{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "46a1f485",
   "metadata": {},
   "source": [
    "# Tutorial:Batch dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7fcca60a",
   "metadata": {},
   "source": [
    "In this tutorial, we will show how to use scMAGCA for batch correction. As an example, we use a mouse Glioblastoma (GBM) dataset 'GSE163120' containing 24559 cells. Specifically, the dataset contains two omicsc and two batches, with ADT containing 174 features and RNA containing 12411 features."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e9df4ff6",
   "metadata": {},
   "source": [
    "## Loading package"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "e3cc9ece",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/zhouzeming/anaconda3/lib/python3.9/site-packages/setuptools_scm/_integration/setuptools.py:30: RuntimeWarning: \n",
      "ERROR: setuptools==58.0.4 is used in combination with setuptools_scm>=8.x\n",
      "\n",
      "Your build configuration is incomplete and previously worked by accident!\n",
      "setuptools_scm requires setuptools>=61\n",
      "\n",
      "Suggested workaround if applicable:\n",
      " - migrating from the deprecated setup_requires mechanism to pep517/518\n",
      "   and using a pyproject.toml to declare build dependencies\n",
      "   which are reliably pre-installed before running the build tools\n",
      "\n",
      "  warnings.warn(\n",
      "<frozen importlib._bootstrap>:228: RuntimeWarning: scipy._lib.messagestream.MessageStream size changed, may indicate binary incompatibility. Expected 56 from C header, got 64 from PyObject\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import torch\n",
    "import scanpy as sc\n",
    "import random\n",
    "import warnings\n",
    "from scipy.sparse import csr_matrix\n",
    "from scipy.io import mmread\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "ae48eb94",
   "metadata": {},
   "outputs": [],
   "source": [
    "from scMAGCA.preprocess import read_dataset, preprocess_dataset\n",
    "from scMAGCA.utils import *\n",
    "from scMAGCA.scMAGCA_batch import scMultiCluster"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "2907ec98",
   "metadata": {},
   "outputs": [],
   "source": [
    "# set seed\n",
    "random.seed(3407)\n",
    "np.random.seed(3407)\n",
    "torch.manual_seed(3407)\n",
    "torch.backends.cudnn.deterministic = True\n",
    "torch.backends.cudnn.enabled = False\n",
    "torch.backends.cudnn.benchmark = False"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4703ab51",
   "metadata": {},
   "source": [
    "## Reading dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c5e7c801",
   "metadata": {},
   "source": [
    "We fully took into account the batch information of the batch dataset when correcting for batch effects.\n",
    "\n",
    "The required input files include: \n",
    "1) x1: protein abundance matrix (data format is csv file) : ADT.csv; \\\n",
    "2) x2: Gene expression matrix (data format is mtx file) : matrix.mtx; \\\n",
    "3) Real label ('cell_label' column in csv file) : GSE163120_label.csv; \\\n",
    "4) Batch information ('batch_id' column in csv file) : GSE163120_label.csv.\n",
    "\n",
    "To ensure reproducibility of the results, please read the above data as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "c09fd528",
   "metadata": {},
   "outputs": [],
   "source": [
    "x1 = np.array(pd.read_csv('../datasets/GSE163120/ADT.csv',index_col=0).T)\n",
    "x2 = csr_matrix(mmread('../datasets/GSE163120/matrix.mtx').T).toarray()\n",
    "y = np.array(pd.read_csv('../datasets/GSE163120/GSE163120_label.csv', index_col=0)['cell_label'])\n",
    "b = np.array(pd.read_csv('../datasets/GSE163120/GSE163120_label.csv', index_col=0)['batch_id'])\n",
    "enc = OneHotEncoder()\n",
    "enc.fit(b.reshape(-1, 1))\n",
    "B = enc.transform(b.reshape(-1, 1)).toarray()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "a906b457",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([[ 0,  0,  0, ...,  0,  0,  0],\n",
       "        [24,  0,  0, ...,  0,  0,  0],\n",
       "        [19,  8,  0, ...,  0,  0,  0],\n",
       "        ...,\n",
       "        [ 4,  0,  3, ...,  3,  0,  0],\n",
       "        [ 2,  1,  0, ...,  2,  0,  0],\n",
       "        [ 8,  1,  0, ...,  2,  0,  0]]),\n",
       " array([[0, 0, 0, ..., 0, 0, 0],\n",
       "        [2, 2, 7, ..., 3, 0, 0],\n",
       "        [0, 0, 1, ..., 0, 0, 0],\n",
       "        ...,\n",
       "        [0, 0, 1, ..., 4, 1, 0],\n",
       "        [0, 1, 0, ..., 0, 1, 0],\n",
       "        [3, 0, 4, ..., 2, 0, 0]]),\n",
       " array([ 5, 11, 17, ...,  1,  3,  0]),\n",
       " array([0, 0, 0, ..., 1, 1, 1]))"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x1,x2,y,b"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dbf2641f",
   "metadata": {},
   "source": [
    "Due to the small number of features in ADT omics data and the large gap between the feature dimensions of RNA omics, for RNA+ADT data, we only select high-expression features for RNA omics (the default number of chosen genes is 2000)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "8202bd5a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Chosen offset: 0.29\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x252 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "importantGenes = geneSelection(x2, n=2000)\n",
    "x2 = x2[:, importantGenes]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "dce9e5cc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "### Autoencoder: Successfully preprocessed 174 features and 24559 cells.\n"
     ]
    }
   ],
   "source": [
    "adata1 = sc.AnnData(x1)\n",
    "adata1 = read_dataset(adata1, copy=True)\n",
    "adata1 = preprocess_dataset(adata1, normalize_input=True, logtrans_input=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "262693cd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AnnData object with n_obs × n_vars = 24559 × 174\n",
       "    obs: 'DCA_split', 'size_factors'\n",
       "    var: 'mean', 'std'\n",
       "    uns: 'log1p'"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adata1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "f923ce51",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "### Autoencoder: Successfully preprocessed 2000 features and 24559 cells.\n"
     ]
    }
   ],
   "source": [
    "adata2 = sc.AnnData(x2)\n",
    "adata2 = read_dataset(adata2, copy=True)\n",
    "adata2 = preprocess_dataset(adata2, normalize_input=True, logtrans_input=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "6fb1b726",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AnnData object with n_obs × n_vars = 24559 × 2000\n",
       "    obs: 'DCA_split', 'size_factors'\n",
       "    var: 'mean', 'std'\n",
       "    uns: 'log1p'"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adata2"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "946ee9b3",
   "metadata": {},
   "source": [
    "## Training the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "5ed2c70a",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = scMultiCluster(input_dim1=adata1.n_vars, input_dim2=adata2.n_vars, n_batch=2,\n",
    "                       alpha=0.2,beta=0.8,gama=0.01,device='cuda').to('cuda')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e1ea8f39",
   "metadata": {},
   "source": [
    "Note: When correcting datasets with batch effects, the actual number of batches need to be provided."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "bacb0d45",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "scMultiCluster(\n",
       "  (encoder): Encoder(\n",
       "    (stacked_gnn): ModuleList(\n",
       "      (0): GCNConv(2176, 1024)\n",
       "      (1): GCNConv(1024, 256)\n",
       "      (2): GCNConv(256, 64)\n",
       "      (3): GCNConv(64, 8)\n",
       "    )\n",
       "    (stacked_bns): ModuleList(\n",
       "      (0): BatchNorm1d(1024, eps=1e-05, momentum=0.01, affine=True, track_running_stats=True)\n",
       "      (1): BatchNorm1d(256, eps=1e-05, momentum=0.01, affine=True, track_running_stats=True)\n",
       "      (2): BatchNorm1d(64, eps=1e-05, momentum=0.01, affine=True, track_running_stats=True)\n",
       "      (3): BatchNorm1d(8, eps=1e-05, momentum=0.01, affine=True, track_running_stats=True)\n",
       "    )\n",
       "    (stacked_prelus): ModuleList(\n",
       "      (0-3): 4 x PReLU(num_parameters=1)\n",
       "    )\n",
       "  )\n",
       "  (decoder): Sequential(\n",
       "    (0): Linear(in_features=10, out_features=512, bias=True)\n",
       "    (1): BatchNorm1d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    (2): PReLU(num_parameters=1)\n",
       "    (3): Linear(in_features=512, out_features=1024, bias=True)\n",
       "    (4): BatchNorm1d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    (5): PReLU(num_parameters=1)\n",
       "    (6): Linear(in_features=1024, out_features=2176, bias=True)\n",
       "  )\n",
       "  (dec_mean): Sequential(\n",
       "    (0): Linear(in_features=10, out_features=256, bias=True)\n",
       "    (1): Linear(in_features=256, out_features=2174, bias=True)\n",
       "    (2): MeanAct()\n",
       "  )\n",
       "  (dec_disp): Sequential(\n",
       "    (0): Linear(in_features=10, out_features=256, bias=True)\n",
       "    (1): Linear(in_features=256, out_features=2174, bias=True)\n",
       "    (2): DispAct()\n",
       "  )\n",
       "  (dec_pi): Sequential(\n",
       "    (0): Linear(in_features=10, out_features=256, bias=True)\n",
       "    (1): Linear(in_features=256, out_features=2174, bias=True)\n",
       "    (2): Sigmoid()\n",
       "  )\n",
       "  (zinb_loss): ZINBLoss()\n",
       ")"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "b61b3be6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pretraining stage\n",
      "Pretrain epoch 1, recon_loss:1.174770, zinb_loss:2.021940, adversial_loss:1.448147\n",
      "Pretrain epoch 2, recon_loss:1.151908, zinb_loss:1.944988, adversial_loss:1.447797\n",
      "Pretrain epoch 3, recon_loss:0.941301, zinb_loss:1.855513, adversial_loss:1.440014\n",
      "Pretrain epoch 4, recon_loss:0.850893, zinb_loss:1.770818, adversial_loss:1.436623\n",
      "Pretrain epoch 5, recon_loss:0.814821, zinb_loss:1.691993, adversial_loss:1.434692\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "Pretrain epoch 379, recon_loss:0.579203, zinb_loss:0.941284, adversial_loss:1.419973\n",
      "Pretrain epoch 380, recon_loss:0.579167, zinb_loss:0.941332, adversial_loss:1.419929\n",
      "Pretrain epoch 381, recon_loss:0.579386, zinb_loss:0.941511, adversial_loss:1.419563\n",
      "Pretrain epoch 382, recon_loss:0.580304, zinb_loss:0.942061, adversial_loss:1.419789\n",
      "Pretrain epoch 383, recon_loss:0.582138, zinb_loss:0.943379, adversial_loss:1.419681\n",
      "Pretrain epoch 384, recon_loss:0.583550, zinb_loss:0.945045, adversial_loss:1.419542\n",
      "Pretrain epoch 385, recon_loss:0.582927, zinb_loss:0.944927, adversial_loss:1.419255\n",
      "Pretrain epoch 386, recon_loss:0.581570, zinb_loss:0.942347, adversial_loss:1.420436\n",
      "Pretrain epoch 387, recon_loss:0.582110, zinb_loss:0.942766, adversial_loss:1.419017\n",
      "Pretrain epoch 388, recon_loss:0.581604, zinb_loss:0.942581, adversial_loss:1.419060\n",
      "Pretrain epoch 389, recon_loss:0.580717, zinb_loss:0.941900, adversial_loss:1.420170\n",
      "Pretrain epoch 390, recon_loss:0.581997, zinb_loss:0.942281, adversial_loss:1.418559\n",
      "Pretrain epoch 391, recon_loss:0.580230, zinb_loss:0.941709, adversial_loss:1.418235\n",
      "Pretrain epoch 392, recon_loss:0.581017, zinb_loss:0.941724, adversial_loss:1.419802\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pretrain epoch 393, recon_loss:0.580346, zinb_loss:0.941516, adversial_loss:1.419295\n",
      "Pretrain epoch 394, recon_loss:0.579912, zinb_loss:0.941409, adversial_loss:1.418395\n",
      "Pretrain epoch 395, recon_loss:0.580094, zinb_loss:0.941453, adversial_loss:1.419263\n",
      "Pretrain epoch 396, recon_loss:0.579287, zinb_loss:0.941191, adversial_loss:1.419363\n",
      "Pretrain epoch 397, recon_loss:0.579749, zinb_loss:0.941160, adversial_loss:1.418412\n",
      "Pretrain epoch 398, recon_loss:0.578888, zinb_loss:0.941031, adversial_loss:1.418818\n",
      "Pretrain epoch 399, recon_loss:0.579094, zinb_loss:0.941045, adversial_loss:1.419081\n",
      "Pretrain epoch 400, recon_loss:0.578716, zinb_loss:0.940966, adversial_loss:1.418836\n"
     ]
    }
   ],
   "source": [
    "pretrain_latent = model.pretrain_autoencoder(\n",
    "                        X1=adata1.X, X2=adata2.X, X1_raw=adata1.raw.X, X2_raw=adata2.raw.X, \n",
    "                        B=B, epochs=400, file='GSE163120')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "ea69fe29",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Clustering stage\n",
      "Initializing cluster centers with kmeans.\n",
      "Initializing k-means: AMI= 0.7055, NMI= 0.7065, ARI= 0.4759, ACC= 0.6091\n",
      "Training epoch 1, recon_loss:0.578283, zinb_loss:0.940858, cluster_loss:0.267897\n",
      "Clustering   1: AMI= 0.7055, NMI= 0.7065, ARI= 0.4759, ACC= 0.6091\n",
      "0.0\n",
      "Training epoch 2, recon_loss:0.604183, zinb_loss:0.950158, cluster_loss:0.267520\n",
      "Clustering   2: AMI= 0.7053, NMI= 0.7063, ARI= 0.4967, ACC= 0.6196\n",
      "0.09955617085386213\n",
      "Training epoch 3, recon_loss:0.624748, zinb_loss:0.975262, cluster_loss:0.251129\n",
      "Clustering   3: AMI= 0.6985, NMI= 0.6995, ARI= 0.4479, ACC= 0.5841\n",
      "0.19475548678692128\n",
      "Training epoch 4, recon_loss:0.641964, zinb_loss:1.007354, cluster_loss:0.248802\n",
      "Clustering   4: AMI= 0.6996, NMI= 0.7006, ARI= 0.4984, ACC= 0.6370\n",
      "0.3092959811067226\n",
      "Training epoch 5, recon_loss:0.620443, zinb_loss:1.014138, cluster_loss:0.258289\n",
      "Clustering   5: AMI= 0.7008, NMI= 0.7018, ARI= 0.4810, ACC= 0.6155\n",
      "0.273382466712814\n",
      "Training epoch 6, recon_loss:0.627468, zinb_loss:1.000923, cluster_loss:0.267297\n",
      "Clustering   6: AMI= 0.7166, NMI= 0.7176, ARI= 0.5168, ACC= 0.6404\n",
      "0.1743963516429822\n",
      "Training epoch 7, recon_loss:0.629639, zinb_loss:0.981455, cluster_loss:0.253169\n",
      "Clustering   7: AMI= 0.6973, NMI= 0.6983, ARI= 0.4921, ACC= 0.6150\n",
      "0.16759640050490654\n",
      "Training epoch 8, recon_loss:0.610951, zinb_loss:0.963163, cluster_loss:0.273688\n",
      "Clustering   8: AMI= 0.7118, NMI= 0.7127, ARI= 0.5102, ACC= 0.6205\n",
      "0.11107944134533165\n",
      "Training epoch 9, recon_loss:0.606674, zinb_loss:0.955225, cluster_loss:0.274060\n",
      "Clustering   9: AMI= 0.7030, NMI= 0.7040, ARI= 0.4945, ACC= 0.6177\n",
      "0.10362799788264994\n",
      "Training epoch 10, recon_loss:0.603629, zinb_loss:0.955168, cluster_loss:0.281613\n",
      "Clustering   10: AMI= 0.7131, NMI= 0.7140, ARI= 0.5062, ACC= 0.6242\n",
      "0.09866036890752881\n",
      "Training epoch 11, recon_loss:0.605655, zinb_loss:0.955295, cluster_loss:0.277240\n",
      "Clustering   11: AMI= 0.7034, NMI= 0.7044, ARI= 0.4917, ACC= 0.6151\n",
      "0.09454782360845311\n",
      "Training epoch 12, recon_loss:0.603617, zinb_loss:0.954015, cluster_loss:0.284091\n",
      "Clustering   12: AMI= 0.7128, NMI= 0.7137, ARI= 0.5038, ACC= 0.6228\n",
      "0.09186041776945315\n",
      "Training epoch 13, recon_loss:0.602957, zinb_loss:0.953503, cluster_loss:0.282067\n",
      "Clustering   13: AMI= 0.7056, NMI= 0.7066, ARI= 0.4964, ACC= 0.6156\n",
      "0.0831874261981351\n",
      "Training epoch 14, recon_loss:0.602049, zinb_loss:0.952359, cluster_loss:0.286583\n",
      "Clustering   14: AMI= 0.7135, NMI= 0.7144, ARI= 0.5025, ACC= 0.6210\n",
      "0.07850482511502911\n",
      "Training epoch 15, recon_loss:0.601373, zinb_loss:0.951753, cluster_loss:0.285459\n",
      "Clustering   15: AMI= 0.7082, NMI= 0.7092, ARI= 0.5006, ACC= 0.6167\n",
      "0.06954680565169592\n",
      "Training epoch 16, recon_loss:0.601026, zinb_loss:0.951113, cluster_loss:0.288606\n",
      "Clustering   16: AMI= 0.7140, NMI= 0.7149, ARI= 0.5009, ACC= 0.6193\n",
      "0.06571928824463537\n",
      "Training epoch 17, recon_loss:0.600724, zinb_loss:0.950717, cluster_loss:0.287691\n",
      "Clustering   17: AMI= 0.7102, NMI= 0.7112, ARI= 0.5031, ACC= 0.6165\n",
      "0.0593265198094385\n",
      "Training epoch 18, recon_loss:0.601057, zinb_loss:0.950548, cluster_loss:0.289826\n",
      "Clustering   18: AMI= 0.7137, NMI= 0.7146, ARI= 0.4987, ACC= 0.6173\n",
      "0.059367238079726374\n",
      "Training epoch 19, recon_loss:0.601084, zinb_loss:0.950544, cluster_loss:0.288866\n",
      "Clustering   19: AMI= 0.7123, NMI= 0.7133, ARI= 0.5068, ACC= 0.6179\n",
      "0.05814568997109003\n",
      "Training epoch 20, recon_loss:0.602567, zinb_loss:0.950983, cluster_loss:0.289988\n",
      "Clustering   20: AMI= 0.7124, NMI= 0.7134, ARI= 0.4946, ACC= 0.6140\n",
      "0.06376481127081722\n",
      "Training epoch 21, recon_loss:0.602428, zinb_loss:0.951537, cluster_loss:0.289339\n",
      "Clustering   21: AMI= 0.7145, NMI= 0.7154, ARI= 0.5100, ACC= 0.6204\n",
      "0.0651085141903172\n",
      "Training epoch 22, recon_loss:0.603932, zinb_loss:0.952431, cluster_loss:0.289908\n",
      "Clustering   22: AMI= 0.7117, NMI= 0.7126, ARI= 0.4923, ACC= 0.6114\n",
      "0.07166415570666558\n",
      "Training epoch 23, recon_loss:0.602905, zinb_loss:0.952970, cluster_loss:0.290755\n",
      "Clustering   23: AMI= 0.7161, NMI= 0.7170, ARI= 0.5114, ACC= 0.6222\n",
      "0.0703204527871656\n",
      "Training epoch 24, recon_loss:0.602801, zinb_loss:0.953425, cluster_loss:0.291247\n",
      "Clustering   24: AMI= 0.7117, NMI= 0.7126, ARI= 0.4924, ACC= 0.6100\n",
      "0.07166415570666558\n",
      "Training epoch 25, recon_loss:0.602533, zinb_loss:0.953915, cluster_loss:0.292682\n",
      "Clustering   25: AMI= 0.7168, NMI= 0.7177, ARI= 0.5112, ACC= 0.6235\n",
      "0.06873244024593836\n",
      "Training epoch 26, recon_loss:0.602246, zinb_loss:0.953963, cluster_loss:0.292619\n",
      "Clustering   26: AMI= 0.7116, NMI= 0.7125, ARI= 0.4930, ACC= 0.6091\n",
      "0.06747017386701414\n",
      "Training epoch 27, recon_loss:0.602707, zinb_loss:0.954565, cluster_loss:0.294073\n",
      "Clustering   27: AMI= 0.7166, NMI= 0.7175, ARI= 0.5095, ACC= 0.6234\n",
      "0.06421271224398388\n",
      "Training epoch 28, recon_loss:0.602441, zinb_loss:0.954300, cluster_loss:0.293615\n",
      "Clustering   28: AMI= 0.7115, NMI= 0.7125, ARI= 0.4956, ACC= 0.6099\n",
      "0.060833095810089985\n",
      "Training epoch 29, recon_loss:0.603012, zinb_loss:0.954816, cluster_loss:0.295198\n",
      "Clustering   29: AMI= 0.7164, NMI= 0.7173, ARI= 0.5078, ACC= 0.6223\n",
      "0.05724988802475671\n",
      "Training epoch 30, recon_loss:0.602607, zinb_loss:0.954239, cluster_loss:0.294651\n",
      "Clustering   30: AMI= 0.7116, NMI= 0.7125, ARI= 0.4970, ACC= 0.6099\n",
      "0.05334093407712041\n",
      "Training epoch 31, recon_loss:0.602986, zinb_loss:0.954600, cluster_loss:0.296351\n",
      "Clustering   31: AMI= 0.7166, NMI= 0.7175, ARI= 0.5077, ACC= 0.6223\n",
      "0.05040921861639318\n",
      "Training epoch 32, recon_loss:0.602366, zinb_loss:0.953835, cluster_loss:0.295951\n",
      "Clustering   32: AMI= 0.7120, NMI= 0.7129, ARI= 0.4981, ACC= 0.6107\n",
      "0.046215236776741726\n",
      "Training epoch 33, recon_loss:0.602762, zinb_loss:0.954243, cluster_loss:0.297542\n",
      "Clustering   33: AMI= 0.7166, NMI= 0.7176, ARI= 0.5072, ACC= 0.6218\n",
      "0.04299849342399935\n",
      "Training epoch 34, recon_loss:0.602057, zinb_loss:0.953480, cluster_loss:0.297270\n",
      "Clustering   34: AMI= 0.7125, NMI= 0.7135, ARI= 0.4988, ACC= 0.6109\n",
      "0.04063683374730241\n",
      "Training epoch 35, recon_loss:0.602738, zinb_loss:0.954083, cluster_loss:0.298615\n",
      "Clustering   35: AMI= 0.7164, NMI= 0.7174, ARI= 0.5074, ACC= 0.6219\n",
      "0.03941528563866607\n",
      "Training epoch 36, recon_loss:0.601913, zinb_loss:0.953373, cluster_loss:0.298413\n",
      "Clustering   36: AMI= 0.7129, NMI= 0.7138, ARI= 0.4989, ACC= 0.6107\n",
      "0.03888594812492365\n",
      "Training epoch 37, recon_loss:0.603167, zinb_loss:0.954282, cluster_loss:0.299491\n",
      "Clustering   37: AMI= 0.7164, NMI= 0.7173, ARI= 0.5077, ACC= 0.6219\n",
      "0.04006677796327212\n",
      "Training epoch 38, recon_loss:0.601935, zinb_loss:0.953599, cluster_loss:0.299274\n",
      "Clustering   38: AMI= 0.7130, NMI= 0.7139, ARI= 0.4980, ACC= 0.6098\n",
      "0.04193981839651452\n",
      "Training epoch 39, recon_loss:0.604018, zinb_loss:0.954999, cluster_loss:0.300035\n",
      "Clustering   39: AMI= 0.7169, NMI= 0.7178, ARI= 0.5091, ACC= 0.6228\n",
      "0.04584877234415082\n",
      "Training epoch 40, recon_loss:0.602190, zinb_loss:0.954309, cluster_loss:0.299712\n",
      "Clustering   40: AMI= 0.7131, NMI= 0.7141, ARI= 0.4965, ACC= 0.6088\n",
      "0.052241540779347694\n",
      "Training epoch 41, recon_loss:0.605564, zinb_loss:0.956435, cluster_loss:0.299894\n",
      "Clustering   41: AMI= 0.7168, NMI= 0.7177, ARI= 0.5106, ACC= 0.6243\n",
      "0.06132171505354453\n",
      "Training epoch 42, recon_loss:0.603153, zinb_loss:0.955624, cluster_loss:0.299327\n",
      "Clustering   42: AMI= 0.7134, NMI= 0.7143, ARI= 0.4946, ACC= 0.6065\n",
      "0.07158271916608983\n",
      "Training epoch 43, recon_loss:0.607563, zinb_loss:0.957940, cluster_loss:0.298822\n",
      "Clustering   43: AMI= 0.7173, NMI= 0.7182, ARI= 0.5127, ACC= 0.6259\n",
      "0.08021499246712\n",
      "Training epoch 44, recon_loss:0.604288, zinb_loss:0.956329, cluster_loss:0.298775\n",
      "Clustering   44: AMI= 0.7127, NMI= 0.7137, ARI= 0.4921, ACC= 0.6047\n",
      "0.08664847917260475\n",
      "Training epoch 45, recon_loss:0.607811, zinb_loss:0.957405, cluster_loss:0.298476\n",
      "Clustering   45: AMI= 0.7167, NMI= 0.7177, ARI= 0.5121, ACC= 0.6249\n",
      "0.08640416955087749\n",
      "Training epoch 46, recon_loss:0.603954, zinb_loss:0.955212, cluster_loss:0.299695\n",
      "Clustering   46: AMI= 0.7127, NMI= 0.7137, ARI= 0.4922, ACC= 0.6059\n",
      "0.08269880695468057\n",
      "Training epoch 47, recon_loss:0.607041, zinb_loss:0.955818, cluster_loss:0.299224\n",
      "Clustering   47: AMI= 0.7166, NMI= 0.7175, ARI= 0.5109, ACC= 0.6228\n",
      "0.07732399527668064\n",
      "Training epoch 48, recon_loss:0.603899, zinb_loss:0.953994, cluster_loss:0.300955\n",
      "Clustering   48: AMI= 0.7137, NMI= 0.7147, ARI= 0.4943, ACC= 0.6086\n",
      "0.07170487397695346\n",
      "Training epoch 49, recon_loss:0.606360, zinb_loss:0.954578, cluster_loss:0.300085\n",
      "Clustering   49: AMI= 0.7166, NMI= 0.7176, ARI= 0.5099, ACC= 0.6214\n",
      "0.06726658251557474\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training epoch 50, recon_loss:0.603710, zinb_loss:0.953259, cluster_loss:0.301935\n",
      "Clustering   50: AMI= 0.7147, NMI= 0.7156, ARI= 0.4966, ACC= 0.6110\n",
      "0.06107740543181726\n",
      "Training epoch 51, recon_loss:0.606071, zinb_loss:0.953811, cluster_loss:0.300916\n",
      "Clustering   51: AMI= 0.7160, NMI= 0.7169, ARI= 0.5091, ACC= 0.6202\n",
      "0.05680198705159005\n",
      "Training epoch 52, recon_loss:0.603947, zinb_loss:0.952961, cluster_loss:0.302709\n",
      "Clustering   52: AMI= 0.7154, NMI= 0.7163, ARI= 0.4980, ACC= 0.6125\n",
      "0.053707398509711304\n",
      "Training epoch 53, recon_loss:0.606021, zinb_loss:0.953407, cluster_loss:0.301580\n",
      "Clustering   53: AMI= 0.7161, NMI= 0.7170, ARI= 0.5082, ACC= 0.6192\n",
      "0.05126430229243862\n",
      "Training epoch 54, recon_loss:0.604311, zinb_loss:0.952948, cluster_loss:0.303314\n",
      "Clustering   54: AMI= 0.7154, NMI= 0.7163, ARI= 0.4985, ACC= 0.6134\n",
      "0.04833258683171139\n",
      "Training epoch 55, recon_loss:0.606182, zinb_loss:0.953258, cluster_loss:0.302095\n",
      "Clustering   55: AMI= 0.7164, NMI= 0.7173, ARI= 0.5081, ACC= 0.6183\n",
      "0.047111038723075045\n",
      "Training epoch 56, recon_loss:0.604760, zinb_loss:0.953142, cluster_loss:0.303795\n",
      "Clustering   56: AMI= 0.7159, NMI= 0.7168, ARI= 0.4998, ACC= 0.6144\n",
      "0.04548230791155992\n",
      "Training epoch 57, recon_loss:0.606341, zinb_loss:0.953292, cluster_loss:0.302517\n",
      "Clustering   57: AMI= 0.7163, NMI= 0.7172, ARI= 0.5072, ACC= 0.6170\n",
      "0.04430147807321145\n",
      "Training epoch 58, recon_loss:0.605274, zinb_loss:0.953513, cluster_loss:0.304217\n",
      "Clustering   58: AMI= 0.7164, NMI= 0.7173, ARI= 0.5015, ACC= 0.6158\n",
      "0.04206197320737815\n",
      "Training epoch 59, recon_loss:0.606405, zinb_loss:0.953505, cluster_loss:0.302888\n",
      "Clustering   59: AMI= 0.7157, NMI= 0.7167, ARI= 0.5058, ACC= 0.6157\n",
      "0.04092186163931756\n",
      "Training epoch 60, recon_loss:0.605611, zinb_loss:0.954064, cluster_loss:0.304581\n",
      "Clustering   60: AMI= 0.7174, NMI= 0.7184, ARI= 0.5040, ACC= 0.6176\n",
      "0.03937456736837819\n",
      "Training epoch 61, recon_loss:0.606343, zinb_loss:0.953926, cluster_loss:0.303256\n",
      "Clustering   61: AMI= 0.7154, NMI= 0.7164, ARI= 0.5039, ACC= 0.6141\n",
      "0.03847876542204487\n",
      "Training epoch 62, recon_loss:0.606012, zinb_loss:0.954873, cluster_loss:0.304893\n",
      "Clustering   62: AMI= 0.7183, NMI= 0.7192, ARI= 0.5064, ACC= 0.6194\n",
      "0.03676859806995399\n",
      "Training epoch 63, recon_loss:0.606218, zinb_loss:0.954626, cluster_loss:0.303585\n",
      "Clustering   63: AMI= 0.7153, NMI= 0.7162, ARI= 0.5024, ACC= 0.6128\n",
      "0.03595423266419642\n",
      "Training epoch 64, recon_loss:0.606223, zinb_loss:0.956046, cluster_loss:0.305074\n",
      "Clustering   64: AMI= 0.7190, NMI= 0.7200, ARI= 0.5089, ACC= 0.6214\n",
      "0.036646443259090354\n",
      "Training epoch 65, recon_loss:0.606071, zinb_loss:0.955746, cluster_loss:0.303877\n",
      "Clustering   65: AMI= 0.7145, NMI= 0.7155, ARI= 0.4999, ACC= 0.6103\n",
      "0.040270369314711514\n",
      "Training epoch 66, recon_loss:0.606696, zinb_loss:0.957720, cluster_loss:0.305082\n",
      "Clustering   66: AMI= 0.7206, NMI= 0.7215, ARI= 0.5126, ACC= 0.6242\n",
      "0.04593020888472658\n",
      "Training epoch 67, recon_loss:0.606087, zinb_loss:0.957220, cluster_loss:0.304076\n",
      "Clustering   67: AMI= 0.7134, NMI= 0.7143, ARI= 0.4964, ACC= 0.6072\n",
      "0.052200822509059816\n",
      "Training epoch 68, recon_loss:0.607118, zinb_loss:0.959537, cluster_loss:0.304932\n",
      "Clustering   68: AMI= 0.7217, NMI= 0.7226, ARI= 0.5150, ACC= 0.6264\n",
      "0.0591229284579991\n",
      "Training epoch 69, recon_loss:0.606211, zinb_loss:0.958407, cluster_loss:0.304311\n",
      "Clustering   69: AMI= 0.7135, NMI= 0.7144, ARI= 0.4948, ACC= 0.6059\n",
      "0.06523066900118082\n",
      "Training epoch 70, recon_loss:0.607683, zinb_loss:0.960386, cluster_loss:0.304953\n",
      "Clustering   70: AMI= 0.7220, NMI= 0.7229, ARI= 0.5161, ACC= 0.6269\n",
      "0.06893603159737774\n",
      "Training epoch 71, recon_loss:0.606178, zinb_loss:0.958425, cluster_loss:0.304747\n",
      "Clustering   71: AMI= 0.7138, NMI= 0.7148, ARI= 0.4947, ACC= 0.6052\n",
      "0.07109409992263528\n",
      "Training epoch 72, recon_loss:0.607365, zinb_loss:0.959681, cluster_loss:0.305366\n",
      "Clustering   72: AMI= 0.7214, NMI= 0.7223, ARI= 0.5155, ACC= 0.6267\n",
      "0.07023901624658985\n",
      "Training epoch 73, recon_loss:0.605811, zinb_loss:0.957490, cluster_loss:0.305452\n",
      "Clustering   73: AMI= 0.7143, NMI= 0.7153, ARI= 0.4951, ACC= 0.6056\n",
      "0.06836597581334745\n",
      "Training epoch 74, recon_loss:0.607197, zinb_loss:0.958380, cluster_loss:0.306015\n",
      "Clustering   74: AMI= 0.7205, NMI= 0.7214, ARI= 0.5141, ACC= 0.6255\n",
      "0.0647420497577263\n",
      "Training epoch 75, recon_loss:0.605477, zinb_loss:0.956487, cluster_loss:0.306114\n",
      "Clustering   75: AMI= 0.7152, NMI= 0.7161, ARI= 0.4972, ACC= 0.6073\n",
      "0.059937293863756666\n",
      "Training epoch 76, recon_loss:0.606875, zinb_loss:0.957310, cluster_loss:0.306613\n",
      "Clustering   76: AMI= 0.7196, NMI= 0.7205, ARI= 0.5121, ACC= 0.6237\n",
      "0.056191212997271874\n",
      "Training epoch 77, recon_loss:0.605363, zinb_loss:0.955818, cluster_loss:0.306662\n",
      "Clustering   77: AMI= 0.7158, NMI= 0.7167, ARI= 0.4983, ACC= 0.6083\n",
      "0.052404413860499204\n",
      "Training epoch 78, recon_loss:0.606800, zinb_loss:0.956598, cluster_loss:0.307085\n",
      "Clustering   78: AMI= 0.7193, NMI= 0.7202, ARI= 0.5109, ACC= 0.6231\n",
      "0.049146952237468955\n",
      "Training epoch 79, recon_loss:0.605479, zinb_loss:0.955451, cluster_loss:0.307041\n",
      "Clustering   79: AMI= 0.7164, NMI= 0.7173, ARI= 0.4998, ACC= 0.6097\n",
      "0.04572661753328719\n",
      "Training epoch 80, recon_loss:0.606916, zinb_loss:0.956171, cluster_loss:0.307393\n",
      "Clustering   80: AMI= 0.7190, NMI= 0.7199, ARI= 0.5100, ACC= 0.6222\n",
      "0.04385357710004479\n",
      "Training epoch 81, recon_loss:0.605828, zinb_loss:0.955307, cluster_loss:0.307235\n",
      "Clustering   81: AMI= 0.7169, NMI= 0.7179, ARI= 0.5012, ACC= 0.6110\n",
      "0.04169550877478725\n",
      "Training epoch 82, recon_loss:0.607365, zinb_loss:0.955926, cluster_loss:0.307506\n",
      "Clustering   82: AMI= 0.7184, NMI= 0.7193, ARI= 0.5087, ACC= 0.6215\n",
      "0.040433242395863024\n",
      "Training epoch 83, recon_loss:0.606410, zinb_loss:0.955293, cluster_loss:0.307216\n",
      "Clustering   83: AMI= 0.7177, NMI= 0.7186, ARI= 0.5031, ACC= 0.6121\n",
      "0.03949672217924183\n",
      "Training epoch 84, recon_loss:0.607947, zinb_loss:0.955779, cluster_loss:0.307419\n",
      "Clustering   84: AMI= 0.7178, NMI= 0.7187, ARI= 0.5070, ACC= 0.6206\n",
      "0.03876379331406002\n",
      "Training epoch 85, recon_loss:0.607013, zinb_loss:0.955298, cluster_loss:0.307125\n",
      "Clustering   85: AMI= 0.7175, NMI= 0.7184, ARI= 0.5034, ACC= 0.6119\n",
      "0.03860092023290851\n",
      "Training epoch 86, recon_loss:0.608465, zinb_loss:0.955660, cluster_loss:0.307329\n",
      "Clustering   86: AMI= 0.7172, NMI= 0.7181, ARI= 0.5054, ACC= 0.6190\n",
      "0.037949427908302455\n",
      "Training epoch 87, recon_loss:0.607330, zinb_loss:0.955233, cluster_loss:0.307181\n",
      "Clustering   87: AMI= 0.7172, NMI= 0.7182, ARI= 0.5035, ACC= 0.6122\n",
      "0.03676859806995399\n",
      "Training epoch 88, recon_loss:0.608503, zinb_loss:0.955554, cluster_loss:0.307461\n",
      "Clustering   88: AMI= 0.7170, NMI= 0.7180, ARI= 0.5046, ACC= 0.6184\n",
      "0.03623926055621157\n",
      "Training epoch 89, recon_loss:0.607349, zinb_loss:0.955141, cluster_loss:0.307450\n",
      "Clustering   89: AMI= 0.7172, NMI= 0.7181, ARI= 0.5039, ACC= 0.6125\n",
      "0.03481412109613584\n",
      "Training epoch 90, recon_loss:0.608406, zinb_loss:0.955515, cluster_loss:0.307786\n",
      "Clustering   90: AMI= 0.7174, NMI= 0.7183, ARI= 0.5046, ACC= 0.6183\n",
      "0.03383688260922676\n",
      "Training epoch 91, recon_loss:0.607378, zinb_loss:0.955110, cluster_loss:0.307764\n",
      "Clustering   91: AMI= 0.7175, NMI= 0.7185, ARI= 0.5048, ACC= 0.6129\n",
      "0.03298179893318132\n",
      "Training epoch 92, recon_loss:0.608392, zinb_loss:0.955573, cluster_loss:0.308129\n",
      "Clustering   92: AMI= 0.7175, NMI= 0.7184, ARI= 0.5046, ACC= 0.6183\n",
      "0.03176025082454497\n",
      "Training epoch 93, recon_loss:0.607579, zinb_loss:0.955176, cluster_loss:0.308014\n",
      "Clustering   93: AMI= 0.7177, NMI= 0.7187, ARI= 0.5054, ACC= 0.6131\n",
      "0.03049798444562075\n",
      "Training epoch 94, recon_loss:0.608668, zinb_loss:0.955735, cluster_loss:0.308408\n",
      "Clustering   94: AMI= 0.7178, NMI= 0.7188, ARI= 0.5048, ACC= 0.6184\n",
      "0.030253674823893482\n",
      "Training epoch 95, recon_loss:0.608084, zinb_loss:0.955347, cluster_loss:0.308127\n",
      "Clustering   95: AMI= 0.7175, NMI= 0.7184, ARI= 0.5058, ACC= 0.6131\n",
      "0.03171953255425709\n",
      "Training epoch 96, recon_loss:0.608954, zinb_loss:0.955956, cluster_loss:0.308523\n",
      "Clustering   96: AMI= 0.7180, NMI= 0.7189, ARI= 0.5047, ACC= 0.6183\n",
      "0.033429699906347976\n",
      "Training epoch 97, recon_loss:0.609026, zinb_loss:0.955630, cluster_loss:0.308012\n",
      "Clustering   97: AMI= 0.7178, NMI= 0.7187, ARI= 0.5068, ACC= 0.6135\n",
      "0.037094344232257014\n",
      "Training epoch 98, recon_loss:0.609937, zinb_loss:0.956232, cluster_loss:0.308301\n",
      "Clustering   98: AMI= 0.7184, NMI= 0.7193, ARI= 0.5041, ACC= 0.6179\n",
      "0.04193981839651452\n",
      "Training epoch 99, recon_loss:0.610543, zinb_loss:0.955965, cluster_loss:0.307413\n",
      "Clustering   99: AMI= 0.7180, NMI= 0.7189, ARI= 0.5077, ACC= 0.6133\n",
      "0.04694816564192353\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training epoch 100, recon_loss:0.610926, zinb_loss:0.956363, cluster_loss:0.307656\n",
      "Clustering   100: AMI= 0.7185, NMI= 0.7194, ARI= 0.5028, ACC= 0.6168\n",
      "0.05317806099596889\n",
      "Training epoch 101, recon_loss:0.611473, zinb_loss:0.955983, cluster_loss:0.306782\n",
      "Clustering   101: AMI= 0.7186, NMI= 0.7196, ARI= 0.5095, ACC= 0.6139\n",
      "0.05765707072763549\n",
      "Training epoch 102, recon_loss:0.610925, zinb_loss:0.956001, cluster_loss:0.307378\n",
      "Clustering   102: AMI= 0.7186, NMI= 0.7196, ARI= 0.5016, ACC= 0.6158\n",
      "0.06148458813469604\n",
      "Training epoch 103, recon_loss:0.611021, zinb_loss:0.955536, cluster_loss:0.306828\n",
      "Clustering   103: AMI= 0.7189, NMI= 0.7199, ARI= 0.5098, ACC= 0.6147\n",
      "0.061688179486135426\n",
      "Training epoch 104, recon_loss:0.610129, zinb_loss:0.955389, cluster_loss:0.307910\n",
      "Clustering   104: AMI= 0.7183, NMI= 0.7192, ARI= 0.5007, ACC= 0.6148\n",
      "0.06160674294555967\n",
      "Training epoch 105, recon_loss:0.610252, zinb_loss:0.955133, cluster_loss:0.307241\n",
      "Clustering   105: AMI= 0.7192, NMI= 0.7201, ARI= 0.5100, ACC= 0.6152\n",
      "0.05973370251231728\n",
      "Training epoch 106, recon_loss:0.609513, zinb_loss:0.954981, cluster_loss:0.308655\n",
      "Clustering   106: AMI= 0.7183, NMI= 0.7192, ARI= 0.5011, ACC= 0.6146\n",
      "0.05737204283562034\n",
      "Training epoch 107, recon_loss:0.609748, zinb_loss:0.954945, cluster_loss:0.307676\n",
      "Clustering   107: AMI= 0.7195, NMI= 0.7204, ARI= 0.5103, ACC= 0.6163\n",
      "0.05480679180748402\n",
      "Training epoch 108, recon_loss:0.609195, zinb_loss:0.954816, cluster_loss:0.309277\n",
      "Clustering   108: AMI= 0.7187, NMI= 0.7196, ARI= 0.5016, ACC= 0.6148\n",
      "0.0520379494279083\n",
      "Training epoch 109, recon_loss:0.609469, zinb_loss:0.954896, cluster_loss:0.308078\n",
      "Clustering   109: AMI= 0.7193, NMI= 0.7202, ARI= 0.5100, ACC= 0.6166\n",
      "0.05069424650840832\n",
      "Training epoch 110, recon_loss:0.609041, zinb_loss:0.954789, cluster_loss:0.309755\n",
      "Clustering   110: AMI= 0.7186, NMI= 0.7195, ARI= 0.5014, ACC= 0.6144\n",
      "0.04853617818315078\n",
      "Training epoch 111, recon_loss:0.609301, zinb_loss:0.954911, cluster_loss:0.308449\n",
      "Clustering   111: AMI= 0.7194, NMI= 0.7203, ARI= 0.5099, ACC= 0.6167\n",
      "0.046378109857893236\n",
      "Training epoch 112, recon_loss:0.608973, zinb_loss:0.954819, cluster_loss:0.310133\n",
      "Clustering   112: AMI= 0.7190, NMI= 0.7200, ARI= 0.5024, ACC= 0.6149\n",
      "0.04397573191090842\n",
      "Training epoch 113, recon_loss:0.609188, zinb_loss:0.954963, cluster_loss:0.308795\n",
      "Clustering   113: AMI= 0.7191, NMI= 0.7201, ARI= 0.5098, ACC= 0.6166\n",
      "0.04169550877478725\n",
      "Training epoch 114, recon_loss:0.608921, zinb_loss:0.954877, cluster_loss:0.310452\n",
      "Clustering   114: AMI= 0.7193, NMI= 0.7202, ARI= 0.5027, ACC= 0.6149\n",
      "0.04035180585528727\n",
      "Training epoch 115, recon_loss:0.609100, zinb_loss:0.955044, cluster_loss:0.309130\n",
      "Clustering   115: AMI= 0.7196, NMI= 0.7205, ARI= 0.5102, ACC= 0.6172\n",
      "0.03835661061118124\n",
      "Training epoch 116, recon_loss:0.608898, zinb_loss:0.954950, cluster_loss:0.310736\n",
      "Clustering   116: AMI= 0.7195, NMI= 0.7204, ARI= 0.5026, ACC= 0.6147\n",
      "0.037216499043120646\n",
      "Training epoch 117, recon_loss:0.609052, zinb_loss:0.955168, cluster_loss:0.309444\n",
      "Clustering   117: AMI= 0.7194, NMI= 0.7203, ARI= 0.5101, ACC= 0.6175\n",
      "0.03542489515045401\n",
      "Training epoch 118, recon_loss:0.608858, zinb_loss:0.955048, cluster_loss:0.310988\n",
      "Clustering   118: AMI= 0.7196, NMI= 0.7205, ARI= 0.5029, ACC= 0.6148\n",
      "0.0346919662852722\n",
      "Training epoch 119, recon_loss:0.609045, zinb_loss:0.955368, cluster_loss:0.309739\n",
      "Clustering   119: AMI= 0.7202, NMI= 0.7211, ARI= 0.5110, ACC= 0.6184\n",
      "0.033266826825196466\n",
      "Training epoch 120, recon_loss:0.608846, zinb_loss:0.955208, cluster_loss:0.311205\n",
      "Clustering   120: AMI= 0.7196, NMI= 0.7206, ARI= 0.5026, ACC= 0.6146\n",
      "0.03298179893318132\n",
      "Training epoch 121, recon_loss:0.609099, zinb_loss:0.955705, cluster_loss:0.310003\n",
      "Clustering   121: AMI= 0.7201, NMI= 0.7210, ARI= 0.5112, ACC= 0.6189\n",
      "0.03200456044627224\n",
      "Training epoch 122, recon_loss:0.608880, zinb_loss:0.955495, cluster_loss:0.311375\n",
      "Clustering   122: AMI= 0.7193, NMI= 0.7202, ARI= 0.5015, ACC= 0.6134\n",
      "0.03119019504051468\n",
      "Training epoch 123, recon_loss:0.609257, zinb_loss:0.956287, cluster_loss:0.310224\n",
      "Clustering   123: AMI= 0.7207, NMI= 0.7216, ARI= 0.5121, ACC= 0.6201\n",
      "0.031108758499938924\n",
      "Training epoch 124, recon_loss:0.608811, zinb_loss:0.956018, cluster_loss:0.311457\n",
      "Clustering   124: AMI= 0.7190, NMI= 0.7200, ARI= 0.5009, ACC= 0.6127\n",
      "0.031678814283969216\n",
      "Training epoch 125, recon_loss:0.609496, zinb_loss:0.957270, cluster_loss:0.310420\n",
      "Clustering   125: AMI= 0.7215, NMI= 0.7224, ARI= 0.5138, ACC= 0.6219\n",
      "0.03424406531210554\n",
      "Training epoch 126, recon_loss:0.608865, zinb_loss:0.956917, cluster_loss:0.311414\n",
      "Clustering   126: AMI= 0.7184, NMI= 0.7193, ARI= 0.4998, ACC= 0.6112\n",
      "0.037094344232257014\n",
      "Training epoch 127, recon_loss:0.609908, zinb_loss:0.958780, cluster_loss:0.310547\n",
      "Clustering   127: AMI= 0.7219, NMI= 0.7228, ARI= 0.5150, ACC= 0.6231\n",
      "0.04116617126104483\n",
      "Training epoch 128, recon_loss:0.608848, zinb_loss:0.958213, cluster_loss:0.311159\n",
      "Clustering   128: AMI= 0.7173, NMI= 0.7182, ARI= 0.4979, ACC= 0.6086\n",
      "0.04548230791155992\n",
      "Training epoch 129, recon_loss:0.610377, zinb_loss:0.960672, cluster_loss:0.310622\n",
      "Clustering   129: AMI= 0.7226, NMI= 0.7235, ARI= 0.5162, ACC= 0.6251\n",
      "0.0511014292112871\n",
      "Training epoch 130, recon_loss:0.608994, zinb_loss:0.959572, cluster_loss:0.310676\n",
      "Clustering   130: AMI= 0.7170, NMI= 0.7179, ARI= 0.4972, ACC= 0.6067\n",
      "0.05610977645669612\n",
      "Training epoch 131, recon_loss:0.610778, zinb_loss:0.962282, cluster_loss:0.310602\n",
      "Clustering   131: AMI= 0.7232, NMI= 0.7241, ARI= 0.5171, ACC= 0.6267\n",
      "0.06124027851296877\n",
      "Training epoch 132, recon_loss:0.608930, zinb_loss:0.960295, cluster_loss:0.310067\n",
      "Clustering   132: AMI= 0.7163, NMI= 0.7173, ARI= 0.4962, ACC= 0.6045\n",
      "0.06543426035262022\n",
      "Training epoch 133, recon_loss:0.610875, zinb_loss:0.962723, cluster_loss:0.310613\n",
      "Clustering   133: AMI= 0.7234, NMI= 0.7243, ARI= 0.5168, ACC= 0.6269\n",
      "0.06690011808298384\n",
      "Training epoch 134, recon_loss:0.609190, zinb_loss:0.959977, cluster_loss:0.309716\n",
      "Clustering   134: AMI= 0.7160, NMI= 0.7170, ARI= 0.4963, ACC= 0.6043\n",
      "0.06779592002931716\n",
      "Training epoch 135, recon_loss:0.610738, zinb_loss:0.961877, cluster_loss:0.310704\n",
      "Clustering   135: AMI= 0.7231, NMI= 0.7241, ARI= 0.5160, ACC= 0.6260\n",
      "0.06571928824463537\n",
      "Training epoch 136, recon_loss:0.609241, zinb_loss:0.958911, cluster_loss:0.309813\n",
      "Clustering   136: AMI= 0.7161, NMI= 0.7171, ARI= 0.4976, ACC= 0.6061\n",
      "0.06327619202736268\n",
      "Training epoch 137, recon_loss:0.610444, zinb_loss:0.960439, cluster_loss:0.310977\n",
      "Clustering   137: AMI= 0.7228, NMI= 0.7237, ARI= 0.5149, ACC= 0.6249\n",
      "0.059937293863756666\n",
      "Training epoch 138, recon_loss:0.609179, zinb_loss:0.957765, cluster_loss:0.310201\n",
      "Clustering   138: AMI= 0.7169, NMI= 0.7179, ARI= 0.4991, ACC= 0.6079\n",
      "0.05562115721324158\n",
      "Training epoch 139, recon_loss:0.610097, zinb_loss:0.959116, cluster_loss:0.311345\n",
      "Clustering   139: AMI= 0.7225, NMI= 0.7234, ARI= 0.5137, ACC= 0.6234\n",
      "0.05118286575186286\n",
      "Training epoch 140, recon_loss:0.609124, zinb_loss:0.956890, cluster_loss:0.310629\n",
      "Clustering   140: AMI= 0.7172, NMI= 0.7181, ARI= 0.5001, ACC= 0.6093\n",
      "0.04698888391221141\n",
      "Training epoch 141, recon_loss:0.609852, zinb_loss:0.958147, cluster_loss:0.311705\n",
      "Clustering   141: AMI= 0.7223, NMI= 0.7232, ARI= 0.5135, ACC= 0.6228\n",
      "0.042876338613135716\n",
      "Training epoch 142, recon_loss:0.609198, zinb_loss:0.956339, cluster_loss:0.310944\n",
      "Clustering   142: AMI= 0.7176, NMI= 0.7185, ARI= 0.5013, ACC= 0.6106\n",
      "0.03856020196262063\n",
      "Training epoch 143, recon_loss:0.609799, zinb_loss:0.957498, cluster_loss:0.312017\n",
      "Clustering   143: AMI= 0.7215, NMI= 0.7224, ARI= 0.5111, ACC= 0.6211\n",
      "0.035180585528726736\n",
      "Training epoch 144, recon_loss:0.609554, zinb_loss:0.956044, cluster_loss:0.311054\n",
      "Clustering   144: AMI= 0.7180, NMI= 0.7190, ARI= 0.5032, ACC= 0.6117\n",
      "0.03094588541878741\n",
      "Training epoch 145, recon_loss:0.609971, zinb_loss:0.957072, cluster_loss:0.312209\n",
      "Clustering   145: AMI= 0.7211, NMI= 0.7220, ARI= 0.5089, ACC= 0.6194\n",
      "0.02964290076957531\n",
      "Training epoch 146, recon_loss:0.610114, zinb_loss:0.955937, cluster_loss:0.310882\n",
      "Clustering   146: AMI= 0.7182, NMI= 0.7191, ARI= 0.5045, ACC= 0.6124\n",
      "0.030579420986196506\n",
      "Training epoch 147, recon_loss:0.610440, zinb_loss:0.956775, cluster_loss:0.312253\n",
      "Clustering   147: AMI= 0.7207, NMI= 0.7216, ARI= 0.5071, ACC= 0.6184\n",
      "0.032574616230302535\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training epoch 148, recon_loss:0.610913, zinb_loss:0.955947, cluster_loss:0.310460\n",
      "Clustering   148: AMI= 0.7186, NMI= 0.7195, ARI= 0.5060, ACC= 0.6133\n",
      "0.03465124801498432\n",
      "Training epoch 149, recon_loss:0.610900, zinb_loss:0.956508, cluster_loss:0.312201\n",
      "Clustering   149: AMI= 0.7205, NMI= 0.7214, ARI= 0.5055, ACC= 0.6172\n",
      "0.03717578077283277\n",
      "Training epoch 150, recon_loss:0.611440, zinb_loss:0.955965, cluster_loss:0.310135\n",
      "Clustering   150: AMI= 0.7192, NMI= 0.7202, ARI= 0.5075, ACC= 0.6146\n",
      "0.040148214503847875\n",
      "Training epoch 151, recon_loss:0.611062, zinb_loss:0.956211, cluster_loss:0.312320\n",
      "Clustering   151: AMI= 0.7203, NMI= 0.7212, ARI= 0.5042, ACC= 0.6163\n",
      "0.04181766358565088\n",
      "Training epoch 152, recon_loss:0.611245, zinb_loss:0.955898, cluster_loss:0.310225\n",
      "Clustering   152: AMI= 0.7199, NMI= 0.7209, ARI= 0.5087, ACC= 0.6153\n",
      "0.04324280304572662\n",
      "Training epoch 153, recon_loss:0.610994, zinb_loss:0.955925, cluster_loss:0.312706\n",
      "Clustering   153: AMI= 0.7201, NMI= 0.7210, ARI= 0.5032, ACC= 0.6153\n",
      "0.044667942505802354\n",
      "Training epoch 154, recon_loss:0.610966, zinb_loss:0.955846, cluster_loss:0.310612\n",
      "Clustering   154: AMI= 0.7207, NMI= 0.7216, ARI= 0.5097, ACC= 0.6163\n",
      "0.04474937904637811\n",
      "Training epoch 155, recon_loss:0.610202, zinb_loss:0.955712, cluster_loss:0.313120\n",
      "Clustering   155: AMI= 0.7199, NMI= 0.7208, ARI= 0.5023, ACC= 0.6144\n",
      "0.04474937904637811\n",
      "Training epoch 156, recon_loss:0.610614, zinb_loss:0.955914, cluster_loss:0.311131\n",
      "Clustering   156: AMI= 0.7214, NMI= 0.7223, ARI= 0.5111, ACC= 0.6175\n",
      "0.04360926747831752\n",
      "Training epoch 157, recon_loss:0.609841, zinb_loss:0.955665, cluster_loss:0.313457\n",
      "Clustering   157: AMI= 0.7197, NMI= 0.7206, ARI= 0.5014, ACC= 0.6135\n",
      "0.043487112667453885\n",
      "Training epoch 158, recon_loss:0.610411, zinb_loss:0.956125, cluster_loss:0.311547\n",
      "Clustering   158: AMI= 0.7219, NMI= 0.7228, ARI= 0.5118, ACC= 0.6186\n",
      "0.0430799299645751\n",
      "Training epoch 159, recon_loss:0.609405, zinb_loss:0.955768, cluster_loss:0.313694\n",
      "Clustering   159: AMI= 0.7194, NMI= 0.7203, ARI= 0.5005, ACC= 0.6127\n",
      "0.04369070401889328\n",
      "Training epoch 160, recon_loss:0.610223, zinb_loss:0.956502, cluster_loss:0.311885\n",
      "Clustering   160: AMI= 0.7222, NMI= 0.7231, ARI= 0.5125, ACC= 0.6197\n",
      "0.044627224235514476\n",
      "Training epoch 161, recon_loss:0.609372, zinb_loss:0.956075, cluster_loss:0.313859\n",
      "Clustering   161: AMI= 0.7192, NMI= 0.7202, ARI= 0.5001, ACC= 0.6121\n",
      "0.04617451850645385\n",
      "Training epoch 162, recon_loss:0.610223, zinb_loss:0.957075, cluster_loss:0.312080\n",
      "Clustering   162: AMI= 0.7222, NMI= 0.7231, ARI= 0.5129, ACC= 0.6208\n",
      "0.04796612239912049\n",
      "Training epoch 163, recon_loss:0.609072, zinb_loss:0.956555, cluster_loss:0.313909\n",
      "Clustering   163: AMI= 0.7193, NMI= 0.7202, ARI= 0.4996, ACC= 0.6111\n",
      "0.04971700802149925\n",
      "Training epoch 164, recon_loss:0.610119, zinb_loss:0.957808, cluster_loss:0.312279\n",
      "Clustering   164: AMI= 0.7225, NMI= 0.7234, ARI= 0.5134, ACC= 0.6214\n",
      "0.05118286575186286\n",
      "Training epoch 165, recon_loss:0.609228, zinb_loss:0.957182, cluster_loss:0.313900\n",
      "Clustering   165: AMI= 0.7189, NMI= 0.7198, ARI= 0.4988, ACC= 0.6101\n",
      "0.05313734272568101\n",
      "Training epoch 166, recon_loss:0.610111, zinb_loss:0.958602, cluster_loss:0.312395\n",
      "Clustering   166: AMI= 0.7230, NMI= 0.7239, ARI= 0.5142, ACC= 0.6225\n",
      "0.05460320045604463\n",
      "Training epoch 167, recon_loss:0.608803, zinb_loss:0.957754, cluster_loss:0.313814\n",
      "Clustering   167: AMI= 0.7189, NMI= 0.7198, ARI= 0.4988, ACC= 0.6097\n",
      "0.05525469278065068\n",
      "Training epoch 168, recon_loss:0.609962, zinb_loss:0.959217, cluster_loss:0.312639\n",
      "Clustering   168: AMI= 0.7229, NMI= 0.7238, ARI= 0.5139, ACC= 0.6226\n",
      "0.05570259375381734\n",
      "Training epoch 169, recon_loss:0.608813, zinb_loss:0.958138, cluster_loss:0.313739\n",
      "Clustering   169: AMI= 0.7190, NMI= 0.7199, ARI= 0.4993, ACC= 0.6096\n",
      "0.05501038315892341\n",
      "Training epoch 170, recon_loss:0.609773, zinb_loss:0.959547, cluster_loss:0.312875\n",
      "Clustering   170: AMI= 0.7227, NMI= 0.7236, ARI= 0.5139, ACC= 0.6230\n",
      "0.05476607353719614\n",
      "Training epoch 171, recon_loss:0.608661, zinb_loss:0.958230, cluster_loss:0.313695\n",
      "Clustering   171: AMI= 0.7189, NMI= 0.7199, ARI= 0.4997, ACC= 0.6094\n",
      "0.05374811677999919\n",
      "Training epoch 172, recon_loss:0.609632, zinb_loss:0.959549, cluster_loss:0.313167\n",
      "Clustering   172: AMI= 0.7228, NMI= 0.7237, ARI= 0.5138, ACC= 0.6230\n",
      "0.05277087829309011\n",
      "Training epoch 173, recon_loss:0.608520, zinb_loss:0.958090, cluster_loss:0.313686\n",
      "Clustering   173: AMI= 0.7192, NMI= 0.7201, ARI= 0.5007, ACC= 0.6098\n",
      "0.05138645710330225\n",
      "Training epoch 174, recon_loss:0.609622, zinb_loss:0.959365, cluster_loss:0.313484\n",
      "Clustering   174: AMI= 0.7226, NMI= 0.7235, ARI= 0.5131, ACC= 0.6228\n",
      "0.04963557148092349\n",
      "Training epoch 175, recon_loss:0.608410, zinb_loss:0.957842, cluster_loss:0.313674\n",
      "Clustering   175: AMI= 0.7194, NMI= 0.7203, ARI= 0.5018, ACC= 0.6106\n",
      "0.048169713750559874\n",
      "Training epoch 176, recon_loss:0.610205, zinb_loss:0.959123, cluster_loss:0.313816\n",
      "Clustering   176: AMI= 0.7225, NMI= 0.7234, ARI= 0.5127, ACC= 0.6224\n",
      "0.04576733580357507\n",
      "Training epoch 177, recon_loss:0.608655, zinb_loss:0.957586, cluster_loss:0.313504\n",
      "Clustering   177: AMI= 0.7194, NMI= 0.7203, ARI= 0.5029, ACC= 0.6109\n",
      "0.04336495785659025\n",
      "Training epoch 178, recon_loss:0.611623, zinb_loss:0.958914, cluster_loss:0.314109\n",
      "Clustering   178: AMI= 0.7224, NMI= 0.7233, ARI= 0.5112, ACC= 0.6216\n",
      "0.0414104808827721\n",
      "Training epoch 179, recon_loss:0.610148, zinb_loss:0.957474, cluster_loss:0.313181\n",
      "Clustering   179: AMI= 0.7193, NMI= 0.7202, ARI= 0.5038, ACC= 0.6111\n",
      "0.03892666639521153\n",
      "Training epoch 180, recon_loss:0.611013, zinb_loss:0.958622, cluster_loss:0.314233\n",
      "Clustering   180: AMI= 0.7221, NMI= 0.7230, ARI= 0.5100, ACC= 0.6209\n",
      "0.03754224520542367\n",
      "Training epoch 181, recon_loss:0.609433, zinb_loss:0.957331, cluster_loss:0.313341\n",
      "Clustering   181: AMI= 0.7194, NMI= 0.7203, ARI= 0.5040, ACC= 0.6113\n",
      "0.03717578077283277\n",
      "Training epoch 182, recon_loss:0.611130, zinb_loss:0.958281, cluster_loss:0.314487\n",
      "Clustering   182: AMI= 0.7220, NMI= 0.7229, ARI= 0.5099, ACC= 0.6207\n",
      "0.03550633169102976\n",
      "Training epoch 183, recon_loss:0.609504, zinb_loss:0.957085, cluster_loss:0.313458\n",
      "Clustering   183: AMI= 0.7194, NMI= 0.7204, ARI= 0.5042, ACC= 0.6116\n",
      "0.03351113644692374\n",
      "Training epoch 184, recon_loss:0.610534, zinb_loss:0.957986, cluster_loss:0.314667\n",
      "Clustering   184: AMI= 0.7217, NMI= 0.7227, ARI= 0.5095, ACC= 0.6205\n",
      "0.032411743149151025\n",
      "Training epoch 185, recon_loss:0.609007, zinb_loss:0.956890, cluster_loss:0.313731\n",
      "Clustering   185: AMI= 0.7195, NMI= 0.7204, ARI= 0.5041, ACC= 0.6118\n",
      "0.03159737774339346\n",
      "Training epoch 186, recon_loss:0.610299, zinb_loss:0.957778, cluster_loss:0.314860\n",
      "Clustering   186: AMI= 0.7219, NMI= 0.7228, ARI= 0.5094, ACC= 0.6204\n",
      "0.030457266175332873\n",
      "Training epoch 187, recon_loss:0.608846, zinb_loss:0.956754, cluster_loss:0.313935\n",
      "Clustering   187: AMI= 0.7196, NMI= 0.7205, ARI= 0.5044, ACC= 0.6122\n",
      "0.02915428152612077\n",
      "Training epoch 188, recon_loss:0.610122, zinb_loss:0.957683, cluster_loss:0.314996\n",
      "Clustering   188: AMI= 0.7218, NMI= 0.7228, ARI= 0.5096, ACC= 0.6204\n",
      "0.028665662282666232\n",
      "Training epoch 189, recon_loss:0.608788, zinb_loss:0.956705, cluster_loss:0.314092\n",
      "Clustering   189: AMI= 0.7198, NMI= 0.7207, ARI= 0.5045, ACC= 0.6124\n",
      "0.028217761309499573\n",
      "Training epoch 190, recon_loss:0.610123, zinb_loss:0.957676, cluster_loss:0.315081\n",
      "Clustering   190: AMI= 0.7221, NMI= 0.7230, ARI= 0.5096, ACC= 0.6203\n",
      "0.02833991612036321\n",
      "Training epoch 191, recon_loss:0.608894, zinb_loss:0.956726, cluster_loss:0.314171\n",
      "Clustering   191: AMI= 0.7197, NMI= 0.7206, ARI= 0.5044, ACC= 0.6123\n",
      "0.028217761309499573\n",
      "Training epoch 192, recon_loss:0.610287, zinb_loss:0.957739, cluster_loss:0.315100\n",
      "Clustering   192: AMI= 0.7222, NMI= 0.7231, ARI= 0.5096, ACC= 0.6202\n",
      "0.028584225742090477\n",
      "Training epoch 193, recon_loss:0.609166, zinb_loss:0.956805, cluster_loss:0.314158\n",
      "Clustering   193: AMI= 0.7198, NMI= 0.7207, ARI= 0.5043, ACC= 0.6119\n",
      "0.028584225742090477\n",
      "Training epoch 194, recon_loss:0.610449, zinb_loss:0.957822, cluster_loss:0.315036\n",
      "Clustering   194: AMI= 0.7223, NMI= 0.7232, ARI= 0.5096, ACC= 0.6202\n",
      "0.029235718066696528\n",
      "Training epoch 195, recon_loss:0.609537, zinb_loss:0.956902, cluster_loss:0.314095\n",
      "Clustering   195: AMI= 0.7195, NMI= 0.7205, ARI= 0.5041, ACC= 0.6116\n",
      "0.029724337310151065\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training epoch 196, recon_loss:0.610897, zinb_loss:0.957871, cluster_loss:0.314917\n",
      "Clustering   196: AMI= 0.7222, NMI= 0.7231, ARI= 0.5091, ACC= 0.6199\n",
      "0.030905167148499533\n",
      "Training epoch 197, recon_loss:0.610099, zinb_loss:0.956976, cluster_loss:0.313954\n",
      "Clustering   197: AMI= 0.7196, NMI= 0.7205, ARI= 0.5041, ACC= 0.6116\n",
      "0.031027321959363165\n",
      "Training epoch 198, recon_loss:0.610682, zinb_loss:0.957800, cluster_loss:0.314737\n",
      "Clustering   198: AMI= 0.7223, NMI= 0.7232, ARI= 0.5091, ACC= 0.6196\n",
      "0.0311494767702268\n",
      "Training epoch 199, recon_loss:0.610417, zinb_loss:0.956985, cluster_loss:0.313953\n",
      "Clustering   199: AMI= 0.7195, NMI= 0.7204, ARI= 0.5042, ACC= 0.6116\n",
      "0.03155665947310558\n",
      "Training epoch 200, recon_loss:0.611210, zinb_loss:0.957638, cluster_loss:0.314636\n",
      "Clustering   200: AMI= 0.7225, NMI= 0.7235, ARI= 0.5091, ACC= 0.6192\n",
      "0.03131234985137831\n",
      "Training epoch 201, recon_loss:0.610836, zinb_loss:0.956913, cluster_loss:0.313926\n",
      "Clustering   201: AMI= 0.7196, NMI= 0.7206, ARI= 0.5047, ACC= 0.6120\n",
      "0.030986603689075288\n",
      "Training epoch 202, recon_loss:0.610744, zinb_loss:0.957363, cluster_loss:0.314636\n",
      "Clustering   202: AMI= 0.7226, NMI= 0.7235, ARI= 0.5091, ACC= 0.6190\n",
      "0.030375829634757115\n",
      "Training epoch 203, recon_loss:0.610859, zinb_loss:0.956804, cluster_loss:0.314162\n",
      "Clustering   203: AMI= 0.7201, NMI= 0.7210, ARI= 0.5051, ACC= 0.6126\n",
      "0.02956146422899955\n",
      "Training epoch 204, recon_loss:0.611062, zinb_loss:0.957097, cluster_loss:0.314771\n",
      "Clustering   204: AMI= 0.7224, NMI= 0.7233, ARI= 0.5090, ACC= 0.6188\n",
      "0.0289099719043935\n",
      "Training epoch 205, recon_loss:0.610963, zinb_loss:0.956711, cluster_loss:0.314350\n",
      "Clustering   205: AMI= 0.7203, NMI= 0.7212, ARI= 0.5056, ACC= 0.6129\n",
      "0.028299197850075328\n",
      "Training epoch 206, recon_loss:0.610374, zinb_loss:0.956868, cluster_loss:0.314935\n",
      "Clustering   206: AMI= 0.7225, NMI= 0.7234, ARI= 0.5091, ACC= 0.6192\n",
      "0.027892015147196546\n",
      "Training epoch 207, recon_loss:0.610833, zinb_loss:0.956670, cluster_loss:0.314713\n",
      "Clustering   207: AMI= 0.7204, NMI= 0.7213, ARI= 0.5055, ACC= 0.6130\n",
      "0.027118368011726863\n",
      "Training epoch 208, recon_loss:0.611048, zinb_loss:0.956745, cluster_loss:0.315123\n",
      "Clustering   208: AMI= 0.7224, NMI= 0.7233, ARI= 0.5085, ACC= 0.6186\n",
      "0.026344720876257176\n",
      "Training epoch 209, recon_loss:0.611142, zinb_loss:0.956708, cluster_loss:0.314809\n",
      "Clustering   209: AMI= 0.7202, NMI= 0.7211, ARI= 0.5057, ACC= 0.6131\n",
      "0.025896819903090517\n",
      "Training epoch 210, recon_loss:0.610380, zinb_loss:0.956690, cluster_loss:0.315211\n",
      "Clustering   210: AMI= 0.7221, NMI= 0.7230, ARI= 0.5078, ACC= 0.6180\n",
      "0.02626328433568142\n",
      "Training epoch 211, recon_loss:0.611125, zinb_loss:0.956820, cluster_loss:0.315070\n",
      "Clustering   211: AMI= 0.7199, NMI= 0.7208, ARI= 0.5052, ACC= 0.6129\n",
      "0.02642615741683293\n",
      "Training epoch 212, recon_loss:0.611538, zinb_loss:0.956747, cluster_loss:0.315299\n",
      "Clustering   212: AMI= 0.7222, NMI= 0.7231, ARI= 0.5073, ACC= 0.6179\n",
      "0.0276069872551814\n",
      "Training epoch 213, recon_loss:0.611816, zinb_loss:0.956997, cluster_loss:0.314945\n",
      "Clustering   213: AMI= 0.7203, NMI= 0.7212, ARI= 0.5058, ACC= 0.6124\n",
      "0.02850278920151472\n",
      "Training epoch 214, recon_loss:0.610891, zinb_loss:0.956852, cluster_loss:0.315233\n",
      "Clustering   214: AMI= 0.7219, NMI= 0.7228, ARI= 0.5067, ACC= 0.6175\n",
      "0.029927928661590456\n",
      "Training epoch 215, recon_loss:0.611641, zinb_loss:0.957161, cluster_loss:0.315076\n",
      "Clustering   215: AMI= 0.7200, NMI= 0.7209, ARI= 0.5051, ACC= 0.6116\n",
      "0.03180096909483285\n",
      "Training epoch 216, recon_loss:0.612018, zinb_loss:0.956984, cluster_loss:0.315229\n",
      "Clustering   216: AMI= 0.7222, NMI= 0.7231, ARI= 0.5068, ACC= 0.6176\n",
      "0.03367400952807525\n",
      "Training epoch 217, recon_loss:0.612204, zinb_loss:0.957281, cluster_loss:0.314864\n",
      "Clustering   217: AMI= 0.7205, NMI= 0.7214, ARI= 0.5060, ACC= 0.6118\n",
      "0.03607638747506006\n",
      "Training epoch 218, recon_loss:0.611100, zinb_loss:0.957062, cluster_loss:0.315160\n",
      "Clustering   218: AMI= 0.7220, NMI= 0.7229, ARI= 0.5065, ACC= 0.6178\n",
      "0.03774583655686307\n",
      "Training epoch 219, recon_loss:0.611481, zinb_loss:0.957245, cluster_loss:0.315038\n",
      "Clustering   219: AMI= 0.7205, NMI= 0.7214, ARI= 0.5053, ACC= 0.6109\n",
      "0.03847876542204487\n",
      "Training epoch 220, recon_loss:0.611518, zinb_loss:0.957060, cluster_loss:0.315289\n",
      "Clustering   220: AMI= 0.7219, NMI= 0.7228, ARI= 0.5062, ACC= 0.6178\n",
      "0.039089539476363046\n",
      "Training epoch 221, recon_loss:0.611382, zinb_loss:0.957115, cluster_loss:0.314988\n",
      "Clustering   221: AMI= 0.7205, NMI= 0.7214, ARI= 0.5054, ACC= 0.6109\n",
      "0.03945600390895395\n",
      "Training epoch 222, recon_loss:0.610697, zinb_loss:0.957030, cluster_loss:0.315394\n",
      "Clustering   222: AMI= 0.7221, NMI= 0.7230, ARI= 0.5064, ACC= 0.6177\n",
      "0.03994462315240849\n",
      "Training epoch 223, recon_loss:0.610627, zinb_loss:0.956958, cluster_loss:0.315195\n",
      "Clustering   223: AMI= 0.7203, NMI= 0.7212, ARI= 0.5051, ACC= 0.6109\n",
      "0.040148214503847875\n",
      "Training epoch 224, recon_loss:0.611064, zinb_loss:0.957010, cluster_loss:0.315591\n",
      "Clustering   224: AMI= 0.7218, NMI= 0.7227, ARI= 0.5065, ACC= 0.6176\n",
      "0.039985341422696365\n",
      "Training epoch 225, recon_loss:0.610524, zinb_loss:0.956813, cluster_loss:0.315186\n",
      "Clustering   225: AMI= 0.7204, NMI= 0.7213, ARI= 0.5052, ACC= 0.6113\n",
      "0.03982246834154485\n",
      "Training epoch 226, recon_loss:0.610389, zinb_loss:0.957008, cluster_loss:0.315705\n",
      "Clustering   226: AMI= 0.7219, NMI= 0.7228, ARI= 0.5068, ACC= 0.6177\n",
      "0.040270369314711514\n",
      "Training epoch 227, recon_loss:0.609957, zinb_loss:0.956723, cluster_loss:0.315373\n",
      "Clustering   227: AMI= 0.7206, NMI= 0.7216, ARI= 0.5053, ACC= 0.6116\n",
      "0.04006677796327212\n",
      "Training epoch 228, recon_loss:0.611048, zinb_loss:0.957057, cluster_loss:0.315885\n",
      "Clustering   228: AMI= 0.7219, NMI= 0.7228, ARI= 0.5071, ACC= 0.6178\n",
      "0.03978175007125697\n",
      "Training epoch 229, recon_loss:0.610076, zinb_loss:0.956653, cluster_loss:0.315313\n",
      "Clustering   229: AMI= 0.7208, NMI= 0.7217, ARI= 0.5054, ACC= 0.6118\n",
      "0.04002605969298424\n",
      "Training epoch 230, recon_loss:0.610408, zinb_loss:0.957107, cluster_loss:0.315973\n",
      "Clustering   230: AMI= 0.7220, NMI= 0.7230, ARI= 0.5073, ACC= 0.6176\n",
      "0.03982246834154485\n",
      "Training epoch 231, recon_loss:0.609650, zinb_loss:0.956651, cluster_loss:0.315515\n",
      "Clustering   231: AMI= 0.7208, NMI= 0.7217, ARI= 0.5052, ACC= 0.6118\n",
      "0.03937456736837819\n",
      "Training epoch 232, recon_loss:0.611161, zinb_loss:0.957212, cluster_loss:0.316144\n",
      "Clustering   232: AMI= 0.7224, NMI= 0.7233, ARI= 0.5080, ACC= 0.6178\n",
      "0.03876379331406002\n",
      "Training epoch 233, recon_loss:0.609798, zinb_loss:0.956637, cluster_loss:0.315479\n",
      "Clustering   233: AMI= 0.7209, NMI= 0.7218, ARI= 0.5052, ACC= 0.6119\n",
      "0.03815301925974185\n",
      "Training epoch 234, recon_loss:0.610485, zinb_loss:0.957318, cluster_loss:0.316237\n",
      "Clustering   234: AMI= 0.7225, NMI= 0.7235, ARI= 0.5084, ACC= 0.6182\n",
      "0.0378679913677267\n",
      "Training epoch 235, recon_loss:0.609417, zinb_loss:0.956715, cluster_loss:0.315751\n",
      "Clustering   235: AMI= 0.7206, NMI= 0.7215, ARI= 0.5045, ACC= 0.6116\n",
      "0.0378679913677267\n",
      "Training epoch 236, recon_loss:0.611155, zinb_loss:0.957497, cluster_loss:0.316398\n",
      "Clustering   236: AMI= 0.7228, NMI= 0.7237, ARI= 0.5089, ACC= 0.6183\n",
      "0.03737937212427216\n",
      "Training epoch 237, recon_loss:0.609453, zinb_loss:0.956778, cluster_loss:0.315803\n",
      "Clustering   237: AMI= 0.7207, NMI= 0.7216, ARI= 0.5046, ACC= 0.6118\n",
      "0.0365650067185146\n",
      "Training epoch 238, recon_loss:0.610534, zinb_loss:0.957710, cluster_loss:0.316471\n",
      "Clustering   238: AMI= 0.7229, NMI= 0.7238, ARI= 0.5092, ACC= 0.6181\n",
      "0.036483570177938844\n",
      "Training epoch 239, recon_loss:0.609130, zinb_loss:0.956963, cluster_loss:0.316109\n",
      "Clustering   239: AMI= 0.7204, NMI= 0.7213, ARI= 0.5034, ACC= 0.6111\n",
      "0.03583207785333279\n",
      "Training epoch 240, recon_loss:0.611087, zinb_loss:0.958045, cluster_loss:0.316533\n",
      "Clustering   240: AMI= 0.7229, NMI= 0.7238, ARI= 0.5098, ACC= 0.6183\n",
      "0.03615782401563582\n",
      "Training epoch 241, recon_loss:0.609130, zinb_loss:0.957183, cluster_loss:0.316235\n",
      "Clustering   241: AMI= 0.7204, NMI= 0.7213, ARI= 0.5028, ACC= 0.6110\n",
      "0.03701290769168126\n",
      "Training epoch 242, recon_loss:0.610920, zinb_loss:0.958479, cluster_loss:0.316449\n",
      "Clustering   242: AMI= 0.7233, NMI= 0.7242, ARI= 0.5108, ACC= 0.6187\n",
      "0.03803086444887821\n",
      "Training epoch 243, recon_loss:0.609061, zinb_loss:0.957554, cluster_loss:0.316484\n",
      "Clustering   243: AMI= 0.7201, NMI= 0.7210, ARI= 0.5017, ACC= 0.6105\n",
      "0.03978175007125697\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training epoch 244, recon_loss:0.611231, zinb_loss:0.959043, cluster_loss:0.316175\n",
      "Clustering   244: AMI= 0.7237, NMI= 0.7246, ARI= 0.5119, ACC= 0.6191\n",
      "0.041736227045075125\n",
      "Training epoch 245, recon_loss:0.609307, zinb_loss:0.958027, cluster_loss:0.316634\n",
      "Clustering   245: AMI= 0.7201, NMI= 0.7210, ARI= 0.5001, ACC= 0.6096\n",
      "0.044220041532635694\n",
      "Training epoch 246, recon_loss:0.611599, zinb_loss:0.959640, cluster_loss:0.315659\n",
      "Clustering   246: AMI= 0.7239, NMI= 0.7248, ARI= 0.5127, ACC= 0.6188\n",
      "0.046826010831059896\n",
      "Training epoch 247, recon_loss:0.609914, zinb_loss:0.958498, cluster_loss:0.316718\n",
      "Clustering   247: AMI= 0.7197, NMI= 0.7206, ARI= 0.4984, ACC= 0.6091\n",
      "0.05065352823812044\n",
      "Training epoch 248, recon_loss:0.611967, zinb_loss:0.959978, cluster_loss:0.315127\n",
      "Clustering   248: AMI= 0.7244, NMI= 0.7253, ARI= 0.5135, ACC= 0.6192\n",
      "0.054236736023453726\n",
      "Training epoch 249, recon_loss:0.610779, zinb_loss:0.958652, cluster_loss:0.316854\n",
      "Clustering   249: AMI= 0.7197, NMI= 0.7206, ARI= 0.4967, ACC= 0.6078\n",
      "0.05753491591677186\n",
      "Training epoch 250, recon_loss:0.611834, zinb_loss:0.959774, cluster_loss:0.314930\n",
      "Clustering   250: AMI= 0.7243, NMI= 0.7252, ARI= 0.5136, ACC= 0.6194\n",
      "0.05924508326886274\n",
      "Training epoch 251, recon_loss:0.611626, zinb_loss:0.958403, cluster_loss:0.317142\n",
      "Clustering   251: AMI= 0.7198, NMI= 0.7207, ARI= 0.4964, ACC= 0.6074\n",
      "0.05900077364713547\n",
      "Training epoch 252, recon_loss:0.612315, zinb_loss:0.959255, cluster_loss:0.314994\n",
      "Clustering   252: AMI= 0.7246, NMI= 0.7255, ARI= 0.5144, ACC= 0.6198\n",
      "0.0587564640254082\n",
      "Training epoch 253, recon_loss:0.610826, zinb_loss:0.957910, cluster_loss:0.317369\n",
      "Clustering   253: AMI= 0.7200, NMI= 0.7209, ARI= 0.4968, ACC= 0.6079\n",
      "0.05692414186245368\n",
      "Training epoch 254, recon_loss:0.611784, zinb_loss:0.958609, cluster_loss:0.315468\n",
      "Clustering   254: AMI= 0.7245, NMI= 0.7254, ARI= 0.5140, ACC= 0.6192\n",
      "0.053096624455393135\n",
      "Training epoch 255, recon_loss:0.611447, zinb_loss:0.957485, cluster_loss:0.317661\n",
      "Clustering   255: AMI= 0.7201, NMI= 0.7210, ARI= 0.4974, ACC= 0.6084\n",
      "0.0507349647786962\n",
      "Training epoch 256, recon_loss:0.612136, zinb_loss:0.958159, cluster_loss:0.315665\n",
      "Clustering   256: AMI= 0.7245, NMI= 0.7254, ARI= 0.5138, ACC= 0.6192\n",
      "0.04910623396718108\n",
      "Training epoch 257, recon_loss:0.610752, zinb_loss:0.957149, cluster_loss:0.317833\n",
      "Clustering   257: AMI= 0.7203, NMI= 0.7212, ARI= 0.4982, ACC= 0.6089\n",
      "0.046215236776741726\n",
      "Training epoch 258, recon_loss:0.611266, zinb_loss:0.957733, cluster_loss:0.316081\n",
      "Clustering   258: AMI= 0.7243, NMI= 0.7252, ARI= 0.5132, ACC= 0.6188\n",
      "0.04360926747831752\n",
      "Training epoch 259, recon_loss:0.611017, zinb_loss:0.956915, cluster_loss:0.318093\n",
      "Clustering   259: AMI= 0.7205, NMI= 0.7214, ARI= 0.4986, ACC= 0.6091\n",
      "0.041573353963923615\n",
      "Training epoch 260, recon_loss:0.611265, zinb_loss:0.957447, cluster_loss:0.316262\n",
      "Clustering   260: AMI= 0.7244, NMI= 0.7253, ARI= 0.5129, ACC= 0.6186\n",
      "0.04035180585528727\n",
      "Training epoch 261, recon_loss:0.610602, zinb_loss:0.956774, cluster_loss:0.318258\n",
      "Clustering   261: AMI= 0.7207, NMI= 0.7216, ARI= 0.4994, ACC= 0.6097\n",
      "0.03831589234089336\n",
      "Training epoch 262, recon_loss:0.610795, zinb_loss:0.957201, cluster_loss:0.316515\n",
      "Clustering   262: AMI= 0.7240, NMI= 0.7249, ARI= 0.5122, ACC= 0.6180\n",
      "0.036483570177938844\n",
      "Training epoch 263, recon_loss:0.610954, zinb_loss:0.956705, cluster_loss:0.318463\n",
      "Clustering   263: AMI= 0.7209, NMI= 0.7218, ARI= 0.5000, ACC= 0.6103\n",
      "0.03534345860987825\n",
      "Training epoch 264, recon_loss:0.610889, zinb_loss:0.957022, cluster_loss:0.316581\n",
      "Clustering   264: AMI= 0.7239, NMI= 0.7248, ARI= 0.5117, ACC= 0.6174\n",
      "0.03497699417728735\n",
      "Training epoch 265, recon_loss:0.610788, zinb_loss:0.956690, cluster_loss:0.318596\n",
      "Clustering   265: AMI= 0.7211, NMI= 0.7220, ARI= 0.5008, ACC= 0.6110\n",
      "0.03383688260922676\n",
      "Training epoch 266, recon_loss:0.610726, zinb_loss:0.956858, cluster_loss:0.316674\n",
      "Clustering   266: AMI= 0.7237, NMI= 0.7246, ARI= 0.5107, ACC= 0.6166\n",
      "0.03290036239260556\n",
      "Training epoch 267, recon_loss:0.611726, zinb_loss:0.956776, cluster_loss:0.318750\n",
      "Clustering   267: AMI= 0.7214, NMI= 0.7224, ARI= 0.5013, ACC= 0.6114\n",
      "0.03265605277087829\n",
      "Training epoch 268, recon_loss:0.611284, zinb_loss:0.956750, cluster_loss:0.316496\n",
      "Clustering   268: AMI= 0.7235, NMI= 0.7244, ARI= 0.5103, ACC= 0.6159\n",
      "0.03420334704181766\n",
      "Training epoch 269, recon_loss:0.611562, zinb_loss:0.956892, cluster_loss:0.318766\n",
      "Clustering   269: AMI= 0.7216, NMI= 0.7226, ARI= 0.5015, ACC= 0.6119\n",
      "0.03473268455556008\n",
      "Training epoch 270, recon_loss:0.611361, zinb_loss:0.956671, cluster_loss:0.316436\n",
      "Clustering   270: AMI= 0.7229, NMI= 0.7238, ARI= 0.5090, ACC= 0.6148\n",
      "0.034447656663544934\n",
      "Training epoch 271, recon_loss:0.612953, zinb_loss:0.957145, cluster_loss:0.318779\n",
      "Clustering   271: AMI= 0.7220, NMI= 0.7229, ARI= 0.5025, ACC= 0.6131\n",
      "0.03440693839325706\n",
      "Training epoch 272, recon_loss:0.612090, zinb_loss:0.956669, cluster_loss:0.316079\n",
      "Clustering   272: AMI= 0.7226, NMI= 0.7235, ARI= 0.5082, ACC= 0.6136\n",
      "0.03587279612362067\n",
      "Training epoch 273, recon_loss:0.612922, zinb_loss:0.957401, cluster_loss:0.318655\n",
      "Clustering   273: AMI= 0.7225, NMI= 0.7234, ARI= 0.5032, ACC= 0.6139\n",
      "0.035913514393908545\n",
      "Training epoch 274, recon_loss:0.612248, zinb_loss:0.956731, cluster_loss:0.316001\n",
      "Clustering   274: AMI= 0.7219, NMI= 0.7228, ARI= 0.5065, ACC= 0.6121\n",
      "0.035017712447575226\n",
      "Training epoch 275, recon_loss:0.613842, zinb_loss:0.957757, cluster_loss:0.318557\n",
      "Clustering   275: AMI= 0.7229, NMI= 0.7239, ARI= 0.5041, ACC= 0.6149\n",
      "0.03497699417728735\n",
      "Training epoch 276, recon_loss:0.612292, zinb_loss:0.956879, cluster_loss:0.315902\n",
      "Clustering   276: AMI= 0.7213, NMI= 0.7222, ARI= 0.5051, ACC= 0.6108\n",
      "0.03632069709678733\n",
      "Training epoch 277, recon_loss:0.613942, zinb_loss:0.958190, cluster_loss:0.318473\n",
      "Clustering   277: AMI= 0.7234, NMI= 0.7243, ARI= 0.5056, ACC= 0.6161\n",
      "0.036361415367075205\n",
      "Training epoch 278, recon_loss:0.612190, zinb_loss:0.957184, cluster_loss:0.315993\n",
      "Clustering   278: AMI= 0.7206, NMI= 0.7216, ARI= 0.5035, ACC= 0.6091\n",
      "0.036035669204772185\n",
      "Training epoch 279, recon_loss:0.613957, zinb_loss:0.958771, cluster_loss:0.318374\n",
      "Clustering   279: AMI= 0.7237, NMI= 0.7246, ARI= 0.5064, ACC= 0.6168\n",
      "0.03615782401563582\n",
      "Training epoch 280, recon_loss:0.612087, zinb_loss:0.957675, cluster_loss:0.316170\n",
      "Clustering   280: AMI= 0.7199, NMI= 0.7209, ARI= 0.5022, ACC= 0.6079\n",
      "0.03550633169102976\n",
      "Training epoch 281, recon_loss:0.613718, zinb_loss:0.959457, cluster_loss:0.318248\n",
      "Clustering   281: AMI= 0.7239, NMI= 0.7248, ARI= 0.5074, ACC= 0.6178\n",
      "0.035180585528726736\n",
      "Training epoch 282, recon_loss:0.612444, zinb_loss:0.958217, cluster_loss:0.316455\n",
      "Clustering   282: AMI= 0.7197, NMI= 0.7206, ARI= 0.5012, ACC= 0.6071\n",
      "0.03542489515045401\n",
      "Training epoch 283, recon_loss:0.613497, zinb_loss:0.960026, cluster_loss:0.318047\n",
      "Clustering   283: AMI= 0.7245, NMI= 0.7254, ARI= 0.5092, ACC= 0.6188\n",
      "0.03595423266419642\n",
      "Training epoch 284, recon_loss:0.612869, zinb_loss:0.958562, cluster_loss:0.316830\n",
      "Clustering   284: AMI= 0.7192, NMI= 0.7201, ARI= 0.4994, ACC= 0.6058\n",
      "0.03668716152937823\n",
      "Training epoch 285, recon_loss:0.613922, zinb_loss:0.960248, cluster_loss:0.317901\n",
      "Clustering   285: AMI= 0.7245, NMI= 0.7254, ARI= 0.5100, ACC= 0.6189\n",
      "0.036646443259090354\n",
      "Training epoch 286, recon_loss:0.611754, zinb_loss:0.958487, cluster_loss:0.317166\n",
      "Clustering   286: AMI= 0.7195, NMI= 0.7204, ARI= 0.4990, ACC= 0.6057\n",
      "0.03627997882649945\n",
      "Training epoch 287, recon_loss:0.612856, zinb_loss:0.960026, cluster_loss:0.318170\n",
      "Clustering   287: AMI= 0.7244, NMI= 0.7253, ARI= 0.5102, ACC= 0.6188\n",
      "0.035465613420741886\n",
      "Training epoch 288, recon_loss:0.612063, zinb_loss:0.958245, cluster_loss:0.317611\n",
      "Clustering   288: AMI= 0.7193, NMI= 0.7202, ARI= 0.4985, ACC= 0.6057\n",
      "0.03404047396066615\n",
      "Training epoch 289, recon_loss:0.612836, zinb_loss:0.959754, cluster_loss:0.318178\n",
      "Clustering   289: AMI= 0.7251, NMI= 0.7260, ARI= 0.5112, ACC= 0.6195\n",
      "0.03379616433893888\n",
      "Training epoch 290, recon_loss:0.610673, zinb_loss:0.957988, cluster_loss:0.317946\n",
      "Clustering   290: AMI= 0.7195, NMI= 0.7204, ARI= 0.4984, ACC= 0.6060\n",
      "0.033144672014332834\n",
      "Training epoch 291, recon_loss:0.611592, zinb_loss:0.959410, cluster_loss:0.318513\n",
      "Clustering   291: AMI= 0.7246, NMI= 0.7255, ARI= 0.5104, ACC= 0.6190\n",
      "0.03155665947310558\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training epoch 292, recon_loss:0.611073, zinb_loss:0.957819, cluster_loss:0.318326\n",
      "Clustering   292: AMI= 0.7198, NMI= 0.7208, ARI= 0.4988, ACC= 0.6065\n",
      "0.0311494767702268\n",
      "Training epoch 293, recon_loss:0.611577, zinb_loss:0.959245, cluster_loss:0.318491\n",
      "Clustering   293: AMI= 0.7247, NMI= 0.7256, ARI= 0.5107, ACC= 0.6189\n",
      "0.032411743149151025\n",
      "Training epoch 294, recon_loss:0.610007, zinb_loss:0.957731, cluster_loss:0.318561\n",
      "Clustering   294: AMI= 0.7197, NMI= 0.7206, ARI= 0.4984, ACC= 0.6066\n",
      "0.03322610855490859\n",
      "Training epoch 295, recon_loss:0.610830, zinb_loss:0.959112, cluster_loss:0.318701\n",
      "Clustering   295: AMI= 0.7247, NMI= 0.7257, ARI= 0.5105, ACC= 0.6187\n",
      "0.033022517203469194\n",
      "Training epoch 296, recon_loss:0.610481, zinb_loss:0.957729, cluster_loss:0.318830\n",
      "Clustering   296: AMI= 0.7197, NMI= 0.7207, ARI= 0.4983, ACC= 0.6064\n",
      "0.033266826825196466\n",
      "Training epoch 297, recon_loss:0.610803, zinb_loss:0.959094, cluster_loss:0.318614\n",
      "Clustering   297: AMI= 0.7249, NMI= 0.7259, ARI= 0.5111, ACC= 0.6191\n",
      "0.035017712447575226\n",
      "Training epoch 298, recon_loss:0.609772, zinb_loss:0.957766, cluster_loss:0.319009\n",
      "Clustering   298: AMI= 0.7198, NMI= 0.7208, ARI= 0.4982, ACC= 0.6066\n",
      "0.0359949509344843\n",
      "Training epoch 299, recon_loss:0.610423, zinb_loss:0.959071, cluster_loss:0.318714\n",
      "Clustering   299: AMI= 0.7247, NMI= 0.7256, ARI= 0.5104, ACC= 0.6187\n",
      "0.03652428844822672\n",
      "Training epoch 300, recon_loss:0.610202, zinb_loss:0.957832, cluster_loss:0.319221\n",
      "Clustering   300: AMI= 0.7199, NMI= 0.7208, ARI= 0.4980, ACC= 0.6066\n",
      "0.037094344232257014\n",
      "Training epoch 301, recon_loss:0.610351, zinb_loss:0.959083, cluster_loss:0.318619\n",
      "Clustering   301: AMI= 0.7249, NMI= 0.7258, ARI= 0.5110, ACC= 0.6187\n",
      "0.03921169428722668\n",
      "Training epoch 302, recon_loss:0.609917, zinb_loss:0.957906, cluster_loss:0.319405\n",
      "Clustering   302: AMI= 0.7200, NMI= 0.7209, ARI= 0.4978, ACC= 0.6064\n",
      "0.03986318661183273\n",
      "Training epoch 303, recon_loss:0.610193, zinb_loss:0.959053, cluster_loss:0.318616\n",
      "Clustering   303: AMI= 0.7249, NMI= 0.7258, ARI= 0.5112, ACC= 0.6183\n",
      "0.04075898855816605\n",
      "Training epoch 304, recon_loss:0.610027, zinb_loss:0.957945, cluster_loss:0.319588\n",
      "Clustering   304: AMI= 0.7199, NMI= 0.7208, ARI= 0.4974, ACC= 0.6064\n",
      "0.04149191742334785\n",
      "Training epoch 305, recon_loss:0.610167, zinb_loss:0.959012, cluster_loss:0.318546\n",
      "Clustering   305: AMI= 0.7248, NMI= 0.7257, ARI= 0.5112, ACC= 0.6179\n",
      "0.042428437639969056\n",
      "Training epoch 306, recon_loss:0.610041, zinb_loss:0.957981, cluster_loss:0.319746\n",
      "Clustering   306: AMI= 0.7198, NMI= 0.7208, ARI= 0.4971, ACC= 0.6065\n",
      "0.0430799299645751\n",
      "Training epoch 307, recon_loss:0.610249, zinb_loss:0.958946, cluster_loss:0.318443\n",
      "Clustering   307: AMI= 0.7244, NMI= 0.7253, ARI= 0.5112, ACC= 0.6172\n",
      "0.04381285882975691\n",
      "Training epoch 308, recon_loss:0.610238, zinb_loss:0.957989, cluster_loss:0.319844\n",
      "Clustering   308: AMI= 0.7202, NMI= 0.7211, ARI= 0.4976, ACC= 0.6075\n",
      "0.04352783093774176\n",
      "Training epoch 309, recon_loss:0.610498, zinb_loss:0.958849, cluster_loss:0.318279\n",
      "Clustering   309: AMI= 0.7245, NMI= 0.7254, ARI= 0.5112, ACC= 0.6168\n",
      "0.044627224235514476\n",
      "Training epoch 310, recon_loss:0.610532, zinb_loss:0.957971, cluster_loss:0.319858\n",
      "Clustering   310: AMI= 0.7203, NMI= 0.7212, ARI= 0.4975, ACC= 0.6080\n",
      "0.04442363288407508\n",
      "Training epoch 311, recon_loss:0.610876, zinb_loss:0.958708, cluster_loss:0.318116\n",
      "Clustering   311: AMI= 0.7241, NMI= 0.7250, ARI= 0.5108, ACC= 0.6160\n",
      "0.04503440693839326\n",
      "Training epoch 312, recon_loss:0.610903, zinb_loss:0.957921, cluster_loss:0.319823\n",
      "Clustering   312: AMI= 0.7204, NMI= 0.7214, ARI= 0.4979, ACC= 0.6084\n",
      "0.04417932326234782\n",
      "Training epoch 313, recon_loss:0.611190, zinb_loss:0.958524, cluster_loss:0.318068\n",
      "Clustering   313: AMI= 0.7239, NMI= 0.7249, ARI= 0.5105, ACC= 0.6156\n",
      "0.04409788672177206\n",
      "Training epoch 314, recon_loss:0.611121, zinb_loss:0.957837, cluster_loss:0.319803\n",
      "Clustering   314: AMI= 0.7208, NMI= 0.7217, ARI= 0.4987, ACC= 0.6091\n",
      "0.04356854920802964\n",
      "Training epoch 315, recon_loss:0.611329, zinb_loss:0.958325, cluster_loss:0.318202\n",
      "Clustering   315: AMI= 0.7238, NMI= 0.7247, ARI= 0.5101, ACC= 0.6152\n",
      "0.042876338613135716\n",
      "Training epoch 316, recon_loss:0.611147, zinb_loss:0.957738, cluster_loss:0.319823\n",
      "Clustering   316: AMI= 0.7211, NMI= 0.7221, ARI= 0.4994, ACC= 0.6096\n",
      "0.041451199153059975\n",
      "Training epoch 317, recon_loss:0.611515, zinb_loss:0.958182, cluster_loss:0.318472\n",
      "Clustering   317: AMI= 0.7240, NMI= 0.7249, ARI= 0.5098, ACC= 0.6152\n",
      "0.04022965104442363\n",
      "Training epoch 318, recon_loss:0.611127, zinb_loss:0.957676, cluster_loss:0.319813\n",
      "Clustering   318: AMI= 0.7211, NMI= 0.7221, ARI= 0.5000, ACC= 0.6099\n",
      "0.03961887699010546\n",
      "Training epoch 319, recon_loss:0.612779, zinb_loss:0.958178, cluster_loss:0.318818\n",
      "Clustering   319: AMI= 0.7236, NMI= 0.7246, ARI= 0.5086, ACC= 0.6148\n",
      "0.03860092023290851\n",
      "Training epoch 320, recon_loss:0.612381, zinb_loss:0.957683, cluster_loss:0.319465\n",
      "Clustering   320: AMI= 0.7213, NMI= 0.7222, ARI= 0.5012, ACC= 0.6099\n",
      "0.03717578077283277\n",
      "Training epoch 321, recon_loss:0.613076, zinb_loss:0.958275, cluster_loss:0.319104\n",
      "Clustering   321: AMI= 0.7231, NMI= 0.7240, ARI= 0.5073, ACC= 0.6148\n",
      "0.036646443259090354\n",
      "Training epoch 322, recon_loss:0.612368, zinb_loss:0.957777, cluster_loss:0.319393\n",
      "Clustering   322: AMI= 0.7209, NMI= 0.7218, ARI= 0.5014, ACC= 0.6094\n",
      "0.03762368174599943\n",
      "Training epoch 323, recon_loss:0.613093, zinb_loss:0.958305, cluster_loss:0.319334\n",
      "Clustering   323: AMI= 0.7231, NMI= 0.7240, ARI= 0.5067, ACC= 0.6148\n",
      "0.03729793558369641\n",
      "Training epoch 324, recon_loss:0.612041, zinb_loss:0.957765, cluster_loss:0.319397\n",
      "Clustering   324: AMI= 0.7209, NMI= 0.7218, ARI= 0.5018, ACC= 0.6093\n",
      "0.03676859806995399\n",
      "Training epoch 325, recon_loss:0.612438, zinb_loss:0.958273, cluster_loss:0.319564\n",
      "Clustering   325: AMI= 0.7233, NMI= 0.7242, ARI= 0.5069, ACC= 0.6154\n",
      "0.03558776823160552\n",
      "Training epoch 326, recon_loss:0.611284, zinb_loss:0.957687, cluster_loss:0.319556\n",
      "Clustering   326: AMI= 0.7209, NMI= 0.7218, ARI= 0.5017, ACC= 0.6092\n",
      "0.03436622012296918\n",
      "Training epoch 327, recon_loss:0.612005, zinb_loss:0.958243, cluster_loss:0.319805\n",
      "Clustering   327: AMI= 0.7232, NMI= 0.7241, ARI= 0.5068, ACC= 0.6157\n",
      "0.032696771041166174\n",
      "Training epoch 328, recon_loss:0.610905, zinb_loss:0.957617, cluster_loss:0.319659\n",
      "Clustering   328: AMI= 0.7209, NMI= 0.7218, ARI= 0.5021, ACC= 0.6093\n",
      "0.03159737774339346\n",
      "Training epoch 329, recon_loss:0.611669, zinb_loss:0.958264, cluster_loss:0.320018\n",
      "Clustering   329: AMI= 0.7234, NMI= 0.7243, ARI= 0.5070, ACC= 0.6160\n",
      "0.030660857526772264\n",
      "Training epoch 330, recon_loss:0.610605, zinb_loss:0.957593, cluster_loss:0.319734\n",
      "Clustering   330: AMI= 0.7208, NMI= 0.7217, ARI= 0.5020, ACC= 0.6089\n",
      "0.029968646931878333\n",
      "Training epoch 331, recon_loss:0.611662, zinb_loss:0.958342, cluster_loss:0.320225\n",
      "Clustering   331: AMI= 0.7234, NMI= 0.7243, ARI= 0.5069, ACC= 0.6163\n",
      "0.028747098823241987\n",
      "Training epoch 332, recon_loss:0.610599, zinb_loss:0.957616, cluster_loss:0.319706\n",
      "Clustering   332: AMI= 0.7211, NMI= 0.7221, ARI= 0.5029, ACC= 0.6091\n",
      "0.027199804552302618\n",
      "Training epoch 333, recon_loss:0.611596, zinb_loss:0.958463, cluster_loss:0.320402\n",
      "Clustering   333: AMI= 0.7233, NMI= 0.7242, ARI= 0.5066, ACC= 0.6162\n",
      "0.026385439146545054\n",
      "Training epoch 334, recon_loss:0.610610, zinb_loss:0.957692, cluster_loss:0.319632\n",
      "Clustering   334: AMI= 0.7211, NMI= 0.7221, ARI= 0.5035, ACC= 0.6092\n",
      "0.02577466509222688\n",
      "Training epoch 335, recon_loss:0.612173, zinb_loss:0.958631, cluster_loss:0.320577\n",
      "Clustering   335: AMI= 0.7229, NMI= 0.7239, ARI= 0.5056, ACC= 0.6158\n",
      "0.02557107374078749\n",
      "Training epoch 336, recon_loss:0.611137, zinb_loss:0.957796, cluster_loss:0.319358\n",
      "Clustering   336: AMI= 0.7211, NMI= 0.7220, ARI= 0.5040, ACC= 0.6085\n",
      "0.025978256443666272\n",
      "Training epoch 337, recon_loss:0.612008, zinb_loss:0.958765, cluster_loss:0.320675\n",
      "Clustering   337: AMI= 0.7229, NMI= 0.7238, ARI= 0.5053, ACC= 0.6161\n",
      "0.026996213200863227\n",
      "Training epoch 338, recon_loss:0.611274, zinb_loss:0.957919, cluster_loss:0.319123\n",
      "Clustering   338: AMI= 0.7210, NMI= 0.7219, ARI= 0.5039, ACC= 0.6080\n",
      "0.027729142066045036\n",
      "Training epoch 339, recon_loss:0.613444, zinb_loss:0.958895, cluster_loss:0.320782\n",
      "Clustering   339: AMI= 0.7231, NMI= 0.7240, ARI= 0.5050, ACC= 0.6161\n",
      "0.028584225742090477\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training epoch 340, recon_loss:0.612424, zinb_loss:0.957956, cluster_loss:0.318562\n",
      "Clustering   340: AMI= 0.7215, NMI= 0.7224, ARI= 0.5052, ACC= 0.6077\n",
      "0.03094588541878741\n",
      "Training epoch 341, recon_loss:0.612770, zinb_loss:0.958796, cluster_loss:0.320738\n",
      "Clustering   341: AMI= 0.7232, NMI= 0.7241, ARI= 0.5047, ACC= 0.6161\n",
      "0.032085996986848\n",
      "Training epoch 342, recon_loss:0.612094, zinb_loss:0.957869, cluster_loss:0.318480\n",
      "Clustering   342: AMI= 0.7216, NMI= 0.7225, ARI= 0.5054, ACC= 0.6076\n",
      "0.03265605277087829\n",
      "Training epoch 343, recon_loss:0.613712, zinb_loss:0.958603, cluster_loss:0.320841\n",
      "Clustering   343: AMI= 0.7228, NMI= 0.7238, ARI= 0.5040, ACC= 0.6155\n",
      "0.03261533450059041\n",
      "Training epoch 344, recon_loss:0.612384, zinb_loss:0.957605, cluster_loss:0.318338\n",
      "Clustering   344: AMI= 0.7218, NMI= 0.7227, ARI= 0.5060, ACC= 0.6081\n",
      "0.03318539028462071\n",
      "Training epoch 345, recon_loss:0.612434, zinb_loss:0.958220, cluster_loss:0.320913\n",
      "Clustering   345: AMI= 0.7228, NMI= 0.7237, ARI= 0.5037, ACC= 0.6150\n",
      "0.033103953744044956\n",
      "Training epoch 346, recon_loss:0.611541, zinb_loss:0.957326, cluster_loss:0.318711\n",
      "Clustering   346: AMI= 0.7221, NMI= 0.7230, ARI= 0.5059, ACC= 0.6085\n",
      "0.03233030660857527\n",
      "Training epoch 347, recon_loss:0.613046, zinb_loss:0.957942, cluster_loss:0.321143\n",
      "Clustering   347: AMI= 0.7227, NMI= 0.7236, ARI= 0.5034, ACC= 0.6143\n",
      "0.03135306812166619\n",
      "Training epoch 348, recon_loss:0.611477, zinb_loss:0.957053, cluster_loss:0.318812\n",
      "Clustering   348: AMI= 0.7225, NMI= 0.7234, ARI= 0.5064, ACC= 0.6094\n",
      "0.030457266175332873\n",
      "Training epoch 349, recon_loss:0.611730, zinb_loss:0.957630, cluster_loss:0.321281\n",
      "Clustering   349: AMI= 0.7226, NMI= 0.7235, ARI= 0.5030, ACC= 0.6137\n",
      "0.03029439309418136\n",
      "Training epoch 350, recon_loss:0.610809, zinb_loss:0.956875, cluster_loss:0.319229\n",
      "Clustering   350: AMI= 0.7225, NMI= 0.7234, ARI= 0.5060, ACC= 0.6096\n",
      "0.02911356325583289\n",
      "Training epoch 351, recon_loss:0.612379, zinb_loss:0.957475, cluster_loss:0.321506\n",
      "Clustering   351: AMI= 0.7224, NMI= 0.7233, ARI= 0.5027, ACC= 0.6132\n",
      "0.028177043039211695\n",
      "Training epoch 352, recon_loss:0.610779, zinb_loss:0.956726, cluster_loss:0.319305\n",
      "Clustering   352: AMI= 0.7224, NMI= 0.7233, ARI= 0.5061, ACC= 0.6099\n",
      "0.02805488822834806\n",
      "Training epoch 353, recon_loss:0.611509, zinb_loss:0.957317, cluster_loss:0.321642\n",
      "Clustering   353: AMI= 0.7223, NMI= 0.7232, ARI= 0.5024, ACC= 0.6127\n",
      "0.0276069872551814\n",
      "Training epoch 354, recon_loss:0.610455, zinb_loss:0.956652, cluster_loss:0.319579\n",
      "Clustering   354: AMI= 0.7223, NMI= 0.7232, ARI= 0.5057, ACC= 0.6099\n",
      "0.026874058389999594\n",
      "Training epoch 355, recon_loss:0.612342, zinb_loss:0.957281, cluster_loss:0.321821\n",
      "Clustering   355: AMI= 0.7222, NMI= 0.7231, ARI= 0.5018, ACC= 0.6120\n",
      "0.026344720876257176\n",
      "Training epoch 356, recon_loss:0.610653, zinb_loss:0.956608, cluster_loss:0.319521\n",
      "Clustering   356: AMI= 0.7227, NMI= 0.7236, ARI= 0.5062, ACC= 0.6105\n",
      "0.026344720876257176\n",
      "Training epoch 357, recon_loss:0.611452, zinb_loss:0.957240, cluster_loss:0.321905\n",
      "Clustering   357: AMI= 0.7222, NMI= 0.7231, ARI= 0.5014, ACC= 0.6112\n",
      "0.02605969298424203\n",
      "Training epoch 358, recon_loss:0.610492, zinb_loss:0.956654, cluster_loss:0.319702\n",
      "Clustering   358: AMI= 0.7227, NMI= 0.7236, ARI= 0.5057, ACC= 0.6106\n",
      "0.025896819903090517\n",
      "Training epoch 359, recon_loss:0.612407, zinb_loss:0.957365, cluster_loss:0.322009\n",
      "Clustering   359: AMI= 0.7220, NMI= 0.7229, ARI= 0.5010, ACC= 0.6106\n",
      "0.027281241092878373\n",
      "Training epoch 360, recon_loss:0.610777, zinb_loss:0.956807, cluster_loss:0.319509\n",
      "Clustering   360: AMI= 0.7229, NMI= 0.7238, ARI= 0.5062, ACC= 0.6113\n",
      "0.03009080174274197\n",
      "Training epoch 361, recon_loss:0.612403, zinb_loss:0.957606, cluster_loss:0.322023\n",
      "Clustering   361: AMI= 0.7216, NMI= 0.7225, ARI= 0.4999, ACC= 0.6095\n",
      "0.032411743149151025\n",
      "Training epoch 362, recon_loss:0.611058, zinb_loss:0.957151, cluster_loss:0.319383\n",
      "Clustering   362: AMI= 0.7226, NMI= 0.7235, ARI= 0.5060, ACC= 0.6115\n",
      "0.035058430717863104\n",
      "Training epoch 363, recon_loss:0.612987, zinb_loss:0.958042, cluster_loss:0.321949\n",
      "Clustering   363: AMI= 0.7214, NMI= 0.7223, ARI= 0.4991, ACC= 0.6085\n",
      "0.03770511828657518\n",
      "Training epoch 364, recon_loss:0.611493, zinb_loss:0.957756, cluster_loss:0.319154\n",
      "Clustering   364: AMI= 0.7229, NMI= 0.7238, ARI= 0.5070, ACC= 0.6128\n",
      "0.041288326071908465\n",
      "Training epoch 365, recon_loss:0.613134, zinb_loss:0.958645, cluster_loss:0.321781\n",
      "Clustering   365: AMI= 0.7214, NMI= 0.7223, ARI= 0.4990, ACC= 0.6081\n",
      "0.042876338613135716\n",
      "Training epoch 366, recon_loss:0.611712, zinb_loss:0.958525, cluster_loss:0.319042\n",
      "Clustering   366: AMI= 0.7229, NMI= 0.7239, ARI= 0.5077, ACC= 0.6139\n",
      "0.04527871656012052\n",
      "Training epoch 367, recon_loss:0.613043, zinb_loss:0.959238, cluster_loss:0.321576\n",
      "Clustering   367: AMI= 0.7216, NMI= 0.7225, ARI= 0.4991, ACC= 0.6080\n",
      "0.046378109857893236\n",
      "Training epoch 368, recon_loss:0.611668, zinb_loss:0.959203, cluster_loss:0.319072\n",
      "Clustering   368: AMI= 0.7230, NMI= 0.7240, ARI= 0.5083, ACC= 0.6148\n",
      "0.04796612239912049\n",
      "Training epoch 369, recon_loss:0.612697, zinb_loss:0.959594, cluster_loss:0.321421\n",
      "Clustering   369: AMI= 0.7220, NMI= 0.7229, ARI= 0.4996, ACC= 0.6082\n",
      "0.04788468585854473\n",
      "Training epoch 370, recon_loss:0.611386, zinb_loss:0.959551, cluster_loss:0.319248\n",
      "Clustering   370: AMI= 0.7234, NMI= 0.7243, ARI= 0.5087, ACC= 0.6156\n",
      "0.04768109450710534\n",
      "Training epoch 371, recon_loss:0.612053, zinb_loss:0.959600, cluster_loss:0.321334\n",
      "Clustering   371: AMI= 0.7217, NMI= 0.7226, ARI= 0.4996, ACC= 0.6085\n",
      "0.04764037623681746\n",
      "Training epoch 372, recon_loss:0.611225, zinb_loss:0.959562, cluster_loss:0.319555\n",
      "Clustering   372: AMI= 0.7234, NMI= 0.7243, ARI= 0.5085, ACC= 0.6159\n",
      "0.04674457429048414\n",
      "Training epoch 373, recon_loss:0.611413, zinb_loss:0.959377, cluster_loss:0.321242\n",
      "Clustering   373: AMI= 0.7217, NMI= 0.7227, ARI= 0.5000, ACC= 0.6083\n",
      "0.04564518099271143\n",
      "Training epoch 374, recon_loss:0.612190, zinb_loss:0.959421, cluster_loss:0.319923\n",
      "Clustering   374: AMI= 0.7232, NMI= 0.7241, ARI= 0.5083, ACC= 0.6163\n",
      "0.044464351154362966\n",
      "Training epoch 375, recon_loss:0.612061, zinb_loss:0.959098, cluster_loss:0.320931\n",
      "Clustering   375: AMI= 0.7217, NMI= 0.7226, ARI= 0.5011, ACC= 0.6085\n",
      "0.0440164501811963\n",
      "Training epoch 376, recon_loss:0.611402, zinb_loss:0.959152, cluster_loss:0.320217\n",
      "Clustering   376: AMI= 0.7228, NMI= 0.7237, ARI= 0.5073, ACC= 0.6161\n",
      "0.04409788672177206\n",
      "Training epoch 377, recon_loss:0.611220, zinb_loss:0.958810, cluster_loss:0.321036\n",
      "Clustering   377: AMI= 0.7215, NMI= 0.7224, ARI= 0.5012, ACC= 0.6082\n",
      "0.043772140559469035\n",
      "Training epoch 378, recon_loss:0.612332, zinb_loss:0.958850, cluster_loss:0.320504\n",
      "Clustering   378: AMI= 0.7228, NMI= 0.7237, ARI= 0.5070, ACC= 0.6157\n",
      "0.042021254937090274\n",
      "Training epoch 379, recon_loss:0.611890, zinb_loss:0.958516, cluster_loss:0.320842\n",
      "Clustering   379: AMI= 0.7215, NMI= 0.7224, ARI= 0.5016, ACC= 0.6082\n",
      "0.040840425098741806\n",
      "Training epoch 380, recon_loss:0.610939, zinb_loss:0.958527, cluster_loss:0.320723\n",
      "Clustering   380: AMI= 0.7222, NMI= 0.7232, ARI= 0.5056, ACC= 0.6147\n",
      "0.039089539476363046\n",
      "Training epoch 381, recon_loss:0.610773, zinb_loss:0.958277, cluster_loss:0.321095\n",
      "Clustering   381: AMI= 0.7216, NMI= 0.7225, ARI= 0.5017, ACC= 0.6082\n",
      "0.03856020196262063\n",
      "Training epoch 382, recon_loss:0.611623, zinb_loss:0.958267, cluster_loss:0.320985\n",
      "Clustering   382: AMI= 0.7223, NMI= 0.7232, ARI= 0.5055, ACC= 0.6147\n",
      "0.03570992304246916\n",
      "Training epoch 383, recon_loss:0.611231, zinb_loss:0.958059, cluster_loss:0.320987\n",
      "Clustering   383: AMI= 0.7216, NMI= 0.7225, ARI= 0.5021, ACC= 0.6083\n",
      "0.0339590374200904\n",
      "Training epoch 384, recon_loss:0.610612, zinb_loss:0.958060, cluster_loss:0.321153\n",
      "Clustering   384: AMI= 0.7222, NMI= 0.7232, ARI= 0.5048, ACC= 0.6144\n",
      "0.033103953744044956\n",
      "Training epoch 385, recon_loss:0.610612, zinb_loss:0.957897, cluster_loss:0.321195\n",
      "Clustering   385: AMI= 0.7213, NMI= 0.7223, ARI= 0.5018, ACC= 0.6081\n",
      "0.03249317968972678\n",
      "Training epoch 386, recon_loss:0.611287, zinb_loss:0.957878, cluster_loss:0.321346\n",
      "Clustering   386: AMI= 0.7221, NMI= 0.7230, ARI= 0.5045, ACC= 0.6141\n",
      "0.031515941202817706\n",
      "Training epoch 387, recon_loss:0.611006, zinb_loss:0.957729, cluster_loss:0.321118\n",
      "Clustering   387: AMI= 0.7219, NMI= 0.7228, ARI= 0.5029, ACC= 0.6089\n",
      "0.03049798444562075\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training epoch 388, recon_loss:0.610489, zinb_loss:0.957716, cluster_loss:0.321491\n",
      "Clustering   388: AMI= 0.7221, NMI= 0.7230, ARI= 0.5043, ACC= 0.6141\n",
      "0.03000936520216621\n",
      "Training epoch 389, recon_loss:0.610534, zinb_loss:0.957594, cluster_loss:0.321332\n",
      "Clustering   389: AMI= 0.7217, NMI= 0.7227, ARI= 0.5026, ACC= 0.6086\n",
      "0.029480027688423796\n",
      "Training epoch 390, recon_loss:0.611218, zinb_loss:0.957561, cluster_loss:0.321651\n",
      "Clustering   390: AMI= 0.7218, NMI= 0.7227, ARI= 0.5040, ACC= 0.6136\n",
      "0.029072844985545014\n",
      "Training epoch 391, recon_loss:0.611045, zinb_loss:0.957472, cluster_loss:0.321251\n",
      "Clustering   391: AMI= 0.7216, NMI= 0.7226, ARI= 0.5033, ACC= 0.6091\n",
      "0.028787817093529868\n",
      "Training epoch 392, recon_loss:0.610319, zinb_loss:0.957408, cluster_loss:0.321749\n",
      "Clustering   392: AMI= 0.7220, NMI= 0.7229, ARI= 0.5039, ACC= 0.6134\n",
      "0.0289099719043935\n",
      "Training epoch 393, recon_loss:0.610565, zinb_loss:0.957379, cluster_loss:0.321485\n",
      "Clustering   393: AMI= 0.7216, NMI= 0.7226, ARI= 0.5033, ACC= 0.6091\n",
      "0.029032126715257137\n",
      "Training epoch 394, recon_loss:0.611208, zinb_loss:0.957261, cluster_loss:0.321855\n",
      "Clustering   394: AMI= 0.7223, NMI= 0.7232, ARI= 0.5038, ACC= 0.6129\n",
      "0.02870638055295411\n",
      "Training epoch 395, recon_loss:0.611272, zinb_loss:0.957302, cluster_loss:0.321345\n",
      "Clustering   395: AMI= 0.7218, NMI= 0.7228, ARI= 0.5039, ACC= 0.6095\n",
      "0.0289099719043935\n",
      "Training epoch 396, recon_loss:0.610405, zinb_loss:0.957117, cluster_loss:0.321881\n",
      "Clustering   396: AMI= 0.7219, NMI= 0.7228, ARI= 0.5031, ACC= 0.6123\n",
      "0.02919499979640865\n",
      "Training epoch 397, recon_loss:0.610923, zinb_loss:0.957248, cluster_loss:0.321498\n",
      "Clustering   397: AMI= 0.7216, NMI= 0.7226, ARI= 0.5039, ACC= 0.6095\n",
      "0.02935787287756016\n",
      "Training epoch 398, recon_loss:0.611492, zinb_loss:0.956972, cluster_loss:0.321903\n",
      "Clustering   398: AMI= 0.7217, NMI= 0.7226, ARI= 0.5024, ACC= 0.6113\n",
      "0.02911356325583289\n",
      "Training epoch 399, recon_loss:0.611919, zinb_loss:0.957258, cluster_loss:0.321228\n",
      "Clustering   399: AMI= 0.7218, NMI= 0.7227, ARI= 0.5044, ACC= 0.6102\n",
      "0.029520745958711674\n",
      "Training epoch 400, recon_loss:0.610812, zinb_loss:0.956875, cluster_loss:0.321805\n",
      "Clustering   400: AMI= 0.7213, NMI= 0.7222, ARI= 0.5015, ACC= 0.6104\n",
      "0.03009080174274197\n",
      "Training epoch 401, recon_loss:0.611748, zinb_loss:0.957325, cluster_loss:0.321240\n",
      "Clustering   401: AMI= 0.7222, NMI= 0.7231, ARI= 0.5047, ACC= 0.6109\n",
      "0.030131520013029846\n",
      "Training epoch 402, recon_loss:0.612115, zinb_loss:0.956860, cluster_loss:0.321695\n",
      "Clustering   402: AMI= 0.7214, NMI= 0.7224, ARI= 0.5012, ACC= 0.6099\n",
      "0.029887210391302578\n",
      "Training epoch 403, recon_loss:0.612885, zinb_loss:0.957579, cluster_loss:0.320801\n",
      "Clustering   403: AMI= 0.7223, NMI= 0.7232, ARI= 0.5054, ACC= 0.6113\n",
      "0.0298464921210147\n",
      "Training epoch 404, recon_loss:0.611599, zinb_loss:0.957057, cluster_loss:0.321466\n",
      "Clustering   404: AMI= 0.7213, NMI= 0.7222, ARI= 0.5005, ACC= 0.6087\n",
      "0.030701575797060142\n",
      "Training epoch 405, recon_loss:0.612743, zinb_loss:0.957893, cluster_loss:0.320753\n",
      "Clustering   405: AMI= 0.7222, NMI= 0.7231, ARI= 0.5051, ACC= 0.6114\n",
      "0.03053870271590863\n",
      "Training epoch 406, recon_loss:0.612582, zinb_loss:0.957354, cluster_loss:0.321315\n",
      "Clustering   406: AMI= 0.7210, NMI= 0.7219, ARI= 0.4996, ACC= 0.6078\n",
      "0.03049798444562075\n",
      "Training epoch 407, recon_loss:0.613140, zinb_loss:0.958314, cluster_loss:0.320480\n",
      "Clustering   407: AMI= 0.7227, NMI= 0.7236, ARI= 0.5061, ACC= 0.6126\n",
      "0.029765055580438942\n",
      "Training epoch 408, recon_loss:0.612347, zinb_loss:0.957737, cluster_loss:0.321242\n",
      "Clustering   408: AMI= 0.7207, NMI= 0.7216, ARI= 0.4990, ACC= 0.6070\n",
      "0.029724337310151065\n",
      "Training epoch 409, recon_loss:0.612787, zinb_loss:0.958571, cluster_loss:0.320596\n",
      "Clustering   409: AMI= 0.7229, NMI= 0.7239, ARI= 0.5063, ACC= 0.6132\n",
      "0.02833991612036321\n",
      "Training epoch 410, recon_loss:0.612681, zinb_loss:0.957941, cluster_loss:0.321315\n",
      "Clustering   410: AMI= 0.7206, NMI= 0.7215, ARI= 0.4989, ACC= 0.6065\n",
      "0.028095606498635937\n",
      "Training epoch 411, recon_loss:0.612647, zinb_loss:0.958743, cluster_loss:0.320666\n",
      "Clustering   411: AMI= 0.7232, NMI= 0.7241, ARI= 0.5067, ACC= 0.6139\n",
      "0.028624944012378355\n",
      "Training epoch 412, recon_loss:0.611901, zinb_loss:0.958040, cluster_loss:0.321463\n",
      "Clustering   412: AMI= 0.7201, NMI= 0.7210, ARI= 0.4984, ACC= 0.6054\n",
      "0.028624944012378355\n",
      "Training epoch 413, recon_loss:0.612109, zinb_loss:0.958791, cluster_loss:0.320991\n",
      "Clustering   413: AMI= 0.7231, NMI= 0.7240, ARI= 0.5066, ACC= 0.6142\n",
      "0.028543507471802596\n",
      "Training epoch 414, recon_loss:0.612273, zinb_loss:0.958039, cluster_loss:0.321650\n",
      "Clustering   414: AMI= 0.7200, NMI= 0.7209, ARI= 0.4984, ACC= 0.6054\n",
      "0.028014169958060182\n",
      "Training epoch 415, recon_loss:0.612047, zinb_loss:0.958805, cluster_loss:0.321121\n",
      "Clustering   415: AMI= 0.7233, NMI= 0.7242, ARI= 0.5071, ACC= 0.6148\n",
      "0.028543507471802596\n",
      "Training epoch 416, recon_loss:0.611124, zinb_loss:0.957996, cluster_loss:0.321843\n",
      "Clustering   416: AMI= 0.7200, NMI= 0.7209, ARI= 0.4983, ACC= 0.6054\n",
      "0.02846207093122684\n",
      "Training epoch 417, recon_loss:0.611489, zinb_loss:0.958785, cluster_loss:0.321511\n",
      "Clustering   417: AMI= 0.7229, NMI= 0.7238, ARI= 0.5063, ACC= 0.6146\n",
      "0.027769860336332913\n",
      "Training epoch 418, recon_loss:0.611729, zinb_loss:0.957937, cluster_loss:0.322041\n",
      "Clustering   418: AMI= 0.7200, NMI= 0.7209, ARI= 0.4984, ACC= 0.6053\n",
      "0.027484832444317764\n",
      "Training epoch 419, recon_loss:0.611693, zinb_loss:0.958809, cluster_loss:0.321563\n",
      "Clustering   419: AMI= 0.7234, NMI= 0.7243, ARI= 0.5073, ACC= 0.6152\n",
      "0.028624944012378355\n",
      "Training epoch 420, recon_loss:0.610701, zinb_loss:0.957900, cluster_loss:0.322214\n",
      "Clustering   420: AMI= 0.7197, NMI= 0.7206, ARI= 0.4979, ACC= 0.6049\n",
      "0.029276436336984405\n",
      "Training epoch 421, recon_loss:0.611211, zinb_loss:0.958833, cluster_loss:0.321861\n",
      "Clustering   421: AMI= 0.7233, NMI= 0.7242, ARI= 0.5065, ACC= 0.6146\n",
      "0.028950690174681378\n",
      "Training epoch 422, recon_loss:0.611436, zinb_loss:0.957880, cluster_loss:0.322373\n",
      "Clustering   422: AMI= 0.7199, NMI= 0.7208, ARI= 0.4981, ACC= 0.6053\n",
      "0.02960218249928743\n",
      "Training epoch 423, recon_loss:0.611850, zinb_loss:0.958967, cluster_loss:0.321785\n",
      "Clustering   423: AMI= 0.7236, NMI= 0.7246, ARI= 0.5072, ACC= 0.6149\n",
      "0.030905167148499533\n",
      "Training epoch 424, recon_loss:0.610583, zinb_loss:0.957922, cluster_loss:0.322403\n",
      "Clustering   424: AMI= 0.7201, NMI= 0.7210, ARI= 0.4980, ACC= 0.6052\n",
      "0.030864448878211652\n",
      "Training epoch 425, recon_loss:0.611445, zinb_loss:0.959120, cluster_loss:0.321937\n",
      "Clustering   425: AMI= 0.7237, NMI= 0.7246, ARI= 0.5069, ACC= 0.6145\n",
      "0.030701575797060142\n",
      "Training epoch 426, recon_loss:0.611854, zinb_loss:0.958023, cluster_loss:0.322406\n",
      "Clustering   426: AMI= 0.7198, NMI= 0.7208, ARI= 0.4976, ACC= 0.6050\n",
      "0.03147522293252983\n",
      "Training epoch 427, recon_loss:0.612569, zinb_loss:0.959346, cluster_loss:0.321661\n",
      "Clustering   427: AMI= 0.7238, NMI= 0.7247, ARI= 0.5075, ACC= 0.6150\n",
      "0.033266826825196466\n",
      "Training epoch 428, recon_loss:0.610698, zinb_loss:0.958133, cluster_loss:0.322243\n",
      "Clustering   428: AMI= 0.7198, NMI= 0.7207, ARI= 0.4974, ACC= 0.6050\n",
      "0.034162628771529785\n",
      "Training epoch 429, recon_loss:0.611660, zinb_loss:0.959434, cluster_loss:0.321847\n",
      "Clustering   429: AMI= 0.7234, NMI= 0.7243, ARI= 0.5064, ACC= 0.6140\n",
      "0.03290036239260556\n",
      "Training epoch 430, recon_loss:0.611642, zinb_loss:0.958203, cluster_loss:0.322239\n",
      "Clustering   430: AMI= 0.7196, NMI= 0.7205, ARI= 0.4973, ACC= 0.6049\n",
      "0.03253389796001466\n",
      "Training epoch 431, recon_loss:0.612234, zinb_loss:0.959476, cluster_loss:0.321760\n",
      "Clustering   431: AMI= 0.7234, NMI= 0.7243, ARI= 0.5067, ACC= 0.6141\n",
      "0.033551854717211615\n",
      "Training epoch 432, recon_loss:0.610446, zinb_loss:0.958211, cluster_loss:0.322194\n",
      "Clustering   432: AMI= 0.7200, NMI= 0.7209, ARI= 0.4975, ACC= 0.6054\n",
      "0.03322610855490859\n",
      "Training epoch 433, recon_loss:0.611379, zinb_loss:0.959386, cluster_loss:0.322108\n",
      "Clustering   433: AMI= 0.7232, NMI= 0.7241, ARI= 0.5057, ACC= 0.6135\n",
      "0.03155665947310558\n",
      "Training epoch 434, recon_loss:0.611210, zinb_loss:0.958182, cluster_loss:0.322315\n",
      "Clustering   434: AMI= 0.7200, NMI= 0.7209, ARI= 0.4979, ACC= 0.6057\n",
      "0.031108758499938924\n",
      "Training epoch 435, recon_loss:0.611736, zinb_loss:0.959324, cluster_loss:0.322129\n",
      "Clustering   435: AMI= 0.7232, NMI= 0.7241, ARI= 0.5060, ACC= 0.6138\n",
      "0.03171953255425709\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training epoch 436, recon_loss:0.610164, zinb_loss:0.958169, cluster_loss:0.322370\n",
      "Clustering   436: AMI= 0.7199, NMI= 0.7208, ARI= 0.4977, ACC= 0.6058\n",
      "0.031230913310802556\n",
      "Training epoch 437, recon_loss:0.611165, zinb_loss:0.959238, cluster_loss:0.322427\n",
      "Clustering   437: AMI= 0.7229, NMI= 0.7238, ARI= 0.5056, ACC= 0.6132\n",
      "0.030457266175332873\n",
      "Training epoch 438, recon_loss:0.610908, zinb_loss:0.958170, cluster_loss:0.322513\n",
      "Clustering   438: AMI= 0.7200, NMI= 0.7209, ARI= 0.4982, ACC= 0.6062\n",
      "0.02964290076957531\n",
      "Training epoch 439, recon_loss:0.611489, zinb_loss:0.959213, cluster_loss:0.322360\n",
      "Clustering   439: AMI= 0.7230, NMI= 0.7239, ARI= 0.5058, ACC= 0.6133\n",
      "0.03005008347245409\n",
      "Training epoch 440, recon_loss:0.610241, zinb_loss:0.958231, cluster_loss:0.322594\n",
      "Clustering   440: AMI= 0.7200, NMI= 0.7209, ARI= 0.4982, ACC= 0.6065\n",
      "0.0302129565536056\n",
      "Training epoch 441, recon_loss:0.611201, zinb_loss:0.959169, cluster_loss:0.322454\n",
      "Clustering   441: AMI= 0.7230, NMI= 0.7239, ARI= 0.5057, ACC= 0.6129\n",
      "0.030131520013029846\n",
      "Training epoch 442, recon_loss:0.611160, zinb_loss:0.958301, cluster_loss:0.322722\n",
      "Clustering   442: AMI= 0.7202, NMI= 0.7211, ARI= 0.4982, ACC= 0.6070\n",
      "0.03053870271590863\n",
      "Training epoch 443, recon_loss:0.611732, zinb_loss:0.959163, cluster_loss:0.322201\n",
      "Clustering   443: AMI= 0.7232, NMI= 0.7241, ARI= 0.5064, ACC= 0.6128\n",
      "0.03294108066289344\n",
      "Training epoch 444, recon_loss:0.610673, zinb_loss:0.958408, cluster_loss:0.322777\n",
      "Clustering   444: AMI= 0.7202, NMI= 0.7212, ARI= 0.4979, ACC= 0.6073\n",
      "0.0339590374200904\n",
      "Training epoch 445, recon_loss:0.611603, zinb_loss:0.959104, cluster_loss:0.322074\n",
      "Clustering   445: AMI= 0.7233, NMI= 0.7242, ARI= 0.5062, ACC= 0.6119\n",
      "0.03554704996131764\n",
      "Training epoch 446, recon_loss:0.612006, zinb_loss:0.958517, cluster_loss:0.322888\n",
      "Clustering   446: AMI= 0.7202, NMI= 0.7211, ARI= 0.4976, ACC= 0.6078\n",
      "0.03672787979966611\n",
      "Training epoch 447, recon_loss:0.612444, zinb_loss:0.959059, cluster_loss:0.321608\n",
      "Clustering   447: AMI= 0.7234, NMI= 0.7243, ARI= 0.5071, ACC= 0.6116\n",
      "0.04018893277413575\n",
      "Training epoch 448, recon_loss:0.611403, zinb_loss:0.958569, cluster_loss:0.322913\n",
      "Clustering   448: AMI= 0.7205, NMI= 0.7214, ARI= 0.4977, ACC= 0.6085\n",
      "0.042021254937090274\n",
      "Training epoch 449, recon_loss:0.612113, zinb_loss:0.958876, cluster_loss:0.321510\n",
      "Clustering   449: AMI= 0.7233, NMI= 0.7242, ARI= 0.5071, ACC= 0.6117\n",
      "0.043324239586302375\n",
      "Training epoch 450, recon_loss:0.612564, zinb_loss:0.958531, cluster_loss:0.323083\n",
      "Clustering   450: AMI= 0.7206, NMI= 0.7215, ARI= 0.4977, ACC= 0.6087\n",
      "0.0436499857486054\n",
      "Training epoch 451, recon_loss:0.612618, zinb_loss:0.958686, cluster_loss:0.321244\n",
      "Clustering   451: AMI= 0.7231, NMI= 0.7240, ARI= 0.5072, ACC= 0.6120\n",
      "0.04487153385724174\n",
      "Training epoch 452, recon_loss:0.611423, zinb_loss:0.958379, cluster_loss:0.323205\n",
      "Clustering   452: AMI= 0.7209, NMI= 0.7218, ARI= 0.4978, ACC= 0.6087\n",
      "0.04487153385724174\n",
      "Training epoch 453, recon_loss:0.611831, zinb_loss:0.958377, cluster_loss:0.321471\n",
      "Clustering   453: AMI= 0.7229, NMI= 0.7238, ARI= 0.5068, ACC= 0.6116\n",
      "0.043935013640620545\n",
      "Training epoch 454, recon_loss:0.612297, zinb_loss:0.958243, cluster_loss:0.323456\n",
      "Clustering   454: AMI= 0.7209, NMI= 0.7218, ARI= 0.4978, ACC= 0.6085\n",
      "0.0432835213160145\n",
      "Training epoch 455, recon_loss:0.612180, zinb_loss:0.958163, cluster_loss:0.321392\n",
      "Clustering   455: AMI= 0.7228, NMI= 0.7237, ARI= 0.5069, ACC= 0.6119\n",
      "0.04336495785659025\n",
      "Training epoch 456, recon_loss:0.611243, zinb_loss:0.958098, cluster_loss:0.323584\n",
      "Clustering   456: AMI= 0.7209, NMI= 0.7218, ARI= 0.4977, ACC= 0.6085\n",
      "0.04250987418054481\n",
      "Training epoch 457, recon_loss:0.611568, zinb_loss:0.957929, cluster_loss:0.321634\n",
      "Clustering   457: AMI= 0.7227, NMI= 0.7236, ARI= 0.5068, ACC= 0.6119\n",
      "0.04149191742334785\n",
      "Training epoch 458, recon_loss:0.612598, zinb_loss:0.958062, cluster_loss:0.323773\n",
      "Clustering   458: AMI= 0.7209, NMI= 0.7218, ARI= 0.4976, ACC= 0.6085\n",
      "0.041003298179893316\n",
      "Training epoch 459, recon_loss:0.612461, zinb_loss:0.957809, cluster_loss:0.321431\n",
      "Clustering   459: AMI= 0.7228, NMI= 0.7237, ARI= 0.5070, ACC= 0.6121\n",
      "0.04132904434219634\n",
      "Training epoch 460, recon_loss:0.611491, zinb_loss:0.957973, cluster_loss:0.323793\n",
      "Clustering   460: AMI= 0.7210, NMI= 0.7219, ARI= 0.4979, ACC= 0.6085\n",
      "0.04039252412557515\n",
      "Training epoch 461, recon_loss:0.611746, zinb_loss:0.957653, cluster_loss:0.321656\n",
      "Clustering   461: AMI= 0.7224, NMI= 0.7233, ARI= 0.5060, ACC= 0.6112\n",
      "0.03819373753002973\n",
      "Training epoch 462, recon_loss:0.612743, zinb_loss:0.957983, cluster_loss:0.323925\n",
      "Clustering   462: AMI= 0.7210, NMI= 0.7219, ARI= 0.4978, ACC= 0.6082\n",
      "0.037664400016287305\n",
      "Training epoch 463, recon_loss:0.612377, zinb_loss:0.957569, cluster_loss:0.321524\n",
      "Clustering   463: AMI= 0.7222, NMI= 0.7232, ARI= 0.5059, ACC= 0.6112\n",
      "0.03746080866484792\n",
      "Training epoch 464, recon_loss:0.611789, zinb_loss:0.957922, cluster_loss:0.323942\n",
      "Clustering   464: AMI= 0.7208, NMI= 0.7217, ARI= 0.4978, ACC= 0.6081\n",
      "0.03623926055621157\n",
      "Training epoch 465, recon_loss:0.611542, zinb_loss:0.957455, cluster_loss:0.321824\n",
      "Clustering   465: AMI= 0.7221, NMI= 0.7230, ARI= 0.5054, ACC= 0.6108\n",
      "0.033755446068651\n",
      "Training epoch 466, recon_loss:0.612862, zinb_loss:0.957950, cluster_loss:0.324093\n",
      "Clustering   466: AMI= 0.7210, NMI= 0.7219, ARI= 0.4983, ACC= 0.6082\n",
      "0.03249317968972678\n",
      "Training epoch 467, recon_loss:0.611930, zinb_loss:0.957389, cluster_loss:0.321774\n",
      "Clustering   467: AMI= 0.7220, NMI= 0.7229, ARI= 0.5053, ACC= 0.6107\n",
      "0.032248870067999515\n",
      "Training epoch 468, recon_loss:0.611627, zinb_loss:0.957895, cluster_loss:0.324144\n",
      "Clustering   468: AMI= 0.7208, NMI= 0.7218, ARI= 0.4984, ACC= 0.6082\n",
      "0.030864448878211652\n",
      "Training epoch 469, recon_loss:0.611020, zinb_loss:0.957322, cluster_loss:0.322138\n",
      "Clustering   469: AMI= 0.7220, NMI= 0.7230, ARI= 0.5042, ACC= 0.6099\n",
      "0.028624944012378355\n",
      "Training epoch 470, recon_loss:0.612589, zinb_loss:0.957969, cluster_loss:0.324309\n",
      "Clustering   470: AMI= 0.7209, NMI= 0.7218, ARI= 0.4987, ACC= 0.6083\n",
      "0.02833991612036321\n",
      "Training epoch 471, recon_loss:0.611303, zinb_loss:0.957298, cluster_loss:0.322109\n",
      "Clustering   471: AMI= 0.7220, NMI= 0.7229, ARI= 0.5045, ACC= 0.6100\n",
      "0.028584225742090477\n",
      "Training epoch 472, recon_loss:0.611849, zinb_loss:0.957999, cluster_loss:0.324385\n",
      "Clustering   472: AMI= 0.7211, NMI= 0.7220, ARI= 0.4993, ACC= 0.6087\n",
      "0.027892015147196546\n",
      "Training epoch 473, recon_loss:0.610809, zinb_loss:0.957292, cluster_loss:0.322352\n",
      "Clustering   473: AMI= 0.7217, NMI= 0.7226, ARI= 0.5036, ACC= 0.6092\n",
      "0.026792621849423836\n",
      "Training epoch 474, recon_loss:0.612848, zinb_loss:0.958121, cluster_loss:0.324499\n",
      "Clustering   474: AMI= 0.7210, NMI= 0.7220, ARI= 0.4991, ACC= 0.6085\n",
      "0.026792621849423836\n",
      "Training epoch 475, recon_loss:0.611250, zinb_loss:0.957302, cluster_loss:0.322294\n",
      "Clustering   475: AMI= 0.7218, NMI= 0.7227, ARI= 0.5034, ACC= 0.6088\n",
      "0.02675190357913596\n",
      "Training epoch 476, recon_loss:0.612049, zinb_loss:0.958157, cluster_loss:0.324497\n",
      "Clustering   476: AMI= 0.7210, NMI= 0.7219, ARI= 0.4992, ACC= 0.6087\n",
      "0.02671118530884808\n",
      "Training epoch 477, recon_loss:0.610837, zinb_loss:0.957326, cluster_loss:0.322525\n",
      "Clustering   477: AMI= 0.7218, NMI= 0.7227, ARI= 0.5029, ACC= 0.6082\n",
      "0.026100411254529908\n",
      "Training epoch 478, recon_loss:0.613139, zinb_loss:0.958317, cluster_loss:0.324517\n",
      "Clustering   478: AMI= 0.7212, NMI= 0.7221, ARI= 0.4995, ACC= 0.6090\n",
      "0.026792621849423836\n",
      "Training epoch 479, recon_loss:0.611337, zinb_loss:0.957358, cluster_loss:0.322418\n",
      "Clustering   479: AMI= 0.7217, NMI= 0.7226, ARI= 0.5027, ACC= 0.6080\n",
      "0.02736267763345413\n",
      "Training epoch 480, recon_loss:0.612758, zinb_loss:0.958397, cluster_loss:0.324429\n",
      "Clustering   480: AMI= 0.7211, NMI= 0.7221, ARI= 0.4996, ACC= 0.6096\n",
      "0.02740339590374201\n",
      "Training epoch 481, recon_loss:0.611188, zinb_loss:0.957408, cluster_loss:0.322532\n",
      "Clustering   481: AMI= 0.7219, NMI= 0.7228, ARI= 0.5025, ACC= 0.6072\n",
      "0.027036931471151104\n",
      "Training epoch 482, recon_loss:0.613756, zinb_loss:0.958558, cluster_loss:0.324323\n",
      "Clustering   482: AMI= 0.7217, NMI= 0.7226, ARI= 0.5006, ACC= 0.6106\n",
      "0.02785129687690867\n",
      "Training epoch 483, recon_loss:0.611801, zinb_loss:0.957476, cluster_loss:0.322425\n",
      "Clustering   483: AMI= 0.7221, NMI= 0.7230, ARI= 0.5026, ACC= 0.6068\n",
      "0.027932733417484427\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training epoch 484, recon_loss:0.613028, zinb_loss:0.958633, cluster_loss:0.324116\n",
      "Clustering   484: AMI= 0.7215, NMI= 0.7225, ARI= 0.5007, ACC= 0.6109\n",
      "0.02715908628201474\n",
      "Training epoch 485, recon_loss:0.611448, zinb_loss:0.957603, cluster_loss:0.322630\n",
      "Clustering   485: AMI= 0.7220, NMI= 0.7230, ARI= 0.5025, ACC= 0.6061\n",
      "0.026670467038560203\n",
      "Training epoch 486, recon_loss:0.614037, zinb_loss:0.958854, cluster_loss:0.323985\n",
      "Clustering   486: AMI= 0.7216, NMI= 0.7225, ARI= 0.5012, ACC= 0.6115\n",
      "0.027525550714605645\n",
      "Training epoch 487, recon_loss:0.611953, zinb_loss:0.957744, cluster_loss:0.322563\n",
      "Clustering   487: AMI= 0.7222, NMI= 0.7231, ARI= 0.5029, ACC= 0.6061\n",
      "0.0276069872551814\n",
      "Training epoch 488, recon_loss:0.612882, zinb_loss:0.959026, cluster_loss:0.323803\n",
      "Clustering   488: AMI= 0.7219, NMI= 0.7228, ARI= 0.5025, ACC= 0.6132\n",
      "0.027566268984893522\n",
      "Training epoch 489, recon_loss:0.611263, zinb_loss:0.958013, cluster_loss:0.322888\n",
      "Clustering   489: AMI= 0.7218, NMI= 0.7227, ARI= 0.5015, ACC= 0.6048\n",
      "0.027688423795757155\n",
      "Training epoch 490, recon_loss:0.613970, zinb_loss:0.959410, cluster_loss:0.323741\n",
      "Clustering   490: AMI= 0.7222, NMI= 0.7231, ARI= 0.5036, ACC= 0.6143\n",
      "0.02956146422899955\n",
      "Training epoch 491, recon_loss:0.611936, zinb_loss:0.958305, cluster_loss:0.322792\n",
      "Clustering   491: AMI= 0.7219, NMI= 0.7228, ARI= 0.5017, ACC= 0.6048\n",
      "0.03009080174274197\n",
      "Training epoch 492, recon_loss:0.612386, zinb_loss:0.959759, cluster_loss:0.323574\n",
      "Clustering   492: AMI= 0.7224, NMI= 0.7233, ARI= 0.5050, ACC= 0.6158\n",
      "0.03159737774339346\n",
      "Training epoch 493, recon_loss:0.610810, zinb_loss:0.958695, cluster_loss:0.323152\n",
      "Clustering   493: AMI= 0.7216, NMI= 0.7225, ARI= 0.5003, ACC= 0.6036\n",
      "0.03359257298749949\n",
      "Training epoch 494, recon_loss:0.613314, zinb_loss:0.960083, cluster_loss:0.323587\n",
      "Clustering   494: AMI= 0.7228, NMI= 0.7237, ARI= 0.5059, ACC= 0.6163\n",
      "0.034895557636711594\n",
      "Training epoch 495, recon_loss:0.611379, zinb_loss:0.958902, cluster_loss:0.323116\n",
      "Clustering   495: AMI= 0.7217, NMI= 0.7226, ARI= 0.5003, ACC= 0.6036\n",
      "0.03534345860987825\n",
      "Training epoch 496, recon_loss:0.611543, zinb_loss:0.960155, cluster_loss:0.323584\n",
      "Clustering   496: AMI= 0.7227, NMI= 0.7236, ARI= 0.5061, ACC= 0.6165\n",
      "0.03587279612362067\n",
      "Training epoch 497, recon_loss:0.610183, zinb_loss:0.959026, cluster_loss:0.323593\n",
      "Clustering   497: AMI= 0.7211, NMI= 0.7220, ARI= 0.4990, ACC= 0.6030\n",
      "0.03672787979966611\n",
      "Training epoch 498, recon_loss:0.612101, zinb_loss:0.960083, cluster_loss:0.323769\n",
      "Clustering   498: AMI= 0.7229, NMI= 0.7238, ARI= 0.5064, ACC= 0.6161\n",
      "0.03615782401563582\n",
      "Training epoch 499, recon_loss:0.610582, zinb_loss:0.959012, cluster_loss:0.323700\n",
      "Clustering   499: AMI= 0.7213, NMI= 0.7222, ARI= 0.4995, ACC= 0.6035\n",
      "0.03513986725843886\n",
      "Training epoch 500, recon_loss:0.610632, zinb_loss:0.959935, cluster_loss:0.323855\n",
      "Clustering   500: AMI= 0.7228, NMI= 0.7237, ARI= 0.5060, ACC= 0.6156\n",
      "0.035017712447575226\n",
      "Training epoch 501, recon_loss:0.609753, zinb_loss:0.959041, cluster_loss:0.324164\n",
      "Clustering   501: AMI= 0.7213, NMI= 0.7222, ARI= 0.4986, ACC= 0.6034\n",
      "0.03579135958304491\n",
      "Training epoch 502, recon_loss:0.611369, zinb_loss:0.959805, cluster_loss:0.324007\n",
      "Clustering   502: AMI= 0.7230, NMI= 0.7239, ARI= 0.5061, ACC= 0.6151\n",
      "0.03513986725843886\n",
      "Training epoch 503, recon_loss:0.610373, zinb_loss:0.959073, cluster_loss:0.324251\n",
      "Clustering   503: AMI= 0.7216, NMI= 0.7225, ARI= 0.4990, ACC= 0.6040\n",
      "0.0339590374200904\n",
      "Training epoch 504, recon_loss:0.610228, zinb_loss:0.959714, cluster_loss:0.324017\n",
      "Clustering   504: AMI= 0.7229, NMI= 0.7239, ARI= 0.5059, ACC= 0.6145\n",
      "0.034162628771529785\n",
      "Training epoch 505, recon_loss:0.609907, zinb_loss:0.959196, cluster_loss:0.324654\n",
      "Clustering   505: AMI= 0.7216, NMI= 0.7226, ARI= 0.4984, ACC= 0.6040\n",
      "0.035058430717863104\n",
      "Training epoch 506, recon_loss:0.611250, zinb_loss:0.959667, cluster_loss:0.324030\n",
      "Clustering   506: AMI= 0.7232, NMI= 0.7241, ARI= 0.5059, ACC= 0.6141\n",
      "0.03509914898815098\n",
      "Training epoch 507, recon_loss:0.610916, zinb_loss:0.959327, cluster_loss:0.324674\n",
      "Clustering   507: AMI= 0.7215, NMI= 0.7224, ARI= 0.4984, ACC= 0.6044\n",
      "0.034610529744696444\n",
      "Training epoch 508, recon_loss:0.610532, zinb_loss:0.959631, cluster_loss:0.323891\n",
      "Clustering   508: AMI= 0.7234, NMI= 0.7243, ARI= 0.5058, ACC= 0.6131\n",
      "0.03566920477218128\n",
      "Training epoch 509, recon_loss:0.610912, zinb_loss:0.959494, cluster_loss:0.324980\n",
      "Clustering   509: AMI= 0.7215, NMI= 0.7224, ARI= 0.4979, ACC= 0.6045\n",
      "0.03672787979966611\n",
      "Training epoch 510, recon_loss:0.612099, zinb_loss:0.959555, cluster_loss:0.323749\n",
      "Clustering   510: AMI= 0.7233, NMI= 0.7242, ARI= 0.5056, ACC= 0.6119\n",
      "0.037216499043120646\n",
      "Training epoch 511, recon_loss:0.612418, zinb_loss:0.959537, cluster_loss:0.324855\n",
      "Clustering   511: AMI= 0.7215, NMI= 0.7225, ARI= 0.4982, ACC= 0.6052\n",
      "0.03615782401563582\n",
      "Training epoch 512, recon_loss:0.611465, zinb_loss:0.959360, cluster_loss:0.323539\n",
      "Clustering   512: AMI= 0.7234, NMI= 0.7243, ARI= 0.5054, ACC= 0.6112\n",
      "0.036646443259090354\n",
      "Training epoch 513, recon_loss:0.612337, zinb_loss:0.959466, cluster_loss:0.325047\n",
      "Clustering   513: AMI= 0.7215, NMI= 0.7225, ARI= 0.4977, ACC= 0.6057\n",
      "0.03652428844822672\n",
      "Training epoch 514, recon_loss:0.612839, zinb_loss:0.959046, cluster_loss:0.323468\n",
      "Clustering   514: AMI= 0.7232, NMI= 0.7241, ARI= 0.5050, ACC= 0.6103\n",
      "0.03611710574534794\n",
      "Training epoch 515, recon_loss:0.613283, zinb_loss:0.959188, cluster_loss:0.324842\n",
      "Clustering   515: AMI= 0.7216, NMI= 0.7225, ARI= 0.4984, ACC= 0.6061\n",
      "0.03440693839325706\n",
      "Training epoch 516, recon_loss:0.611738, zinb_loss:0.958667, cluster_loss:0.323520\n",
      "Clustering   516: AMI= 0.7229, NMI= 0.7239, ARI= 0.5042, ACC= 0.6096\n",
      "0.03334826336577222\n",
      "Training epoch 517, recon_loss:0.612460, zinb_loss:0.958875, cluster_loss:0.325023\n",
      "Clustering   517: AMI= 0.7215, NMI= 0.7225, ARI= 0.4983, ACC= 0.6064\n",
      "0.032574616230302535\n",
      "Training epoch 518, recon_loss:0.612473, zinb_loss:0.958323, cluster_loss:0.323760\n",
      "Clustering   518: AMI= 0.7226, NMI= 0.7235, ARI= 0.5036, ACC= 0.6087\n",
      "0.030335111364469237\n",
      "Training epoch 519, recon_loss:0.612658, zinb_loss:0.958549, cluster_loss:0.324920\n",
      "Clustering   519: AMI= 0.7218, NMI= 0.7227, ARI= 0.4992, ACC= 0.6071\n",
      "0.028217761309499573\n",
      "Training epoch 520, recon_loss:0.611098, zinb_loss:0.958054, cluster_loss:0.324026\n",
      "Clustering   520: AMI= 0.7226, NMI= 0.7235, ARI= 0.5034, ACC= 0.6087\n",
      "0.026874058389999594\n",
      "Training epoch 521, recon_loss:0.611668, zinb_loss:0.958321, cluster_loss:0.325169\n",
      "Clustering   521: AMI= 0.7219, NMI= 0.7228, ARI= 0.4993, ACC= 0.6071\n",
      "0.02622256606539354\n",
      "Training epoch 522, recon_loss:0.611938, zinb_loss:0.957872, cluster_loss:0.324349\n",
      "Clustering   522: AMI= 0.7225, NMI= 0.7234, ARI= 0.5031, ACC= 0.6086\n",
      "0.02455311698359054\n",
      "Training epoch 523, recon_loss:0.612010, zinb_loss:0.958142, cluster_loss:0.325060\n",
      "Clustering   523: AMI= 0.7218, NMI= 0.7228, ARI= 0.4997, ACC= 0.6071\n",
      "0.02251720346919663\n",
      "Training epoch 524, recon_loss:0.610650, zinb_loss:0.957750, cluster_loss:0.324580\n",
      "Clustering   524: AMI= 0.7223, NMI= 0.7232, ARI= 0.5028, ACC= 0.6089\n",
      "0.02153996498228755\n",
      "Training epoch 525, recon_loss:0.611210, zinb_loss:0.958048, cluster_loss:0.325280\n",
      "Clustering   525: AMI= 0.7219, NMI= 0.7228, ARI= 0.4996, ACC= 0.6069\n",
      "0.021254937090272406\n",
      "Training epoch 526, recon_loss:0.611766, zinb_loss:0.957664, cluster_loss:0.324847\n",
      "Clustering   526: AMI= 0.7222, NMI= 0.7231, ARI= 0.5028, ACC= 0.6089\n",
      "0.020440571684514842\n",
      "Training epoch 527, recon_loss:0.611918, zinb_loss:0.957989, cluster_loss:0.325101\n",
      "Clustering   527: AMI= 0.7223, NMI= 0.7232, ARI= 0.5011, ACC= 0.6078\n",
      "0.01917830530559062\n",
      "Training epoch 528, recon_loss:0.610603, zinb_loss:0.957622, cluster_loss:0.324991\n",
      "Clustering   528: AMI= 0.7217, NMI= 0.7227, ARI= 0.5022, ACC= 0.6087\n",
      "0.019015432224439105\n",
      "Training epoch 529, recon_loss:0.611241, zinb_loss:0.957983, cluster_loss:0.325263\n",
      "Clustering   529: AMI= 0.7222, NMI= 0.7231, ARI= 0.5005, ACC= 0.6071\n",
      "0.01913758703530274\n",
      "Training epoch 530, recon_loss:0.611938, zinb_loss:0.957581, cluster_loss:0.325182\n",
      "Clustering   530: AMI= 0.7216, NMI= 0.7225, ARI= 0.5018, ACC= 0.6085\n",
      "0.01848609471069669\n",
      "Training epoch 531, recon_loss:0.612234, zinb_loss:0.958006, cluster_loss:0.325017\n",
      "Clustering   531: AMI= 0.7223, NMI= 0.7233, ARI= 0.5012, ACC= 0.6071\n",
      "0.018608249521560323\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training epoch 532, recon_loss:0.610788, zinb_loss:0.957583, cluster_loss:0.325237\n",
      "Clustering   532: AMI= 0.7215, NMI= 0.7225, ARI= 0.5017, ACC= 0.6084\n",
      "0.01917830530559062\n",
      "Training epoch 533, recon_loss:0.611508, zinb_loss:0.958053, cluster_loss:0.325151\n",
      "Clustering   533: AMI= 0.7223, NMI= 0.7232, ARI= 0.5010, ACC= 0.6066\n",
      "0.019666924549045155\n",
      "Training epoch 534, recon_loss:0.612179, zinb_loss:0.957570, cluster_loss:0.325377\n",
      "Clustering   534: AMI= 0.7213, NMI= 0.7222, ARI= 0.5011, ACC= 0.6078\n",
      "0.019748361089620914\n",
      "Training epoch 535, recon_loss:0.612565, zinb_loss:0.958138, cluster_loss:0.324890\n",
      "Clustering   535: AMI= 0.7226, NMI= 0.7235, ARI= 0.5017, ACC= 0.6070\n",
      "0.019992670711348182\n",
      "Training epoch 536, recon_loss:0.610903, zinb_loss:0.957617, cluster_loss:0.325367\n",
      "Clustering   536: AMI= 0.7210, NMI= 0.7219, ARI= 0.5007, ACC= 0.6073\n",
      "0.02092919092796938\n",
      "Training epoch 537, recon_loss:0.611676, zinb_loss:0.958243, cluster_loss:0.325050\n",
      "Clustering   537: AMI= 0.7225, NMI= 0.7234, ARI= 0.5014, ACC= 0.6067\n",
      "0.020644163035954233\n",
      "Training epoch 538, recon_loss:0.612104, zinb_loss:0.957654, cluster_loss:0.325452\n",
      "Clustering   538: AMI= 0.7209, NMI= 0.7218, ARI= 0.5001, ACC= 0.6067\n",
      "0.020807036117105746\n",
      "Training epoch 539, recon_loss:0.612585, zinb_loss:0.958429, cluster_loss:0.324806\n",
      "Clustering   539: AMI= 0.7229, NMI= 0.7238, ARI= 0.5025, ACC= 0.6074\n",
      "0.022395048658332993\n",
      "Training epoch 540, recon_loss:0.610973, zinb_loss:0.957798, cluster_loss:0.325353\n",
      "Clustering   540: AMI= 0.7208, NMI= 0.7217, ARI= 0.5001, ACC= 0.6070\n",
      "0.02272079482063602\n",
      "Training epoch 541, recon_loss:0.611836, zinb_loss:0.958667, cluster_loss:0.324936\n",
      "Clustering   541: AMI= 0.7233, NMI= 0.7242, ARI= 0.5028, ACC= 0.6077\n",
      "0.023209414064090557\n",
      "Training epoch 542, recon_loss:0.612293, zinb_loss:0.957978, cluster_loss:0.325313\n",
      "Clustering   542: AMI= 0.7207, NMI= 0.7217, ARI= 0.4997, ACC= 0.6066\n",
      "0.023046540982939043\n",
      "Training epoch 543, recon_loss:0.612824, zinb_loss:0.959028, cluster_loss:0.324648\n",
      "Clustering   543: AMI= 0.7238, NMI= 0.7247, ARI= 0.5038, ACC= 0.6085\n",
      "0.02402377946984812\n",
      "Training epoch 544, recon_loss:0.611188, zinb_loss:0.958269, cluster_loss:0.325068\n",
      "Clustering   544: AMI= 0.7208, NMI= 0.7217, ARI= 0.4990, ACC= 0.6058\n",
      "0.024349525632151148\n",
      "Training epoch 545, recon_loss:0.612156, zinb_loss:0.959383, cluster_loss:0.324789\n",
      "Clustering   545: AMI= 0.7238, NMI= 0.7247, ARI= 0.5037, ACC= 0.6087\n",
      "0.023942342929272366\n",
      "Training epoch 546, recon_loss:0.612355, zinb_loss:0.958541, cluster_loss:0.324933\n",
      "Clustering   546: AMI= 0.7207, NMI= 0.7216, ARI= 0.4985, ACC= 0.6049\n",
      "0.023901624658984485\n",
      "Training epoch 547, recon_loss:0.612775, zinb_loss:0.959756, cluster_loss:0.324609\n",
      "Clustering   547: AMI= 0.7241, NMI= 0.7251, ARI= 0.5045, ACC= 0.6095\n",
      "0.024390243902439025\n",
      "Training epoch 548, recon_loss:0.611289, zinb_loss:0.958807, cluster_loss:0.324751\n",
      "Clustering   548: AMI= 0.7207, NMI= 0.7216, ARI= 0.4979, ACC= 0.6039\n",
      "0.023942342929272366\n",
      "Training epoch 549, recon_loss:0.612170, zinb_loss:0.960000, cluster_loss:0.324893\n",
      "Clustering   549: AMI= 0.7238, NMI= 0.7247, ARI= 0.5042, ACC= 0.6098\n",
      "0.023087259253226924\n",
      "Training epoch 550, recon_loss:0.612167, zinb_loss:0.958985, cluster_loss:0.324779\n",
      "Clustering   550: AMI= 0.7209, NMI= 0.7218, ARI= 0.4980, ACC= 0.6035\n",
      "0.023290850604666315\n",
      "Training epoch 551, recon_loss:0.612366, zinb_loss:0.960213, cluster_loss:0.324885\n",
      "Clustering   551: AMI= 0.7237, NMI= 0.7246, ARI= 0.5045, ACC= 0.6104\n",
      "0.023657315037257216\n",
      "Training epoch 552, recon_loss:0.610912, zinb_loss:0.959127, cluster_loss:0.324842\n",
      "Clustering   552: AMI= 0.7211, NMI= 0.7220, ARI= 0.4979, ACC= 0.6030\n",
      "0.023005822712651166\n",
      "Training epoch 553, recon_loss:0.611750, zinb_loss:0.960332, cluster_loss:0.325256\n",
      "Clustering   553: AMI= 0.7237, NMI= 0.7246, ARI= 0.5045, ACC= 0.6108\n",
      "0.02251720346919663\n",
      "Training epoch 554, recon_loss:0.611670, zinb_loss:0.959200, cluster_loss:0.325007\n",
      "Clustering   554: AMI= 0.7211, NMI= 0.7220, ARI= 0.4979, ACC= 0.6028\n",
      "0.023413005415529948\n",
      "Training epoch 555, recon_loss:0.611856, zinb_loss:0.960460, cluster_loss:0.325251\n",
      "Clustering   555: AMI= 0.7238, NMI= 0.7247, ARI= 0.5053, ACC= 0.6119\n",
      "0.024960299686469317\n",
      "Training epoch 556, recon_loss:0.610556, zinb_loss:0.959287, cluster_loss:0.325155\n",
      "Clustering   556: AMI= 0.7211, NMI= 0.7220, ARI= 0.4979, ACC= 0.6025\n",
      "0.02491958141618144\n",
      "Training epoch 557, recon_loss:0.611324, zinb_loss:0.960525, cluster_loss:0.325540\n",
      "Clustering   557: AMI= 0.7237, NMI= 0.7246, ARI= 0.5052, ACC= 0.6122\n",
      "0.02471599006474205\n",
      "Training epoch 558, recon_loss:0.611445, zinb_loss:0.959318, cluster_loss:0.325332\n",
      "Clustering   558: AMI= 0.7213, NMI= 0.7222, ARI= 0.4982, ACC= 0.6024\n",
      "0.025733946821939004\n",
      "Training epoch 559, recon_loss:0.611625, zinb_loss:0.960616, cluster_loss:0.325451\n",
      "Clustering   559: AMI= 0.7242, NMI= 0.7251, ARI= 0.5064, ACC= 0.6132\n",
      "0.027932733417484427\n",
      "Training epoch 560, recon_loss:0.610263, zinb_loss:0.959363, cluster_loss:0.325444\n",
      "Clustering   560: AMI= 0.7215, NMI= 0.7224, ARI= 0.4980, ACC= 0.6021\n",
      "0.028869253634105623\n",
      "Training epoch 561, recon_loss:0.611086, zinb_loss:0.960616, cluster_loss:0.325683\n",
      "Clustering   561: AMI= 0.7241, NMI= 0.7250, ARI= 0.5064, ACC= 0.6136\n",
      "0.02911356325583289\n",
      "Training epoch 562, recon_loss:0.611106, zinb_loss:0.959332, cluster_loss:0.325570\n",
      "Clustering   562: AMI= 0.7216, NMI= 0.7225, ARI= 0.4980, ACC= 0.6018\n",
      "0.030335111364469237\n",
      "Training epoch 563, recon_loss:0.611318, zinb_loss:0.960616, cluster_loss:0.325544\n",
      "Clustering   563: AMI= 0.7244, NMI= 0.7254, ARI= 0.5072, ACC= 0.6146\n",
      "0.03233030660857527\n",
      "Training epoch 564, recon_loss:0.610461, zinb_loss:0.959354, cluster_loss:0.325693\n",
      "Clustering   564: AMI= 0.7218, NMI= 0.7227, ARI= 0.4976, ACC= 0.6015\n",
      "0.03294108066289344\n",
      "Training epoch 565, recon_loss:0.610901, zinb_loss:0.960492, cluster_loss:0.325648\n",
      "Clustering   565: AMI= 0.7242, NMI= 0.7251, ARI= 0.5069, ACC= 0.6142\n",
      "0.032859644122317684\n",
      "Training epoch 566, recon_loss:0.611412, zinb_loss:0.959299, cluster_loss:0.325850\n",
      "Clustering   566: AMI= 0.7217, NMI= 0.7226, ARI= 0.4974, ACC= 0.6015\n",
      "0.033999755690378275\n",
      "Training epoch 567, recon_loss:0.611414, zinb_loss:0.960352, cluster_loss:0.325500\n",
      "Clustering   567: AMI= 0.7244, NMI= 0.7254, ARI= 0.5075, ACC= 0.6143\n",
      "0.035180585528726736\n",
      "Training epoch 568, recon_loss:0.610102, zinb_loss:0.959183, cluster_loss:0.325945\n",
      "Clustering   568: AMI= 0.7216, NMI= 0.7225, ARI= 0.4972, ACC= 0.6014\n",
      "0.034895557636711594\n",
      "Training epoch 569, recon_loss:0.610706, zinb_loss:0.960113, cluster_loss:0.325752\n",
      "Clustering   569: AMI= 0.7242, NMI= 0.7251, ARI= 0.5066, ACC= 0.6133\n",
      "0.03334826336577222\n",
      "Training epoch 570, recon_loss:0.611138, zinb_loss:0.959065, cluster_loss:0.326118\n",
      "Clustering   570: AMI= 0.7215, NMI= 0.7224, ARI= 0.4973, ACC= 0.6015\n",
      "0.03379616433893888\n",
      "Training epoch 571, recon_loss:0.611150, zinb_loss:0.959932, cluster_loss:0.325590\n",
      "Clustering   571: AMI= 0.7241, NMI= 0.7251, ARI= 0.5070, ACC= 0.6134\n",
      "0.034854839366423716\n",
      "Training epoch 572, recon_loss:0.610339, zinb_loss:0.959012, cluster_loss:0.326245\n",
      "Clustering   572: AMI= 0.7215, NMI= 0.7224, ARI= 0.4972, ACC= 0.6017\n",
      "0.035017712447575226\n",
      "Training epoch 573, recon_loss:0.610729, zinb_loss:0.959700, cluster_loss:0.325709\n",
      "Clustering   573: AMI= 0.7239, NMI= 0.7248, ARI= 0.5062, ACC= 0.6125\n",
      "0.03412191050124191\n",
      "Training epoch 574, recon_loss:0.611890, zinb_loss:0.958994, cluster_loss:0.326424\n",
      "Clustering   574: AMI= 0.7216, NMI= 0.7225, ARI= 0.4975, ACC= 0.6023\n",
      "0.03408119223095403\n",
      "Training epoch 575, recon_loss:0.611792, zinb_loss:0.959539, cluster_loss:0.325379\n",
      "Clustering   575: AMI= 0.7241, NMI= 0.7250, ARI= 0.5062, ACC= 0.6122\n",
      "0.035180585528726736\n",
      "Training epoch 576, recon_loss:0.610745, zinb_loss:0.958973, cluster_loss:0.326462\n",
      "Clustering   576: AMI= 0.7216, NMI= 0.7225, ARI= 0.4974, ACC= 0.6027\n",
      "0.03481412109613584\n",
      "Training epoch 577, recon_loss:0.611211, zinb_loss:0.959297, cluster_loss:0.325456\n",
      "Clustering   577: AMI= 0.7236, NMI= 0.7245, ARI= 0.5053, ACC= 0.6109\n",
      "0.0339590374200904\n",
      "Training epoch 578, recon_loss:0.612637, zinb_loss:0.959019, cluster_loss:0.326579\n",
      "Clustering   578: AMI= 0.7220, NMI= 0.7229, ARI= 0.4979, ACC= 0.6035\n",
      "0.0333889816360601\n",
      "Training epoch 579, recon_loss:0.612503, zinb_loss:0.959133, cluster_loss:0.324963\n",
      "Clustering   579: AMI= 0.7237, NMI= 0.7246, ARI= 0.5053, ACC= 0.6102\n",
      "0.035058430717863104\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training epoch 580, recon_loss:0.611996, zinb_loss:0.959068, cluster_loss:0.326474\n",
      "Clustering   580: AMI= 0.7221, NMI= 0.7230, ARI= 0.4981, ACC= 0.6042\n",
      "0.035750641312757035\n",
      "Training epoch 581, recon_loss:0.612218, zinb_loss:0.958875, cluster_loss:0.324901\n",
      "Clustering   581: AMI= 0.7235, NMI= 0.7244, ARI= 0.5046, ACC= 0.6091\n",
      "0.0365650067185146\n",
      "Training epoch 582, recon_loss:0.613952, zinb_loss:0.959163, cluster_loss:0.326452\n",
      "Clustering   582: AMI= 0.7224, NMI= 0.7233, ARI= 0.4988, ACC= 0.6052\n",
      "0.03742009039456004\n",
      "Training epoch 583, recon_loss:0.613304, zinb_loss:0.958679, cluster_loss:0.324485\n",
      "Clustering   583: AMI= 0.7238, NMI= 0.7247, ARI= 0.5054, ACC= 0.6093\n",
      "0.03937456736837819\n",
      "Training epoch 584, recon_loss:0.612764, zinb_loss:0.959113, cluster_loss:0.326334\n",
      "Clustering   584: AMI= 0.7224, NMI= 0.7233, ARI= 0.4991, ACC= 0.6063\n",
      "0.04002605969298424\n",
      "Training epoch 585, recon_loss:0.612335, zinb_loss:0.958373, cluster_loss:0.324767\n",
      "Clustering   585: AMI= 0.7235, NMI= 0.7244, ARI= 0.5044, ACC= 0.6080\n",
      "0.03900810293578729\n",
      "Training epoch 586, recon_loss:0.613786, zinb_loss:0.959079, cluster_loss:0.326462\n",
      "Clustering   586: AMI= 0.7223, NMI= 0.7232, ARI= 0.4991, ACC= 0.6065\n",
      "0.03892666639521153\n",
      "Training epoch 587, recon_loss:0.612502, zinb_loss:0.958153, cluster_loss:0.324776\n",
      "Clustering   587: AMI= 0.7234, NMI= 0.7243, ARI= 0.5046, ACC= 0.6079\n",
      "0.038641638503196386\n",
      "Training epoch 588, recon_loss:0.612265, zinb_loss:0.958920, cluster_loss:0.326548\n",
      "Clustering   588: AMI= 0.7223, NMI= 0.7233, ARI= 0.4992, ACC= 0.6066\n",
      "0.037949427908302455\n",
      "Training epoch 589, recon_loss:0.611437, zinb_loss:0.957920, cluster_loss:0.325267\n",
      "Clustering   589: AMI= 0.7233, NMI= 0.7242, ARI= 0.5040, ACC= 0.6073\n",
      "0.034895557636711594\n",
      "Training epoch 590, recon_loss:0.612691, zinb_loss:0.958847, cluster_loss:0.326727\n",
      "Clustering   590: AMI= 0.7224, NMI= 0.7233, ARI= 0.4995, ACC= 0.6069\n",
      "0.033999755690378275\n",
      "Training epoch 591, recon_loss:0.611297, zinb_loss:0.957767, cluster_loss:0.325408\n",
      "Clustering   591: AMI= 0.7232, NMI= 0.7241, ARI= 0.5038, ACC= 0.6071\n",
      "0.033144672014332834\n",
      "Training epoch 592, recon_loss:0.612462, zinb_loss:0.958781, cluster_loss:0.326866\n",
      "Clustering   592: AMI= 0.7225, NMI= 0.7234, ARI= 0.4996, ACC= 0.6069\n",
      "0.03228958833828739\n",
      "Training epoch 593, recon_loss:0.611059, zinb_loss:0.957626, cluster_loss:0.325615\n",
      "Clustering   593: AMI= 0.7230, NMI= 0.7239, ARI= 0.5038, ACC= 0.6069\n",
      "0.030335111364469237\n",
      "Training epoch 594, recon_loss:0.612400, zinb_loss:0.958701, cluster_loss:0.326958\n",
      "Clustering   594: AMI= 0.7223, NMI= 0.7232, ARI= 0.5000, ACC= 0.6070\n",
      "0.029683619039863187\n",
      "Training epoch 595, recon_loss:0.611010, zinb_loss:0.957526, cluster_loss:0.325799\n",
      "Clustering   595: AMI= 0.7230, NMI= 0.7240, ARI= 0.5038, ACC= 0.6069\n",
      "0.02899140844496926\n",
      "Training epoch 596, recon_loss:0.612373, zinb_loss:0.958645, cluster_loss:0.326990\n",
      "Clustering   596: AMI= 0.7224, NMI= 0.7233, ARI= 0.5004, ACC= 0.6073\n",
      "0.028380634390651086\n",
      "Training epoch 597, recon_loss:0.611010, zinb_loss:0.957461, cluster_loss:0.325951\n",
      "Clustering   597: AMI= 0.7233, NMI= 0.7242, ARI= 0.5038, ACC= 0.6065\n",
      "0.02740339590374201\n",
      "Training epoch 598, recon_loss:0.612617, zinb_loss:0.958632, cluster_loss:0.326941\n",
      "Clustering   598: AMI= 0.7224, NMI= 0.7233, ARI= 0.5008, ACC= 0.6077\n",
      "0.027484832444317764\n",
      "Training epoch 599, recon_loss:0.611217, zinb_loss:0.957445, cluster_loss:0.326012\n",
      "Clustering   599: AMI= 0.7231, NMI= 0.7241, ARI= 0.5033, ACC= 0.6060\n",
      "0.02671118530884808\n",
      "Training epoch 600, recon_loss:0.612507, zinb_loss:0.958639, cluster_loss:0.326811\n",
      "Clustering   600: AMI= 0.7226, NMI= 0.7235, ARI= 0.5012, ACC= 0.6081\n",
      "0.026385439146545054\n",
      "Training epoch 601, recon_loss:0.611145, zinb_loss:0.957476, cluster_loss:0.326109\n",
      "Clustering   601: AMI= 0.7227, NMI= 0.7237, ARI= 0.5026, ACC= 0.6049\n",
      "0.026385439146545054\n",
      "Training epoch 602, recon_loss:0.613439, zinb_loss:0.958714, cluster_loss:0.326657\n",
      "Clustering   602: AMI= 0.7225, NMI= 0.7234, ARI= 0.5015, ACC= 0.6085\n",
      "0.025815383362514762\n",
      "Training epoch 603, recon_loss:0.611768, zinb_loss:0.957527, cluster_loss:0.325969\n",
      "Clustering   603: AMI= 0.7226, NMI= 0.7236, ARI= 0.5026, ACC= 0.6043\n",
      "0.025489637200211735\n",
      "Training epoch 604, recon_loss:0.612319, zinb_loss:0.958708, cluster_loss:0.326452\n",
      "Clustering   604: AMI= 0.7225, NMI= 0.7234, ARI= 0.5017, ACC= 0.6087\n",
      "0.024797426605317807\n",
      "Training epoch 605, recon_loss:0.611027, zinb_loss:0.957626, cluster_loss:0.326203\n",
      "Clustering   605: AMI= 0.7222, NMI= 0.7231, ARI= 0.5013, ACC= 0.6037\n",
      "0.02402377946984812\n",
      "Training epoch 606, recon_loss:0.613183, zinb_loss:0.958778, cluster_loss:0.326439\n",
      "Clustering   606: AMI= 0.7226, NMI= 0.7236, ARI= 0.5022, ACC= 0.6088\n",
      "0.02272079482063602\n",
      "Training epoch 607, recon_loss:0.611402, zinb_loss:0.957682, cluster_loss:0.326106\n",
      "Clustering   607: AMI= 0.7226, NMI= 0.7235, ARI= 0.5017, ACC= 0.6041\n",
      "0.022313612117757238\n",
      "Training epoch 608, recon_loss:0.611842, zinb_loss:0.958793, cluster_loss:0.326423\n",
      "Clustering   608: AMI= 0.7226, NMI= 0.7235, ARI= 0.5024, ACC= 0.6091\n",
      "0.021662119793151188\n",
      "Training epoch 609, recon_loss:0.610599, zinb_loss:0.957795, cluster_loss:0.326422\n",
      "Clustering   609: AMI= 0.7223, NMI= 0.7232, ARI= 0.5006, ACC= 0.6031\n",
      "0.02158068325257543\n",
      "Training epoch 610, recon_loss:0.612557, zinb_loss:0.958881, cluster_loss:0.326563\n",
      "Clustering   610: AMI= 0.7227, NMI= 0.7236, ARI= 0.5031, ACC= 0.6094\n",
      "0.021010627468545137\n",
      "Training epoch 611, recon_loss:0.610848, zinb_loss:0.957866, cluster_loss:0.326388\n",
      "Clustering   611: AMI= 0.7223, NMI= 0.7232, ARI= 0.5008, ACC= 0.6031\n",
      "0.02076631784681787\n",
      "Training epoch 612, recon_loss:0.611238, zinb_loss:0.958953, cluster_loss:0.326627\n",
      "Clustering   612: AMI= 0.7226, NMI= 0.7235, ARI= 0.5032, ACC= 0.6095\n",
      "0.021214218819984528\n",
      "Training epoch 613, recon_loss:0.610166, zinb_loss:0.958025, cluster_loss:0.326709\n",
      "Clustering   613: AMI= 0.7222, NMI= 0.7231, ARI= 0.4998, ACC= 0.6022\n",
      "0.0218249928743027\n",
      "Training epoch 614, recon_loss:0.611976, zinb_loss:0.959094, cluster_loss:0.326766\n",
      "Clustering   614: AMI= 0.7227, NMI= 0.7237, ARI= 0.5037, ACC= 0.6100\n",
      "0.021906429414878456\n",
      "Training epoch 615, recon_loss:0.610451, zinb_loss:0.958153, cluster_loss:0.326654\n",
      "Clustering   615: AMI= 0.7221, NMI= 0.7231, ARI= 0.5001, ACC= 0.6023\n",
      "0.02198786595545421\n",
      "Training epoch 616, recon_loss:0.611214, zinb_loss:0.959269, cluster_loss:0.326833\n",
      "Clustering   616: AMI= 0.7230, NMI= 0.7239, ARI= 0.5040, ACC= 0.6102\n",
      "0.022395048658332993\n",
      "Training epoch 617, recon_loss:0.610005, zinb_loss:0.958373, cluster_loss:0.326845\n",
      "Clustering   617: AMI= 0.7219, NMI= 0.7228, ARI= 0.4996, ACC= 0.6016\n",
      "0.02316869579380268\n",
      "Training epoch 618, recon_loss:0.612333, zinb_loss:0.959503, cluster_loss:0.326937\n",
      "Clustering   618: AMI= 0.7230, NMI= 0.7239, ARI= 0.5042, ACC= 0.6107\n",
      "0.024105216010423876\n",
      "Training epoch 619, recon_loss:0.610875, zinb_loss:0.958621, cluster_loss:0.326707\n",
      "Clustering   619: AMI= 0.7220, NMI= 0.7229, ARI= 0.4999, ACC= 0.6016\n",
      "0.024349525632151148\n",
      "Training epoch 620, recon_loss:0.610831, zinb_loss:0.959706, cluster_loss:0.326894\n",
      "Clustering   620: AMI= 0.7233, NMI= 0.7242, ARI= 0.5047, ACC= 0.6113\n",
      "0.025733946821939004\n",
      "Training epoch 621, recon_loss:0.610062, zinb_loss:0.958964, cluster_loss:0.327015\n",
      "Clustering   621: AMI= 0.7219, NMI= 0.7228, ARI= 0.4992, ACC= 0.6009\n",
      "0.027281241092878373\n",
      "Training epoch 622, recon_loss:0.612200, zinb_loss:0.959960, cluster_loss:0.326971\n",
      "Clustering   622: AMI= 0.7234, NMI= 0.7244, ARI= 0.5053, ACC= 0.6119\n",
      "0.027769860336332913\n",
      "Training epoch 623, recon_loss:0.611082, zinb_loss:0.959248, cluster_loss:0.326826\n",
      "Clustering   623: AMI= 0.7220, NMI= 0.7229, ARI= 0.4996, ACC= 0.6007\n",
      "0.027769860336332913\n",
      "Training epoch 624, recon_loss:0.610971, zinb_loss:0.960161, cluster_loss:0.326897\n",
      "Clustering   624: AMI= 0.7235, NMI= 0.7244, ARI= 0.5055, ACC= 0.6121\n",
      "0.028380634390651086\n",
      "Training epoch 625, recon_loss:0.610429, zinb_loss:0.959564, cluster_loss:0.327070\n",
      "Clustering   625: AMI= 0.7219, NMI= 0.7228, ARI= 0.4990, ACC= 0.6002\n",
      "0.029887210391302578\n",
      "Training epoch 626, recon_loss:0.612555, zinb_loss:0.960313, cluster_loss:0.326950\n",
      "Clustering   626: AMI= 0.7236, NMI= 0.7245, ARI= 0.5061, ACC= 0.6124\n",
      "0.030620139256484383\n",
      "Training epoch 627, recon_loss:0.611834, zinb_loss:0.959776, cluster_loss:0.326822\n",
      "Clustering   627: AMI= 0.7220, NMI= 0.7229, ARI= 0.4993, ACC= 0.5999\n",
      "0.03131234985137831\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training epoch 628, recon_loss:0.611180, zinb_loss:0.960318, cluster_loss:0.326833\n",
      "Clustering   628: AMI= 0.7235, NMI= 0.7244, ARI= 0.5062, ACC= 0.6127\n",
      "0.032126715257135875\n",
      "Training epoch 629, recon_loss:0.611102, zinb_loss:0.959951, cluster_loss:0.327083\n",
      "Clustering   629: AMI= 0.7217, NMI= 0.7226, ARI= 0.4986, ACC= 0.5997\n",
      "0.03379616433893888\n",
      "Training epoch 630, recon_loss:0.612641, zinb_loss:0.960226, cluster_loss:0.326843\n",
      "Clustering   630: AMI= 0.7238, NMI= 0.7248, ARI= 0.5067, ACC= 0.6128\n",
      "0.034610529744696444\n",
      "Training epoch 631, recon_loss:0.612589, zinb_loss:0.960007, cluster_loss:0.326819\n",
      "Clustering   631: AMI= 0.7221, NMI= 0.7230, ARI= 0.4997, ACC= 0.6006\n",
      "0.035017712447575226\n",
      "Training epoch 632, recon_loss:0.611549, zinb_loss:0.960032, cluster_loss:0.326664\n",
      "Clustering   632: AMI= 0.7239, NMI= 0.7248, ARI= 0.5064, ACC= 0.6125\n",
      "0.03607638747506006\n",
      "Training epoch 633, recon_loss:0.612157, zinb_loss:0.960030, cluster_loss:0.327013\n",
      "Clustering   633: AMI= 0.7217, NMI= 0.7226, ARI= 0.4989, ACC= 0.6000\n",
      "0.03807158271916609\n",
      "Training epoch 634, recon_loss:0.613139, zinb_loss:0.959816, cluster_loss:0.326581\n",
      "Clustering   634: AMI= 0.7241, NMI= 0.7250, ARI= 0.5064, ACC= 0.6119\n",
      "0.038804511584347896\n",
      "Training epoch 635, recon_loss:0.613807, zinb_loss:0.960022, cluster_loss:0.326766\n",
      "Clustering   635: AMI= 0.7218, NMI= 0.7227, ARI= 0.4992, ACC= 0.6004\n",
      "0.039252412557514556\n",
      "Training epoch 636, recon_loss:0.612150, zinb_loss:0.959616, cluster_loss:0.326359\n",
      "Clustering   636: AMI= 0.7241, NMI= 0.7250, ARI= 0.5061, ACC= 0.6111\n",
      "0.040677552017590296\n",
      "Training epoch 637, recon_loss:0.613291, zinb_loss:0.959962, cluster_loss:0.326967\n",
      "Clustering   637: AMI= 0.7214, NMI= 0.7223, ARI= 0.4985, ACC= 0.6004\n",
      "0.04222484628852966\n",
      "Training epoch 638, recon_loss:0.613540, zinb_loss:0.959439, cluster_loss:0.326301\n",
      "Clustering   638: AMI= 0.7242, NMI= 0.7251, ARI= 0.5064, ACC= 0.6109\n",
      "0.04316136650515086\n",
      "Training epoch 639, recon_loss:0.614430, zinb_loss:0.959906, cluster_loss:0.326734\n",
      "Clustering   639: AMI= 0.7212, NMI= 0.7222, ARI= 0.4988, ACC= 0.6006\n",
      "0.04336495785659025\n",
      "Training epoch 640, recon_loss:0.612437, zinb_loss:0.959321, cluster_loss:0.326198\n",
      "Clustering   640: AMI= 0.7242, NMI= 0.7251, ARI= 0.5060, ACC= 0.6102\n",
      "0.04413860499205994\n",
      "Training epoch 641, recon_loss:0.613573, zinb_loss:0.959769, cluster_loss:0.326870\n",
      "Clustering   641: AMI= 0.7210, NMI= 0.7219, ARI= 0.4982, ACC= 0.6007\n",
      "0.04397573191090842\n",
      "Training epoch 642, recon_loss:0.613317, zinb_loss:0.959186, cluster_loss:0.326254\n",
      "Clustering   642: AMI= 0.7238, NMI= 0.7247, ARI= 0.5055, ACC= 0.6098\n",
      "0.0440164501811963\n",
      "Training epoch 643, recon_loss:0.613999, zinb_loss:0.959654, cluster_loss:0.326681\n",
      "Clustering   643: AMI= 0.7209, NMI= 0.7219, ARI= 0.4986, ACC= 0.6013\n",
      "0.0432835213160145\n",
      "Training epoch 644, recon_loss:0.612225, zinb_loss:0.959101, cluster_loss:0.326301\n",
      "Clustering   644: AMI= 0.7239, NMI= 0.7248, ARI= 0.5053, ACC= 0.6091\n",
      "0.043039211694287226\n",
      "Training epoch 645, recon_loss:0.613049, zinb_loss:0.959441, cluster_loss:0.326890\n",
      "Clustering   645: AMI= 0.7211, NMI= 0.7220, ARI= 0.4986, ACC= 0.6017\n",
      "0.041776945315363\n",
      "Training epoch 646, recon_loss:0.612810, zinb_loss:0.958959, cluster_loss:0.326489\n",
      "Clustering   646: AMI= 0.7238, NMI= 0.7247, ARI= 0.5050, ACC= 0.6087\n",
      "0.0404739606661509\n",
      "Training epoch 647, recon_loss:0.613028, zinb_loss:0.959248, cluster_loss:0.326871\n",
      "Clustering   647: AMI= 0.7211, NMI= 0.7220, ARI= 0.4989, ACC= 0.6019\n",
      "0.03929313082780243\n",
      "Training epoch 648, recon_loss:0.611491, zinb_loss:0.958852, cluster_loss:0.326693\n",
      "Clustering   648: AMI= 0.7236, NMI= 0.7245, ARI= 0.5040, ACC= 0.6077\n",
      "0.038234455800317604\n",
      "Training epoch 649, recon_loss:0.611999, zinb_loss:0.959020, cluster_loss:0.327241\n",
      "Clustering   649: AMI= 0.7212, NMI= 0.7221, ARI= 0.4989, ACC= 0.6024\n",
      "0.036483570177938844\n",
      "Training epoch 650, recon_loss:0.611629, zinb_loss:0.958713, cluster_loss:0.326917\n",
      "Clustering   650: AMI= 0.7234, NMI= 0.7243, ARI= 0.5038, ACC= 0.6071\n",
      "0.03477340282584796\n",
      "Training epoch 651, recon_loss:0.611713, zinb_loss:0.958877, cluster_loss:0.327382\n",
      "Clustering   651: AMI= 0.7211, NMI= 0.7220, ARI= 0.4988, ACC= 0.6028\n",
      "0.032859644122317684\n",
      "Training epoch 652, recon_loss:0.611937, zinb_loss:0.958700, cluster_loss:0.327164\n",
      "Clustering   652: AMI= 0.7236, NMI= 0.7245, ARI= 0.5033, ACC= 0.6063\n",
      "0.03220815179771163\n",
      "Training epoch 653, recon_loss:0.611684, zinb_loss:0.958698, cluster_loss:0.327497\n",
      "Clustering   653: AMI= 0.7214, NMI= 0.7223, ARI= 0.4993, ACC= 0.6031\n",
      "0.031068040229651043\n",
      "Training epoch 654, recon_loss:0.610590, zinb_loss:0.958588, cluster_loss:0.327323\n",
      "Clustering   654: AMI= 0.7236, NMI= 0.7245, ARI= 0.5031, ACC= 0.6061\n",
      "0.030457266175332873\n",
      "Training epoch 655, recon_loss:0.610982, zinb_loss:0.958620, cluster_loss:0.327888\n",
      "Clustering   655: AMI= 0.7214, NMI= 0.7223, ARI= 0.4990, ACC= 0.6032\n",
      "0.028665662282666232\n",
      "Training epoch 656, recon_loss:0.611002, zinb_loss:0.958525, cluster_loss:0.327448\n",
      "Clustering   656: AMI= 0.7237, NMI= 0.7246, ARI= 0.5034, ACC= 0.6062\n",
      "0.02805488822834806\n",
      "Training epoch 657, recon_loss:0.610951, zinb_loss:0.958580, cluster_loss:0.327980\n",
      "Clustering   657: AMI= 0.7215, NMI= 0.7224, ARI= 0.4993, ACC= 0.6034\n",
      "0.02736267763345413\n",
      "Training epoch 658, recon_loss:0.611439, zinb_loss:0.958565, cluster_loss:0.327585\n",
      "Clustering   658: AMI= 0.7237, NMI= 0.7246, ARI= 0.5032, ACC= 0.6058\n",
      "0.02715908628201474\n",
      "Training epoch 659, recon_loss:0.611143, zinb_loss:0.958489, cluster_loss:0.328023\n",
      "Clustering   659: AMI= 0.7216, NMI= 0.7225, ARI= 0.4996, ACC= 0.6036\n",
      "0.026792621849423836\n",
      "Training epoch 660, recon_loss:0.610792, zinb_loss:0.958525, cluster_loss:0.327694\n",
      "Clustering   660: AMI= 0.7235, NMI= 0.7244, ARI= 0.5029, ACC= 0.6058\n",
      "0.02622256606539354\n",
      "Training epoch 661, recon_loss:0.610788, zinb_loss:0.958477, cluster_loss:0.328229\n",
      "Clustering   661: AMI= 0.7216, NMI= 0.7226, ARI= 0.4993, ACC= 0.6033\n",
      "0.025693228551651126\n",
      "Training epoch 662, recon_loss:0.611275, zinb_loss:0.958501, cluster_loss:0.327786\n",
      "Clustering   662: AMI= 0.7236, NMI= 0.7245, ARI= 0.5030, ACC= 0.6060\n",
      "0.02577466509222688\n",
      "Training epoch 663, recon_loss:0.611111, zinb_loss:0.958480, cluster_loss:0.328246\n",
      "Clustering   663: AMI= 0.7218, NMI= 0.7227, ARI= 0.4996, ACC= 0.6034\n",
      "0.025693228551651126\n",
      "Training epoch 664, recon_loss:0.610959, zinb_loss:0.958503, cluster_loss:0.327862\n",
      "Clustering   664: AMI= 0.7235, NMI= 0.7244, ARI= 0.5029, ACC= 0.6058\n",
      "0.02577466509222688\n",
      "Training epoch 665, recon_loss:0.610946, zinb_loss:0.958480, cluster_loss:0.328348\n",
      "Clustering   665: AMI= 0.7218, NMI= 0.7228, ARI= 0.4994, ACC= 0.6032\n",
      "0.0253674823893481\n",
      "Training epoch 666, recon_loss:0.611344, zinb_loss:0.958499, cluster_loss:0.327917\n",
      "Clustering   666: AMI= 0.7236, NMI= 0.7245, ARI= 0.5029, ACC= 0.6060\n",
      "0.025815383362514762\n",
      "Training epoch 667, recon_loss:0.611333, zinb_loss:0.958556, cluster_loss:0.328321\n",
      "Clustering   667: AMI= 0.7219, NMI= 0.7228, ARI= 0.4994, ACC= 0.6029\n",
      "0.02577466509222688\n",
      "Training epoch 668, recon_loss:0.611165, zinb_loss:0.958542, cluster_loss:0.327911\n",
      "Clustering   668: AMI= 0.7233, NMI= 0.7242, ARI= 0.5027, ACC= 0.6058\n",
      "0.02622256606539354\n",
      "Training epoch 669, recon_loss:0.611426, zinb_loss:0.958696, cluster_loss:0.328324\n",
      "Clustering   669: AMI= 0.7218, NMI= 0.7228, ARI= 0.4991, ACC= 0.6026\n",
      "0.026466875687120812\n",
      "Training epoch 670, recon_loss:0.611905, zinb_loss:0.958645, cluster_loss:0.327848\n",
      "Clustering   670: AMI= 0.7232, NMI= 0.7241, ARI= 0.5026, ACC= 0.6058\n",
      "0.026833340119711713\n",
      "Training epoch 671, recon_loss:0.612252, zinb_loss:0.958981, cluster_loss:0.328111\n",
      "Clustering   671: AMI= 0.7219, NMI= 0.7228, ARI= 0.4992, ACC= 0.6027\n",
      "0.027932733417484427\n",
      "Training epoch 672, recon_loss:0.611258, zinb_loss:0.958822, cluster_loss:0.327635\n",
      "Clustering   672: AMI= 0.7229, NMI= 0.7238, ARI= 0.5024, ACC= 0.6056\n",
      "0.029072844985545014\n",
      "Training epoch 673, recon_loss:0.612292, zinb_loss:0.959432, cluster_loss:0.328056\n",
      "Clustering   673: AMI= 0.7221, NMI= 0.7230, ARI= 0.4991, ACC= 0.6026\n",
      "0.029927928661590456\n",
      "Training epoch 674, recon_loss:0.612783, zinb_loss:0.959129, cluster_loss:0.327408\n",
      "Clustering   674: AMI= 0.7225, NMI= 0.7234, ARI= 0.5021, ACC= 0.6049\n",
      "0.030335111364469237\n",
      "Training epoch 675, recon_loss:0.613632, zinb_loss:0.960027, cluster_loss:0.327577\n",
      "Clustering   675: AMI= 0.7225, NMI= 0.7234, ARI= 0.4999, ACC= 0.6029\n",
      "0.03094588541878741\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training epoch 676, recon_loss:0.611784, zinb_loss:0.959477, cluster_loss:0.327140\n",
      "Clustering   676: AMI= 0.7223, NMI= 0.7232, ARI= 0.5013, ACC= 0.6042\n",
      "0.03159737774339346\n",
      "Training epoch 677, recon_loss:0.613192, zinb_loss:0.960430, cluster_loss:0.327558\n",
      "Clustering   677: AMI= 0.7228, NMI= 0.7237, ARI= 0.4999, ACC= 0.6029\n",
      "0.03119019504051468\n",
      "Training epoch 678, recon_loss:0.612662, zinb_loss:0.959616, cluster_loss:0.327114\n",
      "Clustering   678: AMI= 0.7222, NMI= 0.7231, ARI= 0.5010, ACC= 0.6034\n",
      "0.030375829634757115\n",
      "Training epoch 679, recon_loss:0.613503, zinb_loss:0.960635, cluster_loss:0.327328\n",
      "Clustering   679: AMI= 0.7231, NMI= 0.7240, ARI= 0.5007, ACC= 0.6035\n",
      "0.029072844985545014\n",
      "Training epoch 680, recon_loss:0.611528, zinb_loss:0.959613, cluster_loss:0.327237\n",
      "Clustering   680: AMI= 0.7220, NMI= 0.7229, ARI= 0.5001, ACC= 0.6027\n",
      "0.02736267763345413\n",
      "Training epoch 681, recon_loss:0.612511, zinb_loss:0.960534, cluster_loss:0.327529\n",
      "Clustering   681: AMI= 0.7228, NMI= 0.7238, ARI= 0.5007, ACC= 0.6037\n",
      "0.02577466509222688\n",
      "Training epoch 682, recon_loss:0.611968, zinb_loss:0.959452, cluster_loss:0.327508\n",
      "Clustering   682: AMI= 0.7219, NMI= 0.7229, ARI= 0.4996, ACC= 0.6024\n",
      "0.02455311698359054\n",
      "Training epoch 683, recon_loss:0.612561, zinb_loss:0.960405, cluster_loss:0.327499\n",
      "Clustering   683: AMI= 0.7231, NMI= 0.7240, ARI= 0.5017, ACC= 0.6043\n",
      "0.023087259253226924\n",
      "Training epoch 684, recon_loss:0.610406, zinb_loss:0.959305, cluster_loss:0.327736\n",
      "Clustering   684: AMI= 0.7219, NMI= 0.7228, ARI= 0.4990, ACC= 0.6017\n",
      "0.02162140152286331\n",
      "Training epoch 685, recon_loss:0.611444, zinb_loss:0.960250, cluster_loss:0.327824\n",
      "Clustering   685: AMI= 0.7228, NMI= 0.7238, ARI= 0.5014, ACC= 0.6041\n",
      "0.020114825522211815\n",
      "Training epoch 686, recon_loss:0.611277, zinb_loss:0.959198, cluster_loss:0.327987\n",
      "Clustering   686: AMI= 0.7219, NMI= 0.7228, ARI= 0.4984, ACC= 0.6013\n",
      "0.0195854880084694\n",
      "Training epoch 687, recon_loss:0.611918, zinb_loss:0.960234, cluster_loss:0.327701\n",
      "Clustering   687: AMI= 0.7233, NMI= 0.7242, ARI= 0.5028, ACC= 0.6053\n",
      "0.019056150494726982\n",
      "Training epoch 688, recon_loss:0.610174, zinb_loss:0.959222, cluster_loss:0.328106\n",
      "Clustering   688: AMI= 0.7217, NMI= 0.7226, ARI= 0.4974, ACC= 0.6008\n",
      "0.018567531251272446\n",
      "Training epoch 689, recon_loss:0.611169, zinb_loss:0.960233, cluster_loss:0.327884\n",
      "Clustering   689: AMI= 0.7234, NMI= 0.7244, ARI= 0.5033, ACC= 0.6058\n",
      "0.018526812980984568\n",
      "Training epoch 690, recon_loss:0.611733, zinb_loss:0.959257, cluster_loss:0.328190\n",
      "Clustering   690: AMI= 0.7215, NMI= 0.7224, ARI= 0.4969, ACC= 0.6002\n",
      "0.0195854880084694\n",
      "Training epoch 691, recon_loss:0.612430, zinb_loss:0.960350, cluster_loss:0.327572\n",
      "Clustering   691: AMI= 0.7234, NMI= 0.7243, ARI= 0.5040, ACC= 0.6062\n",
      "0.021906429414878456\n",
      "Training epoch 692, recon_loss:0.610890, zinb_loss:0.959354, cluster_loss:0.328107\n",
      "Clustering   692: AMI= 0.7214, NMI= 0.7223, ARI= 0.4964, ACC= 0.5998\n",
      "0.02292438617207541\n",
      "Training epoch 693, recon_loss:0.611840, zinb_loss:0.960354, cluster_loss:0.327638\n",
      "Clustering   693: AMI= 0.7235, NMI= 0.7244, ARI= 0.5041, ACC= 0.6065\n",
      "0.02357587849668146\n",
      "Training epoch 694, recon_loss:0.612612, zinb_loss:0.959352, cluster_loss:0.328089\n",
      "Clustering   694: AMI= 0.7216, NMI= 0.7226, ARI= 0.4963, ACC= 0.5993\n",
      "0.024105216010423876\n",
      "Training epoch 695, recon_loss:0.612997, zinb_loss:0.960333, cluster_loss:0.327329\n",
      "Clustering   695: AMI= 0.7239, NMI= 0.7249, ARI= 0.5054, ACC= 0.6076\n",
      "0.026141129524817786\n",
      "Training epoch 696, recon_loss:0.611638, zinb_loss:0.959312, cluster_loss:0.328046\n",
      "Clustering   696: AMI= 0.7216, NMI= 0.7225, ARI= 0.4960, ACC= 0.5991\n",
      "0.02642615741683293\n",
      "Training epoch 697, recon_loss:0.612102, zinb_loss:0.960130, cluster_loss:0.327519\n",
      "Clustering   697: AMI= 0.7239, NMI= 0.7248, ARI= 0.5054, ACC= 0.6080\n",
      "0.02650759395740869\n",
      "Training epoch 698, recon_loss:0.612867, zinb_loss:0.959179, cluster_loss:0.328197\n",
      "Clustering   698: AMI= 0.7216, NMI= 0.7225, ARI= 0.4960, ACC= 0.5990\n",
      "0.026670467038560203\n",
      "Training epoch 699, recon_loss:0.612567, zinb_loss:0.959925, cluster_loss:0.327424\n",
      "Clustering   699: AMI= 0.7240, NMI= 0.7249, ARI= 0.5060, ACC= 0.6087\n",
      "0.02781057860662079\n",
      "Training epoch 700, recon_loss:0.611323, zinb_loss:0.959051, cluster_loss:0.328336\n",
      "Clustering   700: AMI= 0.7218, NMI= 0.7227, ARI= 0.4959, ACC= 0.5989\n",
      "0.027892015147196546\n",
      "Training epoch 701, recon_loss:0.611380, zinb_loss:0.959668, cluster_loss:0.327785\n",
      "Clustering   701: AMI= 0.7240, NMI= 0.7249, ARI= 0.5057, ACC= 0.6085\n",
      "0.026833340119711713\n",
      "Training epoch 702, recon_loss:0.612098, zinb_loss:0.958932, cluster_loss:0.328566\n",
      "Clustering   702: AMI= 0.7217, NMI= 0.7227, ARI= 0.4964, ACC= 0.5995\n",
      "0.02650759395740869\n",
      "Training epoch 703, recon_loss:0.611521, zinb_loss:0.959502, cluster_loss:0.327786\n",
      "Clustering   703: AMI= 0.7240, NMI= 0.7249, ARI= 0.5063, ACC= 0.6092\n",
      "0.027892015147196546\n",
      "Training epoch 704, recon_loss:0.610872, zinb_loss:0.958896, cluster_loss:0.328744\n",
      "Clustering   704: AMI= 0.7219, NMI= 0.7228, ARI= 0.4968, ACC= 0.5998\n",
      "0.02785129687690867\n",
      "Training epoch 705, recon_loss:0.610689, zinb_loss:0.959365, cluster_loss:0.328082\n",
      "Clustering   705: AMI= 0.7237, NMI= 0.7247, ARI= 0.5059, ACC= 0.6088\n",
      "0.026833340119711713\n",
      "Training epoch 706, recon_loss:0.611779, zinb_loss:0.958897, cluster_loss:0.328943\n",
      "Clustering   706: AMI= 0.7218, NMI= 0.7227, ARI= 0.4967, ACC= 0.5996\n",
      "0.027077649741438985\n",
      "Training epoch 707, recon_loss:0.611058, zinb_loss:0.959315, cluster_loss:0.328009\n",
      "Clustering   707: AMI= 0.7238, NMI= 0.7248, ARI= 0.5067, ACC= 0.6095\n",
      "0.0289099719043935\n",
      "Training epoch 708, recon_loss:0.610605, zinb_loss:0.958961, cluster_loss:0.329093\n",
      "Clustering   708: AMI= 0.7218, NMI= 0.7227, ARI= 0.4964, ACC= 0.5995\n",
      "0.029235718066696528\n",
      "Training epoch 709, recon_loss:0.610294, zinb_loss:0.959307, cluster_loss:0.328243\n",
      "Clustering   709: AMI= 0.7237, NMI= 0.7246, ARI= 0.5064, ACC= 0.6094\n",
      "0.0289099719043935\n",
      "Training epoch 710, recon_loss:0.611540, zinb_loss:0.959078, cluster_loss:0.329258\n",
      "Clustering   710: AMI= 0.7220, NMI= 0.7229, ARI= 0.4964, ACC= 0.5994\n",
      "0.029520745958711674\n",
      "Training epoch 711, recon_loss:0.610719, zinb_loss:0.959390, cluster_loss:0.328084\n",
      "Clustering   711: AMI= 0.7239, NMI= 0.7248, ARI= 0.5072, ACC= 0.6096\n",
      "0.031515941202817706\n",
      "Training epoch 712, recon_loss:0.610779, zinb_loss:0.959295, cluster_loss:0.329385\n",
      "Clustering   712: AMI= 0.7222, NMI= 0.7231, ARI= 0.4962, ACC= 0.5992\n",
      "0.03261533450059041\n",
      "Training epoch 713, recon_loss:0.610166, zinb_loss:0.959503, cluster_loss:0.328164\n",
      "Clustering   713: AMI= 0.7240, NMI= 0.7249, ARI= 0.5073, ACC= 0.6102\n",
      "0.0333889816360601\n",
      "Training epoch 714, recon_loss:0.612272, zinb_loss:0.959613, cluster_loss:0.329526\n",
      "Clustering   714: AMI= 0.7221, NMI= 0.7230, ARI= 0.4958, ACC= 0.5990\n",
      "0.03481412109613584\n",
      "Training epoch 715, recon_loss:0.611179, zinb_loss:0.959734, cluster_loss:0.327797\n",
      "Clustering   715: AMI= 0.7242, NMI= 0.7251, ARI= 0.5081, ACC= 0.6105\n",
      "0.037664400016287305\n",
      "Training epoch 716, recon_loss:0.610910, zinb_loss:0.959908, cluster_loss:0.329527\n",
      "Clustering   716: AMI= 0.7219, NMI= 0.7228, ARI= 0.4956, ACC= 0.5988\n",
      "0.038845229854635774\n",
      "Training epoch 717, recon_loss:0.610423, zinb_loss:0.959909, cluster_loss:0.327858\n",
      "Clustering   717: AMI= 0.7239, NMI= 0.7248, ARI= 0.5074, ACC= 0.6100\n",
      "0.03941528563866607\n",
      "Training epoch 718, recon_loss:0.612135, zinb_loss:0.960279, cluster_loss:0.329560\n",
      "Clustering   718: AMI= 0.7222, NMI= 0.7232, ARI= 0.4954, ACC= 0.5989\n",
      "0.04181766358565088\n",
      "Training epoch 719, recon_loss:0.611016, zinb_loss:0.960163, cluster_loss:0.327403\n",
      "Clustering   719: AMI= 0.7237, NMI= 0.7246, ARI= 0.5076, ACC= 0.6103\n",
      "0.044790097316665986\n",
      "Training epoch 720, recon_loss:0.612519, zinb_loss:0.960639, cluster_loss:0.329499\n",
      "Clustering   720: AMI= 0.7220, NMI= 0.7229, ARI= 0.4952, ACC= 0.5992\n",
      "0.04556374445213567\n",
      "Training epoch 721, recon_loss:0.611351, zinb_loss:0.960239, cluster_loss:0.327033\n",
      "Clustering   721: AMI= 0.7236, NMI= 0.7245, ARI= 0.5074, ACC= 0.6102\n",
      "0.04674457429048414\n",
      "Training epoch 722, recon_loss:0.613217, zinb_loss:0.960783, cluster_loss:0.329397\n",
      "Clustering   722: AMI= 0.7220, NMI= 0.7230, ARI= 0.4955, ACC= 0.5997\n",
      "0.04601164542530233\n",
      "Training epoch 723, recon_loss:0.611898, zinb_loss:0.960159, cluster_loss:0.326653\n",
      "Clustering   723: AMI= 0.7233, NMI= 0.7242, ARI= 0.5063, ACC= 0.6091\n",
      "0.04572661753328719\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training epoch 724, recon_loss:0.613039, zinb_loss:0.960682, cluster_loss:0.329280\n",
      "Clustering   724: AMI= 0.7222, NMI= 0.7231, ARI= 0.4964, ACC= 0.6011\n",
      "0.04385357710004479\n",
      "Training epoch 725, recon_loss:0.611742, zinb_loss:0.959888, cluster_loss:0.326611\n",
      "Clustering   725: AMI= 0.7233, NMI= 0.7242, ARI= 0.5056, ACC= 0.6079\n",
      "0.04255059245083269\n",
      "Training epoch 726, recon_loss:0.613463, zinb_loss:0.960483, cluster_loss:0.329289\n",
      "Clustering   726: AMI= 0.7220, NMI= 0.7229, ARI= 0.4968, ACC= 0.6019\n",
      "0.0414104808827721\n",
      "Training epoch 727, recon_loss:0.611777, zinb_loss:0.959599, cluster_loss:0.326637\n",
      "Clustering   727: AMI= 0.7233, NMI= 0.7243, ARI= 0.5050, ACC= 0.6072\n",
      "0.03990390488212061\n",
      "Training epoch 728, recon_loss:0.612746, zinb_loss:0.960190, cluster_loss:0.329366\n",
      "Clustering   728: AMI= 0.7220, NMI= 0.7230, ARI= 0.4975, ACC= 0.6028\n",
      "0.03835661061118124\n",
      "Training epoch 729, recon_loss:0.611194, zinb_loss:0.959285, cluster_loss:0.326950\n",
      "Clustering   729: AMI= 0.7232, NMI= 0.7241, ARI= 0.5044, ACC= 0.6060\n",
      "0.03672787979966611\n",
      "Training epoch 730, recon_loss:0.612796, zinb_loss:0.959943, cluster_loss:0.329518\n",
      "Clustering   730: AMI= 0.7218, NMI= 0.7227, ARI= 0.4977, ACC= 0.6032\n",
      "0.036361415367075205\n",
      "Training epoch 731, recon_loss:0.610998, zinb_loss:0.959068, cluster_loss:0.327156\n",
      "Clustering   731: AMI= 0.7233, NMI= 0.7242, ARI= 0.5044, ACC= 0.6056\n",
      "0.0356284865018934\n",
      "Training epoch 732, recon_loss:0.612124, zinb_loss:0.959709, cluster_loss:0.329657\n",
      "Clustering   732: AMI= 0.7218, NMI= 0.7228, ARI= 0.4985, ACC= 0.6039\n",
      "0.03465124801498432\n",
      "Training epoch 733, recon_loss:0.610510, zinb_loss:0.958872, cluster_loss:0.327461\n",
      "Clustering   733: AMI= 0.7232, NMI= 0.7241, ARI= 0.5041, ACC= 0.6050\n",
      "0.03351113644692374\n",
      "Training epoch 734, recon_loss:0.612062, zinb_loss:0.959533, cluster_loss:0.329799\n",
      "Clustering   734: AMI= 0.7219, NMI= 0.7228, ARI= 0.4988, ACC= 0.6042\n",
      "0.032574616230302535\n",
      "Training epoch 735, recon_loss:0.610415, zinb_loss:0.958753, cluster_loss:0.327648\n",
      "Clustering   735: AMI= 0.7230, NMI= 0.7239, ARI= 0.5037, ACC= 0.6048\n",
      "0.031678814283969216\n",
      "Training epoch 736, recon_loss:0.611538, zinb_loss:0.959378, cluster_loss:0.329913\n",
      "Clustering   736: AMI= 0.7218, NMI= 0.7227, ARI= 0.4989, ACC= 0.6041\n",
      "0.031027321959363165\n",
      "Training epoch 737, recon_loss:0.610109, zinb_loss:0.958658, cluster_loss:0.327875\n",
      "Clustering   737: AMI= 0.7231, NMI= 0.7240, ARI= 0.5036, ACC= 0.6046\n",
      "0.030131520013029846\n",
      "Training epoch 738, recon_loss:0.611531, zinb_loss:0.959270, cluster_loss:0.330021\n",
      "Clustering   738: AMI= 0.7218, NMI= 0.7227, ARI= 0.4990, ACC= 0.6043\n",
      "0.02956146422899955\n",
      "Training epoch 739, recon_loss:0.610175, zinb_loss:0.958613, cluster_loss:0.327990\n",
      "Clustering   739: AMI= 0.7231, NMI= 0.7240, ARI= 0.5035, ACC= 0.6044\n",
      "0.02935787287756016\n",
      "Training epoch 740, recon_loss:0.611165, zinb_loss:0.959173, cluster_loss:0.330102\n",
      "Clustering   740: AMI= 0.7220, NMI= 0.7229, ARI= 0.4994, ACC= 0.6048\n",
      "0.0289099719043935\n",
      "Training epoch 741, recon_loss:0.610063, zinb_loss:0.958578, cluster_loss:0.328143\n",
      "Clustering   741: AMI= 0.7233, NMI= 0.7242, ARI= 0.5037, ACC= 0.6041\n",
      "0.028747098823241987\n",
      "Training epoch 742, recon_loss:0.611503, zinb_loss:0.959106, cluster_loss:0.330186\n",
      "Clustering   742: AMI= 0.7219, NMI= 0.7228, ARI= 0.4996, ACC= 0.6049\n",
      "0.02850278920151472\n",
      "Training epoch 743, recon_loss:0.610575, zinb_loss:0.958579, cluster_loss:0.328159\n",
      "Clustering   743: AMI= 0.7236, NMI= 0.7246, ARI= 0.5043, ACC= 0.6043\n",
      "0.028869253634105623\n",
      "Training epoch 744, recon_loss:0.610477, zinb_loss:0.958985, cluster_loss:0.330191\n",
      "Clustering   744: AMI= 0.7222, NMI= 0.7231, ARI= 0.5000, ACC= 0.6053\n",
      "0.028380634390651086\n",
      "Training epoch 745, recon_loss:0.610142, zinb_loss:0.958589, cluster_loss:0.328409\n",
      "Clustering   745: AMI= 0.7236, NMI= 0.7245, ARI= 0.5039, ACC= 0.6039\n",
      "0.027769860336332913\n",
      "Training epoch 746, recon_loss:0.612200, zinb_loss:0.958942, cluster_loss:0.330258\n",
      "Clustering   746: AMI= 0.7221, NMI= 0.7230, ARI= 0.5001, ACC= 0.6058\n",
      "0.02825847957978745\n",
      "Training epoch 747, recon_loss:0.612059, zinb_loss:0.958667, cluster_loss:0.328149\n",
      "Clustering   747: AMI= 0.7237, NMI= 0.7246, ARI= 0.5044, ACC= 0.6039\n",
      "0.029887210391302578\n",
      "Training epoch 748, recon_loss:0.610669, zinb_loss:0.958814, cluster_loss:0.330105\n",
      "Clustering   748: AMI= 0.7222, NMI= 0.7232, ARI= 0.5004, ACC= 0.6062\n",
      "0.029968646931878333\n",
      "Training epoch 749, recon_loss:0.611164, zinb_loss:0.958713, cluster_loss:0.328430\n",
      "Clustering   749: AMI= 0.7236, NMI= 0.7246, ARI= 0.5033, ACC= 0.6028\n",
      "0.028584225742090477\n",
      "Training epoch 750, recon_loss:0.612951, zinb_loss:0.958742, cluster_loss:0.330065\n",
      "Clustering   750: AMI= 0.7227, NMI= 0.7236, ARI= 0.5010, ACC= 0.6067\n",
      "0.028177043039211695\n",
      "Training epoch 751, recon_loss:0.613898, zinb_loss:0.958876, cluster_loss:0.328043\n",
      "Clustering   751: AMI= 0.7236, NMI= 0.7246, ARI= 0.5033, ACC= 0.6019\n",
      "0.02964290076957531\n",
      "Training epoch 752, recon_loss:0.611830, zinb_loss:0.958722, cluster_loss:0.329697\n",
      "Clustering   752: AMI= 0.7224, NMI= 0.7233, ARI= 0.5009, ACC= 0.6069\n",
      "0.029724337310151065\n",
      "Training epoch 753, recon_loss:0.612514, zinb_loss:0.958992, cluster_loss:0.328242\n",
      "Clustering   753: AMI= 0.7233, NMI= 0.7242, ARI= 0.5019, ACC= 0.6004\n",
      "0.0276069872551814\n",
      "Training epoch 754, recon_loss:0.613450, zinb_loss:0.958743, cluster_loss:0.329542\n",
      "Clustering   754: AMI= 0.7228, NMI= 0.7237, ARI= 0.5024, ACC= 0.6082\n",
      "0.027077649741438985\n",
      "Training epoch 755, recon_loss:0.614279, zinb_loss:0.959188, cluster_loss:0.328027\n",
      "Clustering   755: AMI= 0.7229, NMI= 0.7238, ARI= 0.5018, ACC= 0.6003\n",
      "0.027769860336332913\n",
      "Training epoch 756, recon_loss:0.611688, zinb_loss:0.958918, cluster_loss:0.329240\n",
      "Clustering   756: AMI= 0.7231, NMI= 0.7240, ARI= 0.5036, ACC= 0.6090\n",
      "0.028136324768923818\n",
      "Training epoch 757, recon_loss:0.612162, zinb_loss:0.959388, cluster_loss:0.328479\n",
      "Clustering   757: AMI= 0.7225, NMI= 0.7234, ARI= 0.5000, ACC= 0.5990\n",
      "0.02825847957978745\n",
      "Training epoch 758, recon_loss:0.612424, zinb_loss:0.959151, cluster_loss:0.329244\n",
      "Clustering   758: AMI= 0.7234, NMI= 0.7244, ARI= 0.5049, ACC= 0.6098\n",
      "0.028299197850075328\n",
      "Training epoch 759, recon_loss:0.612920, zinb_loss:0.959683, cluster_loss:0.328540\n",
      "Clustering   759: AMI= 0.7223, NMI= 0.7232, ARI= 0.4996, ACC= 0.5990\n",
      "0.028828535363817746\n",
      "Training epoch 760, recon_loss:0.610924, zinb_loss:0.959569, cluster_loss:0.329077\n",
      "Clustering   760: AMI= 0.7234, NMI= 0.7243, ARI= 0.5053, ACC= 0.6100\n",
      "0.0298464921210147\n",
      "Training epoch 761, recon_loss:0.611519, zinb_loss:0.960035, cluster_loss:0.328924\n",
      "Clustering   761: AMI= 0.7219, NMI= 0.7228, ARI= 0.4982, ACC= 0.5981\n",
      "0.030620139256484383\n",
      "Training epoch 762, recon_loss:0.611856, zinb_loss:0.959981, cluster_loss:0.329052\n",
      "Clustering   762: AMI= 0.7235, NMI= 0.7244, ARI= 0.5058, ACC= 0.6102\n",
      "0.031068040229651043\n",
      "Training epoch 763, recon_loss:0.612215, zinb_loss:0.960390, cluster_loss:0.328899\n",
      "Clustering   763: AMI= 0.7218, NMI= 0.7228, ARI= 0.4985, ACC= 0.5982\n",
      "0.03204527871656012\n",
      "Training epoch 764, recon_loss:0.610686, zinb_loss:0.960372, cluster_loss:0.328923\n",
      "Clustering   764: AMI= 0.7236, NMI= 0.7245, ARI= 0.5059, ACC= 0.6104\n",
      "0.03351113644692374\n",
      "Training epoch 765, recon_loss:0.611243, zinb_loss:0.960615, cluster_loss:0.329160\n",
      "Clustering   765: AMI= 0.7220, NMI= 0.7229, ARI= 0.4982, ACC= 0.5981\n",
      "0.0339590374200904\n",
      "Training epoch 766, recon_loss:0.611679, zinb_loss:0.960576, cluster_loss:0.328946\n",
      "Clustering   766: AMI= 0.7238, NMI= 0.7247, ARI= 0.5064, ACC= 0.6109\n",
      "0.0339590374200904\n",
      "Training epoch 767, recon_loss:0.611845, zinb_loss:0.960700, cluster_loss:0.329071\n",
      "Clustering   767: AMI= 0.7218, NMI= 0.7227, ARI= 0.4983, ACC= 0.5983\n",
      "0.033551854717211615\n",
      "Training epoch 768, recon_loss:0.610493, zinb_loss:0.960633, cluster_loss:0.328925\n",
      "Clustering   768: AMI= 0.7236, NMI= 0.7245, ARI= 0.5060, ACC= 0.6105\n",
      "0.033266826825196466\n",
      "Training epoch 769, recon_loss:0.610993, zinb_loss:0.960606, cluster_loss:0.329291\n",
      "Clustering   769: AMI= 0.7218, NMI= 0.7227, ARI= 0.4983, ACC= 0.5987\n",
      "0.03281892585202981\n",
      "Training epoch 770, recon_loss:0.611592, zinb_loss:0.960550, cluster_loss:0.329035\n",
      "Clustering   770: AMI= 0.7234, NMI= 0.7244, ARI= 0.5055, ACC= 0.6101\n",
      "0.03155665947310558\n",
      "Training epoch 771, recon_loss:0.611599, zinb_loss:0.960445, cluster_loss:0.329146\n",
      "Clustering   771: AMI= 0.7219, NMI= 0.7228, ARI= 0.4990, ACC= 0.5993\n",
      "0.030335111364469237\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training epoch 772, recon_loss:0.610479, zinb_loss:0.960406, cluster_loss:0.329071\n",
      "Clustering   772: AMI= 0.7233, NMI= 0.7242, ARI= 0.5046, ACC= 0.6094\n",
      "0.02935787287756016\n",
      "Training epoch 773, recon_loss:0.610947, zinb_loss:0.960182, cluster_loss:0.329299\n",
      "Clustering   773: AMI= 0.7218, NMI= 0.7227, ARI= 0.4990, ACC= 0.5993\n",
      "0.028747098823241987\n",
      "Training epoch 774, recon_loss:0.611481, zinb_loss:0.960237, cluster_loss:0.329168\n",
      "Clustering   774: AMI= 0.7234, NMI= 0.7243, ARI= 0.5043, ACC= 0.6093\n",
      "0.027973451687772304\n",
      "Training epoch 775, recon_loss:0.611420, zinb_loss:0.959906, cluster_loss:0.329074\n",
      "Clustering   775: AMI= 0.7222, NMI= 0.7231, ARI= 0.5006, ACC= 0.6002\n",
      "0.027036931471151104\n",
      "Training epoch 776, recon_loss:0.611515, zinb_loss:0.960099, cluster_loss:0.329198\n",
      "Clustering   776: AMI= 0.7231, NMI= 0.7240, ARI= 0.5030, ACC= 0.6084\n",
      "0.026833340119711713\n",
      "Training epoch 777, recon_loss:0.611344, zinb_loss:0.959599, cluster_loss:0.328973\n",
      "Clustering   777: AMI= 0.7224, NMI= 0.7233, ARI= 0.5012, ACC= 0.6003\n",
      "0.027566268984893522\n",
      "Training epoch 778, recon_loss:0.612500, zinb_loss:0.959978, cluster_loss:0.329248\n",
      "Clustering   778: AMI= 0.7228, NMI= 0.7237, ARI= 0.5023, ACC= 0.6078\n",
      "0.027688423795757155\n",
      "Training epoch 779, recon_loss:0.611854, zinb_loss:0.959319, cluster_loss:0.328671\n",
      "Clustering   779: AMI= 0.7226, NMI= 0.7235, ARI= 0.5022, ACC= 0.6006\n",
      "0.02781057860662079\n",
      "Training epoch 780, recon_loss:0.612013, zinb_loss:0.959812, cluster_loss:0.329235\n",
      "Clustering   780: AMI= 0.7226, NMI= 0.7235, ARI= 0.5014, ACC= 0.6070\n",
      "0.027769860336332913\n",
      "Training epoch 781, recon_loss:0.611347, zinb_loss:0.959082, cluster_loss:0.328706\n",
      "Clustering   781: AMI= 0.7229, NMI= 0.7238, ARI= 0.5025, ACC= 0.6010\n",
      "0.027647705525469277\n",
      "Training epoch 782, recon_loss:0.613026, zinb_loss:0.959728, cluster_loss:0.329353\n",
      "Clustering   782: AMI= 0.7225, NMI= 0.7234, ARI= 0.5010, ACC= 0.6068\n",
      "0.027240522822590495\n",
      "Training epoch 783, recon_loss:0.611567, zinb_loss:0.958872, cluster_loss:0.328517\n",
      "Clustering   783: AMI= 0.7228, NMI= 0.7238, ARI= 0.5028, ACC= 0.6015\n",
      "0.02695549493057535\n",
      "Training epoch 784, recon_loss:0.611766, zinb_loss:0.959579, cluster_loss:0.329437\n",
      "Clustering   784: AMI= 0.7224, NMI= 0.7233, ARI= 0.5000, ACC= 0.6056\n",
      "0.026100411254529908\n",
      "Training epoch 785, recon_loss:0.610812, zinb_loss:0.958714, cluster_loss:0.328758\n",
      "Clustering   785: AMI= 0.7231, NMI= 0.7240, ARI= 0.5027, ACC= 0.6014\n",
      "0.02565251028136325\n",
      "Training epoch 786, recon_loss:0.612360, zinb_loss:0.959524, cluster_loss:0.329620\n",
      "Clustering   786: AMI= 0.7222, NMI= 0.7231, ARI= 0.4996, ACC= 0.6048\n",
      "0.024593835253878416\n",
      "Training epoch 787, recon_loss:0.610678, zinb_loss:0.958608, cluster_loss:0.328740\n",
      "Clustering   787: AMI= 0.7232, NMI= 0.7241, ARI= 0.5032, ACC= 0.6020\n",
      "0.02455311698359054\n",
      "Training epoch 788, recon_loss:0.612137, zinb_loss:0.959507, cluster_loss:0.329784\n",
      "Clustering   788: AMI= 0.7223, NMI= 0.7232, ARI= 0.4995, ACC= 0.6045\n",
      "0.023901624658984485\n",
      "Training epoch 789, recon_loss:0.610463, zinb_loss:0.958511, cluster_loss:0.328824\n",
      "Clustering   789: AMI= 0.7233, NMI= 0.7243, ARI= 0.5032, ACC= 0.6021\n",
      "0.02357587849668146\n",
      "Training epoch 790, recon_loss:0.612088, zinb_loss:0.959498, cluster_loss:0.329934\n",
      "Clustering   790: AMI= 0.7222, NMI= 0.7231, ARI= 0.4991, ACC= 0.6039\n",
      "0.023046540982939043\n",
      "Training epoch 791, recon_loss:0.610388, zinb_loss:0.958476, cluster_loss:0.328908\n",
      "Clustering   791: AMI= 0.7233, NMI= 0.7242, ARI= 0.5033, ACC= 0.6023\n",
      "0.022802231361211775\n",
      "Training epoch 792, recon_loss:0.611866, zinb_loss:0.959511, cluster_loss:0.330045\n",
      "Clustering   792: AMI= 0.7224, NMI= 0.7233, ARI= 0.4991, ACC= 0.6038\n",
      "0.02247648519890875\n",
      "Training epoch 793, recon_loss:0.610223, zinb_loss:0.958478, cluster_loss:0.329022\n",
      "Clustering   793: AMI= 0.7233, NMI= 0.7242, ARI= 0.5033, ACC= 0.6020\n",
      "0.022557921739484506\n",
      "Training epoch 794, recon_loss:0.612171, zinb_loss:0.959576, cluster_loss:0.330133\n",
      "Clustering   794: AMI= 0.7224, NMI= 0.7234, ARI= 0.4989, ACC= 0.6036\n",
      "0.022232175577181483\n",
      "Training epoch 795, recon_loss:0.610507, zinb_loss:0.958509, cluster_loss:0.329040\n",
      "Clustering   795: AMI= 0.7232, NMI= 0.7241, ARI= 0.5035, ACC= 0.6024\n",
      "0.022150739036605725\n",
      "Training epoch 796, recon_loss:0.611815, zinb_loss:0.959642, cluster_loss:0.330156\n",
      "Clustering   796: AMI= 0.7223, NMI= 0.7233, ARI= 0.4985, ACC= 0.6032\n",
      "0.021743556333726943\n",
      "Training epoch 797, recon_loss:0.610287, zinb_loss:0.958578, cluster_loss:0.329160\n",
      "Clustering   797: AMI= 0.7233, NMI= 0.7242, ARI= 0.5037, ACC= 0.6026\n",
      "0.021377091901136038\n",
      "Training epoch 798, recon_loss:0.612861, zinb_loss:0.959781, cluster_loss:0.330162\n",
      "Clustering   798: AMI= 0.7223, NMI= 0.7232, ARI= 0.4986, ACC= 0.6030\n",
      "0.021214218819984528\n",
      "Training epoch 799, recon_loss:0.611265, zinb_loss:0.958678, cluster_loss:0.329039\n",
      "Clustering   799: AMI= 0.7235, NMI= 0.7244, ARI= 0.5040, ACC= 0.6030\n",
      "0.021051345738833015\n",
      "Training epoch 800, recon_loss:0.611929, zinb_loss:0.959847, cluster_loss:0.330011\n",
      "Clustering   800: AMI= 0.7224, NMI= 0.7233, ARI= 0.4989, ACC= 0.6030\n",
      "0.020196262062787573\n",
      "Training epoch 801, recon_loss:0.610707, zinb_loss:0.958819, cluster_loss:0.329228\n",
      "Clustering   801: AMI= 0.7231, NMI= 0.7240, ARI= 0.5030, ACC= 0.6023\n",
      "0.018567531251272446\n",
      "Training epoch 802, recon_loss:0.613347, zinb_loss:0.960002, cluster_loss:0.329913\n",
      "Clustering   802: AMI= 0.7224, NMI= 0.7233, ARI= 0.4991, ACC= 0.6028\n",
      "0.017508856223787613\n",
      "Training epoch 803, recon_loss:0.611844, zinb_loss:0.958947, cluster_loss:0.328967\n",
      "Clustering   803: AMI= 0.7229, NMI= 0.7238, ARI= 0.5025, ACC= 0.6022\n",
      "0.01718311006148459\n",
      "Training epoch 804, recon_loss:0.612660, zinb_loss:0.960006, cluster_loss:0.329748\n",
      "Clustering   804: AMI= 0.7225, NMI= 0.7234, ARI= 0.4991, ACC= 0.6026\n",
      "0.016653772547742172\n",
      "Training epoch 805, recon_loss:0.611269, zinb_loss:0.959083, cluster_loss:0.329048\n",
      "Clustering   805: AMI= 0.7225, NMI= 0.7235, ARI= 0.5016, ACC= 0.6019\n",
      "0.015432224439105826\n",
      "Training epoch 806, recon_loss:0.613909, zinb_loss:0.960081, cluster_loss:0.329627\n",
      "Clustering   806: AMI= 0.7224, NMI= 0.7233, ARI= 0.4993, ACC= 0.6025\n",
      "0.014984323465939167\n",
      "Training epoch 807, recon_loss:0.612263, zinb_loss:0.959204, cluster_loss:0.328822\n",
      "Clustering   807: AMI= 0.7228, NMI= 0.7237, ARI= 0.5016, ACC= 0.6020\n",
      "0.014740013844211898\n",
      "Training epoch 808, recon_loss:0.612030, zinb_loss:0.959992, cluster_loss:0.329450\n",
      "Clustering   808: AMI= 0.7221, NMI= 0.7230, ARI= 0.4993, ACC= 0.6023\n",
      "0.01449570422248463\n",
      "Training epoch 809, recon_loss:0.610946, zinb_loss:0.959328, cluster_loss:0.329140\n",
      "Clustering   809: AMI= 0.7225, NMI= 0.7235, ARI= 0.5007, ACC= 0.6017\n",
      "0.014414267681908873\n",
      "Training epoch 810, recon_loss:0.612461, zinb_loss:0.960052, cluster_loss:0.329412\n",
      "Clustering   810: AMI= 0.7224, NMI= 0.7233, ARI= 0.5000, ACC= 0.6024\n",
      "0.014862168655075532\n",
      "Training epoch 811, recon_loss:0.610942, zinb_loss:0.959408, cluster_loss:0.329093\n",
      "Clustering   811: AMI= 0.7224, NMI= 0.7234, ARI= 0.5007, ACC= 0.6018\n",
      "0.015310069628242192\n",
      "Training epoch 812, recon_loss:0.612471, zinb_loss:0.960055, cluster_loss:0.329404\n",
      "Clustering   812: AMI= 0.7224, NMI= 0.7233, ARI= 0.5002, ACC= 0.6024\n",
      "0.015432224439105826\n",
      "Training epoch 813, recon_loss:0.610961, zinb_loss:0.959451, cluster_loss:0.329065\n",
      "Clustering   813: AMI= 0.7225, NMI= 0.7235, ARI= 0.5005, ACC= 0.6016\n",
      "0.015513660979681583\n",
      "Training epoch 814, recon_loss:0.611969, zinb_loss:0.959949, cluster_loss:0.329394\n",
      "Clustering   814: AMI= 0.7223, NMI= 0.7232, ARI= 0.5002, ACC= 0.6021\n",
      "0.015391506168817948\n",
      "Training epoch 815, recon_loss:0.610706, zinb_loss:0.959463, cluster_loss:0.329147\n",
      "Clustering   815: AMI= 0.7225, NMI= 0.7234, ARI= 0.5001, ACC= 0.6015\n",
      "0.015554379249969462\n",
      "Training epoch 816, recon_loss:0.612151, zinb_loss:0.959825, cluster_loss:0.329420\n",
      "Clustering   816: AMI= 0.7222, NMI= 0.7231, ARI= 0.5007, ACC= 0.6022\n",
      "0.015513660979681583\n",
      "Training epoch 817, recon_loss:0.610955, zinb_loss:0.959436, cluster_loss:0.329135\n",
      "Clustering   817: AMI= 0.7226, NMI= 0.7235, ARI= 0.5000, ACC= 0.6013\n",
      "0.015228633087666437\n",
      "Training epoch 818, recon_loss:0.611550, zinb_loss:0.959633, cluster_loss:0.329433\n",
      "Clustering   818: AMI= 0.7225, NMI= 0.7234, ARI= 0.5012, ACC= 0.6027\n",
      "0.015065760006514923\n",
      "Training epoch 819, recon_loss:0.610729, zinb_loss:0.959402, cluster_loss:0.329274\n",
      "Clustering   819: AMI= 0.7225, NMI= 0.7234, ARI= 0.4995, ACC= 0.6009\n",
      "0.014984323465939167\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training epoch 820, recon_loss:0.612150, zinb_loss:0.959478, cluster_loss:0.329481\n",
      "Clustering   820: AMI= 0.7224, NMI= 0.7233, ARI= 0.5016, ACC= 0.6029\n",
      "0.015187914817378557\n",
      "Training epoch 821, recon_loss:0.611224, zinb_loss:0.959331, cluster_loss:0.329240\n",
      "Clustering   821: AMI= 0.7225, NMI= 0.7235, ARI= 0.4997, ACC= 0.6010\n",
      "0.014821450384787655\n",
      "Training epoch 822, recon_loss:0.611139, zinb_loss:0.959251, cluster_loss:0.329505\n",
      "Clustering   822: AMI= 0.7224, NMI= 0.7233, ARI= 0.5016, ACC= 0.6027\n",
      "0.014658577303636141\n",
      "Training epoch 823, recon_loss:0.610722, zinb_loss:0.959272, cluster_loss:0.329491\n",
      "Clustering   823: AMI= 0.7224, NMI= 0.7233, ARI= 0.4991, ACC= 0.6007\n",
      "0.015025041736227046\n",
      "Training epoch 824, recon_loss:0.611836, zinb_loss:0.959110, cluster_loss:0.329572\n",
      "Clustering   824: AMI= 0.7225, NMI= 0.7234, ARI= 0.5018, ACC= 0.6027\n",
      "0.01514719654709068\n",
      "Training epoch 825, recon_loss:0.611122, zinb_loss:0.959192, cluster_loss:0.329474\n",
      "Clustering   825: AMI= 0.7224, NMI= 0.7233, ARI= 0.4994, ACC= 0.6010\n",
      "0.01490288692536341\n",
      "Training epoch 826, recon_loss:0.610882, zinb_loss:0.958929, cluster_loss:0.329611\n",
      "Clustering   826: AMI= 0.7223, NMI= 0.7232, ARI= 0.5015, ACC= 0.6024\n",
      "0.01469929557392402\n",
      "Training epoch 827, recon_loss:0.610555, zinb_loss:0.959139, cluster_loss:0.329730\n",
      "Clustering   827: AMI= 0.7224, NMI= 0.7233, ARI= 0.4990, ACC= 0.6007\n",
      "0.014943605195651289\n",
      "Training epoch 828, recon_loss:0.611734, zinb_loss:0.958828, cluster_loss:0.329678\n",
      "Clustering   828: AMI= 0.7223, NMI= 0.7232, ARI= 0.5016, ACC= 0.6022\n",
      "0.014943605195651289\n",
      "Training epoch 829, recon_loss:0.611179, zinb_loss:0.959097, cluster_loss:0.329712\n",
      "Clustering   829: AMI= 0.7223, NMI= 0.7232, ARI= 0.4994, ACC= 0.6008\n",
      "0.013966366708742213\n",
      "Training epoch 830, recon_loss:0.610175, zinb_loss:0.958671, cluster_loss:0.329651\n",
      "Clustering   830: AMI= 0.7224, NMI= 0.7233, ARI= 0.5017, ACC= 0.6023\n",
      "0.01449570422248463\n",
      "Training epoch 831, recon_loss:0.610459, zinb_loss:0.959110, cluster_loss:0.330093\n",
      "Clustering   831: AMI= 0.7222, NMI= 0.7231, ARI= 0.4987, ACC= 0.6004\n",
      "0.01559509752025734\n",
      "Training epoch 832, recon_loss:0.610927, zinb_loss:0.958613, cluster_loss:0.329673\n",
      "Clustering   832: AMI= 0.7222, NMI= 0.7232, ARI= 0.5019, ACC= 0.6022\n",
      "0.016083716763711876\n",
      "Training epoch 833, recon_loss:0.610754, zinb_loss:0.959115, cluster_loss:0.330086\n",
      "Clustering   833: AMI= 0.7222, NMI= 0.7231, ARI= 0.4989, ACC= 0.6006\n",
      "0.01579868887169673\n",
      "Training epoch 834, recon_loss:0.610837, zinb_loss:0.958569, cluster_loss:0.329643\n",
      "Clustering   834: AMI= 0.7223, NMI= 0.7232, ARI= 0.5020, ACC= 0.6023\n",
      "0.016042998493424\n",
      "Training epoch 835, recon_loss:0.610827, zinb_loss:0.959158, cluster_loss:0.330195\n",
      "Clustering   835: AMI= 0.7221, NMI= 0.7231, ARI= 0.4989, ACC= 0.6007\n",
      "0.016816645628893685\n",
      "Training epoch 836, recon_loss:0.611254, zinb_loss:0.958544, cluster_loss:0.329562\n",
      "Clustering   836: AMI= 0.7224, NMI= 0.7233, ARI= 0.5022, ACC= 0.6025\n",
      "0.017590292764363368\n",
      "Training epoch 837, recon_loss:0.611343, zinb_loss:0.959253, cluster_loss:0.330244\n",
      "Clustering   837: AMI= 0.7220, NMI= 0.7229, ARI= 0.4985, ACC= 0.6005\n",
      "0.018526812980984568\n",
      "Training epoch 838, recon_loss:0.610474, zinb_loss:0.958522, cluster_loss:0.329393\n",
      "Clustering   838: AMI= 0.7225, NMI= 0.7234, ARI= 0.5025, ACC= 0.6026\n",
      "0.019219023575878496\n",
      "Training epoch 839, recon_loss:0.611027, zinb_loss:0.959392, cluster_loss:0.330501\n",
      "Clustering   839: AMI= 0.7219, NMI= 0.7228, ARI= 0.4981, ACC= 0.6004\n",
      "0.019911234170772424\n",
      "Training epoch 840, recon_loss:0.611801, zinb_loss:0.958586, cluster_loss:0.329221\n",
      "Clustering   840: AMI= 0.7228, NMI= 0.7237, ARI= 0.5032, ACC= 0.6029\n",
      "0.021662119793151188\n",
      "Training epoch 841, recon_loss:0.612148, zinb_loss:0.959538, cluster_loss:0.330404\n",
      "Clustering   841: AMI= 0.7219, NMI= 0.7228, ARI= 0.4977, ACC= 0.6001\n",
      "0.022395048658332993\n",
      "Training epoch 842, recon_loss:0.610595, zinb_loss:0.958672, cluster_loss:0.328962\n",
      "Clustering   842: AMI= 0.7227, NMI= 0.7236, ARI= 0.5036, ACC= 0.6031\n",
      "0.024186652550999634\n",
      "Training epoch 843, recon_loss:0.611585, zinb_loss:0.959698, cluster_loss:0.330691\n",
      "Clustering   843: AMI= 0.7215, NMI= 0.7225, ARI= 0.4967, ACC= 0.5994\n",
      "0.02605969298424203\n",
      "Training epoch 844, recon_loss:0.611796, zinb_loss:0.958823, cluster_loss:0.328788\n",
      "Clustering   844: AMI= 0.7229, NMI= 0.7238, ARI= 0.5040, ACC= 0.6038\n",
      "0.0276069872551814\n",
      "Training epoch 845, recon_loss:0.612320, zinb_loss:0.959830, cluster_loss:0.330560\n",
      "Clustering   845: AMI= 0.7216, NMI= 0.7225, ARI= 0.4967, ACC= 0.5990\n",
      "0.028747098823241987\n",
      "Training epoch 846, recon_loss:0.611025, zinb_loss:0.959043, cluster_loss:0.328636\n",
      "Clustering   846: AMI= 0.7229, NMI= 0.7238, ARI= 0.5043, ACC= 0.6048\n",
      "0.03074229406734802\n",
      "Training epoch 847, recon_loss:0.611805, zinb_loss:0.959948, cluster_loss:0.330701\n",
      "Clustering   847: AMI= 0.7216, NMI= 0.7225, ARI= 0.4963, ACC= 0.5985\n",
      "0.03290036239260556\n",
      "Training epoch 848, recon_loss:0.611897, zinb_loss:0.959276, cluster_loss:0.328547\n",
      "Clustering   848: AMI= 0.7230, NMI= 0.7239, ARI= 0.5044, ACC= 0.6056\n",
      "0.03477340282584796\n",
      "Training epoch 849, recon_loss:0.612395, zinb_loss:0.960066, cluster_loss:0.330529\n",
      "Clustering   849: AMI= 0.7214, NMI= 0.7223, ARI= 0.4963, ACC= 0.5983\n",
      "0.0352620220693025\n",
      "Training epoch 850, recon_loss:0.610840, zinb_loss:0.959477, cluster_loss:0.328502\n",
      "Clustering   850: AMI= 0.7230, NMI= 0.7239, ARI= 0.5047, ACC= 0.6063\n",
      "0.03685003461052974\n",
      "Training epoch 851, recon_loss:0.611636, zinb_loss:0.960118, cluster_loss:0.330665\n",
      "Clustering   851: AMI= 0.7214, NMI= 0.7224, ARI= 0.4956, ACC= 0.5976\n",
      "0.038641638503196386\n",
      "Training epoch 852, recon_loss:0.611372, zinb_loss:0.959598, cluster_loss:0.328585\n",
      "Clustering   852: AMI= 0.7232, NMI= 0.7241, ARI= 0.5049, ACC= 0.6069\n",
      "0.039537440449529705\n",
      "Training epoch 853, recon_loss:0.611864, zinb_loss:0.960161, cluster_loss:0.330538\n",
      "Clustering   853: AMI= 0.7214, NMI= 0.7223, ARI= 0.4961, ACC= 0.5977\n",
      "0.038967384665499406\n",
      "Training epoch 854, recon_loss:0.610982, zinb_loss:0.959681, cluster_loss:0.328773\n",
      "Clustering   854: AMI= 0.7235, NMI= 0.7244, ARI= 0.5052, ACC= 0.6071\n",
      "0.03921169428722668\n",
      "Training epoch 855, recon_loss:0.611362, zinb_loss:0.960096, cluster_loss:0.330581\n",
      "Clustering   855: AMI= 0.7213, NMI= 0.7222, ARI= 0.4960, ACC= 0.5975\n",
      "0.0391709760169388\n",
      "Training epoch 856, recon_loss:0.611143, zinb_loss:0.959677, cluster_loss:0.329032\n",
      "Clustering   856: AMI= 0.7234, NMI= 0.7243, ARI= 0.5052, ACC= 0.6069\n",
      "0.03860092023290851\n",
      "Training epoch 857, recon_loss:0.611339, zinb_loss:0.960030, cluster_loss:0.330563\n",
      "Clustering   857: AMI= 0.7212, NMI= 0.7221, ARI= 0.4964, ACC= 0.5977\n",
      "0.03713506250254489\n",
      "Training epoch 858, recon_loss:0.610535, zinb_loss:0.959647, cluster_loss:0.329306\n",
      "Clustering   858: AMI= 0.7232, NMI= 0.7241, ARI= 0.5047, ACC= 0.6065\n",
      "0.0359949509344843\n",
      "Training epoch 859, recon_loss:0.610763, zinb_loss:0.959941, cluster_loss:0.330661\n",
      "Clustering   859: AMI= 0.7211, NMI= 0.7220, ARI= 0.4965, ACC= 0.5977\n",
      "0.03558776823160552\n",
      "Training epoch 860, recon_loss:0.610844, zinb_loss:0.959634, cluster_loss:0.329587\n",
      "Clustering   860: AMI= 0.7232, NMI= 0.7242, ARI= 0.5046, ACC= 0.6066\n",
      "0.03497699417728735\n",
      "Training epoch 861, recon_loss:0.610909, zinb_loss:0.959889, cluster_loss:0.330620\n",
      "Clustering   861: AMI= 0.7212, NMI= 0.7221, ARI= 0.4969, ACC= 0.5977\n",
      "0.03367400952807525\n",
      "Training epoch 862, recon_loss:0.610157, zinb_loss:0.959632, cluster_loss:0.329825\n",
      "Clustering   862: AMI= 0.7229, NMI= 0.7238, ARI= 0.5041, ACC= 0.6065\n",
      "0.032859644122317684\n",
      "Training epoch 863, recon_loss:0.610353, zinb_loss:0.959833, cluster_loss:0.330714\n",
      "Clustering   863: AMI= 0.7213, NMI= 0.7222, ARI= 0.4969, ACC= 0.5975\n",
      "0.03233030660857527\n",
      "Training epoch 864, recon_loss:0.610901, zinb_loss:0.959675, cluster_loss:0.330059\n",
      "Clustering   864: AMI= 0.7229, NMI= 0.7238, ARI= 0.5039, ACC= 0.6065\n",
      "0.03135306812166619\n",
      "Training epoch 865, recon_loss:0.610920, zinb_loss:0.959828, cluster_loss:0.330578\n",
      "Clustering   865: AMI= 0.7215, NMI= 0.7224, ARI= 0.4975, ACC= 0.5978\n",
      "0.03029439309418136\n",
      "Training epoch 866, recon_loss:0.609779, zinb_loss:0.959725, cluster_loss:0.330202\n",
      "Clustering   866: AMI= 0.7228, NMI= 0.7237, ARI= 0.5036, ACC= 0.6064\n",
      "0.02964290076957531\n",
      "Training epoch 867, recon_loss:0.610319, zinb_loss:0.959830, cluster_loss:0.330694\n",
      "Clustering   867: AMI= 0.7213, NMI= 0.7222, ARI= 0.4971, ACC= 0.5973\n",
      "0.03009080174274197\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training epoch 868, recon_loss:0.610878, zinb_loss:0.959855, cluster_loss:0.330340\n",
      "Clustering   868: AMI= 0.7228, NMI= 0.7238, ARI= 0.5036, ACC= 0.6066\n",
      "0.029765055580438942\n",
      "Training epoch 869, recon_loss:0.611084, zinb_loss:0.959876, cluster_loss:0.330411\n",
      "Clustering   869: AMI= 0.7212, NMI= 0.7221, ARI= 0.4975, ACC= 0.5972\n",
      "0.02915428152612077\n",
      "Training epoch 870, recon_loss:0.610315, zinb_loss:0.960023, cluster_loss:0.330368\n",
      "Clustering   870: AMI= 0.7231, NMI= 0.7240, ARI= 0.5040, ACC= 0.6072\n",
      "0.02943930941813592\n",
      "Training epoch 871, recon_loss:0.610797, zinb_loss:0.959883, cluster_loss:0.330301\n",
      "Clustering   871: AMI= 0.7216, NMI= 0.7225, ARI= 0.4976, ACC= 0.5970\n",
      "0.02964290076957531\n",
      "Training epoch 872, recon_loss:0.611596, zinb_loss:0.960235, cluster_loss:0.330316\n",
      "Clustering   872: AMI= 0.7232, NMI= 0.7241, ARI= 0.5044, ACC= 0.6076\n",
      "0.029683619039863187\n",
      "Training epoch 873, recon_loss:0.611808, zinb_loss:0.959901, cluster_loss:0.329840\n",
      "Clustering   873: AMI= 0.7217, NMI= 0.7227, ARI= 0.4979, ACC= 0.5969\n",
      "0.02960218249928743\n",
      "Training epoch 874, recon_loss:0.611277, zinb_loss:0.960404, cluster_loss:0.330121\n",
      "Clustering   874: AMI= 0.7234, NMI= 0.7243, ARI= 0.5045, ACC= 0.6078\n",
      "0.029683619039863187\n",
      "Training epoch 875, recon_loss:0.611630, zinb_loss:0.959863, cluster_loss:0.329638\n",
      "Clustering   875: AMI= 0.7215, NMI= 0.7224, ARI= 0.4975, ACC= 0.5966\n",
      "0.029480027688423796\n",
      "Training epoch 876, recon_loss:0.612189, zinb_loss:0.960557, cluster_loss:0.329956\n",
      "Clustering   876: AMI= 0.7232, NMI= 0.7242, ARI= 0.5044, ACC= 0.6077\n",
      "0.02919499979640865\n",
      "Training epoch 877, recon_loss:0.611849, zinb_loss:0.959843, cluster_loss:0.329393\n",
      "Clustering   877: AMI= 0.7212, NMI= 0.7221, ARI= 0.4977, ACC= 0.5969\n",
      "0.028421352660938964\n",
      "Training epoch 878, recon_loss:0.611842, zinb_loss:0.960616, cluster_loss:0.329880\n",
      "Clustering   878: AMI= 0.7232, NMI= 0.7241, ARI= 0.5040, ACC= 0.6073\n",
      "0.027566268984893522\n",
      "Training epoch 879, recon_loss:0.611220, zinb_loss:0.959778, cluster_loss:0.329491\n",
      "Clustering   879: AMI= 0.7214, NMI= 0.7223, ARI= 0.4977, ACC= 0.5970\n",
      "0.02675190357913596\n",
      "Training epoch 880, recon_loss:0.611577, zinb_loss:0.960621, cluster_loss:0.329910\n",
      "Clustering   880: AMI= 0.7231, NMI= 0.7241, ARI= 0.5039, ACC= 0.6071\n",
      "0.025530355470499613\n",
      "Training epoch 881, recon_loss:0.610742, zinb_loss:0.959741, cluster_loss:0.329668\n",
      "Clustering   881: AMI= 0.7214, NMI= 0.7223, ARI= 0.4981, ACC= 0.5975\n",
      "0.023983061199560243\n",
      "Training epoch 882, recon_loss:0.611111, zinb_loss:0.960610, cluster_loss:0.329984\n",
      "Clustering   882: AMI= 0.7231, NMI= 0.7240, ARI= 0.5038, ACC= 0.6070\n",
      "0.023005822712651166\n",
      "Training epoch 883, recon_loss:0.610211, zinb_loss:0.959709, cluster_loss:0.329890\n",
      "Clustering   883: AMI= 0.7216, NMI= 0.7225, ARI= 0.4982, ACC= 0.5977\n",
      "0.022313612117757238\n",
      "Training epoch 884, recon_loss:0.610820, zinb_loss:0.960601, cluster_loss:0.330062\n",
      "Clustering   884: AMI= 0.7230, NMI= 0.7239, ARI= 0.5033, ACC= 0.6065\n",
      "0.02198786595545421\n",
      "Training epoch 885, recon_loss:0.609858, zinb_loss:0.959684, cluster_loss:0.330064\n",
      "Clustering   885: AMI= 0.7219, NMI= 0.7228, ARI= 0.4985, ACC= 0.5980\n",
      "0.02162140152286331\n",
      "Training epoch 886, recon_loss:0.610593, zinb_loss:0.960587, cluster_loss:0.330141\n",
      "Clustering   886: AMI= 0.7230, NMI= 0.7239, ARI= 0.5031, ACC= 0.6066\n",
      "0.020807036117105746\n",
      "Training epoch 887, recon_loss:0.609589, zinb_loss:0.959648, cluster_loss:0.330217\n",
      "Clustering   887: AMI= 0.7218, NMI= 0.7227, ARI= 0.4985, ACC= 0.5980\n",
      "0.020562726495378478\n",
      "Training epoch 888, recon_loss:0.610436, zinb_loss:0.960567, cluster_loss:0.330216\n",
      "Clustering   888: AMI= 0.7232, NMI= 0.7241, ARI= 0.5031, ACC= 0.6063\n",
      "0.020359135143939087\n",
      "Training epoch 889, recon_loss:0.609412, zinb_loss:0.959613, cluster_loss:0.330343\n",
      "Clustering   889: AMI= 0.7218, NMI= 0.7228, ARI= 0.4987, ACC= 0.5982\n",
      "0.02003338898163606\n",
      "Training epoch 890, recon_loss:0.610306, zinb_loss:0.960546, cluster_loss:0.330285\n",
      "Clustering   890: AMI= 0.7230, NMI= 0.7239, ARI= 0.5027, ACC= 0.6058\n",
      "0.019748361089620914\n",
      "Training epoch 891, recon_loss:0.609290, zinb_loss:0.959579, cluster_loss:0.330445\n",
      "Clustering   891: AMI= 0.7220, NMI= 0.7229, ARI= 0.4988, ACC= 0.5984\n",
      "0.019666924549045155\n",
      "Training epoch 892, recon_loss:0.610268, zinb_loss:0.960522, cluster_loss:0.330344\n",
      "Clustering   892: AMI= 0.7228, NMI= 0.7237, ARI= 0.5023, ACC= 0.6056\n",
      "0.019219023575878496\n",
      "Training epoch 893, recon_loss:0.609280, zinb_loss:0.959548, cluster_loss:0.330509\n",
      "Clustering   893: AMI= 0.7219, NMI= 0.7229, ARI= 0.4988, ACC= 0.5983\n",
      "0.01893399568386335\n",
      "Training epoch 894, recon_loss:0.610189, zinb_loss:0.960493, cluster_loss:0.330390\n",
      "Clustering   894: AMI= 0.7228, NMI= 0.7237, ARI= 0.5021, ACC= 0.6054\n",
      "0.018811840872999714\n",
      "Training epoch 895, recon_loss:0.609238, zinb_loss:0.959522, cluster_loss:0.330565\n",
      "Clustering   895: AMI= 0.7219, NMI= 0.7228, ARI= 0.4988, ACC= 0.5983\n",
      "0.01868968606213608\n",
      "Training epoch 896, recon_loss:0.610383, zinb_loss:0.960473, cluster_loss:0.330424\n",
      "Clustering   896: AMI= 0.7226, NMI= 0.7236, ARI= 0.5018, ACC= 0.6049\n",
      "0.018445376440408813\n",
      "Training epoch 897, recon_loss:0.609445, zinb_loss:0.959496, cluster_loss:0.330555\n",
      "Clustering   897: AMI= 0.7220, NMI= 0.7229, ARI= 0.4991, ACC= 0.5986\n",
      "0.018160348548393664\n",
      "Training epoch 898, recon_loss:0.610157, zinb_loss:0.960417, cluster_loss:0.330431\n",
      "Clustering   898: AMI= 0.7225, NMI= 0.7234, ARI= 0.5014, ACC= 0.6045\n",
      "0.017956757196954273\n",
      "Training epoch 899, recon_loss:0.609305, zinb_loss:0.959470, cluster_loss:0.330595\n",
      "Clustering   899: AMI= 0.7221, NMI= 0.7230, ARI= 0.4992, ACC= 0.5987\n",
      "0.017916038926666395\n",
      "Training epoch 900, recon_loss:0.610703, zinb_loss:0.960379, cluster_loss:0.330437\n",
      "Clustering   900: AMI= 0.7224, NMI= 0.7233, ARI= 0.5014, ACC= 0.6046\n",
      "0.017875320656378518\n",
      "Training epoch 901, recon_loss:0.609824, zinb_loss:0.959429, cluster_loss:0.330488\n",
      "Clustering   901: AMI= 0.7222, NMI= 0.7231, ARI= 0.4994, ACC= 0.5988\n",
      "0.017427419683211858\n",
      "Training epoch 902, recon_loss:0.610202, zinb_loss:0.960259, cluster_loss:0.330412\n",
      "Clustering   902: AMI= 0.7223, NMI= 0.7232, ARI= 0.5012, ACC= 0.6044\n",
      "0.01718311006148459\n",
      "Training epoch 903, recon_loss:0.609476, zinb_loss:0.959375, cluster_loss:0.330526\n",
      "Clustering   903: AMI= 0.7221, NMI= 0.7231, ARI= 0.4993, ACC= 0.5986\n",
      "0.01783460238609064\n",
      "Training epoch 904, recon_loss:0.611165, zinb_loss:0.960179, cluster_loss:0.330428\n",
      "Clustering   904: AMI= 0.7222, NMI= 0.7232, ARI= 0.5011, ACC= 0.6039\n",
      "0.01718311006148459\n",
      "Training epoch 905, recon_loss:0.610335, zinb_loss:0.959287, cluster_loss:0.330320\n",
      "Clustering   905: AMI= 0.7224, NMI= 0.7233, ARI= 0.4998, ACC= 0.5986\n",
      "0.01718311006148459\n",
      "Training epoch 906, recon_loss:0.609930, zinb_loss:0.959952, cluster_loss:0.330386\n",
      "Clustering   906: AMI= 0.7219, NMI= 0.7228, ARI= 0.5005, ACC= 0.6035\n",
      "0.016653772547742172\n",
      "Training epoch 907, recon_loss:0.609692, zinb_loss:0.959219, cluster_loss:0.330470\n",
      "Clustering   907: AMI= 0.7224, NMI= 0.7233, ARI= 0.4995, ACC= 0.5984\n",
      "0.01714239179119671\n",
      "Training epoch 908, recon_loss:0.610851, zinb_loss:0.959834, cluster_loss:0.330449\n",
      "Clustering   908: AMI= 0.7218, NMI= 0.7227, ARI= 0.5004, ACC= 0.6031\n",
      "0.017020236980333076\n",
      "Training epoch 909, recon_loss:0.610271, zinb_loss:0.959122, cluster_loss:0.330266\n",
      "Clustering   909: AMI= 0.7223, NMI= 0.7233, ARI= 0.4999, ACC= 0.5987\n",
      "0.01649089946659066\n",
      "Training epoch 910, recon_loss:0.610613, zinb_loss:0.959645, cluster_loss:0.330494\n",
      "Clustering   910: AMI= 0.7217, NMI= 0.7226, ARI= 0.5000, ACC= 0.6028\n",
      "0.016368744655727026\n",
      "Training epoch 911, recon_loss:0.610182, zinb_loss:0.959001, cluster_loss:0.330190\n",
      "Clustering   911: AMI= 0.7224, NMI= 0.7233, ARI= 0.4999, ACC= 0.5987\n",
      "0.0169795187100452\n",
      "Training epoch 912, recon_loss:0.611318, zinb_loss:0.959463, cluster_loss:0.330557\n",
      "Clustering   912: AMI= 0.7213, NMI= 0.7222, ARI= 0.4995, ACC= 0.6028\n",
      "0.017427419683211858\n",
      "Training epoch 913, recon_loss:0.610955, zinb_loss:0.958914, cluster_loss:0.329976\n",
      "Clustering   913: AMI= 0.7225, NMI= 0.7234, ARI= 0.5001, ACC= 0.5984\n",
      "0.01779388411580276\n",
      "Training epoch 914, recon_loss:0.610329, zinb_loss:0.959216, cluster_loss:0.330522\n",
      "Clustering   914: AMI= 0.7212, NMI= 0.7221, ARI= 0.4995, ACC= 0.6029\n",
      "0.01783460238609064\n",
      "Training epoch 915, recon_loss:0.610574, zinb_loss:0.958884, cluster_loss:0.330040\n",
      "Clustering   915: AMI= 0.7226, NMI= 0.7235, ARI= 0.5000, ACC= 0.5981\n",
      "0.018323221629545177\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training epoch 916, recon_loss:0.611846, zinb_loss:0.959088, cluster_loss:0.330589\n",
      "Clustering   916: AMI= 0.7213, NMI= 0.7222, ARI= 0.4998, ACC= 0.6031\n",
      "0.01820106681868154\n",
      "Training epoch 917, recon_loss:0.611720, zinb_loss:0.958902, cluster_loss:0.329741\n",
      "Clustering   917: AMI= 0.7227, NMI= 0.7236, ARI= 0.5002, ACC= 0.5983\n",
      "0.019259741846166373\n",
      "Training epoch 918, recon_loss:0.610403, zinb_loss:0.958894, cluster_loss:0.330509\n",
      "Clustering   918: AMI= 0.7210, NMI= 0.7219, ARI= 0.4995, ACC= 0.6030\n",
      "0.019870515900484546\n",
      "Training epoch 919, recon_loss:0.611045, zinb_loss:0.958896, cluster_loss:0.329919\n",
      "Clustering   919: AMI= 0.7224, NMI= 0.7233, ARI= 0.4997, ACC= 0.5978\n",
      "0.02027769860336333\n",
      "Training epoch 920, recon_loss:0.611644, zinb_loss:0.958785, cluster_loss:0.330637\n",
      "Clustering   920: AMI= 0.7211, NMI= 0.7220, ARI= 0.4999, ACC= 0.6032\n",
      "0.020522008225090597\n",
      "Training epoch 921, recon_loss:0.611639, zinb_loss:0.958894, cluster_loss:0.329809\n",
      "Clustering   921: AMI= 0.7225, NMI= 0.7234, ARI= 0.5000, ACC= 0.5980\n",
      "0.020847754387393624\n",
      "Training epoch 922, recon_loss:0.610456, zinb_loss:0.958657, cluster_loss:0.330685\n",
      "Clustering   922: AMI= 0.7211, NMI= 0.7220, ARI= 0.4995, ACC= 0.6031\n",
      "0.02133637363084816\n",
      "Training epoch 923, recon_loss:0.611003, zinb_loss:0.958873, cluster_loss:0.330051\n",
      "Clustering   923: AMI= 0.7226, NMI= 0.7235, ARI= 0.4996, ACC= 0.5977\n",
      "0.02092919092796938\n",
      "Training epoch 924, recon_loss:0.611707, zinb_loss:0.958571, cluster_loss:0.330849\n",
      "Clustering   924: AMI= 0.7210, NMI= 0.7219, ARI= 0.4994, ACC= 0.6029\n",
      "0.020603444765666355\n",
      "Training epoch 925, recon_loss:0.611763, zinb_loss:0.958878, cluster_loss:0.329967\n",
      "Clustering   925: AMI= 0.7227, NMI= 0.7236, ARI= 0.5004, ACC= 0.5986\n",
      "0.020359135143939087\n",
      "Training epoch 926, recon_loss:0.609998, zinb_loss:0.958475, cluster_loss:0.330892\n",
      "Clustering   926: AMI= 0.7211, NMI= 0.7220, ARI= 0.4992, ACC= 0.6023\n",
      "0.019463333197605764\n",
      "Training epoch 927, recon_loss:0.610707, zinb_loss:0.958883, cluster_loss:0.330260\n",
      "Clustering   927: AMI= 0.7226, NMI= 0.7235, ARI= 0.4998, ACC= 0.5985\n",
      "0.01913758703530274\n",
      "Training epoch 928, recon_loss:0.611397, zinb_loss:0.958440, cluster_loss:0.331071\n",
      "Clustering   928: AMI= 0.7212, NMI= 0.7221, ARI= 0.4993, ACC= 0.6021\n",
      "0.019015432224439105\n",
      "Training epoch 929, recon_loss:0.611650, zinb_loss:0.958963, cluster_loss:0.330141\n",
      "Clustering   929: AMI= 0.7227, NMI= 0.7236, ARI= 0.5005, ACC= 0.5993\n",
      "0.01868968606213608\n",
      "Training epoch 930, recon_loss:0.609803, zinb_loss:0.958439, cluster_loss:0.331092\n",
      "Clustering   930: AMI= 0.7211, NMI= 0.7220, ARI= 0.4986, ACC= 0.6012\n",
      "0.018119630278105786\n",
      "Training epoch 931, recon_loss:0.610625, zinb_loss:0.959051, cluster_loss:0.330355\n",
      "Clustering   931: AMI= 0.7225, NMI= 0.7235, ARI= 0.5003, ACC= 0.5996\n",
      "0.01779388411580276\n",
      "Training epoch 932, recon_loss:0.611353, zinb_loss:0.958491, cluster_loss:0.331228\n",
      "Clustering   932: AMI= 0.7214, NMI= 0.7223, ARI= 0.4988, ACC= 0.6012\n",
      "0.018119630278105786\n",
      "Training epoch 933, recon_loss:0.611872, zinb_loss:0.959256, cluster_loss:0.330144\n",
      "Clustering   933: AMI= 0.7227, NMI= 0.7236, ARI= 0.5010, ACC= 0.6006\n",
      "0.01848609471069669\n",
      "Training epoch 934, recon_loss:0.609937, zinb_loss:0.958614, cluster_loss:0.331200\n",
      "Clustering   934: AMI= 0.7212, NMI= 0.7222, ARI= 0.4983, ACC= 0.6007\n",
      "0.018526812980984568\n",
      "Training epoch 935, recon_loss:0.610813, zinb_loss:0.959456, cluster_loss:0.330255\n",
      "Clustering   935: AMI= 0.7225, NMI= 0.7234, ARI= 0.5007, ACC= 0.6008\n",
      "0.018526812980984568\n",
      "Training epoch 936, recon_loss:0.611578, zinb_loss:0.958788, cluster_loss:0.331299\n",
      "Clustering   936: AMI= 0.7214, NMI= 0.7223, ARI= 0.4981, ACC= 0.6001\n",
      "0.018567531251272446\n",
      "Training epoch 937, recon_loss:0.612222, zinb_loss:0.959794, cluster_loss:0.329982\n",
      "Clustering   937: AMI= 0.7226, NMI= 0.7235, ARI= 0.5017, ACC= 0.6021\n",
      "0.018771122602711836\n",
      "Training epoch 938, recon_loss:0.610073, zinb_loss:0.959029, cluster_loss:0.331226\n",
      "Clustering   938: AMI= 0.7214, NMI= 0.7223, ARI= 0.4978, ACC= 0.5997\n",
      "0.019341178386742132\n",
      "Training epoch 939, recon_loss:0.610889, zinb_loss:0.960041, cluster_loss:0.330087\n",
      "Clustering   939: AMI= 0.7227, NMI= 0.7236, ARI= 0.5017, ACC= 0.6026\n",
      "0.019259741846166373\n",
      "Training epoch 940, recon_loss:0.611733, zinb_loss:0.959270, cluster_loss:0.331318\n",
      "Clustering   940: AMI= 0.7213, NMI= 0.7222, ARI= 0.4977, ACC= 0.5991\n",
      "0.01978907935990879\n",
      "Training epoch 941, recon_loss:0.612162, zinb_loss:0.960381, cluster_loss:0.329842\n",
      "Clustering   941: AMI= 0.7227, NMI= 0.7236, ARI= 0.5023, ACC= 0.6034\n",
      "0.020725599576529988\n",
      "Training epoch 942, recon_loss:0.609817, zinb_loss:0.959507, cluster_loss:0.331247\n",
      "Clustering   942: AMI= 0.7210, NMI= 0.7220, ARI= 0.4971, ACC= 0.5986\n",
      "0.021702838063439065\n",
      "Training epoch 943, recon_loss:0.610471, zinb_loss:0.960504, cluster_loss:0.330064\n",
      "Clustering   943: AMI= 0.7227, NMI= 0.7236, ARI= 0.5022, ACC= 0.6037\n",
      "0.02153996498228755\n",
      "Training epoch 944, recon_loss:0.610975, zinb_loss:0.959639, cluster_loss:0.331381\n",
      "Clustering   944: AMI= 0.7211, NMI= 0.7221, ARI= 0.4971, ACC= 0.5981\n",
      "0.0218249928743027\n",
      "Training epoch 945, recon_loss:0.611204, zinb_loss:0.960678, cluster_loss:0.329957\n",
      "Clustering   945: AMI= 0.7231, NMI= 0.7240, ARI= 0.5032, ACC= 0.6048\n",
      "0.023290850604666315\n",
      "Training epoch 946, recon_loss:0.609296, zinb_loss:0.959770, cluster_loss:0.331375\n",
      "Clustering   946: AMI= 0.7209, NMI= 0.7218, ARI= 0.4969, ACC= 0.5980\n",
      "0.024105216010423876\n",
      "Training epoch 947, recon_loss:0.609848, zinb_loss:0.960677, cluster_loss:0.330206\n",
      "Clustering   947: AMI= 0.7230, NMI= 0.7239, ARI= 0.5029, ACC= 0.6048\n",
      "0.02402377946984812\n",
      "Training epoch 948, recon_loss:0.610390, zinb_loss:0.959846, cluster_loss:0.331515\n",
      "Clustering   948: AMI= 0.7210, NMI= 0.7220, ARI= 0.4971, ACC= 0.5981\n",
      "0.024349525632151148\n",
      "Training epoch 949, recon_loss:0.610456, zinb_loss:0.960762, cluster_loss:0.330124\n",
      "Clustering   949: AMI= 0.7230, NMI= 0.7240, ARI= 0.5033, ACC= 0.6052\n",
      "0.025245327578484467\n",
      "Training epoch 950, recon_loss:0.608940, zinb_loss:0.959930, cluster_loss:0.331518\n",
      "Clustering   950: AMI= 0.7210, NMI= 0.7219, ARI= 0.4968, ACC= 0.5979\n",
      "0.02605969298424203\n",
      "Training epoch 951, recon_loss:0.609384, zinb_loss:0.960702, cluster_loss:0.330353\n",
      "Clustering   951: AMI= 0.7228, NMI= 0.7238, ARI= 0.5028, ACC= 0.6048\n",
      "0.025530355470499613\n",
      "Training epoch 952, recon_loss:0.609941, zinb_loss:0.959997, cluster_loss:0.331632\n",
      "Clustering   952: AMI= 0.7212, NMI= 0.7221, ARI= 0.4970, ACC= 0.5979\n",
      "0.025693228551651126\n",
      "Training epoch 953, recon_loss:0.609894, zinb_loss:0.960742, cluster_loss:0.330247\n",
      "Clustering   953: AMI= 0.7226, NMI= 0.7236, ARI= 0.5028, ACC= 0.6048\n",
      "0.026833340119711713\n",
      "Training epoch 954, recon_loss:0.609117, zinb_loss:0.960131, cluster_loss:0.331649\n",
      "Clustering   954: AMI= 0.7211, NMI= 0.7220, ARI= 0.4966, ACC= 0.5978\n",
      "0.0276069872551814\n",
      "Training epoch 955, recon_loss:0.609257, zinb_loss:0.960701, cluster_loss:0.330359\n",
      "Clustering   955: AMI= 0.7228, NMI= 0.7237, ARI= 0.5029, ACC= 0.6049\n",
      "0.027647705525469277\n",
      "Training epoch 956, recon_loss:0.610283, zinb_loss:0.960289, cluster_loss:0.331701\n",
      "Clustering   956: AMI= 0.7214, NMI= 0.7223, ARI= 0.4968, ACC= 0.5981\n",
      "0.028217761309499573\n",
      "Training epoch 957, recon_loss:0.610035, zinb_loss:0.960739, cluster_loss:0.330126\n",
      "Clustering   957: AMI= 0.7229, NMI= 0.7238, ARI= 0.5034, ACC= 0.6053\n",
      "0.02939859114784804\n",
      "Training epoch 958, recon_loss:0.609369, zinb_loss:0.960462, cluster_loss:0.331623\n",
      "Clustering   958: AMI= 0.7215, NMI= 0.7224, ARI= 0.4966, ACC= 0.5983\n",
      "0.0302129565536056\n",
      "Training epoch 959, recon_loss:0.609568, zinb_loss:0.960653, cluster_loss:0.330129\n",
      "Clustering   959: AMI= 0.7230, NMI= 0.7239, ARI= 0.5031, ACC= 0.6052\n",
      "0.030620139256484383\n",
      "Training epoch 960, recon_loss:0.610644, zinb_loss:0.960693, cluster_loss:0.331534\n",
      "Clustering   960: AMI= 0.7215, NMI= 0.7225, ARI= 0.4967, ACC= 0.5984\n",
      "0.031515941202817706\n",
      "Training epoch 961, recon_loss:0.610185, zinb_loss:0.960650, cluster_loss:0.329716\n",
      "Clustering   961: AMI= 0.7230, NMI= 0.7239, ARI= 0.5032, ACC= 0.6049\n",
      "0.03294108066289344\n",
      "Training epoch 962, recon_loss:0.611436, zinb_loss:0.960941, cluster_loss:0.331334\n",
      "Clustering   962: AMI= 0.7215, NMI= 0.7224, ARI= 0.4966, ACC= 0.5988\n",
      "0.033877600879514636\n",
      "Training epoch 963, recon_loss:0.610592, zinb_loss:0.960481, cluster_loss:0.329387\n",
      "Clustering   963: AMI= 0.7226, NMI= 0.7236, ARI= 0.5029, ACC= 0.6042\n",
      "0.03456981147440857\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training epoch 964, recon_loss:0.611869, zinb_loss:0.960985, cluster_loss:0.331134\n",
      "Clustering   964: AMI= 0.7214, NMI= 0.7223, ARI= 0.4965, ACC= 0.5994\n",
      "0.03473268455556008\n",
      "Training epoch 965, recon_loss:0.610613, zinb_loss:0.960195, cluster_loss:0.329287\n",
      "Clustering   965: AMI= 0.7227, NMI= 0.7236, ARI= 0.5029, ACC= 0.6038\n",
      "0.034447656663544934\n",
      "Training epoch 966, recon_loss:0.611821, zinb_loss:0.960821, cluster_loss:0.331055\n",
      "Clustering   966: AMI= 0.7213, NMI= 0.7222, ARI= 0.4968, ACC= 0.5997\n",
      "0.03408119223095403\n",
      "Training epoch 967, recon_loss:0.610288, zinb_loss:0.959847, cluster_loss:0.329402\n",
      "Clustering   967: AMI= 0.7227, NMI= 0.7236, ARI= 0.5029, ACC= 0.6034\n",
      "0.033266826825196466\n",
      "Training epoch 968, recon_loss:0.611671, zinb_loss:0.960551, cluster_loss:0.331113\n",
      "Clustering   968: AMI= 0.7213, NMI= 0.7222, ARI= 0.4968, ACC= 0.5998\n",
      "0.032126715257135875\n",
      "Training epoch 969, recon_loss:0.609994, zinb_loss:0.959516, cluster_loss:0.329609\n",
      "Clustering   969: AMI= 0.7226, NMI= 0.7235, ARI= 0.5027, ACC= 0.6033\n",
      "0.03135306812166619\n",
      "Training epoch 970, recon_loss:0.610943, zinb_loss:0.960237, cluster_loss:0.331224\n",
      "Clustering   970: AMI= 0.7215, NMI= 0.7224, ARI= 0.4972, ACC= 0.6003\n",
      "0.030335111364469237\n",
      "Training epoch 971, recon_loss:0.609405, zinb_loss:0.959237, cluster_loss:0.329913\n",
      "Clustering   971: AMI= 0.7225, NMI= 0.7234, ARI= 0.5024, ACC= 0.6031\n",
      "0.02899140844496926\n",
      "Training epoch 972, recon_loss:0.610865, zinb_loss:0.959987, cluster_loss:0.331363\n",
      "Clustering   972: AMI= 0.7214, NMI= 0.7223, ARI= 0.4972, ACC= 0.6001\n",
      "0.027769860336332913\n",
      "Training epoch 973, recon_loss:0.609349, zinb_loss:0.959041, cluster_loss:0.330101\n",
      "Clustering   973: AMI= 0.7226, NMI= 0.7236, ARI= 0.5023, ACC= 0.6028\n",
      "0.026996213200863227\n",
      "Training epoch 974, recon_loss:0.610223, zinb_loss:0.959749, cluster_loss:0.331459\n",
      "Clustering   974: AMI= 0.7212, NMI= 0.7222, ARI= 0.4973, ACC= 0.6001\n",
      "0.02585610163280264\n",
      "Training epoch 975, recon_loss:0.608933, zinb_loss:0.958888, cluster_loss:0.330341\n",
      "Clustering   975: AMI= 0.7224, NMI= 0.7233, ARI= 0.5018, ACC= 0.6023\n",
      "0.02447168044301478\n",
      "Training epoch 976, recon_loss:0.610644, zinb_loss:0.959613, cluster_loss:0.331554\n",
      "Clustering   976: AMI= 0.7213, NMI= 0.7222, ARI= 0.4973, ACC= 0.6002\n",
      "0.02402377946984812\n",
      "Training epoch 977, recon_loss:0.609367, zinb_loss:0.958801, cluster_loss:0.330394\n",
      "Clustering   977: AMI= 0.7225, NMI= 0.7234, ARI= 0.5021, ACC= 0.6023\n",
      "0.023860906388696607\n",
      "Training epoch 978, recon_loss:0.609604, zinb_loss:0.959413, cluster_loss:0.331558\n",
      "Clustering   978: AMI= 0.7214, NMI= 0.7223, ARI= 0.4975, ACC= 0.6004\n",
      "0.023413005415529948\n",
      "Training epoch 979, recon_loss:0.608881, zinb_loss:0.958721, cluster_loss:0.330639\n",
      "Clustering   979: AMI= 0.7224, NMI= 0.7234, ARI= 0.5016, ACC= 0.6017\n",
      "0.022680076550348142\n",
      "Training epoch 980, recon_loss:0.610788, zinb_loss:0.959348, cluster_loss:0.331591\n",
      "Clustering   980: AMI= 0.7215, NMI= 0.7224, ARI= 0.4978, ACC= 0.6006\n",
      "0.022232175577181483\n",
      "Training epoch 981, recon_loss:0.609959, zinb_loss:0.958700, cluster_loss:0.330500\n",
      "Clustering   981: AMI= 0.7227, NMI= 0.7237, ARI= 0.5020, ACC= 0.6018\n",
      "0.023127977523514802\n",
      "Training epoch 982, recon_loss:0.609656, zinb_loss:0.959146, cluster_loss:0.331452\n",
      "Clustering   982: AMI= 0.7213, NMI= 0.7222, ARI= 0.4976, ACC= 0.6006\n",
      "0.022883667901787533\n",
      "Training epoch 983, recon_loss:0.609618, zinb_loss:0.958661, cluster_loss:0.330727\n",
      "Clustering   983: AMI= 0.7227, NMI= 0.7236, ARI= 0.5017, ACC= 0.6015\n",
      "0.02251720346919663\n",
      "Training epoch 984, recon_loss:0.610345, zinb_loss:0.959015, cluster_loss:0.331336\n",
      "Clustering   984: AMI= 0.7212, NMI= 0.7221, ARI= 0.4978, ACC= 0.6007\n",
      "0.022232175577181483\n",
      "Training epoch 985, recon_loss:0.609989, zinb_loss:0.958681, cluster_loss:0.330677\n",
      "Clustering   985: AMI= 0.7226, NMI= 0.7235, ARI= 0.5017, ACC= 0.6013\n",
      "0.022557921739484506\n",
      "Training epoch 986, recon_loss:0.611669, zinb_loss:0.958906, cluster_loss:0.331235\n",
      "Clustering   986: AMI= 0.7212, NMI= 0.7221, ARI= 0.4981, ACC= 0.6010\n",
      "0.021743556333726943\n",
      "Training epoch 987, recon_loss:0.611288, zinb_loss:0.958659, cluster_loss:0.330559\n",
      "Clustering   987: AMI= 0.7226, NMI= 0.7236, ARI= 0.5019, ACC= 0.6012\n",
      "0.02141781017142392\n",
      "Training epoch 988, recon_loss:0.610336, zinb_loss:0.958699, cluster_loss:0.331047\n",
      "Clustering   988: AMI= 0.7212, NMI= 0.7221, ARI= 0.4984, ACC= 0.6015\n",
      "0.021173500549696647\n",
      "Training epoch 989, recon_loss:0.610446, zinb_loss:0.958669, cluster_loss:0.330837\n",
      "Clustering   989: AMI= 0.7221, NMI= 0.7230, ARI= 0.5005, ACC= 0.6002\n",
      "0.019300460116454254\n",
      "Training epoch 990, recon_loss:0.611673, zinb_loss:0.958598, cluster_loss:0.331036\n",
      "Clustering   990: AMI= 0.7214, NMI= 0.7223, ARI= 0.4990, ACC= 0.6022\n",
      "0.01885255914328759\n",
      "Training epoch 991, recon_loss:0.611709, zinb_loss:0.958750, cluster_loss:0.330696\n",
      "Clustering   991: AMI= 0.7219, NMI= 0.7228, ARI= 0.5006, ACC= 0.6004\n",
      "0.019015432224439105\n",
      "Training epoch 992, recon_loss:0.610490, zinb_loss:0.958523, cluster_loss:0.330916\n",
      "Clustering   992: AMI= 0.7216, NMI= 0.7225, ARI= 0.4992, ACC= 0.6024\n",
      "0.018526812980984568\n",
      "Training epoch 993, recon_loss:0.610951, zinb_loss:0.958777, cluster_loss:0.330862\n",
      "Clustering   993: AMI= 0.7216, NMI= 0.7225, ARI= 0.4998, ACC= 0.5997\n",
      "0.018526812980984568\n",
      "Training epoch 994, recon_loss:0.611697, zinb_loss:0.958499, cluster_loss:0.330948\n",
      "Clustering   994: AMI= 0.7218, NMI= 0.7228, ARI= 0.5001, ACC= 0.6030\n",
      "0.019219023575878496\n",
      "Training epoch 995, recon_loss:0.612222, zinb_loss:0.958881, cluster_loss:0.330688\n",
      "Clustering   995: AMI= 0.7212, NMI= 0.7221, ARI= 0.4994, ACC= 0.5991\n",
      "0.020603444765666355\n",
      "Training epoch 996, recon_loss:0.610555, zinb_loss:0.958512, cluster_loss:0.330856\n",
      "Clustering   996: AMI= 0.7219, NMI= 0.7228, ARI= 0.5002, ACC= 0.6031\n",
      "0.022191457306893602\n",
      "Training epoch 997, recon_loss:0.611274, zinb_loss:0.958945, cluster_loss:0.330811\n",
      "Clustering   997: AMI= 0.7209, NMI= 0.7219, ARI= 0.4989, ACC= 0.5988\n",
      "0.02316869579380268\n",
      "Training epoch 998, recon_loss:0.611928, zinb_loss:0.958566, cluster_loss:0.330886\n",
      "Clustering   998: AMI= 0.7222, NMI= 0.7231, ARI= 0.5010, ACC= 0.6037\n",
      "0.025001017956757198\n",
      "Training epoch 999, recon_loss:0.612632, zinb_loss:0.959115, cluster_loss:0.330605\n",
      "Clustering   999: AMI= 0.7209, NMI= 0.7218, ARI= 0.4985, ACC= 0.5979\n",
      "0.02622256606539354\n",
      "Training epoch 1000, recon_loss:0.610550, zinb_loss:0.958667, cluster_loss:0.330761\n",
      "Clustering   1000: AMI= 0.7223, NMI= 0.7232, ARI= 0.5014, ACC= 0.6041\n",
      "0.027444114174029886\n",
      "Training epoch 1001, recon_loss:0.611369, zinb_loss:0.959248, cluster_loss:0.330734\n",
      "Clustering   1001: AMI= 0.7208, NMI= 0.7218, ARI= 0.4980, ACC= 0.5977\n",
      "0.02850278920151472\n",
      "Training epoch 1002, recon_loss:0.611737, zinb_loss:0.958785, cluster_loss:0.330776\n",
      "Clustering   1002: AMI= 0.7223, NMI= 0.7232, ARI= 0.5020, ACC= 0.6045\n",
      "0.029235718066696528\n",
      "Training epoch 1003, recon_loss:0.612395, zinb_loss:0.959488, cluster_loss:0.330589\n",
      "Clustering   1003: AMI= 0.7209, NMI= 0.7218, ARI= 0.4982, ACC= 0.5975\n",
      "0.030660857526772264\n",
      "Training epoch 1004, recon_loss:0.610386, zinb_loss:0.958958, cluster_loss:0.330664\n",
      "Clustering   1004: AMI= 0.7224, NMI= 0.7233, ARI= 0.5022, ACC= 0.6046\n",
      "0.03184168736512073\n",
      "Training epoch 1005, recon_loss:0.611206, zinb_loss:0.959672, cluster_loss:0.330779\n",
      "Clustering   1005: AMI= 0.7207, NMI= 0.7217, ARI= 0.4975, ACC= 0.5969\n",
      "0.03233030660857527\n",
      "Training epoch 1006, recon_loss:0.611148, zinb_loss:0.959109, cluster_loss:0.330708\n",
      "Clustering   1006: AMI= 0.7225, NMI= 0.7234, ARI= 0.5029, ACC= 0.6051\n",
      "0.032126715257135875\n",
      "Training epoch 1007, recon_loss:0.611767, zinb_loss:0.959929, cluster_loss:0.330736\n",
      "Clustering   1007: AMI= 0.7207, NMI= 0.7217, ARI= 0.4973, ACC= 0.5967\n",
      "0.032411743149151025\n",
      "Training epoch 1008, recon_loss:0.610263, zinb_loss:0.959319, cluster_loss:0.330695\n",
      "Clustering   1008: AMI= 0.7224, NMI= 0.7233, ARI= 0.5031, ACC= 0.6054\n",
      "0.03322610855490859\n",
      "Training epoch 1009, recon_loss:0.610988, zinb_loss:0.960105, cluster_loss:0.330909\n",
      "Clustering   1009: AMI= 0.7207, NMI= 0.7216, ARI= 0.4966, ACC= 0.5963\n",
      "0.03322610855490859\n",
      "Training epoch 1010, recon_loss:0.611094, zinb_loss:0.959499, cluster_loss:0.330757\n",
      "Clustering   1010: AMI= 0.7224, NMI= 0.7234, ARI= 0.5033, ACC= 0.6054\n",
      "0.03322610855490859\n",
      "Training epoch 1011, recon_loss:0.611598, zinb_loss:0.960302, cluster_loss:0.330878\n",
      "Clustering   1011: AMI= 0.7210, NMI= 0.7219, ARI= 0.4967, ACC= 0.5966\n",
      "0.03318539028462071\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training epoch 1012, recon_loss:0.609700, zinb_loss:0.959646, cluster_loss:0.330756\n",
      "Clustering   1012: AMI= 0.7226, NMI= 0.7235, ARI= 0.5038, ACC= 0.6060\n",
      "0.032859644122317684\n",
      "Training epoch 1013, recon_loss:0.610706, zinb_loss:0.960382, cluster_loss:0.331131\n",
      "Clustering   1013: AMI= 0.7209, NMI= 0.7218, ARI= 0.4958, ACC= 0.5963\n",
      "0.03290036239260556\n",
      "Training epoch 1014, recon_loss:0.610280, zinb_loss:0.959770, cluster_loss:0.330830\n",
      "Clustering   1014: AMI= 0.7229, NMI= 0.7238, ARI= 0.5045, ACC= 0.6062\n",
      "0.03322610855490859\n",
      "Training epoch 1015, recon_loss:0.610915, zinb_loss:0.960468, cluster_loss:0.331103\n",
      "Clustering   1015: AMI= 0.7209, NMI= 0.7218, ARI= 0.4957, ACC= 0.5963\n",
      "0.03306323547375707\n",
      "Training epoch 1016, recon_loss:0.610314, zinb_loss:0.959927, cluster_loss:0.330886\n",
      "Clustering   1016: AMI= 0.7225, NMI= 0.7235, ARI= 0.5039, ACC= 0.6056\n",
      "0.03351113644692374\n",
      "Training epoch 1017, recon_loss:0.610775, zinb_loss:0.960445, cluster_loss:0.331110\n",
      "Clustering   1017: AMI= 0.7210, NMI= 0.7219, ARI= 0.4959, ACC= 0.5963\n",
      "0.03383688260922676\n",
      "Training epoch 1018, recon_loss:0.610120, zinb_loss:0.960012, cluster_loss:0.330942\n",
      "Clustering   1018: AMI= 0.7228, NMI= 0.7238, ARI= 0.5045, ACC= 0.6062\n",
      "0.03383688260922676\n",
      "Training epoch 1019, recon_loss:0.610544, zinb_loss:0.960415, cluster_loss:0.331142\n",
      "Clustering   1019: AMI= 0.7211, NMI= 0.7220, ARI= 0.4958, ACC= 0.5962\n",
      "0.03351113644692374\n",
      "Training epoch 1020, recon_loss:0.610024, zinb_loss:0.960078, cluster_loss:0.330986\n",
      "Clustering   1020: AMI= 0.7229, NMI= 0.7238, ARI= 0.5049, ACC= 0.6067\n",
      "0.03359257298749949\n",
      "Training epoch 1021, recon_loss:0.610374, zinb_loss:0.960362, cluster_loss:0.331154\n",
      "Clustering   1021: AMI= 0.7210, NMI= 0.7219, ARI= 0.4958, ACC= 0.5960\n",
      "0.03318539028462071\n",
      "Training epoch 1022, recon_loss:0.609922, zinb_loss:0.960128, cluster_loss:0.331022\n",
      "Clustering   1022: AMI= 0.7228, NMI= 0.7237, ARI= 0.5046, ACC= 0.6065\n",
      "0.03253389796001466\n",
      "Training epoch 1023, recon_loss:0.610174, zinb_loss:0.960295, cluster_loss:0.331168\n",
      "Clustering   1023: AMI= 0.7211, NMI= 0.7220, ARI= 0.4960, ACC= 0.5961\n",
      "0.03192312390569649\n",
      "Training epoch 1024, recon_loss:0.610047, zinb_loss:0.960174, cluster_loss:0.331076\n",
      "Clustering   1024: AMI= 0.7226, NMI= 0.7236, ARI= 0.5045, ACC= 0.6066\n",
      "0.03176025082454497\n",
      "Training epoch 1025, recon_loss:0.610129, zinb_loss:0.960210, cluster_loss:0.331148\n",
      "Clustering   1025: AMI= 0.7211, NMI= 0.7220, ARI= 0.4964, ACC= 0.5964\n",
      "0.030905167148499533\n",
      "Training epoch 1026, recon_loss:0.609685, zinb_loss:0.960179, cluster_loss:0.331135\n",
      "Clustering   1026: AMI= 0.7226, NMI= 0.7235, ARI= 0.5042, ACC= 0.6066\n",
      "0.0302129565536056\n",
      "Training epoch 1027, recon_loss:0.609715, zinb_loss:0.960115, cluster_loss:0.331208\n",
      "Clustering   1027: AMI= 0.7210, NMI= 0.7220, ARI= 0.4965, ACC= 0.5964\n",
      "0.02960218249928743\n",
      "Training epoch 1028, recon_loss:0.609891, zinb_loss:0.960198, cluster_loss:0.331216\n",
      "Clustering   1028: AMI= 0.7225, NMI= 0.7235, ARI= 0.5040, ACC= 0.6067\n",
      "0.028299197850075328\n",
      "Training epoch 1029, recon_loss:0.609767, zinb_loss:0.960030, cluster_loss:0.331190\n",
      "Clustering   1029: AMI= 0.7211, NMI= 0.7221, ARI= 0.4969, ACC= 0.5965\n",
      "0.02715908628201474\n",
      "Training epoch 1030, recon_loss:0.609444, zinb_loss:0.960193, cluster_loss:0.331282\n",
      "Clustering   1030: AMI= 0.7223, NMI= 0.7232, ARI= 0.5035, ACC= 0.6064\n",
      "0.02601897471395415\n",
      "Training epoch 1031, recon_loss:0.609316, zinb_loss:0.959935, cluster_loss:0.331256\n",
      "Clustering   1031: AMI= 0.7212, NMI= 0.7222, ARI= 0.4970, ACC= 0.5965\n",
      "0.02540820065963598\n",
      "Training epoch 1032, recon_loss:0.609840, zinb_loss:0.960213, cluster_loss:0.331378\n",
      "Clustering   1032: AMI= 0.7226, NMI= 0.7235, ARI= 0.5032, ACC= 0.6063\n",
      "0.024512398713302658\n",
      "Training epoch 1033, recon_loss:0.609563, zinb_loss:0.959862, cluster_loss:0.331197\n",
      "Clustering   1033: AMI= 0.7214, NMI= 0.7223, ARI= 0.4977, ACC= 0.5968\n",
      "0.023290850604666315\n",
      "Training epoch 1034, recon_loss:0.609265, zinb_loss:0.960209, cluster_loss:0.331438\n",
      "Clustering   1034: AMI= 0.7224, NMI= 0.7233, ARI= 0.5027, ACC= 0.6060\n",
      "0.02206930249602997\n",
      "Training epoch 1035, recon_loss:0.609066, zinb_loss:0.959772, cluster_loss:0.331246\n",
      "Clustering   1035: AMI= 0.7215, NMI= 0.7224, ARI= 0.4978, ACC= 0.5968\n",
      "0.021295655360560283\n",
      "Training epoch 1036, recon_loss:0.610241, zinb_loss:0.960268, cluster_loss:0.331532\n",
      "Clustering   1036: AMI= 0.7221, NMI= 0.7230, ARI= 0.5020, ACC= 0.6058\n",
      "0.020359135143939087\n",
      "Training epoch 1037, recon_loss:0.609801, zinb_loss:0.959692, cluster_loss:0.331065\n",
      "Clustering   1037: AMI= 0.7218, NMI= 0.7227, ARI= 0.4987, ACC= 0.5968\n",
      "0.02003338898163606\n",
      "Training epoch 1038, recon_loss:0.609121, zinb_loss:0.960238, cluster_loss:0.331528\n",
      "Clustering   1038: AMI= 0.7217, NMI= 0.7227, ARI= 0.5013, ACC= 0.6057\n",
      "0.019666924549045155\n",
      "Training epoch 1039, recon_loss:0.609182, zinb_loss:0.959606, cluster_loss:0.331159\n",
      "Clustering   1039: AMI= 0.7221, NMI= 0.7230, ARI= 0.4992, ACC= 0.5969\n",
      "0.019707642819333036\n",
      "Training epoch 1040, recon_loss:0.610048, zinb_loss:0.960313, cluster_loss:0.331600\n",
      "Clustering   1040: AMI= 0.7219, NMI= 0.7228, ARI= 0.5012, ACC= 0.6056\n",
      "0.018771122602711836\n",
      "Training epoch 1041, recon_loss:0.609575, zinb_loss:0.959528, cluster_loss:0.330943\n",
      "Clustering   1041: AMI= 0.7221, NMI= 0.7230, ARI= 0.4998, ACC= 0.5974\n",
      "0.018445376440408813\n",
      "Training epoch 1042, recon_loss:0.610517, zinb_loss:0.960369, cluster_loss:0.331621\n",
      "Clustering   1042: AMI= 0.7218, NMI= 0.7227, ARI= 0.5005, ACC= 0.6053\n",
      "0.0182825033592573\n",
      "Training epoch 1043, recon_loss:0.609898, zinb_loss:0.959401, cluster_loss:0.330744\n",
      "Clustering   1043: AMI= 0.7220, NMI= 0.7229, ARI= 0.5002, ACC= 0.5976\n",
      "0.019056150494726982\n",
      "Training epoch 1044, recon_loss:0.610806, zinb_loss:0.960372, cluster_loss:0.331617\n",
      "Clustering   1044: AMI= 0.7215, NMI= 0.7224, ARI= 0.4996, ACC= 0.6048\n",
      "0.01938189665703001\n",
      "Training epoch 1045, recon_loss:0.610042, zinb_loss:0.959294, cluster_loss:0.330601\n",
      "Clustering   1045: AMI= 0.7221, NMI= 0.7230, ARI= 0.5008, ACC= 0.5981\n",
      "0.019992670711348182\n",
      "Training epoch 1046, recon_loss:0.610959, zinb_loss:0.960343, cluster_loss:0.331605\n",
      "Clustering   1046: AMI= 0.7214, NMI= 0.7223, ARI= 0.4993, ACC= 0.6045\n",
      "0.02068488130624211\n",
      "Training epoch 1047, recon_loss:0.610050, zinb_loss:0.959209, cluster_loss:0.330521\n",
      "Clustering   1047: AMI= 0.7219, NMI= 0.7229, ARI= 0.5007, ACC= 0.5983\n",
      "0.02092919092796938\n",
      "Training epoch 1048, recon_loss:0.611002, zinb_loss:0.960297, cluster_loss:0.331617\n",
      "Clustering   1048: AMI= 0.7215, NMI= 0.7224, ARI= 0.4991, ACC= 0.6041\n",
      "0.02096990919825726\n",
      "Training epoch 1049, recon_loss:0.609971, zinb_loss:0.959155, cluster_loss:0.330520\n",
      "Clustering   1049: AMI= 0.7220, NMI= 0.7229, ARI= 0.5008, ACC= 0.5986\n",
      "0.0208884726576815\n",
      "Training epoch 1050, recon_loss:0.610953, zinb_loss:0.960247, cluster_loss:0.331665\n",
      "Clustering   1050: AMI= 0.7215, NMI= 0.7224, ARI= 0.4990, ACC= 0.6037\n",
      "0.02096990919825726\n",
      "Training epoch 1051, recon_loss:0.609837, zinb_loss:0.959137, cluster_loss:0.330592\n",
      "Clustering   1051: AMI= 0.7221, NMI= 0.7230, ARI= 0.5008, ACC= 0.5986\n",
      "0.021010627468545137\n",
      "Training epoch 1052, recon_loss:0.610898, zinb_loss:0.960209, cluster_loss:0.331721\n",
      "Clustering   1052: AMI= 0.7215, NMI= 0.7224, ARI= 0.4989, ACC= 0.6031\n",
      "0.020847754387393624\n",
      "Training epoch 1053, recon_loss:0.609748, zinb_loss:0.959162, cluster_loss:0.330699\n",
      "Clustering   1053: AMI= 0.7221, NMI= 0.7230, ARI= 0.5008, ACC= 0.5986\n",
      "0.020725599576529988\n",
      "Training epoch 1054, recon_loss:0.610878, zinb_loss:0.960203, cluster_loss:0.331748\n",
      "Clustering   1054: AMI= 0.7216, NMI= 0.7225, ARI= 0.4989, ACC= 0.6028\n",
      "0.020074107251923937\n",
      "Training epoch 1055, recon_loss:0.609700, zinb_loss:0.959232, cluster_loss:0.330817\n",
      "Clustering   1055: AMI= 0.7220, NMI= 0.7229, ARI= 0.5008, ACC= 0.5988\n",
      "0.019504051467893645\n",
      "Training epoch 1056, recon_loss:0.610896, zinb_loss:0.960227, cluster_loss:0.331733\n",
      "Clustering   1056: AMI= 0.7215, NMI= 0.7224, ARI= 0.4987, ACC= 0.6022\n",
      "0.019056150494726982\n",
      "Training epoch 1057, recon_loss:0.609686, zinb_loss:0.959334, cluster_loss:0.330936\n",
      "Clustering   1057: AMI= 0.7218, NMI= 0.7227, ARI= 0.5004, ACC= 0.5987\n",
      "0.018771122602711836\n",
      "Training epoch 1058, recon_loss:0.610928, zinb_loss:0.960271, cluster_loss:0.331668\n",
      "Clustering   1058: AMI= 0.7214, NMI= 0.7223, ARI= 0.4985, ACC= 0.6017\n",
      "0.018567531251272446\n",
      "Training epoch 1059, recon_loss:0.609675, zinb_loss:0.959451, cluster_loss:0.331051\n",
      "Clustering   1059: AMI= 0.7218, NMI= 0.7228, ARI= 0.5004, ACC= 0.5989\n",
      "0.018567531251272446\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training epoch 1060, recon_loss:0.610933, zinb_loss:0.960323, cluster_loss:0.331556\n",
      "Clustering   1060: AMI= 0.7214, NMI= 0.7223, ARI= 0.4981, ACC= 0.6008\n",
      "0.018404658170120932\n",
      "Training epoch 1061, recon_loss:0.609643, zinb_loss:0.959558, cluster_loss:0.331159\n",
      "Clustering   1061: AMI= 0.7217, NMI= 0.7227, ARI= 0.5003, ACC= 0.5993\n",
      "0.01803819373753003\n",
      "Training epoch 1062, recon_loss:0.610887, zinb_loss:0.960362, cluster_loss:0.331412\n",
      "Clustering   1062: AMI= 0.7216, NMI= 0.7225, ARI= 0.4985, ACC= 0.6007\n",
      "0.01783460238609064\n",
      "Training epoch 1063, recon_loss:0.609577, zinb_loss:0.959632, cluster_loss:0.331265\n",
      "Clustering   1063: AMI= 0.7218, NMI= 0.7227, ARI= 0.5004, ACC= 0.5997\n",
      "0.01799747546724215\n",
      "Training epoch 1064, recon_loss:0.610833, zinb_loss:0.960369, cluster_loss:0.331243\n",
      "Clustering   1064: AMI= 0.7216, NMI= 0.7225, ARI= 0.4987, ACC= 0.6004\n",
      "0.018119630278105786\n",
      "Training epoch 1065, recon_loss:0.609534, zinb_loss:0.959659, cluster_loss:0.331352\n",
      "Clustering   1065: AMI= 0.7218, NMI= 0.7227, ARI= 0.5002, ACC= 0.6003\n",
      "0.01779388411580276\n",
      "Training epoch 1066, recon_loss:0.610720, zinb_loss:0.960324, cluster_loss:0.331073\n",
      "Clustering   1066: AMI= 0.7219, NMI= 0.7228, ARI= 0.4993, ACC= 0.6004\n",
      "0.01763101103465125\n",
      "Training epoch 1067, recon_loss:0.609434, zinb_loss:0.959629, cluster_loss:0.331456\n",
      "Clustering   1067: AMI= 0.7216, NMI= 0.7225, ARI= 0.5000, ACC= 0.6007\n",
      "0.018119630278105786\n",
      "Training epoch 1068, recon_loss:0.610629, zinb_loss:0.960223, cluster_loss:0.330936\n",
      "Clustering   1068: AMI= 0.7222, NMI= 0.7232, ARI= 0.4998, ACC= 0.6003\n",
      "0.019015432224439105\n",
      "Training epoch 1069, recon_loss:0.609353, zinb_loss:0.959542, cluster_loss:0.331559\n",
      "Clustering   1069: AMI= 0.7214, NMI= 0.7223, ARI= 0.4997, ACC= 0.6007\n",
      "0.01917830530559062\n",
      "Training epoch 1070, recon_loss:0.610293, zinb_loss:0.960052, cluster_loss:0.330861\n",
      "Clustering   1070: AMI= 0.7227, NMI= 0.7236, ARI= 0.5006, ACC= 0.6005\n",
      "0.019422614927317887\n",
      "Training epoch 1071, recon_loss:0.609111, zinb_loss:0.959421, cluster_loss:0.331703\n",
      "Clustering   1071: AMI= 0.7212, NMI= 0.7221, ARI= 0.4992, ACC= 0.6006\n",
      "0.01978907935990879\n",
      "Training epoch 1072, recon_loss:0.610211, zinb_loss:0.959854, cluster_loss:0.330849\n",
      "Clustering   1072: AMI= 0.7226, NMI= 0.7236, ARI= 0.5007, ACC= 0.6003\n",
      "0.01938189665703001\n",
      "Training epoch 1073, recon_loss:0.609071, zinb_loss:0.959290, cluster_loss:0.331820\n",
      "Clustering   1073: AMI= 0.7212, NMI= 0.7221, ARI= 0.4991, ACC= 0.6007\n",
      "0.019300460116454254\n",
      "Training epoch 1074, recon_loss:0.609683, zinb_loss:0.959626, cluster_loss:0.330864\n",
      "Clustering   1074: AMI= 0.7228, NMI= 0.7237, ARI= 0.5008, ACC= 0.6004\n",
      "0.018893277413575472\n",
      "Training epoch 1075, recon_loss:0.608750, zinb_loss:0.959192, cluster_loss:0.332005\n",
      "Clustering   1075: AMI= 0.7211, NMI= 0.7220, ARI= 0.4985, ACC= 0.6004\n",
      "0.01873040433242396\n",
      "Training epoch 1076, recon_loss:0.609713, zinb_loss:0.959454, cluster_loss:0.330879\n",
      "Clustering   1076: AMI= 0.7229, NMI= 0.7238, ARI= 0.5009, ACC= 0.6006\n",
      "0.018771122602711836\n",
      "Training epoch 1077, recon_loss:0.608875, zinb_loss:0.959139, cluster_loss:0.332122\n",
      "Clustering   1077: AMI= 0.7210, NMI= 0.7220, ARI= 0.4983, ACC= 0.6004\n",
      "0.0195854880084694\n",
      "Training epoch 1078, recon_loss:0.609290, zinb_loss:0.959302, cluster_loss:0.330851\n",
      "Clustering   1078: AMI= 0.7228, NMI= 0.7237, ARI= 0.5010, ACC= 0.6008\n",
      "0.020725599576529988\n",
      "Training epoch 1079, recon_loss:0.608699, zinb_loss:0.959154, cluster_loss:0.332293\n",
      "Clustering   1079: AMI= 0.7210, NMI= 0.7219, ARI= 0.4981, ACC= 0.6003\n",
      "0.02153996498228755\n",
      "Training epoch 1080, recon_loss:0.609536, zinb_loss:0.959230, cluster_loss:0.330769\n",
      "Clustering   1080: AMI= 0.7227, NMI= 0.7236, ARI= 0.5014, ACC= 0.6013\n",
      "0.022150739036605725\n",
      "Training epoch 1081, recon_loss:0.609139, zinb_loss:0.959252, cluster_loss:0.332348\n",
      "Clustering   1081: AMI= 0.7210, NMI= 0.7220, ARI= 0.4976, ACC= 0.6001\n",
      "0.02357587849668146\n",
      "Training epoch 1082, recon_loss:0.609185, zinb_loss:0.959180, cluster_loss:0.330605\n",
      "Clustering   1082: AMI= 0.7221, NMI= 0.7230, ARI= 0.5014, ACC= 0.6017\n",
      "0.02557107374078749\n",
      "Training epoch 1083, recon_loss:0.609159, zinb_loss:0.959435, cluster_loss:0.332436\n",
      "Clustering   1083: AMI= 0.7212, NMI= 0.7221, ARI= 0.4970, ACC= 0.5994\n",
      "0.027281241092878373\n",
      "Training epoch 1084, recon_loss:0.609690, zinb_loss:0.959197, cluster_loss:0.330419\n",
      "Clustering   1084: AMI= 0.7223, NMI= 0.7232, ARI= 0.5020, ACC= 0.6024\n",
      "0.02919499979640865\n",
      "Training epoch 1085, recon_loss:0.609933, zinb_loss:0.959716, cluster_loss:0.332361\n",
      "Clustering   1085: AMI= 0.7211, NMI= 0.7220, ARI= 0.4965, ACC= 0.5988\n",
      "0.030579420986196506\n",
      "Training epoch 1086, recon_loss:0.609500, zinb_loss:0.959237, cluster_loss:0.330254\n",
      "Clustering   1086: AMI= 0.7222, NMI= 0.7231, ARI= 0.5023, ACC= 0.6028\n",
      "0.03228958833828739\n",
      "Training epoch 1087, recon_loss:0.610142, zinb_loss:0.960031, cluster_loss:0.332363\n",
      "Clustering   1087: AMI= 0.7211, NMI= 0.7221, ARI= 0.4960, ACC= 0.5984\n",
      "0.033103953744044956\n",
      "Training epoch 1088, recon_loss:0.610153, zinb_loss:0.959342, cluster_loss:0.330158\n",
      "Clustering   1088: AMI= 0.7220, NMI= 0.7229, ARI= 0.5024, ACC= 0.6032\n",
      "0.034447656663544934\n",
      "Training epoch 1089, recon_loss:0.610947, zinb_loss:0.960362, cluster_loss:0.332239\n",
      "Clustering   1089: AMI= 0.7211, NMI= 0.7221, ARI= 0.4959, ACC= 0.5978\n",
      "0.03509914898815098\n",
      "Training epoch 1090, recon_loss:0.609900, zinb_loss:0.959421, cluster_loss:0.330148\n",
      "Clustering   1090: AMI= 0.7219, NMI= 0.7229, ARI= 0.5024, ACC= 0.6033\n",
      "0.03570992304246916\n",
      "Training epoch 1091, recon_loss:0.610913, zinb_loss:0.960573, cluster_loss:0.332240\n",
      "Clustering   1091: AMI= 0.7210, NMI= 0.7220, ARI= 0.4957, ACC= 0.5975\n",
      "0.03587279612362067\n",
      "Training epoch 1092, recon_loss:0.610522, zinb_loss:0.959479, cluster_loss:0.330229\n",
      "Clustering   1092: AMI= 0.7219, NMI= 0.7228, ARI= 0.5022, ACC= 0.6032\n",
      "0.03607638747506006\n",
      "Training epoch 1093, recon_loss:0.611422, zinb_loss:0.960695, cluster_loss:0.332107\n",
      "Clustering   1093: AMI= 0.7212, NMI= 0.7221, ARI= 0.4961, ACC= 0.5976\n",
      "0.035058430717863104\n",
      "Training epoch 1094, recon_loss:0.609921, zinb_loss:0.959442, cluster_loss:0.330340\n",
      "Clustering   1094: AMI= 0.7218, NMI= 0.7227, ARI= 0.5019, ACC= 0.6026\n",
      "0.03424406531210554\n",
      "Training epoch 1095, recon_loss:0.610902, zinb_loss:0.960611, cluster_loss:0.332104\n",
      "Clustering   1095: AMI= 0.7214, NMI= 0.7223, ARI= 0.4962, ACC= 0.5977\n",
      "0.03367400952807525\n",
      "Training epoch 1096, recon_loss:0.610453, zinb_loss:0.959350, cluster_loss:0.330530\n",
      "Clustering   1096: AMI= 0.7215, NMI= 0.7224, ARI= 0.5014, ACC= 0.6027\n",
      "0.03265605277087829\n",
      "Training epoch 1097, recon_loss:0.611143, zinb_loss:0.960459, cluster_loss:0.331938\n",
      "Clustering   1097: AMI= 0.7215, NMI= 0.7224, ARI= 0.4970, ACC= 0.5980\n",
      "0.03094588541878741\n",
      "Training epoch 1098, recon_loss:0.609789, zinb_loss:0.959195, cluster_loss:0.330759\n",
      "Clustering   1098: AMI= 0.7214, NMI= 0.7223, ARI= 0.5013, ACC= 0.6028\n",
      "0.02915428152612077\n",
      "Training epoch 1099, recon_loss:0.610558, zinb_loss:0.960185, cluster_loss:0.331920\n",
      "Clustering   1099: AMI= 0.7215, NMI= 0.7224, ARI= 0.4972, ACC= 0.5977\n",
      "0.027729142066045036\n",
      "Training epoch 1100, recon_loss:0.610259, zinb_loss:0.959033, cluster_loss:0.331030\n",
      "Clustering   1100: AMI= 0.7214, NMI= 0.7223, ARI= 0.5012, ACC= 0.6032\n",
      "0.0263040026059693\n",
      "Training epoch 1101, recon_loss:0.610745, zinb_loss:0.959915, cluster_loss:0.331786\n",
      "Clustering   1101: AMI= 0.7215, NMI= 0.7225, ARI= 0.4979, ACC= 0.5980\n",
      "0.024960299686469317\n",
      "Training epoch 1102, recon_loss:0.609636, zinb_loss:0.958857, cluster_loss:0.331255\n",
      "Clustering   1102: AMI= 0.7214, NMI= 0.7224, ARI= 0.5010, ACC= 0.6033\n",
      "0.02357587849668146\n",
      "Training epoch 1103, recon_loss:0.610193, zinb_loss:0.959632, cluster_loss:0.331784\n",
      "Clustering   1103: AMI= 0.7215, NMI= 0.7224, ARI= 0.4983, ACC= 0.5981\n",
      "0.022395048658332993\n",
      "Training epoch 1104, recon_loss:0.610253, zinb_loss:0.958717, cluster_loss:0.331478\n",
      "Clustering   1104: AMI= 0.7213, NMI= 0.7222, ARI= 0.5006, ACC= 0.6032\n",
      "0.021295655360560283\n",
      "Training epoch 1105, recon_loss:0.610539, zinb_loss:0.959412, cluster_loss:0.331647\n",
      "Clustering   1105: AMI= 0.7215, NMI= 0.7224, ARI= 0.4986, ACC= 0.5979\n",
      "0.020399853414226964\n",
      "Training epoch 1106, recon_loss:0.609489, zinb_loss:0.958589, cluster_loss:0.331633\n",
      "Clustering   1106: AMI= 0.7213, NMI= 0.7222, ARI= 0.5000, ACC= 0.6028\n",
      "0.020074107251923937\n",
      "Training epoch 1107, recon_loss:0.609924, zinb_loss:0.959207, cluster_loss:0.331660\n",
      "Clustering   1107: AMI= 0.7220, NMI= 0.7229, ARI= 0.4991, ACC= 0.5979\n",
      "0.020440571684514842\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training epoch 1108, recon_loss:0.610339, zinb_loss:0.958519, cluster_loss:0.331806\n",
      "Clustering   1108: AMI= 0.7212, NMI= 0.7221, ARI= 0.4997, ACC= 0.6025\n",
      "0.019992670711348182\n",
      "Training epoch 1109, recon_loss:0.610485, zinb_loss:0.959085, cluster_loss:0.331511\n",
      "Clustering   1109: AMI= 0.7220, NMI= 0.7229, ARI= 0.4996, ACC= 0.5981\n",
      "0.019748361089620914\n",
      "Training epoch 1110, recon_loss:0.609143, zinb_loss:0.958465, cluster_loss:0.331904\n",
      "Clustering   1110: AMI= 0.7210, NMI= 0.7219, ARI= 0.4989, ACC= 0.6019\n",
      "0.019259741846166373\n",
      "Training epoch 1111, recon_loss:0.609667, zinb_loss:0.958957, cluster_loss:0.331599\n",
      "Clustering   1111: AMI= 0.7219, NMI= 0.7228, ARI= 0.4995, ACC= 0.5981\n",
      "0.018526812980984568\n",
      "Training epoch 1112, recon_loss:0.610311, zinb_loss:0.958470, cluster_loss:0.332087\n",
      "Clustering   1112: AMI= 0.7211, NMI= 0.7220, ARI= 0.4986, ACC= 0.6015\n",
      "0.017590292764363368\n",
      "Training epoch 1113, recon_loss:0.610368, zinb_loss:0.958921, cluster_loss:0.331419\n",
      "Clustering   1113: AMI= 0.7218, NMI= 0.7227, ARI= 0.4995, ACC= 0.5978\n",
      "0.016857363899181563\n",
      "Training epoch 1114, recon_loss:0.609005, zinb_loss:0.958512, cluster_loss:0.332162\n",
      "Clustering   1114: AMI= 0.7209, NMI= 0.7218, ARI= 0.4980, ACC= 0.6012\n",
      "0.016775927358605808\n",
      "Training epoch 1115, recon_loss:0.609502, zinb_loss:0.958874, cluster_loss:0.331498\n",
      "Clustering   1115: AMI= 0.7220, NMI= 0.7229, ARI= 0.4996, ACC= 0.5977\n",
      "0.01645018119630278\n",
      "Training epoch 1116, recon_loss:0.610343, zinb_loss:0.958618, cluster_loss:0.332346\n",
      "Clustering   1116: AMI= 0.7208, NMI= 0.7217, ARI= 0.4978, ACC= 0.6010\n",
      "0.016328026385439145\n",
      "Training epoch 1117, recon_loss:0.610356, zinb_loss:0.958949, cluster_loss:0.331265\n",
      "Clustering   1117: AMI= 0.7220, NMI= 0.7229, ARI= 0.5005, ACC= 0.5985\n",
      "0.016816645628893685\n",
      "Training epoch 1118, recon_loss:0.609073, zinb_loss:0.958790, cluster_loss:0.332406\n",
      "Clustering   1118: AMI= 0.7208, NMI= 0.7217, ARI= 0.4971, ACC= 0.6002\n",
      "0.017468137953499736\n",
      "Training epoch 1119, recon_loss:0.609426, zinb_loss:0.959028, cluster_loss:0.331308\n",
      "Clustering   1119: AMI= 0.7221, NMI= 0.7230, ARI= 0.5006, ACC= 0.5990\n",
      "0.018404658170120932\n",
      "Training epoch 1120, recon_loss:0.610583, zinb_loss:0.959033, cluster_loss:0.332574\n",
      "Clustering   1120: AMI= 0.7206, NMI= 0.7215, ARI= 0.4967, ACC= 0.5999\n",
      "0.01978907935990879\n",
      "Training epoch 1121, recon_loss:0.610432, zinb_loss:0.959269, cluster_loss:0.331019\n",
      "Clustering   1121: AMI= 0.7222, NMI= 0.7231, ARI= 0.5019, ACC= 0.6002\n",
      "0.022110020766317847\n",
      "Training epoch 1122, recon_loss:0.609140, zinb_loss:0.959335, cluster_loss:0.332592\n",
      "Clustering   1122: AMI= 0.7204, NMI= 0.7214, ARI= 0.4962, ACC= 0.5994\n",
      "0.023657315037257216\n",
      "Training epoch 1123, recon_loss:0.609268, zinb_loss:0.959469, cluster_loss:0.331066\n",
      "Clustering   1123: AMI= 0.7224, NMI= 0.7233, ARI= 0.5022, ACC= 0.6008\n",
      "0.023698033307545094\n",
      "Training epoch 1124, recon_loss:0.610600, zinb_loss:0.959650, cluster_loss:0.332738\n",
      "Clustering   1124: AMI= 0.7207, NMI= 0.7216, ARI= 0.4964, ACC= 0.5995\n",
      "0.025001017956757198\n",
      "Training epoch 1125, recon_loss:0.610153, zinb_loss:0.959809, cluster_loss:0.330818\n",
      "Clustering   1125: AMI= 0.7224, NMI= 0.7234, ARI= 0.5033, ACC= 0.6024\n",
      "0.027036931471151104\n",
      "Training epoch 1126, recon_loss:0.609038, zinb_loss:0.959953, cluster_loss:0.332721\n",
      "Clustering   1126: AMI= 0.7205, NMI= 0.7214, ARI= 0.4957, ACC= 0.5987\n",
      "0.02785129687690867\n",
      "Training epoch 1127, recon_loss:0.608965, zinb_loss:0.960000, cluster_loss:0.330938\n",
      "Clustering   1127: AMI= 0.7227, NMI= 0.7236, ARI= 0.5036, ACC= 0.6033\n",
      "0.02805488822834806\n",
      "Training epoch 1128, recon_loss:0.610319, zinb_loss:0.960194, cluster_loss:0.332824\n",
      "Clustering   1128: AMI= 0.7203, NMI= 0.7213, ARI= 0.4953, ACC= 0.5979\n",
      "0.029480027688423796\n",
      "Training epoch 1129, recon_loss:0.609666, zinb_loss:0.960289, cluster_loss:0.330764\n",
      "Clustering   1129: AMI= 0.7228, NMI= 0.7237, ARI= 0.5044, ACC= 0.6046\n",
      "0.03180096909483285\n",
      "Training epoch 1130, recon_loss:0.608910, zinb_loss:0.960374, cluster_loss:0.332760\n",
      "Clustering   1130: AMI= 0.7206, NMI= 0.7215, ARI= 0.4952, ACC= 0.5976\n",
      "0.03249317968972678\n",
      "Training epoch 1131, recon_loss:0.608793, zinb_loss:0.960359, cluster_loss:0.330940\n",
      "Clustering   1131: AMI= 0.7227, NMI= 0.7237, ARI= 0.5039, ACC= 0.6044\n",
      "0.03228958833828739\n",
      "Training epoch 1132, recon_loss:0.609986, zinb_loss:0.960471, cluster_loss:0.332770\n",
      "Clustering   1132: AMI= 0.7207, NMI= 0.7216, ARI= 0.4956, ACC= 0.5976\n",
      "0.03237102487886315\n",
      "Training epoch 1133, recon_loss:0.609352, zinb_loss:0.960510, cluster_loss:0.330812\n",
      "Clustering   1133: AMI= 0.7230, NMI= 0.7239, ARI= 0.5044, ACC= 0.6051\n",
      "0.03359257298749949\n",
      "Training epoch 1134, recon_loss:0.609255, zinb_loss:0.960522, cluster_loss:0.332669\n",
      "Clustering   1134: AMI= 0.7207, NMI= 0.7217, ARI= 0.4956, ACC= 0.5972\n",
      "0.03383688260922676\n",
      "Training epoch 1135, recon_loss:0.608772, zinb_loss:0.960452, cluster_loss:0.330963\n",
      "Clustering   1135: AMI= 0.7225, NMI= 0.7234, ARI= 0.5036, ACC= 0.6050\n",
      "0.03306323547375707\n",
      "Training epoch 1136, recon_loss:0.610303, zinb_loss:0.960485, cluster_loss:0.332595\n",
      "Clustering   1136: AMI= 0.7208, NMI= 0.7217, ARI= 0.4959, ACC= 0.5971\n",
      "0.03277820758174193\n",
      "Training epoch 1137, recon_loss:0.609423, zinb_loss:0.960442, cluster_loss:0.330952\n",
      "Clustering   1137: AMI= 0.7227, NMI= 0.7237, ARI= 0.5039, ACC= 0.6057\n",
      "0.03322610855490859\n",
      "Training epoch 1138, recon_loss:0.608917, zinb_loss:0.960339, cluster_loss:0.332464\n",
      "Clustering   1138: AMI= 0.7208, NMI= 0.7218, ARI= 0.4962, ACC= 0.5970\n",
      "0.032574616230302535\n",
      "Training epoch 1139, recon_loss:0.608584, zinb_loss:0.960221, cluster_loss:0.331329\n",
      "Clustering   1139: AMI= 0.7226, NMI= 0.7235, ARI= 0.5035, ACC= 0.6057\n",
      "0.030783012337635897\n",
      "Training epoch 1140, recon_loss:0.609371, zinb_loss:0.960167, cluster_loss:0.332452\n",
      "Clustering   1140: AMI= 0.7209, NMI= 0.7218, ARI= 0.4963, ACC= 0.5970\n",
      "0.029927928661590456\n",
      "Training epoch 1141, recon_loss:0.608468, zinb_loss:0.960104, cluster_loss:0.331461\n",
      "Clustering   1141: AMI= 0.7225, NMI= 0.7234, ARI= 0.5032, ACC= 0.6055\n",
      "0.029235718066696528\n",
      "Training epoch 1142, recon_loss:0.609715, zinb_loss:0.960066, cluster_loss:0.332452\n",
      "Clustering   1142: AMI= 0.7208, NMI= 0.7217, ARI= 0.4962, ACC= 0.5969\n",
      "0.028421352660938964\n",
      "Training epoch 1143, recon_loss:0.608628, zinb_loss:0.959942, cluster_loss:0.331586\n",
      "Clustering   1143: AMI= 0.7225, NMI= 0.7234, ARI= 0.5032, ACC= 0.6054\n",
      "0.027525550714605645\n",
      "Training epoch 1144, recon_loss:0.609189, zinb_loss:0.959914, cluster_loss:0.332419\n",
      "Clustering   1144: AMI= 0.7208, NMI= 0.7217, ARI= 0.4963, ACC= 0.5969\n",
      "0.026874058389999594\n",
      "Training epoch 1145, recon_loss:0.608169, zinb_loss:0.959790, cluster_loss:0.331824\n",
      "Clustering   1145: AMI= 0.7223, NMI= 0.7232, ARI= 0.5028, ACC= 0.6051\n",
      "0.02605969298424203\n",
      "Training epoch 1146, recon_loss:0.609837, zinb_loss:0.959790, cluster_loss:0.332364\n",
      "Clustering   1146: AMI= 0.7206, NMI= 0.7215, ARI= 0.4964, ACC= 0.5968\n",
      "0.0253674823893481\n",
      "Training epoch 1147, recon_loss:0.608624, zinb_loss:0.959709, cluster_loss:0.331899\n",
      "Clustering   1147: AMI= 0.7224, NMI= 0.7233, ARI= 0.5028, ACC= 0.6053\n",
      "0.025001017956757198\n",
      "Training epoch 1148, recon_loss:0.608866, zinb_loss:0.959628, cluster_loss:0.332217\n",
      "Clustering   1148: AMI= 0.7205, NMI= 0.7214, ARI= 0.4965, ACC= 0.5966\n",
      "0.024186652550999634\n",
      "Training epoch 1149, recon_loss:0.608103, zinb_loss:0.959605, cluster_loss:0.332139\n",
      "Clustering   1149: AMI= 0.7223, NMI= 0.7232, ARI= 0.5027, ACC= 0.6054\n",
      "0.023860906388696607\n",
      "Training epoch 1150, recon_loss:0.610165, zinb_loss:0.959554, cluster_loss:0.332071\n",
      "Clustering   1150: AMI= 0.7207, NMI= 0.7217, ARI= 0.4969, ACC= 0.5966\n",
      "0.023494441956105706\n",
      "Training epoch 1151, recon_loss:0.608921, zinb_loss:0.959568, cluster_loss:0.332046\n",
      "Clustering   1151: AMI= 0.7224, NMI= 0.7233, ARI= 0.5031, ACC= 0.6058\n",
      "0.023983061199560243\n",
      "Training epoch 1152, recon_loss:0.609141, zinb_loss:0.959414, cluster_loss:0.331870\n",
      "Clustering   1152: AMI= 0.7209, NMI= 0.7218, ARI= 0.4970, ACC= 0.5964\n",
      "0.02361659676696934\n",
      "Training epoch 1153, recon_loss:0.608486, zinb_loss:0.959468, cluster_loss:0.332216\n",
      "Clustering   1153: AMI= 0.7222, NMI= 0.7232, ARI= 0.5028, ACC= 0.6056\n",
      "0.023127977523514802\n",
      "Training epoch 1154, recon_loss:0.609932, zinb_loss:0.959352, cluster_loss:0.331754\n",
      "Clustering   1154: AMI= 0.7207, NMI= 0.7216, ARI= 0.4970, ACC= 0.5961\n",
      "0.023005822712651166\n",
      "Training epoch 1155, recon_loss:0.608708, zinb_loss:0.959388, cluster_loss:0.332143\n",
      "Clustering   1155: AMI= 0.7222, NMI= 0.7231, ARI= 0.5028, ACC= 0.6056\n",
      "0.023250132334378434\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training epoch 1156, recon_loss:0.610612, zinb_loss:0.959309, cluster_loss:0.331723\n",
      "Clustering   1156: AMI= 0.7206, NMI= 0.7215, ARI= 0.4968, ACC= 0.5958\n",
      "0.023494441956105706\n",
      "Training epoch 1157, recon_loss:0.609137, zinb_loss:0.959259, cluster_loss:0.332073\n",
      "Clustering   1157: AMI= 0.7224, NMI= 0.7233, ARI= 0.5028, ACC= 0.6055\n",
      "0.023250132334378434\n",
      "Training epoch 1158, recon_loss:0.609814, zinb_loss:0.959209, cluster_loss:0.331759\n",
      "Clustering   1158: AMI= 0.7206, NMI= 0.7215, ARI= 0.4968, ACC= 0.5962\n",
      "0.022761513090923897\n",
      "Training epoch 1159, recon_loss:0.608614, zinb_loss:0.959148, cluster_loss:0.332197\n",
      "Clustering   1159: AMI= 0.7223, NMI= 0.7232, ARI= 0.5025, ACC= 0.6053\n",
      "0.022028584225742092\n",
      "Training epoch 1160, recon_loss:0.610936, zinb_loss:0.959203, cluster_loss:0.331851\n",
      "Clustering   1160: AMI= 0.7205, NMI= 0.7215, ARI= 0.4969, ACC= 0.5965\n",
      "0.021662119793151188\n",
      "Training epoch 1161, recon_loss:0.609322, zinb_loss:0.959069, cluster_loss:0.332056\n",
      "Clustering   1161: AMI= 0.7223, NMI= 0.7232, ARI= 0.5027, ACC= 0.6050\n",
      "0.0218249928743027\n",
      "Training epoch 1162, recon_loss:0.609847, zinb_loss:0.959140, cluster_loss:0.331939\n",
      "Clustering   1162: AMI= 0.7203, NMI= 0.7212, ARI= 0.4961, ACC= 0.5964\n",
      "0.021947147685166334\n",
      "Training epoch 1163, recon_loss:0.609021, zinb_loss:0.959008, cluster_loss:0.332153\n",
      "Clustering   1163: AMI= 0.7223, NMI= 0.7232, ARI= 0.5026, ACC= 0.6046\n",
      "0.022191457306893602\n",
      "Training epoch 1164, recon_loss:0.610594, zinb_loss:0.959185, cluster_loss:0.332007\n",
      "Clustering   1164: AMI= 0.7202, NMI= 0.7212, ARI= 0.4961, ACC= 0.5967\n",
      "0.022598640009772384\n",
      "Training epoch 1165, recon_loss:0.609410, zinb_loss:0.958985, cluster_loss:0.331991\n",
      "Clustering   1165: AMI= 0.7224, NMI= 0.7233, ARI= 0.5027, ACC= 0.6043\n",
      "0.023535160226393584\n",
      "Training epoch 1166, recon_loss:0.611669, zinb_loss:0.959248, cluster_loss:0.332043\n",
      "Clustering   1166: AMI= 0.7201, NMI= 0.7210, ARI= 0.4955, ACC= 0.5970\n",
      "0.02467527179445417\n",
      "Training epoch 1167, recon_loss:0.609941, zinb_loss:0.958929, cluster_loss:0.331734\n",
      "Clustering   1167: AMI= 0.7224, NMI= 0.7234, ARI= 0.5025, ACC= 0.6037\n",
      "0.025163891037908708\n",
      "Training epoch 1168, recon_loss:0.611889, zinb_loss:0.959254, cluster_loss:0.332082\n",
      "Clustering   1168: AMI= 0.7202, NMI= 0.7211, ARI= 0.4954, ACC= 0.5973\n",
      "0.02605969298424203\n",
      "Training epoch 1169, recon_loss:0.610051, zinb_loss:0.958862, cluster_loss:0.331586\n",
      "Clustering   1169: AMI= 0.7223, NMI= 0.7232, ARI= 0.5023, ACC= 0.6031\n",
      "0.025489637200211735\n",
      "Training epoch 1170, recon_loss:0.611450, zinb_loss:0.959211, cluster_loss:0.332125\n",
      "Clustering   1170: AMI= 0.7201, NMI= 0.7210, ARI= 0.4953, ACC= 0.5977\n",
      "0.025286045848772344\n",
      "Training epoch 1171, recon_loss:0.609678, zinb_loss:0.958818, cluster_loss:0.331585\n",
      "Clustering   1171: AMI= 0.7223, NMI= 0.7233, ARI= 0.5020, ACC= 0.6021\n",
      "0.02447168044301478\n",
      "Training epoch 1172, recon_loss:0.611406, zinb_loss:0.959211, cluster_loss:0.332193\n",
      "Clustering   1172: AMI= 0.7201, NMI= 0.7210, ARI= 0.4956, ACC= 0.5984\n",
      "0.023657315037257216\n",
      "Training epoch 1173, recon_loss:0.609611, zinb_loss:0.958808, cluster_loss:0.331581\n",
      "Clustering   1173: AMI= 0.7223, NMI= 0.7232, ARI= 0.5016, ACC= 0.6015\n",
      "0.023005822712651166\n",
      "Training epoch 1174, recon_loss:0.610566, zinb_loss:0.959220, cluster_loss:0.332246\n",
      "Clustering   1174: AMI= 0.7203, NMI= 0.7212, ARI= 0.4958, ACC= 0.5988\n",
      "0.02247648519890875\n",
      "Training epoch 1175, recon_loss:0.609125, zinb_loss:0.958873, cluster_loss:0.331701\n",
      "Clustering   1175: AMI= 0.7221, NMI= 0.7230, ARI= 0.5012, ACC= 0.6007\n",
      "0.021743556333726943\n",
      "Training epoch 1176, recon_loss:0.610807, zinb_loss:0.959369, cluster_loss:0.332285\n",
      "Clustering   1176: AMI= 0.7204, NMI= 0.7213, ARI= 0.4963, ACC= 0.5995\n",
      "0.020562726495378478\n",
      "Training epoch 1177, recon_loss:0.609514, zinb_loss:0.959029, cluster_loss:0.331685\n",
      "Clustering   1177: AMI= 0.7221, NMI= 0.7230, ARI= 0.5008, ACC= 0.5999\n",
      "0.019504051467893645\n",
      "Training epoch 1178, recon_loss:0.609762, zinb_loss:0.959545, cluster_loss:0.332236\n",
      "Clustering   1178: AMI= 0.7203, NMI= 0.7212, ARI= 0.4968, ACC= 0.6008\n",
      "0.01783460238609064\n",
      "Training epoch 1179, recon_loss:0.608985, zinb_loss:0.959308, cluster_loss:0.331813\n",
      "Clustering   1179: AMI= 0.7219, NMI= 0.7228, ARI= 0.5001, ACC= 0.5988\n",
      "0.017468137953499736\n",
      "Training epoch 1180, recon_loss:0.611066, zinb_loss:0.959961, cluster_loss:0.332174\n",
      "Clustering   1180: AMI= 0.7203, NMI= 0.7213, ARI= 0.4974, ACC= 0.6015\n",
      "0.018241785088969422\n",
      "Training epoch 1181, recon_loss:0.610271, zinb_loss:0.959677, cluster_loss:0.331623\n",
      "Clustering   1181: AMI= 0.7220, NMI= 0.7229, ARI= 0.5002, ACC= 0.5983\n",
      "0.019219023575878496\n",
      "Training epoch 1182, recon_loss:0.609301, zinb_loss:0.960231, cluster_loss:0.331953\n",
      "Clustering   1182: AMI= 0.7206, NMI= 0.7215, ARI= 0.4980, ACC= 0.6025\n",
      "0.01938189665703001\n",
      "Training epoch 1183, recon_loss:0.609301, zinb_loss:0.960028, cluster_loss:0.331803\n",
      "Clustering   1183: AMI= 0.7219, NMI= 0.7228, ARI= 0.4995, ACC= 0.5977\n",
      "0.019096868765014863\n",
      "Training epoch 1184, recon_loss:0.609741, zinb_loss:0.960633, cluster_loss:0.331877\n",
      "Clustering   1184: AMI= 0.7207, NMI= 0.7216, ARI= 0.4995, ACC= 0.6041\n",
      "0.019992670711348182\n",
      "Training epoch 1185, recon_loss:0.609245, zinb_loss:0.960241, cluster_loss:0.331711\n",
      "Clustering   1185: AMI= 0.7218, NMI= 0.7228, ARI= 0.4993, ACC= 0.5973\n",
      "0.019951952441060305\n",
      "Training epoch 1186, recon_loss:0.610663, zinb_loss:0.960809, cluster_loss:0.331836\n",
      "Clustering   1186: AMI= 0.7207, NMI= 0.7216, ARI= 0.4998, ACC= 0.6046\n",
      "0.020522008225090597\n",
      "Training epoch 1187, recon_loss:0.610109, zinb_loss:0.960182, cluster_loss:0.331549\n",
      "Clustering   1187: AMI= 0.7216, NMI= 0.7225, ARI= 0.4994, ACC= 0.5968\n",
      "0.021295655360560283\n",
      "Training epoch 1188, recon_loss:0.609019, zinb_loss:0.960602, cluster_loss:0.331761\n",
      "Clustering   1188: AMI= 0.7211, NMI= 0.7220, ARI= 0.5003, ACC= 0.6050\n",
      "0.0218249928743027\n",
      "Training epoch 1189, recon_loss:0.608909, zinb_loss:0.959969, cluster_loss:0.331779\n",
      "Clustering   1189: AMI= 0.7214, NMI= 0.7223, ARI= 0.4990, ACC= 0.5966\n",
      "0.022191457306893602\n",
      "Training epoch 1190, recon_loss:0.609276, zinb_loss:0.960419, cluster_loss:0.331841\n",
      "Clustering   1190: AMI= 0.7210, NMI= 0.7219, ARI= 0.5009, ACC= 0.6055\n",
      "0.022313612117757238\n",
      "Training epoch 1191, recon_loss:0.608938, zinb_loss:0.959752, cluster_loss:0.331778\n",
      "Clustering   1191: AMI= 0.7212, NMI= 0.7222, ARI= 0.4990, ACC= 0.5967\n",
      "0.022232175577181483\n",
      "Training epoch 1192, recon_loss:0.609664, zinb_loss:0.960154, cluster_loss:0.331901\n",
      "Clustering   1192: AMI= 0.7211, NMI= 0.7220, ARI= 0.5007, ACC= 0.6054\n",
      "0.02251720346919663\n",
      "Training epoch 1193, recon_loss:0.609358, zinb_loss:0.959516, cluster_loss:0.331773\n",
      "Clustering   1193: AMI= 0.7214, NMI= 0.7223, ARI= 0.4992, ACC= 0.5968\n",
      "0.023127977523514802\n",
      "Training epoch 1194, recon_loss:0.608401, zinb_loss:0.959834, cluster_loss:0.331911\n",
      "Clustering   1194: AMI= 0.7215, NMI= 0.7225, ARI= 0.5010, ACC= 0.6056\n",
      "0.02251720346919663\n",
      "Training epoch 1195, recon_loss:0.608336, zinb_loss:0.959314, cluster_loss:0.332016\n",
      "Clustering   1195: AMI= 0.7214, NMI= 0.7223, ARI= 0.4988, ACC= 0.5970\n",
      "0.0218249928743027\n",
      "Training epoch 1196, recon_loss:0.609680, zinb_loss:0.959669, cluster_loss:0.332020\n",
      "Clustering   1196: AMI= 0.7218, NMI= 0.7227, ARI= 0.5013, ACC= 0.6054\n",
      "0.021051345738833015\n",
      "Training epoch 1197, recon_loss:0.609490, zinb_loss:0.959237, cluster_loss:0.331967\n",
      "Clustering   1197: AMI= 0.7212, NMI= 0.7222, ARI= 0.4989, ACC= 0.5972\n",
      "0.02141781017142392\n",
      "Training epoch 1198, recon_loss:0.607895, zinb_loss:0.959426, cluster_loss:0.331971\n",
      "Clustering   1198: AMI= 0.7217, NMI= 0.7226, ARI= 0.5012, ACC= 0.6052\n",
      "0.021906429414878456\n",
      "Training epoch 1199, recon_loss:0.608166, zinb_loss:0.959133, cluster_loss:0.332250\n",
      "Clustering   1199: AMI= 0.7212, NMI= 0.7221, ARI= 0.4985, ACC= 0.5970\n",
      "0.02198786595545421\n",
      "Training epoch 1200, recon_loss:0.608864, zinb_loss:0.959337, cluster_loss:0.332079\n",
      "Clustering   1200: AMI= 0.7219, NMI= 0.7228, ARI= 0.5016, ACC= 0.6051\n",
      "0.02186571114459058\n",
      "Training epoch 1201, recon_loss:0.608906, zinb_loss:0.959171, cluster_loss:0.332264\n",
      "Clustering   1201: AMI= 0.7210, NMI= 0.7219, ARI= 0.4983, ACC= 0.5974\n",
      "0.021743556333726943\n",
      "Training epoch 1202, recon_loss:0.608088, zinb_loss:0.959243, cluster_loss:0.332046\n",
      "Clustering   1202: AMI= 0.7218, NMI= 0.7227, ARI= 0.5012, ACC= 0.6047\n",
      "0.02178427460401482\n",
      "Training epoch 1203, recon_loss:0.608398, zinb_loss:0.959182, cluster_loss:0.332424\n",
      "Clustering   1203: AMI= 0.7210, NMI= 0.7219, ARI= 0.4981, ACC= 0.5974\n",
      "0.022191457306893602\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training epoch 1204, recon_loss:0.609128, zinb_loss:0.959220, cluster_loss:0.332050\n",
      "Clustering   1204: AMI= 0.7218, NMI= 0.7227, ARI= 0.5014, ACC= 0.6045\n",
      "0.022354330388045116\n",
      "Training epoch 1205, recon_loss:0.609281, zinb_loss:0.959297, cluster_loss:0.332405\n",
      "Clustering   1205: AMI= 0.7209, NMI= 0.7218, ARI= 0.4981, ACC= 0.5975\n",
      "0.022842949631499652\n",
      "Training epoch 1206, recon_loss:0.608120, zinb_loss:0.959151, cluster_loss:0.331953\n",
      "Clustering   1206: AMI= 0.7219, NMI= 0.7229, ARI= 0.5014, ACC= 0.6044\n",
      "0.02361659676696934\n",
      "Training epoch 1207, recon_loss:0.608624, zinb_loss:0.959374, cluster_loss:0.332605\n",
      "Clustering   1207: AMI= 0.7208, NMI= 0.7218, ARI= 0.4979, ACC= 0.5974\n",
      "0.023657315037257216\n",
      "Training epoch 1208, recon_loss:0.609221, zinb_loss:0.959167, cluster_loss:0.331932\n",
      "Clustering   1208: AMI= 0.7220, NMI= 0.7229, ARI= 0.5016, ACC= 0.6044\n",
      "0.02382018811840873\n",
      "Training epoch 1209, recon_loss:0.609355, zinb_loss:0.959555, cluster_loss:0.332574\n",
      "Clustering   1209: AMI= 0.7208, NMI= 0.7218, ARI= 0.4979, ACC= 0.5975\n",
      "0.024349525632151148\n",
      "Training epoch 1210, recon_loss:0.608070, zinb_loss:0.959139, cluster_loss:0.331846\n",
      "Clustering   1210: AMI= 0.7221, NMI= 0.7230, ARI= 0.5016, ACC= 0.6041\n",
      "0.025082454497332953\n",
      "Training epoch 1211, recon_loss:0.608611, zinb_loss:0.959668, cluster_loss:0.332764\n",
      "Clustering   1211: AMI= 0.7211, NMI= 0.7220, ARI= 0.4977, ACC= 0.5974\n",
      "0.025448918929923858\n",
      "Training epoch 1212, recon_loss:0.608957, zinb_loss:0.959172, cluster_loss:0.331827\n",
      "Clustering   1212: AMI= 0.7220, NMI= 0.7230, ARI= 0.5018, ACC= 0.6041\n",
      "0.02605969298424203\n",
      "Training epoch 1213, recon_loss:0.609161, zinb_loss:0.959895, cluster_loss:0.332728\n",
      "Clustering   1213: AMI= 0.7209, NMI= 0.7218, ARI= 0.4972, ACC= 0.5975\n",
      "0.02650759395740869\n",
      "Training epoch 1214, recon_loss:0.608263, zinb_loss:0.959194, cluster_loss:0.331744\n",
      "Clustering   1214: AMI= 0.7221, NMI= 0.7230, ARI= 0.5018, ACC= 0.6040\n",
      "0.027199804552302618\n",
      "Training epoch 1215, recon_loss:0.608752, zinb_loss:0.960052, cluster_loss:0.332797\n",
      "Clustering   1215: AMI= 0.7208, NMI= 0.7218, ARI= 0.4969, ACC= 0.5975\n",
      "0.028584225742090477\n",
      "Training epoch 1216, recon_loss:0.608861, zinb_loss:0.959239, cluster_loss:0.331671\n",
      "Clustering   1216: AMI= 0.7220, NMI= 0.7229, ARI= 0.5019, ACC= 0.6039\n",
      "0.02939859114784804\n",
      "Training epoch 1217, recon_loss:0.609334, zinb_loss:0.960264, cluster_loss:0.332693\n",
      "Clustering   1217: AMI= 0.7208, NMI= 0.7218, ARI= 0.4966, ACC= 0.5976\n",
      "0.0302129565536056\n",
      "Training epoch 1218, recon_loss:0.608633, zinb_loss:0.959254, cluster_loss:0.331577\n",
      "Clustering   1218: AMI= 0.7221, NMI= 0.7230, ARI= 0.5020, ACC= 0.6037\n",
      "0.030823730607923774\n",
      "Training epoch 1219, recon_loss:0.609293, zinb_loss:0.960366, cluster_loss:0.332640\n",
      "Clustering   1219: AMI= 0.7207, NMI= 0.7216, ARI= 0.4961, ACC= 0.5973\n",
      "0.03131234985137831\n",
      "Training epoch 1220, recon_loss:0.609153, zinb_loss:0.959237, cluster_loss:0.331537\n",
      "Clustering   1220: AMI= 0.7219, NMI= 0.7229, ARI= 0.5022, ACC= 0.6038\n",
      "0.031108758499938924\n",
      "Training epoch 1221, recon_loss:0.609764, zinb_loss:0.960419, cluster_loss:0.332538\n",
      "Clustering   1221: AMI= 0.7207, NMI= 0.7216, ARI= 0.4962, ACC= 0.5976\n",
      "0.03119019504051468\n",
      "Training epoch 1222, recon_loss:0.608791, zinb_loss:0.959165, cluster_loss:0.331540\n",
      "Clustering   1222: AMI= 0.7220, NMI= 0.7229, ARI= 0.5023, ACC= 0.6036\n",
      "0.031230913310802556\n",
      "Training epoch 1223, recon_loss:0.609500, zinb_loss:0.960356, cluster_loss:0.332560\n",
      "Clustering   1223: AMI= 0.7205, NMI= 0.7214, ARI= 0.4960, ACC= 0.5977\n",
      "0.031678814283969216\n",
      "Training epoch 1224, recon_loss:0.609244, zinb_loss:0.959079, cluster_loss:0.331605\n",
      "Clustering   1224: AMI= 0.7221, NMI= 0.7231, ARI= 0.5024, ACC= 0.6035\n",
      "0.031068040229651043\n",
      "Training epoch 1225, recon_loss:0.609812, zinb_loss:0.960263, cluster_loss:0.332528\n",
      "Clustering   1225: AMI= 0.7207, NMI= 0.7216, ARI= 0.4962, ACC= 0.5977\n",
      "0.030620139256484383\n",
      "Training epoch 1226, recon_loss:0.609034, zinb_loss:0.958995, cluster_loss:0.331673\n",
      "Clustering   1226: AMI= 0.7219, NMI= 0.7228, ARI= 0.5017, ACC= 0.6028\n",
      "0.030335111364469237\n",
      "Training epoch 1227, recon_loss:0.609582, zinb_loss:0.960109, cluster_loss:0.332566\n",
      "Clustering   1227: AMI= 0.7205, NMI= 0.7214, ARI= 0.4961, ACC= 0.5977\n",
      "0.02964290076957531\n",
      "Training epoch 1228, recon_loss:0.609399, zinb_loss:0.958926, cluster_loss:0.331773\n",
      "Clustering   1228: AMI= 0.7216, NMI= 0.7225, ARI= 0.5016, ACC= 0.6028\n",
      "0.028543507471802596\n",
      "Training epoch 1229, recon_loss:0.609791, zinb_loss:0.959984, cluster_loss:0.332558\n",
      "Clustering   1229: AMI= 0.7207, NMI= 0.7216, ARI= 0.4965, ACC= 0.5980\n",
      "0.027281241092878373\n",
      "Training epoch 1230, recon_loss:0.609043, zinb_loss:0.958864, cluster_loss:0.331880\n",
      "Clustering   1230: AMI= 0.7213, NMI= 0.7223, ARI= 0.5011, ACC= 0.6026\n",
      "0.026670467038560203\n",
      "Training epoch 1231, recon_loss:0.609423, zinb_loss:0.959855, cluster_loss:0.332635\n",
      "Clustering   1231: AMI= 0.7207, NMI= 0.7216, ARI= 0.4966, ACC= 0.5981\n",
      "0.026100411254529908\n",
      "Training epoch 1232, recon_loss:0.609588, zinb_loss:0.958841, cluster_loss:0.332021\n",
      "Clustering   1232: AMI= 0.7212, NMI= 0.7221, ARI= 0.5009, ACC= 0.6025\n",
      "0.025041736227045076\n",
      "Training epoch 1233, recon_loss:0.609797, zinb_loss:0.959783, cluster_loss:0.332621\n",
      "Clustering   1233: AMI= 0.7206, NMI= 0.7216, ARI= 0.4966, ACC= 0.5977\n",
      "0.024064497740136\n",
      "Training epoch 1234, recon_loss:0.608797, zinb_loss:0.958830, cluster_loss:0.332119\n",
      "Clustering   1234: AMI= 0.7213, NMI= 0.7222, ARI= 0.5007, ACC= 0.6025\n",
      "0.023657315037257216\n",
      "Training epoch 1235, recon_loss:0.609155, zinb_loss:0.959731, cluster_loss:0.332740\n",
      "Clustering   1235: AMI= 0.7206, NMI= 0.7215, ARI= 0.4965, ACC= 0.5976\n",
      "0.02337228714524207\n",
      "Training epoch 1236, recon_loss:0.609454, zinb_loss:0.958888, cluster_loss:0.332244\n",
      "Clustering   1236: AMI= 0.7214, NMI= 0.7223, ARI= 0.5008, ACC= 0.6025\n",
      "0.022842949631499652\n",
      "Training epoch 1237, recon_loss:0.609721, zinb_loss:0.959796, cluster_loss:0.332695\n",
      "Clustering   1237: AMI= 0.7205, NMI= 0.7214, ARI= 0.4963, ACC= 0.5973\n",
      "0.022842949631499652\n",
      "Training epoch 1238, recon_loss:0.608638, zinb_loss:0.958986, cluster_loss:0.332277\n",
      "Clustering   1238: AMI= 0.7216, NMI= 0.7225, ARI= 0.5009, ACC= 0.6024\n",
      "0.02272079482063602\n",
      "Training epoch 1239, recon_loss:0.609174, zinb_loss:0.959883, cluster_loss:0.332781\n",
      "Clustering   1239: AMI= 0.7204, NMI= 0.7213, ARI= 0.4961, ACC= 0.5972\n",
      "0.02337228714524207\n",
      "Training epoch 1240, recon_loss:0.609702, zinb_loss:0.959180, cluster_loss:0.332296\n",
      "Clustering   1240: AMI= 0.7215, NMI= 0.7225, ARI= 0.5011, ACC= 0.6026\n",
      "0.02402377946984812\n",
      "Training epoch 1241, recon_loss:0.610257, zinb_loss:0.960075, cluster_loss:0.332611\n",
      "Clustering   1241: AMI= 0.7204, NMI= 0.7214, ARI= 0.4959, ACC= 0.5968\n",
      "0.024593835253878416\n",
      "Training epoch 1242, recon_loss:0.608778, zinb_loss:0.959359, cluster_loss:0.332170\n",
      "Clustering   1242: AMI= 0.7217, NMI= 0.7226, ARI= 0.5014, ACC= 0.6027\n",
      "0.02565251028136325\n",
      "Training epoch 1243, recon_loss:0.609780, zinb_loss:0.960206, cluster_loss:0.332619\n",
      "Clustering   1243: AMI= 0.7204, NMI= 0.7213, ARI= 0.4956, ACC= 0.5968\n",
      "0.026833340119711713\n",
      "Training epoch 1244, recon_loss:0.610004, zinb_loss:0.959547, cluster_loss:0.332071\n",
      "Clustering   1244: AMI= 0.7217, NMI= 0.7226, ARI= 0.5022, ACC= 0.6034\n",
      "0.028177043039211695\n",
      "Training epoch 1245, recon_loss:0.610758, zinb_loss:0.960283, cluster_loss:0.332337\n",
      "Clustering   1245: AMI= 0.7203, NMI= 0.7212, ARI= 0.4955, ACC= 0.5964\n",
      "0.028217761309499573\n",
      "Training epoch 1246, recon_loss:0.609538, zinb_loss:0.959567, cluster_loss:0.331952\n",
      "Clustering   1246: AMI= 0.7218, NMI= 0.7227, ARI= 0.5025, ACC= 0.6037\n",
      "0.028665662282666232\n",
      "Training epoch 1247, recon_loss:0.610171, zinb_loss:0.960124, cluster_loss:0.332265\n",
      "Clustering   1247: AMI= 0.7200, NMI= 0.7209, ARI= 0.4952, ACC= 0.5961\n",
      "0.028747098823241987\n",
      "Training epoch 1248, recon_loss:0.610350, zinb_loss:0.959499, cluster_loss:0.331966\n",
      "Clustering   1248: AMI= 0.7217, NMI= 0.7226, ARI= 0.5027, ACC= 0.6039\n",
      "0.028299197850075328\n",
      "Training epoch 1249, recon_loss:0.610675, zinb_loss:0.959895, cluster_loss:0.332117\n",
      "Clustering   1249: AMI= 0.7198, NMI= 0.7208, ARI= 0.4954, ACC= 0.5962\n",
      "0.027525550714605645\n",
      "Training epoch 1250, recon_loss:0.608753, zinb_loss:0.959297, cluster_loss:0.331984\n",
      "Clustering   1250: AMI= 0.7216, NMI= 0.7226, ARI= 0.5025, ACC= 0.6038\n",
      "0.026914776660287472\n",
      "Training epoch 1251, recon_loss:0.609330, zinb_loss:0.959619, cluster_loss:0.332313\n",
      "Clustering   1251: AMI= 0.7198, NMI= 0.7208, ARI= 0.4954, ACC= 0.5963\n",
      "0.026589030497984445\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training epoch 1252, recon_loss:0.609363, zinb_loss:0.959204, cluster_loss:0.332134\n",
      "Clustering   1252: AMI= 0.7219, NMI= 0.7228, ARI= 0.5025, ACC= 0.6039\n",
      "0.025693228551651126\n",
      "Training epoch 1253, recon_loss:0.609570, zinb_loss:0.959431, cluster_loss:0.332269\n",
      "Clustering   1253: AMI= 0.7201, NMI= 0.7210, ARI= 0.4962, ACC= 0.5968\n",
      "0.02455311698359054\n",
      "Training epoch 1254, recon_loss:0.608866, zinb_loss:0.959120, cluster_loss:0.332233\n",
      "Clustering   1254: AMI= 0.7217, NMI= 0.7227, ARI= 0.5021, ACC= 0.6037\n",
      "0.02382018811840873\n",
      "Training epoch 1255, recon_loss:0.609042, zinb_loss:0.959237, cluster_loss:0.332328\n",
      "Clustering   1255: AMI= 0.7202, NMI= 0.7211, ARI= 0.4965, ACC= 0.5970\n",
      "0.023494441956105706\n",
      "Training epoch 1256, recon_loss:0.609472, zinb_loss:0.959104, cluster_loss:0.332352\n",
      "Clustering   1256: AMI= 0.7218, NMI= 0.7227, ARI= 0.5020, ACC= 0.6038\n",
      "0.023005822712651166\n",
      "Training epoch 1257, recon_loss:0.609532, zinb_loss:0.959112, cluster_loss:0.332267\n",
      "Clustering   1257: AMI= 0.7200, NMI= 0.7210, ARI= 0.4966, ACC= 0.5970\n",
      "0.02243576692862087\n",
      "Training epoch 1258, recon_loss:0.608643, zinb_loss:0.959053, cluster_loss:0.332391\n",
      "Clustering   1258: AMI= 0.7217, NMI= 0.7226, ARI= 0.5017, ACC= 0.6038\n",
      "0.021743556333726943\n",
      "Training epoch 1259, recon_loss:0.608786, zinb_loss:0.959002, cluster_loss:0.332369\n",
      "Clustering   1259: AMI= 0.7200, NMI= 0.7210, ARI= 0.4966, ACC= 0.5968\n",
      "0.021173500549696647\n",
      "Training epoch 1260, recon_loss:0.609825, zinb_loss:0.959117, cluster_loss:0.332476\n",
      "Clustering   1260: AMI= 0.7216, NMI= 0.7225, ARI= 0.5015, ACC= 0.6037\n",
      "0.02076631784681787\n",
      "Training epoch 1261, recon_loss:0.609785, zinb_loss:0.958942, cluster_loss:0.332206\n",
      "Clustering   1261: AMI= 0.7201, NMI= 0.7210, ARI= 0.4967, ACC= 0.5970\n",
      "0.020399853414226964\n",
      "Training epoch 1262, recon_loss:0.608491, zinb_loss:0.959066, cluster_loss:0.332457\n",
      "Clustering   1262: AMI= 0.7215, NMI= 0.7225, ARI= 0.5014, ACC= 0.6038\n",
      "0.020522008225090597\n",
      "Training epoch 1263, recon_loss:0.608731, zinb_loss:0.958852, cluster_loss:0.332368\n",
      "Clustering   1263: AMI= 0.7202, NMI= 0.7211, ARI= 0.4966, ACC= 0.5970\n",
      "0.019911234170772424\n",
      "Training epoch 1264, recon_loss:0.609479, zinb_loss:0.959153, cluster_loss:0.332560\n",
      "Clustering   1264: AMI= 0.7215, NMI= 0.7224, ARI= 0.5012, ACC= 0.6034\n",
      "0.019300460116454254\n",
      "Training epoch 1265, recon_loss:0.609432, zinb_loss:0.958825, cluster_loss:0.332235\n",
      "Clustering   1265: AMI= 0.7202, NMI= 0.7211, ARI= 0.4969, ACC= 0.5971\n",
      "0.01938189665703001\n",
      "Training epoch 1266, recon_loss:0.608989, zinb_loss:0.959144, cluster_loss:0.332590\n",
      "Clustering   1266: AMI= 0.7214, NMI= 0.7223, ARI= 0.5010, ACC= 0.6036\n",
      "0.019341178386742132\n",
      "Training epoch 1267, recon_loss:0.608900, zinb_loss:0.958753, cluster_loss:0.332275\n",
      "Clustering   1267: AMI= 0.7203, NMI= 0.7213, ARI= 0.4973, ACC= 0.5973\n",
      "0.019300460116454254\n",
      "Training epoch 1268, recon_loss:0.609846, zinb_loss:0.959189, cluster_loss:0.332664\n",
      "Clustering   1268: AMI= 0.7213, NMI= 0.7222, ARI= 0.5009, ACC= 0.6034\n",
      "0.019015432224439105\n",
      "Training epoch 1269, recon_loss:0.609563, zinb_loss:0.958713, cluster_loss:0.332187\n",
      "Clustering   1269: AMI= 0.7205, NMI= 0.7214, ARI= 0.4977, ACC= 0.5976\n",
      "0.018771122602711836\n",
      "Training epoch 1270, recon_loss:0.608372, zinb_loss:0.959100, cluster_loss:0.332655\n",
      "Clustering   1270: AMI= 0.7212, NMI= 0.7221, ARI= 0.5005, ACC= 0.6031\n",
      "0.01799747546724215\n",
      "Training epoch 1271, recon_loss:0.608409, zinb_loss:0.958653, cluster_loss:0.332433\n",
      "Clustering   1271: AMI= 0.7204, NMI= 0.7213, ARI= 0.4975, ACC= 0.5977\n",
      "0.017020236980333076\n",
      "Training epoch 1272, recon_loss:0.609182, zinb_loss:0.959179, cluster_loss:0.332745\n",
      "Clustering   1272: AMI= 0.7213, NMI= 0.7222, ARI= 0.5007, ACC= 0.6034\n",
      "0.01649089946659066\n",
      "Training epoch 1273, recon_loss:0.608848, zinb_loss:0.958660, cluster_loss:0.332417\n",
      "Clustering   1273: AMI= 0.7203, NMI= 0.7212, ARI= 0.4977, ACC= 0.5977\n",
      "0.016083716763711876\n",
      "Training epoch 1274, recon_loss:0.608525, zinb_loss:0.959191, cluster_loss:0.332759\n",
      "Clustering   1274: AMI= 0.7211, NMI= 0.7220, ARI= 0.5000, ACC= 0.6027\n",
      "0.015310069628242192\n",
      "Training epoch 1275, recon_loss:0.608166, zinb_loss:0.958665, cluster_loss:0.332587\n",
      "Clustering   1275: AMI= 0.7204, NMI= 0.7213, ARI= 0.4977, ACC= 0.5976\n",
      "0.014740013844211898\n",
      "Training epoch 1276, recon_loss:0.609246, zinb_loss:0.959346, cluster_loss:0.332772\n",
      "Clustering   1276: AMI= 0.7212, NMI= 0.7221, ARI= 0.5000, ACC= 0.6026\n",
      "0.014210676330469482\n",
      "Training epoch 1277, recon_loss:0.608658, zinb_loss:0.958772, cluster_loss:0.332623\n",
      "Clustering   1277: AMI= 0.7206, NMI= 0.7216, ARI= 0.4980, ACC= 0.5979\n",
      "0.014007084979030091\n",
      "Training epoch 1278, recon_loss:0.607971, zinb_loss:0.959479, cluster_loss:0.332657\n",
      "Clustering   1278: AMI= 0.7210, NMI= 0.7220, ARI= 0.4998, ACC= 0.6026\n",
      "0.013559184005863431\n",
      "Training epoch 1279, recon_loss:0.607707, zinb_loss:0.958985, cluster_loss:0.332910\n",
      "Clustering   1279: AMI= 0.7207, NMI= 0.7216, ARI= 0.4979, ACC= 0.5979\n",
      "0.013722057087014943\n",
      "Training epoch 1280, recon_loss:0.608893, zinb_loss:0.959941, cluster_loss:0.332506\n",
      "Clustering   1280: AMI= 0.7214, NMI= 0.7223, ARI= 0.5002, ACC= 0.6029\n",
      "0.014943605195651289\n",
      "Training epoch 1281, recon_loss:0.608192, zinb_loss:0.959417, cluster_loss:0.332918\n",
      "Clustering   1281: AMI= 0.7207, NMI= 0.7217, ARI= 0.4979, ACC= 0.5981\n",
      "0.015717252331120975\n",
      "Training epoch 1282, recon_loss:0.608470, zinb_loss:0.960490, cluster_loss:0.332186\n",
      "Clustering   1282: AMI= 0.7215, NMI= 0.7224, ARI= 0.5003, ACC= 0.6032\n",
      "0.017264546602060345\n",
      "Training epoch 1283, recon_loss:0.607915, zinb_loss:0.959982, cluster_loss:0.333033\n",
      "Clustering   1283: AMI= 0.7207, NMI= 0.7216, ARI= 0.4977, ACC= 0.5979\n",
      "0.0195854880084694\n",
      "Training epoch 1284, recon_loss:0.609229, zinb_loss:0.961201, cluster_loss:0.331803\n",
      "Clustering   1284: AMI= 0.7215, NMI= 0.7224, ARI= 0.5007, ACC= 0.6033\n",
      "0.02227289384746936\n",
      "Training epoch 1285, recon_loss:0.608332, zinb_loss:0.960533, cluster_loss:0.332969\n",
      "Clustering   1285: AMI= 0.7208, NMI= 0.7217, ARI= 0.4981, ACC= 0.5985\n",
      "0.024756708335029926\n",
      "Training epoch 1286, recon_loss:0.608745, zinb_loss:0.961606, cluster_loss:0.331453\n",
      "Clustering   1286: AMI= 0.7217, NMI= 0.7226, ARI= 0.5014, ACC= 0.6039\n",
      "0.02585610163280264\n",
      "Training epoch 1287, recon_loss:0.608059, zinb_loss:0.960757, cluster_loss:0.333014\n",
      "Clustering   1287: AMI= 0.7211, NMI= 0.7220, ARI= 0.4982, ACC= 0.5990\n",
      "0.02671118530884808\n",
      "Training epoch 1288, recon_loss:0.609013, zinb_loss:0.961639, cluster_loss:0.331300\n",
      "Clustering   1288: AMI= 0.7219, NMI= 0.7228, ARI= 0.5017, ACC= 0.6041\n",
      "0.02785129687690867\n",
      "Training epoch 1289, recon_loss:0.608018, zinb_loss:0.960655, cluster_loss:0.333000\n",
      "Clustering   1289: AMI= 0.7211, NMI= 0.7220, ARI= 0.4983, ACC= 0.5990\n",
      "0.028217761309499573\n",
      "Training epoch 1290, recon_loss:0.608430, zinb_loss:0.961262, cluster_loss:0.331342\n",
      "Clustering   1290: AMI= 0.7217, NMI= 0.7226, ARI= 0.5014, ACC= 0.6038\n",
      "0.027932733417484427\n",
      "Training epoch 1291, recon_loss:0.607707, zinb_loss:0.960346, cluster_loss:0.333128\n",
      "Clustering   1291: AMI= 0.7210, NMI= 0.7219, ARI= 0.4981, ACC= 0.5990\n",
      "0.027525550714605645\n",
      "Training epoch 1292, recon_loss:0.608447, zinb_loss:0.960817, cluster_loss:0.331466\n",
      "Clustering   1292: AMI= 0.7219, NMI= 0.7229, ARI= 0.5017, ACC= 0.6037\n",
      "0.027036931471151104\n",
      "Training epoch 1293, recon_loss:0.607682, zinb_loss:0.960037, cluster_loss:0.333223\n",
      "Clustering   1293: AMI= 0.7209, NMI= 0.7218, ARI= 0.4980, ACC= 0.5991\n",
      "0.026670467038560203\n",
      "Training epoch 1294, recon_loss:0.608053, zinb_loss:0.960364, cluster_loss:0.331605\n",
      "Clustering   1294: AMI= 0.7218, NMI= 0.7227, ARI= 0.5013, ACC= 0.6031\n",
      "0.02605969298424203\n",
      "Training epoch 1295, recon_loss:0.607578, zinb_loss:0.959771, cluster_loss:0.333363\n",
      "Clustering   1295: AMI= 0.7209, NMI= 0.7218, ARI= 0.4980, ACC= 0.5994\n",
      "0.02585610163280264\n",
      "Training epoch 1296, recon_loss:0.608236, zinb_loss:0.960011, cluster_loss:0.331725\n",
      "Clustering   1296: AMI= 0.7219, NMI= 0.7228, ARI= 0.5012, ACC= 0.6028\n",
      "0.024960299686469317\n",
      "Training epoch 1297, recon_loss:0.607789, zinb_loss:0.959576, cluster_loss:0.333434\n",
      "Clustering   1297: AMI= 0.7207, NMI= 0.7217, ARI= 0.4977, ACC= 0.5992\n",
      "0.024878863145893562\n",
      "Training epoch 1298, recon_loss:0.608076, zinb_loss:0.959694, cluster_loss:0.331808\n",
      "Clustering   1298: AMI= 0.7219, NMI= 0.7228, ARI= 0.5012, ACC= 0.6027\n",
      "0.02467527179445417\n",
      "Training epoch 1299, recon_loss:0.607870, zinb_loss:0.959431, cluster_loss:0.333530\n",
      "Clustering   1299: AMI= 0.7207, NMI= 0.7216, ARI= 0.4975, ACC= 0.5995\n",
      "0.02455311698359054\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training epoch 1300, recon_loss:0.608238, zinb_loss:0.959453, cluster_loss:0.331865\n",
      "Clustering   1300: AMI= 0.7217, NMI= 0.7226, ARI= 0.5010, ACC= 0.6024\n",
      "0.02402377946984812\n",
      "Training epoch 1301, recon_loss:0.608138, zinb_loss:0.959347, cluster_loss:0.333585\n",
      "Clustering   1301: AMI= 0.7206, NMI= 0.7215, ARI= 0.4973, ACC= 0.5995\n",
      "0.024145934280711757\n",
      "Training epoch 1302, recon_loss:0.608323, zinb_loss:0.959256, cluster_loss:0.331898\n",
      "Clustering   1302: AMI= 0.7217, NMI= 0.7227, ARI= 0.5010, ACC= 0.6022\n",
      "0.024390243902439025\n",
      "Training epoch 1303, recon_loss:0.608375, zinb_loss:0.959306, cluster_loss:0.333625\n",
      "Clustering   1303: AMI= 0.7206, NMI= 0.7215, ARI= 0.4970, ACC= 0.5994\n",
      "0.024838144875605685\n",
      "Training epoch 1304, recon_loss:0.608550, zinb_loss:0.959109, cluster_loss:0.331922\n",
      "Clustering   1304: AMI= 0.7218, NMI= 0.7227, ARI= 0.5011, ACC= 0.6021\n",
      "0.025163891037908708\n",
      "Training epoch 1305, recon_loss:0.608705, zinb_loss:0.959319, cluster_loss:0.333635\n",
      "Clustering   1305: AMI= 0.7205, NMI= 0.7214, ARI= 0.4968, ACC= 0.5995\n",
      "0.02557107374078749\n",
      "Training epoch 1306, recon_loss:0.608463, zinb_loss:0.958998, cluster_loss:0.331929\n",
      "Clustering   1306: AMI= 0.7219, NMI= 0.7228, ARI= 0.5013, ACC= 0.6020\n",
      "0.02626328433568142\n",
      "Training epoch 1307, recon_loss:0.608797, zinb_loss:0.959381, cluster_loss:0.333653\n",
      "Clustering   1307: AMI= 0.7205, NMI= 0.7214, ARI= 0.4968, ACC= 0.5997\n",
      "0.02671118530884808\n",
      "Training epoch 1308, recon_loss:0.608812, zinb_loss:0.958955, cluster_loss:0.331918\n",
      "Clustering   1308: AMI= 0.7219, NMI= 0.7228, ARI= 0.5015, ACC= 0.6019\n",
      "0.027199804552302618\n",
      "Training epoch 1309, recon_loss:0.609204, zinb_loss:0.959506, cluster_loss:0.333587\n",
      "Clustering   1309: AMI= 0.7207, NMI= 0.7216, ARI= 0.4968, ACC= 0.5999\n",
      "0.027525550714605645\n",
      "Training epoch 1310, recon_loss:0.608846, zinb_loss:0.958954, cluster_loss:0.331905\n",
      "Clustering   1310: AMI= 0.7216, NMI= 0.7225, ARI= 0.5011, ACC= 0.6017\n",
      "0.027892015147196546\n",
      "Training epoch 1311, recon_loss:0.609329, zinb_loss:0.959640, cluster_loss:0.333517\n",
      "Clustering   1311: AMI= 0.7203, NMI= 0.7212, ARI= 0.4964, ACC= 0.5996\n",
      "0.028217761309499573\n",
      "Training epoch 1312, recon_loss:0.609243, zinb_loss:0.958997, cluster_loss:0.331916\n",
      "Clustering   1312: AMI= 0.7216, NMI= 0.7226, ARI= 0.5010, ACC= 0.6014\n",
      "0.028421352660938964\n",
      "Training epoch 1313, recon_loss:0.609705, zinb_loss:0.959788, cluster_loss:0.333378\n",
      "Clustering   1313: AMI= 0.7201, NMI= 0.7210, ARI= 0.4962, ACC= 0.5995\n",
      "0.028177043039211695\n",
      "Training epoch 1314, recon_loss:0.608921, zinb_loss:0.959041, cluster_loss:0.331966\n",
      "Clustering   1314: AMI= 0.7216, NMI= 0.7225, ARI= 0.5009, ACC= 0.6014\n",
      "0.028624944012378355\n",
      "Training epoch 1315, recon_loss:0.609462, zinb_loss:0.959897, cluster_loss:0.333293\n",
      "Clustering   1315: AMI= 0.7200, NMI= 0.7209, ARI= 0.4961, ACC= 0.5995\n",
      "0.028136324768923818\n",
      "Training epoch 1316, recon_loss:0.609293, zinb_loss:0.959102, cluster_loss:0.332077\n",
      "Clustering   1316: AMI= 0.7215, NMI= 0.7225, ARI= 0.5009, ACC= 0.6013\n",
      "0.027892015147196546\n",
      "Training epoch 1317, recon_loss:0.609771, zinb_loss:0.959982, cluster_loss:0.333126\n",
      "Clustering   1317: AMI= 0.7198, NMI= 0.7208, ARI= 0.4961, ACC= 0.5993\n",
      "0.027688423795757155\n",
      "Training epoch 1318, recon_loss:0.608712, zinb_loss:0.959144, cluster_loss:0.332236\n",
      "Clustering   1318: AMI= 0.7216, NMI= 0.7225, ARI= 0.5008, ACC= 0.6012\n",
      "0.027118368011726863\n",
      "Training epoch 1319, recon_loss:0.609243, zinb_loss:0.959980, cluster_loss:0.333026\n",
      "Clustering   1319: AMI= 0.7198, NMI= 0.7207, ARI= 0.4963, ACC= 0.5992\n",
      "0.026344720876257176\n",
      "Training epoch 1320, recon_loss:0.609288, zinb_loss:0.959199, cluster_loss:0.332439\n",
      "Clustering   1320: AMI= 0.7213, NMI= 0.7222, ARI= 0.5002, ACC= 0.6007\n",
      "0.02585610163280264\n",
      "Training epoch 1321, recon_loss:0.609782, zinb_loss:0.959974, cluster_loss:0.332774\n",
      "Clustering   1321: AMI= 0.7198, NMI= 0.7208, ARI= 0.4965, ACC= 0.5994\n",
      "0.025082454497332953\n",
      "Training epoch 1322, recon_loss:0.608222, zinb_loss:0.959230, cluster_loss:0.332613\n",
      "Clustering   1322: AMI= 0.7213, NMI= 0.7222, ARI= 0.5000, ACC= 0.6008\n",
      "0.024878863145893562\n",
      "Training epoch 1323, recon_loss:0.608797, zinb_loss:0.959888, cluster_loss:0.332684\n",
      "Clustering   1323: AMI= 0.7196, NMI= 0.7205, ARI= 0.4964, ACC= 0.5993\n",
      "0.024593835253878416\n",
      "Training epoch 1324, recon_loss:0.609228, zinb_loss:0.959290, cluster_loss:0.332852\n",
      "Clustering   1324: AMI= 0.7211, NMI= 0.7221, ARI= 0.4995, ACC= 0.6004\n",
      "0.023860906388696607\n",
      "Training epoch 1325, recon_loss:0.609616, zinb_loss:0.959867, cluster_loss:0.332413\n",
      "Clustering   1325: AMI= 0.7197, NMI= 0.7206, ARI= 0.4971, ACC= 0.5997\n",
      "0.023698033307545094\n",
      "Training epoch 1326, recon_loss:0.607635, zinb_loss:0.959324, cluster_loss:0.333024\n",
      "Clustering   1326: AMI= 0.7212, NMI= 0.7221, ARI= 0.4993, ACC= 0.6006\n",
      "0.023005822712651166\n",
      "Training epoch 1327, recon_loss:0.608238, zinb_loss:0.959755, cluster_loss:0.332483\n",
      "Clustering   1327: AMI= 0.7199, NMI= 0.7208, ARI= 0.4974, ACC= 0.5998\n",
      "0.022150739036605725\n",
      "Training epoch 1328, recon_loss:0.608104, zinb_loss:0.959371, cluster_loss:0.333250\n",
      "Clustering   1328: AMI= 0.7212, NMI= 0.7221, ARI= 0.4992, ACC= 0.6006\n",
      "0.021295655360560283\n",
      "Training epoch 1329, recon_loss:0.608435, zinb_loss:0.959727, cluster_loss:0.332318\n",
      "Clustering   1329: AMI= 0.7198, NMI= 0.7207, ARI= 0.4982, ACC= 0.6004\n",
      "0.021662119793151188\n",
      "Training epoch 1330, recon_loss:0.608364, zinb_loss:0.959492, cluster_loss:0.333398\n",
      "Clustering   1330: AMI= 0.7210, NMI= 0.7220, ARI= 0.4985, ACC= 0.6002\n",
      "0.022557921739484506\n",
      "Training epoch 1331, recon_loss:0.608483, zinb_loss:0.959691, cluster_loss:0.332146\n",
      "Clustering   1331: AMI= 0.7200, NMI= 0.7210, ARI= 0.4991, ACC= 0.6007\n",
      "0.023535160226393584\n",
      "Training epoch 1332, recon_loss:0.607964, zinb_loss:0.959563, cluster_loss:0.333482\n",
      "Clustering   1332: AMI= 0.7209, NMI= 0.7218, ARI= 0.4977, ACC= 0.6001\n",
      "0.023901624658984485\n",
      "Training epoch 1333, recon_loss:0.608092, zinb_loss:0.959619, cluster_loss:0.332086\n",
      "Clustering   1333: AMI= 0.7201, NMI= 0.7210, ARI= 0.4988, ACC= 0.6004\n",
      "0.02426808909157539\n",
      "Training epoch 1334, recon_loss:0.608564, zinb_loss:0.959640, cluster_loss:0.333526\n",
      "Clustering   1334: AMI= 0.7207, NMI= 0.7216, ARI= 0.4973, ACC= 0.6000\n",
      "0.024512398713302658\n",
      "Training epoch 1335, recon_loss:0.608497, zinb_loss:0.959565, cluster_loss:0.331923\n",
      "Clustering   1335: AMI= 0.7204, NMI= 0.7214, ARI= 0.4994, ACC= 0.6005\n",
      "0.025489637200211735\n",
      "Training epoch 1336, recon_loss:0.608048, zinb_loss:0.959667, cluster_loss:0.333475\n",
      "Clustering   1336: AMI= 0.7206, NMI= 0.7215, ARI= 0.4970, ACC= 0.5999\n",
      "0.026181847795105663\n",
      "Training epoch 1337, recon_loss:0.608060, zinb_loss:0.959429, cluster_loss:0.331898\n",
      "Clustering   1337: AMI= 0.7207, NMI= 0.7217, ARI= 0.4996, ACC= 0.6006\n",
      "0.02626328433568142\n",
      "Training epoch 1338, recon_loss:0.608661, zinb_loss:0.959698, cluster_loss:0.333446\n",
      "Clustering   1338: AMI= 0.7204, NMI= 0.7213, ARI= 0.4967, ACC= 0.6000\n",
      "0.026344720876257176\n",
      "Training epoch 1339, recon_loss:0.608401, zinb_loss:0.959334, cluster_loss:0.331801\n",
      "Clustering   1339: AMI= 0.7208, NMI= 0.7217, ARI= 0.5000, ACC= 0.6010\n",
      "0.026792621849423836\n",
      "Training epoch 1340, recon_loss:0.608291, zinb_loss:0.959710, cluster_loss:0.333379\n",
      "Clustering   1340: AMI= 0.7202, NMI= 0.7212, ARI= 0.4967, ACC= 0.6001\n",
      "0.027281241092878373\n",
      "Training epoch 1341, recon_loss:0.608097, zinb_loss:0.959219, cluster_loss:0.331837\n",
      "Clustering   1341: AMI= 0.7208, NMI= 0.7217, ARI= 0.5002, ACC= 0.6012\n",
      "0.027566268984893522\n",
      "Training epoch 1342, recon_loss:0.608829, zinb_loss:0.959769, cluster_loss:0.333369\n",
      "Clustering   1342: AMI= 0.7204, NMI= 0.7213, ARI= 0.4968, ACC= 0.6003\n",
      "0.027444114174029886\n",
      "Training epoch 1343, recon_loss:0.608350, zinb_loss:0.959213, cluster_loss:0.331835\n",
      "Clustering   1343: AMI= 0.7209, NMI= 0.7218, ARI= 0.5007, ACC= 0.6021\n",
      "0.027281241092878373\n",
      "Training epoch 1344, recon_loss:0.608501, zinb_loss:0.959853, cluster_loss:0.333340\n",
      "Clustering   1344: AMI= 0.7202, NMI= 0.7212, ARI= 0.4964, ACC= 0.5998\n",
      "0.027240522822590495\n",
      "Training epoch 1345, recon_loss:0.608153, zinb_loss:0.959266, cluster_loss:0.331925\n",
      "Clustering   1345: AMI= 0.7210, NMI= 0.7219, ARI= 0.5012, ACC= 0.6028\n",
      "0.027199804552302618\n",
      "Training epoch 1346, recon_loss:0.609064, zinb_loss:0.960054, cluster_loss:0.333353\n",
      "Clustering   1346: AMI= 0.7201, NMI= 0.7211, ARI= 0.4959, ACC= 0.5991\n",
      "0.027036931471151104\n",
      "Training epoch 1347, recon_loss:0.608515, zinb_loss:0.959488, cluster_loss:0.331944\n",
      "Clustering   1347: AMI= 0.7210, NMI= 0.7219, ARI= 0.5015, ACC= 0.6034\n",
      "0.027892015147196546\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training epoch 1348, recon_loss:0.608811, zinb_loss:0.960314, cluster_loss:0.333319\n",
      "Clustering   1348: AMI= 0.7200, NMI= 0.7209, ARI= 0.4958, ACC= 0.5988\n",
      "0.028217761309499573\n",
      "Training epoch 1349, recon_loss:0.608484, zinb_loss:0.959792, cluster_loss:0.332018\n",
      "Clustering   1349: AMI= 0.7209, NMI= 0.7219, ARI= 0.5016, ACC= 0.6039\n",
      "0.028584225742090477\n",
      "Training epoch 1350, recon_loss:0.609421, zinb_loss:0.960684, cluster_loss:0.333282\n",
      "Clustering   1350: AMI= 0.7199, NMI= 0.7208, ARI= 0.4954, ACC= 0.5981\n",
      "0.029480027688423796\n",
      "Training epoch 1351, recon_loss:0.608945, zinb_loss:0.960221, cluster_loss:0.332000\n",
      "Clustering   1351: AMI= 0.7212, NMI= 0.7221, ARI= 0.5019, ACC= 0.6043\n",
      "0.030375829634757115\n",
      "Training epoch 1352, recon_loss:0.609311, zinb_loss:0.961008, cluster_loss:0.333196\n",
      "Clustering   1352: AMI= 0.7200, NMI= 0.7209, ARI= 0.4953, ACC= 0.5974\n",
      "0.03163809601368134\n",
      "Training epoch 1353, recon_loss:0.608977, zinb_loss:0.960575, cluster_loss:0.332029\n",
      "Clustering   1353: AMI= 0.7213, NMI= 0.7223, ARI= 0.5022, ACC= 0.6049\n",
      "0.032248870067999515\n",
      "Training epoch 1354, recon_loss:0.609695, zinb_loss:0.961203, cluster_loss:0.333139\n",
      "Clustering   1354: AMI= 0.7200, NMI= 0.7209, ARI= 0.4952, ACC= 0.5970\n",
      "0.032696771041166174\n",
      "Training epoch 1355, recon_loss:0.609232, zinb_loss:0.960812, cluster_loss:0.332047\n",
      "Clustering   1355: AMI= 0.7213, NMI= 0.7222, ARI= 0.5021, ACC= 0.6048\n",
      "0.03277820758174193\n",
      "Training epoch 1356, recon_loss:0.609215, zinb_loss:0.961139, cluster_loss:0.333083\n",
      "Clustering   1356: AMI= 0.7196, NMI= 0.7205, ARI= 0.4949, ACC= 0.5963\n",
      "0.03318539028462071\n",
      "Training epoch 1357, recon_loss:0.609020, zinb_loss:0.960824, cluster_loss:0.332143\n",
      "Clustering   1357: AMI= 0.7215, NMI= 0.7224, ARI= 0.5020, ACC= 0.6049\n",
      "0.03273748931145405\n",
      "Training epoch 1358, recon_loss:0.609429, zinb_loss:0.960903, cluster_loss:0.333078\n",
      "Clustering   1358: AMI= 0.7198, NMI= 0.7207, ARI= 0.4952, ACC= 0.5964\n",
      "0.032126715257135875\n",
      "Training epoch 1359, recon_loss:0.609209, zinb_loss:0.960728, cluster_loss:0.332195\n",
      "Clustering   1359: AMI= 0.7216, NMI= 0.7225, ARI= 0.5021, ACC= 0.6049\n",
      "0.03163809601368134\n",
      "Training epoch 1360, recon_loss:0.609096, zinb_loss:0.960520, cluster_loss:0.333059\n",
      "Clustering   1360: AMI= 0.7195, NMI= 0.7204, ARI= 0.4953, ACC= 0.5965\n",
      "0.031108758499938924\n",
      "Training epoch 1361, recon_loss:0.609073, zinb_loss:0.960504, cluster_loss:0.332297\n",
      "Clustering   1361: AMI= 0.7214, NMI= 0.7223, ARI= 0.5018, ACC= 0.6047\n",
      "0.02980577385072682\n",
      "Training epoch 1362, recon_loss:0.609437, zinb_loss:0.960117, cluster_loss:0.333047\n",
      "Clustering   1362: AMI= 0.7194, NMI= 0.7203, ARI= 0.4954, ACC= 0.5964\n",
      "0.028869253634105623\n",
      "Training epoch 1363, recon_loss:0.609406, zinb_loss:0.960272, cluster_loss:0.332350\n",
      "Clustering   1363: AMI= 0.7212, NMI= 0.7221, ARI= 0.5016, ACC= 0.6043\n",
      "0.028014169958060182\n",
      "Training epoch 1364, recon_loss:0.609037, zinb_loss:0.959677, cluster_loss:0.332996\n",
      "Clustering   1364: AMI= 0.7194, NMI= 0.7204, ARI= 0.4958, ACC= 0.5973\n",
      "0.026792621849423836\n",
      "Training epoch 1365, recon_loss:0.609155, zinb_loss:0.959978, cluster_loss:0.332448\n",
      "Clustering   1365: AMI= 0.7210, NMI= 0.7219, ARI= 0.5007, ACC= 0.6032\n",
      "0.0253674823893481\n",
      "Training epoch 1366, recon_loss:0.609652, zinb_loss:0.959330, cluster_loss:0.332991\n",
      "Clustering   1366: AMI= 0.7195, NMI= 0.7204, ARI= 0.4962, ACC= 0.5980\n",
      "0.024105216010423876\n",
      "Training epoch 1367, recon_loss:0.609606, zinb_loss:0.959745, cluster_loss:0.332435\n",
      "Clustering   1367: AMI= 0.7209, NMI= 0.7218, ARI= 0.5001, ACC= 0.6023\n",
      "0.02247648519890875\n",
      "Training epoch 1368, recon_loss:0.608894, zinb_loss:0.958987, cluster_loss:0.332955\n",
      "Clustering   1368: AMI= 0.7194, NMI= 0.7203, ARI= 0.4964, ACC= 0.5985\n",
      "0.020847754387393624\n",
      "Training epoch 1369, recon_loss:0.608959, zinb_loss:0.959463, cluster_loss:0.332509\n",
      "Clustering   1369: AMI= 0.7208, NMI= 0.7217, ARI= 0.4995, ACC= 0.6013\n",
      "0.019300460116454254\n",
      "Training epoch 1370, recon_loss:0.609403, zinb_loss:0.958779, cluster_loss:0.333045\n",
      "Clustering   1370: AMI= 0.7194, NMI= 0.7203, ARI= 0.4966, ACC= 0.5988\n",
      "0.01754957449407549\n",
      "Training epoch 1371, recon_loss:0.609173, zinb_loss:0.959274, cluster_loss:0.332498\n",
      "Clustering   1371: AMI= 0.7208, NMI= 0.7218, ARI= 0.4994, ACC= 0.6010\n",
      "0.01645018119630278\n",
      "Training epoch 1372, recon_loss:0.608428, zinb_loss:0.958589, cluster_loss:0.333129\n",
      "Clustering   1372: AMI= 0.7194, NMI= 0.7203, ARI= 0.4966, ACC= 0.5996\n",
      "0.01490288692536341\n",
      "Training epoch 1373, recon_loss:0.608404, zinb_loss:0.959074, cluster_loss:0.332614\n",
      "Clustering   1373: AMI= 0.7208, NMI= 0.7217, ARI= 0.4992, ACC= 0.6005\n",
      "0.013966366708742213\n",
      "Training epoch 1374, recon_loss:0.608800, zinb_loss:0.958515, cluster_loss:0.333310\n",
      "Clustering   1374: AMI= 0.7195, NMI= 0.7204, ARI= 0.4969, ACC= 0.6002\n",
      "0.01311128303269677\n",
      "Training epoch 1375, recon_loss:0.608586, zinb_loss:0.958985, cluster_loss:0.332636\n",
      "Clustering   1375: AMI= 0.7208, NMI= 0.7217, ARI= 0.4993, ACC= 0.6000\n",
      "0.01311128303269677\n",
      "Training epoch 1376, recon_loss:0.608093, zinb_loss:0.958448, cluster_loss:0.333427\n",
      "Clustering   1376: AMI= 0.7194, NMI= 0.7204, ARI= 0.4969, ACC= 0.6004\n",
      "0.012744818600105868\n",
      "Training epoch 1377, recon_loss:0.608107, zinb_loss:0.958896, cluster_loss:0.332733\n",
      "Clustering   1377: AMI= 0.7207, NMI= 0.7217, ARI= 0.4991, ACC= 0.5996\n",
      "0.012581945518954354\n",
      "Training epoch 1378, recon_loss:0.608493, zinb_loss:0.958454, cluster_loss:0.333574\n",
      "Clustering   1378: AMI= 0.7194, NMI= 0.7203, ARI= 0.4970, ACC= 0.6007\n",
      "0.012785536870393745\n",
      "Training epoch 1379, recon_loss:0.608381, zinb_loss:0.958882, cluster_loss:0.332743\n",
      "Clustering   1379: AMI= 0.7207, NMI= 0.7217, ARI= 0.4991, ACC= 0.5992\n",
      "0.013396310924711918\n",
      "Training epoch 1380, recon_loss:0.608152, zinb_loss:0.958459, cluster_loss:0.333647\n",
      "Clustering   1380: AMI= 0.7194, NMI= 0.7203, ARI= 0.4968, ACC= 0.6008\n",
      "0.013599902276151309\n",
      "Training epoch 1381, recon_loss:0.608182, zinb_loss:0.958878, cluster_loss:0.332789\n",
      "Clustering   1381: AMI= 0.7207, NMI= 0.7216, ARI= 0.4986, ACC= 0.5982\n",
      "0.014373549411620994\n",
      "Training epoch 1382, recon_loss:0.608621, zinb_loss:0.958526, cluster_loss:0.333715\n",
      "Clustering   1382: AMI= 0.7195, NMI= 0.7204, ARI= 0.4971, ACC= 0.6015\n",
      "0.01469929557392402\n",
      "Training epoch 1383, recon_loss:0.608563, zinb_loss:0.958937, cluster_loss:0.332771\n",
      "Clustering   1383: AMI= 0.7207, NMI= 0.7216, ARI= 0.4986, ACC= 0.5979\n",
      "0.015432224439105826\n",
      "Training epoch 1384, recon_loss:0.608235, zinb_loss:0.958602, cluster_loss:0.333701\n",
      "Clustering   1384: AMI= 0.7196, NMI= 0.7205, ARI= 0.4976, ACC= 0.6022\n",
      "0.01600228022313612\n",
      "Training epoch 1385, recon_loss:0.608342, zinb_loss:0.959019, cluster_loss:0.332809\n",
      "Clustering   1385: AMI= 0.7207, NMI= 0.7216, ARI= 0.4983, ACC= 0.5973\n",
      "0.016042998493424\n",
      "Training epoch 1386, recon_loss:0.608834, zinb_loss:0.958780, cluster_loss:0.333681\n",
      "Clustering   1386: AMI= 0.7197, NMI= 0.7207, ARI= 0.4981, ACC= 0.6031\n",
      "0.016816645628893685\n",
      "Training epoch 1387, recon_loss:0.608804, zinb_loss:0.959191, cluster_loss:0.332772\n",
      "Clustering   1387: AMI= 0.7206, NMI= 0.7215, ARI= 0.4981, ACC= 0.5969\n",
      "0.017020236980333076\n",
      "Training epoch 1388, recon_loss:0.608437, zinb_loss:0.958993, cluster_loss:0.333556\n",
      "Clustering   1388: AMI= 0.7198, NMI= 0.7208, ARI= 0.4985, ACC= 0.6038\n",
      "0.017956757196954273\n",
      "Training epoch 1389, recon_loss:0.608555, zinb_loss:0.959390, cluster_loss:0.332784\n",
      "Clustering   1389: AMI= 0.7204, NMI= 0.7213, ARI= 0.4978, ACC= 0.5962\n",
      "0.018160348548393664\n",
      "Training epoch 1390, recon_loss:0.609077, zinb_loss:0.959327, cluster_loss:0.333418\n",
      "Clustering   1390: AMI= 0.7199, NMI= 0.7208, ARI= 0.4993, ACC= 0.6048\n",
      "0.018811840872999714\n",
      "Training epoch 1391, recon_loss:0.608954, zinb_loss:0.959662, cluster_loss:0.332711\n",
      "Clustering   1391: AMI= 0.7202, NMI= 0.7211, ARI= 0.4974, ACC= 0.5954\n",
      "0.018893277413575472\n",
      "Training epoch 1392, recon_loss:0.608487, zinb_loss:0.959654, cluster_loss:0.333186\n",
      "Clustering   1392: AMI= 0.7202, NMI= 0.7211, ARI= 0.4999, ACC= 0.6057\n",
      "0.019504051467893645\n",
      "Training epoch 1393, recon_loss:0.608480, zinb_loss:0.959866, cluster_loss:0.332716\n",
      "Clustering   1393: AMI= 0.7201, NMI= 0.7210, ARI= 0.4973, ACC= 0.5951\n",
      "0.020359135143939087\n",
      "Training epoch 1394, recon_loss:0.608964, zinb_loss:0.960000, cluster_loss:0.333008\n",
      "Clustering   1394: AMI= 0.7204, NMI= 0.7213, ARI= 0.5003, ACC= 0.6062\n",
      "0.020725599576529988\n",
      "Training epoch 1395, recon_loss:0.608581, zinb_loss:0.960024, cluster_loss:0.332661\n",
      "Clustering   1395: AMI= 0.7201, NMI= 0.7211, ARI= 0.4976, ACC= 0.5951\n",
      "0.021377091901136038\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training epoch 1396, recon_loss:0.608302, zinb_loss:0.960194, cluster_loss:0.332823\n",
      "Clustering   1396: AMI= 0.7205, NMI= 0.7215, ARI= 0.5007, ACC= 0.6067\n",
      "0.022395048658332993\n",
      "Training epoch 1397, recon_loss:0.607963, zinb_loss:0.960024, cluster_loss:0.332733\n",
      "Clustering   1397: AMI= 0.7200, NMI= 0.7209, ARI= 0.4974, ACC= 0.5952\n",
      "0.022680076550348142\n",
      "Training epoch 1398, recon_loss:0.608444, zinb_loss:0.960312, cluster_loss:0.332760\n",
      "Clustering   1398: AMI= 0.7204, NMI= 0.7214, ARI= 0.5008, ACC= 0.6067\n",
      "0.023453723685817825\n",
      "Training epoch 1399, recon_loss:0.607860, zinb_loss:0.960000, cluster_loss:0.332796\n",
      "Clustering   1399: AMI= 0.7198, NMI= 0.7207, ARI= 0.4972, ACC= 0.5955\n",
      "0.023290850604666315\n",
      "Training epoch 1400, recon_loss:0.607839, zinb_loss:0.960330, cluster_loss:0.332713\n",
      "Clustering   1400: AMI= 0.7205, NMI= 0.7214, ARI= 0.5009, ACC= 0.6065\n",
      "0.023494441956105706\n",
      "Training epoch 1401, recon_loss:0.607381, zinb_loss:0.959911, cluster_loss:0.332931\n",
      "Clustering   1401: AMI= 0.7198, NMI= 0.7207, ARI= 0.4970, ACC= 0.5956\n",
      "0.023331568874954193\n",
      "Training epoch 1402, recon_loss:0.608067, zinb_loss:0.960354, cluster_loss:0.332730\n",
      "Clustering   1402: AMI= 0.7206, NMI= 0.7215, ARI= 0.5009, ACC= 0.6064\n",
      "0.023494441956105706\n",
      "Training epoch 1403, recon_loss:0.607445, zinb_loss:0.959843, cluster_loss:0.332987\n",
      "Clustering   1403: AMI= 0.7197, NMI= 0.7207, ARI= 0.4968, ACC= 0.5959\n",
      "0.023209414064090557\n",
      "Training epoch 1404, recon_loss:0.607790, zinb_loss:0.960325, cluster_loss:0.332720\n",
      "Clustering   1404: AMI= 0.7205, NMI= 0.7214, ARI= 0.5009, ACC= 0.6061\n",
      "0.02316869579380268\n",
      "Training epoch 1405, recon_loss:0.607267, zinb_loss:0.959748, cluster_loss:0.333057\n",
      "Clustering   1405: AMI= 0.7199, NMI= 0.7208, ARI= 0.4970, ACC= 0.5963\n",
      "0.022842949631499652\n",
      "Training epoch 1406, recon_loss:0.608228, zinb_loss:0.960305, cluster_loss:0.332739\n",
      "Clustering   1406: AMI= 0.7209, NMI= 0.7218, ARI= 0.5013, ACC= 0.6059\n",
      "0.02263935828006026\n",
      "Training epoch 1407, recon_loss:0.607551, zinb_loss:0.959650, cluster_loss:0.333041\n",
      "Clustering   1407: AMI= 0.7197, NMI= 0.7206, ARI= 0.4967, ACC= 0.5966\n",
      "0.022680076550348142\n",
      "Training epoch 1408, recon_loss:0.608040, zinb_loss:0.960209, cluster_loss:0.332728\n",
      "Clustering   1408: AMI= 0.7208, NMI= 0.7218, ARI= 0.5011, ACC= 0.6052\n",
      "0.02272079482063602\n",
      "Training epoch 1409, recon_loss:0.607448, zinb_loss:0.959530, cluster_loss:0.333055\n",
      "Clustering   1409: AMI= 0.7195, NMI= 0.7204, ARI= 0.4965, ACC= 0.5969\n",
      "0.022883667901787533\n",
      "Training epoch 1410, recon_loss:0.608474, zinb_loss:0.960114, cluster_loss:0.332739\n",
      "Clustering   1410: AMI= 0.7210, NMI= 0.7219, ARI= 0.5011, ACC= 0.6050\n",
      "0.022680076550348142\n",
      "Training epoch 1411, recon_loss:0.607766, zinb_loss:0.959422, cluster_loss:0.333011\n",
      "Clustering   1411: AMI= 0.7195, NMI= 0.7205, ARI= 0.4965, ACC= 0.5972\n",
      "0.02251720346919663\n",
      "Training epoch 1412, recon_loss:0.608172, zinb_loss:0.959952, cluster_loss:0.332724\n",
      "Clustering   1412: AMI= 0.7210, NMI= 0.7219, ARI= 0.5010, ACC= 0.6045\n",
      "0.022232175577181483\n",
      "Training epoch 1413, recon_loss:0.607618, zinb_loss:0.959311, cluster_loss:0.333046\n",
      "Clustering   1413: AMI= 0.7194, NMI= 0.7203, ARI= 0.4965, ACC= 0.5973\n",
      "0.02247648519890875\n",
      "Training epoch 1414, recon_loss:0.608541, zinb_loss:0.959822, cluster_loss:0.332750\n",
      "Clustering   1414: AMI= 0.7210, NMI= 0.7219, ARI= 0.5009, ACC= 0.6043\n",
      "0.022150739036605725\n",
      "Training epoch 1415, recon_loss:0.607895, zinb_loss:0.959233, cluster_loss:0.333039\n",
      "Clustering   1415: AMI= 0.7194, NMI= 0.7204, ARI= 0.4967, ACC= 0.5976\n",
      "0.022232175577181483\n",
      "Training epoch 1416, recon_loss:0.608129, zinb_loss:0.959655, cluster_loss:0.332751\n",
      "Clustering   1416: AMI= 0.7209, NMI= 0.7218, ARI= 0.5007, ACC= 0.6040\n",
      "0.02272079482063602\n",
      "Training epoch 1417, recon_loss:0.607697, zinb_loss:0.959172, cluster_loss:0.333110\n",
      "Clustering   1417: AMI= 0.7194, NMI= 0.7203, ARI= 0.4966, ACC= 0.5978\n",
      "0.022802231361211775\n",
      "Training epoch 1418, recon_loss:0.608319, zinb_loss:0.959542, cluster_loss:0.332785\n",
      "Clustering   1418: AMI= 0.7211, NMI= 0.7220, ARI= 0.5011, ACC= 0.6042\n",
      "0.023250132334378434\n",
      "Training epoch 1419, recon_loss:0.607894, zinb_loss:0.959161, cluster_loss:0.333143\n",
      "Clustering   1419: AMI= 0.7195, NMI= 0.7204, ARI= 0.4966, ACC= 0.5979\n",
      "0.02357587849668146\n",
      "Training epoch 1420, recon_loss:0.608097, zinb_loss:0.959413, cluster_loss:0.332790\n",
      "Clustering   1420: AMI= 0.7213, NMI= 0.7222, ARI= 0.5014, ACC= 0.6043\n",
      "0.024145934280711757\n",
      "Training epoch 1421, recon_loss:0.607870, zinb_loss:0.959184, cluster_loss:0.333213\n",
      "Clustering   1421: AMI= 0.7194, NMI= 0.7203, ARI= 0.4963, ACC= 0.5978\n",
      "0.024634553524166294\n",
      "Training epoch 1422, recon_loss:0.607975, zinb_loss:0.959328, cluster_loss:0.332792\n",
      "Clustering   1422: AMI= 0.7214, NMI= 0.7223, ARI= 0.5019, ACC= 0.6048\n",
      "0.025815383362514762\n",
      "Training epoch 1423, recon_loss:0.607976, zinb_loss:0.959276, cluster_loss:0.333276\n",
      "Clustering   1423: AMI= 0.7192, NMI= 0.7202, ARI= 0.4956, ACC= 0.5972\n",
      "0.026589030497984445\n",
      "Training epoch 1424, recon_loss:0.608015, zinb_loss:0.959283, cluster_loss:0.332755\n",
      "Clustering   1424: AMI= 0.7215, NMI= 0.7224, ARI= 0.5024, ACC= 0.6052\n",
      "0.026996213200863227\n",
      "Training epoch 1425, recon_loss:0.608227, zinb_loss:0.959426, cluster_loss:0.333312\n",
      "Clustering   1425: AMI= 0.7191, NMI= 0.7201, ARI= 0.4952, ACC= 0.5972\n",
      "0.027892015147196546\n",
      "Training epoch 1426, recon_loss:0.608041, zinb_loss:0.959265, cluster_loss:0.332674\n",
      "Clustering   1426: AMI= 0.7215, NMI= 0.7224, ARI= 0.5027, ACC= 0.6055\n",
      "0.02911356325583289\n",
      "Training epoch 1427, recon_loss:0.608489, zinb_loss:0.959613, cluster_loss:0.333332\n",
      "Clustering   1427: AMI= 0.7190, NMI= 0.7199, ARI= 0.4946, ACC= 0.5967\n",
      "0.030375829634757115\n",
      "Training epoch 1428, recon_loss:0.608139, zinb_loss:0.959270, cluster_loss:0.332583\n",
      "Clustering   1428: AMI= 0.7213, NMI= 0.7222, ARI= 0.5026, ACC= 0.6054\n",
      "0.03143450466224195\n",
      "Training epoch 1429, recon_loss:0.608760, zinb_loss:0.959809, cluster_loss:0.333331\n",
      "Clustering   1429: AMI= 0.7188, NMI= 0.7197, ARI= 0.4942, ACC= 0.5966\n",
      "0.03290036239260556\n",
      "Training epoch 1430, recon_loss:0.608195, zinb_loss:0.959273, cluster_loss:0.332512\n",
      "Clustering   1430: AMI= 0.7211, NMI= 0.7220, ARI= 0.5023, ACC= 0.6049\n",
      "0.033470418176635854\n",
      "Training epoch 1431, recon_loss:0.608887, zinb_loss:0.959959, cluster_loss:0.333346\n",
      "Clustering   1431: AMI= 0.7188, NMI= 0.7198, ARI= 0.4939, ACC= 0.5965\n",
      "0.034162628771529785\n",
      "Training epoch 1432, recon_loss:0.608211, zinb_loss:0.959248, cluster_loss:0.332515\n",
      "Clustering   1432: AMI= 0.7210, NMI= 0.7219, ARI= 0.5023, ACC= 0.6048\n",
      "0.03424406531210554\n",
      "Training epoch 1433, recon_loss:0.608868, zinb_loss:0.960035, cluster_loss:0.333386\n",
      "Clustering   1433: AMI= 0.7189, NMI= 0.7198, ARI= 0.4940, ACC= 0.5967\n",
      "0.03436622012296918\n",
      "Training epoch 1434, recon_loss:0.608019, zinb_loss:0.959180, cluster_loss:0.332616\n",
      "Clustering   1434: AMI= 0.7209, NMI= 0.7219, ARI= 0.5020, ACC= 0.6042\n",
      "0.033714727798363125\n",
      "Training epoch 1435, recon_loss:0.608671, zinb_loss:0.960032, cluster_loss:0.333453\n",
      "Clustering   1435: AMI= 0.7191, NMI= 0.7200, ARI= 0.4942, ACC= 0.5969\n",
      "0.033144672014332834\n",
      "Training epoch 1436, recon_loss:0.607844, zinb_loss:0.959089, cluster_loss:0.332778\n",
      "Clustering   1436: AMI= 0.7210, NMI= 0.7219, ARI= 0.5017, ACC= 0.6037\n",
      "0.032248870067999515\n",
      "Training epoch 1437, recon_loss:0.608502, zinb_loss:0.959991, cluster_loss:0.333523\n",
      "Clustering   1437: AMI= 0.7192, NMI= 0.7201, ARI= 0.4947, ACC= 0.5973\n",
      "0.03131234985137831\n",
      "Training epoch 1438, recon_loss:0.607608, zinb_loss:0.958988, cluster_loss:0.332953\n",
      "Clustering   1438: AMI= 0.7207, NMI= 0.7216, ARI= 0.5011, ACC= 0.6032\n",
      "0.03029439309418136\n",
      "Training epoch 1439, recon_loss:0.608373, zinb_loss:0.959957, cluster_loss:0.333576\n",
      "Clustering   1439: AMI= 0.7193, NMI= 0.7202, ARI= 0.4951, ACC= 0.5978\n",
      "0.02939859114784804\n",
      "Training epoch 1440, recon_loss:0.607712, zinb_loss:0.958911, cluster_loss:0.333112\n",
      "Clustering   1440: AMI= 0.7206, NMI= 0.7216, ARI= 0.5006, ACC= 0.6027\n",
      "0.028869253634105623\n",
      "Training epoch 1441, recon_loss:0.608503, zinb_loss:0.959932, cluster_loss:0.333571\n",
      "Clustering   1441: AMI= 0.7194, NMI= 0.7203, ARI= 0.4956, ACC= 0.5978\n",
      "0.02781057860662079\n",
      "Training epoch 1442, recon_loss:0.607560, zinb_loss:0.958836, cluster_loss:0.333231\n",
      "Clustering   1442: AMI= 0.7207, NMI= 0.7217, ARI= 0.5005, ACC= 0.6028\n",
      "0.02695549493057535\n",
      "Training epoch 1443, recon_loss:0.608532, zinb_loss:0.959927, cluster_loss:0.333545\n",
      "Clustering   1443: AMI= 0.7196, NMI= 0.7205, ARI= 0.4962, ACC= 0.5983\n",
      "0.025815383362514762\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training epoch 1444, recon_loss:0.607957, zinb_loss:0.958794, cluster_loss:0.333327\n",
      "Clustering   1444: AMI= 0.7205, NMI= 0.7214, ARI= 0.4999, ACC= 0.6024\n",
      "0.025001017956757198\n",
      "Training epoch 1445, recon_loss:0.608907, zinb_loss:0.959919, cluster_loss:0.333430\n",
      "Clustering   1445: AMI= 0.7192, NMI= 0.7201, ARI= 0.4964, ACC= 0.5984\n",
      "0.023738751577832975\n",
      "Training epoch 1446, recon_loss:0.608127, zinb_loss:0.958754, cluster_loss:0.333377\n",
      "Clustering   1446: AMI= 0.7203, NMI= 0.7212, ARI= 0.4996, ACC= 0.6021\n",
      "0.022761513090923897\n",
      "Training epoch 1447, recon_loss:0.609028, zinb_loss:0.959878, cluster_loss:0.333296\n",
      "Clustering   1447: AMI= 0.7195, NMI= 0.7204, ARI= 0.4971, ACC= 0.5986\n",
      "0.02178427460401482\n",
      "Training epoch 1448, recon_loss:0.608713, zinb_loss:0.958751, cluster_loss:0.333423\n",
      "Clustering   1448: AMI= 0.7204, NMI= 0.7213, ARI= 0.4990, ACC= 0.6017\n",
      "0.02048128995480272\n",
      "Training epoch 1449, recon_loss:0.609297, zinb_loss:0.959794, cluster_loss:0.333130\n",
      "Clustering   1449: AMI= 0.7196, NMI= 0.7205, ARI= 0.4979, ACC= 0.5993\n",
      "0.019748361089620914\n",
      "Training epoch 1450, recon_loss:0.608689, zinb_loss:0.958740, cluster_loss:0.333435\n",
      "Clustering   1450: AMI= 0.7202, NMI= 0.7211, ARI= 0.4983, ACC= 0.6012\n",
      "0.018323221629545177\n",
      "Training epoch 1451, recon_loss:0.609042, zinb_loss:0.959688, cluster_loss:0.333040\n",
      "Clustering   1451: AMI= 0.7200, NMI= 0.7209, ARI= 0.4985, ACC= 0.5996\n",
      "0.01710167352090883\n",
      "Training epoch 1452, recon_loss:0.609051, zinb_loss:0.958763, cluster_loss:0.333452\n",
      "Clustering   1452: AMI= 0.7201, NMI= 0.7210, ARI= 0.4981, ACC= 0.6009\n",
      "0.016042998493424\n",
      "Training epoch 1453, recon_loss:0.609048, zinb_loss:0.959591, cluster_loss:0.332955\n",
      "Clustering   1453: AMI= 0.7200, NMI= 0.7209, ARI= 0.4993, ACC= 0.6003\n",
      "0.014821450384787655\n",
      "Training epoch 1454, recon_loss:0.608903, zinb_loss:0.958783, cluster_loss:0.333443\n",
      "Clustering   1454: AMI= 0.7202, NMI= 0.7211, ARI= 0.4978, ACC= 0.6007\n",
      "0.014373549411620994\n",
      "Training epoch 1455, recon_loss:0.608736, zinb_loss:0.959493, cluster_loss:0.332928\n",
      "Clustering   1455: AMI= 0.7202, NMI= 0.7211, ARI= 0.4996, ACC= 0.6006\n",
      "0.014088521519605848\n",
      "Training epoch 1456, recon_loss:0.608992, zinb_loss:0.958812, cluster_loss:0.333439\n",
      "Clustering   1456: AMI= 0.7202, NMI= 0.7211, ARI= 0.4977, ACC= 0.6006\n",
      "0.013844211897878577\n",
      "Training epoch 1457, recon_loss:0.608628, zinb_loss:0.959410, cluster_loss:0.332906\n",
      "Clustering   1457: AMI= 0.7203, NMI= 0.7212, ARI= 0.4999, ACC= 0.6006\n",
      "0.014169958060181604\n",
      "Training epoch 1458, recon_loss:0.608811, zinb_loss:0.958826, cluster_loss:0.333435\n",
      "Clustering   1458: AMI= 0.7200, NMI= 0.7209, ARI= 0.4973, ACC= 0.6004\n",
      "0.013925648438454334\n",
      "Training epoch 1459, recon_loss:0.608420, zinb_loss:0.959330, cluster_loss:0.332932\n",
      "Clustering   1459: AMI= 0.7203, NMI= 0.7212, ARI= 0.5000, ACC= 0.6006\n",
      "0.013884930168166457\n",
      "Training epoch 1460, recon_loss:0.608782, zinb_loss:0.958824, cluster_loss:0.333453\n",
      "Clustering   1460: AMI= 0.7199, NMI= 0.7208, ARI= 0.4969, ACC= 0.6002\n",
      "0.013844211897878577\n",
      "Training epoch 1461, recon_loss:0.608339, zinb_loss:0.959260, cluster_loss:0.332985\n",
      "Clustering   1461: AMI= 0.7205, NMI= 0.7214, ARI= 0.5000, ACC= 0.6004\n",
      "0.013925648438454334\n",
      "Training epoch 1462, recon_loss:0.608640, zinb_loss:0.958799, cluster_loss:0.333479\n",
      "Clustering   1462: AMI= 0.7199, NMI= 0.7208, ARI= 0.4969, ACC= 0.6004\n",
      "0.013722057087014943\n",
      "Training epoch 1463, recon_loss:0.608207, zinb_loss:0.959186, cluster_loss:0.333076\n",
      "Clustering   1463: AMI= 0.7204, NMI= 0.7214, ARI= 0.5000, ACC= 0.6003\n",
      "0.014007084979030091\n",
      "Training epoch 1464, recon_loss:0.608501, zinb_loss:0.958767, cluster_loss:0.333504\n",
      "Clustering   1464: AMI= 0.7201, NMI= 0.7210, ARI= 0.4971, ACC= 0.6004\n",
      "0.013844211897878577\n",
      "Training epoch 1465, recon_loss:0.608134, zinb_loss:0.959118, cluster_loss:0.333166\n",
      "Clustering   1465: AMI= 0.7200, NMI= 0.7210, ARI= 0.4995, ACC= 0.5998\n",
      "0.01335559265442404\n",
      "Training epoch 1466, recon_loss:0.608402, zinb_loss:0.958729, cluster_loss:0.333517\n",
      "Clustering   1466: AMI= 0.7201, NMI= 0.7210, ARI= 0.4971, ACC= 0.6007\n",
      "0.013681338816727066\n",
      "Training epoch 1467, recon_loss:0.608120, zinb_loss:0.959049, cluster_loss:0.333263\n",
      "Clustering   1467: AMI= 0.7199, NMI= 0.7208, ARI= 0.4990, ACC= 0.5997\n",
      "0.013314874384136161\n",
      "Training epoch 1468, recon_loss:0.608356, zinb_loss:0.958696, cluster_loss:0.333517\n",
      "Clustering   1468: AMI= 0.7202, NMI= 0.7211, ARI= 0.4973, ACC= 0.6009\n",
      "0.013192719573272527\n",
      "Training epoch 1469, recon_loss:0.608147, zinb_loss:0.958980, cluster_loss:0.333333\n",
      "Clustering   1469: AMI= 0.7200, NMI= 0.7209, ARI= 0.4992, ACC= 0.5999\n",
      "0.012948409951545259\n",
      "Training epoch 1470, recon_loss:0.608174, zinb_loss:0.958648, cluster_loss:0.333483\n",
      "Clustering   1470: AMI= 0.7200, NMI= 0.7209, ARI= 0.4976, ACC= 0.6011\n",
      "0.013396310924711918\n",
      "Training epoch 1471, recon_loss:0.608090, zinb_loss:0.958897, cluster_loss:0.333424\n",
      "Clustering   1471: AMI= 0.7202, NMI= 0.7211, ARI= 0.4990, ACC= 0.5995\n",
      "0.014088521519605848\n",
      "Training epoch 1472, recon_loss:0.608157, zinb_loss:0.958622, cluster_loss:0.333425\n",
      "Clustering   1472: AMI= 0.7202, NMI= 0.7212, ARI= 0.4979, ACC= 0.6013\n",
      "0.014821450384787655\n",
      "Training epoch 1473, recon_loss:0.608156, zinb_loss:0.958825, cluster_loss:0.333487\n",
      "Clustering   1473: AMI= 0.7197, NMI= 0.7207, ARI= 0.4984, ACC= 0.5993\n",
      "0.015269351357954314\n",
      "Training epoch 1474, recon_loss:0.608100, zinb_loss:0.958603, cluster_loss:0.333299\n",
      "Clustering   1474: AMI= 0.7204, NMI= 0.7214, ARI= 0.4981, ACC= 0.6013\n",
      "0.016735209088317927\n",
      "Training epoch 1475, recon_loss:0.608211, zinb_loss:0.958765, cluster_loss:0.333534\n",
      "Clustering   1475: AMI= 0.7197, NMI= 0.7206, ARI= 0.4981, ACC= 0.5995\n",
      "0.017712447575227004\n",
      "Training epoch 1476, recon_loss:0.608097, zinb_loss:0.958629, cluster_loss:0.333097\n",
      "Clustering   1476: AMI= 0.7206, NMI= 0.7215, ARI= 0.4986, ACC= 0.6015\n",
      "0.019422614927317887\n",
      "Training epoch 1477, recon_loss:0.608308, zinb_loss:0.958778, cluster_loss:0.333532\n",
      "Clustering   1477: AMI= 0.7195, NMI= 0.7204, ARI= 0.4973, ACC= 0.5988\n",
      "0.0208884726576815\n",
      "Training epoch 1478, recon_loss:0.608209, zinb_loss:0.958759, cluster_loss:0.332785\n",
      "Clustering   1478: AMI= 0.7205, NMI= 0.7214, ARI= 0.4991, ACC= 0.6019\n",
      "0.02296510444236329\n",
      "Training epoch 1479, recon_loss:0.608443, zinb_loss:0.958940, cluster_loss:0.333476\n",
      "Clustering   1479: AMI= 0.7193, NMI= 0.7203, ARI= 0.4967, ACC= 0.5984\n",
      "0.024186652550999634\n",
      "Training epoch 1480, recon_loss:0.608288, zinb_loss:0.959072, cluster_loss:0.332424\n",
      "Clustering   1480: AMI= 0.7202, NMI= 0.7212, ARI= 0.4993, ACC= 0.6023\n",
      "0.026181847795105663\n",
      "Training epoch 1481, recon_loss:0.608474, zinb_loss:0.959308, cluster_loss:0.333398\n",
      "Clustering   1481: AMI= 0.7196, NMI= 0.7205, ARI= 0.4965, ACC= 0.5983\n",
      "0.027729142066045036\n",
      "Training epoch 1482, recon_loss:0.608314, zinb_loss:0.959540, cluster_loss:0.332132\n",
      "Clustering   1482: AMI= 0.7204, NMI= 0.7214, ARI= 0.4996, ACC= 0.6026\n",
      "0.02870638055295411\n",
      "Training epoch 1483, recon_loss:0.608297, zinb_loss:0.959743, cluster_loss:0.333350\n",
      "Clustering   1483: AMI= 0.7198, NMI= 0.7208, ARI= 0.4966, ACC= 0.5983\n",
      "0.02956146422899955\n",
      "Training epoch 1484, recon_loss:0.608165, zinb_loss:0.959942, cluster_loss:0.332051\n",
      "Clustering   1484: AMI= 0.7204, NMI= 0.7213, ARI= 0.4998, ACC= 0.6027\n",
      "0.02943930941813592\n",
      "Training epoch 1485, recon_loss:0.607902, zinb_loss:0.960022, cluster_loss:0.333413\n",
      "Clustering   1485: AMI= 0.7199, NMI= 0.7208, ARI= 0.4969, ACC= 0.5986\n",
      "0.02870638055295411\n",
      "Training epoch 1486, recon_loss:0.607847, zinb_loss:0.960117, cluster_loss:0.332185\n",
      "Clustering   1486: AMI= 0.7204, NMI= 0.7213, ARI= 0.4997, ACC= 0.6028\n",
      "0.0276069872551814\n",
      "Training epoch 1487, recon_loss:0.607436, zinb_loss:0.960076, cluster_loss:0.333540\n",
      "Clustering   1487: AMI= 0.7204, NMI= 0.7213, ARI= 0.4975, ACC= 0.5990\n",
      "0.02650759395740869\n",
      "Training epoch 1488, recon_loss:0.607514, zinb_loss:0.960092, cluster_loss:0.332418\n",
      "Clustering   1488: AMI= 0.7204, NMI= 0.7213, ARI= 0.4996, ACC= 0.6030\n",
      "0.02532676411906022\n",
      "Training epoch 1489, recon_loss:0.607049, zinb_loss:0.960007, cluster_loss:0.333685\n",
      "Clustering   1489: AMI= 0.7202, NMI= 0.7211, ARI= 0.4975, ACC= 0.5991\n",
      "0.023698033307545094\n",
      "Training epoch 1490, recon_loss:0.607304, zinb_loss:0.959987, cluster_loss:0.332672\n",
      "Clustering   1490: AMI= 0.7201, NMI= 0.7210, ARI= 0.4992, ACC= 0.6027\n",
      "0.022313612117757238\n",
      "Training epoch 1491, recon_loss:0.606829, zinb_loss:0.959904, cluster_loss:0.333800\n",
      "Clustering   1491: AMI= 0.7202, NMI= 0.7211, ARI= 0.4976, ACC= 0.5991\n",
      "0.0208884726576815\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training epoch 1492, recon_loss:0.607259, zinb_loss:0.959871, cluster_loss:0.332906\n",
      "Clustering   1492: AMI= 0.7200, NMI= 0.7209, ARI= 0.4988, ACC= 0.6024\n",
      "0.019544769738181523\n",
      "Training epoch 1493, recon_loss:0.606786, zinb_loss:0.959808, cluster_loss:0.333854\n",
      "Clustering   1493: AMI= 0.7201, NMI= 0.7211, ARI= 0.4976, ACC= 0.5990\n",
      "0.018608249521560323\n",
      "Training epoch 1494, recon_loss:0.607413, zinb_loss:0.959778, cluster_loss:0.333113\n",
      "Clustering   1494: AMI= 0.7197, NMI= 0.7206, ARI= 0.4981, ACC= 0.6021\n",
      "0.01738670141292398\n",
      "Training epoch 1495, recon_loss:0.606947, zinb_loss:0.959722, cluster_loss:0.333829\n",
      "Clustering   1495: AMI= 0.7202, NMI= 0.7211, ARI= 0.4977, ACC= 0.5988\n",
      "0.016572336007166417\n",
      "Training epoch 1496, recon_loss:0.607549, zinb_loss:0.959703, cluster_loss:0.333281\n",
      "Clustering   1496: AMI= 0.7195, NMI= 0.7204, ARI= 0.4975, ACC= 0.6020\n",
      "0.015839407141984608\n",
      "Training epoch 1497, recon_loss:0.607159, zinb_loss:0.959656, cluster_loss:0.333733\n",
      "Clustering   1497: AMI= 0.7205, NMI= 0.7215, ARI= 0.4981, ACC= 0.5986\n",
      "0.01559509752025734\n",
      "Training epoch 1498, recon_loss:0.608075, zinb_loss:0.959657, cluster_loss:0.333412\n",
      "Clustering   1498: AMI= 0.7195, NMI= 0.7204, ARI= 0.4974, ACC= 0.6021\n",
      "0.014984323465939167\n",
      "Training epoch 1499, recon_loss:0.607664, zinb_loss:0.959609, cluster_loss:0.333517\n",
      "Clustering   1499: AMI= 0.7206, NMI= 0.7215, ARI= 0.4983, ACC= 0.5986\n",
      "0.014169958060181604\n",
      "Training epoch 1500, recon_loss:0.608307, zinb_loss:0.959605, cluster_loss:0.333476\n",
      "Clustering   1500: AMI= 0.7192, NMI= 0.7202, ARI= 0.4970, ACC= 0.6017\n",
      "0.013884930168166457\n",
      "Training epoch 1501, recon_loss:0.607977, zinb_loss:0.959574, cluster_loss:0.333260\n",
      "Clustering   1501: AMI= 0.7205, NMI= 0.7214, ARI= 0.4985, ACC= 0.5988\n",
      "0.014292112871045239\n",
      "Training epoch 1502, recon_loss:0.608844, zinb_loss:0.959581, cluster_loss:0.333482\n",
      "Clustering   1502: AMI= 0.7192, NMI= 0.7201, ARI= 0.4968, ACC= 0.6017\n",
      "0.01449570422248463\n",
      "Training epoch 1503, recon_loss:0.608369, zinb_loss:0.959532, cluster_loss:0.332963\n",
      "Clustering   1503: AMI= 0.7205, NMI= 0.7214, ARI= 0.4987, ACC= 0.5986\n",
      "0.015757970601408853\n",
      "Training epoch 1504, recon_loss:0.608963, zinb_loss:0.959536, cluster_loss:0.333446\n",
      "Clustering   1504: AMI= 0.7191, NMI= 0.7200, ARI= 0.4969, ACC= 0.6019\n",
      "0.016531617736878536\n",
      "Training epoch 1505, recon_loss:0.608396, zinb_loss:0.959466, cluster_loss:0.332745\n",
      "Clustering   1505: AMI= 0.7202, NMI= 0.7211, ARI= 0.4983, ACC= 0.5980\n",
      "0.01783460238609064\n",
      "Training epoch 1506, recon_loss:0.608976, zinb_loss:0.959494, cluster_loss:0.333451\n",
      "Clustering   1506: AMI= 0.7193, NMI= 0.7202, ARI= 0.4971, ACC= 0.6022\n",
      "0.01885255914328759\n",
      "Training epoch 1507, recon_loss:0.608362, zinb_loss:0.959375, cluster_loss:0.332569\n",
      "Clustering   1507: AMI= 0.7202, NMI= 0.7211, ARI= 0.4983, ACC= 0.5981\n",
      "0.020155543792499696\n",
      "Training epoch 1508, recon_loss:0.608819, zinb_loss:0.959433, cluster_loss:0.333489\n",
      "Clustering   1508: AMI= 0.7194, NMI= 0.7203, ARI= 0.4973, ACC= 0.6023\n",
      "0.020847754387393624\n",
      "Training epoch 1509, recon_loss:0.608223, zinb_loss:0.959260, cluster_loss:0.332459\n",
      "Clustering   1509: AMI= 0.7200, NMI= 0.7210, ARI= 0.4982, ACC= 0.5982\n",
      "0.021947147685166334\n",
      "Training epoch 1510, recon_loss:0.608614, zinb_loss:0.959375, cluster_loss:0.333571\n",
      "Clustering   1510: AMI= 0.7193, NMI= 0.7202, ARI= 0.4973, ACC= 0.6021\n",
      "0.022598640009772384\n",
      "Training epoch 1511, recon_loss:0.608064, zinb_loss:0.959155, cluster_loss:0.332409\n",
      "Clustering   1511: AMI= 0.7201, NMI= 0.7211, ARI= 0.4984, ACC= 0.5984\n",
      "0.02263935828006026\n",
      "Training epoch 1512, recon_loss:0.608461, zinb_loss:0.959325, cluster_loss:0.333679\n",
      "Clustering   1512: AMI= 0.7192, NMI= 0.7202, ARI= 0.4969, ACC= 0.6019\n",
      "0.023535160226393584\n",
      "Training epoch 1513, recon_loss:0.607920, zinb_loss:0.959069, cluster_loss:0.332399\n",
      "Clustering   1513: AMI= 0.7201, NMI= 0.7210, ARI= 0.4987, ACC= 0.5991\n",
      "0.02382018811840873\n",
      "Training epoch 1514, recon_loss:0.608356, zinb_loss:0.959302, cluster_loss:0.333805\n",
      "Clustering   1514: AMI= 0.7193, NMI= 0.7202, ARI= 0.4965, ACC= 0.6013\n",
      "0.02402377946984812\n",
      "Training epoch 1515, recon_loss:0.607783, zinb_loss:0.959032, cluster_loss:0.332412\n",
      "Clustering   1515: AMI= 0.7201, NMI= 0.7210, ARI= 0.4990, ACC= 0.5997\n",
      "0.02402377946984812\n",
      "Training epoch 1516, recon_loss:0.608271, zinb_loss:0.959331, cluster_loss:0.333923\n",
      "Clustering   1516: AMI= 0.7193, NMI= 0.7202, ARI= 0.4959, ACC= 0.6007\n",
      "0.024512398713302658\n",
      "Training epoch 1517, recon_loss:0.607678, zinb_loss:0.959068, cluster_loss:0.332433\n",
      "Clustering   1517: AMI= 0.7203, NMI= 0.7212, ARI= 0.4994, ACC= 0.6004\n",
      "0.02426808909157539\n",
      "Training epoch 1518, recon_loss:0.608249, zinb_loss:0.959446, cluster_loss:0.334028\n",
      "Clustering   1518: AMI= 0.7193, NMI= 0.7203, ARI= 0.4957, ACC= 0.6005\n",
      "0.024430962172726903\n",
      "Training epoch 1519, recon_loss:0.607615, zinb_loss:0.959189, cluster_loss:0.332452\n",
      "Clustering   1519: AMI= 0.7203, NMI= 0.7212, ARI= 0.4996, ACC= 0.6009\n",
      "0.025001017956757198\n",
      "Training epoch 1520, recon_loss:0.608242, zinb_loss:0.959657, cluster_loss:0.334098\n",
      "Clustering   1520: AMI= 0.7190, NMI= 0.7199, ARI= 0.4949, ACC= 0.5996\n",
      "0.025286045848772344\n",
      "Training epoch 1521, recon_loss:0.607568, zinb_loss:0.959400, cluster_loss:0.332471\n",
      "Clustering   1521: AMI= 0.7203, NMI= 0.7213, ARI= 0.5001, ACC= 0.6019\n",
      "0.02622256606539354\n",
      "Training epoch 1522, recon_loss:0.608359, zinb_loss:0.959958, cluster_loss:0.334139\n",
      "Clustering   1522: AMI= 0.7190, NMI= 0.7199, ARI= 0.4945, ACC= 0.5990\n",
      "0.027199804552302618\n",
      "Training epoch 1523, recon_loss:0.607604, zinb_loss:0.959673, cluster_loss:0.332486\n",
      "Clustering   1523: AMI= 0.7204, NMI= 0.7213, ARI= 0.5004, ACC= 0.6024\n",
      "0.02833991612036321\n",
      "Training epoch 1524, recon_loss:0.608426, zinb_loss:0.960296, cluster_loss:0.334126\n",
      "Clustering   1524: AMI= 0.7189, NMI= 0.7198, ARI= 0.4944, ACC= 0.5986\n",
      "0.02915428152612077\n",
      "Training epoch 1525, recon_loss:0.607629, zinb_loss:0.959951, cluster_loss:0.332524\n",
      "Clustering   1525: AMI= 0.7207, NMI= 0.7216, ARI= 0.5012, ACC= 0.6034\n",
      "0.030375829634757115\n",
      "Training epoch 1526, recon_loss:0.608786, zinb_loss:0.960607, cluster_loss:0.334083\n",
      "Clustering   1526: AMI= 0.7189, NMI= 0.7198, ARI= 0.4941, ACC= 0.5983\n",
      "0.030986603689075288\n",
      "Training epoch 1527, recon_loss:0.607841, zinb_loss:0.960173, cluster_loss:0.332556\n",
      "Clustering   1527: AMI= 0.7208, NMI= 0.7218, ARI= 0.5016, ACC= 0.6037\n",
      "0.031515941202817706\n",
      "Training epoch 1528, recon_loss:0.608799, zinb_loss:0.960801, cluster_loss:0.333992\n",
      "Clustering   1528: AMI= 0.7188, NMI= 0.7197, ARI= 0.4940, ACC= 0.5978\n",
      "0.03143450466224195\n",
      "Training epoch 1529, recon_loss:0.607878, zinb_loss:0.960271, cluster_loss:0.332620\n",
      "Clustering   1529: AMI= 0.7208, NMI= 0.7218, ARI= 0.5020, ACC= 0.6043\n",
      "0.03143450466224195\n",
      "Training epoch 1530, recon_loss:0.608983, zinb_loss:0.960837, cluster_loss:0.333932\n",
      "Clustering   1530: AMI= 0.7187, NMI= 0.7196, ARI= 0.4943, ACC= 0.5978\n",
      "0.030864448878211652\n",
      "Training epoch 1531, recon_loss:0.607930, zinb_loss:0.960215, cluster_loss:0.332726\n",
      "Clustering   1531: AMI= 0.7209, NMI= 0.7218, ARI= 0.5021, ACC= 0.6045\n",
      "0.029968646931878333\n",
      "Training epoch 1532, recon_loss:0.608523, zinb_loss:0.960707, cluster_loss:0.333899\n",
      "Clustering   1532: AMI= 0.7188, NMI= 0.7197, ARI= 0.4944, ACC= 0.5977\n",
      "0.02956146422899955\n",
      "Training epoch 1533, recon_loss:0.607618, zinb_loss:0.960117, cluster_loss:0.332851\n",
      "Clustering   1533: AMI= 0.7206, NMI= 0.7215, ARI= 0.5018, ACC= 0.6043\n",
      "0.0289099719043935\n",
      "Training epoch 1534, recon_loss:0.608371, zinb_loss:0.960546, cluster_loss:0.333925\n",
      "Clustering   1534: AMI= 0.7188, NMI= 0.7198, ARI= 0.4945, ACC= 0.5977\n",
      "0.028380634390651086\n",
      "Training epoch 1535, recon_loss:0.607447, zinb_loss:0.959953, cluster_loss:0.332985\n",
      "Clustering   1535: AMI= 0.7205, NMI= 0.7214, ARI= 0.5016, ACC= 0.6042\n",
      "0.027892015147196546\n",
      "Training epoch 1536, recon_loss:0.608425, zinb_loss:0.960369, cluster_loss:0.333979\n",
      "Clustering   1536: AMI= 0.7189, NMI= 0.7198, ARI= 0.4945, ACC= 0.5975\n",
      "0.027688423795757155\n",
      "Training epoch 1537, recon_loss:0.607513, zinb_loss:0.959788, cluster_loss:0.333027\n",
      "Clustering   1537: AMI= 0.7203, NMI= 0.7212, ARI= 0.5016, ACC= 0.6042\n",
      "0.027688423795757155\n",
      "Training epoch 1538, recon_loss:0.608391, zinb_loss:0.960191, cluster_loss:0.334021\n",
      "Clustering   1538: AMI= 0.7189, NMI= 0.7199, ARI= 0.4945, ACC= 0.5976\n",
      "0.027240522822590495\n",
      "Training epoch 1539, recon_loss:0.607583, zinb_loss:0.959596, cluster_loss:0.333054\n",
      "Clustering   1539: AMI= 0.7202, NMI= 0.7211, ARI= 0.5017, ACC= 0.6039\n",
      "0.02622256606539354\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training epoch 1540, recon_loss:0.608443, zinb_loss:0.960045, cluster_loss:0.334045\n",
      "Clustering   1540: AMI= 0.7189, NMI= 0.7198, ARI= 0.4945, ACC= 0.5979\n",
      "0.02585610163280264\n",
      "Training epoch 1541, recon_loss:0.607745, zinb_loss:0.959400, cluster_loss:0.332992\n",
      "Clustering   1541: AMI= 0.7202, NMI= 0.7212, ARI= 0.5019, ACC= 0.6038\n",
      "0.02561179201107537\n",
      "Training epoch 1542, recon_loss:0.608658, zinb_loss:0.959905, cluster_loss:0.334030\n",
      "Clustering   1542: AMI= 0.7191, NMI= 0.7201, ARI= 0.4947, ACC= 0.5981\n",
      "0.0253674823893481\n",
      "Training epoch 1543, recon_loss:0.607969, zinb_loss:0.959186, cluster_loss:0.332925\n",
      "Clustering   1543: AMI= 0.7200, NMI= 0.7209, ARI= 0.5018, ACC= 0.6036\n",
      "0.025163891037908708\n",
      "Training epoch 1544, recon_loss:0.608847, zinb_loss:0.959751, cluster_loss:0.334002\n",
      "Clustering   1544: AMI= 0.7194, NMI= 0.7203, ARI= 0.4951, ACC= 0.5986\n",
      "0.02447168044301478\n",
      "Training epoch 1545, recon_loss:0.608129, zinb_loss:0.958958, cluster_loss:0.332877\n",
      "Clustering   1545: AMI= 0.7198, NMI= 0.7207, ARI= 0.5016, ACC= 0.6032\n",
      "0.023983061199560243\n",
      "Training epoch 1546, recon_loss:0.608747, zinb_loss:0.959566, cluster_loss:0.333996\n",
      "Clustering   1546: AMI= 0.7195, NMI= 0.7204, ARI= 0.4953, ACC= 0.5991\n",
      "0.023046540982939043\n",
      "Training epoch 1547, recon_loss:0.608019, zinb_loss:0.958760, cluster_loss:0.332906\n",
      "Clustering   1547: AMI= 0.7200, NMI= 0.7209, ARI= 0.5013, ACC= 0.6028\n",
      "0.02141781017142392\n",
      "Training epoch 1548, recon_loss:0.608556, zinb_loss:0.959407, cluster_loss:0.334003\n",
      "Clustering   1548: AMI= 0.7197, NMI= 0.7206, ARI= 0.4955, ACC= 0.5994\n",
      "0.020807036117105746\n",
      "Training epoch 1549, recon_loss:0.607865, zinb_loss:0.958596, cluster_loss:0.332994\n",
      "Clustering   1549: AMI= 0.7199, NMI= 0.7208, ARI= 0.5009, ACC= 0.6023\n",
      "0.01938189665703001\n",
      "Training epoch 1550, recon_loss:0.608295, zinb_loss:0.959279, cluster_loss:0.334008\n",
      "Clustering   1550: AMI= 0.7195, NMI= 0.7204, ARI= 0.4955, ACC= 0.5997\n",
      "0.01873040433242396\n",
      "Training epoch 1551, recon_loss:0.607677, zinb_loss:0.958495, cluster_loss:0.333093\n",
      "Clustering   1551: AMI= 0.7198, NMI= 0.7207, ARI= 0.5005, ACC= 0.6020\n",
      "0.01803819373753003\n",
      "Training epoch 1552, recon_loss:0.608181, zinb_loss:0.959229, cluster_loss:0.333982\n",
      "Clustering   1552: AMI= 0.7194, NMI= 0.7204, ARI= 0.4955, ACC= 0.5997\n",
      "0.017060955250620954\n",
      "Training epoch 1553, recon_loss:0.607609, zinb_loss:0.958455, cluster_loss:0.333149\n",
      "Clustering   1553: AMI= 0.7196, NMI= 0.7205, ARI= 0.4996, ACC= 0.6008\n",
      "0.016368744655727026\n",
      "Training epoch 1554, recon_loss:0.608127, zinb_loss:0.959255, cluster_loss:0.333908\n",
      "Clustering   1554: AMI= 0.7194, NMI= 0.7203, ARI= 0.4958, ACC= 0.6001\n",
      "0.015920843682560366\n",
      "Training epoch 1555, recon_loss:0.607584, zinb_loss:0.958479, cluster_loss:0.333149\n",
      "Clustering   1555: AMI= 0.7199, NMI= 0.7208, ARI= 0.4993, ACC= 0.6000\n",
      "0.015554379249969462\n",
      "Training epoch 1556, recon_loss:0.608123, zinb_loss:0.959351, cluster_loss:0.333777\n",
      "Clustering   1556: AMI= 0.7193, NMI= 0.7202, ARI= 0.4961, ACC= 0.6005\n",
      "0.015513660979681583\n",
      "Training epoch 1557, recon_loss:0.607568, zinb_loss:0.958570, cluster_loss:0.333113\n",
      "Clustering   1557: AMI= 0.7197, NMI= 0.7206, ARI= 0.4986, ACC= 0.5992\n",
      "0.015676534060833094\n",
      "Training epoch 1558, recon_loss:0.608094, zinb_loss:0.959501, cluster_loss:0.333603\n",
      "Clustering   1558: AMI= 0.7191, NMI= 0.7200, ARI= 0.4964, ACC= 0.6010\n",
      "0.015310069628242192\n",
      "Training epoch 1559, recon_loss:0.607488, zinb_loss:0.958726, cluster_loss:0.333097\n",
      "Clustering   1559: AMI= 0.7197, NMI= 0.7206, ARI= 0.4982, ACC= 0.5985\n",
      "0.015391506168817948\n",
      "Training epoch 1560, recon_loss:0.607997, zinb_loss:0.959683, cluster_loss:0.333430\n",
      "Clustering   1560: AMI= 0.7194, NMI= 0.7203, ARI= 0.4971, ACC= 0.6021\n",
      "0.015432224439105826\n",
      "Training epoch 1561, recon_loss:0.607345, zinb_loss:0.958944, cluster_loss:0.333142\n",
      "Clustering   1561: AMI= 0.7195, NMI= 0.7205, ARI= 0.4976, ACC= 0.5976\n",
      "0.015432224439105826\n",
      "Training epoch 1562, recon_loss:0.607895, zinb_loss:0.959889, cluster_loss:0.333296\n",
      "Clustering   1562: AMI= 0.7195, NMI= 0.7204, ARI= 0.4980, ACC= 0.6032\n",
      "0.01579868887169673\n",
      "Training epoch 1563, recon_loss:0.607267, zinb_loss:0.959220, cluster_loss:0.333259\n",
      "Clustering   1563: AMI= 0.7198, NMI= 0.7207, ARI= 0.4974, ACC= 0.5970\n",
      "0.016572336007166417\n",
      "Training epoch 1564, recon_loss:0.607824, zinb_loss:0.960106, cluster_loss:0.333206\n",
      "Clustering   1564: AMI= 0.7198, NMI= 0.7208, ARI= 0.4991, ACC= 0.6044\n",
      "0.01714239179119671\n",
      "Training epoch 1565, recon_loss:0.607303, zinb_loss:0.959532, cluster_loss:0.333400\n",
      "Clustering   1565: AMI= 0.7196, NMI= 0.7205, ARI= 0.4972, ACC= 0.5967\n",
      "0.018363939899833055\n",
      "Training epoch 1566, recon_loss:0.607685, zinb_loss:0.960314, cluster_loss:0.333142\n",
      "Clustering   1566: AMI= 0.7203, NMI= 0.7212, ARI= 0.5000, ACC= 0.6054\n",
      "0.019504051467893645\n",
      "Training epoch 1567, recon_loss:0.607348, zinb_loss:0.959846, cluster_loss:0.333537\n",
      "Clustering   1567: AMI= 0.7194, NMI= 0.7203, ARI= 0.4967, ACC= 0.5961\n",
      "0.020399853414226964\n",
      "Training epoch 1568, recon_loss:0.607330, zinb_loss:0.960478, cluster_loss:0.333101\n",
      "Clustering   1568: AMI= 0.7204, NMI= 0.7214, ARI= 0.5013, ACC= 0.6065\n",
      "0.02153996498228755\n",
      "Training epoch 1569, recon_loss:0.607265, zinb_loss:0.960119, cluster_loss:0.333669\n",
      "Clustering   1569: AMI= 0.7194, NMI= 0.7203, ARI= 0.4963, ACC= 0.5959\n",
      "0.023331568874954193\n",
      "Training epoch 1570, recon_loss:0.607236, zinb_loss:0.960596, cluster_loss:0.333108\n",
      "Clustering   1570: AMI= 0.7208, NMI= 0.7217, ARI= 0.5025, ACC= 0.6075\n",
      "0.024960299686469317\n",
      "Training epoch 1571, recon_loss:0.607349, zinb_loss:0.960313, cluster_loss:0.333748\n",
      "Clustering   1571: AMI= 0.7192, NMI= 0.7202, ARI= 0.4957, ACC= 0.5956\n",
      "0.02626328433568142\n",
      "Training epoch 1572, recon_loss:0.607144, zinb_loss:0.960622, cluster_loss:0.333149\n",
      "Clustering   1572: AMI= 0.7208, NMI= 0.7217, ARI= 0.5029, ACC= 0.6076\n",
      "0.027240522822590495\n",
      "Training epoch 1573, recon_loss:0.607403, zinb_loss:0.960418, cluster_loss:0.333816\n",
      "Clustering   1573: AMI= 0.7190, NMI= 0.7199, ARI= 0.4954, ACC= 0.5956\n",
      "0.028380634390651086\n",
      "Training epoch 1574, recon_loss:0.607089, zinb_loss:0.960567, cluster_loss:0.333215\n",
      "Clustering   1574: AMI= 0.7208, NMI= 0.7217, ARI= 0.5029, ACC= 0.6076\n",
      "0.028828535363817746\n",
      "Training epoch 1575, recon_loss:0.607453, zinb_loss:0.960433, cluster_loss:0.333874\n",
      "Clustering   1575: AMI= 0.7188, NMI= 0.7197, ARI= 0.4949, ACC= 0.5952\n",
      "0.029480027688423796\n",
      "Training epoch 1576, recon_loss:0.607082, zinb_loss:0.960446, cluster_loss:0.333297\n",
      "Clustering   1576: AMI= 0.7208, NMI= 0.7218, ARI= 0.5027, ACC= 0.6072\n",
      "0.029520745958711674\n",
      "Training epoch 1577, recon_loss:0.607503, zinb_loss:0.960374, cluster_loss:0.333917\n",
      "Clustering   1577: AMI= 0.7186, NMI= 0.7195, ARI= 0.4945, ACC= 0.5953\n",
      "0.02960218249928743\n",
      "Training epoch 1578, recon_loss:0.607267, zinb_loss:0.960291, cluster_loss:0.333391\n",
      "Clustering   1578: AMI= 0.7210, NMI= 0.7219, ARI= 0.5026, ACC= 0.6071\n",
      "0.02980577385072682\n",
      "Training epoch 1579, recon_loss:0.607635, zinb_loss:0.960247, cluster_loss:0.333928\n",
      "Clustering   1579: AMI= 0.7186, NMI= 0.7196, ARI= 0.4944, ACC= 0.5953\n",
      "0.03005008347245409\n",
      "Training epoch 1580, recon_loss:0.607299, zinb_loss:0.960094, cluster_loss:0.333483\n",
      "Clustering   1580: AMI= 0.7211, NMI= 0.7220, ARI= 0.5026, ACC= 0.6066\n",
      "0.030335111364469237\n",
      "Training epoch 1581, recon_loss:0.607652, zinb_loss:0.960078, cluster_loss:0.333932\n",
      "Clustering   1581: AMI= 0.7185, NMI= 0.7195, ARI= 0.4943, ACC= 0.5954\n",
      "0.03029439309418136\n",
      "Training epoch 1582, recon_loss:0.607518, zinb_loss:0.959890, cluster_loss:0.333581\n",
      "Clustering   1582: AMI= 0.7211, NMI= 0.7220, ARI= 0.5025, ACC= 0.6062\n",
      "0.029968646931878333\n",
      "Training epoch 1583, recon_loss:0.607802, zinb_loss:0.959896, cluster_loss:0.333902\n",
      "Clustering   1583: AMI= 0.7186, NMI= 0.7195, ARI= 0.4943, ACC= 0.5956\n",
      "0.03009080174274197\n",
      "Training epoch 1584, recon_loss:0.607594, zinb_loss:0.959673, cluster_loss:0.333662\n",
      "Clustering   1584: AMI= 0.7210, NMI= 0.7219, ARI= 0.5023, ACC= 0.6059\n",
      "0.02960218249928743\n",
      "Training epoch 1585, recon_loss:0.607866, zinb_loss:0.959725, cluster_loss:0.333859\n",
      "Clustering   1585: AMI= 0.7187, NMI= 0.7196, ARI= 0.4943, ACC= 0.5959\n",
      "0.02956146422899955\n",
      "Training epoch 1586, recon_loss:0.607857, zinb_loss:0.959467, cluster_loss:0.333729\n",
      "Clustering   1586: AMI= 0.7209, NMI= 0.7219, ARI= 0.5020, ACC= 0.6055\n",
      "0.02943930941813592\n",
      "Training epoch 1587, recon_loss:0.608077, zinb_loss:0.959565, cluster_loss:0.333786\n",
      "Clustering   1587: AMI= 0.7184, NMI= 0.7194, ARI= 0.4940, ACC= 0.5960\n",
      "0.02956146422899955\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training epoch 1588, recon_loss:0.607972, zinb_loss:0.959256, cluster_loss:0.333775\n",
      "Clustering   1588: AMI= 0.7210, NMI= 0.7219, ARI= 0.5021, ACC= 0.6053\n",
      "0.02911356325583289\n",
      "Training epoch 1589, recon_loss:0.608140, zinb_loss:0.959418, cluster_loss:0.333713\n",
      "Clustering   1589: AMI= 0.7185, NMI= 0.7194, ARI= 0.4940, ACC= 0.5963\n",
      "0.0289099719043935\n",
      "Training epoch 1590, recon_loss:0.608074, zinb_loss:0.959057, cluster_loss:0.333828\n",
      "Clustering   1590: AMI= 0.7207, NMI= 0.7217, ARI= 0.5018, ACC= 0.6048\n",
      "0.02833991612036321\n",
      "Training epoch 1591, recon_loss:0.608192, zinb_loss:0.959291, cluster_loss:0.333652\n",
      "Clustering   1591: AMI= 0.7183, NMI= 0.7192, ARI= 0.4940, ACC= 0.5966\n",
      "0.027932733417484427\n",
      "Training epoch 1592, recon_loss:0.608065, zinb_loss:0.958881, cluster_loss:0.333903\n",
      "Clustering   1592: AMI= 0.7205, NMI= 0.7214, ARI= 0.5011, ACC= 0.6041\n",
      "0.027118368011726863\n",
      "Training epoch 1593, recon_loss:0.608119, zinb_loss:0.959188, cluster_loss:0.333630\n",
      "Clustering   1593: AMI= 0.7181, NMI= 0.7191, ARI= 0.4942, ACC= 0.5969\n",
      "0.026466875687120812\n",
      "Training epoch 1594, recon_loss:0.607983, zinb_loss:0.958758, cluster_loss:0.334013\n",
      "Clustering   1594: AMI= 0.7201, NMI= 0.7210, ARI= 0.5002, ACC= 0.6030\n",
      "0.025489637200211735\n",
      "Training epoch 1595, recon_loss:0.608020, zinb_loss:0.959125, cluster_loss:0.333634\n",
      "Clustering   1595: AMI= 0.7183, NMI= 0.7192, ARI= 0.4947, ACC= 0.5975\n",
      "0.024960299686469317\n",
      "Training epoch 1596, recon_loss:0.607889, zinb_loss:0.958706, cluster_loss:0.334143\n",
      "Clustering   1596: AMI= 0.7201, NMI= 0.7210, ARI= 0.4999, ACC= 0.6028\n",
      "0.023779469848120852\n",
      "Training epoch 1597, recon_loss:0.607975, zinb_loss:0.959154, cluster_loss:0.333635\n",
      "Clustering   1597: AMI= 0.7185, NMI= 0.7194, ARI= 0.4953, ACC= 0.5978\n",
      "0.023046540982939043\n",
      "Training epoch 1598, recon_loss:0.607963, zinb_loss:0.958763, cluster_loss:0.334279\n",
      "Clustering   1598: AMI= 0.7202, NMI= 0.7211, ARI= 0.4998, ACC= 0.6027\n",
      "0.02243576692862087\n",
      "Training epoch 1599, recon_loss:0.608146, zinb_loss:0.959293, cluster_loss:0.333563\n",
      "Clustering   1599: AMI= 0.7189, NMI= 0.7198, ARI= 0.4957, ACC= 0.5986\n",
      "0.021906429414878456\n",
      "Training epoch 1600, recon_loss:0.608106, zinb_loss:0.958939, cluster_loss:0.334359\n",
      "Clustering   1600: AMI= 0.7203, NMI= 0.7212, ARI= 0.4998, ACC= 0.6023\n",
      "0.02096990919825726\n",
      "Training epoch 1601, recon_loss:0.608426, zinb_loss:0.959583, cluster_loss:0.333445\n",
      "Clustering   1601: AMI= 0.7188, NMI= 0.7197, ARI= 0.4958, ACC= 0.5985\n",
      "0.021051345738833015\n",
      "Training epoch 1602, recon_loss:0.608380, zinb_loss:0.959264, cluster_loss:0.334388\n",
      "Clustering   1602: AMI= 0.7201, NMI= 0.7210, ARI= 0.4990, ACC= 0.6016\n",
      "0.021254937090272406\n",
      "Training epoch 1603, recon_loss:0.608784, zinb_loss:0.960008, cluster_loss:0.333252\n",
      "Clustering   1603: AMI= 0.7192, NMI= 0.7202, ARI= 0.4964, ACC= 0.5988\n",
      "0.02158068325257543\n",
      "Training epoch 1604, recon_loss:0.608795, zinb_loss:0.959670, cluster_loss:0.334341\n",
      "Clustering   1604: AMI= 0.7199, NMI= 0.7209, ARI= 0.4988, ACC= 0.6014\n",
      "0.02206930249602997\n",
      "Training epoch 1605, recon_loss:0.609118, zinb_loss:0.960465, cluster_loss:0.333084\n",
      "Clustering   1605: AMI= 0.7195, NMI= 0.7205, ARI= 0.4970, ACC= 0.5995\n",
      "0.022191457306893602\n",
      "Training epoch 1606, recon_loss:0.608830, zinb_loss:0.960015, cluster_loss:0.334258\n",
      "Clustering   1606: AMI= 0.7197, NMI= 0.7206, ARI= 0.4983, ACC= 0.6010\n",
      "0.023087259253226924\n",
      "Training epoch 1607, recon_loss:0.608993, zinb_loss:0.960759, cluster_loss:0.332988\n",
      "Clustering   1607: AMI= 0.7198, NMI= 0.7208, ARI= 0.4975, ACC= 0.6001\n",
      "0.02292438617207541\n",
      "Training epoch 1608, recon_loss:0.608585, zinb_loss:0.960145, cluster_loss:0.334192\n",
      "Clustering   1608: AMI= 0.7198, NMI= 0.7207, ARI= 0.4983, ACC= 0.6008\n",
      "0.022842949631499652\n",
      "Training epoch 1609, recon_loss:0.608532, zinb_loss:0.960796, cluster_loss:0.333049\n",
      "Clustering   1609: AMI= 0.7201, NMI= 0.7210, ARI= 0.4978, ACC= 0.6008\n",
      "0.022110020766317847\n",
      "Training epoch 1610, recon_loss:0.608281, zinb_loss:0.960074, cluster_loss:0.334151\n",
      "Clustering   1610: AMI= 0.7196, NMI= 0.7206, ARI= 0.4982, ACC= 0.6007\n",
      "0.021377091901136038\n",
      "Training epoch 1611, recon_loss:0.608064, zinb_loss:0.960644, cluster_loss:0.333181\n",
      "Clustering   1611: AMI= 0.7202, NMI= 0.7211, ARI= 0.4982, ACC= 0.6015\n",
      "0.020644163035954233\n",
      "Training epoch 1612, recon_loss:0.607623, zinb_loss:0.959892, cluster_loss:0.334161\n",
      "Clustering   1612: AMI= 0.7197, NMI= 0.7206, ARI= 0.4983, ACC= 0.6002\n",
      "0.019544769738181523\n",
      "Training epoch 1613, recon_loss:0.607441, zinb_loss:0.960459, cluster_loss:0.333375\n",
      "Clustering   1613: AMI= 0.7202, NMI= 0.7211, ARI= 0.4982, ACC= 0.6018\n",
      "0.01873040433242396\n",
      "Training epoch 1614, recon_loss:0.607199, zinb_loss:0.959694, cluster_loss:0.334149\n",
      "Clustering   1614: AMI= 0.7198, NMI= 0.7208, ARI= 0.4985, ACC= 0.6002\n",
      "0.017590292764363368\n",
      "Training epoch 1615, recon_loss:0.607092, zinb_loss:0.960277, cluster_loss:0.333560\n",
      "Clustering   1615: AMI= 0.7199, NMI= 0.7209, ARI= 0.4977, ACC= 0.6017\n",
      "0.01649089946659066\n",
      "Training epoch 1616, recon_loss:0.606985, zinb_loss:0.959525, cluster_loss:0.334119\n",
      "Clustering   1616: AMI= 0.7198, NMI= 0.7207, ARI= 0.4986, ACC= 0.6000\n",
      "0.016205871574575512\n",
      "Training epoch 1617, recon_loss:0.606938, zinb_loss:0.960150, cluster_loss:0.333704\n",
      "Clustering   1617: AMI= 0.7197, NMI= 0.7206, ARI= 0.4975, ACC= 0.6019\n",
      "0.015676534060833094\n",
      "Training epoch 1618, recon_loss:0.606894, zinb_loss:0.959383, cluster_loss:0.334031\n",
      "Clustering   1618: AMI= 0.7198, NMI= 0.7208, ARI= 0.4988, ACC= 0.5999\n",
      "0.01559509752025734\n",
      "Training epoch 1619, recon_loss:0.606913, zinb_loss:0.960059, cluster_loss:0.333811\n",
      "Clustering   1619: AMI= 0.7197, NMI= 0.7206, ARI= 0.4974, ACC= 0.6021\n",
      "0.014984323465939167\n",
      "Training epoch 1620, recon_loss:0.606958, zinb_loss:0.959265, cluster_loss:0.333886\n",
      "Clustering   1620: AMI= 0.7198, NMI= 0.7208, ARI= 0.4985, ACC= 0.5996\n",
      "0.01490288692536341\n",
      "Training epoch 1621, recon_loss:0.607037, zinb_loss:0.960003, cluster_loss:0.333852\n",
      "Clustering   1621: AMI= 0.7196, NMI= 0.7205, ARI= 0.4971, ACC= 0.6020\n",
      "0.015025041736227046\n",
      "Training epoch 1622, recon_loss:0.607051, zinb_loss:0.959160, cluster_loss:0.333710\n",
      "Clustering   1622: AMI= 0.7201, NMI= 0.7210, ARI= 0.4992, ACC= 0.5998\n",
      "0.014740013844211898\n",
      "Training epoch 1623, recon_loss:0.607164, zinb_loss:0.959946, cluster_loss:0.333859\n",
      "Clustering   1623: AMI= 0.7193, NMI= 0.7203, ARI= 0.4967, ACC= 0.6017\n",
      "0.014373549411620994\n",
      "Training epoch 1624, recon_loss:0.607101, zinb_loss:0.959059, cluster_loss:0.333549\n",
      "Clustering   1624: AMI= 0.7201, NMI= 0.7210, ARI= 0.4991, ACC= 0.5996\n",
      "0.0138034936275907\n",
      "Training epoch 1625, recon_loss:0.607205, zinb_loss:0.959865, cluster_loss:0.333856\n",
      "Clustering   1625: AMI= 0.7191, NMI= 0.7200, ARI= 0.4962, ACC= 0.6013\n",
      "0.013762775357302822\n",
      "Training epoch 1626, recon_loss:0.607031, zinb_loss:0.958953, cluster_loss:0.333430\n",
      "Clustering   1626: AMI= 0.7202, NMI= 0.7211, ARI= 0.4994, ACC= 0.5996\n",
      "0.013844211897878577\n",
      "Training epoch 1627, recon_loss:0.607097, zinb_loss:0.959757, cluster_loss:0.333867\n",
      "Clustering   1627: AMI= 0.7191, NMI= 0.7200, ARI= 0.4960, ACC= 0.6012\n",
      "0.013844211897878577\n",
      "Training epoch 1628, recon_loss:0.606838, zinb_loss:0.958851, cluster_loss:0.333399\n",
      "Clustering   1628: AMI= 0.7202, NMI= 0.7212, ARI= 0.4994, ACC= 0.5996\n",
      "0.0138034936275907\n",
      "Training epoch 1629, recon_loss:0.606865, zinb_loss:0.959636, cluster_loss:0.333906\n",
      "Clustering   1629: AMI= 0.7190, NMI= 0.7199, ARI= 0.4958, ACC= 0.6009\n",
      "0.013762775357302822\n",
      "Training epoch 1630, recon_loss:0.606567, zinb_loss:0.958767, cluster_loss:0.333448\n",
      "Clustering   1630: AMI= 0.7201, NMI= 0.7211, ARI= 0.4991, ACC= 0.5996\n",
      "0.013314874384136161\n",
      "Training epoch 1631, recon_loss:0.606597, zinb_loss:0.959529, cluster_loss:0.333959\n",
      "Clustering   1631: AMI= 0.7190, NMI= 0.7199, ARI= 0.4960, ACC= 0.6009\n",
      "0.013070564762408893\n",
      "Training epoch 1632, recon_loss:0.606303, zinb_loss:0.958704, cluster_loss:0.333530\n",
      "Clustering   1632: AMI= 0.7201, NMI= 0.7210, ARI= 0.4989, ACC= 0.5995\n",
      "0.012256199356651329\n",
      "Training epoch 1633, recon_loss:0.606372, zinb_loss:0.959454, cluster_loss:0.334012\n",
      "Clustering   1633: AMI= 0.7192, NMI= 0.7202, ARI= 0.4966, ACC= 0.6013\n",
      "0.011930453194348304\n",
      "Training epoch 1634, recon_loss:0.606089, zinb_loss:0.958662, cluster_loss:0.333612\n",
      "Clustering   1634: AMI= 0.7201, NMI= 0.7210, ARI= 0.4985, ACC= 0.5992\n",
      "0.011726861842908913\n",
      "Training epoch 1635, recon_loss:0.606254, zinb_loss:0.959424, cluster_loss:0.334036\n",
      "Clustering   1635: AMI= 0.7191, NMI= 0.7201, ARI= 0.4967, ACC= 0.6015\n",
      "0.01180829838348467\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training epoch 1636, recon_loss:0.605960, zinb_loss:0.958644, cluster_loss:0.333684\n",
      "Clustering   1636: AMI= 0.7199, NMI= 0.7208, ARI= 0.4983, ACC= 0.5990\n",
      "0.01180829838348467\n",
      "Training epoch 1637, recon_loss:0.606283, zinb_loss:0.959443, cluster_loss:0.334028\n",
      "Clustering   1637: AMI= 0.7193, NMI= 0.7202, ARI= 0.4971, ACC= 0.6015\n",
      "0.011441833950893767\n",
      "Training epoch 1638, recon_loss:0.605971, zinb_loss:0.958657, cluster_loss:0.333742\n",
      "Clustering   1638: AMI= 0.7198, NMI= 0.7207, ARI= 0.4980, ACC= 0.5990\n",
      "0.011156806058878619\n",
      "Training epoch 1639, recon_loss:0.606538, zinb_loss:0.959509, cluster_loss:0.333974\n",
      "Clustering   1639: AMI= 0.7194, NMI= 0.7203, ARI= 0.4975, ACC= 0.6017\n",
      "0.011278960869742253\n",
      "Training epoch 1640, recon_loss:0.606225, zinb_loss:0.958700, cluster_loss:0.333776\n",
      "Clustering   1640: AMI= 0.7198, NMI= 0.7207, ARI= 0.4978, ACC= 0.5990\n",
      "0.011238242599454376\n",
      "Training epoch 1641, recon_loss:0.607005, zinb_loss:0.959585, cluster_loss:0.333887\n",
      "Clustering   1641: AMI= 0.7198, NMI= 0.7207, ARI= 0.4983, ACC= 0.6020\n",
      "0.01136039741031801\n",
      "Training epoch 1642, recon_loss:0.606661, zinb_loss:0.958771, cluster_loss:0.333815\n",
      "Clustering   1642: AMI= 0.7198, NMI= 0.7207, ARI= 0.4978, ACC= 0.5988\n",
      "0.011563988761757401\n",
      "Training epoch 1643, recon_loss:0.607419, zinb_loss:0.959617, cluster_loss:0.333804\n",
      "Clustering   1643: AMI= 0.7199, NMI= 0.7208, ARI= 0.4989, ACC= 0.6023\n",
      "0.011726861842908913\n",
      "Training epoch 1644, recon_loss:0.606972, zinb_loss:0.958856, cluster_loss:0.333894\n",
      "Clustering   1644: AMI= 0.7196, NMI= 0.7206, ARI= 0.4976, ACC= 0.5987\n",
      "0.012174762816075574\n",
      "Training epoch 1645, recon_loss:0.607590, zinb_loss:0.959590, cluster_loss:0.333765\n",
      "Clustering   1645: AMI= 0.7201, NMI= 0.7210, ARI= 0.4994, ACC= 0.6027\n",
      "0.012581945518954354\n",
      "Training epoch 1646, recon_loss:0.607100, zinb_loss:0.958927, cluster_loss:0.334006\n",
      "Clustering   1646: AMI= 0.7196, NMI= 0.7206, ARI= 0.4973, ACC= 0.5985\n",
      "0.013477747465287675\n",
      "Training epoch 1647, recon_loss:0.607581, zinb_loss:0.959542, cluster_loss:0.333758\n",
      "Clustering   1647: AMI= 0.7202, NMI= 0.7211, ARI= 0.5000, ACC= 0.6035\n",
      "0.014780732114499776\n",
      "Training epoch 1648, recon_loss:0.607081, zinb_loss:0.959014, cluster_loss:0.334166\n",
      "Clustering   1648: AMI= 0.7195, NMI= 0.7204, ARI= 0.4970, ACC= 0.5987\n",
      "0.015635815790545217\n",
      "Training epoch 1649, recon_loss:0.607482, zinb_loss:0.959511, cluster_loss:0.333772\n",
      "Clustering   1649: AMI= 0.7202, NMI= 0.7211, ARI= 0.5001, ACC= 0.6036\n",
      "0.016042998493424\n",
      "Training epoch 1650, recon_loss:0.607144, zinb_loss:0.959086, cluster_loss:0.334273\n",
      "Clustering   1650: AMI= 0.7192, NMI= 0.7201, ARI= 0.4963, ACC= 0.5981\n",
      "0.017020236980333076\n",
      "Training epoch 1651, recon_loss:0.607331, zinb_loss:0.959496, cluster_loss:0.333779\n",
      "Clustering   1651: AMI= 0.7202, NMI= 0.7211, ARI= 0.5006, ACC= 0.6041\n",
      "0.01807891200781791\n",
      "Training epoch 1652, recon_loss:0.607027, zinb_loss:0.959228, cluster_loss:0.334422\n",
      "Clustering   1652: AMI= 0.7189, NMI= 0.7199, ARI= 0.4959, ACC= 0.5979\n",
      "0.019300460116454254\n",
      "Training epoch 1653, recon_loss:0.607164, zinb_loss:0.959525, cluster_loss:0.333753\n",
      "Clustering   1653: AMI= 0.7203, NMI= 0.7212, ARI= 0.5009, ACC= 0.6045\n",
      "0.020603444765666355\n",
      "Training epoch 1654, recon_loss:0.607055, zinb_loss:0.959406, cluster_loss:0.334536\n",
      "Clustering   1654: AMI= 0.7189, NMI= 0.7198, ARI= 0.4956, ACC= 0.5978\n",
      "0.02162140152286331\n",
      "Training epoch 1655, recon_loss:0.607113, zinb_loss:0.959604, cluster_loss:0.333698\n",
      "Clustering   1655: AMI= 0.7206, NMI= 0.7215, ARI= 0.5014, ACC= 0.6053\n",
      "0.023250132334378434\n",
      "Training epoch 1656, recon_loss:0.607153, zinb_loss:0.959651, cluster_loss:0.334623\n",
      "Clustering   1656: AMI= 0.7186, NMI= 0.7196, ARI= 0.4945, ACC= 0.5971\n",
      "0.025041736227045076\n",
      "Training epoch 1657, recon_loss:0.607076, zinb_loss:0.959718, cluster_loss:0.333613\n",
      "Clustering   1657: AMI= 0.7208, NMI= 0.7217, ARI= 0.5022, ACC= 0.6058\n",
      "0.02675190357913596\n",
      "Training epoch 1658, recon_loss:0.607297, zinb_loss:0.959953, cluster_loss:0.334671\n",
      "Clustering   1658: AMI= 0.7184, NMI= 0.7193, ARI= 0.4940, ACC= 0.5966\n",
      "0.028421352660938964\n",
      "Training epoch 1659, recon_loss:0.607014, zinb_loss:0.959865, cluster_loss:0.333503\n",
      "Clustering   1659: AMI= 0.7209, NMI= 0.7218, ARI= 0.5027, ACC= 0.6064\n",
      "0.02964290076957531\n",
      "Training epoch 1660, recon_loss:0.607395, zinb_loss:0.960267, cluster_loss:0.334691\n",
      "Clustering   1660: AMI= 0.7184, NMI= 0.7193, ARI= 0.4938, ACC= 0.5966\n",
      "0.03135306812166619\n",
      "Training epoch 1661, recon_loss:0.606914, zinb_loss:0.960017, cluster_loss:0.333414\n",
      "Clustering   1661: AMI= 0.7206, NMI= 0.7215, ARI= 0.5019, ACC= 0.6059\n",
      "0.03237102487886315\n",
      "Training epoch 1662, recon_loss:0.607454, zinb_loss:0.960568, cluster_loss:0.334699\n",
      "Clustering   1662: AMI= 0.7183, NMI= 0.7192, ARI= 0.4935, ACC= 0.5964\n",
      "0.03367400952807525\n",
      "Training epoch 1663, recon_loss:0.606763, zinb_loss:0.960149, cluster_loss:0.333342\n",
      "Clustering   1663: AMI= 0.7204, NMI= 0.7214, ARI= 0.5018, ACC= 0.6056\n",
      "0.03448837493383281\n",
      "Training epoch 1664, recon_loss:0.607468, zinb_loss:0.960812, cluster_loss:0.334709\n",
      "Clustering   1664: AMI= 0.7184, NMI= 0.7193, ARI= 0.4936, ACC= 0.5966\n",
      "0.035017712447575226\n",
      "Training epoch 1665, recon_loss:0.606610, zinb_loss:0.960234, cluster_loss:0.333294\n",
      "Clustering   1665: AMI= 0.7205, NMI= 0.7214, ARI= 0.5013, ACC= 0.6049\n",
      "0.03550633169102976\n",
      "Training epoch 1666, recon_loss:0.607540, zinb_loss:0.961000, cluster_loss:0.334726\n",
      "Clustering   1666: AMI= 0.7185, NMI= 0.7194, ARI= 0.4939, ACC= 0.5969\n",
      "0.03554704996131764\n",
      "Training epoch 1667, recon_loss:0.606587, zinb_loss:0.960271, cluster_loss:0.333239\n",
      "Clustering   1667: AMI= 0.7202, NMI= 0.7211, ARI= 0.5007, ACC= 0.6042\n",
      "0.03542489515045401\n",
      "Training epoch 1668, recon_loss:0.607708, zinb_loss:0.961123, cluster_loss:0.334725\n",
      "Clustering   1668: AMI= 0.7188, NMI= 0.7197, ARI= 0.4944, ACC= 0.5975\n",
      "0.03538417688016613\n",
      "Training epoch 1669, recon_loss:0.606774, zinb_loss:0.960248, cluster_loss:0.333182\n",
      "Clustering   1669: AMI= 0.7201, NMI= 0.7210, ARI= 0.5004, ACC= 0.6037\n",
      "0.035465613420741886\n",
      "Training epoch 1670, recon_loss:0.607967, zinb_loss:0.961158, cluster_loss:0.334675\n",
      "Clustering   1670: AMI= 0.7186, NMI= 0.7195, ARI= 0.4942, ACC= 0.5975\n",
      "0.03497699417728735\n",
      "Training epoch 1671, recon_loss:0.607159, zinb_loss:0.960150, cluster_loss:0.333116\n",
      "Clustering   1671: AMI= 0.7198, NMI= 0.7207, ARI= 0.4999, ACC= 0.6029\n",
      "0.034895557636711594\n",
      "Training epoch 1672, recon_loss:0.608174, zinb_loss:0.961053, cluster_loss:0.334573\n",
      "Clustering   1672: AMI= 0.7189, NMI= 0.7199, ARI= 0.4951, ACC= 0.5984\n",
      "0.0339590374200904\n",
      "Training epoch 1673, recon_loss:0.607453, zinb_loss:0.959946, cluster_loss:0.333088\n",
      "Clustering   1673: AMI= 0.7198, NMI= 0.7208, ARI= 0.4998, ACC= 0.6028\n",
      "0.03318539028462071\n",
      "Training epoch 1674, recon_loss:0.608132, zinb_loss:0.960803, cluster_loss:0.334483\n",
      "Clustering   1674: AMI= 0.7191, NMI= 0.7201, ARI= 0.4955, ACC= 0.5987\n",
      "0.03204527871656012\n",
      "Training epoch 1675, recon_loss:0.607469, zinb_loss:0.959675, cluster_loss:0.333166\n",
      "Clustering   1675: AMI= 0.7198, NMI= 0.7208, ARI= 0.4998, ACC= 0.6028\n",
      "0.030864448878211652\n",
      "Training epoch 1676, recon_loss:0.607840, zinb_loss:0.960501, cluster_loss:0.334454\n",
      "Clustering   1676: AMI= 0.7193, NMI= 0.7202, ARI= 0.4958, ACC= 0.5990\n",
      "0.030253674823893482\n",
      "Training epoch 1677, recon_loss:0.607321, zinb_loss:0.959424, cluster_loss:0.333325\n",
      "Clustering   1677: AMI= 0.7197, NMI= 0.7207, ARI= 0.4995, ACC= 0.6026\n",
      "0.02915428152612077\n",
      "Training epoch 1678, recon_loss:0.607540, zinb_loss:0.960231, cluster_loss:0.334465\n",
      "Clustering   1678: AMI= 0.7196, NMI= 0.7205, ARI= 0.4961, ACC= 0.5993\n",
      "0.028095606498635937\n",
      "Training epoch 1679, recon_loss:0.607230, zinb_loss:0.959226, cluster_loss:0.333504\n",
      "Clustering   1679: AMI= 0.7198, NMI= 0.7207, ARI= 0.4993, ACC= 0.6026\n",
      "0.026914776660287472\n",
      "Training epoch 1680, recon_loss:0.607357, zinb_loss:0.960018, cluster_loss:0.334480\n",
      "Clustering   1680: AMI= 0.7197, NMI= 0.7206, ARI= 0.4961, ACC= 0.5992\n",
      "0.026141129524817786\n",
      "Training epoch 1681, recon_loss:0.607382, zinb_loss:0.959096, cluster_loss:0.333673\n",
      "Clustering   1681: AMI= 0.7199, NMI= 0.7209, ARI= 0.4995, ACC= 0.6028\n",
      "0.024838144875605685\n",
      "Training epoch 1682, recon_loss:0.607416, zinb_loss:0.959844, cluster_loss:0.334458\n",
      "Clustering   1682: AMI= 0.7196, NMI= 0.7205, ARI= 0.4962, ACC= 0.5990\n",
      "0.024145934280711757\n",
      "Training epoch 1683, recon_loss:0.607936, zinb_loss:0.959019, cluster_loss:0.333821\n",
      "Clustering   1683: AMI= 0.7203, NMI= 0.7212, ARI= 0.4997, ACC= 0.6032\n",
      "0.023127977523514802\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training epoch 1684, recon_loss:0.607858, zinb_loss:0.959684, cluster_loss:0.334358\n",
      "Clustering   1684: AMI= 0.7194, NMI= 0.7203, ARI= 0.4960, ACC= 0.5987\n",
      "0.022842949631499652\n",
      "Training epoch 1685, recon_loss:0.608214, zinb_loss:0.958967, cluster_loss:0.333941\n",
      "Clustering   1685: AMI= 0.7202, NMI= 0.7211, ARI= 0.4994, ACC= 0.6029\n",
      "0.021254937090272406\n",
      "Training epoch 1686, recon_loss:0.608003, zinb_loss:0.959587, cluster_loss:0.334261\n",
      "Clustering   1686: AMI= 0.7191, NMI= 0.7201, ARI= 0.4960, ACC= 0.5985\n",
      "0.02068488130624211\n",
      "Training epoch 1687, recon_loss:0.608220, zinb_loss:0.958947, cluster_loss:0.334041\n",
      "Clustering   1687: AMI= 0.7201, NMI= 0.7211, ARI= 0.4995, ACC= 0.6031\n",
      "0.019748361089620914\n",
      "Training epoch 1688, recon_loss:0.607935, zinb_loss:0.959503, cluster_loss:0.334171\n",
      "Clustering   1688: AMI= 0.7190, NMI= 0.7199, ARI= 0.4960, ACC= 0.5984\n",
      "0.019219023575878496\n",
      "Training epoch 1689, recon_loss:0.608167, zinb_loss:0.958957, cluster_loss:0.334129\n",
      "Clustering   1689: AMI= 0.7201, NMI= 0.7211, ARI= 0.4994, ACC= 0.6029\n",
      "0.018771122602711836\n",
      "Training epoch 1690, recon_loss:0.607744, zinb_loss:0.959455, cluster_loss:0.334142\n",
      "Clustering   1690: AMI= 0.7190, NMI= 0.7199, ARI= 0.4958, ACC= 0.5979\n",
      "0.018404658170120932\n",
      "Training epoch 1691, recon_loss:0.608103, zinb_loss:0.959016, cluster_loss:0.334201\n",
      "Clustering   1691: AMI= 0.7202, NMI= 0.7212, ARI= 0.4993, ACC= 0.6030\n",
      "0.017712447575227004\n",
      "Training epoch 1692, recon_loss:0.607673, zinb_loss:0.959397, cluster_loss:0.334105\n",
      "Clustering   1692: AMI= 0.7188, NMI= 0.7198, ARI= 0.4956, ACC= 0.5975\n",
      "0.01669449081803005\n",
      "Training epoch 1693, recon_loss:0.608055, zinb_loss:0.959088, cluster_loss:0.334268\n",
      "Clustering   1693: AMI= 0.7200, NMI= 0.7209, ARI= 0.4989, ACC= 0.6028\n",
      "0.016775927358605808\n",
      "Training epoch 1694, recon_loss:0.607590, zinb_loss:0.959339, cluster_loss:0.334094\n",
      "Clustering   1694: AMI= 0.7187, NMI= 0.7197, ARI= 0.4957, ACC= 0.5977\n",
      "0.016328026385439145\n",
      "Training epoch 1695, recon_loss:0.608018, zinb_loss:0.959168, cluster_loss:0.334332\n",
      "Clustering   1695: AMI= 0.7203, NMI= 0.7212, ARI= 0.4989, ACC= 0.6030\n",
      "0.01689808216946944\n",
      "Training epoch 1696, recon_loss:0.607539, zinb_loss:0.959275, cluster_loss:0.334076\n",
      "Clustering   1696: AMI= 0.7187, NMI= 0.7197, ARI= 0.4957, ACC= 0.5976\n",
      "0.016287308115151267\n",
      "Training epoch 1697, recon_loss:0.607985, zinb_loss:0.959237, cluster_loss:0.334397\n",
      "Clustering   1697: AMI= 0.7201, NMI= 0.7211, ARI= 0.4989, ACC= 0.6031\n",
      "0.01669449081803005\n",
      "Training epoch 1698, recon_loss:0.607516, zinb_loss:0.959226, cluster_loss:0.334056\n",
      "Clustering   1698: AMI= 0.7187, NMI= 0.7196, ARI= 0.4959, ACC= 0.5977\n",
      "0.01669449081803005\n",
      "Training epoch 1699, recon_loss:0.607954, zinb_loss:0.959317, cluster_loss:0.334448\n",
      "Clustering   1699: AMI= 0.7202, NMI= 0.7211, ARI= 0.4989, ACC= 0.6033\n",
      "0.016816645628893685\n",
      "Training epoch 1700, recon_loss:0.607496, zinb_loss:0.959197, cluster_loss:0.334030\n",
      "Clustering   1700: AMI= 0.7189, NMI= 0.7199, ARI= 0.4965, ACC= 0.5981\n",
      "0.017264546602060345\n",
      "Training epoch 1701, recon_loss:0.607946, zinb_loss:0.959415, cluster_loss:0.334488\n",
      "Clustering   1701: AMI= 0.7200, NMI= 0.7209, ARI= 0.4986, ACC= 0.6031\n",
      "0.017508856223787613\n",
      "Training epoch 1702, recon_loss:0.607540, zinb_loss:0.959196, cluster_loss:0.333984\n",
      "Clustering   1702: AMI= 0.7192, NMI= 0.7201, ARI= 0.4972, ACC= 0.5986\n",
      "0.01714239179119671\n",
      "Training epoch 1703, recon_loss:0.608060, zinb_loss:0.959537, cluster_loss:0.334502\n",
      "Clustering   1703: AMI= 0.7199, NMI= 0.7209, ARI= 0.4985, ACC= 0.6032\n",
      "0.017060955250620954\n",
      "Training epoch 1704, recon_loss:0.607678, zinb_loss:0.959224, cluster_loss:0.333906\n",
      "Clustering   1704: AMI= 0.7192, NMI= 0.7201, ARI= 0.4979, ACC= 0.5990\n",
      "0.0173459831426361\n",
      "Training epoch 1705, recon_loss:0.608231, zinb_loss:0.959659, cluster_loss:0.334465\n",
      "Clustering   1705: AMI= 0.7199, NMI= 0.7208, ARI= 0.4984, ACC= 0.6030\n",
      "0.01779388411580276\n",
      "Training epoch 1706, recon_loss:0.607900, zinb_loss:0.959269, cluster_loss:0.333806\n",
      "Clustering   1706: AMI= 0.7195, NMI= 0.7204, ARI= 0.4984, ACC= 0.5995\n",
      "0.017956757196954273\n",
      "Training epoch 1707, recon_loss:0.608417, zinb_loss:0.959732, cluster_loss:0.334377\n",
      "Clustering   1707: AMI= 0.7195, NMI= 0.7204, ARI= 0.4977, ACC= 0.6024\n",
      "0.018241785088969422\n",
      "Training epoch 1708, recon_loss:0.608087, zinb_loss:0.959293, cluster_loss:0.333723\n",
      "Clustering   1708: AMI= 0.7197, NMI= 0.7206, ARI= 0.4986, ACC= 0.5999\n",
      "0.018119630278105786\n",
      "Training epoch 1709, recon_loss:0.608539, zinb_loss:0.959739, cluster_loss:0.334256\n",
      "Clustering   1709: AMI= 0.7193, NMI= 0.7202, ARI= 0.4971, ACC= 0.6020\n",
      "0.018567531251272446\n",
      "Training epoch 1710, recon_loss:0.608202, zinb_loss:0.959256, cluster_loss:0.333639\n",
      "Clustering   1710: AMI= 0.7198, NMI= 0.7207, ARI= 0.4991, ACC= 0.6006\n",
      "0.018363939899833055\n",
      "Training epoch 1711, recon_loss:0.608503, zinb_loss:0.959643, cluster_loss:0.334123\n",
      "Clustering   1711: AMI= 0.7191, NMI= 0.7200, ARI= 0.4968, ACC= 0.6017\n",
      "0.018404658170120932\n",
      "Training epoch 1712, recon_loss:0.608025, zinb_loss:0.959160, cluster_loss:0.333675\n",
      "Clustering   1712: AMI= 0.7198, NMI= 0.7207, ARI= 0.4992, ACC= 0.6006\n",
      "0.018526812980984568\n",
      "Training epoch 1713, recon_loss:0.608255, zinb_loss:0.959506, cluster_loss:0.334051\n",
      "Clustering   1713: AMI= 0.7187, NMI= 0.7196, ARI= 0.4961, ACC= 0.6008\n",
      "0.017956757196954273\n",
      "Training epoch 1714, recon_loss:0.607712, zinb_loss:0.959018, cluster_loss:0.333720\n",
      "Clustering   1714: AMI= 0.7197, NMI= 0.7207, ARI= 0.4994, ACC= 0.6009\n",
      "0.01803819373753003\n",
      "Training epoch 1715, recon_loss:0.607788, zinb_loss:0.959325, cluster_loss:0.334046\n",
      "Clustering   1715: AMI= 0.7185, NMI= 0.7195, ARI= 0.4958, ACC= 0.6005\n",
      "0.017427419683211858\n",
      "Training epoch 1716, recon_loss:0.607213, zinb_loss:0.958897, cluster_loss:0.333873\n",
      "Clustering   1716: AMI= 0.7197, NMI= 0.7206, ARI= 0.4995, ACC= 0.6009\n",
      "0.01718311006148459\n",
      "Training epoch 1717, recon_loss:0.607421, zinb_loss:0.959172, cluster_loss:0.334099\n",
      "Clustering   1717: AMI= 0.7186, NMI= 0.7196, ARI= 0.4958, ACC= 0.6004\n",
      "0.01649089946659066\n",
      "Training epoch 1718, recon_loss:0.606915, zinb_loss:0.958781, cluster_loss:0.333994\n",
      "Clustering   1718: AMI= 0.7197, NMI= 0.7207, ARI= 0.4996, ACC= 0.6011\n",
      "0.016613054277454294\n",
      "Training epoch 1719, recon_loss:0.607083, zinb_loss:0.959038, cluster_loss:0.334182\n",
      "Clustering   1719: AMI= 0.7185, NMI= 0.7195, ARI= 0.4956, ACC= 0.6002\n",
      "0.01649089946659066\n",
      "Training epoch 1720, recon_loss:0.606654, zinb_loss:0.958717, cluster_loss:0.334148\n",
      "Clustering   1720: AMI= 0.7197, NMI= 0.7206, ARI= 0.4994, ACC= 0.6010\n",
      "0.01645018119630278\n",
      "Training epoch 1721, recon_loss:0.606961, zinb_loss:0.958940, cluster_loss:0.334255\n",
      "Clustering   1721: AMI= 0.7186, NMI= 0.7195, ARI= 0.4955, ACC= 0.5998\n",
      "0.016775927358605808\n",
      "Training epoch 1722, recon_loss:0.606626, zinb_loss:0.958668, cluster_loss:0.334240\n",
      "Clustering   1722: AMI= 0.7198, NMI= 0.7207, ARI= 0.4995, ACC= 0.6012\n",
      "0.01738670141292398\n",
      "Training epoch 1723, recon_loss:0.606930, zinb_loss:0.958865, cluster_loss:0.334326\n",
      "Clustering   1723: AMI= 0.7186, NMI= 0.7196, ARI= 0.4955, ACC= 0.5998\n",
      "0.017427419683211858\n",
      "Training epoch 1724, recon_loss:0.606659, zinb_loss:0.958657, cluster_loss:0.334324\n",
      "Clustering   1724: AMI= 0.7200, NMI= 0.7209, ARI= 0.4995, ACC= 0.6015\n",
      "0.017427419683211858\n",
      "Training epoch 1725, recon_loss:0.607061, zinb_loss:0.958819, cluster_loss:0.334369\n",
      "Clustering   1725: AMI= 0.7186, NMI= 0.7196, ARI= 0.4953, ACC= 0.5995\n",
      "0.017590292764363368\n",
      "Training epoch 1726, recon_loss:0.606863, zinb_loss:0.958665, cluster_loss:0.334353\n",
      "Clustering   1726: AMI= 0.7201, NMI= 0.7210, ARI= 0.4998, ACC= 0.6020\n",
      "0.018363939899833055\n",
      "Training epoch 1727, recon_loss:0.607222, zinb_loss:0.958804, cluster_loss:0.334420\n",
      "Clustering   1727: AMI= 0.7187, NMI= 0.7196, ARI= 0.4952, ACC= 0.5992\n",
      "0.018404658170120932\n",
      "Training epoch 1728, recon_loss:0.607053, zinb_loss:0.958708, cluster_loss:0.334372\n",
      "Clustering   1728: AMI= 0.7201, NMI= 0.7210, ARI= 0.4997, ACC= 0.6022\n",
      "0.01873040433242396\n",
      "Training epoch 1729, recon_loss:0.607500, zinb_loss:0.958802, cluster_loss:0.334450\n",
      "Clustering   1729: AMI= 0.7189, NMI= 0.7198, ARI= 0.4952, ACC= 0.5993\n",
      "0.01873040433242396\n",
      "Training epoch 1730, recon_loss:0.607351, zinb_loss:0.958764, cluster_loss:0.334337\n",
      "Clustering   1730: AMI= 0.7200, NMI= 0.7209, ARI= 0.4998, ACC= 0.6025\n",
      "0.019219023575878496\n",
      "Training epoch 1731, recon_loss:0.607693, zinb_loss:0.958848, cluster_loss:0.334509\n",
      "Clustering   1731: AMI= 0.7189, NMI= 0.7198, ARI= 0.4951, ACC= 0.5991\n",
      "0.019626206278757278\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training epoch 1732, recon_loss:0.607535, zinb_loss:0.958887, cluster_loss:0.334325\n",
      "Clustering   1732: AMI= 0.7199, NMI= 0.7209, ARI= 0.5000, ACC= 0.6026\n",
      "0.019992670711348182\n",
      "Training epoch 1733, recon_loss:0.607825, zinb_loss:0.958911, cluster_loss:0.334542\n",
      "Clustering   1733: AMI= 0.7188, NMI= 0.7197, ARI= 0.4951, ACC= 0.5988\n",
      "0.02027769860336333\n",
      "Training epoch 1734, recon_loss:0.607604, zinb_loss:0.959033, cluster_loss:0.334260\n",
      "Clustering   1734: AMI= 0.7197, NMI= 0.7207, ARI= 0.5000, ACC= 0.6028\n",
      "0.02076631784681787\n",
      "Training epoch 1735, recon_loss:0.607858, zinb_loss:0.959068, cluster_loss:0.334614\n",
      "Clustering   1735: AMI= 0.7188, NMI= 0.7197, ARI= 0.4950, ACC= 0.5987\n",
      "0.020725599576529988\n",
      "Training epoch 1736, recon_loss:0.607644, zinb_loss:0.959252, cluster_loss:0.334225\n",
      "Clustering   1736: AMI= 0.7196, NMI= 0.7206, ARI= 0.5001, ACC= 0.6030\n",
      "0.021092064009120892\n",
      "Training epoch 1737, recon_loss:0.607647, zinb_loss:0.959219, cluster_loss:0.334644\n",
      "Clustering   1737: AMI= 0.7190, NMI= 0.7199, ARI= 0.4954, ACC= 0.5987\n",
      "0.021458528441711797\n",
      "Training epoch 1738, recon_loss:0.607448, zinb_loss:0.959509, cluster_loss:0.334187\n",
      "Clustering   1738: AMI= 0.7197, NMI= 0.7207, ARI= 0.5002, ACC= 0.6033\n",
      "0.021702838063439065\n",
      "Training epoch 1739, recon_loss:0.607592, zinb_loss:0.959449, cluster_loss:0.334684\n",
      "Clustering   1739: AMI= 0.7192, NMI= 0.7201, ARI= 0.4957, ACC= 0.5988\n",
      "0.022191457306893602\n",
      "Training epoch 1740, recon_loss:0.607330, zinb_loss:0.959772, cluster_loss:0.334129\n",
      "Clustering   1740: AMI= 0.7196, NMI= 0.7206, ARI= 0.5002, ACC= 0.6034\n",
      "0.022883667901787533\n",
      "Training epoch 1741, recon_loss:0.607509, zinb_loss:0.959689, cluster_loss:0.334720\n",
      "Clustering   1741: AMI= 0.7193, NMI= 0.7203, ARI= 0.4958, ACC= 0.5989\n",
      "0.023331568874954193\n",
      "Training epoch 1742, recon_loss:0.607210, zinb_loss:0.960006, cluster_loss:0.334072\n",
      "Clustering   1742: AMI= 0.7196, NMI= 0.7206, ARI= 0.5005, ACC= 0.6038\n",
      "0.023738751577832975\n",
      "Training epoch 1743, recon_loss:0.607473, zinb_loss:0.959883, cluster_loss:0.334736\n",
      "Clustering   1743: AMI= 0.7194, NMI= 0.7203, ARI= 0.4960, ACC= 0.5990\n",
      "0.023535160226393584\n",
      "Training epoch 1744, recon_loss:0.607073, zinb_loss:0.960187, cluster_loss:0.334029\n",
      "Clustering   1744: AMI= 0.7195, NMI= 0.7205, ARI= 0.5006, ACC= 0.6042\n",
      "0.023413005415529948\n",
      "Training epoch 1745, recon_loss:0.607387, zinb_loss:0.960014, cluster_loss:0.334728\n",
      "Clustering   1745: AMI= 0.7194, NMI= 0.7203, ARI= 0.4959, ACC= 0.5988\n",
      "0.023005822712651166\n",
      "Training epoch 1746, recon_loss:0.606911, zinb_loss:0.960262, cluster_loss:0.333945\n",
      "Clustering   1746: AMI= 0.7198, NMI= 0.7207, ARI= 0.5007, ACC= 0.6044\n",
      "0.02247648519890875\n",
      "Training epoch 1747, recon_loss:0.607351, zinb_loss:0.960074, cluster_loss:0.334690\n",
      "Clustering   1747: AMI= 0.7195, NMI= 0.7204, ARI= 0.4962, ACC= 0.5986\n",
      "0.022028584225742092\n",
      "Training epoch 1748, recon_loss:0.606827, zinb_loss:0.960214, cluster_loss:0.333875\n",
      "Clustering   1748: AMI= 0.7197, NMI= 0.7206, ARI= 0.5004, ACC= 0.6043\n",
      "0.021254937090272406\n",
      "Training epoch 1749, recon_loss:0.607304, zinb_loss:0.960031, cluster_loss:0.334625\n",
      "Clustering   1749: AMI= 0.7196, NMI= 0.7206, ARI= 0.4967, ACC= 0.5986\n",
      "0.020318416873651206\n",
      "Training epoch 1750, recon_loss:0.606667, zinb_loss:0.960073, cluster_loss:0.333784\n",
      "Clustering   1750: AMI= 0.7195, NMI= 0.7204, ARI= 0.5000, ACC= 0.6044\n",
      "0.019870515900484546\n",
      "Training epoch 1751, recon_loss:0.607144, zinb_loss:0.959922, cluster_loss:0.334531\n",
      "Clustering   1751: AMI= 0.7197, NMI= 0.7206, ARI= 0.4969, ACC= 0.5986\n",
      "0.01913758703530274\n",
      "Training epoch 1752, recon_loss:0.606563, zinb_loss:0.959858, cluster_loss:0.333762\n",
      "Clustering   1752: AMI= 0.7193, NMI= 0.7202, ARI= 0.4993, ACC= 0.6041\n",
      "0.018893277413575472\n",
      "Training epoch 1753, recon_loss:0.606937, zinb_loss:0.959785, cluster_loss:0.334485\n",
      "Clustering   1753: AMI= 0.7199, NMI= 0.7208, ARI= 0.4975, ACC= 0.5988\n",
      "0.018119630278105786\n",
      "Training epoch 1754, recon_loss:0.606439, zinb_loss:0.959652, cluster_loss:0.333812\n",
      "Clustering   1754: AMI= 0.7191, NMI= 0.7200, ARI= 0.4987, ACC= 0.6037\n",
      "0.017916038926666395\n",
      "Training epoch 1755, recon_loss:0.606725, zinb_loss:0.959681, cluster_loss:0.334494\n",
      "Clustering   1755: AMI= 0.7198, NMI= 0.7208, ARI= 0.4976, ACC= 0.5990\n",
      "0.017468137953499736\n",
      "Training epoch 1756, recon_loss:0.606375, zinb_loss:0.959492, cluster_loss:0.333890\n",
      "Clustering   1756: AMI= 0.7190, NMI= 0.7200, ARI= 0.4983, ACC= 0.6032\n",
      "0.01714239179119671\n",
      "Training epoch 1757, recon_loss:0.606575, zinb_loss:0.959634, cluster_loss:0.334535\n",
      "Clustering   1757: AMI= 0.7198, NMI= 0.7208, ARI= 0.4979, ACC= 0.5992\n",
      "0.017671729304939127\n",
      "Training epoch 1758, recon_loss:0.606295, zinb_loss:0.959394, cluster_loss:0.334005\n",
      "Clustering   1758: AMI= 0.7192, NMI= 0.7201, ARI= 0.4986, ACC= 0.6037\n",
      "0.01807891200781791\n",
      "Training epoch 1759, recon_loss:0.606463, zinb_loss:0.959635, cluster_loss:0.334616\n",
      "Clustering   1759: AMI= 0.7199, NMI= 0.7208, ARI= 0.4978, ACC= 0.5992\n",
      "0.018648967791848204\n",
      "Training epoch 1760, recon_loss:0.606222, zinb_loss:0.959374, cluster_loss:0.334087\n",
      "Clustering   1760: AMI= 0.7194, NMI= 0.7204, ARI= 0.4989, ACC= 0.6042\n",
      "0.019341178386742132\n",
      "Training epoch 1761, recon_loss:0.606292, zinb_loss:0.959685, cluster_loss:0.334696\n",
      "Clustering   1761: AMI= 0.7197, NMI= 0.7207, ARI= 0.4973, ACC= 0.5985\n",
      "0.020196262062787573\n",
      "Training epoch 1762, recon_loss:0.606089, zinb_loss:0.959435, cluster_loss:0.334165\n",
      "Clustering   1762: AMI= 0.7197, NMI= 0.7206, ARI= 0.4991, ACC= 0.6045\n",
      "0.0208884726576815\n",
      "Training epoch 1763, recon_loss:0.606165, zinb_loss:0.959794, cluster_loss:0.334781\n",
      "Clustering   1763: AMI= 0.7196, NMI= 0.7206, ARI= 0.4970, ACC= 0.5981\n",
      "0.021743556333726943\n",
      "Training epoch 1764, recon_loss:0.606018, zinb_loss:0.959558, cluster_loss:0.334207\n",
      "Clustering   1764: AMI= 0.7196, NMI= 0.7205, ARI= 0.4993, ACC= 0.6047\n",
      "0.022191457306893602\n",
      "Training epoch 1765, recon_loss:0.606119, zinb_loss:0.959969, cluster_loss:0.334855\n",
      "Clustering   1765: AMI= 0.7193, NMI= 0.7203, ARI= 0.4963, ACC= 0.5977\n",
      "0.022883667901787533\n",
      "Training epoch 1766, recon_loss:0.606039, zinb_loss:0.959744, cluster_loss:0.334207\n",
      "Clustering   1766: AMI= 0.7200, NMI= 0.7209, ARI= 0.5000, ACC= 0.6052\n",
      "0.024308807361863267\n",
      "Training epoch 1767, recon_loss:0.606170, zinb_loss:0.960202, cluster_loss:0.334914\n",
      "Clustering   1767: AMI= 0.7191, NMI= 0.7200, ARI= 0.4961, ACC= 0.5975\n",
      "0.025163891037908708\n",
      "Training epoch 1768, recon_loss:0.606149, zinb_loss:0.959958, cluster_loss:0.334167\n",
      "Clustering   1768: AMI= 0.7201, NMI= 0.7210, ARI= 0.5002, ACC= 0.6054\n",
      "0.02622256606539354\n",
      "Training epoch 1769, recon_loss:0.606313, zinb_loss:0.960461, cluster_loss:0.334954\n",
      "Clustering   1769: AMI= 0.7189, NMI= 0.7198, ARI= 0.4955, ACC= 0.5973\n",
      "0.027647705525469277\n",
      "Training epoch 1770, recon_loss:0.606399, zinb_loss:0.960173, cluster_loss:0.334090\n",
      "Clustering   1770: AMI= 0.7202, NMI= 0.7211, ARI= 0.5005, ACC= 0.6055\n",
      "0.029235718066696528\n",
      "Training epoch 1771, recon_loss:0.606568, zinb_loss:0.960687, cluster_loss:0.334948\n",
      "Clustering   1771: AMI= 0.7186, NMI= 0.7196, ARI= 0.4952, ACC= 0.5967\n",
      "0.030375829634757115\n",
      "Training epoch 1772, recon_loss:0.606900, zinb_loss:0.960347, cluster_loss:0.333973\n",
      "Clustering   1772: AMI= 0.7202, NMI= 0.7211, ARI= 0.5001, ACC= 0.6053\n",
      "0.031108758499938924\n",
      "Training epoch 1773, recon_loss:0.607030, zinb_loss:0.960864, cluster_loss:0.334893\n",
      "Clustering   1773: AMI= 0.7186, NMI= 0.7195, ARI= 0.4950, ACC= 0.5968\n",
      "0.0324524614194389\n",
      "Training epoch 1774, recon_loss:0.607388, zinb_loss:0.960410, cluster_loss:0.333799\n",
      "Clustering   1774: AMI= 0.7202, NMI= 0.7211, ARI= 0.4998, ACC= 0.6046\n",
      "0.033429699906347976\n",
      "Training epoch 1775, recon_loss:0.607480, zinb_loss:0.960924, cluster_loss:0.334801\n",
      "Clustering   1775: AMI= 0.7186, NMI= 0.7195, ARI= 0.4950, ACC= 0.5975\n",
      "0.03404047396066615\n",
      "Training epoch 1776, recon_loss:0.607827, zinb_loss:0.960321, cluster_loss:0.333601\n",
      "Clustering   1776: AMI= 0.7202, NMI= 0.7211, ARI= 0.4991, ACC= 0.6038\n",
      "0.03440693839325706\n",
      "Training epoch 1777, recon_loss:0.607776, zinb_loss:0.960791, cluster_loss:0.334701\n",
      "Clustering   1777: AMI= 0.7186, NMI= 0.7196, ARI= 0.4954, ACC= 0.5984\n",
      "0.0346919662852722\n",
      "Training epoch 1778, recon_loss:0.607682, zinb_loss:0.960039, cluster_loss:0.333477\n",
      "Clustering   1778: AMI= 0.7202, NMI= 0.7211, ARI= 0.4989, ACC= 0.6030\n",
      "0.034325501852681295\n",
      "Training epoch 1779, recon_loss:0.607523, zinb_loss:0.960501, cluster_loss:0.334691\n",
      "Clustering   1779: AMI= 0.7184, NMI= 0.7194, ARI= 0.4955, ACC= 0.5990\n",
      "0.033714727798363125\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training epoch 1780, recon_loss:0.607247, zinb_loss:0.959692, cluster_loss:0.333465\n",
      "Clustering   1780: AMI= 0.7202, NMI= 0.7211, ARI= 0.4989, ACC= 0.6026\n",
      "0.03237102487886315\n",
      "Training epoch 1781, recon_loss:0.607050, zinb_loss:0.960118, cluster_loss:0.334746\n",
      "Clustering   1781: AMI= 0.7186, NMI= 0.7195, ARI= 0.4959, ACC= 0.5995\n",
      "0.03159737774339346\n",
      "Training epoch 1782, recon_loss:0.606755, zinb_loss:0.959372, cluster_loss:0.333563\n",
      "Clustering   1782: AMI= 0.7201, NMI= 0.7210, ARI= 0.4989, ACC= 0.6022\n",
      "0.030701575797060142\n",
      "Training epoch 1783, recon_loss:0.606571, zinb_loss:0.959756, cluster_loss:0.334836\n",
      "Clustering   1783: AMI= 0.7187, NMI= 0.7197, ARI= 0.4960, ACC= 0.5998\n",
      "0.02915428152612077\n",
      "Training epoch 1784, recon_loss:0.606345, zinb_loss:0.959132, cluster_loss:0.333699\n",
      "Clustering   1784: AMI= 0.7199, NMI= 0.7208, ARI= 0.4986, ACC= 0.6017\n",
      "0.02785129687690867\n",
      "Training epoch 1785, recon_loss:0.606178, zinb_loss:0.959453, cluster_loss:0.334922\n",
      "Clustering   1785: AMI= 0.7187, NMI= 0.7197, ARI= 0.4962, ACC= 0.6002\n",
      "0.026548312227696567\n",
      "Training epoch 1786, recon_loss:0.606079, zinb_loss:0.958974, cluster_loss:0.333833\n",
      "Clustering   1786: AMI= 0.7198, NMI= 0.7208, ARI= 0.4985, ACC= 0.6014\n",
      "0.025693228551651126\n",
      "Training epoch 1787, recon_loss:0.605921, zinb_loss:0.959228, cluster_loss:0.334991\n",
      "Clustering   1787: AMI= 0.7188, NMI= 0.7197, ARI= 0.4962, ACC= 0.6001\n",
      "0.024512398713302658\n",
      "Training epoch 1788, recon_loss:0.605934, zinb_loss:0.958885, cluster_loss:0.333936\n",
      "Clustering   1788: AMI= 0.7199, NMI= 0.7208, ARI= 0.4983, ACC= 0.6010\n",
      "0.02382018811840873\n",
      "Training epoch 1789, recon_loss:0.605769, zinb_loss:0.959050, cluster_loss:0.335038\n",
      "Clustering   1789: AMI= 0.7186, NMI= 0.7196, ARI= 0.4965, ACC= 0.6004\n",
      "0.022761513090923897\n",
      "Training epoch 1790, recon_loss:0.605881, zinb_loss:0.958845, cluster_loss:0.334015\n",
      "Clustering   1790: AMI= 0.7198, NMI= 0.7208, ARI= 0.4980, ACC= 0.6007\n",
      "0.021662119793151188\n",
      "Training epoch 1791, recon_loss:0.605695, zinb_loss:0.958910, cluster_loss:0.335061\n",
      "Clustering   1791: AMI= 0.7188, NMI= 0.7198, ARI= 0.4968, ACC= 0.6006\n",
      "0.02076631784681787\n",
      "Training epoch 1792, recon_loss:0.605901, zinb_loss:0.958846, cluster_loss:0.334059\n",
      "Clustering   1792: AMI= 0.7197, NMI= 0.7207, ARI= 0.4979, ACC= 0.6006\n",
      "0.020114825522211815\n",
      "Training epoch 1793, recon_loss:0.605677, zinb_loss:0.958801, cluster_loss:0.335064\n",
      "Clustering   1793: AMI= 0.7191, NMI= 0.7200, ARI= 0.4972, ACC= 0.6008\n",
      "0.019666924549045155\n",
      "Training epoch 1794, recon_loss:0.605973, zinb_loss:0.958892, cluster_loss:0.334078\n",
      "Clustering   1794: AMI= 0.7196, NMI= 0.7205, ARI= 0.4976, ACC= 0.6002\n",
      "0.019056150494726982\n",
      "Training epoch 1795, recon_loss:0.605714, zinb_loss:0.958728, cluster_loss:0.335039\n",
      "Clustering   1795: AMI= 0.7194, NMI= 0.7203, ARI= 0.4977, ACC= 0.6011\n",
      "0.01783460238609064\n",
      "Training epoch 1796, recon_loss:0.606098, zinb_loss:0.958993, cluster_loss:0.334064\n",
      "Clustering   1796: AMI= 0.7197, NMI= 0.7206, ARI= 0.4973, ACC= 0.5999\n",
      "0.017468137953499736\n",
      "Training epoch 1797, recon_loss:0.605802, zinb_loss:0.958701, cluster_loss:0.334980\n",
      "Clustering   1797: AMI= 0.7193, NMI= 0.7202, ARI= 0.4976, ACC= 0.6010\n",
      "0.0169795187100452\n",
      "Training epoch 1798, recon_loss:0.606281, zinb_loss:0.959169, cluster_loss:0.334018\n",
      "Clustering   1798: AMI= 0.7194, NMI= 0.7204, ARI= 0.4970, ACC= 0.5996\n",
      "0.01669449081803005\n",
      "Training epoch 1799, recon_loss:0.605934, zinb_loss:0.958740, cluster_loss:0.334871\n",
      "Clustering   1799: AMI= 0.7194, NMI= 0.7203, ARI= 0.4981, ACC= 0.6013\n",
      "0.015880125412272485\n",
      "Training epoch 1800, recon_loss:0.606487, zinb_loss:0.959430, cluster_loss:0.333952\n",
      "Clustering   1800: AMI= 0.7193, NMI= 0.7202, ARI= 0.4966, ACC= 0.5994\n",
      "0.015310069628242192\n",
      "Training epoch 1801, recon_loss:0.606050, zinb_loss:0.958859, cluster_loss:0.334720\n",
      "Clustering   1801: AMI= 0.7197, NMI= 0.7206, ARI= 0.4986, ACC= 0.6017\n",
      "0.015350787898530071\n",
      "Training epoch 1802, recon_loss:0.606652, zinb_loss:0.959755, cluster_loss:0.333898\n",
      "Clustering   1802: AMI= 0.7194, NMI= 0.7204, ARI= 0.4967, ACC= 0.5993\n",
      "0.015228633087666437\n",
      "Training epoch 1803, recon_loss:0.606180, zinb_loss:0.959041, cluster_loss:0.334550\n",
      "Clustering   1803: AMI= 0.7201, NMI= 0.7210, ARI= 0.4997, ACC= 0.6023\n",
      "0.01449570422248463\n",
      "Training epoch 1804, recon_loss:0.606860, zinb_loss:0.960100, cluster_loss:0.333878\n",
      "Clustering   1804: AMI= 0.7192, NMI= 0.7202, ARI= 0.4962, ACC= 0.5989\n",
      "0.015513660979681583\n",
      "Training epoch 1805, recon_loss:0.606502, zinb_loss:0.959242, cluster_loss:0.334379\n",
      "Clustering   1805: AMI= 0.7202, NMI= 0.7211, ARI= 0.5004, ACC= 0.6029\n",
      "0.016938800439757318\n",
      "Training epoch 1806, recon_loss:0.607275, zinb_loss:0.960381, cluster_loss:0.333903\n",
      "Clustering   1806: AMI= 0.7189, NMI= 0.7199, ARI= 0.4958, ACC= 0.5990\n",
      "0.018323221629545177\n",
      "Training epoch 1807, recon_loss:0.607320, zinb_loss:0.959392, cluster_loss:0.334247\n",
      "Clustering   1807: AMI= 0.7198, NMI= 0.7208, ARI= 0.5008, ACC= 0.6035\n",
      "0.019341178386742132\n",
      "Training epoch 1808, recon_loss:0.608071, zinb_loss:0.960508, cluster_loss:0.333964\n",
      "Clustering   1808: AMI= 0.7188, NMI= 0.7197, ARI= 0.4953, ACC= 0.5987\n",
      "0.021051345738833015\n",
      "Training epoch 1809, recon_loss:0.608486, zinb_loss:0.959440, cluster_loss:0.334188\n",
      "Clustering   1809: AMI= 0.7201, NMI= 0.7210, ARI= 0.5013, ACC= 0.6040\n",
      "0.0218249928743027\n",
      "Training epoch 1810, recon_loss:0.608709, zinb_loss:0.960428, cluster_loss:0.334057\n",
      "Clustering   1810: AMI= 0.7185, NMI= 0.7194, ARI= 0.4950, ACC= 0.5984\n",
      "0.02296510444236329\n",
      "Training epoch 1811, recon_loss:0.608256, zinb_loss:0.959342, cluster_loss:0.334227\n",
      "Clustering   1811: AMI= 0.7200, NMI= 0.7209, ARI= 0.5013, ACC= 0.6041\n",
      "0.023046540982939043\n",
      "Training epoch 1812, recon_loss:0.608267, zinb_loss:0.960259, cluster_loss:0.334254\n",
      "Clustering   1812: AMI= 0.7187, NMI= 0.7197, ARI= 0.4948, ACC= 0.5984\n",
      "0.023331568874954193\n",
      "Training epoch 1813, recon_loss:0.608249, zinb_loss:0.959208, cluster_loss:0.334327\n",
      "Clustering   1813: AMI= 0.7199, NMI= 0.7208, ARI= 0.5014, ACC= 0.6041\n",
      "0.02316869579380268\n",
      "Training epoch 1814, recon_loss:0.608367, zinb_loss:0.959990, cluster_loss:0.334351\n",
      "Clustering   1814: AMI= 0.7185, NMI= 0.7194, ARI= 0.4944, ACC= 0.5982\n",
      "0.023290850604666315\n",
      "Training epoch 1815, recon_loss:0.607845, zinb_loss:0.959054, cluster_loss:0.334431\n",
      "Clustering   1815: AMI= 0.7200, NMI= 0.7209, ARI= 0.5014, ACC= 0.6041\n",
      "0.023046540982939043\n",
      "Training epoch 1816, recon_loss:0.607789, zinb_loss:0.959752, cluster_loss:0.334486\n",
      "Clustering   1816: AMI= 0.7187, NMI= 0.7197, ARI= 0.4948, ACC= 0.5984\n",
      "0.022598640009772384\n",
      "Training epoch 1817, recon_loss:0.607431, zinb_loss:0.958950, cluster_loss:0.334531\n",
      "Clustering   1817: AMI= 0.7199, NMI= 0.7209, ARI= 0.5011, ACC= 0.6037\n",
      "0.02206930249602997\n",
      "Training epoch 1818, recon_loss:0.607571, zinb_loss:0.959549, cluster_loss:0.334568\n",
      "Clustering   1818: AMI= 0.7186, NMI= 0.7196, ARI= 0.4950, ACC= 0.5984\n",
      "0.02133637363084816\n",
      "Training epoch 1819, recon_loss:0.607299, zinb_loss:0.958889, cluster_loss:0.334633\n",
      "Clustering   1819: AMI= 0.7199, NMI= 0.7208, ARI= 0.5009, ACC= 0.6033\n",
      "0.02092919092796938\n",
      "Training epoch 1820, recon_loss:0.607481, zinb_loss:0.959392, cluster_loss:0.334559\n",
      "Clustering   1820: AMI= 0.7188, NMI= 0.7198, ARI= 0.4951, ACC= 0.5985\n",
      "0.020807036117105746\n",
      "Training epoch 1821, recon_loss:0.607238, zinb_loss:0.958860, cluster_loss:0.334702\n",
      "Clustering   1821: AMI= 0.7197, NMI= 0.7206, ARI= 0.5006, ACC= 0.6030\n",
      "0.020562726495378478\n",
      "Training epoch 1822, recon_loss:0.607378, zinb_loss:0.959287, cluster_loss:0.334546\n",
      "Clustering   1822: AMI= 0.7187, NMI= 0.7196, ARI= 0.4949, ACC= 0.5983\n",
      "0.020359135143939087\n",
      "Training epoch 1823, recon_loss:0.607242, zinb_loss:0.958873, cluster_loss:0.334739\n",
      "Clustering   1823: AMI= 0.7197, NMI= 0.7206, ARI= 0.5005, ACC= 0.6032\n",
      "0.020114825522211815\n",
      "Training epoch 1824, recon_loss:0.607344, zinb_loss:0.959195, cluster_loss:0.334496\n",
      "Clustering   1824: AMI= 0.7187, NMI= 0.7197, ARI= 0.4953, ACC= 0.5982\n",
      "0.019463333197605764\n",
      "Training epoch 1825, recon_loss:0.607199, zinb_loss:0.958891, cluster_loss:0.334753\n",
      "Clustering   1825: AMI= 0.7199, NMI= 0.7208, ARI= 0.5004, ACC= 0.6033\n",
      "0.01913758703530274\n",
      "Training epoch 1826, recon_loss:0.607212, zinb_loss:0.959131, cluster_loss:0.334415\n",
      "Clustering   1826: AMI= 0.7190, NMI= 0.7199, ARI= 0.4957, ACC= 0.5982\n",
      "0.01885255914328759\n",
      "Training epoch 1827, recon_loss:0.607110, zinb_loss:0.958914, cluster_loss:0.334726\n",
      "Clustering   1827: AMI= 0.7198, NMI= 0.7207, ARI= 0.5003, ACC= 0.6037\n",
      "0.018771122602711836\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training epoch 1828, recon_loss:0.607041, zinb_loss:0.959091, cluster_loss:0.334352\n",
      "Clustering   1828: AMI= 0.7191, NMI= 0.7200, ARI= 0.4960, ACC= 0.5983\n",
      "0.018323221629545177\n",
      "Training epoch 1829, recon_loss:0.606971, zinb_loss:0.958974, cluster_loss:0.334693\n",
      "Clustering   1829: AMI= 0.7197, NMI= 0.7206, ARI= 0.4999, ACC= 0.6035\n",
      "0.01820106681868154\n",
      "Training epoch 1830, recon_loss:0.606839, zinb_loss:0.959086, cluster_loss:0.334282\n",
      "Clustering   1830: AMI= 0.7193, NMI= 0.7202, ARI= 0.4967, ACC= 0.5987\n",
      "0.01775316584551488\n",
      "Training epoch 1831, recon_loss:0.606852, zinb_loss:0.959069, cluster_loss:0.334638\n",
      "Clustering   1831: AMI= 0.7195, NMI= 0.7205, ARI= 0.4998, ACC= 0.6038\n",
      "0.017956757196954273\n",
      "Training epoch 1832, recon_loss:0.606681, zinb_loss:0.959114, cluster_loss:0.334167\n",
      "Clustering   1832: AMI= 0.7193, NMI= 0.7202, ARI= 0.4972, ACC= 0.5987\n",
      "0.01783460238609064\n",
      "Training epoch 1833, recon_loss:0.606872, zinb_loss:0.959223, cluster_loss:0.334550\n",
      "Clustering   1833: AMI= 0.7196, NMI= 0.7205, ARI= 0.4996, ACC= 0.6040\n",
      "0.018404658170120932\n",
      "Training epoch 1834, recon_loss:0.606723, zinb_loss:0.959192, cluster_loss:0.334027\n",
      "Clustering   1834: AMI= 0.7195, NMI= 0.7204, ARI= 0.4975, ACC= 0.5983\n",
      "0.019422614927317887\n",
      "Training epoch 1835, recon_loss:0.607051, zinb_loss:0.959456, cluster_loss:0.334434\n",
      "Clustering   1835: AMI= 0.7194, NMI= 0.7203, ARI= 0.4990, ACC= 0.6042\n",
      "0.02068488130624211\n",
      "Training epoch 1836, recon_loss:0.606956, zinb_loss:0.959314, cluster_loss:0.333832\n",
      "Clustering   1836: AMI= 0.7194, NMI= 0.7203, ARI= 0.4976, ACC= 0.5979\n",
      "0.021743556333726943\n",
      "Training epoch 1837, recon_loss:0.607364, zinb_loss:0.959717, cluster_loss:0.334286\n",
      "Clustering   1837: AMI= 0.7194, NMI= 0.7203, ARI= 0.4989, ACC= 0.6046\n",
      "0.02227289384746936\n",
      "Training epoch 1838, recon_loss:0.607255, zinb_loss:0.959398, cluster_loss:0.333606\n",
      "Clustering   1838: AMI= 0.7194, NMI= 0.7204, ARI= 0.4978, ACC= 0.5974\n",
      "0.023494441956105706\n",
      "Training epoch 1839, recon_loss:0.607461, zinb_loss:0.959882, cluster_loss:0.334191\n",
      "Clustering   1839: AMI= 0.7195, NMI= 0.7205, ARI= 0.4989, ACC= 0.6050\n",
      "0.023535160226393584\n",
      "Training epoch 1840, recon_loss:0.607179, zinb_loss:0.959446, cluster_loss:0.333621\n",
      "Clustering   1840: AMI= 0.7192, NMI= 0.7201, ARI= 0.4975, ACC= 0.5969\n",
      "0.023494441956105706\n",
      "Training epoch 1841, recon_loss:0.607080, zinb_loss:0.959957, cluster_loss:0.334239\n",
      "Clustering   1841: AMI= 0.7196, NMI= 0.7205, ARI= 0.4990, ACC= 0.6054\n",
      "0.02272079482063602\n",
      "Training epoch 1842, recon_loss:0.606731, zinb_loss:0.959438, cluster_loss:0.333818\n",
      "Clustering   1842: AMI= 0.7192, NMI= 0.7202, ARI= 0.4975, ACC= 0.5970\n",
      "0.0218249928743027\n",
      "Training epoch 1843, recon_loss:0.606539, zinb_loss:0.959964, cluster_loss:0.334357\n",
      "Clustering   1843: AMI= 0.7196, NMI= 0.7206, ARI= 0.4995, ACC= 0.6056\n",
      "0.021010627468545137\n",
      "Training epoch 1844, recon_loss:0.606223, zinb_loss:0.959418, cluster_loss:0.334081\n",
      "Clustering   1844: AMI= 0.7192, NMI= 0.7202, ARI= 0.4972, ACC= 0.5968\n",
      "0.019422614927317887\n",
      "Training epoch 1845, recon_loss:0.606021, zinb_loss:0.959947, cluster_loss:0.334483\n",
      "Clustering   1845: AMI= 0.7196, NMI= 0.7206, ARI= 0.4997, ACC= 0.6056\n",
      "0.018811840872999714\n",
      "Training epoch 1846, recon_loss:0.605818, zinb_loss:0.959424, cluster_loss:0.334320\n",
      "Clustering   1846: AMI= 0.7190, NMI= 0.7199, ARI= 0.4970, ACC= 0.5967\n",
      "0.01763101103465125\n",
      "Training epoch 1847, recon_loss:0.605640, zinb_loss:0.959948, cluster_loss:0.334588\n",
      "Clustering   1847: AMI= 0.7197, NMI= 0.7206, ARI= 0.4996, ACC= 0.6053\n",
      "0.016938800439757318\n",
      "Training epoch 1848, recon_loss:0.605549, zinb_loss:0.959475, cluster_loss:0.334524\n",
      "Clustering   1848: AMI= 0.7190, NMI= 0.7199, ARI= 0.4968, ACC= 0.5968\n",
      "0.01669449081803005\n",
      "Training epoch 1849, recon_loss:0.605360, zinb_loss:0.959984, cluster_loss:0.334658\n",
      "Clustering   1849: AMI= 0.7195, NMI= 0.7204, ARI= 0.4998, ACC= 0.6053\n",
      "0.016613054277454294\n",
      "Training epoch 1850, recon_loss:0.605397, zinb_loss:0.959583, cluster_loss:0.334696\n",
      "Clustering   1850: AMI= 0.7190, NMI= 0.7199, ARI= 0.4968, ACC= 0.5972\n",
      "0.016613054277454294\n",
      "Training epoch 1851, recon_loss:0.605141, zinb_loss:0.960058, cluster_loss:0.334677\n",
      "Clustering   1851: AMI= 0.7197, NMI= 0.7206, ARI= 0.5000, ACC= 0.6056\n",
      "0.0169795187100452\n",
      "Training epoch 1852, recon_loss:0.605368, zinb_loss:0.959766, cluster_loss:0.334840\n",
      "Clustering   1852: AMI= 0.7191, NMI= 0.7200, ARI= 0.4966, ACC= 0.5973\n",
      "0.017468137953499736\n",
      "Training epoch 1853, recon_loss:0.604977, zinb_loss:0.960185, cluster_loss:0.334642\n",
      "Clustering   1853: AMI= 0.7200, NMI= 0.7209, ARI= 0.5003, ACC= 0.6057\n",
      "0.018363939899833055\n",
      "Training epoch 1854, recon_loss:0.605527, zinb_loss:0.960063, cluster_loss:0.334969\n",
      "Clustering   1854: AMI= 0.7189, NMI= 0.7199, ARI= 0.4962, ACC= 0.5973\n",
      "0.0195854880084694\n",
      "Training epoch 1855, recon_loss:0.604998, zinb_loss:0.960387, cluster_loss:0.334537\n",
      "Clustering   1855: AMI= 0.7202, NMI= 0.7211, ARI= 0.5010, ACC= 0.6063\n",
      "0.02068488130624211\n",
      "Training epoch 1856, recon_loss:0.605717, zinb_loss:0.960432, cluster_loss:0.335070\n",
      "Clustering   1856: AMI= 0.7190, NMI= 0.7199, ARI= 0.4958, ACC= 0.5973\n",
      "0.02227289384746936\n",
      "Training epoch 1857, recon_loss:0.605095, zinb_loss:0.960617, cluster_loss:0.334447\n",
      "Clustering   1857: AMI= 0.7202, NMI= 0.7211, ARI= 0.5016, ACC= 0.6065\n",
      "0.02402377946984812\n",
      "Training epoch 1858, recon_loss:0.605902, zinb_loss:0.960699, cluster_loss:0.335121\n",
      "Clustering   1858: AMI= 0.7188, NMI= 0.7198, ARI= 0.4953, ACC= 0.5971\n",
      "0.025448918929923858\n",
      "Training epoch 1859, recon_loss:0.605263, zinb_loss:0.960783, cluster_loss:0.334367\n",
      "Clustering   1859: AMI= 0.7204, NMI= 0.7213, ARI= 0.5022, ACC= 0.6065\n",
      "0.026385439146545054\n",
      "Training epoch 1860, recon_loss:0.606260, zinb_loss:0.960889, cluster_loss:0.335172\n",
      "Clustering   1860: AMI= 0.7188, NMI= 0.7197, ARI= 0.4950, ACC= 0.5973\n",
      "0.027444114174029886\n",
      "Training epoch 1861, recon_loss:0.605583, zinb_loss:0.960872, cluster_loss:0.334256\n",
      "Clustering   1861: AMI= 0.7205, NMI= 0.7215, ARI= 0.5022, ACC= 0.6060\n",
      "0.027932733417484427\n",
      "Training epoch 1862, recon_loss:0.606770, zinb_loss:0.960967, cluster_loss:0.335185\n",
      "Clustering   1862: AMI= 0.7190, NMI= 0.7199, ARI= 0.4953, ACC= 0.5981\n",
      "0.027892015147196546\n",
      "Training epoch 1863, recon_loss:0.606170, zinb_loss:0.960838, cluster_loss:0.334154\n",
      "Clustering   1863: AMI= 0.7203, NMI= 0.7212, ARI= 0.5019, ACC= 0.6050\n",
      "0.028217761309499573\n",
      "Training epoch 1864, recon_loss:0.607202, zinb_loss:0.960921, cluster_loss:0.335198\n",
      "Clustering   1864: AMI= 0.7189, NMI= 0.7199, ARI= 0.4953, ACC= 0.5984\n",
      "0.02850278920151472\n",
      "Training epoch 1865, recon_loss:0.606419, zinb_loss:0.960747, cluster_loss:0.334037\n",
      "Clustering   1865: AMI= 0.7205, NMI= 0.7214, ARI= 0.5021, ACC= 0.6048\n",
      "0.029072844985545014\n",
      "Training epoch 1866, recon_loss:0.607195, zinb_loss:0.960729, cluster_loss:0.335172\n",
      "Clustering   1866: AMI= 0.7190, NMI= 0.7199, ARI= 0.4953, ACC= 0.5989\n",
      "0.028828535363817746\n",
      "Training epoch 1867, recon_loss:0.606642, zinb_loss:0.960493, cluster_loss:0.334012\n",
      "Clustering   1867: AMI= 0.7206, NMI= 0.7215, ARI= 0.5020, ACC= 0.6043\n",
      "0.028380634390651086\n",
      "Training epoch 1868, recon_loss:0.607282, zinb_loss:0.960474, cluster_loss:0.335169\n",
      "Clustering   1868: AMI= 0.7189, NMI= 0.7198, ARI= 0.4954, ACC= 0.5993\n",
      "0.028014169958060182\n",
      "Training epoch 1869, recon_loss:0.606828, zinb_loss:0.960189, cluster_loss:0.333993\n",
      "Clustering   1869: AMI= 0.7204, NMI= 0.7213, ARI= 0.5016, ACC= 0.6038\n",
      "0.02736267763345413\n",
      "Training epoch 1870, recon_loss:0.607353, zinb_loss:0.960199, cluster_loss:0.335175\n",
      "Clustering   1870: AMI= 0.7188, NMI= 0.7197, ARI= 0.4953, ACC= 0.5994\n",
      "0.027199804552302618\n",
      "Training epoch 1871, recon_loss:0.607007, zinb_loss:0.959902, cluster_loss:0.333942\n",
      "Clustering   1871: AMI= 0.7202, NMI= 0.7211, ARI= 0.5012, ACC= 0.6033\n",
      "0.026100411254529908\n",
      "Training epoch 1872, recon_loss:0.607614, zinb_loss:0.959973, cluster_loss:0.335135\n",
      "Clustering   1872: AMI= 0.7188, NMI= 0.7197, ARI= 0.4950, ACC= 0.5995\n",
      "0.02577466509222688\n",
      "Training epoch 1873, recon_loss:0.607287, zinb_loss:0.959629, cluster_loss:0.333876\n",
      "Clustering   1873: AMI= 0.7204, NMI= 0.7213, ARI= 0.5014, ACC= 0.6033\n",
      "0.024960299686469317\n",
      "Training epoch 1874, recon_loss:0.607803, zinb_loss:0.959776, cluster_loss:0.335079\n",
      "Clustering   1874: AMI= 0.7186, NMI= 0.7196, ARI= 0.4945, ACC= 0.5995\n",
      "0.02471599006474205\n",
      "Training epoch 1875, recon_loss:0.607372, zinb_loss:0.959389, cluster_loss:0.333823\n",
      "Clustering   1875: AMI= 0.7203, NMI= 0.7212, ARI= 0.5010, ACC= 0.6032\n",
      "0.024593835253878416\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training epoch 1876, recon_loss:0.607736, zinb_loss:0.959605, cluster_loss:0.335014\n",
      "Clustering   1876: AMI= 0.7186, NMI= 0.7195, ARI= 0.4944, ACC= 0.5998\n",
      "0.024512398713302658\n",
      "Training epoch 1877, recon_loss:0.607232, zinb_loss:0.959190, cluster_loss:0.333817\n",
      "Clustering   1877: AMI= 0.7202, NMI= 0.7212, ARI= 0.5010, ACC= 0.6032\n",
      "0.024186652550999634\n",
      "Training epoch 1878, recon_loss:0.607479, zinb_loss:0.959451, cluster_loss:0.334976\n",
      "Clustering   1878: AMI= 0.7184, NMI= 0.7193, ARI= 0.4943, ACC= 0.5997\n",
      "0.02382018811840873\n",
      "Training epoch 1879, recon_loss:0.606948, zinb_loss:0.959024, cluster_loss:0.333881\n",
      "Clustering   1879: AMI= 0.7202, NMI= 0.7212, ARI= 0.5006, ACC= 0.6028\n",
      "0.023087259253226924\n",
      "Training epoch 1880, recon_loss:0.607104, zinb_loss:0.959308, cluster_loss:0.334969\n",
      "Clustering   1880: AMI= 0.7184, NMI= 0.7194, ARI= 0.4943, ACC= 0.5997\n",
      "0.022598640009772384\n",
      "Training epoch 1881, recon_loss:0.606576, zinb_loss:0.958887, cluster_loss:0.334022\n",
      "Clustering   1881: AMI= 0.7202, NMI= 0.7211, ARI= 0.5004, ACC= 0.6026\n",
      "0.022313612117757238\n",
      "Training epoch 1882, recon_loss:0.606698, zinb_loss:0.959189, cluster_loss:0.334999\n",
      "Clustering   1882: AMI= 0.7184, NMI= 0.7193, ARI= 0.4941, ACC= 0.5995\n",
      "0.02178427460401482\n",
      "Training epoch 1883, recon_loss:0.606221, zinb_loss:0.958786, cluster_loss:0.334191\n",
      "Clustering   1883: AMI= 0.7202, NMI= 0.7211, ARI= 0.5004, ACC= 0.6026\n",
      "0.021377091901136038\n",
      "Training epoch 1884, recon_loss:0.606358, zinb_loss:0.959106, cluster_loss:0.335041\n",
      "Clustering   1884: AMI= 0.7182, NMI= 0.7191, ARI= 0.4942, ACC= 0.5994\n",
      "0.02068488130624211\n",
      "Training epoch 1885, recon_loss:0.605929, zinb_loss:0.958723, cluster_loss:0.334366\n",
      "Clustering   1885: AMI= 0.7202, NMI= 0.7211, ARI= 0.5004, ACC= 0.6027\n",
      "0.02023698033307545\n",
      "Training epoch 1886, recon_loss:0.606120, zinb_loss:0.959056, cluster_loss:0.335075\n",
      "Clustering   1886: AMI= 0.7182, NMI= 0.7191, ARI= 0.4941, ACC= 0.5992\n",
      "0.019056150494726982\n",
      "Training epoch 1887, recon_loss:0.605718, zinb_loss:0.958692, cluster_loss:0.334526\n",
      "Clustering   1887: AMI= 0.7201, NMI= 0.7210, ARI= 0.5001, ACC= 0.6026\n",
      "0.018323221629545177\n",
      "Training epoch 1888, recon_loss:0.605982, zinb_loss:0.959044, cluster_loss:0.335094\n",
      "Clustering   1888: AMI= 0.7181, NMI= 0.7190, ARI= 0.4943, ACC= 0.5993\n",
      "0.01807891200781791\n",
      "Training epoch 1889, recon_loss:0.605584, zinb_loss:0.958693, cluster_loss:0.334655\n",
      "Clustering   1889: AMI= 0.7200, NMI= 0.7209, ARI= 0.4999, ACC= 0.6024\n",
      "0.017671729304939127\n",
      "Training epoch 1890, recon_loss:0.605911, zinb_loss:0.959064, cluster_loss:0.335092\n",
      "Clustering   1890: AMI= 0.7181, NMI= 0.7190, ARI= 0.4946, ACC= 0.5994\n",
      "0.01738670141292398\n",
      "Training epoch 1891, recon_loss:0.605514, zinb_loss:0.958721, cluster_loss:0.334746\n",
      "Clustering   1891: AMI= 0.7200, NMI= 0.7209, ARI= 0.4998, ACC= 0.6024\n",
      "0.017508856223787613\n",
      "Training epoch 1892, recon_loss:0.605909, zinb_loss:0.959119, cluster_loss:0.335067\n",
      "Clustering   1892: AMI= 0.7183, NMI= 0.7192, ARI= 0.4946, ACC= 0.5992\n",
      "0.017590292764363368\n",
      "Training epoch 1893, recon_loss:0.605484, zinb_loss:0.958773, cluster_loss:0.334816\n",
      "Clustering   1893: AMI= 0.7200, NMI= 0.7209, ARI= 0.4999, ACC= 0.6026\n",
      "0.01754957449407549\n",
      "Training epoch 1894, recon_loss:0.605952, zinb_loss:0.959202, cluster_loss:0.335021\n",
      "Clustering   1894: AMI= 0.7184, NMI= 0.7193, ARI= 0.4949, ACC= 0.5996\n",
      "0.01779388411580276\n",
      "Training epoch 1895, recon_loss:0.605491, zinb_loss:0.958857, cluster_loss:0.334869\n",
      "Clustering   1895: AMI= 0.7199, NMI= 0.7208, ARI= 0.5001, ACC= 0.6030\n",
      "0.018363939899833055\n",
      "Training epoch 1896, recon_loss:0.606021, zinb_loss:0.959317, cluster_loss:0.334959\n",
      "Clustering   1896: AMI= 0.7184, NMI= 0.7193, ARI= 0.4949, ACC= 0.5996\n",
      "0.018648967791848204\n",
      "Training epoch 1897, recon_loss:0.605518, zinb_loss:0.958968, cluster_loss:0.334916\n",
      "Clustering   1897: AMI= 0.7200, NMI= 0.7210, ARI= 0.5001, ACC= 0.6028\n",
      "0.01893399568386335\n",
      "Training epoch 1898, recon_loss:0.606103, zinb_loss:0.959455, cluster_loss:0.334886\n",
      "Clustering   1898: AMI= 0.7186, NMI= 0.7195, ARI= 0.4952, ACC= 0.5997\n",
      "0.018771122602711836\n",
      "Training epoch 1899, recon_loss:0.605548, zinb_loss:0.959103, cluster_loss:0.334958\n",
      "Clustering   1899: AMI= 0.7197, NMI= 0.7206, ARI= 0.4995, ACC= 0.6026\n",
      "0.019056150494726982\n",
      "Training epoch 1900, recon_loss:0.606190, zinb_loss:0.959610, cluster_loss:0.334819\n",
      "Clustering   1900: AMI= 0.7186, NMI= 0.7195, ARI= 0.4954, ACC= 0.5996\n",
      "0.01917830530559062\n",
      "Training epoch 1901, recon_loss:0.605514, zinb_loss:0.959246, cluster_loss:0.335001\n",
      "Clustering   1901: AMI= 0.7196, NMI= 0.7205, ARI= 0.4993, ACC= 0.6024\n",
      "0.01893399568386335\n",
      "Training epoch 1902, recon_loss:0.606265, zinb_loss:0.959765, cluster_loss:0.334763\n",
      "Clustering   1902: AMI= 0.7188, NMI= 0.7197, ARI= 0.4958, ACC= 0.5998\n",
      "0.019015432224439105\n",
      "Training epoch 1903, recon_loss:0.605517, zinb_loss:0.959376, cluster_loss:0.335040\n",
      "Clustering   1903: AMI= 0.7196, NMI= 0.7206, ARI= 0.4992, ACC= 0.6023\n",
      "0.0182825033592573\n",
      "Training epoch 1904, recon_loss:0.606337, zinb_loss:0.959901, cluster_loss:0.334721\n",
      "Clustering   1904: AMI= 0.7190, NMI= 0.7199, ARI= 0.4959, ACC= 0.5996\n",
      "0.017916038926666395\n",
      "Training epoch 1905, recon_loss:0.605587, zinb_loss:0.959489, cluster_loss:0.335069\n",
      "Clustering   1905: AMI= 0.7195, NMI= 0.7205, ARI= 0.4990, ACC= 0.6022\n",
      "0.01763101103465125\n",
      "Training epoch 1906, recon_loss:0.606476, zinb_loss:0.960017, cluster_loss:0.334675\n",
      "Clustering   1906: AMI= 0.7192, NMI= 0.7202, ARI= 0.4963, ACC= 0.5997\n",
      "0.0169795187100452\n",
      "Training epoch 1907, recon_loss:0.605800, zinb_loss:0.959575, cluster_loss:0.335068\n",
      "Clustering   1907: AMI= 0.7197, NMI= 0.7206, ARI= 0.4989, ACC= 0.6021\n",
      "0.015839407141984608\n",
      "Training epoch 1908, recon_loss:0.606698, zinb_loss:0.960090, cluster_loss:0.334622\n",
      "Clustering   1908: AMI= 0.7193, NMI= 0.7202, ARI= 0.4966, ACC= 0.5998\n",
      "0.015025041736227046\n",
      "Training epoch 1909, recon_loss:0.606132, zinb_loss:0.959632, cluster_loss:0.335031\n",
      "Clustering   1909: AMI= 0.7197, NMI= 0.7206, ARI= 0.4989, ACC= 0.6020\n",
      "0.014007084979030091\n",
      "Training epoch 1910, recon_loss:0.606933, zinb_loss:0.960102, cluster_loss:0.334562\n",
      "Clustering   1910: AMI= 0.7194, NMI= 0.7203, ARI= 0.4970, ACC= 0.6002\n",
      "0.013681338816727066\n",
      "Training epoch 1911, recon_loss:0.606328, zinb_loss:0.959634, cluster_loss:0.334972\n",
      "Clustering   1911: AMI= 0.7198, NMI= 0.7207, ARI= 0.4993, ACC= 0.6022\n",
      "0.013233437843560406\n",
      "Training epoch 1912, recon_loss:0.606954, zinb_loss:0.960033, cluster_loss:0.334506\n",
      "Clustering   1912: AMI= 0.7192, NMI= 0.7201, ARI= 0.4969, ACC= 0.6002\n",
      "0.013681338816727066\n",
      "Training epoch 1913, recon_loss:0.606723, zinb_loss:0.959584, cluster_loss:0.334904\n",
      "Clustering   1913: AMI= 0.7198, NMI= 0.7207, ARI= 0.4992, ACC= 0.6020\n",
      "0.013722057087014943\n",
      "Training epoch 1914, recon_loss:0.606877, zinb_loss:0.959866, cluster_loss:0.334476\n",
      "Clustering   1914: AMI= 0.7194, NMI= 0.7203, ARI= 0.4974, ACC= 0.6008\n",
      "0.013722057087014943\n",
      "Training epoch 1915, recon_loss:0.606532, zinb_loss:0.959473, cluster_loss:0.334898\n",
      "Clustering   1915: AMI= 0.7196, NMI= 0.7206, ARI= 0.4989, ACC= 0.6017\n",
      "0.013966366708742213\n",
      "Training epoch 1916, recon_loss:0.606300, zinb_loss:0.959732, cluster_loss:0.334519\n",
      "Clustering   1916: AMI= 0.7193, NMI= 0.7202, ARI= 0.4975, ACC= 0.6010\n",
      "0.014414267681908873\n",
      "Training epoch 1917, recon_loss:0.606095, zinb_loss:0.959327, cluster_loss:0.334858\n",
      "Clustering   1917: AMI= 0.7197, NMI= 0.7207, ARI= 0.4987, ACC= 0.6014\n",
      "0.014577140763060385\n",
      "Training epoch 1918, recon_loss:0.605958, zinb_loss:0.959562, cluster_loss:0.334541\n",
      "Clustering   1918: AMI= 0.7194, NMI= 0.7203, ARI= 0.4979, ACC= 0.6017\n",
      "0.015513660979681583\n",
      "Training epoch 1919, recon_loss:0.605863, zinb_loss:0.959186, cluster_loss:0.334778\n",
      "Clustering   1919: AMI= 0.7193, NMI= 0.7202, ARI= 0.4980, ACC= 0.6006\n",
      "0.01579868887169673\n",
      "Training epoch 1920, recon_loss:0.605606, zinb_loss:0.959456, cluster_loss:0.334563\n",
      "Clustering   1920: AMI= 0.7196, NMI= 0.7205, ARI= 0.4983, ACC= 0.6023\n",
      "0.015757970601408853\n",
      "Training epoch 1921, recon_loss:0.605803, zinb_loss:0.959048, cluster_loss:0.334576\n",
      "Clustering   1921: AMI= 0.7191, NMI= 0.7200, ARI= 0.4974, ACC= 0.6000\n",
      "0.016042998493424\n",
      "Training epoch 1922, recon_loss:0.605598, zinb_loss:0.959375, cluster_loss:0.334469\n",
      "Clustering   1922: AMI= 0.7196, NMI= 0.7206, ARI= 0.4985, ACC= 0.6028\n",
      "0.016287308115151267\n",
      "Training epoch 1923, recon_loss:0.605974, zinb_loss:0.958972, cluster_loss:0.334296\n",
      "Clustering   1923: AMI= 0.7190, NMI= 0.7199, ARI= 0.4973, ACC= 0.5994\n",
      "0.016816645628893685\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training epoch 1924, recon_loss:0.605757, zinb_loss:0.959337, cluster_loss:0.334303\n",
      "Clustering   1924: AMI= 0.7196, NMI= 0.7206, ARI= 0.4990, ACC= 0.6034\n",
      "0.0173459831426361\n",
      "Training epoch 1925, recon_loss:0.606281, zinb_loss:0.958966, cluster_loss:0.334080\n",
      "Clustering   1925: AMI= 0.7190, NMI= 0.7199, ARI= 0.4970, ACC= 0.5988\n",
      "0.018241785088969422\n",
      "Training epoch 1926, recon_loss:0.605992, zinb_loss:0.959305, cluster_loss:0.334175\n",
      "Clustering   1926: AMI= 0.7198, NMI= 0.7207, ARI= 0.4995, ACC= 0.6041\n",
      "0.018893277413575472\n",
      "Training epoch 1927, recon_loss:0.606537, zinb_loss:0.959028, cluster_loss:0.334060\n",
      "Clustering   1927: AMI= 0.7190, NMI= 0.7199, ARI= 0.4971, ACC= 0.5986\n",
      "0.019504051467893645\n",
      "Training epoch 1928, recon_loss:0.606091, zinb_loss:0.959269, cluster_loss:0.334158\n",
      "Clustering   1928: AMI= 0.7196, NMI= 0.7206, ARI= 0.4996, ACC= 0.6042\n",
      "0.019626206278757278\n",
      "Training epoch 1929, recon_loss:0.606596, zinb_loss:0.959121, cluster_loss:0.334227\n",
      "Clustering   1929: AMI= 0.7188, NMI= 0.7197, ARI= 0.4966, ACC= 0.5981\n",
      "0.020399853414226964\n",
      "Training epoch 1930, recon_loss:0.606056, zinb_loss:0.959235, cluster_loss:0.334249\n",
      "Clustering   1930: AMI= 0.7198, NMI= 0.7207, ARI= 0.5002, ACC= 0.6048\n",
      "0.02092919092796938\n",
      "Training epoch 1931, recon_loss:0.606479, zinb_loss:0.959222, cluster_loss:0.334468\n",
      "Clustering   1931: AMI= 0.7189, NMI= 0.7198, ARI= 0.4967, ACC= 0.5982\n",
      "0.02113278227940877\n",
      "Training epoch 1932, recon_loss:0.605939, zinb_loss:0.959223, cluster_loss:0.334383\n",
      "Clustering   1932: AMI= 0.7199, NMI= 0.7208, ARI= 0.5007, ACC= 0.6053\n",
      "0.021458528441711797\n",
      "Training epoch 1933, recon_loss:0.606300, zinb_loss:0.959337, cluster_loss:0.334699\n",
      "Clustering   1933: AMI= 0.7187, NMI= 0.7196, ARI= 0.4960, ACC= 0.5978\n",
      "0.022150739036605725\n",
      "Training epoch 1934, recon_loss:0.605811, zinb_loss:0.959245, cluster_loss:0.334515\n",
      "Clustering   1934: AMI= 0.7200, NMI= 0.7210, ARI= 0.5009, ACC= 0.6054\n",
      "0.02251720346919663\n",
      "Training epoch 1935, recon_loss:0.606099, zinb_loss:0.959430, cluster_loss:0.334885\n",
      "Clustering   1935: AMI= 0.7187, NMI= 0.7197, ARI= 0.4960, ACC= 0.5979\n",
      "0.022110020766317847\n",
      "Training epoch 1936, recon_loss:0.605703, zinb_loss:0.959283, cluster_loss:0.334632\n",
      "Clustering   1936: AMI= 0.7201, NMI= 0.7210, ARI= 0.5010, ACC= 0.6055\n",
      "0.02227289384746936\n",
      "Training epoch 1937, recon_loss:0.605927, zinb_loss:0.959522, cluster_loss:0.335018\n",
      "Clustering   1937: AMI= 0.7186, NMI= 0.7195, ARI= 0.4954, ACC= 0.5973\n",
      "0.02272079482063602\n",
      "Training epoch 1938, recon_loss:0.605633, zinb_loss:0.959328, cluster_loss:0.334725\n",
      "Clustering   1938: AMI= 0.7201, NMI= 0.7210, ARI= 0.5011, ACC= 0.6055\n",
      "0.023087259253226924\n",
      "Training epoch 1939, recon_loss:0.605807, zinb_loss:0.959616, cluster_loss:0.335111\n",
      "Clustering   1939: AMI= 0.7184, NMI= 0.7193, ARI= 0.4949, ACC= 0.5972\n",
      "0.023779469848120852\n",
      "Training epoch 1940, recon_loss:0.605624, zinb_loss:0.959383, cluster_loss:0.334795\n",
      "Clustering   1940: AMI= 0.7201, NMI= 0.7211, ARI= 0.5013, ACC= 0.6057\n",
      "0.02455311698359054\n",
      "Training epoch 1941, recon_loss:0.605768, zinb_loss:0.959723, cluster_loss:0.335168\n",
      "Clustering   1941: AMI= 0.7183, NMI= 0.7192, ARI= 0.4945, ACC= 0.5969\n",
      "0.025082454497332953\n",
      "Training epoch 1942, recon_loss:0.605657, zinb_loss:0.959435, cluster_loss:0.334840\n",
      "Clustering   1942: AMI= 0.7201, NMI= 0.7211, ARI= 0.5013, ACC= 0.6057\n",
      "0.025245327578484467\n",
      "Training epoch 1943, recon_loss:0.605807, zinb_loss:0.959845, cluster_loss:0.335184\n",
      "Clustering   1943: AMI= 0.7183, NMI= 0.7192, ARI= 0.4944, ACC= 0.5968\n",
      "0.025978256443666272\n",
      "Training epoch 1944, recon_loss:0.605750, zinb_loss:0.959489, cluster_loss:0.334862\n",
      "Clustering   1944: AMI= 0.7202, NMI= 0.7211, ARI= 0.5015, ACC= 0.6058\n",
      "0.026833340119711713\n",
      "Training epoch 1945, recon_loss:0.605933, zinb_loss:0.959961, cluster_loss:0.335163\n",
      "Clustering   1945: AMI= 0.7183, NMI= 0.7192, ARI= 0.4942, ACC= 0.5968\n",
      "0.027688423795757155\n",
      "Training epoch 1946, recon_loss:0.605932, zinb_loss:0.959524, cluster_loss:0.334858\n",
      "Clustering   1946: AMI= 0.7201, NMI= 0.7210, ARI= 0.5016, ACC= 0.6060\n",
      "0.028299197850075328\n",
      "Training epoch 1947, recon_loss:0.606133, zinb_loss:0.960068, cluster_loss:0.335108\n",
      "Clustering   1947: AMI= 0.7179, NMI= 0.7189, ARI= 0.4937, ACC= 0.5966\n",
      "0.02935787287756016\n",
      "Training epoch 1948, recon_loss:0.606176, zinb_loss:0.959525, cluster_loss:0.334831\n",
      "Clustering   1948: AMI= 0.7203, NMI= 0.7212, ARI= 0.5019, ACC= 0.6059\n",
      "0.029887210391302578\n",
      "Training epoch 1949, recon_loss:0.606422, zinb_loss:0.960121, cluster_loss:0.335002\n",
      "Clustering   1949: AMI= 0.7180, NMI= 0.7189, ARI= 0.4937, ACC= 0.5963\n",
      "0.03049798444562075\n",
      "Training epoch 1950, recon_loss:0.606410, zinb_loss:0.959465, cluster_loss:0.334790\n",
      "Clustering   1950: AMI= 0.7203, NMI= 0.7212, ARI= 0.5019, ACC= 0.6060\n",
      "0.03074229406734802\n",
      "Training epoch 1951, recon_loss:0.606590, zinb_loss:0.960135, cluster_loss:0.334929\n",
      "Clustering   1951: AMI= 0.7180, NMI= 0.7189, ARI= 0.4936, ACC= 0.5962\n",
      "0.03131234985137831\n",
      "Training epoch 1952, recon_loss:0.606514, zinb_loss:0.959360, cluster_loss:0.334773\n",
      "Clustering   1952: AMI= 0.7201, NMI= 0.7210, ARI= 0.5016, ACC= 0.6056\n",
      "0.0311494767702268\n",
      "Training epoch 1953, recon_loss:0.606668, zinb_loss:0.960067, cluster_loss:0.334868\n",
      "Clustering   1953: AMI= 0.7181, NMI= 0.7190, ARI= 0.4938, ACC= 0.5966\n",
      "0.03094588541878741\n",
      "Training epoch 1954, recon_loss:0.606474, zinb_loss:0.959226, cluster_loss:0.334799\n",
      "Clustering   1954: AMI= 0.7200, NMI= 0.7210, ARI= 0.5015, ACC= 0.6052\n",
      "0.0302129565536056\n",
      "Training epoch 1955, recon_loss:0.606507, zinb_loss:0.959962, cluster_loss:0.334919\n",
      "Clustering   1955: AMI= 0.7182, NMI= 0.7191, ARI= 0.4940, ACC= 0.5970\n",
      "0.02935787287756016\n",
      "Training epoch 1956, recon_loss:0.606317, zinb_loss:0.959109, cluster_loss:0.334871\n",
      "Clustering   1956: AMI= 0.7200, NMI= 0.7210, ARI= 0.5012, ACC= 0.6050\n",
      "0.02846207093122684\n",
      "Training epoch 1957, recon_loss:0.606381, zinb_loss:0.959849, cluster_loss:0.334980\n",
      "Clustering   1957: AMI= 0.7183, NMI= 0.7193, ARI= 0.4945, ACC= 0.5975\n",
      "0.027036931471151104\n",
      "Training epoch 1958, recon_loss:0.606262, zinb_loss:0.959033, cluster_loss:0.334924\n",
      "Clustering   1958: AMI= 0.7200, NMI= 0.7209, ARI= 0.5012, ACC= 0.6051\n",
      "0.026833340119711713\n",
      "Training epoch 1959, recon_loss:0.606342, zinb_loss:0.959794, cluster_loss:0.335074\n",
      "Clustering   1959: AMI= 0.7184, NMI= 0.7193, ARI= 0.4946, ACC= 0.5978\n",
      "0.025978256443666272\n",
      "Training epoch 1960, recon_loss:0.606278, zinb_loss:0.959013, cluster_loss:0.334951\n",
      "Clustering   1960: AMI= 0.7199, NMI= 0.7208, ARI= 0.5006, ACC= 0.6046\n",
      "0.024960299686469317\n",
      "Training epoch 1961, recon_loss:0.606451, zinb_loss:0.959770, cluster_loss:0.335135\n",
      "Clustering   1961: AMI= 0.7186, NMI= 0.7196, ARI= 0.4950, ACC= 0.5982\n",
      "0.02455311698359054\n",
      "Training epoch 1962, recon_loss:0.606459, zinb_loss:0.959033, cluster_loss:0.334923\n",
      "Clustering   1962: AMI= 0.7198, NMI= 0.7207, ARI= 0.5003, ACC= 0.6044\n",
      "0.023657315037257216\n",
      "Training epoch 1963, recon_loss:0.606658, zinb_loss:0.959760, cluster_loss:0.335146\n",
      "Clustering   1963: AMI= 0.7188, NMI= 0.7198, ARI= 0.4953, ACC= 0.5986\n",
      "0.022598640009772384\n",
      "Training epoch 1964, recon_loss:0.606666, zinb_loss:0.959080, cluster_loss:0.334813\n",
      "Clustering   1964: AMI= 0.7199, NMI= 0.7208, ARI= 0.5003, ACC= 0.6044\n",
      "0.022354330388045116\n",
      "Training epoch 1965, recon_loss:0.606817, zinb_loss:0.959821, cluster_loss:0.335185\n",
      "Clustering   1965: AMI= 0.7188, NMI= 0.7197, ARI= 0.4954, ACC= 0.5988\n",
      "0.022232175577181483\n",
      "Training epoch 1966, recon_loss:0.606611, zinb_loss:0.959148, cluster_loss:0.334657\n",
      "Clustering   1966: AMI= 0.7198, NMI= 0.7207, ARI= 0.4999, ACC= 0.6045\n",
      "0.022802231361211775\n",
      "Training epoch 1967, recon_loss:0.606774, zinb_loss:0.959860, cluster_loss:0.335206\n",
      "Clustering   1967: AMI= 0.7187, NMI= 0.7196, ARI= 0.4954, ACC= 0.5989\n",
      "0.023127977523514802\n",
      "Training epoch 1968, recon_loss:0.606447, zinb_loss:0.959205, cluster_loss:0.334507\n",
      "Clustering   1968: AMI= 0.7197, NMI= 0.7206, ARI= 0.4998, ACC= 0.6043\n",
      "0.023209414064090557\n",
      "Training epoch 1969, recon_loss:0.606616, zinb_loss:0.959855, cluster_loss:0.335209\n",
      "Clustering   1969: AMI= 0.7187, NMI= 0.7197, ARI= 0.4955, ACC= 0.5991\n",
      "0.023250132334378434\n",
      "Training epoch 1970, recon_loss:0.606132, zinb_loss:0.959230, cluster_loss:0.334404\n",
      "Clustering   1970: AMI= 0.7197, NMI= 0.7206, ARI= 0.4998, ACC= 0.6045\n",
      "0.023779469848120852\n",
      "Training epoch 1971, recon_loss:0.606307, zinb_loss:0.959861, cluster_loss:0.335274\n",
      "Clustering   1971: AMI= 0.7188, NMI= 0.7197, ARI= 0.4956, ACC= 0.5992\n",
      "0.023494441956105706\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training epoch 1972, recon_loss:0.605632, zinb_loss:0.959246, cluster_loss:0.334363\n",
      "Clustering   1972: AMI= 0.7197, NMI= 0.7207, ARI= 0.4999, ACC= 0.6046\n",
      "0.024145934280711757\n",
      "Training epoch 1973, recon_loss:0.606015, zinb_loss:0.959912, cluster_loss:0.335367\n",
      "Clustering   1973: AMI= 0.7188, NMI= 0.7197, ARI= 0.4957, ACC= 0.5992\n",
      "0.02426808909157539\n",
      "Training epoch 1974, recon_loss:0.605209, zinb_loss:0.959302, cluster_loss:0.334345\n",
      "Clustering   1974: AMI= 0.7198, NMI= 0.7207, ARI= 0.5001, ACC= 0.6048\n",
      "0.02467527179445417\n",
      "Training epoch 1975, recon_loss:0.605772, zinb_loss:0.960024, cluster_loss:0.335473\n",
      "Clustering   1975: AMI= 0.7187, NMI= 0.7197, ARI= 0.4956, ACC= 0.5991\n",
      "0.024797426605317807\n",
      "Training epoch 1976, recon_loss:0.604984, zinb_loss:0.959394, cluster_loss:0.334374\n",
      "Clustering   1976: AMI= 0.7198, NMI= 0.7207, ARI= 0.5001, ACC= 0.6048\n",
      "0.02491958141618144\n",
      "Training epoch 1977, recon_loss:0.605607, zinb_loss:0.960098, cluster_loss:0.335537\n",
      "Clustering   1977: AMI= 0.7187, NMI= 0.7197, ARI= 0.4957, ACC= 0.5990\n",
      "0.02471599006474205\n",
      "Training epoch 1978, recon_loss:0.604827, zinb_loss:0.959498, cluster_loss:0.334457\n",
      "Clustering   1978: AMI= 0.7198, NMI= 0.7207, ARI= 0.5002, ACC= 0.6049\n",
      "0.024838144875605685\n",
      "Training epoch 1979, recon_loss:0.605558, zinb_loss:0.960154, cluster_loss:0.335587\n",
      "Clustering   1979: AMI= 0.7189, NMI= 0.7199, ARI= 0.4958, ACC= 0.5992\n",
      "0.024756708335029926\n",
      "Training epoch 1980, recon_loss:0.604826, zinb_loss:0.959612, cluster_loss:0.334499\n",
      "Clustering   1980: AMI= 0.7197, NMI= 0.7206, ARI= 0.5000, ACC= 0.6049\n",
      "0.025041736227045076\n",
      "Training epoch 1981, recon_loss:0.605503, zinb_loss:0.960216, cluster_loss:0.335614\n",
      "Clustering   1981: AMI= 0.7189, NMI= 0.7199, ARI= 0.4960, ACC= 0.5992\n",
      "0.024838144875605685\n",
      "Training epoch 1982, recon_loss:0.604883, zinb_loss:0.959741, cluster_loss:0.334547\n",
      "Clustering   1982: AMI= 0.7196, NMI= 0.7206, ARI= 0.4995, ACC= 0.6041\n",
      "0.025163891037908708\n",
      "Training epoch 1983, recon_loss:0.605573, zinb_loss:0.960283, cluster_loss:0.335636\n",
      "Clustering   1983: AMI= 0.7189, NMI= 0.7198, ARI= 0.4960, ACC= 0.5992\n",
      "0.025489637200211735\n",
      "Training epoch 1984, recon_loss:0.605068, zinb_loss:0.959876, cluster_loss:0.334556\n",
      "Clustering   1984: AMI= 0.7196, NMI= 0.7205, ARI= 0.4994, ACC= 0.6038\n",
      "0.02565251028136325\n",
      "Training epoch 1985, recon_loss:0.605587, zinb_loss:0.960336, cluster_loss:0.335645\n",
      "Clustering   1985: AMI= 0.7191, NMI= 0.7201, ARI= 0.4964, ACC= 0.5996\n",
      "0.026141129524817786\n",
      "Training epoch 1986, recon_loss:0.605221, zinb_loss:0.959998, cluster_loss:0.334574\n",
      "Clustering   1986: AMI= 0.7195, NMI= 0.7204, ARI= 0.4993, ACC= 0.6037\n",
      "0.026181847795105663\n",
      "Training epoch 1987, recon_loss:0.605749, zinb_loss:0.960375, cluster_loss:0.335653\n",
      "Clustering   1987: AMI= 0.7191, NMI= 0.7200, ARI= 0.4965, ACC= 0.5999\n",
      "0.026344720876257176\n",
      "Training epoch 1988, recon_loss:0.605552, zinb_loss:0.960100, cluster_loss:0.334548\n",
      "Clustering   1988: AMI= 0.7195, NMI= 0.7205, ARI= 0.4992, ACC= 0.6033\n",
      "0.026385439146545054\n",
      "Training epoch 1989, recon_loss:0.605869, zinb_loss:0.960350, cluster_loss:0.335642\n",
      "Clustering   1989: AMI= 0.7189, NMI= 0.7198, ARI= 0.4965, ACC= 0.6002\n",
      "0.026141129524817786\n",
      "Training epoch 1990, recon_loss:0.605865, zinb_loss:0.960153, cluster_loss:0.334548\n",
      "Clustering   1990: AMI= 0.7194, NMI= 0.7203, ARI= 0.4987, ACC= 0.6026\n",
      "0.025937538173378395\n",
      "Training epoch 1991, recon_loss:0.606142, zinb_loss:0.960293, cluster_loss:0.335633\n",
      "Clustering   1991: AMI= 0.7191, NMI= 0.7200, ARI= 0.4969, ACC= 0.6008\n",
      "0.02601897471395415\n",
      "Training epoch 1992, recon_loss:0.606289, zinb_loss:0.960128, cluster_loss:0.334522\n",
      "Clustering   1992: AMI= 0.7195, NMI= 0.7204, ARI= 0.4985, ACC= 0.6021\n",
      "0.02605969298424203\n",
      "Training epoch 1993, recon_loss:0.606394, zinb_loss:0.960175, cluster_loss:0.335607\n",
      "Clustering   1993: AMI= 0.7189, NMI= 0.7199, ARI= 0.4970, ACC= 0.6009\n",
      "0.026100411254529908\n",
      "Training epoch 1994, recon_loss:0.606670, zinb_loss:0.960048, cluster_loss:0.334504\n",
      "Clustering   1994: AMI= 0.7192, NMI= 0.7201, ARI= 0.4981, ACC= 0.6018\n",
      "0.025733946821939004\n",
      "Training epoch 1995, recon_loss:0.606513, zinb_loss:0.959983, cluster_loss:0.335580\n",
      "Clustering   1995: AMI= 0.7190, NMI= 0.7199, ARI= 0.4972, ACC= 0.6011\n",
      "0.025448918929923858\n",
      "Training epoch 1996, recon_loss:0.606802, zinb_loss:0.959894, cluster_loss:0.334518\n",
      "Clustering   1996: AMI= 0.7191, NMI= 0.7200, ARI= 0.4978, ACC= 0.6013\n",
      "0.025082454497332953\n",
      "Training epoch 1997, recon_loss:0.606578, zinb_loss:0.959801, cluster_loss:0.335564\n",
      "Clustering   1997: AMI= 0.7191, NMI= 0.7200, ARI= 0.4973, ACC= 0.6014\n",
      "0.02471599006474205\n",
      "Training epoch 1998, recon_loss:0.606799, zinb_loss:0.959686, cluster_loss:0.334551\n",
      "Clustering   1998: AMI= 0.7190, NMI= 0.7199, ARI= 0.4976, ACC= 0.6009\n",
      "0.023983061199560243\n",
      "Training epoch 1999, recon_loss:0.606388, zinb_loss:0.959575, cluster_loss:0.335586\n",
      "Clustering   1999: AMI= 0.7191, NMI= 0.7201, ARI= 0.4976, ACC= 0.6017\n",
      "0.023494441956105706\n",
      "Training epoch 2000, recon_loss:0.606603, zinb_loss:0.959469, cluster_loss:0.334630\n",
      "Clustering   2000: AMI= 0.7189, NMI= 0.7199, ARI= 0.4976, ACC= 0.6008\n",
      "0.022680076550348142\n",
      "Final Result : AMI= 0.7189, NMI= 0.7199, ARI= 0.4976, ACC= 0.6008\n"
     ]
    }
   ],
   "source": [
    "y_pred, final_latent = model.fit(y=y, n_clusters=23, num_epochs=2000, file='GSE163120')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "ae8a2ff8",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "np.savetxt(\"../results/GSE163120_pred.csv\", y_pred, delimiter=\",\")\n",
    "np.savetxt(\"../results/GSE163120_embedding.csv\", final_latent.cpu().detach().numpy(), delimiter=\",\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "f1c9e57c",
   "metadata": {},
   "outputs": [],
   "source": [
    "from scib_metrics.benchmark import Benchmarker,BioConservation,BatchCorrection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "abf8f100",
   "metadata": {},
   "outputs": [],
   "source": [
    "adata2.obs[\"cell_type\"] = np.array(pd.read_csv('../datasets/GSE163120/GSE163120_label.csv', index_col=0)['cell_label'])\n",
    "adata2.obs[\"batch\"] = np.array(pd.read_csv('../datasets/GSE163120/GSE163120_label.csv', index_col=0)['batch_id'])\n",
    "adata2.obsm[\"scMAGCA\"] = np.array(pd.read_csv('../results/GSE163120_embedding.csv',header=None))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "04aeb4ea",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Computing neighbors: 100%|███████████████████████████████████████████████████████████████| 1/1 [02:16<00:00, 136.67s/it]\n",
      "Embeddings:   0%|\u001b[32m                                                                                 \u001b[0m| 0/1 [00:00<?, ?it/s]\u001b[0m\n",
      "Metrics:   0%|\u001b[34m                                                                                   \u001b[0m| 0/10 [00:00<?, ?it/s]\u001b[0m\u001b[A\n",
      "Metrics:   0%|\u001b[34m                                                \u001b[0m| 0/10 [00:00<?, ?it/s, Bio conservation: isolated_labels]\u001b[0m\u001b[AWARNING:jax._src.xla_bridge:An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n",
      "\n",
      "Metrics:  10%|\u001b[34m████                                    \u001b[0m| 1/10 [00:03<00:28,  3.13s/it, Bio conservation: isolated_labels]\u001b[0m\u001b[A\n",
      "Metrics:  10%|\u001b[34m██▌                       \u001b[0m| 1/10 [00:03<00:28,  3.13s/it, Bio conservation: nmi_ari_cluster_labels_kmeans]\u001b[0m\u001b[A\n",
      "Metrics:  20%|\u001b[34m█████▏                    \u001b[0m| 2/10 [00:08<00:35,  4.39s/it, Bio conservation: nmi_ari_cluster_labels_kmeans]\u001b[0m\u001b[A\n",
      "Metrics:  20%|\u001b[34m███████▊                               \u001b[0m| 2/10 [00:08<00:35,  4.39s/it, Bio conservation: silhouette_label]\u001b[0m\u001b[A\n",
      "Metrics:  30%|\u001b[34m███████████▋                           \u001b[0m| 3/10 [00:10<00:22,  3.24s/it, Bio conservation: silhouette_label]\u001b[0m\u001b[A\n",
      "Metrics:  30%|\u001b[34m█████████████▊                                \u001b[0m| 3/10 [00:10<00:22,  3.24s/it, Bio conservation: clisi_knn]\u001b[0m\u001b[A\n",
      "Metrics:  40%|\u001b[34m██████████████████▍                           \u001b[0m| 4/10 [00:11<00:14,  2.42s/it, Bio conservation: clisi_knn]\u001b[0m\u001b[A\n",
      "Metrics:  40%|\u001b[34m███████████████▌                       \u001b[0m| 4/10 [00:11<00:14,  2.42s/it, Batch correction: silhouette_batch]\u001b[0m\u001b[A\n",
      "Metrics:  50%|\u001b[34m███████████████████▌                   \u001b[0m| 5/10 [00:22<00:27,  5.50s/it, Batch correction: silhouette_batch]\u001b[0m\u001b[A\n",
      "Metrics:  50%|\u001b[34m███████████████████████                       \u001b[0m| 5/10 [00:22<00:27,  5.50s/it, Batch correction: ilisi_knn]\u001b[0m\u001b[A\n",
      "Metrics:  60%|\u001b[34m███████████████████████████▌                  \u001b[0m| 6/10 [00:22<00:14,  3.70s/it, Batch correction: ilisi_knn]\u001b[0m\u001b[A\n",
      "Metrics:  60%|\u001b[34m████████████████████████▌                \u001b[0m| 6/10 [00:22<00:14,  3.70s/it, Batch correction: kbet_per_label]\u001b[0m\u001b[A\n",
      "Metrics:  70%|\u001b[34m████████████████████████████▋            \u001b[0m| 7/10 [00:50<00:35, 11.72s/it, Batch correction: kbet_per_label]\u001b[0m\u001b[A\n",
      "Embeddings: 100%|\u001b[32m█████████████████████████████████████████████████████████████████████████\u001b[0m| 1/1 [00:50<00:00, 50.88s/it]\u001b[0m\u001b[A\n",
      "\n",
      "                                                                                                                        \u001b[A"
     ]
    }
   ],
   "source": [
    "bm = Benchmarker(\n",
    "    adata2,\n",
    "    label_key=\"cell_type\",\n",
    "    batch_key=\"batch\",\n",
    "    embedding_obsm_keys=[\"scMAGCA\"],\n",
    "    bio_conservation_metrics=BioConservation(nmi_ari_cluster_labels_kmeans=True,isolated_labels=True,silhouette_label=True,clisi_knn=True),\n",
    "    batch_correction_metrics=BatchCorrection(silhouette_batch=True,ilisi_knn=True,kbet_per_label=True,graph_connectivity=True,pcr_comparison=False),\n",
    "    n_jobs=1,\n",
    ")\n",
    "bm.benchmark()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "f4c5faf0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Isolated labels</th>\n",
       "      <th>KMeans NMI</th>\n",
       "      <th>KMeans ARI</th>\n",
       "      <th>Silhouette label</th>\n",
       "      <th>cLISI</th>\n",
       "      <th>Silhouette batch</th>\n",
       "      <th>iLISI</th>\n",
       "      <th>KBET</th>\n",
       "      <th>Graph connectivity</th>\n",
       "      <th>Batch correction</th>\n",
       "      <th>Bio conservation</th>\n",
       "      <th>Total</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Embedding</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>scMAGCA</th>\n",
       "      <td>0.621098</td>\n",
       "      <td>0.714943</td>\n",
       "      <td>0.476137</td>\n",
       "      <td>0.588032</td>\n",
       "      <td>0.99651</td>\n",
       "      <td>0.845322</td>\n",
       "      <td>0.449404</td>\n",
       "      <td>0.648513</td>\n",
       "      <td>0.928204</td>\n",
       "      <td>0.717861</td>\n",
       "      <td>0.679344</td>\n",
       "      <td>0.694751</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Metric Type</th>\n",
       "      <td>Bio conservation</td>\n",
       "      <td>Bio conservation</td>\n",
       "      <td>Bio conservation</td>\n",
       "      <td>Bio conservation</td>\n",
       "      <td>Bio conservation</td>\n",
       "      <td>Batch correction</td>\n",
       "      <td>Batch correction</td>\n",
       "      <td>Batch correction</td>\n",
       "      <td>Batch correction</td>\n",
       "      <td>Aggregate score</td>\n",
       "      <td>Aggregate score</td>\n",
       "      <td>Aggregate score</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Isolated labels        KMeans NMI        KMeans ARI  \\\n",
       "Embedding                                                           \n",
       "scMAGCA              0.621098          0.714943          0.476137   \n",
       "Metric Type  Bio conservation  Bio conservation  Bio conservation   \n",
       "\n",
       "             Silhouette label             cLISI  Silhouette batch  \\\n",
       "Embedding                                                           \n",
       "scMAGCA              0.588032           0.99651          0.845322   \n",
       "Metric Type  Bio conservation  Bio conservation  Batch correction   \n",
       "\n",
       "                        iLISI              KBET Graph connectivity  \\\n",
       "Embedding                                                            \n",
       "scMAGCA              0.449404          0.648513           0.928204   \n",
       "Metric Type  Batch correction  Batch correction   Batch correction   \n",
       "\n",
       "            Batch correction Bio conservation            Total  \n",
       "Embedding                                                       \n",
       "scMAGCA             0.717861         0.679344         0.694751  \n",
       "Metric Type  Aggregate score  Aggregate score  Aggregate score  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bm.get_results(min_max_scale=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "26dc390e",
   "metadata": {},
   "outputs": [],
   "source": [
    "library(umap)\n",
    "library(ggplot2)\n",
    "library(scattermore)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "6831205c",
   "metadata": {},
   "outputs": [],
   "source": [
    "latent <- read.csv(file = \"../results/GSE163120_embedding.csv\", sep = \",\", header = FALSE)\n",
    "batch_label <- read.csv(file = \"../datasets/GSE163120/GSE163120_label.csv\", sep = \",\")['batch_id']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "9eb6959a",
   "metadata": {},
   "outputs": [],
   "source": [
    "z.umap<-umap(latent)\n",
    "batch_label<-as.factor(batch_label$batch_id)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "a666bb37",
   "metadata": {},
   "outputs": [
    {
     "data": {
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W4EHE1XLVlMeVznzzlgaACCEouFERG000Hx+TEzZ/nzN+3dQ3AcoW1kSZJkWQBQ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sOceLZjeUzjtYfXZxuunmGVnj80m9cfah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\nU8vCJfYNG4KnThqn1lV870HHhuvtEycMvvOm+IfBE0TRxuuHdLmD9rJ1tXm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U85R2QpVJBQgm2jQz8nTt/gQnYADU5In3hlOcgAu18tsn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\nMmP9Eup8C+ZYgiRTWXPBM6cyWo9I2Dgp8NDdu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MXG9QHHc+u/cMPXv7DMQGv+dB8A9BY1nCtb7opxMorEGEspssaifseZOyLJN0d8AsaL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\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O840u+ak6Ld2GoFgPPS9WubLAAwjvQU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\nqf60ODdB+Lm816VZqmc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dNk/AFngGVB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O7BH43Xa5TrNkWbSrw7RlU9TtBoAJkSpl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\nQmCC",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "bm_table_num <- data.frame(UMAP1=z.umap$layout[,1], UMAP2=z.umap$layout[,2],Cluster = batch_label)\n",
    "bm_plot1 <- ggplot(bm_table_num, aes(x = UMAP1, y = UMAP2, color = Cluster)) +\n",
    "  geom_point(size = 0.1) +\n",
    "  ggtitle('scMAGCA') +\n",
    "  theme_bw(base_line_size = 1,base_rect_size = 1)+\n",
    "  scale_color_manual(values = c(\"0\"=\"#55A9CF\", \"1\"=\"#C24640\"))+\n",
    "  labs(x = \"\", y = \"\", color = \"Label\")+  # Set x and y axis labels\n",
    "  theme(\n",
    "    plot.title = element_text(size = 20, hjust = 0.5, face = 'bold'),  # Set title to center\n",
    "    panel.border = element_blank(),\n",
    "    axis.ticks.length=unit(0, \"lines\"),  # Remove axis ticks but keep axis labels\n",
    "    axis.text = element_blank(),\n",
    "    panel.grid.minor = element_blank(),\n",
    "    panel.grid.major = element_line(color = NA),\n",
    "    axis.title.x = element_text(size = 15, hjust = 0.5, vjust = 0.5, color = \"black\"),\n",
    "    axis.title.y = element_text(size = 15, hjust = 0.5, vjust = 0.5, color = \"black\"),\n",
    "    legend.title = element_blank(),\n",
    "    legend.text = element_text(size = 15),\n",
    "    legend.position = \"none\")+\n",
    "  guides(color = guide_legend(override.aes = list(size = 5), nrow=2))\n",
    "bm_plot1"
   ]
  }
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