{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "42363f2f",
   "metadata": {},
   "source": [
    "#  Tutorial:RNA+ADT+ATAC (3omics)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "13d46422",
   "metadata": {},
   "source": [
    "In this tutorial, we will show how to cluster RNA+ADT+ATAC(3omics) data using scMAGCA. We use a processed human peripheral blood mononuclear sample dataset 'GSE158013' containing 7084 cells with three omics. Among them, ADT has 46 features, ATAC includes 2500 features and RNA contains 15000 features."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fe815756",
   "metadata": {},
   "source": [
    "## Loading package"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "1dec408a",
   "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",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "3cb16af3",
   "metadata": {},
   "outputs": [],
   "source": [
    "from scMAGCA.preprocess import read_dataset, preprocess_dataset\n",
    "from scMAGCA.utils import *\n",
    "from scMAGCA.scMAGCA_3omics import scMultiCluster"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "14488673",
   "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": "31119358",
   "metadata": {},
   "source": [
    "## Reading dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a2864de3",
   "metadata": {},
   "source": [
    "The required input files include: \n",
    "1) x1: protein abundance matrix (data format is h5ad file) : GSE158013_adt.h5ad;\\\n",
    "2) x2: Chromatin accessibility matrix (data format is h5ad file) : GSE158013_atac.h5ad;\\\n",
    "3) x3: Gene expression matrix (data format is h5ad file) : GSE158013_rna.h5ad.\n",
    "\n",
    "To ensure reproducibility of the results, please read the above data as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "39efaf2b",
   "metadata": {},
   "outputs": [],
   "source": [
    "x1 = sc.read_h5ad('../datasets/GSE158013/GSE158013_adt.h5ad').layers['counts']\n",
    "feature1 = sc.read_h5ad('../datasets/GSE158013/GSE158013_adt.h5ad').var.index\n",
    "x2 = sc.read_h5ad('../datasets/GSE158013/GSE158013_atac.h5ad').layers['counts'].A\n",
    "feature2 = sc.read_h5ad('../datasets/GSE158013/GSE158013_atac.h5ad').var.index\n",
    "x3 = sc.read_h5ad('../datasets/GSE158013/GSE158013_rna.h5ad').layers['counts'].A\n",
    "feature3 = sc.read_h5ad('../datasets/GSE158013/GSE158013_rna.h5ad').var.index\n",
    "y = None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "f56062a8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([[ 1.,  1., 15., ...,  0., 22., 44.],\n",
       "        [30.,  3., 14., ...,  1., 20., 65.],\n",
       "        [ 2.,  1., 17., ...,  1., 18., 37.],\n",
       "        ...,\n",
       "        [ 0.,  2., 12., ..., 31., 15., 30.],\n",
       "        [ 0.,  0.,  8., ...,  1., 22., 35.],\n",
       "        [ 0.,  0., 18., ...,  0., 24., 44.]], dtype=float32),\n",
       " array([[0., 0., 0., ..., 0., 0., 0.],\n",
       "        [0., 0., 0., ..., 0., 0., 0.],\n",
       "        [0., 0., 0., ..., 0., 0., 0.],\n",
       "        ...,\n",
       "        [0., 0., 0., ..., 0., 0., 0.],\n",
       "        [0., 0., 0., ..., 0., 0., 0.],\n",
       "        [0., 0., 0., ..., 0., 0., 0.]], dtype=float32),\n",
       " array([[ 0.,  0.,  0., ...,  0., 14., 28.],\n",
       "        [ 0.,  0.,  1., ...,  0., 15., 24.],\n",
       "        [ 0.,  0.,  1., ...,  0., 19., 33.],\n",
       "        ...,\n",
       "        [ 0.,  0.,  0., ...,  0., 15., 56.],\n",
       "        [ 0.,  0.,  0., ...,  0., 19., 49.],\n",
       "        [ 0.,  0.,  5., ...,  0., 23., 25.]], dtype=float32))"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x1,x2,x3"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5b303705",
   "metadata": {},
   "source": [
    "We select the ATAC and RNA omics for high expression, and the number of chosen features are both set to 2000."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "5987a299",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Chosen offset: 1.11\n",
      "Chosen offset: 0.17\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x252 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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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]\n",
    "feature2 = feature2[importantGenes]\n",
    "importantGenes = geneSelection(x3, n=2000)\n",
    "x3 = x3[:, importantGenes]\n",
    "feature3 = feature3[importantGenes]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "b7e2fb2b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "### Autoencoder: Successfully preprocessed 46 features and 7084 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)\n",
    "adata1.var['importantGenes'] = feature1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "81d57028",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AnnData object with n_obs × n_vars = 7084 × 46\n",
       "    obs: 'DCA_split', 'size_factors'\n",
       "    var: 'mean', 'std', 'importantGenes'\n",
       "    uns: 'log1p'"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adata1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "797823bb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "### Autoencoder: Successfully preprocessed 2000 features and 7084 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)\n",
    "adata2.var['importantGenes'] = feature2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "e040e96d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AnnData object with n_obs × n_vars = 7084 × 2000\n",
       "    obs: 'DCA_split', 'size_factors'\n",
       "    var: 'mean', 'std', 'importantGenes'\n",
       "    uns: 'log1p'"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adata2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "f6dd4f50",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "### Autoencoder: Successfully preprocessed 2000 features and 7084 cells.\n"
     ]
    }
   ],
   "source": [
    "adata3 = sc.AnnData(x3)\n",
    "adata3 = read_dataset(adata3, copy=True)\n",
    "adata3 = preprocess_dataset(adata3, normalize_input=True, logtrans_input=True)\n",
    "adata3.var['importantGenes'] = feature3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "6e62b554",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AnnData object with n_obs × n_vars = 7084 × 2000\n",
       "    obs: 'DCA_split', 'size_factors'\n",
       "    var: 'mean', 'std', 'importantGenes'\n",
       "    uns: 'log1p'"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adata3"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f4857f7c",
   "metadata": {},
   "source": [
    "## Training the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "e3d29d1d",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = scMultiCluster(input_dim1=adata1.n_vars,input_dim2=adata2.n_vars,input_dim3=adata3.n_vars,\n",
    "                           alpha=0.2,beta=0.8,gama=0.01,device='cuda').to('cuda')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "191c1af2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "scMultiCluster(\n",
       "  (encoder): Encoder(\n",
       "    (stacked_gnn): ModuleList(\n",
       "      (0): GCNConv(4046, 1024)\n",
       "      (1): GCNConv(1024, 256)\n",
       "      (2): GCNConv(256, 64)\n",
       "      (3): GCNConv(64, 32)\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(32, 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=32, 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=4046, bias=True)\n",
       "  )\n",
       "  (dec_mean): Sequential(\n",
       "    (0): Linear(in_features=32, out_features=256, bias=True)\n",
       "    (1): Linear(in_features=256, out_features=512, bias=True)\n",
       "    (2): Linear(in_features=512, out_features=4046, bias=True)\n",
       "    (3): MeanAct()\n",
       "  )\n",
       "  (dec_disp): Sequential(\n",
       "    (0): Linear(in_features=32, out_features=256, bias=True)\n",
       "    (1): Linear(in_features=256, out_features=512, bias=True)\n",
       "    (2): Linear(in_features=512, out_features=4046, bias=True)\n",
       "    (3): DispAct()\n",
       "  )\n",
       "  (dec_pi): Sequential(\n",
       "    (0): Linear(in_features=32, out_features=256, bias=True)\n",
       "    (1): Linear(in_features=256, out_features=512, bias=True)\n",
       "    (2): Linear(in_features=512, out_features=4046, bias=True)\n",
       "    (3): Sigmoid()\n",
       "  )\n",
       "  (zinb_loss): ZINBLoss()\n",
       ")"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "7a7b89b6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pretraining stage\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Processing...\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing full batch data\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Done!\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pretrain epoch 1, recon_loss:1.175199, zinb_loss:0.855500, adversial_loss:1.378395\n",
      "Pretrain epoch 2, recon_loss:1.108052, zinb_loss:0.790036, adversial_loss:1.353847\n",
      "Pretrain epoch 3, recon_loss:1.013116, zinb_loss:0.732100, adversial_loss:1.349079\n",
      "Pretrain epoch 4, recon_loss:0.967926, zinb_loss:0.685367, adversial_loss:1.347783\n",
      "Pretrain epoch 5, recon_loss:0.955065, zinb_loss:0.643808, adversial_loss:1.350997\n",
      "Pretrain epoch 6, recon_loss:0.948277, zinb_loss:0.607989, adversial_loss:1.351554\n",
      "Pretrain epoch 7, recon_loss:0.940977, zinb_loss:0.578766, adversial_loss:1.349777\n",
      "Pretrain epoch 8, recon_loss:0.933745, zinb_loss:0.558008, adversial_loss:1.347673\n",
      "Pretrain epoch 9, recon_loss:0.928594, zinb_loss:0.546180, adversial_loss:1.345650\n",
      "Pretrain epoch 10, recon_loss:0.924947, zinb_loss:0.540460, adversial_loss:1.343964\n",
      "Pretrain epoch 11, recon_loss:0.921956, zinb_loss:0.537047, adversial_loss:1.342250\n",
      "Pretrain epoch 12, recon_loss:0.919373, zinb_loss:0.533806, adversial_loss:1.340244\n",
      "Pretrain epoch 13, recon_loss:0.916590, zinb_loss:0.530386, adversial_loss:1.338154\n",
      "Pretrain epoch 14, recon_loss:0.913909, zinb_loss:0.527035, adversial_loss:1.335480\n",
      "Pretrain epoch 15, recon_loss:0.911839, zinb_loss:0.523866, adversial_loss:1.331557\n",
      "Pretrain epoch 16, recon_loss:0.910443, zinb_loss:0.521004, adversial_loss:1.326380\n",
      "Pretrain epoch 17, recon_loss:0.909288, zinb_loss:0.518576, adversial_loss:1.322320\n",
      "Pretrain epoch 18, recon_loss:0.907878, zinb_loss:0.516333, adversial_loss:1.319946\n",
      "Pretrain epoch 19, recon_loss:0.906563, zinb_loss:0.514146, adversial_loss:1.318467\n",
      "Pretrain epoch 20, recon_loss:0.905531, zinb_loss:0.512105, adversial_loss:1.317480\n",
      "Pretrain epoch 21, recon_loss:0.904526, zinb_loss:0.510200, adversial_loss:1.316783\n",
      "Pretrain epoch 22, recon_loss:0.903540, zinb_loss:0.508358, adversial_loss:1.316315\n",
      "Pretrain epoch 23, recon_loss:0.902707, zinb_loss:0.506648, adversial_loss:1.316024\n",
      "Pretrain epoch 24, recon_loss:0.901874, zinb_loss:0.505102, adversial_loss:1.315779\n",
      "Pretrain epoch 25, recon_loss:0.901007, zinb_loss:0.503651, adversial_loss:1.315447\n",
      "Pretrain epoch 26, recon_loss:0.900225, zinb_loss:0.502290, adversial_loss:1.314963\n",
      "Pretrain epoch 27, recon_loss:0.899482, zinb_loss:0.501040, adversial_loss:1.314348\n",
      "Pretrain epoch 28, recon_loss:0.898717, zinb_loss:0.499883, adversial_loss:1.313620\n",
      "Pretrain epoch 29, recon_loss:0.897974, zinb_loss:0.498816, adversial_loss:1.312802\n",
      "Pretrain epoch 30, recon_loss:0.897277, zinb_loss:0.497838, adversial_loss:1.311924\n",
      "Pretrain epoch 31, recon_loss:0.896619, zinb_loss:0.496917, adversial_loss:1.310987\n",
      "Pretrain epoch 32, recon_loss:0.895992, zinb_loss:0.496037, adversial_loss:1.309985\n",
      "Pretrain epoch 33, recon_loss:0.895375, zinb_loss:0.495203, adversial_loss:1.308975\n",
      "Pretrain epoch 34, recon_loss:0.894778, zinb_loss:0.494410, adversial_loss:1.308025\n",
      "Pretrain epoch 35, recon_loss:0.894228, zinb_loss:0.493646, adversial_loss:1.307193\n",
      "Pretrain epoch 36, recon_loss:0.893697, zinb_loss:0.492919, adversial_loss:1.306482\n",
      "Pretrain epoch 37, recon_loss:0.893168, zinb_loss:0.492235, adversial_loss:1.305805\n",
      "Pretrain epoch 38, recon_loss:0.892660, zinb_loss:0.491593, adversial_loss:1.305066\n",
      "Pretrain epoch 39, recon_loss:0.892151, zinb_loss:0.490987, adversial_loss:1.304236\n",
      "Pretrain epoch 40, recon_loss:0.891640, zinb_loss:0.490410, adversial_loss:1.303369\n",
      "Pretrain epoch 41, recon_loss:0.891153, zinb_loss:0.489853, adversial_loss:1.302559\n",
      "Pretrain epoch 42, recon_loss:0.890687, zinb_loss:0.489309, adversial_loss:1.301869\n",
      "Pretrain epoch 43, recon_loss:0.890251, zinb_loss:0.488777, adversial_loss:1.301257\n",
      "Pretrain epoch 44, recon_loss:0.889843, zinb_loss:0.488256, adversial_loss:1.300650\n",
      "Pretrain epoch 45, recon_loss:0.889454, zinb_loss:0.487751, adversial_loss:1.300038\n",
      "Pretrain epoch 46, recon_loss:0.889082, zinb_loss:0.487263, adversial_loss:1.299444\n",
      "Pretrain epoch 47, recon_loss:0.888712, zinb_loss:0.486794, adversial_loss:1.298817\n",
      "Pretrain epoch 48, recon_loss:0.888351, zinb_loss:0.486342, adversial_loss:1.298034\n",
      "Pretrain epoch 49, recon_loss:0.888008, zinb_loss:0.485909, adversial_loss:1.297121\n",
      "Pretrain epoch 50, recon_loss:0.887662, zinb_loss:0.485487, adversial_loss:1.296121\n",
      "Pretrain epoch 51, recon_loss:0.887324, zinb_loss:0.485070, adversial_loss:1.295191\n",
      "Pretrain epoch 52, recon_loss:0.886997, zinb_loss:0.484660, adversial_loss:1.294396\n",
      "Pretrain epoch 53, recon_loss:0.886684, zinb_loss:0.484257, adversial_loss:1.293579\n",
      "Pretrain epoch 54, recon_loss:0.886377, zinb_loss:0.483865, adversial_loss:1.292739\n",
      "Pretrain epoch 55, recon_loss:0.886101, zinb_loss:0.483481, adversial_loss:1.291661\n",
      "Pretrain epoch 56, recon_loss:0.885851, zinb_loss:0.483127, adversial_loss:1.291050\n",
      "Pretrain epoch 57, recon_loss:0.885890, zinb_loss:0.482857, adversial_loss:1.289647\n",
      "Pretrain epoch 58, recon_loss:0.886180, zinb_loss:0.482728, adversial_loss:1.289851\n",
      "Pretrain epoch 59, recon_loss:0.885215, zinb_loss:0.482178, adversial_loss:1.288190\n",
      "Pretrain epoch 60, recon_loss:0.885347, zinb_loss:0.481868, adversial_loss:1.286787\n",
      "Pretrain epoch 61, recon_loss:0.884960, zinb_loss:0.481549, adversial_loss:1.287031\n",
      "Pretrain epoch 62, recon_loss:0.884647, zinb_loss:0.481240, adversial_loss:1.286084\n",
      "Pretrain epoch 63, recon_loss:0.884216, zinb_loss:0.480799, adversial_loss:1.284261\n",
      "Pretrain epoch 64, recon_loss:0.884266, zinb_loss:0.480610, adversial_loss:1.283846\n",
      "Pretrain epoch 65, recon_loss:0.883800, zinb_loss:0.480226, adversial_loss:1.284666\n",
      "Pretrain epoch 66, recon_loss:0.883520, zinb_loss:0.479906, adversial_loss:1.283720\n",
      "Pretrain epoch 67, recon_loss:0.883410, zinb_loss:0.479631, adversial_loss:1.282337\n",
      "Pretrain epoch 68, recon_loss:0.883198, zinb_loss:0.479352, adversial_loss:1.282573\n",
      "Pretrain epoch 69, recon_loss:0.882770, zinb_loss:0.478971, adversial_loss:1.282615\n",
      "Pretrain epoch 70, recon_loss:0.882769, zinb_loss:0.478766, adversial_loss:1.281491\n",
      "Pretrain epoch 71, recon_loss:0.882391, zinb_loss:0.478445, adversial_loss:1.281171\n",
      "Pretrain epoch 72, recon_loss:0.882230, zinb_loss:0.478219, adversial_loss:1.281228\n",
      "Pretrain epoch 73, recon_loss:0.881932, zinb_loss:0.477906, adversial_loss:1.280578\n",
      "Pretrain epoch 74, recon_loss:0.881811, zinb_loss:0.477741, adversial_loss:1.280275\n",
      "Pretrain epoch 75, recon_loss:0.881506, zinb_loss:0.477418, adversial_loss:1.280187\n",
      "Pretrain epoch 76, recon_loss:0.881352, zinb_loss:0.477194, adversial_loss:1.279860\n",
      "Pretrain epoch 77, recon_loss:0.881011, zinb_loss:0.476965, adversial_loss:1.279087\n",
      "Pretrain epoch 78, recon_loss:0.881010, zinb_loss:0.476766, adversial_loss:1.279005\n",
      "Pretrain epoch 79, recon_loss:0.880806, zinb_loss:0.476561, adversial_loss:1.279081\n",
      "Pretrain epoch 80, recon_loss:0.880632, zinb_loss:0.476394, adversial_loss:1.277914\n",
      "Pretrain epoch 81, recon_loss:0.880353, zinb_loss:0.476192, adversial_loss:1.278324\n",
      "Pretrain epoch 82, recon_loss:0.880257, zinb_loss:0.476009, adversial_loss:1.277439\n",
      "Pretrain epoch 83, recon_loss:0.879993, zinb_loss:0.475797, adversial_loss:1.277913\n",
      "Pretrain epoch 84, recon_loss:0.879785, zinb_loss:0.475625, adversial_loss:1.276413\n",
      "Pretrain epoch 85, recon_loss:0.879605, zinb_loss:0.475492, adversial_loss:1.277489\n",
      "Pretrain epoch 86, recon_loss:0.879356, zinb_loss:0.475285, adversial_loss:1.276016\n",
      "Pretrain epoch 87, recon_loss:0.879262, zinb_loss:0.475160, adversial_loss:1.276432\n",
      "Pretrain epoch 88, recon_loss:0.879032, zinb_loss:0.474996, adversial_loss:1.275238\n",
      "Pretrain epoch 89, recon_loss:0.878745, zinb_loss:0.474845, adversial_loss:1.275868\n",
      "Pretrain epoch 90, recon_loss:0.878577, zinb_loss:0.474718, adversial_loss:1.274301\n",
      "Pretrain epoch 91, recon_loss:0.878290, zinb_loss:0.474551, adversial_loss:1.275026\n",
      "Pretrain epoch 92, recon_loss:0.878254, zinb_loss:0.474455, adversial_loss:1.273386\n",
      "Pretrain epoch 93, recon_loss:0.878239, zinb_loss:0.474504, adversial_loss:1.274436\n",
      "Pretrain epoch 94, recon_loss:0.878525, zinb_loss:0.474740, adversial_loss:1.272796\n",
      "Pretrain epoch 95, recon_loss:0.878902, zinb_loss:0.475089, adversial_loss:1.275385\n",
      "Pretrain epoch 96, recon_loss:0.877561, zinb_loss:0.474054, adversial_loss:1.272375\n",
      "Pretrain epoch 97, recon_loss:0.877778, zinb_loss:0.474259, adversial_loss:1.271871\n",
      "Pretrain epoch 98, recon_loss:0.877807, zinb_loss:0.474278, adversial_loss:1.273904\n",
      "Pretrain epoch 99, recon_loss:0.877096, zinb_loss:0.473776, adversial_loss:1.272743\n",
      "Pretrain epoch 100, recon_loss:0.877512, zinb_loss:0.474059, adversial_loss:1.270871\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pretrain epoch 101, recon_loss:0.876652, zinb_loss:0.473544, adversial_loss:1.271894\n",
      "Pretrain epoch 102, recon_loss:0.876898, zinb_loss:0.473643, adversial_loss:1.271924\n",
      "Pretrain epoch 103, recon_loss:0.876329, zinb_loss:0.473405, adversial_loss:1.270079\n",
      "Pretrain epoch 104, recon_loss:0.876395, zinb_loss:0.473419, adversial_loss:1.269913\n",
      "Pretrain epoch 105, recon_loss:0.876021, zinb_loss:0.473242, adversial_loss:1.271329\n",
      "Pretrain epoch 106, recon_loss:0.876009, zinb_loss:0.473213, adversial_loss:1.269614\n",
      "Pretrain epoch 107, recon_loss:0.875709, zinb_loss:0.473094, adversial_loss:1.269238\n",
      "Pretrain epoch 108, recon_loss:0.875638, zinb_loss:0.473099, adversial_loss:1.269217\n",
      "Pretrain epoch 109, recon_loss:0.875589, zinb_loss:0.473085, adversial_loss:1.270183\n",
      "Pretrain epoch 110, recon_loss:0.875630, zinb_loss:0.472942, adversial_loss:1.268777\n",
      "Pretrain epoch 111, recon_loss:0.875412, zinb_loss:0.472890, adversial_loss:1.269828\n",
      "Pretrain epoch 112, recon_loss:0.875219, zinb_loss:0.472672, adversial_loss:1.269079\n",
      "Pretrain epoch 113, recon_loss:0.875006, zinb_loss:0.472692, adversial_loss:1.267742\n",
      "Pretrain epoch 114, recon_loss:0.874811, zinb_loss:0.472569, adversial_loss:1.267557\n",
      "Pretrain epoch 115, recon_loss:0.874585, zinb_loss:0.472548, adversial_loss:1.266879\n",
      "Pretrain epoch 116, recon_loss:0.874229, zinb_loss:0.472359, adversial_loss:1.267354\n",
      "Pretrain epoch 117, recon_loss:0.874126, zinb_loss:0.472386, adversial_loss:1.267169\n",
      "Pretrain epoch 118, recon_loss:0.874022, zinb_loss:0.472283, adversial_loss:1.265722\n",
      "Pretrain epoch 119, recon_loss:0.873713, zinb_loss:0.472187, adversial_loss:1.266729\n",
      "Pretrain epoch 120, recon_loss:0.873618, zinb_loss:0.472110, adversial_loss:1.266035\n",
      "Pretrain epoch 121, recon_loss:0.873415, zinb_loss:0.472064, adversial_loss:1.264639\n",
      "Pretrain epoch 122, recon_loss:0.873320, zinb_loss:0.472021, adversial_loss:1.265379\n",
      "Pretrain epoch 123, recon_loss:0.873136, zinb_loss:0.471912, adversial_loss:1.264864\n",
      "Pretrain epoch 124, recon_loss:0.872942, zinb_loss:0.471887, adversial_loss:1.264485\n",
      "Pretrain epoch 125, recon_loss:0.872812, zinb_loss:0.471785, adversial_loss:1.263999\n",
      "Pretrain epoch 126, recon_loss:0.872581, zinb_loss:0.471736, adversial_loss:1.263726\n",
      "Pretrain epoch 127, recon_loss:0.872448, zinb_loss:0.471673, adversial_loss:1.263884\n",
      "Pretrain epoch 128, recon_loss:0.872232, zinb_loss:0.471598, adversial_loss:1.263259\n",
      "Pretrain epoch 129, recon_loss:0.872227, zinb_loss:0.471601, adversial_loss:1.262720\n",
      "Pretrain epoch 130, recon_loss:0.872051, zinb_loss:0.471535, adversial_loss:1.262570\n",
      "Pretrain epoch 131, recon_loss:0.872017, zinb_loss:0.471547, adversial_loss:1.262810\n",
      "Pretrain epoch 132, recon_loss:0.872062, zinb_loss:0.471590, adversial_loss:1.261674\n",
      "Pretrain epoch 133, recon_loss:0.872316, zinb_loss:0.471761, adversial_loss:1.263152\n",
      "Pretrain epoch 134, recon_loss:0.872393, zinb_loss:0.471765, adversial_loss:1.261133\n",
      "Pretrain epoch 135, recon_loss:0.871755, zinb_loss:0.471515, adversial_loss:1.262774\n",
      "Pretrain epoch 136, recon_loss:0.871299, zinb_loss:0.471261, adversial_loss:1.261143\n",
      "Pretrain epoch 137, recon_loss:0.871308, zinb_loss:0.471298, adversial_loss:1.260310\n",
      "Pretrain epoch 138, recon_loss:0.871430, zinb_loss:0.471387, adversial_loss:1.261964\n",
      "Pretrain epoch 139, recon_loss:0.871076, zinb_loss:0.471219, adversial_loss:1.260673\n",
      "Pretrain epoch 140, recon_loss:0.870721, zinb_loss:0.471069, adversial_loss:1.259987\n",
      "Pretrain epoch 141, recon_loss:0.870777, zinb_loss:0.471149, adversial_loss:1.260930\n",
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     ]
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     "output_type": "stream",
     "text": [
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     ]
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     "text": [
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      "Pretrain epoch 330, recon_loss:0.845143, zinb_loss:0.467363, adversial_loss:1.241377\n",
      "Pretrain epoch 331, recon_loss:0.845097, zinb_loss:0.467369, adversial_loss:1.239908\n",
      "Pretrain epoch 332, recon_loss:0.845454, zinb_loss:0.467401, adversial_loss:1.241301\n",
      "Pretrain epoch 333, recon_loss:0.845199, zinb_loss:0.467356, adversial_loss:1.240106\n",
      "Pretrain epoch 334, recon_loss:0.844103, zinb_loss:0.467207, adversial_loss:1.240155\n",
      "Pretrain epoch 335, recon_loss:0.843578, zinb_loss:0.467161, adversial_loss:1.240351\n",
      "Pretrain epoch 336, recon_loss:0.843672, zinb_loss:0.467193, adversial_loss:1.239885\n",
      "Pretrain epoch 337, recon_loss:0.843534, zinb_loss:0.467206, adversial_loss:1.240476\n",
      "Pretrain epoch 338, recon_loss:0.842944, zinb_loss:0.467159, adversial_loss:1.239558\n",
      "Pretrain epoch 339, recon_loss:0.842923, zinb_loss:0.467131, adversial_loss:1.240264\n",
      "Pretrain epoch 340, recon_loss:0.842735, zinb_loss:0.467122, adversial_loss:1.239618\n",
      "Pretrain epoch 341, recon_loss:0.842605, zinb_loss:0.467096, adversial_loss:1.239977\n",
      "Pretrain epoch 342, recon_loss:0.842695, zinb_loss:0.467142, adversial_loss:1.239616\n",
      "Pretrain epoch 343, recon_loss:0.843179, zinb_loss:0.467215, adversial_loss:1.239887\n",
      "Pretrain epoch 344, recon_loss:0.843544, zinb_loss:0.467264, adversial_loss:1.239293\n",
      "Pretrain epoch 345, recon_loss:0.844045, zinb_loss:0.467300, adversial_loss:1.240098\n",
      "Pretrain epoch 346, recon_loss:0.843314, zinb_loss:0.467207, adversial_loss:1.239053\n",
      "Pretrain epoch 347, recon_loss:0.843255, zinb_loss:0.467197, adversial_loss:1.239673\n",
      "Pretrain epoch 348, recon_loss:0.845292, zinb_loss:0.467485, adversial_loss:1.239450\n",
      "Pretrain epoch 349, recon_loss:0.845225, zinb_loss:0.467512, adversial_loss:1.239694\n",
      "Pretrain epoch 350, recon_loss:0.843269, zinb_loss:0.467207, adversial_loss:1.238909\n",
      "Pretrain epoch 351, recon_loss:0.842528, zinb_loss:0.467131, adversial_loss:1.239758\n",
      "Pretrain epoch 352, recon_loss:0.842872, zinb_loss:0.467215, adversial_loss:1.238955\n",
      "Pretrain epoch 353, recon_loss:0.842390, zinb_loss:0.467134, adversial_loss:1.239320\n",
      "Pretrain epoch 354, recon_loss:0.841689, zinb_loss:0.467045, adversial_loss:1.239301\n",
      "Pretrain epoch 355, recon_loss:0.842337, zinb_loss:0.467061, adversial_loss:1.239071\n",
      "Pretrain epoch 356, recon_loss:0.840696, zinb_loss:0.466971, adversial_loss:1.239088\n",
      "Pretrain epoch 357, recon_loss:0.841387, zinb_loss:0.467036, adversial_loss:1.239297\n",
      "Pretrain epoch 358, recon_loss:0.840836, zinb_loss:0.466996, adversial_loss:1.238446\n",
      "Pretrain epoch 359, recon_loss:0.840246, zinb_loss:0.466915, adversial_loss:1.239055\n",
      "Pretrain epoch 360, recon_loss:0.840125, zinb_loss:0.466916, adversial_loss:1.238636\n",
      "Pretrain epoch 361, recon_loss:0.839874, zinb_loss:0.466939, adversial_loss:1.238719\n",
      "Pretrain epoch 362, recon_loss:0.839598, zinb_loss:0.466944, adversial_loss:1.238369\n",
      "Pretrain epoch 363, recon_loss:0.839547, zinb_loss:0.466925, adversial_loss:1.239099\n",
      "Pretrain epoch 364, recon_loss:0.840207, zinb_loss:0.466978, adversial_loss:1.237722\n",
      "Pretrain epoch 365, recon_loss:0.839584, zinb_loss:0.466963, adversial_loss:1.239180\n",
      "Pretrain epoch 366, recon_loss:0.839261, zinb_loss:0.466924, adversial_loss:1.237848\n",
      "Pretrain epoch 367, recon_loss:0.838322, zinb_loss:0.466845, adversial_loss:1.238451\n",
      "Pretrain epoch 368, recon_loss:0.838026, zinb_loss:0.466815, adversial_loss:1.237877\n",
      "Pretrain epoch 369, recon_loss:0.837955, zinb_loss:0.466827, adversial_loss:1.238253\n",
      "Pretrain epoch 370, recon_loss:0.838767, zinb_loss:0.466914, adversial_loss:1.238142\n",
      "Pretrain epoch 371, recon_loss:0.839370, zinb_loss:0.466960, adversial_loss:1.238243\n",
      "Pretrain epoch 372, recon_loss:0.839189, zinb_loss:0.467017, adversial_loss:1.238283\n",
      "Pretrain epoch 373, recon_loss:0.838442, zinb_loss:0.466924, adversial_loss:1.238102\n",
      "Pretrain epoch 374, recon_loss:0.839289, zinb_loss:0.466973, adversial_loss:1.237743\n",
      "Pretrain epoch 375, recon_loss:0.839590, zinb_loss:0.466964, adversial_loss:1.237983\n",
      "Pretrain epoch 376, recon_loss:0.837254, zinb_loss:0.466791, adversial_loss:1.238129\n",
      "Pretrain epoch 377, recon_loss:0.837483, zinb_loss:0.466839, adversial_loss:1.237256\n",
      "Pretrain epoch 378, recon_loss:0.837679, zinb_loss:0.466831, adversial_loss:1.237994\n",
      "Pretrain epoch 379, recon_loss:0.836292, zinb_loss:0.466735, adversial_loss:1.237572\n",
      "Pretrain epoch 380, recon_loss:0.836792, zinb_loss:0.466757, adversial_loss:1.237486\n",
      "Pretrain epoch 381, recon_loss:0.836292, zinb_loss:0.466720, adversial_loss:1.237280\n",
      "Pretrain epoch 382, recon_loss:0.835759, zinb_loss:0.466707, adversial_loss:1.237738\n",
      "Pretrain epoch 383, recon_loss:0.836404, zinb_loss:0.466765, adversial_loss:1.237188\n",
      "Pretrain epoch 384, recon_loss:0.836765, zinb_loss:0.466811, adversial_loss:1.237186\n",
      "Pretrain epoch 385, recon_loss:0.838624, zinb_loss:0.466941, adversial_loss:1.237713\n",
      "Pretrain epoch 386, recon_loss:0.840880, zinb_loss:0.467184, adversial_loss:1.238024\n",
      "Pretrain epoch 387, recon_loss:0.836855, zinb_loss:0.466834, adversial_loss:1.236656\n",
      "Pretrain epoch 388, recon_loss:0.835517, zinb_loss:0.466672, adversial_loss:1.236978\n",
      "Pretrain epoch 389, recon_loss:0.836959, zinb_loss:0.466825, adversial_loss:1.237627\n",
      "Pretrain epoch 390, recon_loss:0.835768, zinb_loss:0.466703, adversial_loss:1.237139\n",
      "Pretrain epoch 391, recon_loss:0.834794, zinb_loss:0.466677, adversial_loss:1.236547\n",
      "Pretrain epoch 392, recon_loss:0.834709, zinb_loss:0.466679, adversial_loss:1.237424\n",
      "Pretrain epoch 393, recon_loss:0.834526, zinb_loss:0.466649, adversial_loss:1.237094\n",
      "Pretrain epoch 394, recon_loss:0.833730, zinb_loss:0.466616, adversial_loss:1.236221\n",
      "Pretrain epoch 395, recon_loss:0.833320, zinb_loss:0.466582, adversial_loss:1.237042\n",
      "Pretrain epoch 396, recon_loss:0.833140, zinb_loss:0.466565, adversial_loss:1.236657\n",
      "Pretrain epoch 397, recon_loss:0.832359, zinb_loss:0.466515, adversial_loss:1.236342\n",
      "Pretrain epoch 398, recon_loss:0.832398, zinb_loss:0.466531, adversial_loss:1.236686\n",
      "Pretrain epoch 399, recon_loss:0.831918, zinb_loss:0.466505, adversial_loss:1.236133\n",
      "Pretrain epoch 400, recon_loss:0.832006, zinb_loss:0.466512, adversial_loss:1.236845\n"
     ]
    }
   ],
   "source": [
    "pretrain_latent = model.pretrain_autoencoder(\n",
    "                        X1=adata1.X, X2=adata2.X, X3=adata3.X, \n",
    "                        X1_raw=adata1.raw.X, X2_raw=adata2.raw.X, X3_raw=adata3.raw.X,\n",
    "                        epochs=400, file='GSE158013')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "2cf079af",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Clustering stage\n",
      "Estimated n_clusters is:  7\n",
      "Initializing cluster centers with kmeans.\n",
      "Initializing k-means: ASW= 0.4344, DB= 0.9470, CH= 4032.3028\n",
      "Training epoch 1, recon_loss:0.832919, zinb_loss:0.466575, cluster_loss:0.207182\n",
      "Clustering   1: ASW= 0.4344, DB= 0.9470, CH= 4032.3028\n",
      "Training epoch 2, recon_loss:0.887874, zinb_loss:0.473593, cluster_loss:0.212438\n",
      "Clustering   2: ASW= 0.5285, DB= 0.7033, CH= 7507.6300\n",
      "Training epoch 3, recon_loss:0.913830, zinb_loss:0.476516, cluster_loss:0.226155\n",
      "Clustering   3: ASW= 0.5506, DB= 0.6721, CH= 7760.1150\n",
      "Training epoch 4, recon_loss:0.897848, zinb_loss:0.476857, cluster_loss:0.211880\n",
      "Clustering   4: ASW= 0.5716, DB= 0.6261, CH= 9449.4753\n",
      "Training epoch 5, recon_loss:0.895170, zinb_loss:0.474653, cluster_loss:0.212168\n",
      "Clustering   5: ASW= 0.6029, DB= 0.5841, CH= 9793.6500\n",
      "Training epoch 6, recon_loss:0.895097, zinb_loss:0.475862, cluster_loss:0.204507\n",
      "Clustering   6: ASW= 0.6064, DB= 0.5652, CH= 11096.5971\n",
      "Training epoch 7, recon_loss:0.896039, zinb_loss:0.475649, cluster_loss:0.204745\n",
      "Clustering   7: ASW= 0.6305, DB= 0.5359, CH= 11486.3060\n",
      "Training epoch 8, recon_loss:0.895833, zinb_loss:0.476619, cluster_loss:0.201410\n",
      "Clustering   8: ASW= 0.6321, DB= 0.5252, CH= 12620.9400\n",
      "Training epoch 9, recon_loss:0.897844, zinb_loss:0.476831, cluster_loss:0.199865\n",
      "Clustering   9: ASW= 0.6488, DB= 0.5042, CH= 12946.5050\n",
      "Training epoch 10, recon_loss:0.897870, zinb_loss:0.477306, cluster_loss:0.198004\n",
      "Clustering   10: ASW= 0.6493, DB= 0.4980, CH= 13838.1399\n",
      "Training epoch 11, recon_loss:0.898041, zinb_loss:0.477441, cluster_loss:0.195625\n",
      "Clustering   11: ASW= 0.6619, DB= 0.4832, CH= 14086.1586\n",
      "Training epoch 12, recon_loss:0.900377, zinb_loss:0.477832, cluster_loss:0.195087\n",
      "Clustering   12: ASW= 0.6617, DB= 0.4760, CH= 14750.1328\n",
      "Training epoch 13, recon_loss:0.898935, zinb_loss:0.477983, cluster_loss:0.193765\n",
      "Clustering   13: ASW= 0.6716, DB= 0.4669, CH= 15034.0546\n",
      "Training epoch 14, recon_loss:0.901775, zinb_loss:0.478386, cluster_loss:0.193867\n",
      "Clustering   14: ASW= 0.6725, DB= 0.4592, CH= 15470.8353\n",
      "Training epoch 15, recon_loss:0.899275, zinb_loss:0.478428, cluster_loss:0.192754\n",
      "Clustering   15: ASW= 0.6819, DB= 0.4501, CH= 15918.6746\n",
      "Training epoch 16, recon_loss:0.899870, zinb_loss:0.478486, cluster_loss:0.190681\n",
      "Clustering   16: ASW= 0.6825, DB= 0.4447, CH= 16188.6884\n",
      "Training epoch 17, recon_loss:0.897410, zinb_loss:0.478531, cluster_loss:0.189571\n",
      "Clustering   17: ASW= 0.6899, DB= 0.4371, CH= 16554.1309\n",
      "Training epoch 18, recon_loss:0.898436, zinb_loss:0.478700, cluster_loss:0.188050\n",
      "Clustering   18: ASW= 0.6894, DB= 0.4340, CH= 16799.0709\n",
      "Training epoch 19, recon_loss:0.896228, zinb_loss:0.478602, cluster_loss:0.186756\n",
      "Clustering   19: ASW= 0.6961, DB= 0.4269, CH= 17137.5097\n",
      "Training epoch 20, recon_loss:0.896804, zinb_loss:0.478696, cluster_loss:0.185572\n",
      "Clustering   20: ASW= 0.6961, DB= 0.4243, CH= 17385.2659\n",
      "Training epoch 21, recon_loss:0.895796, zinb_loss:0.478736, cluster_loss:0.185207\n",
      "Clustering   21: ASW= 0.7015, DB= 0.4181, CH= 17618.5395\n",
      "Training epoch 22, recon_loss:0.897048, zinb_loss:0.478910, cluster_loss:0.184882\n",
      "Clustering   22: ASW= 0.7006, DB= 0.4170, CH= 17867.8379\n",
      "Training epoch 23, recon_loss:0.895957, zinb_loss:0.478829, cluster_loss:0.183780\n",
      "Clustering   23: ASW= 0.7064, DB= 0.4096, CH= 18118.7101\n",
      "Training epoch 24, recon_loss:0.896396, zinb_loss:0.478856, cluster_loss:0.183362\n",
      "Clustering   24: ASW= 0.7054, DB= 0.4103, CH= 18352.4814\n",
      "Training epoch 25, recon_loss:0.895911, zinb_loss:0.478955, cluster_loss:0.182951\n",
      "Clustering   25: ASW= 0.7110, DB= 0.4031, CH= 18525.4366\n",
      "Training epoch 26, recon_loss:0.896150, zinb_loss:0.478998, cluster_loss:0.182563\n",
      "Clustering   26: ASW= 0.7090, DB= 0.4046, CH= 18797.3669\n",
      "Training epoch 27, recon_loss:0.895114, zinb_loss:0.479047, cluster_loss:0.181674\n",
      "Clustering   27: ASW= 0.7153, DB= 0.3970, CH= 18957.1880\n",
      "Training epoch 28, recon_loss:0.895192, zinb_loss:0.479065, cluster_loss:0.181096\n",
      "Clustering   28: ASW= 0.7126, DB= 0.3987, CH= 19162.9736\n",
      "Training epoch 29, recon_loss:0.894739, zinb_loss:0.479080, cluster_loss:0.180419\n",
      "Clustering   29: ASW= 0.7188, DB= 0.3913, CH= 19395.1208\n",
      "Training epoch 30, recon_loss:0.894749, zinb_loss:0.479093, cluster_loss:0.180082\n",
      "Clustering   30: ASW= 0.7168, DB= 0.3926, CH= 19617.5343\n",
      "Training epoch 31, recon_loss:0.893745, zinb_loss:0.479171, cluster_loss:0.179076\n",
      "Clustering   31: ASW= 0.7220, DB= 0.3863, CH= 19752.8483\n",
      "Training epoch 32, recon_loss:0.893349, zinb_loss:0.479098, cluster_loss:0.178515\n",
      "Clustering   32: ASW= 0.7202, DB= 0.3871, CH= 19961.7235\n",
      "Training epoch 33, recon_loss:0.894343, zinb_loss:0.479299, cluster_loss:0.178610\n",
      "Clustering   33: ASW= 0.7251, DB= 0.3816, CH= 20120.6732\n",
      "Training epoch 34, recon_loss:0.893603, zinb_loss:0.479254, cluster_loss:0.178313\n",
      "Clustering   34: ASW= 0.7230, DB= 0.3831, CH= 20385.6509\n",
      "Training epoch 35, recon_loss:0.893213, zinb_loss:0.479364, cluster_loss:0.177528\n",
      "Clustering   35: ASW= 0.7281, DB= 0.3767, CH= 20473.3641\n",
      "Training epoch 36, recon_loss:0.892874, zinb_loss:0.479267, cluster_loss:0.177341\n",
      "Clustering   36: ASW= 0.7259, DB= 0.3789, CH= 20706.4483\n",
      "Training epoch 37, recon_loss:0.894445, zinb_loss:0.479532, cluster_loss:0.177554\n",
      "Clustering   37: ASW= 0.7307, DB= 0.3729, CH= 20812.5273\n",
      "Training epoch 38, recon_loss:0.893082, zinb_loss:0.479409, cluster_loss:0.177175\n",
      "Clustering   38: ASW= 0.7283, DB= 0.3752, CH= 21108.6846\n",
      "Training epoch 39, recon_loss:0.892983, zinb_loss:0.479543, cluster_loss:0.176366\n",
      "Clustering   39: ASW= 0.7333, DB= 0.3687, CH= 21152.8818\n",
      "Training epoch 40, recon_loss:0.892380, zinb_loss:0.479396, cluster_loss:0.176257\n",
      "Clustering   40: ASW= 0.7308, DB= 0.3716, CH= 21372.6040\n",
      "Training epoch 41, recon_loss:0.894487, zinb_loss:0.479722, cluster_loss:0.176668\n",
      "Clustering   41: ASW= 0.7354, DB= 0.3649, CH= 21465.4217\n",
      "Training epoch 42, recon_loss:0.893596, zinb_loss:0.479575, cluster_loss:0.176681\n",
      "Clustering   42: ASW= 0.7330, DB= 0.3679, CH= 21758.6817\n",
      "Training epoch 43, recon_loss:0.895516, zinb_loss:0.479868, cluster_loss:0.176862\n",
      "Clustering   43: ASW= 0.7372, DB= 0.3616, CH= 21762.7217\n",
      "Training epoch 44, recon_loss:0.895423, zinb_loss:0.479748, cluster_loss:0.177448\n",
      "Clustering   44: ASW= 0.7353, DB= 0.3643, CH= 22065.1812\n",
      "Training epoch 45, recon_loss:0.898613, zinb_loss:0.480126, cluster_loss:0.178379\n",
      "Clustering   45: ASW= 0.7382, DB= 0.3593, CH= 22015.9119\n",
      "Training epoch 46, recon_loss:0.896574, zinb_loss:0.479918, cluster_loss:0.177674\n",
      "Clustering   46: ASW= 0.7373, DB= 0.3608, CH= 22419.9690\n",
      "Training epoch 47, recon_loss:0.897652, zinb_loss:0.480130, cluster_loss:0.177398\n",
      "Clustering   47: ASW= 0.7405, DB= 0.3556, CH= 22393.7405\n",
      "Training epoch 48, recon_loss:0.895305, zinb_loss:0.479887, cluster_loss:0.176258\n",
      "Clustering   48: ASW= 0.7395, DB= 0.3583, CH= 22665.8365\n",
      "Training epoch 49, recon_loss:0.896349, zinb_loss:0.480105, cluster_loss:0.176355\n",
      "Clustering   49: ASW= 0.7421, DB= 0.3532, CH= 22678.0671\n",
      "Training epoch 50, recon_loss:0.894359, zinb_loss:0.479916, cluster_loss:0.175221\n",
      "Clustering   50: ASW= 0.7416, DB= 0.3546, CH= 22956.7053\n",
      "Training epoch 51, recon_loss:0.894614, zinb_loss:0.480043, cluster_loss:0.174864\n",
      "Clustering   51: ASW= 0.7442, DB= 0.3503, CH= 22974.5795\n",
      "Training epoch 52, recon_loss:0.893814, zinb_loss:0.479914, cluster_loss:0.174496\n",
      "Clustering   52: ASW= 0.7435, DB= 0.3520, CH= 23185.0945\n",
      "Training epoch 53, recon_loss:0.894629, zinb_loss:0.480092, cluster_loss:0.174665\n",
      "Clustering   53: ASW= 0.7458, DB= 0.3484, CH= 23221.1699\n",
      "Training epoch 54, recon_loss:0.893462, zinb_loss:0.479974, cluster_loss:0.173979\n",
      "Clustering   54: ASW= 0.7451, DB= 0.3492, CH= 23451.2198\n",
      "Training epoch 55, recon_loss:0.893438, zinb_loss:0.480076, cluster_loss:0.173588\n",
      "Clustering   55: ASW= 0.7477, DB= 0.3453, CH= 23502.7829\n",
      "Training epoch 56, recon_loss:0.893491, zinb_loss:0.479992, cluster_loss:0.173689\n",
      "Clustering   56: ASW= 0.7466, DB= 0.3474, CH= 23635.6027\n",
      "Training epoch 57, recon_loss:0.894415, zinb_loss:0.480190, cluster_loss:0.174059\n",
      "Clustering   57: ASW= 0.7491, DB= 0.3432, CH= 23741.7676\n",
      "Training epoch 58, recon_loss:0.893781, zinb_loss:0.480108, cluster_loss:0.173735\n",
      "Clustering   58: ASW= 0.7478, DB= 0.3456, CH= 23877.9914\n",
      "Training epoch 59, recon_loss:0.893560, zinb_loss:0.480226, cluster_loss:0.173335\n",
      "Clustering   59: ASW= 0.7508, DB= 0.3405, CH= 24025.6308\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training epoch 60, recon_loss:0.894262, zinb_loss:0.480159, cluster_loss:0.173799\n",
      "Clustering   60: ASW= 0.7490, DB= 0.3448, CH= 23987.2433\n",
      "Training epoch 61, recon_loss:0.895237, zinb_loss:0.480413, cluster_loss:0.174413\n",
      "Clustering   61: ASW= 0.7522, DB= 0.3381, CH= 24290.5140\n",
      "Training epoch 62, recon_loss:0.895414, zinb_loss:0.480332, cluster_loss:0.174617\n",
      "Clustering   62: ASW= 0.7497, DB= 0.3440, CH= 24145.7464\n",
      "Training epoch 63, recon_loss:0.895203, zinb_loss:0.480538, cluster_loss:0.174288\n",
      "Clustering   63: ASW= 0.7535, DB= 0.3354, CH= 24563.4381\n",
      "Training epoch 64, recon_loss:0.895713, zinb_loss:0.480397, cluster_loss:0.174410\n",
      "Clustering   64: ASW= 0.7511, DB= 0.3422, CH= 24279.4558\n",
      "Training epoch 65, recon_loss:0.896016, zinb_loss:0.480663, cluster_loss:0.174676\n",
      "Clustering   65: ASW= 0.7551, DB= 0.3326, CH= 24814.4452\n",
      "Training epoch 66, recon_loss:0.895584, zinb_loss:0.480459, cluster_loss:0.174344\n",
      "Clustering   66: ASW= 0.7522, DB= 0.3407, CH= 24514.2996\n",
      "Training epoch 67, recon_loss:0.894853, zinb_loss:0.480617, cluster_loss:0.173478\n",
      "Clustering   67: ASW= 0.7558, DB= 0.3307, CH= 25015.9516\n",
      "Training epoch 68, recon_loss:0.894613, zinb_loss:0.480430, cluster_loss:0.173252\n",
      "Clustering   68: ASW= 0.7541, DB= 0.3373, CH= 24698.5128\n",
      "Training epoch 69, recon_loss:0.894791, zinb_loss:0.480619, cluster_loss:0.172987\n",
      "Clustering   69: ASW= 0.7566, DB= 0.3293, CH= 25229.5517\n",
      "Training epoch 70, recon_loss:0.894065, zinb_loss:0.480442, cluster_loss:0.172799\n",
      "Clustering   70: ASW= 0.7555, DB= 0.3349, CH= 24934.7299\n",
      "Training epoch 71, recon_loss:0.893844, zinb_loss:0.480544, cluster_loss:0.171928\n",
      "Clustering   71: ASW= 0.7572, DB= 0.3281, CH= 25422.1790\n",
      "Training epoch 72, recon_loss:0.893937, zinb_loss:0.480462, cluster_loss:0.172407\n",
      "Clustering   72: ASW= 0.7572, DB= 0.3336, CH= 25115.0504\n",
      "Training epoch 73, recon_loss:0.894792, zinb_loss:0.480613, cluster_loss:0.172183\n",
      "Clustering   73: ASW= 0.7577, DB= 0.3276, CH= 25614.3808\n",
      "Training epoch 74, recon_loss:0.894381, zinb_loss:0.480524, cluster_loss:0.172488\n",
      "Clustering   74: ASW= 0.7582, DB= 0.3315, CH= 25297.2656\n",
      "Training epoch 75, recon_loss:0.894270, zinb_loss:0.480571, cluster_loss:0.171556\n",
      "Clustering   75: ASW= 0.7583, DB= 0.3266, CH= 25785.3651\n",
      "Training epoch 76, recon_loss:0.894531, zinb_loss:0.480569, cluster_loss:0.172188\n",
      "Clustering   76: ASW= 0.7595, DB= 0.3297, CH= 25445.4490\n",
      "Training epoch 77, recon_loss:0.894970, zinb_loss:0.480648, cluster_loss:0.171734\n",
      "Clustering   77: ASW= 0.7590, DB= 0.3257, CH= 25950.6494\n",
      "Training epoch 78, recon_loss:0.894357, zinb_loss:0.480590, cluster_loss:0.171848\n",
      "Clustering   78: ASW= 0.7602, DB= 0.3292, CH= 25668.2558\n",
      "Training epoch 79, recon_loss:0.893997, zinb_loss:0.480600, cluster_loss:0.170892\n",
      "Clustering   79: ASW= 0.7598, DB= 0.3246, CH= 26135.3464\n",
      "Training epoch 80, recon_loss:0.894154, zinb_loss:0.480609, cluster_loss:0.171461\n",
      "Clustering   80: ASW= 0.7613, DB= 0.3280, CH= 25832.8497\n",
      "Training epoch 81, recon_loss:0.894252, zinb_loss:0.480653, cluster_loss:0.170831\n",
      "Clustering   81: ASW= 0.7606, DB= 0.3237, CH= 26292.4655\n",
      "Training epoch 82, recon_loss:0.893606, zinb_loss:0.480614, cluster_loss:0.170895\n",
      "Clustering   82: ASW= 0.7622, DB= 0.3274, CH= 26032.5352\n",
      "Training epoch 83, recon_loss:0.893237, zinb_loss:0.480604, cluster_loss:0.170013\n",
      "Clustering   83: ASW= 0.7614, DB= 0.3225, CH= 26471.8783\n",
      "Training epoch 84, recon_loss:0.893415, zinb_loss:0.480637, cluster_loss:0.170561\n",
      "Clustering   84: ASW= 0.7632, DB= 0.3259, CH= 26164.3040\n",
      "Training epoch 85, recon_loss:0.893549, zinb_loss:0.480664, cluster_loss:0.170062\n",
      "Clustering   85: ASW= 0.7621, DB= 0.3218, CH= 26642.4422\n",
      "Training epoch 86, recon_loss:0.893335, zinb_loss:0.480667, cluster_loss:0.170320\n",
      "Clustering   86: ASW= 0.7639, DB= 0.3250, CH= 26334.8406\n",
      "Training epoch 87, recon_loss:0.893091, zinb_loss:0.480656, cluster_loss:0.169645\n",
      "Clustering   87: ASW= 0.7628, DB= 0.3205, CH= 26811.5244\n",
      "Training epoch 88, recon_loss:0.893380, zinb_loss:0.480707, cluster_loss:0.170172\n",
      "Clustering   88: ASW= 0.7648, DB= 0.3239, CH= 26464.6821\n",
      "Training epoch 89, recon_loss:0.893470, zinb_loss:0.480717, cluster_loss:0.169729\n",
      "Clustering   89: ASW= 0.7634, DB= 0.3195, CH= 26977.5673\n",
      "Training epoch 90, recon_loss:0.893284, zinb_loss:0.480737, cluster_loss:0.169959\n",
      "Clustering   90: ASW= 0.7655, DB= 0.3246, CH= 26628.6192\n",
      "Training epoch 91, recon_loss:0.892930, zinb_loss:0.480700, cluster_loss:0.169277\n",
      "Clustering   91: ASW= 0.7641, DB= 0.3196, CH= 27158.2050\n",
      "Training epoch 92, recon_loss:0.893235, zinb_loss:0.480768, cluster_loss:0.169756\n",
      "Clustering   92: ASW= 0.7663, DB= 0.3231, CH= 26748.3523\n",
      "Training epoch 93, recon_loss:0.893196, zinb_loss:0.480756, cluster_loss:0.169295\n",
      "Clustering   93: ASW= 0.7649, DB= 0.3185, CH= 27317.8769\n",
      "Training epoch 94, recon_loss:0.893107, zinb_loss:0.480791, cluster_loss:0.169534\n",
      "Clustering   94: ASW= 0.7668, DB= 0.3235, CH= 26905.5525\n",
      "Training epoch 95, recon_loss:0.892720, zinb_loss:0.480749, cluster_loss:0.168880\n",
      "Clustering   95: ASW= 0.7655, DB= 0.3174, CH= 27470.2044\n",
      "Training epoch 96, recon_loss:0.893005, zinb_loss:0.480816, cluster_loss:0.169311\n",
      "Clustering   96: ASW= 0.7674, DB= 0.3224, CH= 27033.3678\n",
      "Training epoch 97, recon_loss:0.892904, zinb_loss:0.480799, cluster_loss:0.168855\n",
      "Clustering   97: ASW= 0.7663, DB= 0.3162, CH= 27622.9765\n",
      "Training epoch 98, recon_loss:0.892905, zinb_loss:0.480838, cluster_loss:0.169137\n",
      "Clustering   98: ASW= 0.7679, DB= 0.3224, CH= 27186.8839\n",
      "Training epoch 99, recon_loss:0.892539, zinb_loss:0.480796, cluster_loss:0.168516\n",
      "Clustering   99: ASW= 0.7670, DB= 0.3149, CH= 27758.0330\n",
      "Training epoch 100, recon_loss:0.892821, zinb_loss:0.480861, cluster_loss:0.168957\n",
      "Clustering   100: ASW= 0.7685, DB= 0.3219, CH= 27314.3810\n",
      "Training epoch 101, recon_loss:0.892670, zinb_loss:0.480839, cluster_loss:0.168478\n",
      "Clustering   101: ASW= 0.7677, DB= 0.3142, CH= 27906.7729\n",
      "Training epoch 102, recon_loss:0.892753, zinb_loss:0.480881, cluster_loss:0.168821\n",
      "Clustering   102: ASW= 0.7691, DB= 0.3215, CH= 27458.2040\n",
      "Training epoch 103, recon_loss:0.892437, zinb_loss:0.480845, cluster_loss:0.168226\n",
      "Clustering   103: ASW= 0.7683, DB= 0.3132, CH= 28036.2029\n",
      "Training epoch 104, recon_loss:0.892694, zinb_loss:0.480901, cluster_loss:0.168690\n",
      "Clustering   104: ASW= 0.7696, DB= 0.3210, CH= 27581.7720\n",
      "Training epoch 105, recon_loss:0.892518, zinb_loss:0.480880, cluster_loss:0.168172\n",
      "Clustering   105: ASW= 0.7690, DB= 0.3135, CH= 28181.2068\n",
      "Training epoch 106, recon_loss:0.892671, zinb_loss:0.480921, cluster_loss:0.168612\n",
      "Clustering   106: ASW= 0.7701, DB= 0.3205, CH= 27712.1551\n",
      "Training epoch 107, recon_loss:0.892425, zinb_loss:0.480894, cluster_loss:0.168017\n",
      "Clustering   107: ASW= 0.7696, DB= 0.3125, CH= 28305.6802\n",
      "Training epoch 108, recon_loss:0.892636, zinb_loss:0.480939, cluster_loss:0.168535\n",
      "Clustering   108: ASW= 0.7705, DB= 0.3204, CH= 27830.1395\n",
      "Training epoch 109, recon_loss:0.892486, zinb_loss:0.480923, cluster_loss:0.167963\n",
      "Clustering   109: ASW= 0.7703, DB= 0.3125, CH= 28448.6944\n",
      "Training epoch 110, recon_loss:0.892660, zinb_loss:0.480957, cluster_loss:0.168522\n",
      "Clustering   110: ASW= 0.7710, DB= 0.3199, CH= 27948.0113\n",
      "Training epoch 111, recon_loss:0.892543, zinb_loss:0.480947, cluster_loss:0.167915\n",
      "Clustering   111: ASW= 0.7709, DB= 0.3130, CH= 28588.7524\n",
      "Training epoch 112, recon_loss:0.892682, zinb_loss:0.480971, cluster_loss:0.168520\n",
      "Clustering   112: ASW= 0.7714, DB= 0.3193, CH= 28067.2763\n",
      "Training epoch 113, recon_loss:0.892673, zinb_loss:0.480978, cluster_loss:0.167925\n",
      "Clustering   113: ASW= 0.7715, DB= 0.3127, CH= 28716.8068\n",
      "Training epoch 114, recon_loss:0.892753, zinb_loss:0.480985, cluster_loss:0.168567\n",
      "Clustering   114: ASW= 0.7717, DB= 0.3188, CH= 28186.0703\n",
      "Training epoch 115, recon_loss:0.892864, zinb_loss:0.481015, cluster_loss:0.167990\n",
      "Clustering   115: ASW= 0.7722, DB= 0.3140, CH= 28849.9874\n",
      "Training epoch 116, recon_loss:0.892796, zinb_loss:0.480995, cluster_loss:0.168603\n",
      "Clustering   116: ASW= 0.7721, DB= 0.3185, CH= 28310.0429\n",
      "Training epoch 117, recon_loss:0.893018, zinb_loss:0.481057, cluster_loss:0.168060\n",
      "Clustering   117: ASW= 0.7728, DB= 0.3136, CH= 28937.7455\n",
      "Training epoch 118, recon_loss:0.892859, zinb_loss:0.481007, cluster_loss:0.168634\n",
      "Clustering   118: ASW= 0.7724, DB= 0.3188, CH= 28440.2501\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training epoch 119, recon_loss:0.893094, zinb_loss:0.481098, cluster_loss:0.168114\n",
      "Clustering   119: ASW= 0.7732, DB= 0.3130, CH= 29007.3892\n",
      "Training epoch 120, recon_loss:0.893008, zinb_loss:0.481034, cluster_loss:0.168699\n",
      "Clustering   120: ASW= 0.7728, DB= 0.3180, CH= 28579.3903\n",
      "Training epoch 121, recon_loss:0.893333, zinb_loss:0.481158, cluster_loss:0.168299\n",
      "Clustering   121: ASW= 0.7737, DB= 0.3126, CH= 29056.7675\n",
      "Training epoch 122, recon_loss:0.893283, zinb_loss:0.481080, cluster_loss:0.168843\n",
      "Clustering   122: ASW= 0.7730, DB= 0.3183, CH= 28701.7644\n",
      "Training epoch 123, recon_loss:0.893618, zinb_loss:0.481235, cluster_loss:0.168535\n",
      "Clustering   123: ASW= 0.7742, DB= 0.3124, CH= 29095.0453\n",
      "Training epoch 124, recon_loss:0.893566, zinb_loss:0.481138, cluster_loss:0.168963\n",
      "Clustering   124: ASW= 0.7733, DB= 0.3172, CH= 28845.0855\n",
      "Training epoch 125, recon_loss:0.893796, zinb_loss:0.481306, cluster_loss:0.168674\n",
      "Clustering   125: ASW= 0.7745, DB= 0.3125, CH= 29120.5798\n",
      "Training epoch 126, recon_loss:0.893715, zinb_loss:0.481190, cluster_loss:0.168961\n",
      "Clustering   126: ASW= 0.7735, DB= 0.3170, CH= 28977.4228\n",
      "Training epoch 127, recon_loss:0.893830, zinb_loss:0.481344, cluster_loss:0.168646\n",
      "Clustering   127: ASW= 0.7749, DB= 0.3118, CH= 29144.7787\n",
      "Training epoch 128, recon_loss:0.893660, zinb_loss:0.481217, cluster_loss:0.168805\n",
      "Clustering   128: ASW= 0.7738, DB= 0.3158, CH= 29109.5086\n",
      "Training epoch 129, recon_loss:0.893656, zinb_loss:0.481345, cluster_loss:0.168426\n",
      "Clustering   129: ASW= 0.7753, DB= 0.3111, CH= 29189.1133\n",
      "Training epoch 130, recon_loss:0.893436, zinb_loss:0.481223, cluster_loss:0.168510\n",
      "Clustering   130: ASW= 0.7741, DB= 0.3155, CH= 29237.6815\n",
      "Training epoch 131, recon_loss:0.893374, zinb_loss:0.481324, cluster_loss:0.168114\n",
      "Clustering   131: ASW= 0.7756, DB= 0.3109, CH= 29249.7209\n",
      "Training epoch 132, recon_loss:0.893151, zinb_loss:0.481219, cluster_loss:0.168170\n",
      "Clustering   132: ASW= 0.7745, DB= 0.3153, CH= 29372.4262\n",
      "Training epoch 133, recon_loss:0.893067, zinb_loss:0.481295, cluster_loss:0.167802\n",
      "Clustering   133: ASW= 0.7760, DB= 0.3109, CH= 29331.4902\n",
      "Training epoch 134, recon_loss:0.892884, zinb_loss:0.481212, cluster_loss:0.167856\n",
      "Clustering   134: ASW= 0.7750, DB= 0.3150, CH= 29512.5408\n",
      "Training epoch 135, recon_loss:0.892767, zinb_loss:0.481264, cluster_loss:0.167518\n",
      "Clustering   135: ASW= 0.7764, DB= 0.3104, CH= 29416.8779\n",
      "Training epoch 136, recon_loss:0.892682, zinb_loss:0.481211, cluster_loss:0.167598\n",
      "Clustering   136: ASW= 0.7755, DB= 0.3142, CH= 29636.2899\n",
      "Training epoch 137, recon_loss:0.892538, zinb_loss:0.481241, cluster_loss:0.167294\n",
      "Clustering   137: ASW= 0.7767, DB= 0.3099, CH= 29509.3410\n",
      "Training epoch 138, recon_loss:0.892568, zinb_loss:0.481216, cluster_loss:0.167417\n",
      "Clustering   138: ASW= 0.7760, DB= 0.3135, CH= 29753.1783\n",
      "Training epoch 139, recon_loss:0.892380, zinb_loss:0.481224, cluster_loss:0.167137\n",
      "Clustering   139: ASW= 0.7771, DB= 0.3093, CH= 29606.1557\n",
      "Training epoch 140, recon_loss:0.892529, zinb_loss:0.481230, cluster_loss:0.167294\n",
      "Clustering   140: ASW= 0.7765, DB= 0.3128, CH= 29861.2074\n",
      "Training epoch 141, recon_loss:0.892259, zinb_loss:0.481211, cluster_loss:0.167028\n",
      "Clustering   141: ASW= 0.7773, DB= 0.3092, CH= 29706.6672\n",
      "Training epoch 142, recon_loss:0.892519, zinb_loss:0.481248, cluster_loss:0.167201\n",
      "Clustering   142: ASW= 0.7769, DB= 0.3122, CH= 29963.8996\n",
      "Training epoch 143, recon_loss:0.892149, zinb_loss:0.481201, cluster_loss:0.166951\n",
      "Clustering   143: ASW= 0.7776, DB= 0.3085, CH= 29804.3779\n",
      "Training epoch 144, recon_loss:0.892493, zinb_loss:0.481268, cluster_loss:0.167125\n",
      "Clustering   144: ASW= 0.7774, DB= 0.3115, CH= 30059.6885\n",
      "Training epoch 145, recon_loss:0.892039, zinb_loss:0.481193, cluster_loss:0.166897\n",
      "Clustering   145: ASW= 0.7780, DB= 0.3081, CH= 29909.3386\n",
      "Training epoch 146, recon_loss:0.892453, zinb_loss:0.481287, cluster_loss:0.167060\n",
      "Clustering   146: ASW= 0.7778, DB= 0.3114, CH= 30172.0641\n",
      "Training epoch 147, recon_loss:0.891934, zinb_loss:0.481186, cluster_loss:0.166863\n",
      "Clustering   147: ASW= 0.7782, DB= 0.3080, CH= 30005.9945\n",
      "Training epoch 148, recon_loss:0.892418, zinb_loss:0.481306, cluster_loss:0.167005\n",
      "Clustering   148: ASW= 0.7782, DB= 0.3106, CH= 30268.2671\n",
      "Training epoch 149, recon_loss:0.891854, zinb_loss:0.481181, cluster_loss:0.166843\n",
      "Clustering   149: ASW= 0.7785, DB= 0.3076, CH= 30099.1895\n",
      "Training epoch 150, recon_loss:0.892392, zinb_loss:0.481323, cluster_loss:0.166944\n",
      "Clustering   150: ASW= 0.7786, DB= 0.3099, CH= 30367.9863\n",
      "Training epoch 151, recon_loss:0.891791, zinb_loss:0.481178, cluster_loss:0.166833\n",
      "Clustering   151: ASW= 0.7788, DB= 0.3073, CH= 30191.3522\n",
      "Training epoch 152, recon_loss:0.892369, zinb_loss:0.481342, cluster_loss:0.166879\n",
      "Clustering   152: ASW= 0.7790, DB= 0.3089, CH= 30468.7984\n",
      "Training epoch 153, recon_loss:0.891734, zinb_loss:0.481180, cluster_loss:0.166826\n",
      "Clustering   153: ASW= 0.7792, DB= 0.3071, CH= 30284.4784\n",
      "Training epoch 154, recon_loss:0.892348, zinb_loss:0.481361, cluster_loss:0.166815\n",
      "Clustering   154: ASW= 0.7795, DB= 0.3082, CH= 30564.8087\n",
      "Training epoch 155, recon_loss:0.891679, zinb_loss:0.481184, cluster_loss:0.166828\n",
      "Clustering   155: ASW= 0.7794, DB= 0.3066, CH= 30360.4960\n",
      "Training epoch 156, recon_loss:0.892337, zinb_loss:0.481382, cluster_loss:0.166765\n",
      "Clustering   156: ASW= 0.7799, DB= 0.3073, CH= 30659.5433\n",
      "Training epoch 157, recon_loss:0.891642, zinb_loss:0.481192, cluster_loss:0.166844\n",
      "Clustering   157: ASW= 0.7796, DB= 0.3062, CH= 30431.2781\n",
      "Training epoch 158, recon_loss:0.892344, zinb_loss:0.481405, cluster_loss:0.166731\n",
      "Clustering   158: ASW= 0.7803, DB= 0.3063, CH= 30749.4587\n",
      "Training epoch 159, recon_loss:0.891606, zinb_loss:0.481205, cluster_loss:0.166864\n",
      "Clustering   159: ASW= 0.7798, DB= 0.3057, CH= 30497.7766\n",
      "Training epoch 160, recon_loss:0.892360, zinb_loss:0.481432, cluster_loss:0.166709\n",
      "Clustering   160: ASW= 0.7807, DB= 0.3054, CH= 30848.7077\n",
      "Training epoch 161, recon_loss:0.891572, zinb_loss:0.481222, cluster_loss:0.166883\n",
      "Clustering   161: ASW= 0.7800, DB= 0.3053, CH= 30567.1032\n",
      "Training epoch 162, recon_loss:0.892365, zinb_loss:0.481460, cluster_loss:0.166684\n",
      "Clustering   162: ASW= 0.7811, DB= 0.3052, CH= 30943.4569\n",
      "Training epoch 163, recon_loss:0.891516, zinb_loss:0.481241, cluster_loss:0.166880\n",
      "Clustering   163: ASW= 0.7804, DB= 0.3053, CH= 30642.1487\n",
      "Training epoch 164, recon_loss:0.892321, zinb_loss:0.481482, cluster_loss:0.166638\n",
      "Clustering   164: ASW= 0.7815, DB= 0.3044, CH= 31020.7238\n",
      "Training epoch 165, recon_loss:0.891425, zinb_loss:0.481258, cluster_loss:0.166842\n",
      "Clustering   165: ASW= 0.7806, DB= 0.3047, CH= 30717.2671\n",
      "Training epoch 166, recon_loss:0.892219, zinb_loss:0.481498, cluster_loss:0.166568\n",
      "Clustering   166: ASW= 0.7818, DB= 0.3038, CH= 31093.2231\n",
      "Training epoch 167, recon_loss:0.891317, zinb_loss:0.481275, cluster_loss:0.166781\n",
      "Clustering   167: ASW= 0.7809, DB= 0.3040, CH= 30805.5826\n",
      "Training epoch 168, recon_loss:0.892087, zinb_loss:0.481511, cluster_loss:0.166485\n",
      "Clustering   168: ASW= 0.7821, DB= 0.3036, CH= 31154.9556\n",
      "Training epoch 169, recon_loss:0.891229, zinb_loss:0.481292, cluster_loss:0.166717\n",
      "Clustering   169: ASW= 0.7813, DB= 0.3034, CH= 30904.6501\n",
      "Training epoch 170, recon_loss:0.891958, zinb_loss:0.481521, cluster_loss:0.166410\n",
      "Clustering   170: ASW= 0.7823, DB= 0.3031, CH= 31201.0036\n",
      "Training epoch 171, recon_loss:0.891185, zinb_loss:0.481310, cluster_loss:0.166662\n",
      "Clustering   171: ASW= 0.7817, DB= 0.3024, CH= 31013.0621\n",
      "Training epoch 172, recon_loss:0.891860, zinb_loss:0.481530, cluster_loss:0.166355\n",
      "Clustering   172: ASW= 0.7825, DB= 0.3030, CH= 31231.2577\n",
      "Training epoch 173, recon_loss:0.891216, zinb_loss:0.481330, cluster_loss:0.166627\n",
      "Clustering   173: ASW= 0.7821, DB= 0.3020, CH= 31144.6293\n",
      "Training epoch 174, recon_loss:0.891849, zinb_loss:0.481544, cluster_loss:0.166341\n",
      "Clustering   174: ASW= 0.7827, DB= 0.3030, CH= 31236.5920\n",
      "Training epoch 175, recon_loss:0.891401, zinb_loss:0.481360, cluster_loss:0.166635\n",
      "Clustering   175: ASW= 0.7825, DB= 0.3015, CH= 31287.2036\n",
      "Training epoch 176, recon_loss:0.892020, zinb_loss:0.481572, cluster_loss:0.166405\n",
      "Clustering   176: ASW= 0.7828, DB= 0.3030, CH= 31204.2851\n",
      "Training epoch 177, recon_loss:0.891816, zinb_loss:0.481408, cluster_loss:0.166715\n",
      "Clustering   177: ASW= 0.7828, DB= 0.3011, CH= 31470.0230\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training epoch 178, recon_loss:0.892422, zinb_loss:0.481615, cluster_loss:0.166583\n",
      "Clustering   178: ASW= 0.7830, DB= 0.3022, CH= 31138.7196\n",
      "Training epoch 179, recon_loss:0.892407, zinb_loss:0.481467, cluster_loss:0.166890\n",
      "Clustering   179: ASW= 0.7829, DB= 0.3009, CH= 31637.0119\n",
      "Training epoch 180, recon_loss:0.892806, zinb_loss:0.481647, cluster_loss:0.166786\n",
      "Clustering   180: ASW= 0.7832, DB= 0.3016, CH= 31093.5441\n",
      "Training epoch 181, recon_loss:0.892767, zinb_loss:0.481504, cluster_loss:0.167002\n",
      "Clustering   181: ASW= 0.7829, DB= 0.3011, CH= 31748.2067\n",
      "Training epoch 182, recon_loss:0.892773, zinb_loss:0.481636, cluster_loss:0.166807\n",
      "Clustering   182: ASW= 0.7835, DB= 0.3010, CH= 31095.2856\n",
      "Training epoch 183, recon_loss:0.892576, zinb_loss:0.481487, cluster_loss:0.166869\n",
      "Clustering   183: ASW= 0.7829, DB= 0.3013, CH= 31834.0013\n",
      "Training epoch 184, recon_loss:0.892434, zinb_loss:0.481593, cluster_loss:0.166661\n",
      "Clustering   184: ASW= 0.7837, DB= 0.3004, CH= 31143.0711\n",
      "Training epoch 185, recon_loss:0.892168, zinb_loss:0.481446, cluster_loss:0.166601\n",
      "Clustering   185: ASW= 0.7831, DB= 0.3009, CH= 31911.5906\n",
      "Training epoch 186, recon_loss:0.892026, zinb_loss:0.481541, cluster_loss:0.166459\n",
      "Clustering   186: ASW= 0.7839, DB= 0.3001, CH= 31228.5488\n",
      "Training epoch 187, recon_loss:0.891800, zinb_loss:0.481405, cluster_loss:0.166351\n",
      "Clustering   187: ASW= 0.7833, DB= 0.3007, CH= 31960.0517\n",
      "Training epoch 188, recon_loss:0.891740, zinb_loss:0.481501, cluster_loss:0.166305\n",
      "Clustering   188: ASW= 0.7843, DB= 0.2996, CH= 31344.3444\n",
      "Training epoch 189, recon_loss:0.891615, zinb_loss:0.481378, cluster_loss:0.166203\n",
      "Clustering   189: ASW= 0.7836, DB= 0.3003, CH= 32000.9455\n",
      "Training epoch 190, recon_loss:0.891636, zinb_loss:0.481472, cluster_loss:0.166242\n",
      "Clustering   190: ASW= 0.7846, DB= 0.2990, CH= 31467.0890\n",
      "Training epoch 191, recon_loss:0.891608, zinb_loss:0.481362, cluster_loss:0.166176\n",
      "Clustering   191: ASW= 0.7839, DB= 0.3000, CH= 32042.3774\n",
      "Training epoch 192, recon_loss:0.891695, zinb_loss:0.481455, cluster_loss:0.166267\n",
      "Clustering   192: ASW= 0.7848, DB= 0.2984, CH= 31592.5033\n",
      "Training epoch 193, recon_loss:0.891723, zinb_loss:0.481355, cluster_loss:0.166230\n",
      "Clustering   193: ASW= 0.7843, DB= 0.2997, CH= 32069.7896\n",
      "Training epoch 194, recon_loss:0.891793, zinb_loss:0.481445, cluster_loss:0.166308\n",
      "Clustering   194: ASW= 0.7852, DB= 0.2974, CH= 31713.2497\n",
      "Training epoch 195, recon_loss:0.891775, zinb_loss:0.481350, cluster_loss:0.166258\n",
      "Clustering   195: ASW= 0.7846, DB= 0.2993, CH= 32100.5806\n",
      "Training epoch 196, recon_loss:0.891795, zinb_loss:0.481433, cluster_loss:0.166295\n",
      "Clustering   196: ASW= 0.7855, DB= 0.2969, CH= 31822.0128\n",
      "Training epoch 197, recon_loss:0.891681, zinb_loss:0.481340, cluster_loss:0.166203\n",
      "Clustering   197: ASW= 0.7849, DB= 0.2990, CH= 32137.4152\n",
      "Training epoch 198, recon_loss:0.891666, zinb_loss:0.481418, cluster_loss:0.166207\n",
      "Clustering   198: ASW= 0.7858, DB= 0.2965, CH= 31923.5227\n",
      "Training epoch 199, recon_loss:0.891461, zinb_loss:0.481327, cluster_loss:0.166074\n",
      "Clustering   199: ASW= 0.7851, DB= 0.2991, CH= 32186.4243\n",
      "Training epoch 200, recon_loss:0.891445, zinb_loss:0.481399, cluster_loss:0.166063\n",
      "Clustering   200: ASW= 0.7861, DB= 0.2959, CH= 32011.4007\n",
      "Training epoch 201, recon_loss:0.891208, zinb_loss:0.481313, cluster_loss:0.165925\n",
      "Clustering   201: ASW= 0.7854, DB= 0.2990, CH= 32244.5177\n",
      "Training epoch 202, recon_loss:0.891207, zinb_loss:0.481378, cluster_loss:0.165904\n",
      "Clustering   202: ASW= 0.7863, DB= 0.2953, CH= 32092.7632\n",
      "Training epoch 203, recon_loss:0.890973, zinb_loss:0.481299, cluster_loss:0.165783\n",
      "Clustering   203: ASW= 0.7857, DB= 0.2986, CH= 32297.9588\n",
      "Training epoch 204, recon_loss:0.891023, zinb_loss:0.481360, cluster_loss:0.165771\n",
      "Clustering   204: ASW= 0.7866, DB= 0.2953, CH= 32176.4718\n",
      "Training epoch 205, recon_loss:0.890796, zinb_loss:0.481287, cluster_loss:0.165677\n",
      "Clustering   205: ASW= 0.7859, DB= 0.2987, CH= 32355.3645\n",
      "Training epoch 206, recon_loss:0.890900, zinb_loss:0.481345, cluster_loss:0.165670\n",
      "Clustering   206: ASW= 0.7868, DB= 0.2947, CH= 32253.7288\n",
      "Training epoch 207, recon_loss:0.890686, zinb_loss:0.481278, cluster_loss:0.165605\n",
      "Clustering   207: ASW= 0.7861, DB= 0.2983, CH= 32404.9415\n",
      "Training epoch 208, recon_loss:0.890849, zinb_loss:0.481333, cluster_loss:0.165600\n",
      "Clustering   208: ASW= 0.7871, DB= 0.2941, CH= 32333.0215\n",
      "Training epoch 209, recon_loss:0.890644, zinb_loss:0.481271, cluster_loss:0.165560\n",
      "Clustering   209: ASW= 0.7863, DB= 0.2980, CH= 32451.2028\n",
      "Training epoch 210, recon_loss:0.890870, zinb_loss:0.481324, cluster_loss:0.165553\n",
      "Clustering   210: ASW= 0.7874, DB= 0.2934, CH= 32410.0156\n",
      "Training epoch 211, recon_loss:0.890661, zinb_loss:0.481266, cluster_loss:0.165544\n",
      "Clustering   211: ASW= 0.7865, DB= 0.2977, CH= 32486.9799\n",
      "Training epoch 212, recon_loss:0.890959, zinb_loss:0.481318, cluster_loss:0.165528\n",
      "Clustering   212: ASW= 0.7877, DB= 0.2930, CH= 32501.3319\n",
      "Training epoch 213, recon_loss:0.890735, zinb_loss:0.481264, cluster_loss:0.165546\n",
      "Clustering   213: ASW= 0.7867, DB= 0.2973, CH= 32511.8702\n",
      "Training epoch 214, recon_loss:0.891122, zinb_loss:0.481318, cluster_loss:0.165518\n",
      "Clustering   214: ASW= 0.7880, DB= 0.2924, CH= 32595.8948\n",
      "Training epoch 215, recon_loss:0.890866, zinb_loss:0.481268, cluster_loss:0.165568\n",
      "Clustering   215: ASW= 0.7868, DB= 0.2969, CH= 32532.7367\n",
      "Training epoch 216, recon_loss:0.891363, zinb_loss:0.481325, cluster_loss:0.165533\n",
      "Clustering   216: ASW= 0.7884, DB= 0.2921, CH= 32704.1919\n",
      "Training epoch 217, recon_loss:0.891046, zinb_loss:0.481282, cluster_loss:0.165616\n",
      "Clustering   217: ASW= 0.7869, DB= 0.2968, CH= 32548.1372\n",
      "Training epoch 218, recon_loss:0.891627, zinb_loss:0.481342, cluster_loss:0.165563\n",
      "Clustering   218: ASW= 0.7887, DB= 0.2920, CH= 32819.8869\n",
      "Training epoch 219, recon_loss:0.891232, zinb_loss:0.481303, cluster_loss:0.165679\n",
      "Clustering   219: ASW= 0.7871, DB= 0.2966, CH= 32557.7512\n",
      "Training epoch 220, recon_loss:0.891837, zinb_loss:0.481365, cluster_loss:0.165594\n",
      "Clustering   220: ASW= 0.7890, DB= 0.2913, CH= 32931.6840\n",
      "Training epoch 221, recon_loss:0.891333, zinb_loss:0.481331, cluster_loss:0.165730\n",
      "Clustering   221: ASW= 0.7873, DB= 0.2962, CH= 32563.4161\n",
      "Training epoch 222, recon_loss:0.891922, zinb_loss:0.481391, cluster_loss:0.165610\n",
      "Clustering   222: ASW= 0.7893, DB= 0.2907, CH= 33056.6239\n",
      "Training epoch 223, recon_loss:0.891337, zinb_loss:0.481360, cluster_loss:0.165764\n",
      "Clustering   223: ASW= 0.7876, DB= 0.2958, CH= 32560.0274\n",
      "Training epoch 224, recon_loss:0.891891, zinb_loss:0.481415, cluster_loss:0.165618\n",
      "Clustering   224: ASW= 0.7896, DB= 0.2900, CH= 33173.4896\n",
      "Training epoch 225, recon_loss:0.891309, zinb_loss:0.481391, cluster_loss:0.165799\n",
      "Clustering   225: ASW= 0.7878, DB= 0.2954, CH= 32569.7543\n",
      "Training epoch 226, recon_loss:0.891835, zinb_loss:0.481437, cluster_loss:0.165643\n",
      "Clustering   226: ASW= 0.7898, DB= 0.2899, CH= 33296.6829\n",
      "Training epoch 227, recon_loss:0.891257, zinb_loss:0.481424, cluster_loss:0.165826\n",
      "Clustering   227: ASW= 0.7880, DB= 0.2948, CH= 32564.2125\n",
      "Training epoch 228, recon_loss:0.891754, zinb_loss:0.481454, cluster_loss:0.165666\n",
      "Clustering   228: ASW= 0.7900, DB= 0.2900, CH= 33412.5602\n",
      "Training epoch 229, recon_loss:0.891173, zinb_loss:0.481451, cluster_loss:0.165830\n",
      "Clustering   229: ASW= 0.7882, DB= 0.2944, CH= 32550.6102\n",
      "Training epoch 230, recon_loss:0.891623, zinb_loss:0.481461, cluster_loss:0.165662\n",
      "Clustering   230: ASW= 0.7901, DB= 0.2898, CH= 33513.2533\n",
      "Training epoch 231, recon_loss:0.891093, zinb_loss:0.481469, cluster_loss:0.165818\n",
      "Clustering   231: ASW= 0.7884, DB= 0.2936, CH= 32540.3557\n",
      "Training epoch 232, recon_loss:0.891446, zinb_loss:0.481454, cluster_loss:0.165633\n",
      "Clustering   232: ASW= 0.7901, DB= 0.2900, CH= 33592.0757\n",
      "Training epoch 233, recon_loss:0.891031, zinb_loss:0.481478, cluster_loss:0.165777\n",
      "Clustering   233: ASW= 0.7887, DB= 0.2929, CH= 32556.6437\n",
      "Training epoch 234, recon_loss:0.891242, zinb_loss:0.481438, cluster_loss:0.165584\n",
      "Clustering   234: ASW= 0.7900, DB= 0.2902, CH= 33645.4607\n",
      "Training epoch 235, recon_loss:0.890982, zinb_loss:0.481478, cluster_loss:0.165704\n",
      "Clustering   235: ASW= 0.7890, DB= 0.2923, CH= 32609.4532\n",
      "Training epoch 236, recon_loss:0.891014, zinb_loss:0.481413, cluster_loss:0.165513\n",
      "Clustering   236: ASW= 0.7901, DB= 0.2903, CH= 33666.6997\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training epoch 237, recon_loss:0.890904, zinb_loss:0.481468, cluster_loss:0.165587\n",
      "Clustering   237: ASW= 0.7892, DB= 0.2913, CH= 32701.4470\n",
      "Training epoch 238, recon_loss:0.890806, zinb_loss:0.481386, cluster_loss:0.165450\n",
      "Clustering   238: ASW= 0.7902, DB= 0.2901, CH= 33665.3216\n",
      "Training epoch 239, recon_loss:0.890821, zinb_loss:0.481458, cluster_loss:0.165475\n",
      "Clustering   239: ASW= 0.7894, DB= 0.2906, CH= 32829.6369\n",
      "Training epoch 240, recon_loss:0.890672, zinb_loss:0.481367, cluster_loss:0.165434\n",
      "Clustering   240: ASW= 0.7903, DB= 0.2899, CH= 33650.5155\n",
      "Training epoch 241, recon_loss:0.890788, zinb_loss:0.481450, cluster_loss:0.165428\n",
      "Clustering   241: ASW= 0.7897, DB= 0.2901, CH= 32973.4013\n",
      "Training epoch 242, recon_loss:0.890619, zinb_loss:0.481359, cluster_loss:0.165490\n",
      "Clustering   242: ASW= 0.7905, DB= 0.2899, CH= 33634.3285\n",
      "Training epoch 243, recon_loss:0.890779, zinb_loss:0.481447, cluster_loss:0.165458\n",
      "Clustering   243: ASW= 0.7899, DB= 0.2896, CH= 33111.1364\n",
      "Training epoch 244, recon_loss:0.890655, zinb_loss:0.481360, cluster_loss:0.165625\n",
      "Clustering   244: ASW= 0.7907, DB= 0.2903, CH= 33611.4834\n",
      "Training epoch 245, recon_loss:0.890785, zinb_loss:0.481443, cluster_loss:0.165555\n",
      "Clustering   245: ASW= 0.7900, DB= 0.2892, CH= 33243.1679\n",
      "Training epoch 246, recon_loss:0.890765, zinb_loss:0.481366, cluster_loss:0.165830\n",
      "Clustering   246: ASW= 0.7909, DB= 0.2903, CH= 33589.8376\n",
      "Training epoch 247, recon_loss:0.890807, zinb_loss:0.481427, cluster_loss:0.165667\n",
      "Clustering   247: ASW= 0.7901, DB= 0.2890, CH= 33375.8103\n",
      "Training epoch 248, recon_loss:0.890908, zinb_loss:0.481372, cluster_loss:0.166050\n",
      "Clustering   248: ASW= 0.7911, DB= 0.2903, CH= 33585.9665\n",
      "Training epoch 249, recon_loss:0.890748, zinb_loss:0.481396, cluster_loss:0.165705\n",
      "Clustering   249: ASW= 0.7902, DB= 0.2886, CH= 33487.0685\n",
      "Training epoch 250, recon_loss:0.890888, zinb_loss:0.481370, cluster_loss:0.166114\n",
      "Clustering   250: ASW= 0.7913, DB= 0.2904, CH= 33575.5664\n",
      "Training epoch 251, recon_loss:0.890536, zinb_loss:0.481358, cluster_loss:0.165617\n",
      "Clustering   251: ASW= 0.7903, DB= 0.2879, CH= 33596.8745\n",
      "Training epoch 252, recon_loss:0.890665, zinb_loss:0.481363, cluster_loss:0.165985\n",
      "Clustering   252: ASW= 0.7915, DB= 0.2901, CH= 33569.4899\n",
      "Training epoch 253, recon_loss:0.890270, zinb_loss:0.481330, cluster_loss:0.165497\n",
      "Clustering   253: ASW= 0.7905, DB= 0.2873, CH= 33699.6549\n",
      "Training epoch 254, recon_loss:0.890447, zinb_loss:0.481358, cluster_loss:0.165852\n",
      "Clustering   254: ASW= 0.7917, DB= 0.2908, CH= 33588.1915\n",
      "Training epoch 255, recon_loss:0.890013, zinb_loss:0.481309, cluster_loss:0.165368\n",
      "Clustering   255: ASW= 0.7908, DB= 0.2868, CH= 33787.6780\n",
      "Training epoch 256, recon_loss:0.890238, zinb_loss:0.481353, cluster_loss:0.165717\n",
      "Clustering   256: ASW= 0.7919, DB= 0.2907, CH= 33614.9089\n",
      "Training epoch 257, recon_loss:0.889794, zinb_loss:0.481295, cluster_loss:0.165258\n",
      "Clustering   257: ASW= 0.7910, DB= 0.2863, CH= 33859.2352\n",
      "Training epoch 258, recon_loss:0.890077, zinb_loss:0.481349, cluster_loss:0.165619\n",
      "Clustering   258: ASW= 0.7921, DB= 0.2904, CH= 33657.5837\n",
      "Training epoch 259, recon_loss:0.889656, zinb_loss:0.481289, cluster_loss:0.165188\n",
      "Clustering   259: ASW= 0.7913, DB= 0.2858, CH= 33918.7850\n",
      "Training epoch 260, recon_loss:0.889974, zinb_loss:0.481346, cluster_loss:0.165554\n",
      "Clustering   260: ASW= 0.7922, DB= 0.2900, CH= 33716.2731\n",
      "Training epoch 261, recon_loss:0.889581, zinb_loss:0.481289, cluster_loss:0.165153\n",
      "Clustering   261: ASW= 0.7915, DB= 0.2855, CH= 33962.2576\n",
      "Training epoch 262, recon_loss:0.889914, zinb_loss:0.481339, cluster_loss:0.165529\n",
      "Clustering   262: ASW= 0.7923, DB= 0.2899, CH= 33775.6273\n",
      "Training epoch 263, recon_loss:0.889596, zinb_loss:0.481297, cluster_loss:0.165163\n",
      "Clustering   263: ASW= 0.7918, DB= 0.2850, CH= 33996.5302\n",
      "Training epoch 264, recon_loss:0.889940, zinb_loss:0.481334, cluster_loss:0.165565\n",
      "Clustering   264: ASW= 0.7924, DB= 0.2900, CH= 33832.3937\n",
      "Training epoch 265, recon_loss:0.889795, zinb_loss:0.481319, cluster_loss:0.165241\n",
      "Clustering   265: ASW= 0.7921, DB= 0.2854, CH= 34035.4840\n",
      "Training epoch 266, recon_loss:0.890173, zinb_loss:0.481340, cluster_loss:0.165683\n",
      "Clustering   266: ASW= 0.7924, DB= 0.2897, CH= 33904.7260\n",
      "Training epoch 267, recon_loss:0.890376, zinb_loss:0.481372, cluster_loss:0.165434\n",
      "Clustering   267: ASW= 0.7924, DB= 0.2849, CH= 34036.4644\n",
      "Training epoch 268, recon_loss:0.890738, zinb_loss:0.481366, cluster_loss:0.165910\n",
      "Clustering   268: ASW= 0.7924, DB= 0.2889, CH= 33977.2369\n",
      "Training epoch 269, recon_loss:0.891233, zinb_loss:0.481458, cluster_loss:0.165702\n",
      "Clustering   269: ASW= 0.7928, DB= 0.2851, CH= 34015.8647\n",
      "Training epoch 270, recon_loss:0.891342, zinb_loss:0.481408, cluster_loss:0.166113\n",
      "Clustering   270: ASW= 0.7923, DB= 0.2887, CH= 34057.5459\n",
      "Training epoch 271, recon_loss:0.891808, zinb_loss:0.481544, cluster_loss:0.165870\n",
      "Clustering   271: ASW= 0.7931, DB= 0.2847, CH= 33968.2105\n",
      "Training epoch 272, recon_loss:0.891560, zinb_loss:0.481446, cluster_loss:0.166115\n",
      "Clustering   272: ASW= 0.7924, DB= 0.2881, CH= 34147.0999\n",
      "Training epoch 273, recon_loss:0.891827, zinb_loss:0.481599, cluster_loss:0.165820\n",
      "Clustering   273: ASW= 0.7933, DB= 0.2845, CH= 33937.9436\n",
      "Training epoch 274, recon_loss:0.891390, zinb_loss:0.481466, cluster_loss:0.165945\n",
      "Clustering   274: ASW= 0.7926, DB= 0.2878, CH= 34237.7798\n",
      "Training epoch 275, recon_loss:0.891503, zinb_loss:0.481614, cluster_loss:0.165636\n",
      "Clustering   275: ASW= 0.7935, DB= 0.2844, CH= 33931.0509\n",
      "Training epoch 276, recon_loss:0.891008, zinb_loss:0.481465, cluster_loss:0.165706\n",
      "Clustering   276: ASW= 0.7928, DB= 0.2873, CH= 34341.6208\n",
      "Training epoch 277, recon_loss:0.891056, zinb_loss:0.481594, cluster_loss:0.165420\n",
      "Clustering   277: ASW= 0.7936, DB= 0.2846, CH= 33923.4752\n",
      "Training epoch 278, recon_loss:0.890603, zinb_loss:0.481445, cluster_loss:0.165484\n",
      "Clustering   278: ASW= 0.7930, DB= 0.2866, CH= 34448.5020\n",
      "Training epoch 279, recon_loss:0.890661, zinb_loss:0.481558, cluster_loss:0.165241\n",
      "Clustering   279: ASW= 0.7937, DB= 0.2847, CH= 33945.9715\n",
      "Training epoch 280, recon_loss:0.890259, zinb_loss:0.481415, cluster_loss:0.165284\n",
      "Clustering   280: ASW= 0.7932, DB= 0.2863, CH= 34533.5507\n",
      "Training epoch 281, recon_loss:0.890328, zinb_loss:0.481515, cluster_loss:0.165091\n",
      "Clustering   281: ASW= 0.7938, DB= 0.2845, CH= 33969.3260\n",
      "Training epoch 282, recon_loss:0.889994, zinb_loss:0.481383, cluster_loss:0.165115\n",
      "Clustering   282: ASW= 0.7934, DB= 0.2859, CH= 34613.7850\n",
      "Training epoch 283, recon_loss:0.890048, zinb_loss:0.481471, cluster_loss:0.164960\n",
      "Clustering   283: ASW= 0.7940, DB= 0.2842, CH= 34000.0794\n",
      "Training epoch 284, recon_loss:0.889762, zinb_loss:0.481350, cluster_loss:0.164955\n",
      "Clustering   284: ASW= 0.7936, DB= 0.2856, CH= 34680.3544\n",
      "Training epoch 285, recon_loss:0.889800, zinb_loss:0.481429, cluster_loss:0.164839\n",
      "Clustering   285: ASW= 0.7941, DB= 0.2842, CH= 34053.5827\n",
      "Training epoch 286, recon_loss:0.889565, zinb_loss:0.481320, cluster_loss:0.164814\n",
      "Clustering   286: ASW= 0.7938, DB= 0.2851, CH= 34741.1015\n",
      "Training epoch 287, recon_loss:0.889626, zinb_loss:0.481394, cluster_loss:0.164739\n",
      "Clustering   287: ASW= 0.7944, DB= 0.2842, CH= 34115.6181\n",
      "Training epoch 288, recon_loss:0.889436, zinb_loss:0.481294, cluster_loss:0.164692\n",
      "Clustering   288: ASW= 0.7941, DB= 0.2845, CH= 34796.5930\n",
      "Training epoch 289, recon_loss:0.889491, zinb_loss:0.481363, cluster_loss:0.164657\n",
      "Clustering   289: ASW= 0.7946, DB= 0.2842, CH= 34189.3677\n",
      "Training epoch 290, recon_loss:0.889360, zinb_loss:0.481275, cluster_loss:0.164605\n",
      "Clustering   290: ASW= 0.7943, DB= 0.2842, CH= 34846.4341\n",
      "Training epoch 291, recon_loss:0.889426, zinb_loss:0.481338, cluster_loss:0.164604\n",
      "Clustering   291: ASW= 0.7947, DB= 0.2838, CH= 34255.0651\n",
      "Training epoch 292, recon_loss:0.889341, zinb_loss:0.481260, cluster_loss:0.164539\n",
      "Clustering   292: ASW= 0.7944, DB= 0.2839, CH= 34887.0441\n",
      "Training epoch 293, recon_loss:0.889395, zinb_loss:0.481317, cluster_loss:0.164572\n",
      "Clustering   293: ASW= 0.7950, DB= 0.2831, CH= 34328.9244\n",
      "Training epoch 294, recon_loss:0.889353, zinb_loss:0.481248, cluster_loss:0.164501\n",
      "Clustering   294: ASW= 0.7946, DB= 0.2837, CH= 34925.3238\n",
      "Training epoch 295, recon_loss:0.889397, zinb_loss:0.481300, cluster_loss:0.164556\n",
      "Clustering   295: ASW= 0.7951, DB= 0.2828, CH= 34400.8529\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training epoch 296, recon_loss:0.889398, zinb_loss:0.481240, cluster_loss:0.164475\n",
      "Clustering   296: ASW= 0.7947, DB= 0.2834, CH= 34957.6112\n",
      "Training epoch 297, recon_loss:0.889404, zinb_loss:0.481282, cluster_loss:0.164548\n",
      "Clustering   297: ASW= 0.7953, DB= 0.2824, CH= 34476.6676\n",
      "Training epoch 298, recon_loss:0.889428, zinb_loss:0.481231, cluster_loss:0.164453\n",
      "Clustering   298: ASW= 0.7949, DB= 0.2833, CH= 34982.3217\n",
      "Training epoch 299, recon_loss:0.889406, zinb_loss:0.481265, cluster_loss:0.164539\n",
      "Clustering   299: ASW= 0.7955, DB= 0.2819, CH= 34556.2117\n",
      "Training epoch 300, recon_loss:0.889446, zinb_loss:0.481223, cluster_loss:0.164427\n",
      "Clustering   300: ASW= 0.7950, DB= 0.2839, CH= 35004.3850\n",
      "Training epoch 301, recon_loss:0.889394, zinb_loss:0.481249, cluster_loss:0.164535\n",
      "Clustering   301: ASW= 0.7958, DB= 0.2816, CH= 34645.2435\n",
      "Training epoch 302, recon_loss:0.889428, zinb_loss:0.481215, cluster_loss:0.164395\n",
      "Clustering   302: ASW= 0.7950, DB= 0.2841, CH= 35019.4613\n",
      "Training epoch 303, recon_loss:0.889360, zinb_loss:0.481230, cluster_loss:0.164528\n",
      "Clustering   303: ASW= 0.7961, DB= 0.2811, CH= 34734.8829\n",
      "Training epoch 304, recon_loss:0.889396, zinb_loss:0.481208, cluster_loss:0.164358\n",
      "Clustering   304: ASW= 0.7951, DB= 0.2841, CH= 35021.0447\n",
      "Training epoch 305, recon_loss:0.889335, zinb_loss:0.481215, cluster_loss:0.164535\n",
      "Clustering   305: ASW= 0.7964, DB= 0.2805, CH= 34829.0822\n",
      "Training epoch 306, recon_loss:0.889347, zinb_loss:0.481204, cluster_loss:0.164324\n",
      "Clustering   306: ASW= 0.7951, DB= 0.2841, CH= 35017.5958\n",
      "Training epoch 307, recon_loss:0.889339, zinb_loss:0.481204, cluster_loss:0.164554\n",
      "Clustering   307: ASW= 0.7967, DB= 0.2798, CH= 34925.4743\n",
      "Training epoch 308, recon_loss:0.889319, zinb_loss:0.481205, cluster_loss:0.164306\n",
      "Clustering   308: ASW= 0.7951, DB= 0.2842, CH= 35008.2854\n",
      "Training epoch 309, recon_loss:0.889379, zinb_loss:0.481202, cluster_loss:0.164588\n",
      "Clustering   309: ASW= 0.7970, DB= 0.2796, CH= 35014.5594\n",
      "Training epoch 310, recon_loss:0.889296, zinb_loss:0.481208, cluster_loss:0.164310\n",
      "Clustering   310: ASW= 0.7951, DB= 0.2843, CH= 35005.0613\n",
      "Training epoch 311, recon_loss:0.889504, zinb_loss:0.481212, cluster_loss:0.164628\n",
      "Clustering   311: ASW= 0.7973, DB= 0.2791, CH= 35110.5454\n",
      "Training epoch 312, recon_loss:0.889366, zinb_loss:0.481218, cluster_loss:0.164350\n",
      "Clustering   312: ASW= 0.7950, DB= 0.2843, CH= 34986.5680\n",
      "Training epoch 313, recon_loss:0.889752, zinb_loss:0.481237, cluster_loss:0.164685\n",
      "Clustering   313: ASW= 0.7976, DB= 0.2785, CH= 35214.4314\n",
      "Training epoch 314, recon_loss:0.889537, zinb_loss:0.481239, cluster_loss:0.164411\n",
      "Clustering   314: ASW= 0.7950, DB= 0.2846, CH= 34951.9537\n",
      "Training epoch 315, recon_loss:0.890141, zinb_loss:0.481274, cluster_loss:0.164738\n",
      "Clustering   315: ASW= 0.7979, DB= 0.2782, CH= 35309.1185\n",
      "Training epoch 316, recon_loss:0.889791, zinb_loss:0.481265, cluster_loss:0.164474\n",
      "Clustering   316: ASW= 0.7951, DB= 0.2843, CH= 34917.8505\n",
      "Training epoch 317, recon_loss:0.890451, zinb_loss:0.481304, cluster_loss:0.164752\n",
      "Clustering   317: ASW= 0.7982, DB= 0.2782, CH= 35428.4536\n",
      "Training epoch 318, recon_loss:0.889882, zinb_loss:0.481281, cluster_loss:0.164491\n",
      "Clustering   318: ASW= 0.7952, DB= 0.2834, CH= 34892.0572\n",
      "Training epoch 319, recon_loss:0.890507, zinb_loss:0.481317, cluster_loss:0.164695\n",
      "Clustering   319: ASW= 0.7983, DB= 0.2781, CH= 35536.3149\n",
      "Training epoch 320, recon_loss:0.889777, zinb_loss:0.481282, cluster_loss:0.164448\n",
      "Clustering   320: ASW= 0.7955, DB= 0.2828, CH= 34890.5981\n",
      "Training epoch 321, recon_loss:0.890326, zinb_loss:0.481309, cluster_loss:0.164600\n",
      "Clustering   321: ASW= 0.7984, DB= 0.2782, CH= 35629.1329\n",
      "Training epoch 322, recon_loss:0.889551, zinb_loss:0.481274, cluster_loss:0.164376\n",
      "Clustering   322: ASW= 0.7958, DB= 0.2821, CH= 34905.3541\n",
      "Training epoch 323, recon_loss:0.890025, zinb_loss:0.481291, cluster_loss:0.164501\n",
      "Clustering   323: ASW= 0.7985, DB= 0.2782, CH= 35699.8194\n",
      "Training epoch 324, recon_loss:0.889337, zinb_loss:0.481265, cluster_loss:0.164311\n",
      "Clustering   324: ASW= 0.7961, DB= 0.2817, CH= 34943.7529\n",
      "Training epoch 325, recon_loss:0.889752, zinb_loss:0.481273, cluster_loss:0.164445\n",
      "Clustering   325: ASW= 0.7985, DB= 0.2786, CH= 35757.1625\n",
      "Training epoch 326, recon_loss:0.889181, zinb_loss:0.481258, cluster_loss:0.164273\n",
      "Clustering   326: ASW= 0.7963, DB= 0.2814, CH= 34978.4944\n",
      "Training epoch 327, recon_loss:0.889525, zinb_loss:0.481256, cluster_loss:0.164423\n",
      "Clustering   327: ASW= 0.7984, DB= 0.2787, CH= 35803.0877\n",
      "Training epoch 328, recon_loss:0.889123, zinb_loss:0.481258, cluster_loss:0.164269\n",
      "Clustering   328: ASW= 0.7965, DB= 0.2807, CH= 35032.0391\n",
      "Training epoch 329, recon_loss:0.889402, zinb_loss:0.481244, cluster_loss:0.164471\n",
      "Clustering   329: ASW= 0.7984, DB= 0.2791, CH= 35828.8888\n",
      "Training epoch 330, recon_loss:0.889151, zinb_loss:0.481265, cluster_loss:0.164307\n",
      "Clustering   330: ASW= 0.7966, DB= 0.2798, CH= 35086.6794\n",
      "Training epoch 331, recon_loss:0.889356, zinb_loss:0.481239, cluster_loss:0.164570\n",
      "Clustering   331: ASW= 0.7984, DB= 0.2793, CH= 35837.8995\n",
      "Training epoch 332, recon_loss:0.889251, zinb_loss:0.481282, cluster_loss:0.164383\n",
      "Clustering   332: ASW= 0.7968, DB= 0.2792, CH= 35159.1892\n",
      "Training epoch 333, recon_loss:0.889329, zinb_loss:0.481237, cluster_loss:0.164689\n",
      "Clustering   333: ASW= 0.7984, DB= 0.2795, CH= 35834.1748\n",
      "Training epoch 334, recon_loss:0.889321, zinb_loss:0.481300, cluster_loss:0.164452\n",
      "Clustering   334: ASW= 0.7969, DB= 0.2789, CH= 35229.0798\n",
      "Training epoch 335, recon_loss:0.889243, zinb_loss:0.481232, cluster_loss:0.164768\n",
      "Clustering   335: ASW= 0.7984, DB= 0.2796, CH= 35827.3957\n",
      "Training epoch 336, recon_loss:0.889279, zinb_loss:0.481309, cluster_loss:0.164484\n",
      "Clustering   336: ASW= 0.7972, DB= 0.2783, CH= 35311.5011\n",
      "Training epoch 337, recon_loss:0.889048, zinb_loss:0.481218, cluster_loss:0.164777\n",
      "Clustering   337: ASW= 0.7984, DB= 0.2796, CH= 35813.8799\n",
      "Training epoch 338, recon_loss:0.889140, zinb_loss:0.481305, cluster_loss:0.164452\n",
      "Clustering   338: ASW= 0.7975, DB= 0.2777, CH= 35401.7175\n",
      "Training epoch 339, recon_loss:0.888808, zinb_loss:0.481197, cluster_loss:0.164731\n",
      "Clustering   339: ASW= 0.7984, DB= 0.2796, CH= 35802.7218\n",
      "Training epoch 340, recon_loss:0.888974, zinb_loss:0.481295, cluster_loss:0.164391\n",
      "Clustering   340: ASW= 0.7979, DB= 0.2769, CH= 35502.4453\n",
      "Training epoch 341, recon_loss:0.888576, zinb_loss:0.481171, cluster_loss:0.164661\n",
      "Clustering   341: ASW= 0.7985, DB= 0.2796, CH= 35795.4400\n",
      "Training epoch 342, recon_loss:0.888856, zinb_loss:0.481282, cluster_loss:0.164328\n",
      "Clustering   342: ASW= 0.7983, DB= 0.2764, CH= 35606.4713\n",
      "Training epoch 343, recon_loss:0.888408, zinb_loss:0.481142, cluster_loss:0.164594\n",
      "Clustering   343: ASW= 0.7985, DB= 0.2796, CH= 35788.0310\n",
      "Training epoch 344, recon_loss:0.888809, zinb_loss:0.481271, cluster_loss:0.164276\n",
      "Clustering   344: ASW= 0.7987, DB= 0.2758, CH= 35695.3809\n",
      "Training epoch 345, recon_loss:0.888321, zinb_loss:0.481114, cluster_loss:0.164535\n",
      "Clustering   345: ASW= 0.7985, DB= 0.2796, CH= 35783.2135\n",
      "Training epoch 346, recon_loss:0.888857, zinb_loss:0.481262, cluster_loss:0.164243\n",
      "Clustering   346: ASW= 0.7990, DB= 0.2753, CH= 35787.0243\n",
      "Training epoch 347, recon_loss:0.888334, zinb_loss:0.481089, cluster_loss:0.164499\n",
      "Clustering   347: ASW= 0.7985, DB= 0.2794, CH= 35787.2041\n",
      "Training epoch 348, recon_loss:0.888982, zinb_loss:0.481256, cluster_loss:0.164223\n",
      "Clustering   348: ASW= 0.7994, DB= 0.2749, CH= 35874.1495\n",
      "Training epoch 349, recon_loss:0.888403, zinb_loss:0.481069, cluster_loss:0.164467\n",
      "Clustering   349: ASW= 0.7984, DB= 0.2792, CH= 35785.2223\n",
      "Training epoch 350, recon_loss:0.889114, zinb_loss:0.481249, cluster_loss:0.164210\n",
      "Clustering   350: ASW= 0.7997, DB= 0.2746, CH= 35952.9088\n",
      "Training epoch 351, recon_loss:0.888429, zinb_loss:0.481050, cluster_loss:0.164415\n",
      "Clustering   351: ASW= 0.7985, DB= 0.2788, CH= 35810.0731\n",
      "Training epoch 352, recon_loss:0.889147, zinb_loss:0.481240, cluster_loss:0.164160\n",
      "Clustering   352: ASW= 0.8000, DB= 0.2742, CH= 36023.2587\n",
      "Training epoch 353, recon_loss:0.888380, zinb_loss:0.481033, cluster_loss:0.164335\n",
      "Clustering   353: ASW= 0.7985, DB= 0.2788, CH= 35846.6527\n",
      "Training epoch 354, recon_loss:0.889094, zinb_loss:0.481227, cluster_loss:0.164102\n",
      "Clustering   354: ASW= 0.8002, DB= 0.2738, CH= 36082.9316\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training epoch 355, recon_loss:0.888308, zinb_loss:0.481019, cluster_loss:0.164256\n",
      "Clustering   355: ASW= 0.7986, DB= 0.2786, CH= 35883.1942\n",
      "Training epoch 356, recon_loss:0.889027, zinb_loss:0.481215, cluster_loss:0.164056\n",
      "Clustering   356: ASW= 0.8005, DB= 0.2737, CH= 36147.9577\n",
      "Training epoch 357, recon_loss:0.888243, zinb_loss:0.481007, cluster_loss:0.164185\n",
      "Clustering   357: ASW= 0.7987, DB= 0.2785, CH= 35926.4697\n",
      "Training epoch 358, recon_loss:0.888963, zinb_loss:0.481204, cluster_loss:0.164028\n",
      "Clustering   358: ASW= 0.8006, DB= 0.2734, CH= 36198.9902\n",
      "Training epoch 359, recon_loss:0.888218, zinb_loss:0.480998, cluster_loss:0.164130\n",
      "Clustering   359: ASW= 0.7988, DB= 0.2782, CH= 35969.8988\n",
      "Training epoch 360, recon_loss:0.888947, zinb_loss:0.481194, cluster_loss:0.164024\n",
      "Clustering   360: ASW= 0.8008, DB= 0.2729, CH= 36255.8438\n",
      "Training epoch 361, recon_loss:0.888277, zinb_loss:0.480994, cluster_loss:0.164108\n",
      "Clustering   361: ASW= 0.7989, DB= 0.2779, CH= 36006.7356\n",
      "Training epoch 362, recon_loss:0.889012, zinb_loss:0.481188, cluster_loss:0.164055\n",
      "Clustering   362: ASW= 0.8010, DB= 0.2729, CH= 36325.8163\n",
      "Training epoch 363, recon_loss:0.888457, zinb_loss:0.481004, cluster_loss:0.164116\n",
      "Clustering   363: ASW= 0.7990, DB= 0.2780, CH= 36037.5862\n",
      "Training epoch 364, recon_loss:0.889173, zinb_loss:0.481190, cluster_loss:0.164134\n",
      "Clustering   364: ASW= 0.8011, DB= 0.2730, CH= 36402.5490\n",
      "Training epoch 365, recon_loss:0.888776, zinb_loss:0.481034, cluster_loss:0.164153\n",
      "Clustering   365: ASW= 0.7991, DB= 0.2775, CH= 36044.3017\n",
      "Training epoch 366, recon_loss:0.889397, zinb_loss:0.481205, cluster_loss:0.164242\n",
      "Clustering   366: ASW= 0.8012, DB= 0.2733, CH= 36490.3290\n",
      "Training epoch 367, recon_loss:0.889159, zinb_loss:0.481084, cluster_loss:0.164213\n",
      "Clustering   367: ASW= 0.7992, DB= 0.2772, CH= 36020.9367\n",
      "Training epoch 368, recon_loss:0.889589, zinb_loss:0.481230, cluster_loss:0.164354\n",
      "Clustering   368: ASW= 0.8013, DB= 0.2730, CH= 36591.7856\n",
      "Training epoch 369, recon_loss:0.889511, zinb_loss:0.481145, cluster_loss:0.164318\n",
      "Clustering   369: ASW= 0.7992, DB= 0.2769, CH= 35967.4657\n",
      "Training epoch 370, recon_loss:0.889672, zinb_loss:0.481250, cluster_loss:0.164458\n",
      "Clustering   370: ASW= 0.8013, DB= 0.2734, CH= 36696.2782\n",
      "Training epoch 371, recon_loss:0.889717, zinb_loss:0.481192, cluster_loss:0.164455\n",
      "Clustering   371: ASW= 0.7994, DB= 0.2764, CH= 35888.3103\n",
      "Training epoch 372, recon_loss:0.889561, zinb_loss:0.481251, cluster_loss:0.164524\n",
      "Clustering   372: ASW= 0.8012, DB= 0.2733, CH= 36781.1342\n",
      "Training epoch 373, recon_loss:0.889705, zinb_loss:0.481210, cluster_loss:0.164553\n",
      "Clustering   373: ASW= 0.7997, DB= 0.2758, CH= 35829.8205\n",
      "Training epoch 374, recon_loss:0.889222, zinb_loss:0.481222, cluster_loss:0.164472\n",
      "Clustering   374: ASW= 0.8010, DB= 0.2736, CH= 36832.2167\n",
      "Training epoch 375, recon_loss:0.889430, zinb_loss:0.481199, cluster_loss:0.164489\n",
      "Clustering   375: ASW= 0.7999, DB= 0.2757, CH= 35821.0156\n",
      "Training epoch 376, recon_loss:0.888787, zinb_loss:0.481177, cluster_loss:0.164320\n",
      "Clustering   376: ASW= 0.8010, DB= 0.2738, CH= 36868.7189\n",
      "Training epoch 377, recon_loss:0.889091, zinb_loss:0.481171, cluster_loss:0.164329\n",
      "Clustering   377: ASW= 0.8002, DB= 0.2752, CH= 35869.5556\n",
      "Training epoch 378, recon_loss:0.888455, zinb_loss:0.481135, cluster_loss:0.164145\n",
      "Clustering   378: ASW= 0.8010, DB= 0.2738, CH= 36907.2006\n",
      "Training epoch 379, recon_loss:0.888836, zinb_loss:0.481143, cluster_loss:0.164162\n",
      "Clustering   379: ASW= 0.8006, DB= 0.2746, CH= 35949.4165\n",
      "Training epoch 380, recon_loss:0.888244, zinb_loss:0.481101, cluster_loss:0.163977\n",
      "Clustering   380: ASW= 0.8011, DB= 0.2738, CH= 36948.1858\n",
      "Training epoch 381, recon_loss:0.888634, zinb_loss:0.481114, cluster_loss:0.164004\n",
      "Clustering   381: ASW= 0.8008, DB= 0.2742, CH= 36033.6238\n",
      "Training epoch 382, recon_loss:0.888081, zinb_loss:0.481073, cluster_loss:0.163820\n",
      "Clustering   382: ASW= 0.8011, DB= 0.2737, CH= 36985.5634\n",
      "Training epoch 383, recon_loss:0.888462, zinb_loss:0.481089, cluster_loss:0.163857\n",
      "Clustering   383: ASW= 0.8011, DB= 0.2741, CH= 36125.3992\n",
      "Training epoch 384, recon_loss:0.887944, zinb_loss:0.481048, cluster_loss:0.163666\n",
      "Clustering   384: ASW= 0.8013, DB= 0.2736, CH= 37018.4820\n",
      "Training epoch 385, recon_loss:0.888290, zinb_loss:0.481062, cluster_loss:0.163722\n",
      "Clustering   385: ASW= 0.8013, DB= 0.2738, CH= 36212.3440\n",
      "Training epoch 386, recon_loss:0.887854, zinb_loss:0.481028, cluster_loss:0.163540\n",
      "Clustering   386: ASW= 0.8014, DB= 0.2733, CH= 37060.9739\n",
      "Training epoch 387, recon_loss:0.888190, zinb_loss:0.481040, cluster_loss:0.163629\n",
      "Clustering   387: ASW= 0.8015, DB= 0.2736, CH= 36287.2802\n",
      "Training epoch 388, recon_loss:0.887859, zinb_loss:0.481015, cluster_loss:0.163459\n",
      "Clustering   388: ASW= 0.8015, DB= 0.2733, CH= 37117.7609\n",
      "Training epoch 389, recon_loss:0.888212, zinb_loss:0.481024, cluster_loss:0.163593\n",
      "Clustering   389: ASW= 0.8017, DB= 0.2735, CH= 36343.3951\n",
      "Training epoch 390, recon_loss:0.888022, zinb_loss:0.481013, cluster_loss:0.163451\n",
      "Clustering   390: ASW= 0.8017, DB= 0.2729, CH= 37180.0164\n",
      "Training epoch 391, recon_loss:0.888405, zinb_loss:0.481016, cluster_loss:0.163638\n",
      "Clustering   391: ASW= 0.8017, DB= 0.2735, CH= 36374.6015\n",
      "Training epoch 392, recon_loss:0.888343, zinb_loss:0.481020, cluster_loss:0.163538\n",
      "Clustering   392: ASW= 0.8019, DB= 0.2725, CH= 37249.8259\n",
      "Training epoch 393, recon_loss:0.888710, zinb_loss:0.481013, cluster_loss:0.163762\n",
      "Clustering   393: ASW= 0.8017, DB= 0.2738, CH= 36381.0892\n",
      "Training epoch 394, recon_loss:0.888753, zinb_loss:0.481037, cluster_loss:0.163721\n",
      "Clustering   394: ASW= 0.8022, DB= 0.2719, CH= 37321.4734\n",
      "Training epoch 395, recon_loss:0.889021, zinb_loss:0.481013, cluster_loss:0.163935\n",
      "Clustering   395: ASW= 0.8015, DB= 0.2738, CH= 36373.5043\n",
      "Training epoch 396, recon_loss:0.889123, zinb_loss:0.481054, cluster_loss:0.163933\n",
      "Clustering   396: ASW= 0.8025, DB= 0.2715, CH= 37366.6558\n",
      "Training epoch 397, recon_loss:0.889158, zinb_loss:0.481002, cluster_loss:0.164063\n",
      "Clustering   397: ASW= 0.8013, DB= 0.2738, CH= 36370.8788\n",
      "Training epoch 398, recon_loss:0.889302, zinb_loss:0.481058, cluster_loss:0.164068\n",
      "Clustering   398: ASW= 0.8028, DB= 0.2712, CH= 37372.6577\n",
      "Training epoch 399, recon_loss:0.888978, zinb_loss:0.480974, cluster_loss:0.164053\n",
      "Clustering   399: ASW= 0.8011, DB= 0.2740, CH= 36423.2070\n",
      "Training epoch 400, recon_loss:0.889208, zinb_loss:0.481042, cluster_loss:0.164079\n",
      "Clustering   400: ASW= 0.8032, DB= 0.2707, CH= 37331.0075\n",
      "Training epoch 401, recon_loss:0.888645, zinb_loss:0.480942, cluster_loss:0.163961\n",
      "Clustering   401: ASW= 0.8009, DB= 0.2741, CH= 36501.6993\n",
      "Training epoch 402, recon_loss:0.888991, zinb_loss:0.481022, cluster_loss:0.164024\n",
      "Clustering   402: ASW= 0.8035, DB= 0.2707, CH= 37296.4142\n",
      "Training epoch 403, recon_loss:0.888293, zinb_loss:0.480922, cluster_loss:0.163842\n",
      "Clustering   403: ASW= 0.8009, DB= 0.2741, CH= 36601.7510\n",
      "Training epoch 404, recon_loss:0.888737, zinb_loss:0.481004, cluster_loss:0.163938\n",
      "Clustering   404: ASW= 0.8037, DB= 0.2706, CH= 37271.8778\n",
      "Training epoch 405, recon_loss:0.887983, zinb_loss:0.480910, cluster_loss:0.163720\n",
      "Clustering   405: ASW= 0.8010, DB= 0.2738, CH= 36697.7447\n",
      "Training epoch 406, recon_loss:0.888476, zinb_loss:0.480986, cluster_loss:0.163826\n",
      "Clustering   406: ASW= 0.8038, DB= 0.2705, CH= 37274.0784\n",
      "Training epoch 407, recon_loss:0.887719, zinb_loss:0.480903, cluster_loss:0.163593\n",
      "Clustering   407: ASW= 0.8012, DB= 0.2736, CH= 36785.0310\n",
      "Training epoch 408, recon_loss:0.888238, zinb_loss:0.480971, cluster_loss:0.163708\n",
      "Clustering   408: ASW= 0.8040, DB= 0.2700, CH= 37301.8732\n",
      "Training epoch 409, recon_loss:0.887524, zinb_loss:0.480896, cluster_loss:0.163474\n",
      "Clustering   409: ASW= 0.8013, DB= 0.2732, CH= 36860.8921\n",
      "Training epoch 410, recon_loss:0.888051, zinb_loss:0.480956, cluster_loss:0.163605\n",
      "Clustering   410: ASW= 0.8041, DB= 0.2698, CH= 37337.0903\n",
      "Training epoch 411, recon_loss:0.887397, zinb_loss:0.480891, cluster_loss:0.163372\n",
      "Clustering   411: ASW= 0.8015, DB= 0.2731, CH= 36930.3683\n",
      "Training epoch 412, recon_loss:0.887917, zinb_loss:0.480942, cluster_loss:0.163520\n",
      "Clustering   412: ASW= 0.8042, DB= 0.2696, CH= 37377.6610\n",
      "Training epoch 413, recon_loss:0.887311, zinb_loss:0.480886, cluster_loss:0.163288\n",
      "Clustering   413: ASW= 0.8017, DB= 0.2730, CH= 36996.1161\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training epoch 414, recon_loss:0.887807, zinb_loss:0.480930, cluster_loss:0.163453\n",
      "Clustering   414: ASW= 0.8044, DB= 0.2694, CH= 37418.9066\n",
      "Training epoch 415, recon_loss:0.887259, zinb_loss:0.480883, cluster_loss:0.163220\n",
      "Clustering   415: ASW= 0.8019, DB= 0.2727, CH= 37061.5015\n",
      "Training epoch 416, recon_loss:0.887722, zinb_loss:0.480917, cluster_loss:0.163415\n",
      "Clustering   416: ASW= 0.8045, DB= 0.2692, CH= 37453.7369\n",
      "Training epoch 417, recon_loss:0.887230, zinb_loss:0.480882, cluster_loss:0.163172\n",
      "Clustering   417: ASW= 0.8020, DB= 0.2725, CH= 37125.6886\n",
      "Training epoch 418, recon_loss:0.887662, zinb_loss:0.480905, cluster_loss:0.163402\n",
      "Clustering   418: ASW= 0.8046, DB= 0.2690, CH= 37485.9543\n",
      "Training epoch 419, recon_loss:0.887236, zinb_loss:0.480885, cluster_loss:0.163146\n",
      "Clustering   419: ASW= 0.8022, DB= 0.2724, CH= 37189.3257\n",
      "Training epoch 420, recon_loss:0.887626, zinb_loss:0.480893, cluster_loss:0.163424\n",
      "Clustering   420: ASW= 0.8048, DB= 0.2688, CH= 37510.2313\n",
      "Training epoch 421, recon_loss:0.887288, zinb_loss:0.480893, cluster_loss:0.163154\n",
      "Clustering   421: ASW= 0.8023, DB= 0.2723, CH= 37252.4561\n",
      "Training epoch 422, recon_loss:0.887624, zinb_loss:0.480885, cluster_loss:0.163487\n",
      "Clustering   422: ASW= 0.8049, DB= 0.2686, CH= 37527.7016\n",
      "Training epoch 423, recon_loss:0.887378, zinb_loss:0.480909, cluster_loss:0.163202\n",
      "Clustering   423: ASW= 0.8025, DB= 0.2721, CH= 37322.2583\n",
      "Training epoch 424, recon_loss:0.887631, zinb_loss:0.480880, cluster_loss:0.163592\n",
      "Clustering   424: ASW= 0.8050, DB= 0.2685, CH= 37535.6939\n",
      "Training epoch 425, recon_loss:0.887492, zinb_loss:0.480932, cluster_loss:0.163287\n",
      "Clustering   425: ASW= 0.8026, DB= 0.2719, CH= 37396.5673\n",
      "Training epoch 426, recon_loss:0.887614, zinb_loss:0.480875, cluster_loss:0.163711\n",
      "Clustering   426: ASW= 0.8050, DB= 0.2684, CH= 37529.3049\n",
      "Training epoch 427, recon_loss:0.887556, zinb_loss:0.480955, cluster_loss:0.163371\n",
      "Clustering   427: ASW= 0.8028, DB= 0.2716, CH= 37472.4582\n",
      "Training epoch 428, recon_loss:0.887514, zinb_loss:0.480868, cluster_loss:0.163790\n",
      "Clustering   428: ASW= 0.8050, DB= 0.2683, CH= 37518.1707\n",
      "Training epoch 429, recon_loss:0.887569, zinb_loss:0.480973, cluster_loss:0.163424\n",
      "Clustering   429: ASW= 0.8031, DB= 0.2711, CH= 37549.8290\n",
      "Training epoch 430, recon_loss:0.887374, zinb_loss:0.480851, cluster_loss:0.163819\n",
      "Clustering   430: ASW= 0.8048, DB= 0.2682, CH= 37501.7017\n",
      "Training epoch 431, recon_loss:0.887576, zinb_loss:0.480981, cluster_loss:0.163437\n",
      "Clustering   431: ASW= 0.8034, DB= 0.2706, CH= 37627.6240\n",
      "Training epoch 432, recon_loss:0.887307, zinb_loss:0.480831, cluster_loss:0.163807\n",
      "Clustering   432: ASW= 0.8047, DB= 0.2683, CH= 37486.3702\n",
      "Training epoch 433, recon_loss:0.887752, zinb_loss:0.480992, cluster_loss:0.163458\n",
      "Clustering   433: ASW= 0.8037, DB= 0.2705, CH= 37701.2743\n",
      "Training epoch 434, recon_loss:0.887465, zinb_loss:0.480818, cluster_loss:0.163818\n",
      "Clustering   434: ASW= 0.8044, DB= 0.2685, CH= 37469.6473\n",
      "Training epoch 435, recon_loss:0.888156, zinb_loss:0.481005, cluster_loss:0.163490\n",
      "Clustering   435: ASW= 0.8042, DB= 0.2701, CH= 37771.1933\n",
      "Training epoch 436, recon_loss:0.887768, zinb_loss:0.480811, cluster_loss:0.163808\n",
      "Clustering   436: ASW= 0.8042, DB= 0.2682, CH= 37454.6337\n",
      "Training epoch 437, recon_loss:0.888635, zinb_loss:0.481021, cluster_loss:0.163516\n",
      "Clustering   437: ASW= 0.8046, DB= 0.2696, CH= 37823.3655\n",
      "Training epoch 438, recon_loss:0.888067, zinb_loss:0.480818, cluster_loss:0.163794\n",
      "Clustering   438: ASW= 0.8039, DB= 0.2682, CH= 37467.9434\n",
      "Training epoch 439, recon_loss:0.889070, zinb_loss:0.481046, cluster_loss:0.163588\n",
      "Clustering   439: ASW= 0.8050, DB= 0.2697, CH= 37839.1272\n",
      "Training epoch 440, recon_loss:0.888316, zinb_loss:0.480844, cluster_loss:0.163859\n",
      "Clustering   440: ASW= 0.8036, DB= 0.2676, CH= 37502.8480\n",
      "Training epoch 441, recon_loss:0.889574, zinb_loss:0.481094, cluster_loss:0.163853\n",
      "Clustering   441: ASW= 0.8054, DB= 0.2699, CH= 37804.4902\n",
      "Training epoch 442, recon_loss:0.888667, zinb_loss:0.480900, cluster_loss:0.164150\n",
      "Clustering   442: ASW= 0.8033, DB= 0.2670, CH= 37535.9547\n",
      "Training epoch 443, recon_loss:0.890039, zinb_loss:0.481151, cluster_loss:0.164231\n",
      "Clustering   443: ASW= 0.8055, DB= 0.2703, CH= 37742.4376\n",
      "Training epoch 444, recon_loss:0.888660, zinb_loss:0.480920, cluster_loss:0.164353\n",
      "Clustering   444: ASW= 0.8031, DB= 0.2669, CH= 37519.2888\n",
      "Training epoch 445, recon_loss:0.889749, zinb_loss:0.481118, cluster_loss:0.164222\n",
      "Clustering   445: ASW= 0.8052, DB= 0.2705, CH= 37739.9830\n",
      "Training epoch 446, recon_loss:0.888032, zinb_loss:0.480854, cluster_loss:0.164155\n",
      "Clustering   446: ASW= 0.8033, DB= 0.2669, CH= 37519.1856\n",
      "Training epoch 447, recon_loss:0.889060, zinb_loss:0.481017, cluster_loss:0.164011\n",
      "Clustering   447: ASW= 0.8049, DB= 0.2707, CH= 37781.2890\n",
      "Training epoch 448, recon_loss:0.887600, zinb_loss:0.480793, cluster_loss:0.163916\n",
      "Clustering   448: ASW= 0.8036, DB= 0.2670, CH= 37500.6651\n",
      "Training epoch 449, recon_loss:0.888536, zinb_loss:0.480923, cluster_loss:0.163821\n",
      "Clustering   449: ASW= 0.8049, DB= 0.2701, CH= 37845.0322\n",
      "Training epoch 450, recon_loss:0.887388, zinb_loss:0.480757, cluster_loss:0.163744\n",
      "Clustering   450: ASW= 0.8037, DB= 0.2672, CH= 37462.3765\n",
      "Training epoch 451, recon_loss:0.888210, zinb_loss:0.480859, cluster_loss:0.163698\n",
      "Clustering   451: ASW= 0.8049, DB= 0.2695, CH= 37944.9075\n",
      "Training epoch 452, recon_loss:0.887331, zinb_loss:0.480752, cluster_loss:0.163622\n",
      "Clustering   452: ASW= 0.8037, DB= 0.2674, CH= 37416.3065\n",
      "Training epoch 453, recon_loss:0.887999, zinb_loss:0.480820, cluster_loss:0.163616\n",
      "Clustering   453: ASW= 0.8050, DB= 0.2691, CH= 38054.3954\n",
      "Training epoch 454, recon_loss:0.887298, zinb_loss:0.480764, cluster_loss:0.163505\n",
      "Clustering   454: ASW= 0.8038, DB= 0.2676, CH= 37391.4553\n",
      "Training epoch 455, recon_loss:0.887745, zinb_loss:0.480793, cluster_loss:0.163519\n",
      "Clustering   455: ASW= 0.8052, DB= 0.2687, CH= 38157.6084\n",
      "Training epoch 456, recon_loss:0.887244, zinb_loss:0.480783, cluster_loss:0.163378\n",
      "Clustering   456: ASW= 0.8039, DB= 0.2678, CH= 37400.1524\n",
      "Training epoch 457, recon_loss:0.887546, zinb_loss:0.480779, cluster_loss:0.163421\n",
      "Clustering   457: ASW= 0.8053, DB= 0.2684, CH= 38238.5914\n",
      "Training epoch 458, recon_loss:0.887200, zinb_loss:0.480804, cluster_loss:0.163271\n",
      "Clustering   458: ASW= 0.8041, DB= 0.2679, CH= 37440.3486\n",
      "Training epoch 459, recon_loss:0.887350, zinb_loss:0.480769, cluster_loss:0.163347\n",
      "Clustering   459: ASW= 0.8054, DB= 0.2681, CH= 38300.8958\n",
      "Training epoch 460, recon_loss:0.887210, zinb_loss:0.480825, cluster_loss:0.163189\n",
      "Clustering   460: ASW= 0.8044, DB= 0.2679, CH= 37509.4297\n",
      "Training epoch 461, recon_loss:0.887265, zinb_loss:0.480764, cluster_loss:0.163319\n",
      "Clustering   461: ASW= 0.8054, DB= 0.2680, CH= 38340.6502\n",
      "Training epoch 462, recon_loss:0.887283, zinb_loss:0.480850, cluster_loss:0.163151\n",
      "Clustering   462: ASW= 0.8047, DB= 0.2676, CH= 37586.6045\n",
      "Training epoch 463, recon_loss:0.887260, zinb_loss:0.480763, cluster_loss:0.163345\n",
      "Clustering   463: ASW= 0.8054, DB= 0.2678, CH= 38360.5012\n",
      "Training epoch 464, recon_loss:0.887556, zinb_loss:0.480884, cluster_loss:0.163184\n",
      "Clustering   464: ASW= 0.8051, DB= 0.2673, CH= 37680.3256\n",
      "Training epoch 465, recon_loss:0.887557, zinb_loss:0.480776, cluster_loss:0.163463\n",
      "Clustering   465: ASW= 0.8054, DB= 0.2676, CH= 38349.6061\n",
      "Training epoch 466, recon_loss:0.888095, zinb_loss:0.480929, cluster_loss:0.163337\n",
      "Clustering   466: ASW= 0.8054, DB= 0.2669, CH= 37783.4612\n",
      "Training epoch 467, recon_loss:0.887996, zinb_loss:0.480788, cluster_loss:0.163627\n",
      "Clustering   467: ASW= 0.8052, DB= 0.2676, CH= 38321.5946\n",
      "Training epoch 468, recon_loss:0.888438, zinb_loss:0.480948, cluster_loss:0.163463\n",
      "Clustering   468: ASW= 0.8057, DB= 0.2664, CH= 37872.1469\n",
      "Training epoch 469, recon_loss:0.888003, zinb_loss:0.480766, cluster_loss:0.163618\n",
      "Clustering   469: ASW= 0.8052, DB= 0.2676, CH= 38294.5390\n",
      "Training epoch 470, recon_loss:0.888168, zinb_loss:0.480915, cluster_loss:0.163394\n",
      "Clustering   470: ASW= 0.8060, DB= 0.2660, CH= 37967.9295\n",
      "Training epoch 471, recon_loss:0.887571, zinb_loss:0.480718, cluster_loss:0.163425\n",
      "Clustering   471: ASW= 0.8052, DB= 0.2680, CH= 38286.8330\n",
      "Training epoch 472, recon_loss:0.887628, zinb_loss:0.480857, cluster_loss:0.163216\n",
      "Clustering   472: ASW= 0.8063, DB= 0.2657, CH= 38064.6629\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training epoch 473, recon_loss:0.887053, zinb_loss:0.480672, cluster_loss:0.163205\n",
      "Clustering   473: ASW= 0.8053, DB= 0.2679, CH= 38290.7630\n",
      "Training epoch 474, recon_loss:0.887154, zinb_loss:0.480805, cluster_loss:0.163052\n",
      "Clustering   474: ASW= 0.8066, DB= 0.2654, CH= 38153.9915\n",
      "Training epoch 475, recon_loss:0.886672, zinb_loss:0.480638, cluster_loss:0.163055\n",
      "Clustering   475: ASW= 0.8054, DB= 0.2682, CH= 38306.3581\n",
      "Training epoch 476, recon_loss:0.886848, zinb_loss:0.480769, cluster_loss:0.162952\n",
      "Clustering   476: ASW= 0.8067, DB= 0.2651, CH= 38234.4542\n",
      "Training epoch 477, recon_loss:0.886445, zinb_loss:0.480613, cluster_loss:0.162980\n",
      "Clustering   477: ASW= 0.8054, DB= 0.2682, CH= 38314.9601\n",
      "Training epoch 478, recon_loss:0.886699, zinb_loss:0.480746, cluster_loss:0.162913\n",
      "Clustering   478: ASW= 0.8069, DB= 0.2648, CH= 38319.4844\n",
      "Training epoch 479, recon_loss:0.886348, zinb_loss:0.480595, cluster_loss:0.162969\n",
      "Clustering   479: ASW= 0.8055, DB= 0.2683, CH= 38312.9540\n",
      "Training epoch 480, recon_loss:0.886680, zinb_loss:0.480734, cluster_loss:0.162929\n",
      "Clustering   480: ASW= 0.8071, DB= 0.2645, CH= 38403.7924\n",
      "Training epoch 481, recon_loss:0.886370, zinb_loss:0.480583, cluster_loss:0.163019\n",
      "Clustering   481: ASW= 0.8055, DB= 0.2685, CH= 38290.9186\n",
      "Training epoch 482, recon_loss:0.886776, zinb_loss:0.480728, cluster_loss:0.162995\n",
      "Clustering   482: ASW= 0.8073, DB= 0.2641, CH= 38495.4376\n",
      "Training epoch 483, recon_loss:0.886498, zinb_loss:0.480577, cluster_loss:0.163123\n",
      "Clustering   483: ASW= 0.8055, DB= 0.2688, CH= 38240.9363\n",
      "Training epoch 484, recon_loss:0.886945, zinb_loss:0.480729, cluster_loss:0.163105\n",
      "Clustering   484: ASW= 0.8075, DB= 0.2636, CH= 38601.9123\n",
      "Training epoch 485, recon_loss:0.886656, zinb_loss:0.480578, cluster_loss:0.163269\n",
      "Clustering   485: ASW= 0.8054, DB= 0.2692, CH= 38161.8657\n",
      "Training epoch 486, recon_loss:0.887127, zinb_loss:0.480739, cluster_loss:0.163244\n",
      "Clustering   486: ASW= 0.8077, DB= 0.2627, CH= 38724.8171\n",
      "Training epoch 487, recon_loss:0.886791, zinb_loss:0.480590, cluster_loss:0.163428\n",
      "Clustering   487: ASW= 0.8053, DB= 0.2696, CH= 38061.5059\n",
      "Training epoch 488, recon_loss:0.887269, zinb_loss:0.480763, cluster_loss:0.163365\n",
      "Clustering   488: ASW= 0.8080, DB= 0.2623, CH= 38860.8198\n",
      "Training epoch 489, recon_loss:0.886908, zinb_loss:0.480618, cluster_loss:0.163542\n",
      "Clustering   489: ASW= 0.8053, DB= 0.2700, CH= 37967.1935\n",
      "Training epoch 490, recon_loss:0.887460, zinb_loss:0.480803, cluster_loss:0.163455\n",
      "Clustering   490: ASW= 0.8081, DB= 0.2618, CH= 38991.9888\n",
      "Training epoch 491, recon_loss:0.887087, zinb_loss:0.480660, cluster_loss:0.163582\n",
      "Clustering   491: ASW= 0.8054, DB= 0.2700, CH= 37907.3923\n",
      "Training epoch 492, recon_loss:0.887731, zinb_loss:0.480842, cluster_loss:0.163464\n",
      "Clustering   492: ASW= 0.8084, DB= 0.2616, CH= 39100.3204\n",
      "Training epoch 493, recon_loss:0.887218, zinb_loss:0.480691, cluster_loss:0.163470\n",
      "Clustering   493: ASW= 0.8055, DB= 0.2696, CH= 37889.6464\n",
      "Training epoch 494, recon_loss:0.887848, zinb_loss:0.480841, cluster_loss:0.163310\n",
      "Clustering   494: ASW= 0.8085, DB= 0.2614, CH= 39165.2683\n",
      "Training epoch 495, recon_loss:0.887164, zinb_loss:0.480686, cluster_loss:0.163249\n",
      "Clustering   495: ASW= 0.8057, DB= 0.2688, CH= 37935.2989\n",
      "Training epoch 496, recon_loss:0.887801, zinb_loss:0.480810, cluster_loss:0.163106\n",
      "Clustering   496: ASW= 0.8086, DB= 0.2614, CH= 39201.6270\n",
      "Training epoch 497, recon_loss:0.887136, zinb_loss:0.480671, cluster_loss:0.163083\n",
      "Clustering   497: ASW= 0.8058, DB= 0.2682, CH= 37995.0341\n",
      "Training epoch 498, recon_loss:0.887773, zinb_loss:0.480780, cluster_loss:0.162995\n",
      "Clustering   498: ASW= 0.8086, DB= 0.2616, CH= 39211.9503\n",
      "Training epoch 499, recon_loss:0.887159, zinb_loss:0.480658, cluster_loss:0.163015\n",
      "Clustering   499: ASW= 0.8059, DB= 0.2677, CH= 38054.7063\n",
      "Training epoch 500, recon_loss:0.887706, zinb_loss:0.480752, cluster_loss:0.162960\n",
      "Clustering   500: ASW= 0.8086, DB= 0.2618, CH= 39208.0264\n",
      "Training epoch 501, recon_loss:0.887140, zinb_loss:0.480648, cluster_loss:0.162986\n",
      "Clustering   501: ASW= 0.8060, DB= 0.2673, CH= 38112.9132\n",
      "Training epoch 502, recon_loss:0.887522, zinb_loss:0.480720, cluster_loss:0.162920\n",
      "Clustering   502: ASW= 0.8086, DB= 0.2624, CH= 39224.6874\n",
      "Training epoch 503, recon_loss:0.886976, zinb_loss:0.480630, cluster_loss:0.162935\n",
      "Clustering   503: ASW= 0.8061, DB= 0.2667, CH= 38155.0517\n",
      "Training epoch 504, recon_loss:0.887232, zinb_loss:0.480683, cluster_loss:0.162853\n",
      "Clustering   504: ASW= 0.8086, DB= 0.2627, CH= 39261.2201\n",
      "Training epoch 505, recon_loss:0.886731, zinb_loss:0.480612, cluster_loss:0.162851\n",
      "Clustering   505: ASW= 0.8062, DB= 0.2663, CH= 38202.2748\n",
      "Training epoch 506, recon_loss:0.886909, zinb_loss:0.480648, cluster_loss:0.162772\n",
      "Clustering   506: ASW= 0.8086, DB= 0.2628, CH= 39305.9181\n",
      "Training epoch 507, recon_loss:0.886488, zinb_loss:0.480591, cluster_loss:0.162768\n",
      "Clustering   507: ASW= 0.8063, DB= 0.2660, CH= 38241.5796\n",
      "Training epoch 508, recon_loss:0.886634, zinb_loss:0.480618, cluster_loss:0.162707\n",
      "Clustering   508: ASW= 0.8087, DB= 0.2630, CH= 39351.2363\n",
      "Training epoch 509, recon_loss:0.886303, zinb_loss:0.480575, cluster_loss:0.162701\n",
      "Clustering   509: ASW= 0.8063, DB= 0.2661, CH= 38273.1081\n",
      "Training epoch 510, recon_loss:0.886440, zinb_loss:0.480599, cluster_loss:0.162659\n",
      "Clustering   510: ASW= 0.8087, DB= 0.2630, CH= 39389.1863\n",
      "Training epoch 511, recon_loss:0.886258, zinb_loss:0.480572, cluster_loss:0.162691\n",
      "Clustering   511: ASW= 0.8065, DB= 0.2658, CH= 38304.6281\n",
      "Training epoch 512, recon_loss:0.886395, zinb_loss:0.480595, cluster_loss:0.162661\n",
      "Clustering   512: ASW= 0.8088, DB= 0.2630, CH= 39433.2527\n",
      "Training epoch 513, recon_loss:0.886346, zinb_loss:0.480580, cluster_loss:0.162736\n",
      "Clustering   513: ASW= 0.8066, DB= 0.2656, CH= 38340.4673\n",
      "Training epoch 514, recon_loss:0.886463, zinb_loss:0.480602, cluster_loss:0.162698\n",
      "Clustering   514: ASW= 0.8088, DB= 0.2631, CH= 39479.4873\n",
      "Training epoch 515, recon_loss:0.886468, zinb_loss:0.480593, cluster_loss:0.162789\n",
      "Clustering   515: ASW= 0.8067, DB= 0.2653, CH= 38378.0547\n",
      "Training epoch 516, recon_loss:0.886491, zinb_loss:0.480610, cluster_loss:0.162716\n",
      "Clustering   516: ASW= 0.8089, DB= 0.2628, CH= 39516.1509\n",
      "Training epoch 517, recon_loss:0.886475, zinb_loss:0.480596, cluster_loss:0.162801\n",
      "Clustering   517: ASW= 0.8069, DB= 0.2651, CH= 38416.7605\n",
      "Training epoch 518, recon_loss:0.886413, zinb_loss:0.480613, cluster_loss:0.162693\n",
      "Clustering   518: ASW= 0.8090, DB= 0.2627, CH= 39554.6603\n",
      "Training epoch 519, recon_loss:0.886354, zinb_loss:0.480592, cluster_loss:0.162752\n",
      "Clustering   519: ASW= 0.8070, DB= 0.2648, CH= 38464.0529\n",
      "Training epoch 520, recon_loss:0.886274, zinb_loss:0.480611, cluster_loss:0.162644\n",
      "Clustering   520: ASW= 0.8090, DB= 0.2629, CH= 39582.2927\n",
      "Training epoch 521, recon_loss:0.886195, zinb_loss:0.480584, cluster_loss:0.162692\n",
      "Clustering   521: ASW= 0.8071, DB= 0.2645, CH= 38511.8491\n",
      "Training epoch 522, recon_loss:0.886175, zinb_loss:0.480609, cluster_loss:0.162610\n",
      "Clustering   522: ASW= 0.8091, DB= 0.2630, CH= 39604.7958\n",
      "Training epoch 523, recon_loss:0.886093, zinb_loss:0.480578, cluster_loss:0.162661\n",
      "Clustering   523: ASW= 0.8072, DB= 0.2641, CH= 38563.2819\n",
      "Training epoch 524, recon_loss:0.886182, zinb_loss:0.480609, cluster_loss:0.162630\n",
      "Clustering   524: ASW= 0.8091, DB= 0.2631, CH= 39607.6261\n",
      "Training epoch 525, recon_loss:0.886115, zinb_loss:0.480575, cluster_loss:0.162691\n",
      "Clustering   525: ASW= 0.8073, DB= 0.2636, CH= 38614.8834\n",
      "Training epoch 526, recon_loss:0.886326, zinb_loss:0.480613, cluster_loss:0.162730\n",
      "Clustering   526: ASW= 0.8090, DB= 0.2635, CH= 39591.6293\n",
      "Training epoch 527, recon_loss:0.886280, zinb_loss:0.480570, cluster_loss:0.162789\n",
      "Clustering   527: ASW= 0.8073, DB= 0.2632, CH= 38676.9047\n",
      "Training epoch 528, recon_loss:0.886575, zinb_loss:0.480616, cluster_loss:0.162897\n",
      "Clustering   528: ASW= 0.8089, DB= 0.2641, CH= 39537.2997\n",
      "Training epoch 529, recon_loss:0.886540, zinb_loss:0.480560, cluster_loss:0.162959\n",
      "Clustering   529: ASW= 0.8073, DB= 0.2627, CH= 38748.7622\n",
      "Training epoch 530, recon_loss:0.886877, zinb_loss:0.480615, cluster_loss:0.163112\n",
      "Clustering   530: ASW= 0.8088, DB= 0.2647, CH= 39440.9989\n",
      "Training epoch 531, recon_loss:0.886820, zinb_loss:0.480543, cluster_loss:0.163143\n",
      "Clustering   531: ASW= 0.8072, DB= 0.2619, CH= 38830.2054\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training epoch 532, recon_loss:0.887078, zinb_loss:0.480602, cluster_loss:0.163265\n",
      "Clustering   532: ASW= 0.8086, DB= 0.2653, CH= 39313.3982\n",
      "Training epoch 533, recon_loss:0.886834, zinb_loss:0.480515, cluster_loss:0.163210\n",
      "Clustering   533: ASW= 0.8073, DB= 0.2614, CH= 38937.4144\n",
      "Training epoch 534, recon_loss:0.886969, zinb_loss:0.480572, cluster_loss:0.163231\n",
      "Clustering   534: ASW= 0.8085, DB= 0.2657, CH= 39198.6706\n",
      "Training epoch 535, recon_loss:0.886579, zinb_loss:0.480477, cluster_loss:0.163092\n",
      "Clustering   535: ASW= 0.8076, DB= 0.2612, CH= 39044.0949\n",
      "Training epoch 536, recon_loss:0.886749, zinb_loss:0.480537, cluster_loss:0.163068\n",
      "Clustering   536: ASW= 0.8084, DB= 0.2661, CH= 39111.6179\n",
      "Training epoch 537, recon_loss:0.886248, zinb_loss:0.480448, cluster_loss:0.162908\n",
      "Clustering   537: ASW= 0.8080, DB= 0.2610, CH= 39159.7555\n",
      "Training epoch 538, recon_loss:0.886450, zinb_loss:0.480507, cluster_loss:0.162854\n",
      "Clustering   538: ASW= 0.8084, DB= 0.2661, CH= 39067.1900\n",
      "Training epoch 539, recon_loss:0.885983, zinb_loss:0.480430, cluster_loss:0.162739\n",
      "Clustering   539: ASW= 0.8084, DB= 0.2602, CH= 39263.5409\n",
      "Training epoch 540, recon_loss:0.886266, zinb_loss:0.480492, cluster_loss:0.162690\n",
      "Clustering   540: ASW= 0.8084, DB= 0.2662, CH= 39038.5089\n",
      "Training epoch 541, recon_loss:0.885818, zinb_loss:0.480432, cluster_loss:0.162627\n",
      "Clustering   541: ASW= 0.8088, DB= 0.2598, CH= 39384.1668\n",
      "Training epoch 542, recon_loss:0.886132, zinb_loss:0.480490, cluster_loss:0.162583\n",
      "Clustering   542: ASW= 0.8084, DB= 0.2662, CH= 39029.4582\n",
      "Training epoch 543, recon_loss:0.885792, zinb_loss:0.480445, cluster_loss:0.162582\n",
      "Clustering   543: ASW= 0.8092, DB= 0.2595, CH= 39490.3538\n",
      "Training epoch 544, recon_loss:0.886127, zinb_loss:0.480499, cluster_loss:0.162541\n",
      "Clustering   544: ASW= 0.8083, DB= 0.2663, CH= 39011.7598\n",
      "Training epoch 545, recon_loss:0.885851, zinb_loss:0.480472, cluster_loss:0.162579\n",
      "Clustering   545: ASW= 0.8096, DB= 0.2591, CH= 39600.6926\n",
      "Training epoch 546, recon_loss:0.886132, zinb_loss:0.480512, cluster_loss:0.162517\n",
      "Clustering   546: ASW= 0.8083, DB= 0.2665, CH= 39002.6718\n",
      "Training epoch 547, recon_loss:0.885942, zinb_loss:0.480495, cluster_loss:0.162592\n",
      "Clustering   547: ASW= 0.8100, DB= 0.2589, CH= 39679.8708\n",
      "Training epoch 548, recon_loss:0.886155, zinb_loss:0.480521, cluster_loss:0.162506\n",
      "Clustering   548: ASW= 0.8082, DB= 0.2663, CH= 39001.4343\n",
      "Training epoch 549, recon_loss:0.886038, zinb_loss:0.480518, cluster_loss:0.162611\n",
      "Clustering   549: ASW= 0.8104, DB= 0.2586, CH= 39773.6669\n",
      "Training epoch 550, recon_loss:0.886136, zinb_loss:0.480527, cluster_loss:0.162474\n",
      "Clustering   550: ASW= 0.8082, DB= 0.2660, CH= 39040.7109\n",
      "Training epoch 551, recon_loss:0.886081, zinb_loss:0.480524, cluster_loss:0.162623\n",
      "Clustering   551: ASW= 0.8107, DB= 0.2583, CH= 39826.6856\n",
      "Training epoch 552, recon_loss:0.886069, zinb_loss:0.480515, cluster_loss:0.162444\n",
      "Clustering   552: ASW= 0.8081, DB= 0.2659, CH= 39099.3149\n",
      "Training epoch 553, recon_loss:0.886159, zinb_loss:0.480526, cluster_loss:0.162655\n",
      "Clustering   553: ASW= 0.8110, DB= 0.2580, CH= 39876.4750\n",
      "Training epoch 554, recon_loss:0.886075, zinb_loss:0.480501, cluster_loss:0.162420\n",
      "Clustering   554: ASW= 0.8080, DB= 0.2656, CH= 39181.1713\n",
      "Training epoch 555, recon_loss:0.886279, zinb_loss:0.480517, cluster_loss:0.162720\n",
      "Clustering   555: ASW= 0.8114, DB= 0.2578, CH= 39879.5065\n",
      "Training epoch 556, recon_loss:0.886072, zinb_loss:0.480469, cluster_loss:0.162416\n",
      "Clustering   556: ASW= 0.8078, DB= 0.2655, CH= 39254.0694\n",
      "Training epoch 557, recon_loss:0.886454, zinb_loss:0.480523, cluster_loss:0.162790\n",
      "Clustering   557: ASW= 0.8117, DB= 0.2575, CH= 39880.9049\n",
      "Training epoch 558, recon_loss:0.886126, zinb_loss:0.480438, cluster_loss:0.162397\n",
      "Clustering   558: ASW= 0.8077, DB= 0.2653, CH= 39332.2258\n",
      "Training epoch 559, recon_loss:0.886627, zinb_loss:0.480518, cluster_loss:0.162845\n",
      "Clustering   559: ASW= 0.8120, DB= 0.2575, CH= 39836.8422\n",
      "Training epoch 560, recon_loss:0.886185, zinb_loss:0.480402, cluster_loss:0.162417\n",
      "Clustering   560: ASW= 0.8076, DB= 0.2653, CH= 39400.0624\n",
      "Training epoch 561, recon_loss:0.886905, zinb_loss:0.480534, cluster_loss:0.162889\n",
      "Clustering   561: ASW= 0.8121, DB= 0.2576, CH= 39828.1629\n",
      "Training epoch 562, recon_loss:0.886310, zinb_loss:0.480388, cluster_loss:0.162424\n",
      "Clustering   562: ASW= 0.8077, DB= 0.2649, CH= 39473.0340\n",
      "Training epoch 563, recon_loss:0.887053, zinb_loss:0.480536, cluster_loss:0.162834\n",
      "Clustering   563: ASW= 0.8121, DB= 0.2580, CH= 39825.8305\n",
      "Training epoch 564, recon_loss:0.886234, zinb_loss:0.480380, cluster_loss:0.162385\n",
      "Clustering   564: ASW= 0.8080, DB= 0.2644, CH= 39549.4522\n",
      "Training epoch 565, recon_loss:0.886873, zinb_loss:0.480519, cluster_loss:0.162681\n",
      "Clustering   565: ASW= 0.8119, DB= 0.2583, CH= 39846.3956\n",
      "Training epoch 566, recon_loss:0.885937, zinb_loss:0.480366, cluster_loss:0.162313\n",
      "Clustering   566: ASW= 0.8083, DB= 0.2637, CH= 39618.4176\n",
      "Training epoch 567, recon_loss:0.886503, zinb_loss:0.480491, cluster_loss:0.162532\n",
      "Clustering   567: ASW= 0.8117, DB= 0.2586, CH= 39874.0506\n",
      "Training epoch 568, recon_loss:0.885598, zinb_loss:0.480354, cluster_loss:0.162254\n",
      "Clustering   568: ASW= 0.8087, DB= 0.2633, CH= 39678.2314\n",
      "Training epoch 569, recon_loss:0.886167, zinb_loss:0.480461, cluster_loss:0.162439\n",
      "Clustering   569: ASW= 0.8116, DB= 0.2588, CH= 39893.2430\n",
      "Training epoch 570, recon_loss:0.885390, zinb_loss:0.480349, cluster_loss:0.162245\n",
      "Clustering   570: ASW= 0.8090, DB= 0.2629, CH= 39727.1029\n",
      "Training epoch 571, recon_loss:0.885987, zinb_loss:0.480437, cluster_loss:0.162413\n",
      "Clustering   571: ASW= 0.8114, DB= 0.2587, CH= 39925.8728\n",
      "Training epoch 572, recon_loss:0.885311, zinb_loss:0.480350, cluster_loss:0.162275\n",
      "Clustering   572: ASW= 0.8092, DB= 0.2626, CH= 39767.7985\n",
      "Training epoch 573, recon_loss:0.885923, zinb_loss:0.480415, cluster_loss:0.162427\n",
      "Clustering   573: ASW= 0.8112, DB= 0.2587, CH= 39954.8189\n",
      "Training epoch 574, recon_loss:0.885323, zinb_loss:0.480352, cluster_loss:0.162332\n",
      "Clustering   574: ASW= 0.8094, DB= 0.2625, CH= 39791.6173\n",
      "Training epoch 575, recon_loss:0.885930, zinb_loss:0.480393, cluster_loss:0.162455\n",
      "Clustering   575: ASW= 0.8111, DB= 0.2586, CH= 39999.3721\n",
      "Training epoch 576, recon_loss:0.885413, zinb_loss:0.480353, cluster_loss:0.162395\n",
      "Clustering   576: ASW= 0.8097, DB= 0.2627, CH= 39790.4234\n",
      "Training epoch 577, recon_loss:0.885999, zinb_loss:0.480369, cluster_loss:0.162490\n",
      "Clustering   577: ASW= 0.8108, DB= 0.2586, CH= 40023.3906\n",
      "Training epoch 578, recon_loss:0.885658, zinb_loss:0.480369, cluster_loss:0.162485\n",
      "Clustering   578: ASW= 0.8100, DB= 0.2628, CH= 39795.0033\n",
      "Training epoch 579, recon_loss:0.886190, zinb_loss:0.480361, cluster_loss:0.162539\n",
      "Clustering   579: ASW= 0.8105, DB= 0.2587, CH= 40044.9357\n",
      "Training epoch 580, recon_loss:0.885958, zinb_loss:0.480385, cluster_loss:0.162593\n",
      "Clustering   580: ASW= 0.8103, DB= 0.2627, CH= 39774.0808\n",
      "Training epoch 581, recon_loss:0.886347, zinb_loss:0.480360, cluster_loss:0.162589\n",
      "Clustering   581: ASW= 0.8101, DB= 0.2585, CH= 40032.6315\n",
      "Training epoch 582, recon_loss:0.886315, zinb_loss:0.480427, cluster_loss:0.162739\n",
      "Clustering   582: ASW= 0.8106, DB= 0.2629, CH= 39769.4109\n",
      "Training epoch 583, recon_loss:0.886475, zinb_loss:0.480386, cluster_loss:0.162645\n",
      "Clustering   583: ASW= 0.8096, DB= 0.2582, CH= 40028.5304\n",
      "Training epoch 584, recon_loss:0.886546, zinb_loss:0.480464, cluster_loss:0.162994\n",
      "Clustering   584: ASW= 0.8109, DB= 0.2630, CH= 39702.7830\n",
      "Training epoch 585, recon_loss:0.886517, zinb_loss:0.480427, cluster_loss:0.162775\n",
      "Clustering   585: ASW= 0.8092, DB= 0.2581, CH= 40024.8730\n",
      "Training epoch 586, recon_loss:0.886650, zinb_loss:0.480504, cluster_loss:0.163341\n",
      "Clustering   586: ASW= 0.8110, DB= 0.2631, CH= 39632.8129\n",
      "Training epoch 587, recon_loss:0.886502, zinb_loss:0.480477, cluster_loss:0.162958\n",
      "Clustering   587: ASW= 0.8090, DB= 0.2581, CH= 40016.4190\n",
      "Training epoch 588, recon_loss:0.886442, zinb_loss:0.480499, cluster_loss:0.163596\n",
      "Clustering   588: ASW= 0.8108, DB= 0.2631, CH= 39567.8452\n",
      "Training epoch 589, recon_loss:0.886276, zinb_loss:0.480495, cluster_loss:0.163077\n",
      "Clustering   589: ASW= 0.8090, DB= 0.2580, CH= 40011.7263\n",
      "Training epoch 590, recon_loss:0.886063, zinb_loss:0.480457, cluster_loss:0.163688\n",
      "Clustering   590: ASW= 0.8106, DB= 0.2631, CH= 39574.6982\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training epoch 591, recon_loss:0.885976, zinb_loss:0.480490, cluster_loss:0.163132\n",
      "Clustering   591: ASW= 0.8093, DB= 0.2578, CH= 39992.8491\n",
      "Training epoch 592, recon_loss:0.885732, zinb_loss:0.480393, cluster_loss:0.163698\n",
      "Clustering   592: ASW= 0.8104, DB= 0.2631, CH= 39606.9068\n",
      "Training epoch 593, recon_loss:0.885755, zinb_loss:0.480472, cluster_loss:0.163161\n",
      "Clustering   593: ASW= 0.8097, DB= 0.2578, CH= 39964.8155\n",
      "Training epoch 594, recon_loss:0.885595, zinb_loss:0.480329, cluster_loss:0.163640\n",
      "Clustering   594: ASW= 0.8101, DB= 0.2629, CH= 39646.6730\n",
      "Training epoch 595, recon_loss:0.885679, zinb_loss:0.480452, cluster_loss:0.163160\n",
      "Clustering   595: ASW= 0.8101, DB= 0.2579, CH= 39936.7055\n",
      "Training epoch 596, recon_loss:0.885594, zinb_loss:0.480272, cluster_loss:0.163512\n",
      "Clustering   596: ASW= 0.8100, DB= 0.2628, CH= 39702.3775\n",
      "Training epoch 597, recon_loss:0.885656, zinb_loss:0.480427, cluster_loss:0.163086\n",
      "Clustering   597: ASW= 0.8105, DB= 0.2581, CH= 39923.7005\n",
      "Training epoch 598, recon_loss:0.885599, zinb_loss:0.480228, cluster_loss:0.163297\n",
      "Clustering   598: ASW= 0.8099, DB= 0.2626, CH= 39772.4999\n",
      "Training epoch 599, recon_loss:0.885563, zinb_loss:0.480394, cluster_loss:0.162907\n",
      "Clustering   599: ASW= 0.8109, DB= 0.2582, CH= 39953.1126\n",
      "Training epoch 600, recon_loss:0.885486, zinb_loss:0.480193, cluster_loss:0.163004\n",
      "Clustering   600: ASW= 0.8099, DB= 0.2625, CH= 39842.8243\n",
      "Training epoch 601, recon_loss:0.885388, zinb_loss:0.480362, cluster_loss:0.162649\n",
      "Clustering   601: ASW= 0.8113, DB= 0.2582, CH= 40017.6635\n",
      "Training epoch 602, recon_loss:0.885308, zinb_loss:0.480169, cluster_loss:0.162684\n",
      "Clustering   602: ASW= 0.8100, DB= 0.2622, CH= 39919.3261\n",
      "Training epoch 603, recon_loss:0.885207, zinb_loss:0.480337, cluster_loss:0.162398\n",
      "Clustering   603: ASW= 0.8116, DB= 0.2582, CH= 40104.0560\n",
      "Training epoch 604, recon_loss:0.885122, zinb_loss:0.480154, cluster_loss:0.162407\n",
      "Clustering   604: ASW= 0.8101, DB= 0.2617, CH= 40003.9246\n",
      "Training epoch 605, recon_loss:0.885082, zinb_loss:0.480321, cluster_loss:0.162195\n",
      "Clustering   605: ASW= 0.8118, DB= 0.2580, CH= 40179.1729\n",
      "Training epoch 606, recon_loss:0.885003, zinb_loss:0.480150, cluster_loss:0.162192\n",
      "Clustering   606: ASW= 0.8102, DB= 0.2616, CH= 40072.5719\n",
      "Training epoch 607, recon_loss:0.885011, zinb_loss:0.480308, cluster_loss:0.162066\n",
      "Clustering   607: ASW= 0.8122, DB= 0.2576, CH= 40250.7387\n",
      "Training epoch 608, recon_loss:0.884930, zinb_loss:0.480151, cluster_loss:0.162037\n",
      "Clustering   608: ASW= 0.8102, DB= 0.2615, CH= 40137.7942\n",
      "Training epoch 609, recon_loss:0.884978, zinb_loss:0.480301, cluster_loss:0.161991\n",
      "Clustering   609: ASW= 0.8124, DB= 0.2576, CH= 40312.8701\n",
      "Training epoch 610, recon_loss:0.884868, zinb_loss:0.480152, cluster_loss:0.161920\n",
      "Clustering   610: ASW= 0.8103, DB= 0.2614, CH= 40203.0812\n",
      "Training epoch 611, recon_loss:0.884924, zinb_loss:0.480289, cluster_loss:0.161936\n",
      "Clustering   611: ASW= 0.8126, DB= 0.2573, CH= 40360.1570\n",
      "Training epoch 612, recon_loss:0.884794, zinb_loss:0.480150, cluster_loss:0.161831\n",
      "Clustering   612: ASW= 0.8103, DB= 0.2614, CH= 40243.1525\n",
      "Training epoch 613, recon_loss:0.884890, zinb_loss:0.480281, cluster_loss:0.161899\n",
      "Clustering   613: ASW= 0.8128, DB= 0.2570, CH= 40406.1705\n",
      "Training epoch 614, recon_loss:0.884764, zinb_loss:0.480149, cluster_loss:0.161771\n",
      "Clustering   614: ASW= 0.8103, DB= 0.2614, CH= 40269.4793\n",
      "Training epoch 615, recon_loss:0.884946, zinb_loss:0.480276, cluster_loss:0.161896\n",
      "Clustering   615: ASW= 0.8130, DB= 0.2567, CH= 40463.9131\n",
      "Training epoch 616, recon_loss:0.884862, zinb_loss:0.480154, cluster_loss:0.161769\n",
      "Clustering   616: ASW= 0.8103, DB= 0.2607, CH= 40285.0590\n",
      "Training epoch 617, recon_loss:0.885170, zinb_loss:0.480280, cluster_loss:0.161959\n",
      "Clustering   617: ASW= 0.8131, DB= 0.2568, CH= 40525.1923\n",
      "Training epoch 618, recon_loss:0.885077, zinb_loss:0.480162, cluster_loss:0.161839\n",
      "Clustering   618: ASW= 0.8103, DB= 0.2606, CH= 40291.6470\n",
      "Training epoch 619, recon_loss:0.885496, zinb_loss:0.480288, cluster_loss:0.162091\n",
      "Clustering   619: ASW= 0.8132, DB= 0.2565, CH= 40595.9169\n",
      "Training epoch 620, recon_loss:0.885291, zinb_loss:0.480168, cluster_loss:0.161952\n",
      "Clustering   620: ASW= 0.8104, DB= 0.2605, CH= 40297.3823\n",
      "Training epoch 621, recon_loss:0.885696, zinb_loss:0.480286, cluster_loss:0.162195\n",
      "Clustering   621: ASW= 0.8132, DB= 0.2564, CH= 40654.9034\n",
      "Training epoch 622, recon_loss:0.885215, zinb_loss:0.480157, cluster_loss:0.161996\n",
      "Clustering   622: ASW= 0.8105, DB= 0.2608, CH= 40320.6040\n",
      "Training epoch 623, recon_loss:0.885550, zinb_loss:0.480262, cluster_loss:0.162165\n",
      "Clustering   623: ASW= 0.8131, DB= 0.2563, CH= 40685.7392\n",
      "Training epoch 624, recon_loss:0.884862, zinb_loss:0.480129, cluster_loss:0.161957\n",
      "Clustering   624: ASW= 0.8107, DB= 0.2594, CH= 40321.5305\n",
      "Training epoch 625, recon_loss:0.885240, zinb_loss:0.480238, cluster_loss:0.162048\n",
      "Clustering   625: ASW= 0.8131, DB= 0.2563, CH= 40753.7106\n",
      "Training epoch 626, recon_loss:0.884513, zinb_loss:0.480108, cluster_loss:0.161883\n",
      "Clustering   626: ASW= 0.8110, DB= 0.2591, CH= 40351.9890\n",
      "Training epoch 627, recon_loss:0.884913, zinb_loss:0.480207, cluster_loss:0.161941\n",
      "Clustering   627: ASW= 0.8130, DB= 0.2562, CH= 40802.1985\n",
      "Training epoch 628, recon_loss:0.884254, zinb_loss:0.480090, cluster_loss:0.161870\n",
      "Clustering   628: ASW= 0.8112, DB= 0.2590, CH= 40352.3772\n",
      "Training epoch 629, recon_loss:0.884756, zinb_loss:0.480195, cluster_loss:0.161911\n",
      "Clustering   629: ASW= 0.8129, DB= 0.2563, CH= 40886.1677\n",
      "Training epoch 630, recon_loss:0.884196, zinb_loss:0.480081, cluster_loss:0.161936\n",
      "Clustering   630: ASW= 0.8113, DB= 0.2591, CH= 40313.4506\n",
      "Training epoch 631, recon_loss:0.884797, zinb_loss:0.480192, cluster_loss:0.161991\n",
      "Clustering   631: ASW= 0.8128, DB= 0.2562, CH= 40977.6882\n",
      "Training epoch 632, recon_loss:0.884509, zinb_loss:0.480094, cluster_loss:0.162189\n",
      "Clustering   632: ASW= 0.8114, DB= 0.2593, CH= 40173.1794\n",
      "Training epoch 633, recon_loss:0.885427, zinb_loss:0.480239, cluster_loss:0.162342\n",
      "Clustering   633: ASW= 0.8127, DB= 0.2563, CH= 41094.9614\n",
      "Training epoch 634, recon_loss:0.885633, zinb_loss:0.480188, cluster_loss:0.162790\n",
      "Clustering   634: ASW= 0.8113, DB= 0.2599, CH= 39911.4973\n",
      "Training epoch 635, recon_loss:0.887015, zinb_loss:0.480405, cluster_loss:0.163070\n",
      "Clustering   635: ASW= 0.8124, DB= 0.2561, CH= 41185.7935\n",
      "Training epoch 636, recon_loss:0.886758, zinb_loss:0.480349, cluster_loss:0.163260\n",
      "Clustering   636: ASW= 0.8111, DB= 0.2597, CH= 39700.3384\n",
      "Training epoch 637, recon_loss:0.887367, zinb_loss:0.480491, cluster_loss:0.163163\n",
      "Clustering   637: ASW= 0.8127, DB= 0.2556, CH= 41307.1560\n",
      "Training epoch 638, recon_loss:0.886372, zinb_loss:0.480376, cluster_loss:0.163086\n",
      "Clustering   638: ASW= 0.8111, DB= 0.2592, CH= 39653.4286\n",
      "Training epoch 639, recon_loss:0.886416, zinb_loss:0.480423, cluster_loss:0.162728\n",
      "Clustering   639: ASW= 0.8131, DB= 0.2559, CH= 41413.0118\n",
      "Training epoch 640, recon_loss:0.885691, zinb_loss:0.480344, cluster_loss:0.162778\n",
      "Clustering   640: ASW= 0.8111, DB= 0.2589, CH= 39692.2786\n",
      "Training epoch 641, recon_loss:0.885690, zinb_loss:0.480374, cluster_loss:0.162415\n",
      "Clustering   641: ASW= 0.8133, DB= 0.2557, CH= 41467.9075\n",
      "Training epoch 642, recon_loss:0.885126, zinb_loss:0.480303, cluster_loss:0.162498\n",
      "Clustering   642: ASW= 0.8111, DB= 0.2597, CH= 39785.9226\n",
      "Training epoch 643, recon_loss:0.885160, zinb_loss:0.480339, cluster_loss:0.162184\n",
      "Clustering   643: ASW= 0.8134, DB= 0.2556, CH= 41515.7465\n",
      "Training epoch 644, recon_loss:0.884692, zinb_loss:0.480262, cluster_loss:0.162280\n",
      "Clustering   644: ASW= 0.8112, DB= 0.2595, CH= 39875.8721\n",
      "Training epoch 645, recon_loss:0.884803, zinb_loss:0.480311, cluster_loss:0.162020\n",
      "Clustering   645: ASW= 0.8136, DB= 0.2555, CH= 41556.2366\n",
      "Training epoch 646, recon_loss:0.884434, zinb_loss:0.480225, cluster_loss:0.162124\n",
      "Clustering   646: ASW= 0.8113, DB= 0.2594, CH= 39959.4670\n",
      "Training epoch 647, recon_loss:0.884674, zinb_loss:0.480298, cluster_loss:0.161925\n",
      "Clustering   647: ASW= 0.8137, DB= 0.2554, CH= 41590.2891\n",
      "Training epoch 648, recon_loss:0.884423, zinb_loss:0.480200, cluster_loss:0.162045\n",
      "Clustering   648: ASW= 0.8113, DB= 0.2592, CH= 40028.4024\n",
      "Training epoch 649, recon_loss:0.884815, zinb_loss:0.480302, cluster_loss:0.161909\n",
      "Clustering   649: ASW= 0.8139, DB= 0.2543, CH= 41598.7952\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training epoch 650, recon_loss:0.884701, zinb_loss:0.480185, cluster_loss:0.162049\n",
      "Clustering   650: ASW= 0.8113, DB= 0.2591, CH= 40086.6641\n",
      "Training epoch 651, recon_loss:0.885281, zinb_loss:0.480323, cluster_loss:0.161987\n",
      "Clustering   651: ASW= 0.8140, DB= 0.2542, CH= 41617.0335\n",
      "Training epoch 652, recon_loss:0.885220, zinb_loss:0.480180, cluster_loss:0.162125\n",
      "Clustering   652: ASW= 0.8113, DB= 0.2591, CH= 40140.0635\n",
      "Training epoch 653, recon_loss:0.885902, zinb_loss:0.480348, cluster_loss:0.162139\n",
      "Clustering   653: ASW= 0.8142, DB= 0.2542, CH= 41609.0301\n",
      "Training epoch 654, recon_loss:0.885703, zinb_loss:0.480177, cluster_loss:0.162212\n",
      "Clustering   654: ASW= 0.8112, DB= 0.2588, CH= 40206.8011\n",
      "Training epoch 655, recon_loss:0.886306, zinb_loss:0.480350, cluster_loss:0.162271\n",
      "Clustering   655: ASW= 0.8143, DB= 0.2543, CH= 41580.1058\n",
      "Training epoch 656, recon_loss:0.885762, zinb_loss:0.480158, cluster_loss:0.162200\n",
      "Clustering   656: ASW= 0.8113, DB= 0.2574, CH= 40288.3060\n",
      "Training epoch 657, recon_loss:0.886114, zinb_loss:0.480308, cluster_loss:0.162256\n",
      "Clustering   657: ASW= 0.8143, DB= 0.2550, CH= 41528.9607\n",
      "Training epoch 658, recon_loss:0.885386, zinb_loss:0.480123, cluster_loss:0.162082\n",
      "Clustering   658: ASW= 0.8114, DB= 0.2568, CH= 40393.4522\n",
      "Training epoch 659, recon_loss:0.885615, zinb_loss:0.480249, cluster_loss:0.162159\n",
      "Clustering   659: ASW= 0.8143, DB= 0.2554, CH= 41466.8259\n",
      "Training epoch 660, recon_loss:0.884878, zinb_loss:0.480086, cluster_loss:0.161955\n",
      "Clustering   660: ASW= 0.8116, DB= 0.2562, CH= 40507.8840\n",
      "Training epoch 661, recon_loss:0.885092, zinb_loss:0.480195, cluster_loss:0.162084\n",
      "Clustering   661: ASW= 0.8141, DB= 0.2559, CH= 41383.8563\n",
      "Training epoch 662, recon_loss:0.884483, zinb_loss:0.480062, cluster_loss:0.161925\n",
      "Clustering   662: ASW= 0.8117, DB= 0.2557, CH= 40619.2631\n",
      "Training epoch 663, recon_loss:0.884785, zinb_loss:0.480159, cluster_loss:0.162127\n",
      "Clustering   663: ASW= 0.8139, DB= 0.2566, CH= 41263.2111\n",
      "Training epoch 664, recon_loss:0.884290, zinb_loss:0.480061, cluster_loss:0.162028\n",
      "Clustering   664: ASW= 0.8118, DB= 0.2551, CH= 40719.6197\n",
      "Training epoch 665, recon_loss:0.884690, zinb_loss:0.480141, cluster_loss:0.162280\n",
      "Clustering   665: ASW= 0.8136, DB= 0.2574, CH= 41101.7030\n",
      "Training epoch 666, recon_loss:0.884208, zinb_loss:0.480077, cluster_loss:0.162192\n",
      "Clustering   666: ASW= 0.8121, DB= 0.2546, CH= 40831.1666\n",
      "Training epoch 667, recon_loss:0.884646, zinb_loss:0.480117, cluster_loss:0.162421\n",
      "Clustering   667: ASW= 0.8133, DB= 0.2577, CH= 40936.6567\n",
      "Training epoch 668, recon_loss:0.884149, zinb_loss:0.480091, cluster_loss:0.162271\n",
      "Clustering   668: ASW= 0.8124, DB= 0.2540, CH= 40946.3979\n",
      "Training epoch 669, recon_loss:0.884526, zinb_loss:0.480074, cluster_loss:0.162467\n",
      "Clustering   669: ASW= 0.8130, DB= 0.2583, CH= 40795.2248\n",
      "Training epoch 670, recon_loss:0.884043, zinb_loss:0.480089, cluster_loss:0.162236\n",
      "Clustering   670: ASW= 0.8128, DB= 0.2537, CH= 41078.4011\n",
      "Training epoch 671, recon_loss:0.884260, zinb_loss:0.480025, cluster_loss:0.162459\n",
      "Clustering   671: ASW= 0.8128, DB= 0.2585, CH= 40693.0578\n",
      "Training epoch 672, recon_loss:0.883914, zinb_loss:0.480087, cluster_loss:0.162166\n",
      "Clustering   672: ASW= 0.8133, DB= 0.2533, CH= 41202.6225\n",
      "Training epoch 673, recon_loss:0.883993, zinb_loss:0.479980, cluster_loss:0.162458\n",
      "Clustering   673: ASW= 0.8127, DB= 0.2588, CH= 40614.8118\n",
      "Training epoch 674, recon_loss:0.883844, zinb_loss:0.480092, cluster_loss:0.162130\n",
      "Clustering   674: ASW= 0.8135, DB= 0.2529, CH= 41327.6920\n",
      "Training epoch 675, recon_loss:0.883796, zinb_loss:0.479947, cluster_loss:0.162508\n",
      "Clustering   675: ASW= 0.8127, DB= 0.2591, CH= 40550.7705\n",
      "Training epoch 676, recon_loss:0.883879, zinb_loss:0.480109, cluster_loss:0.162130\n",
      "Clustering   676: ASW= 0.8137, DB= 0.2525, CH= 41438.5071\n",
      "Training epoch 677, recon_loss:0.883703, zinb_loss:0.479929, cluster_loss:0.162580\n",
      "Clustering   677: ASW= 0.8126, DB= 0.2593, CH= 40494.0332\n",
      "Training epoch 678, recon_loss:0.884007, zinb_loss:0.480136, cluster_loss:0.162154\n",
      "Clustering   678: ASW= 0.8138, DB= 0.2522, CH= 41535.1124\n",
      "Training epoch 679, recon_loss:0.883686, zinb_loss:0.479926, cluster_loss:0.162622\n",
      "Clustering   679: ASW= 0.8126, DB= 0.2594, CH= 40458.7466\n",
      "Training epoch 680, recon_loss:0.884164, zinb_loss:0.480164, cluster_loss:0.162162\n",
      "Clustering   680: ASW= 0.8139, DB= 0.2518, CH= 41604.4546\n",
      "Training epoch 681, recon_loss:0.883711, zinb_loss:0.479937, cluster_loss:0.162584\n",
      "Clustering   681: ASW= 0.8127, DB= 0.2593, CH= 40465.2326\n",
      "Training epoch 682, recon_loss:0.884244, zinb_loss:0.480179, cluster_loss:0.162091\n",
      "Clustering   682: ASW= 0.8140, DB= 0.2515, CH= 41645.1902\n",
      "Training epoch 683, recon_loss:0.883726, zinb_loss:0.479959, cluster_loss:0.162464\n",
      "Clustering   683: ASW= 0.8129, DB= 0.2589, CH= 40519.2764\n",
      "Training epoch 684, recon_loss:0.884276, zinb_loss:0.480183, cluster_loss:0.161978\n",
      "Clustering   684: ASW= 0.8141, DB= 0.2513, CH= 41669.9263\n",
      "Training epoch 685, recon_loss:0.883774, zinb_loss:0.479986, cluster_loss:0.162328\n",
      "Clustering   685: ASW= 0.8130, DB= 0.2587, CH= 40590.8577\n",
      "Training epoch 686, recon_loss:0.884324, zinb_loss:0.480180, cluster_loss:0.161881\n",
      "Clustering   686: ASW= 0.8141, DB= 0.2507, CH= 41684.4933\n",
      "Training epoch 687, recon_loss:0.883895, zinb_loss:0.480016, cluster_loss:0.162241\n",
      "Clustering   687: ASW= 0.8132, DB= 0.2585, CH= 40673.1172\n",
      "Training epoch 688, recon_loss:0.884441, zinb_loss:0.480170, cluster_loss:0.161844\n",
      "Clustering   688: ASW= 0.8139, DB= 0.2506, CH= 41671.7160\n",
      "Training epoch 689, recon_loss:0.884120, zinb_loss:0.480050, cluster_loss:0.162238\n",
      "Clustering   689: ASW= 0.8135, DB= 0.2582, CH= 40751.4084\n",
      "Training epoch 690, recon_loss:0.884617, zinb_loss:0.480155, cluster_loss:0.161887\n",
      "Clustering   690: ASW= 0.8137, DB= 0.2502, CH= 41646.8601\n",
      "Training epoch 691, recon_loss:0.884446, zinb_loss:0.480085, cluster_loss:0.162313\n",
      "Clustering   691: ASW= 0.8137, DB= 0.2584, CH= 40821.0246\n",
      "Training epoch 692, recon_loss:0.884781, zinb_loss:0.480125, cluster_loss:0.161983\n",
      "Clustering   692: ASW= 0.8133, DB= 0.2504, CH= 41588.3800\n",
      "Training epoch 693, recon_loss:0.884755, zinb_loss:0.480110, cluster_loss:0.162430\n",
      "Clustering   693: ASW= 0.8140, DB= 0.2584, CH= 40875.8324\n",
      "Training epoch 694, recon_loss:0.884823, zinb_loss:0.480075, cluster_loss:0.162062\n",
      "Clustering   694: ASW= 0.8131, DB= 0.2508, CH= 41536.8223\n",
      "Training epoch 695, recon_loss:0.884836, zinb_loss:0.480110, cluster_loss:0.162457\n",
      "Clustering   695: ASW= 0.8141, DB= 0.2585, CH= 40929.8067\n",
      "Training epoch 696, recon_loss:0.884604, zinb_loss:0.480008, cluster_loss:0.162040\n",
      "Clustering   696: ASW= 0.8131, DB= 0.2512, CH= 41504.7813\n",
      "Training epoch 697, recon_loss:0.884606, zinb_loss:0.480083, cluster_loss:0.162327\n",
      "Clustering   697: ASW= 0.8143, DB= 0.2582, CH= 41003.2953\n",
      "Training epoch 698, recon_loss:0.884242, zinb_loss:0.479943, cluster_loss:0.161932\n",
      "Clustering   698: ASW= 0.8131, DB= 0.2504, CH= 41471.8160\n",
      "Training epoch 900, recon_loss:0.882355, zinb_loss:0.479461, cluster_loss:0.161423\n",
      "Clustering   900: ASW= 0.8188, DB= 0.2425, CH= 43597.3875\n",
      "Training epoch 901, recon_loss:0.882180, zinb_loss:0.479366, cluster_loss:0.161362\n",
      "Clustering   901: ASW= 0.8182, DB= 0.2474, CH= 43790.5240\n",
      "Training epoch 902, recon_loss:0.882247, zinb_loss:0.479424, cluster_loss:0.161290\n",
      "Clustering   902: ASW= 0.8190, DB= 0.2422, CH= 43771.0397\n",
      "Training epoch 903, recon_loss:0.882044, zinb_loss:0.479312, cluster_loss:0.161259\n",
      "Clustering   903: ASW= 0.8183, DB= 0.2473, CH= 43744.5329\n",
      "Training epoch 904, recon_loss:0.882270, zinb_loss:0.479399, cluster_loss:0.161190\n",
      "Clustering   904: ASW= 0.8192, DB= 0.2420, CH= 43951.6577\n",
      "Training epoch 905, recon_loss:0.882057, zinb_loss:0.479281, cluster_loss:0.161197\n",
      "Clustering   905: ASW= 0.8184, DB= 0.2472, CH= 43720.2498\n",
      "Training epoch 906, recon_loss:0.882458, zinb_loss:0.479387, cluster_loss:0.161155\n",
      "Clustering   906: ASW= 0.8193, DB= 0.2428, CH= 44112.1691\n",
      "Training epoch 907, recon_loss:0.882277, zinb_loss:0.479271, cluster_loss:0.161187\n",
      "Clustering   907: ASW= 0.8186, DB= 0.2470, CH= 43700.3907\n",
      "Training epoch 908, recon_loss:0.882765, zinb_loss:0.479387, cluster_loss:0.161182\n",
      "Clustering   908: ASW= 0.8194, DB= 0.2429, CH= 44250.9346\n",
      "Training epoch 909, recon_loss:0.882565, zinb_loss:0.479279, cluster_loss:0.161215\n",
      "Clustering   909: ASW= 0.8187, DB= 0.2468, CH= 43676.3820\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training epoch 910, recon_loss:0.883069, zinb_loss:0.479398, cluster_loss:0.161212\n",
      "Clustering   910: ASW= 0.8194, DB= 0.2431, CH= 44344.6931\n",
      "Training epoch 911, recon_loss:0.882860, zinb_loss:0.479299, cluster_loss:0.161228\n",
      "Clustering   911: ASW= 0.8188, DB= 0.2466, CH= 43664.7818\n",
      "Training epoch 912, recon_loss:0.883321, zinb_loss:0.479415, cluster_loss:0.161221\n",
      "Clustering   912: ASW= 0.8194, DB= 0.2424, CH= 44409.2906\n",
      "Training epoch 913, recon_loss:0.883115, zinb_loss:0.479330, cluster_loss:0.161226\n",
      "Clustering   913: ASW= 0.8189, DB= 0.2457, CH= 43700.5773\n",
      "Training epoch 914, recon_loss:0.883488, zinb_loss:0.479434, cluster_loss:0.161205\n",
      "Clustering   914: ASW= 0.8194, DB= 0.2432, CH= 44455.6877\n",
      "Training epoch 915, recon_loss:0.883298, zinb_loss:0.479366, cluster_loss:0.161217\n",
      "Clustering   915: ASW= 0.8190, DB= 0.2452, CH= 43728.3713\n",
      "Training epoch 916, recon_loss:0.883579, zinb_loss:0.479457, cluster_loss:0.161198\n",
      "Clustering   916: ASW= 0.8193, DB= 0.2437, CH= 44453.2845\n",
      "Training epoch 917, recon_loss:0.883349, zinb_loss:0.479397, cluster_loss:0.161241\n",
      "Clustering   917: ASW= 0.8191, DB= 0.2450, CH= 43778.6389\n",
      "Training epoch 918, recon_loss:0.883576, zinb_loss:0.479479, cluster_loss:0.161193\n",
      "Clustering   918: ASW= 0.8192, DB= 0.2440, CH= 44398.7678\n",
      "Training epoch 919, recon_loss:0.883243, zinb_loss:0.479415, cluster_loss:0.161281\n",
      "Clustering   919: ASW= 0.8191, DB= 0.2444, CH= 43836.6423\n",
      "Training epoch 920, recon_loss:0.883458, zinb_loss:0.479498, cluster_loss:0.161154\n",
      "Clustering   920: ASW= 0.8192, DB= 0.2442, CH= 44350.8879\n",
      "Training epoch 921, recon_loss:0.882958, zinb_loss:0.479413, cluster_loss:0.161268\n",
      "Clustering   921: ASW= 0.8192, DB= 0.2439, CH= 43916.8048\n",
      "Training epoch 922, recon_loss:0.883244, zinb_loss:0.479523, cluster_loss:0.161033\n",
      "Clustering   922: ASW= 0.8191, DB= 0.2442, CH= 44340.8930\n",
      "Training epoch 923, recon_loss:0.882428, zinb_loss:0.479344, cluster_loss:0.161174\n",
      "Clustering   923: ASW= 0.8192, DB= 0.2436, CH= 43955.1540\n",
      "Training epoch 924, recon_loss:0.882695, zinb_loss:0.479472, cluster_loss:0.160904\n",
      "Clustering   924: ASW= 0.8190, DB= 0.2446, CH= 44343.4315\n",
      "Training epoch 925, recon_loss:0.882216, zinb_loss:0.479354, cluster_loss:0.161101\n",
      "Clustering   925: ASW= 0.8196, DB= 0.2421, CH= 44087.2715\n",
      "Training epoch 926, recon_loss:0.882542, zinb_loss:0.479469, cluster_loss:0.160787\n",
      "Clustering   926: ASW= 0.8191, DB= 0.2447, CH= 44364.0545\n",
      "Training epoch 927, recon_loss:0.882115, zinb_loss:0.479347, cluster_loss:0.160996\n",
      "Clustering   927: ASW= 0.8198, DB= 0.2416, CH= 44231.8847\n",
      "Training epoch 928, recon_loss:0.882318, zinb_loss:0.479433, cluster_loss:0.160668\n",
      "Clustering   928: ASW= 0.8192, DB= 0.2447, CH= 44376.9193\n",
      "Training epoch 929, recon_loss:0.881924, zinb_loss:0.479328, cluster_loss:0.160844\n",
      "Clustering   929: ASW= 0.8200, DB= 0.2412, CH= 44411.8732\n",
      "Training epoch 930, recon_loss:0.881935, zinb_loss:0.479384, cluster_loss:0.160519\n",
      "Clustering   930: ASW= 0.8194, DB= 0.2445, CH= 44401.5556\n",
      "Training epoch 931, recon_loss:0.881512, zinb_loss:0.479259, cluster_loss:0.160724\n",
      "Clustering   931: ASW= 0.8200, DB= 0.2411, CH= 44532.7182\n",
      "Training epoch 932, recon_loss:0.881639, zinb_loss:0.479323, cluster_loss:0.160497\n",
      "Clustering   932: ASW= 0.8195, DB= 0.2446, CH= 44399.3685\n",
      "Training epoch 933, recon_loss:0.881587, zinb_loss:0.479277, cluster_loss:0.160733\n",
      "Clustering   933: ASW= 0.8203, DB= 0.2406, CH= 44711.8253\n",
      "Training epoch 934, recon_loss:0.881563, zinb_loss:0.479301, cluster_loss:0.160518\n",
      "Clustering   934: ASW= 0.8197, DB= 0.2448, CH= 44392.0027\n",
      "Training epoch 935, recon_loss:0.881476, zinb_loss:0.479265, cluster_loss:0.160754\n",
      "Clustering   935: ASW= 0.8202, DB= 0.2402, CH= 44848.3396\n",
      "Training epoch 936, recon_loss:0.881450, zinb_loss:0.479274, cluster_loss:0.160620\n",
      "Clustering   936: ASW= 0.8198, DB= 0.2452, CH= 44331.9653\n",
      "Training epoch 937, recon_loss:0.881422, zinb_loss:0.479266, cluster_loss:0.160842\n",
      "Clustering   937: ASW= 0.8202, DB= 0.2396, CH= 44996.2191\n",
      "Training epoch 938, recon_loss:0.881477, zinb_loss:0.479262, cluster_loss:0.160801\n",
      "Clustering   938: ASW= 0.8199, DB= 0.2457, CH= 44233.9570\n",
      "Training epoch 939, recon_loss:0.881464, zinb_loss:0.479265, cluster_loss:0.160992\n",
      "Clustering   939: ASW= 0.8201, DB= 0.2394, CH= 45090.3165\n",
      "Training epoch 940, recon_loss:0.881676, zinb_loss:0.479271, cluster_loss:0.160972\n",
      "Clustering   940: ASW= 0.8201, DB= 0.2460, CH= 44156.7755\n",
      "Training epoch 941, recon_loss:0.881662, zinb_loss:0.479271, cluster_loss:0.161149\n",
      "Clustering   941: ASW= 0.8197, DB= 0.2394, CH= 45128.4600\n",
      "Training epoch 942, recon_loss:0.882028, zinb_loss:0.479292, cluster_loss:0.161224\n",
      "Clustering   942: ASW= 0.8203, DB= 0.2465, CH= 44074.4559\n",
      "Training epoch 943, recon_loss:0.882009, zinb_loss:0.479281, cluster_loss:0.161318\n",
      "Clustering   943: ASW= 0.8192, DB= 0.2396, CH= 45091.4130\n",
      "Training epoch 944, recon_loss:0.882397, zinb_loss:0.479320, cluster_loss:0.161399\n",
      "Clustering   944: ASW= 0.8204, DB= 0.2466, CH= 44054.6106\n",
      "Training epoch 945, recon_loss:0.882191, zinb_loss:0.479265, cluster_loss:0.161308\n",
      "Clustering   945: ASW= 0.8190, DB= 0.2399, CH= 45016.5419\n",
      "Training epoch 946, recon_loss:0.882371, zinb_loss:0.479313, cluster_loss:0.161291\n",
      "Clustering   946: ASW= 0.8206, DB= 0.2463, CH= 44140.0365\n",
      "Training epoch 947, recon_loss:0.881974, zinb_loss:0.479215, cluster_loss:0.161084\n",
      "Clustering   947: ASW= 0.8190, DB= 0.2400, CH= 44960.3335\n",
      "Training epoch 948, recon_loss:0.882060, zinb_loss:0.479287, cluster_loss:0.161066\n",
      "Clustering   948: ASW= 0.8207, DB= 0.2459, CH= 44291.3123\n",
      "Training epoch 949, recon_loss:0.881612, zinb_loss:0.479163, cluster_loss:0.160880\n",
      "Clustering   949: ASW= 0.8191, DB= 0.2402, CH= 44924.3614\n",
      "Training epoch 950, recon_loss:0.881745, zinb_loss:0.479259, cluster_loss:0.160894\n",
      "Clustering   950: ASW= 0.8208, DB= 0.2455, CH= 44439.4805\n",
      "Training epoch 951, recon_loss:0.881335, zinb_loss:0.479121, cluster_loss:0.160786\n",
      "Clustering   951: ASW= 0.8191, DB= 0.2407, CH= 44901.2880\n",
      "Training epoch 952, recon_loss:0.881580, zinb_loss:0.479240, cluster_loss:0.160829\n",
      "Clustering   952: ASW= 0.8209, DB= 0.2448, CH= 44569.8462\n",
      "Training epoch 953, recon_loss:0.881256, zinb_loss:0.479090, cluster_loss:0.160816\n",
      "Clustering   953: ASW= 0.8191, DB= 0.2409, CH= 44867.0944\n",
      "Training epoch 954, recon_loss:0.881550, zinb_loss:0.479231, cluster_loss:0.160860\n",
      "Clustering   954: ASW= 0.8211, DB= 0.2446, CH= 44658.9690\n",
      "Training epoch 955, recon_loss:0.881452, zinb_loss:0.479080, cluster_loss:0.160997\n",
      "Clustering   955: ASW= 0.8190, DB= 0.2413, CH= 44775.8357\n",
      "Training epoch 956, recon_loss:0.881847, zinb_loss:0.479239, cluster_loss:0.161118\n",
      "Clustering   956: ASW= 0.8211, DB= 0.2445, CH= 44652.8229\n",
      "Training epoch 957, recon_loss:0.882240, zinb_loss:0.479116, cluster_loss:0.161552\n",
      "Clustering   957: ASW= 0.8184, DB= 0.2422, CH= 44449.8946\n",
      "Training epoch 958, recon_loss:0.883100, zinb_loss:0.479406, cluster_loss:0.161816\n",
      "Clustering   958: ASW= 0.8211, DB= 0.2444, CH= 44560.3603\n",
      "Training epoch 959, recon_loss:0.883819, zinb_loss:0.479329, cluster_loss:0.162451\n",
      "Clustering   959: ASW= 0.8176, DB= 0.2433, CH= 43917.5632\n",
      "Training epoch 960, recon_loss:0.884027, zinb_loss:0.479558, cluster_loss:0.162521\n",
      "Clustering   960: ASW= 0.8206, DB= 0.2442, CH= 44387.1846\n",
      "Training epoch 961, recon_loss:0.883190, zinb_loss:0.479297, cluster_loss:0.162481\n",
      "Clustering   961: ASW= 0.8177, DB= 0.2435, CH= 43639.6782\n",
      "Training epoch 962, recon_loss:0.883008, zinb_loss:0.479520, cluster_loss:0.162029\n",
      "Clustering   962: ASW= 0.8207, DB= 0.2429, CH= 44623.7948\n",
      "Training epoch 963, recon_loss:0.881880, zinb_loss:0.479210, cluster_loss:0.161677\n",
      "Clustering   963: ASW= 0.8185, DB= 0.2431, CH= 43792.8122\n",
      "Training epoch 964, recon_loss:0.881466, zinb_loss:0.479371, cluster_loss:0.161049\n",
      "Clustering   964: ASW= 0.8211, DB= 0.2419, CH= 44953.9023\n",
      "Training epoch 965, recon_loss:0.880860, zinb_loss:0.479129, cluster_loss:0.161099\n",
      "Clustering   965: ASW= 0.8190, DB= 0.2430, CH= 43960.0003\n",
      "Training epoch 966, recon_loss:0.880765, zinb_loss:0.479302, cluster_loss:0.160647\n",
      "Clustering   966: ASW= 0.8211, DB= 0.2415, CH= 45136.8896\n",
      "Training epoch 967, recon_loss:0.880460, zinb_loss:0.479108, cluster_loss:0.160877\n",
      "Clustering   967: ASW= 0.8193, DB= 0.2430, CH= 44082.4272\n",
      "Training epoch 968, recon_loss:0.880538, zinb_loss:0.479284, cluster_loss:0.160518\n",
      "Clustering   968: ASW= 0.8211, DB= 0.2412, CH= 45263.1010\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training epoch 969, recon_loss:0.880378, zinb_loss:0.479112, cluster_loss:0.160850\n",
      "Clustering   969: ASW= 0.8195, DB= 0.2430, CH= 44157.6636\n",
      "Training epoch 970, recon_loss:0.880584, zinb_loss:0.479294, cluster_loss:0.160529\n",
      "Clustering   970: ASW= 0.8210, DB= 0.2409, CH= 45355.2155\n",
      "Training epoch 971, recon_loss:0.880517, zinb_loss:0.479135, cluster_loss:0.160933\n",
      "Clustering   971: ASW= 0.8197, DB= 0.2430, CH= 44191.2874\n",
      "Training epoch 972, recon_loss:0.880830, zinb_loss:0.479319, cluster_loss:0.160625\n",
      "Clustering   972: ASW= 0.8210, DB= 0.2402, CH= 45414.3692\n",
      "Training epoch 973, recon_loss:0.880842, zinb_loss:0.479174, cluster_loss:0.161078\n",
      "Clustering   973: ASW= 0.8198, DB= 0.2433, CH= 44198.4642\n",
      "Training epoch 974, recon_loss:0.881257, zinb_loss:0.479357, cluster_loss:0.160763\n",
      "Clustering   974: ASW= 0.8210, DB= 0.2399, CH= 45441.1686\n",
      "Training epoch 975, recon_loss:0.881260, zinb_loss:0.479224, cluster_loss:0.161211\n",
      "Clustering   975: ASW= 0.8198, DB= 0.2435, CH= 44216.7948\n",
      "Training epoch 976, recon_loss:0.881692, zinb_loss:0.479388, cluster_loss:0.160874\n",
      "Clustering   976: ASW= 0.8210, DB= 0.2394, CH= 45453.2077\n",
      "Training epoch 977, recon_loss:0.881544, zinb_loss:0.479260, cluster_loss:0.161256\n",
      "Clustering   977: ASW= 0.8199, DB= 0.2438, CH= 44254.9954\n",
      "Training epoch 978, recon_loss:0.881871, zinb_loss:0.479390, cluster_loss:0.160883\n",
      "Clustering   978: ASW= 0.8210, DB= 0.2392, CH= 45455.7965\n",
      "Training epoch 979, recon_loss:0.881507, zinb_loss:0.479265, cluster_loss:0.161166\n",
      "Clustering   979: ASW= 0.8199, DB= 0.2438, CH= 44337.2448\n",
      "Training epoch 980, recon_loss:0.881741, zinb_loss:0.479367, cluster_loss:0.160783\n",
      "Clustering   980: ASW= 0.8211, DB= 0.2391, CH= 45460.6853\n",
      "Training epoch 981, recon_loss:0.881257, zinb_loss:0.479253, cluster_loss:0.160994\n",
      "Clustering   981: ASW= 0.8200, DB= 0.2437, CH= 44449.2126\n",
      "Training epoch 982, recon_loss:0.881457, zinb_loss:0.479331, cluster_loss:0.160639\n",
      "Clustering   982: ASW= 0.8212, DB= 0.2387, CH= 45474.1905\n",
      "Training epoch 983, recon_loss:0.880988, zinb_loss:0.479236, cluster_loss:0.160825\n",
      "Clustering   983: ASW= 0.8201, DB= 0.2437, CH= 44562.1178\n",
      "Training epoch 984, recon_loss:0.881213, zinb_loss:0.479303, cluster_loss:0.160521\n",
      "Clustering   984: ASW= 0.8213, DB= 0.2386, CH= 45483.4119\n",
      "Training epoch 985, recon_loss:0.880798, zinb_loss:0.479222, cluster_loss:0.160698\n",
      "Clustering   985: ASW= 0.8201, DB= 0.2437, CH= 44667.6741\n",
      "Training epoch 986, recon_loss:0.881087, zinb_loss:0.479288, cluster_loss:0.160449\n",
      "Clustering   986: ASW= 0.8214, DB= 0.2385, CH= 45490.4611\n",
      "Training epoch 987, recon_loss:0.880743, zinb_loss:0.479214, cluster_loss:0.160627\n",
      "Clustering   987: ASW= 0.8200, DB= 0.2437, CH= 44752.4645\n",
      "Training epoch 988, recon_loss:0.881114, zinb_loss:0.479289, cluster_loss:0.160431\n",
      "Clustering   988: ASW= 0.8215, DB= 0.2383, CH= 45483.8785\n",
      "Training epoch 989, recon_loss:0.880826, zinb_loss:0.479215, cluster_loss:0.160598\n",
      "Clustering   989: ASW= 0.8199, DB= 0.2439, CH= 44828.1920\n",
      "Training epoch 990, recon_loss:0.881306, zinb_loss:0.479303, cluster_loss:0.160467\n",
      "Clustering   990: ASW= 0.8217, DB= 0.2381, CH= 45461.5483\n",
      "Training epoch 991, recon_loss:0.881053, zinb_loss:0.479220, cluster_loss:0.160605\n",
      "Clustering   991: ASW= 0.8198, DB= 0.2441, CH= 44894.3339\n",
      "Training epoch 992, recon_loss:0.881649, zinb_loss:0.479327, cluster_loss:0.160548\n",
      "Clustering   992: ASW= 0.8220, DB= 0.2381, CH= 45415.7009\n",
      "Training epoch 993, recon_loss:0.881382, zinb_loss:0.479222, cluster_loss:0.160638\n",
      "Clustering   993: ASW= 0.8196, DB= 0.2443, CH= 44962.3426\n",
      "Training epoch 994, recon_loss:0.882052, zinb_loss:0.479351, cluster_loss:0.160660\n",
      "Clustering   994: ASW= 0.8222, DB= 0.2379, CH= 45362.6136\n",
      "Training epoch 995, recon_loss:0.881717, zinb_loss:0.479215, cluster_loss:0.160681\n",
      "Clustering   995: ASW= 0.8195, DB= 0.2446, CH= 45041.9946\n",
      "Training epoch 996, recon_loss:0.882360, zinb_loss:0.479358, cluster_loss:0.160788\n",
      "Clustering   996: ASW= 0.8225, DB= 0.2378, CH= 45321.1435\n",
      "Training epoch 997, recon_loss:0.881966, zinb_loss:0.479198, cluster_loss:0.160706\n",
      "Clustering   997: ASW= 0.8195, DB= 0.2446, CH= 45168.8987\n",
      "Training epoch 998, recon_loss:0.882417, zinb_loss:0.479333, cluster_loss:0.160899\n",
      "Clustering   998: ASW= 0.8227, DB= 0.2377, CH= 45251.8730\n",
      "Training epoch 999, recon_loss:0.882127, zinb_loss:0.479180, cluster_loss:0.160736\n",
      "Clustering   999: ASW= 0.8196, DB= 0.2441, CH= 45324.5408\n",
      "Training epoch 1000, recon_loss:0.882388, zinb_loss:0.479288, cluster_loss:0.161058\n",
      "Clustering   1000: ASW= 0.8227, DB= 0.2379, CH= 45153.4518\n",
      "Training epoch 1001, recon_loss:0.882292, zinb_loss:0.479164, cluster_loss:0.160835\n",
      "Clustering   1001: ASW= 0.8196, DB= 0.2443, CH= 45463.8211\n",
      "Training epoch 1002, recon_loss:0.882380, zinb_loss:0.479244, cluster_loss:0.161279\n",
      "Clustering   1002: ASW= 0.8224, DB= 0.2379, CH= 44999.3072\n",
      "Training epoch 1003, recon_loss:0.882369, zinb_loss:0.479151, cluster_loss:0.160935\n",
      "Clustering   1003: ASW= 0.8197, DB= 0.2440, CH= 45555.2682\n",
      "Training epoch 1004, recon_loss:0.882220, zinb_loss:0.479194, cluster_loss:0.161386\n",
      "Clustering   1004: ASW= 0.8222, DB= 0.2383, CH= 44908.4971\n",
      "Training epoch 1005, recon_loss:0.882184, zinb_loss:0.479133, cluster_loss:0.160927\n",
      "Clustering   1005: ASW= 0.8198, DB= 0.2436, CH= 45621.2431\n",
      "Training epoch 1006, recon_loss:0.881832, zinb_loss:0.479139, cluster_loss:0.161293\n",
      "Clustering   1006: ASW= 0.8219, DB= 0.2384, CH= 44890.5101\n",
      "Training epoch 1007, recon_loss:0.881755, zinb_loss:0.479117, cluster_loss:0.160805\n",
      "Clustering   1007: ASW= 0.8201, DB= 0.2432, CH= 45716.5670\n",
      "Training epoch 1008, recon_loss:0.881449, zinb_loss:0.479100, cluster_loss:0.161139\n",
      "Clustering   1008: ASW= 0.8217, DB= 0.2383, CH= 44932.7183\n",
      "Training epoch 1009, recon_loss:0.881432, zinb_loss:0.479125, cluster_loss:0.160718\n",
      "Clustering   1009: ASW= 0.8205, DB= 0.2428, CH= 45819.3562\n",
      "Training epoch 1010, recon_loss:0.881397, zinb_loss:0.479107, cluster_loss:0.161088\n",
      "Clustering   1010: ASW= 0.8215, DB= 0.2391, CH= 45001.2266\n",
      "Training epoch 1011, recon_loss:0.881496, zinb_loss:0.479187, cluster_loss:0.160802\n",
      "Clustering   1011: ASW= 0.8208, DB= 0.2425, CH= 45892.4972\n",
      "Training epoch 1012, recon_loss:0.881723, zinb_loss:0.479163, cluster_loss:0.161233\n",
      "Clustering   1012: ASW= 0.8211, DB= 0.2394, CH= 45016.6265\n",
      "Training epoch 1013, recon_loss:0.881717, zinb_loss:0.479283, cluster_loss:0.161038\n",
      "Clustering   1013: ASW= 0.8212, DB= 0.2423, CH= 45887.2383\n",
      "Training epoch 1014, recon_loss:0.881966, zinb_loss:0.479181, cluster_loss:0.161472\n",
      "Clustering   1014: ASW= 0.8206, DB= 0.2399, CH= 45000.2504\n",
      "Training epoch 1015, recon_loss:0.881667, zinb_loss:0.479314, cluster_loss:0.161255\n",
      "Clustering   1015: ASW= 0.8216, DB= 0.2424, CH= 45833.2268\n",
      "Training epoch 1016, recon_loss:0.881729, zinb_loss:0.479181, cluster_loss:0.161477\n",
      "Clustering   1016: ASW= 0.8204, DB= 0.2403, CH= 45027.5029\n",
      "Training epoch 1017, recon_loss:0.881072, zinb_loss:0.479260, cluster_loss:0.161190\n",
      "Clustering   1017: ASW= 0.8220, DB= 0.2422, CH= 45798.7842\n",
      "Training epoch 1018, recon_loss:0.881176, zinb_loss:0.479105, cluster_loss:0.161294\n",
      "Clustering   1018: ASW= 0.8203, DB= 0.2404, CH= 45031.5577\n",
      "Training epoch 1019, recon_loss:0.880718, zinb_loss:0.479210, cluster_loss:0.161012\n",
      "Clustering   1019: ASW= 0.8222, DB= 0.2420, CH= 45834.4028\n",
      "Training epoch 1020, recon_loss:0.880840, zinb_loss:0.479040, cluster_loss:0.161092\n",
      "Clustering   1020: ASW= 0.8203, DB= 0.2405, CH= 45104.0572\n",
      "Training epoch 1021, recon_loss:0.880578, zinb_loss:0.479156, cluster_loss:0.160862\n",
      "Clustering   1021: ASW= 0.8224, DB= 0.2419, CH= 45863.1150\n",
      "Training epoch 1022, recon_loss:0.880751, zinb_loss:0.478991, cluster_loss:0.160983\n",
      "Clustering   1022: ASW= 0.8203, DB= 0.2405, CH= 45185.1676\n",
      "Training epoch 1023, recon_loss:0.880683, zinb_loss:0.479128, cluster_loss:0.160780\n",
      "Clustering   1023: ASW= 0.8225, DB= 0.2418, CH= 45896.7745\n",
      "Training epoch 1024, recon_loss:0.880896, zinb_loss:0.478965, cluster_loss:0.160956\n",
      "Clustering   1024: ASW= 0.8204, DB= 0.2404, CH= 45257.6919\n",
      "Training epoch 1025, recon_loss:0.880903, zinb_loss:0.479114, cluster_loss:0.160731\n",
      "Clustering   1025: ASW= 0.8226, DB= 0.2416, CH= 45921.3175\n",
      "Training epoch 1026, recon_loss:0.881051, zinb_loss:0.478948, cluster_loss:0.160927\n",
      "Clustering   1026: ASW= 0.8204, DB= 0.2405, CH= 45321.1565\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training epoch 1027, recon_loss:0.881050, zinb_loss:0.479104, cluster_loss:0.160671\n",
      "Clustering   1027: ASW= 0.8226, DB= 0.2414, CH= 45941.9553\n",
      "Training epoch 1028, recon_loss:0.881042, zinb_loss:0.478935, cluster_loss:0.160861\n",
      "Clustering   1028: ASW= 0.8204, DB= 0.2405, CH= 45390.7695\n",
      "Training epoch 1029, recon_loss:0.881064, zinb_loss:0.479092, cluster_loss:0.160595\n",
      "Clustering   1029: ASW= 0.8227, DB= 0.2412, CH= 45950.8497\n",
      "Training epoch 1030, recon_loss:0.881018, zinb_loss:0.478936, cluster_loss:0.160784\n",
      "Clustering   1030: ASW= 0.8205, DB= 0.2403, CH= 45468.5291\n",
      "Training epoch 1031, recon_loss:0.881103, zinb_loss:0.479084, cluster_loss:0.160537\n",
      "Clustering   1031: ASW= 0.8227, DB= 0.2411, CH= 45950.3279\n",
      "Training epoch 1032, recon_loss:0.881122, zinb_loss:0.478957, cluster_loss:0.160736\n",
      "Clustering   1032: ASW= 0.8205, DB= 0.2402, CH= 45556.9521\n",
      "Training epoch 1033, recon_loss:0.881283, zinb_loss:0.479088, cluster_loss:0.160518\n",
      "Clustering   1033: ASW= 0.8226, DB= 0.2406, CH= 45958.6949\n",
      "Training epoch 1034, recon_loss:0.881355, zinb_loss:0.478998, cluster_loss:0.160719\n",
      "Clustering   1034: ASW= 0.8206, DB= 0.2402, CH= 45655.1760\n",
      "Training epoch 1035, recon_loss:0.881562, zinb_loss:0.479102, cluster_loss:0.160536\n",
      "Clustering   1035: ASW= 0.8226, DB= 0.2405, CH= 45943.8217\n",
      "Training epoch 1036, recon_loss:0.881752, zinb_loss:0.479063, cluster_loss:0.160751\n",
      "Clustering   1036: ASW= 0.8207, DB= 0.2402, CH= 45749.8755\n",
      "Training epoch 1037, recon_loss:0.881981, zinb_loss:0.479135, cluster_loss:0.160603\n",
      "Clustering   1037: ASW= 0.8225, DB= 0.2407, CH= 45927.8960\n",
      "Training epoch 1038, recon_loss:0.882186, zinb_loss:0.479142, cluster_loss:0.160838\n",
      "Clustering   1038: ASW= 0.8208, DB= 0.2402, CH= 45836.7056\n",
      "Training epoch 1039, recon_loss:0.882489, zinb_loss:0.479175, cluster_loss:0.160801\n",
      "Clustering   1039: ASW= 0.8223, DB= 0.2405, CH= 45873.2429\n",
      "Training epoch 1040, recon_loss:0.882700, zinb_loss:0.479233, cluster_loss:0.161070\n",
      "Clustering   1040: ASW= 0.8208, DB= 0.2400, CH= 45858.4984\n",
      "Training epoch 1041, recon_loss:0.883072, zinb_loss:0.479211, cluster_loss:0.161177\n",
      "Clustering   1041: ASW= 0.8219, DB= 0.2408, CH= 45759.9860\n",
      "Training epoch 1042, recon_loss:0.882965, zinb_loss:0.479299, cluster_loss:0.161265\n",
      "Clustering   1042: ASW= 0.8209, DB= 0.2402, CH= 45755.1790\n",
      "Training epoch 1043, recon_loss:0.882870, zinb_loss:0.479152, cluster_loss:0.161286\n",
      "Clustering   1043: ASW= 0.8215, DB= 0.2410, CH= 45694.4043\n",
      "Training epoch 1044, recon_loss:0.882374, zinb_loss:0.479251, cluster_loss:0.161109\n",
      "Clustering   1044: ASW= 0.8210, DB= 0.2402, CH= 45624.2213\n",
      "Training epoch 1045, recon_loss:0.882152, zinb_loss:0.479038, cluster_loss:0.161173\n",
      "Clustering   1045: ASW= 0.8213, DB= 0.2410, CH= 45709.8297\n",
      "Training epoch 1046, recon_loss:0.881748, zinb_loss:0.479172, cluster_loss:0.160938\n",
      "Clustering   1046: ASW= 0.8212, DB= 0.2399, CH= 45496.1729\n",
      "Training epoch 1047, recon_loss:0.881773, zinb_loss:0.478954, cluster_loss:0.161217\n",
      "Clustering   1047: ASW= 0.8210, DB= 0.2413, CH= 45676.3243\n",
      "Training epoch 1048, recon_loss:0.881653, zinb_loss:0.479133, cluster_loss:0.161020\n",
      "Clustering   1048: ASW= 0.8212, DB= 0.2398, CH= 45274.3667\n",
      "Training epoch 1049, recon_loss:0.881664, zinb_loss:0.478913, cluster_loss:0.161327\n",
      "Clustering   1049: ASW= 0.8207, DB= 0.2414, CH= 45541.1838\n",
      "Training epoch 1050, recon_loss:0.881520, zinb_loss:0.479118, cluster_loss:0.161075\n",
      "Clustering   1050: ASW= 0.8215, DB= 0.2393, CH= 45240.2814\n",
      "Training epoch 1051, recon_loss:0.881263, zinb_loss:0.478888, cluster_loss:0.161205\n",
      "Clustering   1051: ASW= 0.8206, DB= 0.2415, CH= 45418.4169\n",
      "Training epoch 1052, recon_loss:0.880976, zinb_loss:0.479086, cluster_loss:0.160855\n",
      "Clustering   1052: ASW= 0.8219, DB= 0.2388, CH= 45506.6536\n",
      "Training epoch 1053, recon_loss:0.880805, zinb_loss:0.478861, cluster_loss:0.160995\n",
      "Clustering   1053: ASW= 0.8207, DB= 0.2415, CH= 45410.2090\n",
      "Training epoch 1054, recon_loss:0.880702, zinb_loss:0.479079, cluster_loss:0.160640\n",
      "Clustering   1054: ASW= 0.8222, DB= 0.2383, CH= 45804.8595\n",
      "Training epoch 1055, recon_loss:0.880612, zinb_loss:0.478858, cluster_loss:0.160896\n",
      "Clustering   1055: ASW= 0.8209, DB= 0.2416, CH= 45414.3267\n",
      "Training epoch 1056, recon_loss:0.880707, zinb_loss:0.479089, cluster_loss:0.160560\n",
      "Clustering   1056: ASW= 0.8224, DB= 0.2379, CH= 46022.3662\n",
      "Training epoch 1057, recon_loss:0.880697, zinb_loss:0.478852, cluster_loss:0.160962\n",
      "Clustering   1057: ASW= 0.8209, DB= 0.2419, CH= 45380.2067\n",
      "Training epoch 1058, recon_loss:0.880948, zinb_loss:0.479108, cluster_loss:0.160640\n",
      "Clustering   1058: ASW= 0.8224, DB= 0.2372, CH= 46195.8401\n",
      "Training epoch 1059, recon_loss:0.880991, zinb_loss:0.478874, cluster_loss:0.161142\n",
      "Clustering   1059: ASW= 0.8210, DB= 0.2422, CH= 45328.6961\n",
      "Training epoch 1060, recon_loss:0.881205, zinb_loss:0.479124, cluster_loss:0.160764\n",
      "Clustering   1060: ASW= 0.8224, DB= 0.2371, CH= 46296.7610\n",
      "Training epoch 1061, recon_loss:0.881182, zinb_loss:0.478904, cluster_loss:0.161227\n",
      "Clustering   1061: ASW= 0.8211, DB= 0.2427, CH= 45318.5010\n",
      "Training epoch 1062, recon_loss:0.881266, zinb_loss:0.479132, cluster_loss:0.160802\n",
      "Clustering   1062: ASW= 0.8224, DB= 0.2369, CH= 46369.1260\n",
      "Training epoch 1063, recon_loss:0.881213, zinb_loss:0.478944, cluster_loss:0.161244\n",
      "Clustering   1063: ASW= 0.8213, DB= 0.2427, CH= 45349.8002\n",
      "Training epoch 1064, recon_loss:0.881169, zinb_loss:0.479137, cluster_loss:0.160783\n",
      "Clustering   1064: ASW= 0.8223, DB= 0.2365, CH= 46387.5934\n",
      "Training epoch 1065, recon_loss:0.881135, zinb_loss:0.478985, cluster_loss:0.161192\n",
      "Clustering   1065: ASW= 0.8216, DB= 0.2432, CH= 45410.5624\n",
      "Training epoch 1066, recon_loss:0.880953, zinb_loss:0.479130, cluster_loss:0.160716\n",
      "Clustering   1066: ASW= 0.8222, DB= 0.2366, CH= 46375.0637\n",
      "Training epoch 1067, recon_loss:0.880973, zinb_loss:0.479024, cluster_loss:0.161106\n",
      "Clustering   1067: ASW= 0.8218, DB= 0.2431, CH= 45491.0470\n",
      "Training epoch 1068, recon_loss:0.880710, zinb_loss:0.479115, cluster_loss:0.160644\n",
      "Clustering   1068: ASW= 0.8220, DB= 0.2367, CH= 46344.2368\n",
      "Training epoch 1069, recon_loss:0.880848, zinb_loss:0.479052, cluster_loss:0.161024\n",
      "Clustering   1069: ASW= 0.8220, DB= 0.2429, CH= 45570.9231\n",
      "Training epoch 1070, recon_loss:0.880534, zinb_loss:0.479098, cluster_loss:0.160592\n",
      "Clustering   1070: ASW= 0.8218, DB= 0.2368, CH= 46301.4685\n",
      "Training epoch 1071, recon_loss:0.880782, zinb_loss:0.479070, cluster_loss:0.160962\n",
      "Clustering   1071: ASW= 0.8221, DB= 0.2432, CH= 45646.1445\n",
      "Training epoch 1072, recon_loss:0.880455, zinb_loss:0.479080, cluster_loss:0.160558\n",
      "Clustering   1072: ASW= 0.8218, DB= 0.2369, CH= 46278.1315\n",
      "Training epoch 1073, recon_loss:0.880793, zinb_loss:0.479073, cluster_loss:0.160908\n",
      "Clustering   1073: ASW= 0.8221, DB= 0.2432, CH= 45728.1732\n",
      "Training epoch 1074, recon_loss:0.880453, zinb_loss:0.479054, cluster_loss:0.160557\n",
      "Clustering   1074: ASW= 0.8218, DB= 0.2371, CH= 46266.1820\n",
      "Training epoch 1075, recon_loss:0.880845, zinb_loss:0.479062, cluster_loss:0.160866\n",
      "Clustering   1075: ASW= 0.8222, DB= 0.2432, CH= 45818.4270\n",
      "Training epoch 1076, recon_loss:0.880464, zinb_loss:0.479026, cluster_loss:0.160559\n",
      "Clustering   1076: ASW= 0.8218, DB= 0.2370, CH= 46267.2191\n",
      "Training epoch 1077, recon_loss:0.880854, zinb_loss:0.479038, cluster_loss:0.160807\n",
      "Clustering   1077: ASW= 0.8222, DB= 0.2431, CH= 45906.7686\n",
      "Training epoch 1078, recon_loss:0.880452, zinb_loss:0.478997, cluster_loss:0.160532\n",
      "Clustering   1078: ASW= 0.8219, DB= 0.2370, CH= 46314.4883\n",
      "Training epoch 1079, recon_loss:0.880815, zinb_loss:0.479012, cluster_loss:0.160729\n",
      "Clustering   1079: ASW= 0.8223, DB= 0.2429, CH= 45993.8108\n",
      "Training epoch 1080, recon_loss:0.880428, zinb_loss:0.478971, cluster_loss:0.160493\n",
      "Clustering   1080: ASW= 0.8221, DB= 0.2372, CH= 46375.6777\n",
      "Training epoch 1081, recon_loss:0.880788, zinb_loss:0.478989, cluster_loss:0.160644\n",
      "Clustering   1081: ASW= 0.8224, DB= 0.2428, CH= 46094.2661\n",
      "Training epoch 1082, recon_loss:0.880377, zinb_loss:0.478938, cluster_loss:0.160456\n",
      "Clustering   1082: ASW= 0.8222, DB= 0.2370, CH= 46432.8270\n",
      "Training epoch 1083, recon_loss:0.880752, zinb_loss:0.478965, cluster_loss:0.160578\n",
      "Clustering   1083: ASW= 0.8224, DB= 0.2424, CH= 46197.3424\n",
      "Training epoch 1084, recon_loss:0.880409, zinb_loss:0.478917, cluster_loss:0.160435\n",
      "Clustering   1084: ASW= 0.8223, DB= 0.2371, CH= 46505.2358\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training epoch 1085, recon_loss:0.880799, zinb_loss:0.478952, cluster_loss:0.160536\n",
      "Clustering   1085: ASW= 0.8226, DB= 0.2421, CH= 46274.7420\n",
      "Training epoch 1086, recon_loss:0.880466, zinb_loss:0.478889, cluster_loss:0.160460\n",
      "Clustering   1086: ASW= 0.8223, DB= 0.2371, CH= 46602.9807\n",
      "Training epoch 1087, recon_loss:0.880949, zinb_loss:0.478943, cluster_loss:0.160586\n",
      "Clustering   1087: ASW= 0.8227, DB= 0.2417, CH= 46306.1056\n",
      "Training epoch 1088, recon_loss:0.880796, zinb_loss:0.478883, cluster_loss:0.160607\n",
      "Clustering   1088: ASW= 0.8222, DB= 0.2373, CH= 46724.3168\n",
      "Training epoch 1089, recon_loss:0.881343, zinb_loss:0.478948, cluster_loss:0.160787\n",
      "Clustering   1089: ASW= 0.8228, DB= 0.2417, CH= 46264.0621\n",
      "Training epoch 1090, recon_loss:0.881358, zinb_loss:0.478891, cluster_loss:0.160954\n",
      "Clustering   1090: ASW= 0.8220, DB= 0.2377, CH= 46773.9311\n",
      "Training epoch 1091, recon_loss:0.881800, zinb_loss:0.478958, cluster_loss:0.161159\n",
      "Clustering   1091: ASW= 0.8226, DB= 0.2412, CH= 46060.8972\n",
      "Training epoch 1092, recon_loss:0.881976, zinb_loss:0.478911, cluster_loss:0.161453\n",
      "Clustering   1092: ASW= 0.8214, DB= 0.2385, CH= 46612.9808\n",
      "Training epoch 1093, recon_loss:0.882204, zinb_loss:0.478981, cluster_loss:0.161666\n",
      "Clustering   1093: ASW= 0.8220, DB= 0.2412, CH= 45602.9900\n",
      "Training epoch 1094, recon_loss:0.882452, zinb_loss:0.478932, cluster_loss:0.161865\n",
      "Clustering   1094: ASW= 0.8207, DB= 0.2392, CH= 46299.0919\n",
      "Training epoch 1095, recon_loss:0.882387, zinb_loss:0.479014, cluster_loss:0.161942\n",
      "Clustering   1095: ASW= 0.8214, DB= 0.2414, CH= 45191.8830\n",
      "Training epoch 1096, recon_loss:0.882205, zinb_loss:0.478920, cluster_loss:0.161651\n",
      "Clustering   1096: ASW= 0.8209, DB= 0.2394, CH= 46271.6592\n",
      "Training epoch 1097, recon_loss:0.881550, zinb_loss:0.478983, cluster_loss:0.161424\n",
      "Clustering   1097: ASW= 0.8220, DB= 0.2406, CH= 45423.1098\n",
      "Training epoch 1098, recon_loss:0.881541, zinb_loss:0.478867, cluster_loss:0.161088\n",
      "Clustering   1098: ASW= 0.8213, DB= 0.2392, CH= 46450.5239\n",
      "Training epoch 1099, recon_loss:0.880833, zinb_loss:0.478940, cluster_loss:0.160854\n",
      "Clustering   1099: ASW= 0.8226, DB= 0.2401, CH= 45773.8050\n",
      "Training epoch 1100, recon_loss:0.881142, zinb_loss:0.478843, cluster_loss:0.160694\n",
      "Clustering   1100: ASW= 0.8217, DB= 0.2389, CH= 46651.5469\n",
      "Training epoch 1101, recon_loss:0.880578, zinb_loss:0.478911, cluster_loss:0.160588\n",
      "Clustering   1101: ASW= 0.8230, DB= 0.2396, CH= 45969.2636\n",
      "Training epoch 1102, recon_loss:0.881129, zinb_loss:0.478848, cluster_loss:0.160522\n",
      "Clustering   1102: ASW= 0.8219, DB= 0.2388, CH= 46829.3420\n",
      "Training epoch 1103, recon_loss:0.880564, zinb_loss:0.478892, cluster_loss:0.160508\n",
      "Clustering   1103: ASW= 0.8231, DB= 0.2396, CH= 46059.8844\n",
      "Training epoch 1104, recon_loss:0.881198, zinb_loss:0.478855, cluster_loss:0.160442\n",
      "Clustering   1104: ASW= 0.8220, DB= 0.2388, CH= 46974.1687\n",
      "Training epoch 1105, recon_loss:0.880539, zinb_loss:0.478871, cluster_loss:0.160496\n",
      "Clustering   1105: ASW= 0.8233, DB= 0.2395, CH= 46110.9143\n",
      "Training epoch 1106, recon_loss:0.881183, zinb_loss:0.478858, cluster_loss:0.160383\n",
      "Clustering   1106: ASW= 0.8221, DB= 0.2391, CH= 47104.5119\n",
      "Training epoch 1107, recon_loss:0.880419, zinb_loss:0.478833, cluster_loss:0.160511\n",
      "Clustering   1107: ASW= 0.8234, DB= 0.2395, CH= 46134.7523\n",
      "Training epoch 1108, recon_loss:0.881106, zinb_loss:0.478850, cluster_loss:0.160347\n",
      "Clustering   1108: ASW= 0.8221, DB= 0.2389, CH= 47226.4736\n",
      "Training epoch 1109, recon_loss:0.880337, zinb_loss:0.478804, cluster_loss:0.160561\n",
      "Clustering   1109: ASW= 0.8236, DB= 0.2396, CH= 46150.8646\n",
      "Training epoch 1110, recon_loss:0.881082, zinb_loss:0.478861, cluster_loss:0.160342\n",
      "Clustering   1110: ASW= 0.8222, DB= 0.2386, CH= 47348.6323\n",
      "Training epoch 1111, recon_loss:0.880327, zinb_loss:0.478772, cluster_loss:0.160702\n",
      "Clustering   1111: ASW= 0.8236, DB= 0.2400, CH= 46139.7076\n",
      "Training epoch 1112, recon_loss:0.881194, zinb_loss:0.478897, cluster_loss:0.160479\n",
      "Clustering   1112: ASW= 0.8223, DB= 0.2383, CH= 47463.0066\n",
      "Training epoch 1113, recon_loss:0.880590, zinb_loss:0.478819, cluster_loss:0.160966\n",
      "Clustering   1113: ASW= 0.8236, DB= 0.2407, CH= 46102.8253\n",
      "Training epoch 1114, recon_loss:0.881641, zinb_loss:0.479037, cluster_loss:0.160724\n",
      "Clustering   1114: ASW= 0.8225, DB= 0.2373, CH= 47563.0910\n",
      "Training epoch 1115, recon_loss:0.880805, zinb_loss:0.478867, cluster_loss:0.161225\n",
      "Clustering   1115: ASW= 0.8232, DB= 0.2414, CH= 45985.7549\n",
      "Training epoch 1116, recon_loss:0.881896, zinb_loss:0.479125, cluster_loss:0.160883\n",
      "Clustering   1116: ASW= 0.8225, DB= 0.2371, CH= 47564.3720\n",
      "Training epoch 1117, recon_loss:0.880981, zinb_loss:0.478973, cluster_loss:0.161227\n",
      "Clustering   1117: ASW= 0.8231, DB= 0.2413, CH= 45942.5680\n",
      "Training epoch 1118, recon_loss:0.881894, zinb_loss:0.479175, cluster_loss:0.160743\n",
      "Clustering   1118: ASW= 0.8225, DB= 0.2372, CH= 47562.3066\n",
      "Training epoch 1119, recon_loss:0.881115, zinb_loss:0.479032, cluster_loss:0.161150\n",
      "Clustering   1119: ASW= 0.8230, DB= 0.2411, CH= 45999.0382\n",
      "Training epoch 1120, recon_loss:0.881855, zinb_loss:0.479182, cluster_loss:0.160660\n",
      "Clustering   1120: ASW= 0.8224, DB= 0.2372, CH= 47482.3123\n",
      "Training epoch 1121, recon_loss:0.881164, zinb_loss:0.479079, cluster_loss:0.161052\n",
      "Clustering   1121: ASW= 0.8229, DB= 0.2408, CH= 46077.3019\n",
      "Training epoch 1122, recon_loss:0.881727, zinb_loss:0.479176, cluster_loss:0.160619\n",
      "Clustering   1122: ASW= 0.8223, DB= 0.2373, CH= 47385.2958\n",
      "Training epoch 1123, recon_loss:0.881082, zinb_loss:0.479097, cluster_loss:0.160994\n",
      "Clustering   1123: ASW= 0.8228, DB= 0.2400, CH= 46147.0346\n",
      "Training epoch 1124, recon_loss:0.881464, zinb_loss:0.479136, cluster_loss:0.160661\n",
      "Clustering   1124: ASW= 0.8223, DB= 0.2374, CH= 47265.9943\n",
      "Training epoch 1125, recon_loss:0.880966, zinb_loss:0.479074, cluster_loss:0.160983\n",
      "Clustering   1125: ASW= 0.8228, DB= 0.2396, CH= 46214.8066\n",
      "Training epoch 1126, recon_loss:0.881230, zinb_loss:0.479086, cluster_loss:0.160721\n",
      "Clustering   1126: ASW= 0.8225, DB= 0.2372, CH= 47247.6272\n",
      "Training epoch 1127, recon_loss:0.880970, zinb_loss:0.479024, cluster_loss:0.161035\n",
      "Clustering   1127: ASW= 0.8228, DB= 0.2394, CH= 46298.7570\n",
      "Training epoch 1128, recon_loss:0.881045, zinb_loss:0.479016, cluster_loss:0.160830\n",
      "Clustering   1128: ASW= 0.8229, DB= 0.2369, CH= 47248.8160\n",
      "Training epoch 1129, recon_loss:0.881069, zinb_loss:0.478939, cluster_loss:0.161103\n",
      "Clustering   1129: ASW= 0.8227, DB= 0.2393, CH= 46349.9719\n",
      "Training epoch 1130, recon_loss:0.880887, zinb_loss:0.478957, cluster_loss:0.160883\n",
      "Clustering   1130: ASW= 0.8234, DB= 0.2366, CH= 47270.4956\n",
      "Training epoch 1131, recon_loss:0.880975, zinb_loss:0.478852, cluster_loss:0.161116\n",
      "Clustering   1131: ASW= 0.8225, DB= 0.2391, CH= 46365.4473\n",
      "Training epoch 1132, recon_loss:0.880650, zinb_loss:0.478918, cluster_loss:0.160865\n",
      "Clustering   1132: ASW= 0.8238, DB= 0.2366, CH= 47238.2403\n",
      "Training epoch 1133, recon_loss:0.880777, zinb_loss:0.478791, cluster_loss:0.161077\n",
      "Clustering   1133: ASW= 0.8223, DB= 0.2393, CH= 46346.0835\n",
      "Training epoch 1134, recon_loss:0.880505, zinb_loss:0.478925, cluster_loss:0.160799\n",
      "Clustering   1134: ASW= 0.8242, DB= 0.2366, CH= 47243.3657\n",
      "Training epoch 1135, recon_loss:0.880628, zinb_loss:0.478769, cluster_loss:0.161006\n",
      "Clustering   1135: ASW= 0.8221, DB= 0.2395, CH= 46321.0900\n",
      "Training epoch 1136, recon_loss:0.880620, zinb_loss:0.478969, cluster_loss:0.160714\n",
      "Clustering   1136: ASW= 0.8246, DB= 0.2366, CH= 47317.7749\n",
      "Training epoch 1137, recon_loss:0.880726, zinb_loss:0.478777, cluster_loss:0.160936\n",
      "Clustering   1137: ASW= 0.8219, DB= 0.2396, CH= 46299.7274\n",
      "Training epoch 1138, recon_loss:0.881071, zinb_loss:0.479025, cluster_loss:0.160677\n",
      "Clustering   1138: ASW= 0.8249, DB= 0.2369, CH= 47410.5603\n",
      "Training epoch 1139, recon_loss:0.881128, zinb_loss:0.478800, cluster_loss:0.160939\n",
      "Clustering   1139: ASW= 0.8217, DB= 0.2396, CH= 46309.5493\n",
      "Training epoch 1140, recon_loss:0.881789, zinb_loss:0.479102, cluster_loss:0.160741\n",
      "Clustering   1140: ASW= 0.8251, DB= 0.2368, CH= 47539.8168\n",
      "Training epoch 1141, recon_loss:0.881471, zinb_loss:0.478834, cluster_loss:0.160930\n",
      "Clustering   1141: ASW= 0.8216, DB= 0.2394, CH= 46353.9708\n",
      "Training epoch 1142, recon_loss:0.881866, zinb_loss:0.479087, cluster_loss:0.160784\n",
      "Clustering   1142: ASW= 0.8252, DB= 0.2373, CH= 47604.8378\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training epoch 1143, recon_loss:0.881412, zinb_loss:0.478861, cluster_loss:0.160794\n",
      "Clustering   1143: ASW= 0.8216, DB= 0.2391, CH= 46503.5583\n",
      "Training epoch 1144, recon_loss:0.881632, zinb_loss:0.479056, cluster_loss:0.160720\n",
      "Clustering   1144: ASW= 0.8253, DB= 0.2376, CH= 47663.7736\n",
      "Training epoch 1145, recon_loss:0.881164, zinb_loss:0.478872, cluster_loss:0.160641\n",
      "Clustering   1145: ASW= 0.8218, DB= 0.2388, CH= 46660.5166\n",
      "Training epoch 1146, recon_loss:0.881296, zinb_loss:0.479016, cluster_loss:0.160647\n",
      "Clustering   1146: ASW= 0.8252, DB= 0.2373, CH= 47722.6410\n",
      "Training epoch 1147, recon_loss:0.880789, zinb_loss:0.478866, cluster_loss:0.160522\n",
      "Clustering   1147: ASW= 0.8219, DB= 0.2385, CH= 46786.7283\n",
      "Training epoch 1148, recon_loss:0.880947, zinb_loss:0.478970, cluster_loss:0.160620\n",
      "Clustering   1148: ASW= 0.8251, DB= 0.2375, CH= 47754.9351\n",
      "Training epoch 1149, recon_loss:0.880501, zinb_loss:0.478857, cluster_loss:0.160484\n",
      "Clustering   1149: ASW= 0.8219, DB= 0.2382, CH= 46866.5401\n",
      "Training epoch 1150, recon_loss:0.880830, zinb_loss:0.478947, cluster_loss:0.160687\n",
      "Clustering   1150: ASW= 0.8250, DB= 0.2377, CH= 47737.2190\n",
      "Training epoch 1151, recon_loss:0.880469, zinb_loss:0.478872, cluster_loss:0.160530\n",
      "Clustering   1151: ASW= 0.8219, DB= 0.2380, CH= 46942.8415\n",
      "Training epoch 1152, recon_loss:0.880882, zinb_loss:0.478929, cluster_loss:0.160794\n",
      "Clustering   1152: ASW= 0.8248, DB= 0.2381, CH= 47635.9562\n",
      "Training epoch 1153, recon_loss:0.880568, zinb_loss:0.478895, cluster_loss:0.160618\n",
      "Clustering   1153: ASW= 0.8219, DB= 0.2378, CH= 46972.0114\n",
      "Training epoch 1154, recon_loss:0.880941, zinb_loss:0.478923, cluster_loss:0.160902\n",
      "Clustering   1154: ASW= 0.8245, DB= 0.2384, CH= 47458.7140\n",
      "Training epoch 1155, recon_loss:0.880614, zinb_loss:0.478921, cluster_loss:0.160625\n",
      "Clustering   1155: ASW= 0.8220, DB= 0.2375, CH= 47038.2162\n",
      "Training epoch 1156, recon_loss:0.880781, zinb_loss:0.478874, cluster_loss:0.160918\n",
      "Clustering   1156: ASW= 0.8242, DB= 0.2387, CH= 47290.4096\n",
      "Training epoch 1157, recon_loss:0.880462, zinb_loss:0.478916, cluster_loss:0.160558\n",
      "Clustering   1157: ASW= 0.8221, DB= 0.2371, CH= 47122.1185\n",
      "Training epoch 1158, recon_loss:0.880546, zinb_loss:0.478811, cluster_loss:0.160855\n",
      "Clustering   1158: ASW= 0.8240, DB= 0.2389, CH= 47201.8624\n",
      "Training epoch 1159, recon_loss:0.880275, zinb_loss:0.478893, cluster_loss:0.160502\n",
      "Clustering   1159: ASW= 0.8224, DB= 0.2369, CH= 47240.4991\n",
      "Training epoch 1160, recon_loss:0.880369, zinb_loss:0.478758, cluster_loss:0.160804\n",
      "Clustering   1160: ASW= 0.8238, DB= 0.2392, CH= 47175.1451\n",
      "Training epoch 1161, recon_loss:0.880256, zinb_loss:0.478876, cluster_loss:0.160510\n",
      "Clustering   1161: ASW= 0.8228, DB= 0.2369, CH= 47343.2728\n",
      "Training epoch 1162, recon_loss:0.880417, zinb_loss:0.478722, cluster_loss:0.160816\n",
      "Clustering   1162: ASW= 0.8235, DB= 0.2394, CH= 47173.2044\n",
      "Training epoch 1163, recon_loss:0.880406, zinb_loss:0.478870, cluster_loss:0.160590\n",
      "Clustering   1163: ASW= 0.8232, DB= 0.2365, CH= 47434.1629\n",
      "Training epoch 1164, recon_loss:0.880612, zinb_loss:0.478705, cluster_loss:0.160891\n",
      "Clustering   1164: ASW= 0.8233, DB= 0.2396, CH= 47199.6866\n",
      "Training epoch 1165, recon_loss:0.880650, zinb_loss:0.478872, cluster_loss:0.160714\n",
      "Clustering   1165: ASW= 0.8235, DB= 0.2364, CH= 47484.1552\n",
      "Training epoch 1166, recon_loss:0.880899, zinb_loss:0.478704, cluster_loss:0.161000\n",
      "Clustering   1166: ASW= 0.8231, DB= 0.2397, CH= 47235.9800\n",
      "Training epoch 1167, recon_loss:0.880797, zinb_loss:0.478882, cluster_loss:0.160826\n",
      "Clustering   1167: ASW= 0.8239, DB= 0.2362, CH= 47477.9972\n",
      "Training epoch 1168, recon_loss:0.881003, zinb_loss:0.478690, cluster_loss:0.161060\n",
      "Clustering   1168: ASW= 0.8230, DB= 0.2398, CH= 47318.7697\n",
      "Training epoch 1169, recon_loss:0.880657, zinb_loss:0.478868, cluster_loss:0.160861\n",
      "Clustering   1169: ASW= 0.8242, DB= 0.2361, CH= 47357.0149\n",
      "Training epoch 1170, recon_loss:0.880802, zinb_loss:0.478646, cluster_loss:0.161002\n",
      "Clustering   1170: ASW= 0.8229, DB= 0.2399, CH= 47380.1191\n",
      "Training epoch 1171, recon_loss:0.880236, zinb_loss:0.478824, cluster_loss:0.160783\n",
      "Clustering   1171: ASW= 0.8244, DB= 0.2359, CH= 47132.4651\n",
      "Training epoch 1172, recon_loss:0.880260, zinb_loss:0.478566, cluster_loss:0.160805\n",
      "Clustering   1172: ASW= 0.8229, DB= 0.2397, CH= 47393.7160\n",
      "Training epoch 1173, recon_loss:0.879753, zinb_loss:0.478769, cluster_loss:0.160617\n",
      "Clustering   1173: ASW= 0.8245, DB= 0.2357, CH= 46970.9149\n",
      "Training epoch 1174, recon_loss:0.879761, zinb_loss:0.478512, cluster_loss:0.160611\n",
      "Clustering   1174: ASW= 0.8229, DB= 0.2397, CH= 47365.2253\n",
      "Training epoch 1175, recon_loss:0.879553, zinb_loss:0.478763, cluster_loss:0.160509\n",
      "Clustering   1175: ASW= 0.8245, DB= 0.2354, CH= 46921.7427\n",
      "Training epoch 1176, recon_loss:0.879514, zinb_loss:0.478520, cluster_loss:0.160523\n",
      "Clustering   1176: ASW= 0.8229, DB= 0.2398, CH= 47288.0951\n",
      "Training epoch 1177, recon_loss:0.879613, zinb_loss:0.478814, cluster_loss:0.160460\n",
      "Clustering   1177: ASW= 0.8246, DB= 0.2350, CH= 47006.5664\n",
      "Training epoch 1178, recon_loss:0.879530, zinb_loss:0.478577, cluster_loss:0.160561\n",
      "Clustering   1178: ASW= 0.8228, DB= 0.2399, CH= 47162.4388\n",
      "Training epoch 1179, recon_loss:0.879772, zinb_loss:0.478873, cluster_loss:0.160423\n",
      "Clustering   1179: ASW= 0.8246, DB= 0.2346, CH= 47126.4608\n",
      "Training epoch 1180, recon_loss:0.879570, zinb_loss:0.478624, cluster_loss:0.160608\n",
      "Clustering   1180: ASW= 0.8228, DB= 0.2401, CH= 47035.5807\n",
      "Training epoch 1181, recon_loss:0.879893, zinb_loss:0.478895, cluster_loss:0.160397\n",
      "Clustering   1181: ASW= 0.8244, DB= 0.2344, CH= 47216.7435\n",
      "Training epoch 1182, recon_loss:0.879595, zinb_loss:0.478641, cluster_loss:0.160623\n",
      "Clustering   1182: ASW= 0.8226, DB= 0.2402, CH= 46890.2421\n",
      "Training epoch 1183, recon_loss:0.880052, zinb_loss:0.478900, cluster_loss:0.160411\n",
      "Clustering   1183: ASW= 0.8241, DB= 0.2341, CH= 47252.5038\n",
      "Training epoch 1184, recon_loss:0.879707, zinb_loss:0.478648, cluster_loss:0.160618\n",
      "Clustering   1184: ASW= 0.8226, DB= 0.2402, CH= 46812.2518\n",
      "Training epoch 1185, recon_loss:0.880253, zinb_loss:0.478895, cluster_loss:0.160400\n",
      "Clustering   1185: ASW= 0.8241, DB= 0.2342, CH= 47353.9102\n",
      "Training epoch 1186, recon_loss:0.879906, zinb_loss:0.478649, cluster_loss:0.160583\n",
      "Clustering   1186: ASW= 0.8227, DB= 0.2399, CH= 46839.8885\n",
      "Training epoch 1187, recon_loss:0.880494, zinb_loss:0.478887, cluster_loss:0.160381\n",
      "Clustering   1187: ASW= 0.8242, DB= 0.2345, CH= 47494.9396\n",
      "Training epoch 1188, recon_loss:0.880236, zinb_loss:0.478661, cluster_loss:0.160581\n",
      "Clustering   1188: ASW= 0.8230, DB= 0.2396, CH= 46927.9208\n",
      "Training epoch 1189, recon_loss:0.880783, zinb_loss:0.478883, cluster_loss:0.160403\n",
      "Clustering   1189: ASW= 0.8243, DB= 0.2348, CH= 47564.9870\n",
      "Training epoch 1190, recon_loss:0.880479, zinb_loss:0.478687, cluster_loss:0.160569\n",
      "Clustering   1190: ASW= 0.8232, DB= 0.2390, CH= 47048.3288\n",
      "Training epoch 1191, recon_loss:0.880660, zinb_loss:0.478851, cluster_loss:0.160320\n",
      "Clustering   1191: ASW= 0.8243, DB= 0.2353, CH= 47644.1058\n",
      "Training epoch 1192, recon_loss:0.880936, zinb_loss:0.478774, cluster_loss:0.160568\n",
      "Clustering   1192: ASW= 0.8236, DB= 0.2382, CH= 47249.7316\n",
      "Training epoch 1193, recon_loss:0.880518, zinb_loss:0.478763, cluster_loss:0.160240\n",
      "Clustering   1193: ASW= 0.8238, DB= 0.2365, CH= 47576.5463\n",
      "Training epoch 1194, recon_loss:0.880743, zinb_loss:0.478768, cluster_loss:0.160650\n",
      "Clustering   1194: ASW= 0.8234, DB= 0.2378, CH= 47299.7292\n",
      "Training epoch 1195, recon_loss:0.881405, zinb_loss:0.478817, cluster_loss:0.160813\n",
      "Clustering   1195: ASW= 0.8238, DB= 0.2374, CH= 47294.8501\n",
      "Training epoch 1196, recon_loss:0.881738, zinb_loss:0.478822, cluster_loss:0.160951\n",
      "Clustering   1196: ASW= 0.8236, DB= 0.2370, CH= 47542.2205\n",
      "Training epoch 1197, recon_loss:0.881616, zinb_loss:0.478788, cluster_loss:0.161188\n",
      "Clustering   1197: ASW= 0.8237, DB= 0.2383, CH= 46989.9168\n",
      "Training epoch 1198, recon_loss:0.881667, zinb_loss:0.478793, cluster_loss:0.160991\n",
      "Clustering   1198: ASW= 0.8242, DB= 0.2360, CH= 47743.2987\n",
      "Training epoch 1199, recon_loss:0.880867, zinb_loss:0.478711, cluster_loss:0.161056\n",
      "Clustering   1199: ASW= 0.8238, DB= 0.2385, CH= 46948.0838\n",
      "Training epoch 1200, recon_loss:0.881164, zinb_loss:0.478742, cluster_loss:0.160834\n",
      "Clustering   1200: ASW= 0.8244, DB= 0.2352, CH= 47927.3534\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training epoch 1201, recon_loss:0.880280, zinb_loss:0.478636, cluster_loss:0.160847\n",
      "Clustering   1201: ASW= 0.8239, DB= 0.2386, CH= 46957.1404\n",
      "Training epoch 1202, recon_loss:0.880766, zinb_loss:0.478716, cluster_loss:0.160705\n",
      "Clustering   1202: ASW= 0.8245, DB= 0.2344, CH= 48029.9994\n",
      "Training epoch 1203, recon_loss:0.879973, zinb_loss:0.478597, cluster_loss:0.160693\n",
      "Clustering   1203: ASW= 0.8240, DB= 0.2386, CH= 47003.8757\n",
      "Training epoch 1204, recon_loss:0.880479, zinb_loss:0.478695, cluster_loss:0.160622\n",
      "Clustering   1204: ASW= 0.8243, DB= 0.2344, CH= 48087.9316\n",
      "Training epoch 1205, recon_loss:0.879823, zinb_loss:0.478581, cluster_loss:0.160624\n",
      "Clustering   1205: ASW= 0.8240, DB= 0.2386, CH= 47036.8224\n",
      "Training epoch 1206, recon_loss:0.880288, zinb_loss:0.478691, cluster_loss:0.160581\n",
      "Clustering   1206: ASW= 0.8242, DB= 0.2345, CH= 48121.0510\n",
      "Training epoch 1207, recon_loss:0.879755, zinb_loss:0.478593, cluster_loss:0.160577\n",
      "Clustering   1207: ASW= 0.8239, DB= 0.2391, CH= 47077.6242\n",
      "Training epoch 1208, recon_loss:0.880146, zinb_loss:0.478695, cluster_loss:0.160528\n",
      "Clustering   1208: ASW= 0.8241, DB= 0.2346, CH= 48165.9418\n",
      "Training epoch 1209, recon_loss:0.879748, zinb_loss:0.478619, cluster_loss:0.160529\n",
      "Clustering   1209: ASW= 0.8238, DB= 0.2391, CH= 47136.3378\n",
      "Training epoch 1210, recon_loss:0.880074, zinb_loss:0.478715, cluster_loss:0.160459\n",
      "Clustering   1210: ASW= 0.8242, DB= 0.2349, CH= 48215.2331\n",
      "Training epoch 1211, recon_loss:0.879865, zinb_loss:0.478662, cluster_loss:0.160466\n",
      "Clustering   1211: ASW= 0.8238, DB= 0.2389, CH= 47247.9810\n",
      "Training epoch 1212, recon_loss:0.880145, zinb_loss:0.478753, cluster_loss:0.160393\n",
      "Clustering   1212: ASW= 0.8243, DB= 0.2349, CH= 48279.0440\n",
      "Training epoch 1213, recon_loss:0.880215, zinb_loss:0.478726, cluster_loss:0.160430\n",
      "Clustering   1213: ASW= 0.8238, DB= 0.2387, CH= 47396.7441\n",
      "Training epoch 1214, recon_loss:0.880469, zinb_loss:0.478820, cluster_loss:0.160356\n",
      "Clustering   1214: ASW= 0.8245, DB= 0.2352, CH= 48306.6814\n",
      "Training epoch 1215, recon_loss:0.880823, zinb_loss:0.478813, cluster_loss:0.160440\n",
      "Clustering   1215: ASW= 0.8238, DB= 0.2387, CH= 47555.1722\n",
      "Training epoch 1216, recon_loss:0.880968, zinb_loss:0.478905, cluster_loss:0.160382\n",
      "Clustering   1216: ASW= 0.8247, DB= 0.2356, CH= 48258.3163\n",
      "Training epoch 1217, recon_loss:0.881429, zinb_loss:0.478884, cluster_loss:0.160500\n",
      "Clustering   1217: ASW= 0.8238, DB= 0.2386, CH= 47724.5579\n",
      "Training epoch 1218, recon_loss:0.881189, zinb_loss:0.478936, cluster_loss:0.160436\n",
      "Clustering   1218: ASW= 0.8248, DB= 0.2359, CH= 48066.6917\n",
      "Training epoch 1219, recon_loss:0.881579, zinb_loss:0.478883, cluster_loss:0.160561\n",
      "Clustering   1219: ASW= 0.8239, DB= 0.2378, CH= 47823.3365\n",
      "Training epoch 1220, recon_loss:0.880971, zinb_loss:0.478890, cluster_loss:0.160432\n",
      "Clustering   1220: ASW= 0.8249, DB= 0.2361, CH= 47857.8296\n",
      "Training epoch 1221, recon_loss:0.881254, zinb_loss:0.478812, cluster_loss:0.160515\n",
      "Clustering   1221: ASW= 0.8239, DB= 0.2370, CH= 47866.0513\n",
      "Training epoch 1222, recon_loss:0.880626, zinb_loss:0.478828, cluster_loss:0.160320\n",
      "Clustering   1222: ASW= 0.8252, DB= 0.2361, CH= 47865.8064\n",
      "Training epoch 1223, recon_loss:0.880821, zinb_loss:0.478733, cluster_loss:0.160388\n",
      "Clustering   1223: ASW= 0.8239, DB= 0.2369, CH= 47888.2204\n",
      "Training epoch 1224, recon_loss:0.880434, zinb_loss:0.478772, cluster_loss:0.160219\n",
      "Clustering   1224: ASW= 0.8255, DB= 0.2361, CH= 47993.8028\n",
      "Training epoch 1225, recon_loss:0.880610, zinb_loss:0.478675, cluster_loss:0.160348\n",
      "Clustering   1225: ASW= 0.8238, DB= 0.2370, CH= 47883.3981\n",
      "Training epoch 1226, recon_loss:0.880463, zinb_loss:0.478744, cluster_loss:0.160216\n",
      "Clustering   1226: ASW= 0.8258, DB= 0.2361, CH= 48145.7068\n",
      "Training epoch 1227, recon_loss:0.880510, zinb_loss:0.478627, cluster_loss:0.160413\n",
      "Clustering   1227: ASW= 0.8235, DB= 0.2372, CH= 47821.1602\n",
      "Training epoch 1228, recon_loss:0.880513, zinb_loss:0.478732, cluster_loss:0.160295\n",
      "Clustering   1228: ASW= 0.8259, DB= 0.2372, CH= 48270.5452\n",
      "Training epoch 1229, recon_loss:0.880366, zinb_loss:0.478586, cluster_loss:0.160565\n",
      "Clustering   1229: ASW= 0.8233, DB= 0.2374, CH= 47676.5778\n",
      "Training epoch 1230, recon_loss:0.880372, zinb_loss:0.478718, cluster_loss:0.160416\n",
      "Clustering   1230: ASW= 0.8259, DB= 0.2372, CH= 48352.5257\n",
      "Training epoch 1231, recon_loss:0.880095, zinb_loss:0.478556, cluster_loss:0.160706\n",
      "Clustering   1231: ASW= 0.8231, DB= 0.2376, CH= 47493.0450\n",
      "Training epoch 1232, recon_loss:0.880113, zinb_loss:0.478702, cluster_loss:0.160562\n",
      "Clustering   1232: ASW= 0.8257, DB= 0.2372, CH= 48336.8705\n",
      "Training epoch 1233, recon_loss:0.879785, zinb_loss:0.478525, cluster_loss:0.160872\n",
      "Clustering   1233: ASW= 0.8229, DB= 0.2376, CH= 47274.9189\n",
      "Training epoch 1234, recon_loss:0.879905, zinb_loss:0.478690, cluster_loss:0.160741\n",
      "Clustering   1234: ASW= 0.8254, DB= 0.2373, CH= 48266.2666\n",
      "Training epoch 1235, recon_loss:0.879563, zinb_loss:0.478495, cluster_loss:0.160986\n",
      "Clustering   1235: ASW= 0.8229, DB= 0.2374, CH= 47152.7408\n",
      "Training epoch 1236, recon_loss:0.879774, zinb_loss:0.478675, cluster_loss:0.160806\n",
      "Clustering   1236: ASW= 0.8253, DB= 0.2372, CH= 48293.2738\n",
      "Training epoch 1237, recon_loss:0.879399, zinb_loss:0.478470, cluster_loss:0.160950\n",
      "Clustering   1237: ASW= 0.8232, DB= 0.2373, CH= 47200.2243\n",
      "Training epoch 1238, recon_loss:0.879614, zinb_loss:0.478661, cluster_loss:0.160667\n",
      "Clustering   1238: ASW= 0.8254, DB= 0.2370, CH= 48447.3265\n",
      "Training epoch 1239, recon_loss:0.879311, zinb_loss:0.478463, cluster_loss:0.160799\n",
      "Clustering   1239: ASW= 0.8236, DB= 0.2373, CH= 47343.0332\n",
      "Training epoch 1240, recon_loss:0.879547, zinb_loss:0.478659, cluster_loss:0.160498\n",
      "Clustering   1240: ASW= 0.8256, DB= 0.2366, CH= 48549.8096\n",
      "Training epoch 1241, recon_loss:0.879396, zinb_loss:0.478473, cluster_loss:0.160670\n",
      "Clustering   1241: ASW= 0.8238, DB= 0.2372, CH= 47484.5219\n",
      "Training epoch 1242, recon_loss:0.879652, zinb_loss:0.478675, cluster_loss:0.160424\n",
      "Clustering   1242: ASW= 0.8257, DB= 0.2356, CH= 48576.7856\n",
      "Training epoch 1243, recon_loss:0.879611, zinb_loss:0.478492, cluster_loss:0.160625\n",
      "Clustering   1243: ASW= 0.8239, DB= 0.2374, CH= 47582.7841\n",
      "Training epoch 1244, recon_loss:0.879841, zinb_loss:0.478697, cluster_loss:0.160416\n",
      "Clustering   1244: ASW= 0.8257, DB= 0.2351, CH= 48514.4036\n",
      "Training epoch 1245, recon_loss:0.879761, zinb_loss:0.478500, cluster_loss:0.160609\n",
      "Clustering   1245: ASW= 0.8239, DB= 0.2369, CH= 47624.8942\n",
      "Training epoch 1246, recon_loss:0.879907, zinb_loss:0.478698, cluster_loss:0.160394\n",
      "Clustering   1246: ASW= 0.8256, DB= 0.2348, CH= 48361.4774\n",
      "Training epoch 1247, recon_loss:0.879702, zinb_loss:0.478481, cluster_loss:0.160557\n",
      "Clustering   1247: ASW= 0.8239, DB= 0.2371, CH= 47677.5488\n",
      "Training epoch 1248, recon_loss:0.879778, zinb_loss:0.478669, cluster_loss:0.160343\n",
      "Clustering   1248: ASW= 0.8254, DB= 0.2344, CH= 48144.8753\n",
      "Training epoch 1249, recon_loss:0.879470, zinb_loss:0.478452, cluster_loss:0.160479\n",
      "Clustering   1249: ASW= 0.8240, DB= 0.2372, CH= 47745.4799\n",
      "Training epoch 1250, recon_loss:0.879507, zinb_loss:0.478629, cluster_loss:0.160275\n",
      "Clustering   1250: ASW= 0.8252, DB= 0.2343, CH= 47981.4335\n",
      "Training epoch 1251, recon_loss:0.879156, zinb_loss:0.478426, cluster_loss:0.160384\n",
      "Clustering   1251: ASW= 0.8242, DB= 0.2372, CH= 47837.3217\n",
      "Training epoch 1252, recon_loss:0.879140, zinb_loss:0.478593, cluster_loss:0.160172\n",
      "Clustering   1252: ASW= 0.8251, DB= 0.2336, CH= 47940.9815\n",
      "Training epoch 1253, recon_loss:0.878897, zinb_loss:0.478414, cluster_loss:0.160312\n",
      "Clustering   1253: ASW= 0.8244, DB= 0.2371, CH= 47939.3841\n",
      "Training epoch 1254, recon_loss:0.878903, zinb_loss:0.478572, cluster_loss:0.160109\n",
      "Clustering   1254: ASW= 0.8250, DB= 0.2335, CH= 47928.0837\n",
      "Training epoch 1255, recon_loss:0.878813, zinb_loss:0.478428, cluster_loss:0.160298\n",
      "Clustering   1255: ASW= 0.8245, DB= 0.2372, CH= 48027.6940\n",
      "Training epoch 1256, recon_loss:0.878892, zinb_loss:0.478579, cluster_loss:0.160100\n",
      "Clustering   1256: ASW= 0.8249, DB= 0.2334, CH= 47879.3458\n",
      "Training epoch 1257, recon_loss:0.878948, zinb_loss:0.478462, cluster_loss:0.160323\n",
      "Clustering   1257: ASW= 0.8246, DB= 0.2382, CH= 48094.9462\n",
      "Training epoch 1258, recon_loss:0.879058, zinb_loss:0.478589, cluster_loss:0.160140\n",
      "Clustering   1258: ASW= 0.8247, DB= 0.2334, CH= 47852.5900\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training epoch 1259, recon_loss:0.879292, zinb_loss:0.478518, cluster_loss:0.160410\n",
      "Clustering   1259: ASW= 0.8247, DB= 0.2377, CH= 48141.7664\n",
      "Training epoch 1260, recon_loss:0.879384, zinb_loss:0.478626, cluster_loss:0.160202\n",
      "Clustering   1260: ASW= 0.8246, DB= 0.2335, CH= 47805.7758\n",
      "Training epoch 1261, recon_loss:0.879637, zinb_loss:0.478581, cluster_loss:0.160459\n",
      "Clustering   1261: ASW= 0.8248, DB= 0.2390, CH= 48237.8276\n",
      "Training epoch 1262, recon_loss:0.879597, zinb_loss:0.478638, cluster_loss:0.160238\n",
      "Clustering   1262: ASW= 0.8244, DB= 0.2338, CH= 47807.2051\n",
      "Training epoch 1263, recon_loss:0.879882, zinb_loss:0.478624, cluster_loss:0.160506\n",
      "Clustering   1263: ASW= 0.8248, DB= 0.2389, CH= 48285.4290\n",
      "Training epoch 1264, recon_loss:0.879837, zinb_loss:0.478672, cluster_loss:0.160247\n",
      "Clustering   1264: ASW= 0.8244, DB= 0.2336, CH= 47889.0698\n",
      "Training epoch 1265, recon_loss:0.879963, zinb_loss:0.478660, cluster_loss:0.160482\n",
      "Clustering   1265: ASW= 0.8250, DB= 0.2387, CH= 48371.8369\n",
      "Training epoch 1266, recon_loss:0.879870, zinb_loss:0.478697, cluster_loss:0.160236\n",
      "Clustering   1266: ASW= 0.8244, DB= 0.2335, CH= 48070.4233\n",
      "Training epoch 1267, recon_loss:0.879963, zinb_loss:0.478708, cluster_loss:0.160527\n",
      "Clustering   1267: ASW= 0.8253, DB= 0.2384, CH= 48410.9999\n",
      "Training epoch 1268, recon_loss:0.879910, zinb_loss:0.478734, cluster_loss:0.160311\n",
      "Clustering   1268: ASW= 0.8243, DB= 0.2340, CH= 48312.5576\n",
      "Training epoch 1269, recon_loss:0.879883, zinb_loss:0.478755, cluster_loss:0.160664\n",
      "Clustering   1269: ASW= 0.8255, DB= 0.2382, CH= 48375.1510\n",
      "Training epoch 1270, recon_loss:0.879811, zinb_loss:0.478742, cluster_loss:0.160372\n",
      "Clustering   1270: ASW= 0.8242, DB= 0.2341, CH= 48480.0817\n",
      "Training epoch 1271, recon_loss:0.879489, zinb_loss:0.478740, cluster_loss:0.160680\n",
      "Clustering   1271: ASW= 0.8258, DB= 0.2382, CH= 48341.9828\n",
      "Training epoch 1272, recon_loss:0.879466, zinb_loss:0.478702, cluster_loss:0.160290\n",
      "Clustering   1272: ASW= 0.8242, DB= 0.2345, CH= 48536.7004\n",
      "Training epoch 1273, recon_loss:0.879000, zinb_loss:0.478689, cluster_loss:0.160521\n",
      "Clustering   1273: ASW= 0.8261, DB= 0.2380, CH= 48394.4114\n",
      "Training epoch 1274, recon_loss:0.879138, zinb_loss:0.478613, cluster_loss:0.160170\n",
      "Clustering   1274: ASW= 0.8242, DB= 0.2348, CH= 48577.2974\n",
      "Training epoch 1275, recon_loss:0.878869, zinb_loss:0.478614, cluster_loss:0.160374\n",
      "Clustering   1275: ASW= 0.8263, DB= 0.2379, CH= 48461.0304\n",
      "Training epoch 1276, recon_loss:0.879258, zinb_loss:0.478564, cluster_loss:0.160130\n",
      "Clustering   1276: ASW= 0.8242, DB= 0.2348, CH= 48615.9688\n",
      "Training epoch 1277, recon_loss:0.879190, zinb_loss:0.478595, cluster_loss:0.160324\n",
      "Clustering   1277: ASW= 0.8264, DB= 0.2378, CH= 48514.4083\n",
      "Training epoch 1278, recon_loss:0.879767, zinb_loss:0.478526, cluster_loss:0.160181\n",
      "Clustering   1278: ASW= 0.8242, DB= 0.2352, CH= 48676.3907\n",
      "Training epoch 1279, recon_loss:0.879676, zinb_loss:0.478553, cluster_loss:0.160363\n",
      "Clustering   1279: ASW= 0.8265, DB= 0.2375, CH= 48489.9912\n",
      "Training epoch 1280, recon_loss:0.880212, zinb_loss:0.478489, cluster_loss:0.160260\n",
      "Clustering   1280: ASW= 0.8242, DB= 0.2352, CH= 48723.3606\n",
      "Training epoch 1281, recon_loss:0.879900, zinb_loss:0.478530, cluster_loss:0.160361\n",
      "Clustering   1281: ASW= 0.8264, DB= 0.2375, CH= 48420.4681\n",
      "Training epoch 1282, recon_loss:0.880335, zinb_loss:0.478451, cluster_loss:0.160264\n",
      "Clustering   1282: ASW= 0.8242, DB= 0.2355, CH= 48818.3173\n",
      "Training epoch 1283, recon_loss:0.879859, zinb_loss:0.478473, cluster_loss:0.160379\n",
      "Clustering   1283: ASW= 0.8263, DB= 0.2377, CH= 48255.1107\n",
      "Training epoch 1284, recon_loss:0.880193, zinb_loss:0.478408, cluster_loss:0.160245\n",
      "Clustering   1284: ASW= 0.8241, DB= 0.2355, CH= 48921.7115\n",
      "Training epoch 1285, recon_loss:0.879698, zinb_loss:0.478456, cluster_loss:0.160394\n",
      "Clustering   1285: ASW= 0.8261, DB= 0.2377, CH= 48028.6916\n",
      "Training epoch 1286, recon_loss:0.880081, zinb_loss:0.478416, cluster_loss:0.160215\n",
      "Clustering   1286: ASW= 0.8242, DB= 0.2354, CH= 49078.3951\n",
      "Training epoch 1287, recon_loss:0.879602, zinb_loss:0.478438, cluster_loss:0.160531\n",
      "Clustering   1287: ASW= 0.8258, DB= 0.2378, CH= 47694.7980\n",
      "Training epoch 1288, recon_loss:0.880106, zinb_loss:0.478433, cluster_loss:0.160281\n",
      "Clustering   1288: ASW= 0.8241, DB= 0.2351, CH= 49181.0274\n",
      "Training epoch 1289, recon_loss:0.879740, zinb_loss:0.478480, cluster_loss:0.160733\n",
      "Clustering   1289: ASW= 0.8255, DB= 0.2378, CH= 47290.8068\n",
      "Training epoch 1290, recon_loss:0.880379, zinb_loss:0.478512, cluster_loss:0.160380\n",
      "Clustering   1290: ASW= 0.8241, DB= 0.2350, CH= 49294.5640\n",
      "Training epoch 1291, recon_loss:0.879833, zinb_loss:0.478492, cluster_loss:0.160849\n",
      "Clustering   1291: ASW= 0.8250, DB= 0.2379, CH= 46997.9553\n",
      "Training epoch 1292, recon_loss:0.880310, zinb_loss:0.478501, cluster_loss:0.160516\n",
      "Clustering   1292: ASW= 0.8238, DB= 0.2350, CH= 49213.1278\n",
      "Training epoch 1293, recon_loss:0.880008, zinb_loss:0.478530, cluster_loss:0.161094\n",
      "Clustering   1293: ASW= 0.8249, DB= 0.2376, CH= 46837.6785\n",
      "Training epoch 1294, recon_loss:0.880221, zinb_loss:0.478514, cluster_loss:0.160448\n",
      "Clustering   1294: ASW= 0.8239, DB= 0.2351, CH= 49130.9385\n",
      "Training epoch 1295, recon_loss:0.879370, zinb_loss:0.478488, cluster_loss:0.160665\n",
      "Clustering   1295: ASW= 0.8253, DB= 0.2366, CH= 47148.7321\n",
      "Training epoch 1296, recon_loss:0.879472, zinb_loss:0.478478, cluster_loss:0.160016\n",
      "Clustering   1296: ASW= 0.8245, DB= 0.2349, CH= 49298.9176\n",
      "Training epoch 1297, recon_loss:0.878830, zinb_loss:0.478453, cluster_loss:0.160241\n",
      "Clustering   1297: ASW= 0.8258, DB= 0.2356, CH= 47536.8491\n",
      "Training epoch 1298, recon_loss:0.879112, zinb_loss:0.478473, cluster_loss:0.159750\n",
      "Clustering   1298: ASW= 0.8249, DB= 0.2349, CH= 49464.1305\n",
      "Training epoch 1299, recon_loss:0.878894, zinb_loss:0.478455, cluster_loss:0.160110\n",
      "Clustering   1299: ASW= 0.8260, DB= 0.2352, CH= 47709.2776\n",
      "Training epoch 1300, recon_loss:0.879413, zinb_loss:0.478507, cluster_loss:0.159723\n",
      "Clustering   1300: ASW= 0.8249, DB= 0.2349, CH= 49542.1449\n",
      "Training epoch 1301, recon_loss:0.879475, zinb_loss:0.478492, cluster_loss:0.160152\n",
      "Clustering   1301: ASW= 0.8262, DB= 0.2351, CH= 47737.7234\n",
      "Training epoch 1302, recon_loss:0.880061, zinb_loss:0.478554, cluster_loss:0.159824\n",
      "Clustering   1302: ASW= 0.8248, DB= 0.2352, CH= 49504.3802\n",
      "Training epoch 1303, recon_loss:0.880289, zinb_loss:0.478569, cluster_loss:0.160234\n",
      "Clustering   1303: ASW= 0.8263, DB= 0.2347, CH= 47743.9265\n",
      "Training epoch 1304, recon_loss:0.880594, zinb_loss:0.478592, cluster_loss:0.159900\n",
      "Clustering   1304: ASW= 0.8247, DB= 0.2356, CH= 49381.1080\n",
      "Training epoch 1305, recon_loss:0.880640, zinb_loss:0.478608, cluster_loss:0.160225\n",
      "Clustering   1305: ASW= 0.8265, DB= 0.2343, CH= 47862.9574\n",
      "Training epoch 1306, recon_loss:0.880634, zinb_loss:0.478588, cluster_loss:0.159897\n",
      "Clustering   1306: ASW= 0.8245, DB= 0.2362, CH= 49226.3354\n",
      "Training epoch 1307, recon_loss:0.880615, zinb_loss:0.478637, cluster_loss:0.160116\n",
      "Clustering   1307: ASW= 0.8267, DB= 0.2337, CH= 48152.7405\n",
      "Training epoch 1308, recon_loss:0.880470, zinb_loss:0.478564, cluster_loss:0.159861\n",
      "Clustering   1308: ASW= 0.8245, DB= 0.2366, CH= 49065.1903\n",
      "Training epoch 1309, recon_loss:0.880338, zinb_loss:0.478632, cluster_loss:0.160041\n",
      "Clustering   1309: ASW= 0.8267, DB= 0.2333, CH= 48430.3579\n",
      "Training epoch 1310, recon_loss:0.880298, zinb_loss:0.478550, cluster_loss:0.159855\n",
      "Clustering   1310: ASW= 0.8244, DB= 0.2369, CH= 48881.6845\n",
      "Training epoch 1311, recon_loss:0.880088, zinb_loss:0.478631, cluster_loss:0.160040\n",
      "Clustering   1311: ASW= 0.8267, DB= 0.2331, CH= 48630.3848\n",
      "Training epoch 1312, recon_loss:0.880253, zinb_loss:0.478567, cluster_loss:0.159915\n",
      "Clustering   1312: ASW= 0.8242, DB= 0.2373, CH= 48650.2262\n",
      "Training epoch 1313, recon_loss:0.880009, zinb_loss:0.478665, cluster_loss:0.160081\n",
      "Clustering   1313: ASW= 0.8267, DB= 0.2329, CH= 48813.6304\n",
      "Training epoch 1314, recon_loss:0.880280, zinb_loss:0.478597, cluster_loss:0.160016\n",
      "Clustering   1314: ASW= 0.8239, DB= 0.2375, CH= 48375.6091\n",
      "Training epoch 1315, recon_loss:0.879941, zinb_loss:0.478693, cluster_loss:0.160076\n",
      "Clustering   1315: ASW= 0.8267, DB= 0.2328, CH= 49043.1863\n",
      "Training epoch 1316, recon_loss:0.880089, zinb_loss:0.478583, cluster_loss:0.160069\n",
      "Clustering   1316: ASW= 0.8238, DB= 0.2377, CH= 48160.2578\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training epoch 1317, recon_loss:0.879719, zinb_loss:0.478671, cluster_loss:0.160013\n",
      "Clustering   1317: ASW= 0.8267, DB= 0.2329, CH= 49278.1978\n",
      "Training epoch 1318, recon_loss:0.879735, zinb_loss:0.478534, cluster_loss:0.160070\n",
      "Clustering   1318: ASW= 0.8239, DB= 0.2375, CH= 48066.0604\n",
      "Training epoch 1319, recon_loss:0.879565, zinb_loss:0.478641, cluster_loss:0.159949\n",
      "Clustering   1319: ASW= 0.8268, DB= 0.2330, CH= 49536.0034\n",
      "Training epoch 1320, recon_loss:0.879565, zinb_loss:0.478476, cluster_loss:0.160201\n",
      "Clustering   1320: ASW= 0.8240, DB= 0.2374, CH= 47964.5023\n",
      "Training epoch 1321, recon_loss:0.879530, zinb_loss:0.478606, cluster_loss:0.159984\n",
      "Clustering   1321: ASW= 0.8268, DB= 0.2333, CH= 49676.5984\n",
      "Training epoch 1322, recon_loss:0.879524, zinb_loss:0.478435, cluster_loss:0.160437\n",
      "Clustering   1322: ASW= 0.8242, DB= 0.2372, CH= 47897.5307\n",
      "Training epoch 1323, recon_loss:0.879610, zinb_loss:0.478607, cluster_loss:0.160130\n",
      "Clustering   1323: ASW= 0.8267, DB= 0.2336, CH= 49675.9337\n",
      "Training epoch 1324, recon_loss:0.879640, zinb_loss:0.478433, cluster_loss:0.160859\n",
      "Clustering   1324: ASW= 0.8243, DB= 0.2368, CH= 47810.5018\n",
      "Training epoch 1325, recon_loss:0.879798, zinb_loss:0.478647, cluster_loss:0.160423\n",
      "Clustering   1325: ASW= 0.8263, DB= 0.2339, CH= 49397.9426\n",
      "Training epoch 1326, recon_loss:0.879644, zinb_loss:0.478460, cluster_loss:0.161232\n",
      "Clustering   1326: ASW= 0.8244, DB= 0.2366, CH= 47709.8639\n",
      "Training epoch 1327, recon_loss:0.879704, zinb_loss:0.478650, cluster_loss:0.160546\n",
      "Clustering   1327: ASW= 0.8260, DB= 0.2342, CH= 49070.8441\n",
      "Training epoch 1328, recon_loss:0.879190, zinb_loss:0.478448, cluster_loss:0.161162\n",
      "Clustering   1328: ASW= 0.8246, DB= 0.2364, CH= 47691.7492\n",
      "Training epoch 1329, recon_loss:0.879279, zinb_loss:0.478594, cluster_loss:0.160393\n",
      "Clustering   1329: ASW= 0.8257, DB= 0.2342, CH= 48944.3655\n",
      "Training epoch 1330, recon_loss:0.878726, zinb_loss:0.478407, cluster_loss:0.160936\n",
      "Clustering   1330: ASW= 0.8247, DB= 0.2364, CH= 47714.2843\n",
      "Training epoch 1331, recon_loss:0.879047, zinb_loss:0.478540, cluster_loss:0.160342\n",
      "Clustering   1331: ASW= 0.8253, DB= 0.2345, CH= 48754.2777\n",
      "Training epoch 1332, recon_loss:0.878467, zinb_loss:0.478368, cluster_loss:0.160724\n",
      "Clustering   1332: ASW= 0.8247, DB= 0.2360, CH= 47827.5104\n",
      "Training epoch 1333, recon_loss:0.878849, zinb_loss:0.478491, cluster_loss:0.160251\n",
      "Clustering   1333: ASW= 0.8254, DB= 0.2344, CH= 48713.8323\n",
      "Training epoch 1334, recon_loss:0.878277, zinb_loss:0.478329, cluster_loss:0.160481\n",
      "Clustering   1334: ASW= 0.8249, DB= 0.2357, CH= 48064.0369\n",
      "Training epoch 1335, recon_loss:0.878638, zinb_loss:0.478450, cluster_loss:0.160069\n",
      "Clustering   1335: ASW= 0.8259, DB= 0.2341, CH= 48904.2449\n",
      "Training epoch 1336, recon_loss:0.878273, zinb_loss:0.478299, cluster_loss:0.160311\n",
      "Clustering   1336: ASW= 0.8251, DB= 0.2355, CH= 48290.7903\n",
      "Training epoch 1337, recon_loss:0.878744, zinb_loss:0.478439, cluster_loss:0.160032\n",
      "Clustering   1337: ASW= 0.8263, DB= 0.2338, CH= 49014.6202\n",
      "Training epoch 1338, recon_loss:0.878500, zinb_loss:0.478282, cluster_loss:0.160254\n",
      "Clustering   1338: ASW= 0.8251, DB= 0.2356, CH= 48438.3886\n",
      "Training epoch 1339, recon_loss:0.879078, zinb_loss:0.478435, cluster_loss:0.160106\n",
      "Clustering   1339: ASW= 0.8266, DB= 0.2335, CH= 49064.5915\n",
      "Training epoch 1340, recon_loss:0.878870, zinb_loss:0.478270, cluster_loss:0.160284\n",
      "Clustering   1340: ASW= 0.8250, DB= 0.2359, CH= 48531.1286\n",
      "Training epoch 1341, recon_loss:0.879527, zinb_loss:0.478447, cluster_loss:0.160245\n",
      "Clustering   1341: ASW= 0.8269, DB= 0.2331, CH= 49100.0617\n",
      "Training epoch 1342, recon_loss:0.879129, zinb_loss:0.478253, cluster_loss:0.160326\n",
      "Clustering   1342: ASW= 0.8249, DB= 0.2362, CH= 48590.1922\n",
      "Training epoch 1343, recon_loss:0.879729, zinb_loss:0.478447, cluster_loss:0.160356\n",
      "Clustering   1343: ASW= 0.8272, DB= 0.2328, CH= 49181.1123\n",
      "Training epoch 1344, recon_loss:0.879153, zinb_loss:0.478232, cluster_loss:0.160347\n",
      "Clustering   1344: ASW= 0.8249, DB= 0.2365, CH= 48632.7797\n",
      "Training epoch 1345, recon_loss:0.879749, zinb_loss:0.478458, cluster_loss:0.160436\n",
      "Clustering   1345: ASW= 0.8275, DB= 0.2328, CH= 49314.6047\n",
      "Training epoch 1346, recon_loss:0.879172, zinb_loss:0.478226, cluster_loss:0.160405\n",
      "Clustering   1346: ASW= 0.8249, DB= 0.2368, CH= 48669.8038\n",
      "Training epoch 1347, recon_loss:0.879778, zinb_loss:0.478462, cluster_loss:0.160585\n",
      "Clustering   1347: ASW= 0.8277, DB= 0.2326, CH= 49471.2466\n",
      "Training epoch 1348, recon_loss:0.879214, zinb_loss:0.478247, cluster_loss:0.160396\n",
      "Clustering   1348: ASW= 0.8251, DB= 0.2367, CH= 48730.7164\n",
      "Training epoch 1349, recon_loss:0.879940, zinb_loss:0.478496, cluster_loss:0.160673\n",
      "Clustering   1349: ASW= 0.8278, DB= 0.2325, CH= 49667.3710\n",
      "Training epoch 1350, recon_loss:0.879461, zinb_loss:0.478299, cluster_loss:0.160428\n",
      "Clustering   1350: ASW= 0.8253, DB= 0.2366, CH= 48668.5083\n",
      "Training epoch 1351, recon_loss:0.880413, zinb_loss:0.478533, cluster_loss:0.160800\n",
      "Clustering   1351: ASW= 0.8276, DB= 0.2326, CH= 49732.3206\n",
      "Training epoch 1352, recon_loss:0.880064, zinb_loss:0.478399, cluster_loss:0.160535\n",
      "Clustering   1352: ASW= 0.8255, DB= 0.2363, CH= 48245.2217\n",
      "Training epoch 1353, recon_loss:0.880529, zinb_loss:0.478547, cluster_loss:0.160689\n",
      "Clustering   1353: ASW= 0.8271, DB= 0.2329, CH= 49573.1098\n",
      "Training epoch 1354, recon_loss:0.879941, zinb_loss:0.478448, cluster_loss:0.160369\n",
      "Clustering   1354: ASW= 0.8257, DB= 0.2358, CH= 48179.6642\n",
      "Training epoch 1355, recon_loss:0.879958, zinb_loss:0.478517, cluster_loss:0.160334\n",
      "Clustering   1355: ASW= 0.8268, DB= 0.2326, CH= 49457.3448\n",
      "Training epoch 1356, recon_loss:0.879381, zinb_loss:0.478427, cluster_loss:0.160018\n",
      "Clustering   1356: ASW= 0.8261, DB= 0.2354, CH= 48521.4011\n",
      "Training epoch 1357, recon_loss:0.879506, zinb_loss:0.478481, cluster_loss:0.160111\n",
      "Clustering   1357: ASW= 0.8266, DB= 0.2328, CH= 49417.1599\n",
      "Training epoch 1358, recon_loss:0.879186, zinb_loss:0.478415, cluster_loss:0.159889\n",
      "Clustering   1358: ASW= 0.8263, DB= 0.2352, CH= 48731.9214\n",
      "Training epoch 1359, recon_loss:0.879414, zinb_loss:0.478483, cluster_loss:0.160058\n",
      "Clustering   1359: ASW= 0.8264, DB= 0.2328, CH= 49408.5509\n",
      "Training epoch 1360, recon_loss:0.879228, zinb_loss:0.478419, cluster_loss:0.159920\n",
      "Clustering   1360: ASW= 0.8263, DB= 0.2353, CH= 48810.0713\n",
      "Training epoch 1361, recon_loss:0.879496, zinb_loss:0.478511, cluster_loss:0.160112\n",
      "Clustering   1361: ASW= 0.8263, DB= 0.2328, CH= 49376.2528\n",
      "Training epoch 1362, recon_loss:0.879385, zinb_loss:0.478427, cluster_loss:0.160013\n",
      "Clustering   1362: ASW= 0.8263, DB= 0.2354, CH= 48823.2246\n",
      "Training epoch 1363, recon_loss:0.879613, zinb_loss:0.478552, cluster_loss:0.160161\n",
      "Clustering   1363: ASW= 0.8261, DB= 0.2328, CH= 49360.8025\n",
      "Training epoch 1364, recon_loss:0.879409, zinb_loss:0.478420, cluster_loss:0.160086\n",
      "Clustering   1364: ASW= 0.8261, DB= 0.2359, CH= 48812.2836\n",
      "Training epoch 1365, recon_loss:0.879516, zinb_loss:0.478560, cluster_loss:0.160172\n",
      "Clustering   1365: ASW= 0.8260, DB= 0.2329, CH= 49310.4545\n",
      "Training epoch 1366, recon_loss:0.879430, zinb_loss:0.478388, cluster_loss:0.160105\n",
      "Clustering   1366: ASW= 0.8261, DB= 0.2361, CH= 48823.2152\n",
      "Training epoch 1367, recon_loss:0.879460, zinb_loss:0.478570, cluster_loss:0.160115\n",
      "Clustering   1367: ASW= 0.8259, DB= 0.2322, CH= 49304.0934\n",
      "Training epoch 1368, recon_loss:0.879271, zinb_loss:0.478360, cluster_loss:0.160106\n",
      "Clustering   1368: ASW= 0.8260, DB= 0.2362, CH= 48873.3312\n",
      "Training epoch 1369, recon_loss:0.879260, zinb_loss:0.478541, cluster_loss:0.160025\n",
      "Clustering   1369: ASW= 0.8259, DB= 0.2324, CH= 49283.8807\n",
      "Training epoch 1370, recon_loss:0.879190, zinb_loss:0.478295, cluster_loss:0.160037\n",
      "Clustering   1370: ASW= 0.8260, DB= 0.2360, CH= 48996.3251\n",
      "Training epoch 1371, recon_loss:0.879034, zinb_loss:0.478500, cluster_loss:0.159926\n",
      "Clustering   1371: ASW= 0.8260, DB= 0.2321, CH= 49276.6994\n",
      "Training epoch 1372, recon_loss:0.879105, zinb_loss:0.478254, cluster_loss:0.160052\n",
      "Clustering   1372: ASW= 0.8260, DB= 0.2361, CH= 49144.7793\n",
      "Training epoch 1373, recon_loss:0.878988, zinb_loss:0.478468, cluster_loss:0.159920\n",
      "Clustering   1373: ASW= 0.8261, DB= 0.2320, CH= 49113.6961\n",
      "Training epoch 1374, recon_loss:0.879461, zinb_loss:0.478217, cluster_loss:0.160292\n",
      "Clustering   1374: ASW= 0.8259, DB= 0.2354, CH= 49160.5871\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training epoch 1375, recon_loss:0.879431, zinb_loss:0.478473, cluster_loss:0.160256\n",
      "Clustering   1375: ASW= 0.8259, DB= 0.2322, CH= 48442.9244\n",
      "Training epoch 1376, recon_loss:0.879593, zinb_loss:0.478185, cluster_loss:0.160524\n",
      "Clustering   1376: ASW= 0.8257, DB= 0.2355, CH= 48960.0937\n",
      "Training epoch 1377, recon_loss:0.879098, zinb_loss:0.478443, cluster_loss:0.160250\n",
      "Clustering   1377: ASW= 0.8260, DB= 0.2320, CH= 48320.0098\n",
      "Training epoch 1378, recon_loss:0.878762, zinb_loss:0.478139, cluster_loss:0.160269\n",
      "Clustering   1378: ASW= 0.8260, DB= 0.2360, CH= 48855.4890\n",
      "Training epoch 1379, recon_loss:0.878167, zinb_loss:0.478392, cluster_loss:0.159819\n",
      "Clustering   1379: ASW= 0.8266, DB= 0.2316, CH= 49003.8801\n",
      "Training epoch 1380, recon_loss:0.878295, zinb_loss:0.478132, cluster_loss:0.160182\n",
      "Clustering   1380: ASW= 0.8262, DB= 0.2356, CH= 48889.5837\n",
      "Training epoch 1381, recon_loss:0.878154, zinb_loss:0.478437, cluster_loss:0.159804\n",
      "Clustering   1381: ASW= 0.8267, DB= 0.2315, CH= 49266.2588\n",
      "Training epoch 1382, recon_loss:0.878443, zinb_loss:0.478180, cluster_loss:0.160333\n",
      "Clustering   1382: ASW= 0.8262, DB= 0.2363, CH= 48801.0122\n",
      "Training epoch 1383, recon_loss:0.878408, zinb_loss:0.478481, cluster_loss:0.159974\n",
      "Clustering   1383: ASW= 0.8265, DB= 0.2313, CH= 49328.3835\n",
      "Training epoch 1384, recon_loss:0.878633, zinb_loss:0.478239, cluster_loss:0.160488\n",
      "Clustering   1384: ASW= 0.8263, DB= 0.2363, CH= 48682.1929\n",
      "Training epoch 1385, recon_loss:0.878495, zinb_loss:0.478528, cluster_loss:0.160028\n",
      "Clustering   1385: ASW= 0.8265, DB= 0.2313, CH= 49425.5806\n",
      "Training epoch 1386, recon_loss:0.878486, zinb_loss:0.478285, cluster_loss:0.160564\n",
      "Clustering   1386: ASW= 0.8262, DB= 0.2364, CH= 48579.8373\n",
      "Training epoch 1387, recon_loss:0.878340, zinb_loss:0.478529, cluster_loss:0.160046\n",
      "Clustering   1387: ASW= 0.8264, DB= 0.2313, CH= 49504.7142\n",
      "Training epoch 1388, recon_loss:0.878387, zinb_loss:0.478343, cluster_loss:0.160583\n",
      "Clustering   1388: ASW= 0.8263, DB= 0.2364, CH= 48557.9574\n",
      "Training epoch 1389, recon_loss:0.878330, zinb_loss:0.478548, cluster_loss:0.160035\n",
      "Clustering   1389: ASW= 0.8263, DB= 0.2313, CH= 49559.5827\n",
      "Training epoch 1390, recon_loss:0.878469, zinb_loss:0.478402, cluster_loss:0.160568\n",
      "Clustering   1390: ASW= 0.8264, DB= 0.2364, CH= 48567.9000\n",
      "Training epoch 1391, recon_loss:0.878496, zinb_loss:0.478553, cluster_loss:0.160023\n",
      "Clustering   1391: ASW= 0.8262, DB= 0.2314, CH= 49619.8954\n",
      "Training epoch 1392, recon_loss:0.878727, zinb_loss:0.478474, cluster_loss:0.160549\n",
      "Clustering   1392: ASW= 0.8266, DB= 0.2362, CH= 48635.5599\n",
      "Training epoch 1393, recon_loss:0.878866, zinb_loss:0.478576, cluster_loss:0.160057\n",
      "Clustering   1393: ASW= 0.8260, DB= 0.2317, CH= 49649.8605\n",
      "Training epoch 1394, recon_loss:0.879141, zinb_loss:0.478529, cluster_loss:0.160508\n",
      "Clustering   1394: ASW= 0.8268, DB= 0.2362, CH= 48709.2305\n",
      "Training epoch 1395, recon_loss:0.879199, zinb_loss:0.478583, cluster_loss:0.160049\n",
      "Clustering   1395: ASW= 0.8259, DB= 0.2318, CH= 49653.7306\n",
      "Training epoch 1396, recon_loss:0.879235, zinb_loss:0.478530, cluster_loss:0.160437\n",
      "Clustering   1396: ASW= 0.8269, DB= 0.2362, CH= 48801.9570\n",
      "Training epoch 1397, recon_loss:0.879298, zinb_loss:0.478552, cluster_loss:0.160043\n",
      "Clustering   1397: ASW= 0.8257, DB= 0.2324, CH= 49644.0004\n",
      "Training epoch 1398, recon_loss:0.879235, zinb_loss:0.478504, cluster_loss:0.160365\n",
      "Clustering   1398: ASW= 0.8271, DB= 0.2361, CH= 48889.3616\n",
      "Training epoch 1399, recon_loss:0.879270, zinb_loss:0.478504, cluster_loss:0.160005\n",
      "Clustering   1399: ASW= 0.8255, DB= 0.2327, CH= 49617.1000\n",
      "Training epoch 1400, recon_loss:0.879099, zinb_loss:0.478454, cluster_loss:0.160322\n",
      "Clustering   1400: ASW= 0.8271, DB= 0.2365, CH= 48953.6823\n",
      "Training epoch 1401, recon_loss:0.879271, zinb_loss:0.478449, cluster_loss:0.159979\n",
      "Clustering   1401: ASW= 0.8253, DB= 0.2331, CH= 49581.3545\n",
      "Training epoch 1402, recon_loss:0.879016, zinb_loss:0.478406, cluster_loss:0.160299\n",
      "Clustering   1402: ASW= 0.8272, DB= 0.2364, CH= 49016.0737\n",
      "Training epoch 1403, recon_loss:0.879368, zinb_loss:0.478390, cluster_loss:0.159975\n",
      "Clustering   1403: ASW= 0.8252, DB= 0.2335, CH= 49503.9330\n",
      "Training epoch 1404, recon_loss:0.879008, zinb_loss:0.478368, cluster_loss:0.160270\n",
      "Clustering   1404: ASW= 0.8271, DB= 0.2358, CH= 49092.6427\n",
      "Training epoch 1405, recon_loss:0.879414, zinb_loss:0.478322, cluster_loss:0.159968\n",
      "Clustering   1405: ASW= 0.8251, DB= 0.2337, CH= 49405.5792\n",
      "Training epoch 1406, recon_loss:0.878976, zinb_loss:0.478300, cluster_loss:0.160275\n",
      "Clustering   1406: ASW= 0.8271, DB= 0.2355, CH= 49176.5666\n",
      "Training epoch 1407, recon_loss:0.879625, zinb_loss:0.478261, cluster_loss:0.160046\n",
      "Clustering   1407: ASW= 0.8250, DB= 0.2339, CH= 49263.2897\n",
      "Training epoch 1408, recon_loss:0.879005, zinb_loss:0.478249, cluster_loss:0.160144\n",
      "Clustering   1408: ASW= 0.8268, DB= 0.2354, CH= 49230.0677\n",
      "Training epoch 1409, recon_loss:0.879598, zinb_loss:0.478222, cluster_loss:0.160010\n",
      "Clustering   1409: ASW= 0.8252, DB= 0.2337, CH= 49141.6521\n",
      "Training epoch 1410, recon_loss:0.879391, zinb_loss:0.478232, cluster_loss:0.160333\n",
      "Clustering   1410: ASW= 0.8267, DB= 0.2343, CH= 49356.2905\n",
      "Training epoch 1411, recon_loss:0.880342, zinb_loss:0.478249, cluster_loss:0.160377\n",
      "Clustering   1411: ASW= 0.8252, DB= 0.2341, CH= 48773.5493\n",
      "Training epoch 1412, recon_loss:0.879754, zinb_loss:0.478221, cluster_loss:0.160491\n",
      "Clustering   1412: ASW= 0.8264, DB= 0.2339, CH= 49407.7485\n",
      "Training epoch 1413, recon_loss:0.880094, zinb_loss:0.478229, cluster_loss:0.160407\n",
      "Clustering   1413: ASW= 0.8253, DB= 0.2340, CH= 48544.4518\n",
      "Training epoch 1414, recon_loss:0.879223, zinb_loss:0.478180, cluster_loss:0.160292\n",
      "Clustering   1414: ASW= 0.8265, DB= 0.2334, CH= 49535.2176\n",
      "Training epoch 1415, recon_loss:0.879193, zinb_loss:0.478180, cluster_loss:0.160062\n",
      "Clustering   1415: ASW= 0.8255, DB= 0.2338, CH= 48595.2318\n",
      "Training epoch 1416, recon_loss:0.878476, zinb_loss:0.478154, cluster_loss:0.159922\n",
      "Clustering   1416: ASW= 0.8268, DB= 0.2339, CH= 49758.4755\n",
      "Training epoch 1417, recon_loss:0.878659, zinb_loss:0.478194, cluster_loss:0.160004\n",
      "Clustering   1417: ASW= 0.8256, DB= 0.2338, CH= 48604.0300\n",
      "Training epoch 1418, recon_loss:0.878618, zinb_loss:0.478199, cluster_loss:0.159831\n",
      "Clustering   1418: ASW= 0.8267, DB= 0.2342, CH= 49833.8481\n",
      "Training epoch 1419, recon_loss:0.878810, zinb_loss:0.478260, cluster_loss:0.160003\n",
      "Clustering   1419: ASW= 0.8258, DB= 0.2332, CH= 48479.1999\n",
      "Training epoch 1420, recon_loss:0.879276, zinb_loss:0.478320, cluster_loss:0.160197\n",
      "Clustering   1420: ASW= 0.8262, DB= 0.2335, CH= 49756.7992\n",
      "Training epoch 1421, recon_loss:0.879472, zinb_loss:0.478359, cluster_loss:0.160400\n",
      "Clustering   1421: ASW= 0.8258, DB= 0.2333, CH= 48126.9525\n",
      "Training epoch 1422, recon_loss:0.879592, zinb_loss:0.478369, cluster_loss:0.160221\n",
      "Clustering   1422: ASW= 0.8261, DB= 0.2343, CH= 49763.4282\n",
      "Training epoch 1423, recon_loss:0.879351, zinb_loss:0.478342, cluster_loss:0.160323\n",
      "Clustering   1423: ASW= 0.8261, DB= 0.2333, CH= 48225.4605\n",
      "Training epoch 1424, recon_loss:0.879236, zinb_loss:0.478331, cluster_loss:0.160009\n",
      "Clustering   1424: ASW= 0.8263, DB= 0.2338, CH= 49880.7209\n",
      "Training epoch 1425, recon_loss:0.878823, zinb_loss:0.478270, cluster_loss:0.160080\n",
      "Clustering   1425: ASW= 0.8266, DB= 0.2334, CH= 48542.4932\n",
      "Training epoch 1426, recon_loss:0.878880, zinb_loss:0.478274, cluster_loss:0.159863\n",
      "Clustering   1426: ASW= 0.8264, DB= 0.2334, CH= 49991.5615\n",
      "Training epoch 1427, recon_loss:0.878456, zinb_loss:0.478207, cluster_loss:0.159985\n",
      "Clustering   1427: ASW= 0.8269, DB= 0.2336, CH= 48763.5771\n",
      "Training epoch 1428, recon_loss:0.878795, zinb_loss:0.478230, cluster_loss:0.159927\n",
      "Clustering   1428: ASW= 0.8264, DB= 0.2333, CH= 50023.8749\n",
      "Training epoch 1429, recon_loss:0.878424, zinb_loss:0.478184, cluster_loss:0.160188\n",
      "Clustering   1429: ASW= 0.8271, DB= 0.2339, CH= 48710.4379\n",
      "Training epoch 1430, recon_loss:0.879029, zinb_loss:0.478203, cluster_loss:0.160323\n",
      "Clustering   1430: ASW= 0.8261, DB= 0.2333, CH= 49904.2890\n",
      "Training epoch 1431, recon_loss:0.878809, zinb_loss:0.478223, cluster_loss:0.160718\n",
      "Clustering   1431: ASW= 0.8271, DB= 0.2350, CH= 48462.2378\n",
      "Training epoch 1432, recon_loss:0.879596, zinb_loss:0.478221, cluster_loss:0.160901\n",
      "Clustering   1432: ASW= 0.8256, DB= 0.2336, CH= 49649.8342\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training epoch 1433, recon_loss:0.878979, zinb_loss:0.478191, cluster_loss:0.160896\n",
      "Clustering   1433: ASW= 0.8268, DB= 0.2355, CH= 48334.4961\n",
      "Training epoch 1434, recon_loss:0.879000, zinb_loss:0.478058, cluster_loss:0.160843\n",
      "Clustering   1434: ASW= 0.8251, DB= 0.2342, CH= 49206.3067\n",
      "Training epoch 1435, recon_loss:0.878706, zinb_loss:0.478205, cluster_loss:0.160665\n",
      "Clustering   1435: ASW= 0.8276, DB= 0.2350, CH= 48862.8095\n",
      "Training epoch 1436, recon_loss:0.878595, zinb_loss:0.478003, cluster_loss:0.160543\n",
      "Clustering   1436: ASW= 0.8251, DB= 0.2340, CH= 49153.3508\n",
      "Training epoch 1437, recon_loss:0.878399, zinb_loss:0.478203, cluster_loss:0.160393\n",
      "Clustering   1437: ASW= 0.8280, DB= 0.2349, CH= 49205.4554\n",
      "Training epoch 1438, recon_loss:0.878189, zinb_loss:0.477983, cluster_loss:0.160269\n",
      "Clustering   1438: ASW= 0.8252, DB= 0.2340, CH= 49197.9238\n",
      "Training epoch 1439, recon_loss:0.878107, zinb_loss:0.478210, cluster_loss:0.160187\n",
      "Clustering   1439: ASW= 0.8283, DB= 0.2344, CH= 49461.8766\n",
      "Training epoch 1440, recon_loss:0.877882, zinb_loss:0.477986, cluster_loss:0.160080\n",
      "Clustering   1440: ASW= 0.8253, DB= 0.2340, CH= 49268.3457\n",
      "Training epoch 1441, recon_loss:0.877890, zinb_loss:0.478223, cluster_loss:0.160053\n",
      "Clustering   1441: ASW= 0.8285, DB= 0.2340, CH= 49659.8781\n",
      "Training epoch 1442, recon_loss:0.877685, zinb_loss:0.478004, cluster_loss:0.159961\n",
      "Clustering   1442: ASW= 0.8255, DB= 0.2340, CH= 49326.4254\n",
      "Training epoch 1443, recon_loss:0.877766, zinb_loss:0.478248, cluster_loss:0.159980\n",
      "Clustering   1443: ASW= 0.8287, DB= 0.2336, CH= 49809.5325\n",
      "Training epoch 1444, recon_loss:0.877577, zinb_loss:0.478035, cluster_loss:0.159896\n",
      "Clustering   1444: ASW= 0.8255, DB= 0.2341, CH= 49358.8570\n",
      "Training epoch 1445, recon_loss:0.877727, zinb_loss:0.478281, cluster_loss:0.159933\n",
      "Clustering   1445: ASW= 0.8289, DB= 0.2330, CH= 49952.8808\n",
      "Training epoch 1446, recon_loss:0.877547, zinb_loss:0.478069, cluster_loss:0.159870\n",
      "Clustering   1446: ASW= 0.8256, DB= 0.2343, CH= 49349.3892\n",
      "Training epoch 1447, recon_loss:0.877749, zinb_loss:0.478316, cluster_loss:0.159890\n",
      "Clustering   1447: ASW= 0.8291, DB= 0.2320, CH= 50087.1429\n",
      "Training epoch 1448, recon_loss:0.877553, zinb_loss:0.478096, cluster_loss:0.159870\n",
      "Clustering   1448: ASW= 0.8257, DB= 0.2335, CH= 49287.0000\n",
      "Training epoch 1449, recon_loss:0.877800, zinb_loss:0.478346, cluster_loss:0.159836\n",
      "Clustering   1449: ASW= 0.8291, DB= 0.2315, CH= 50213.0226\n",
      "Training epoch 1450, recon_loss:0.877583, zinb_loss:0.478102, cluster_loss:0.159908\n",
      "Clustering   1450: ASW= 0.8257, DB= 0.2337, CH= 49178.1300\n",
      "Training epoch 1451, recon_loss:0.877888, zinb_loss:0.478364, cluster_loss:0.159791\n",
      "Clustering   1451: ASW= 0.8290, DB= 0.2313, CH= 50308.6141\n",
      "Training epoch 1452, recon_loss:0.877714, zinb_loss:0.478093, cluster_loss:0.160014\n",
      "Clustering   1452: ASW= 0.8257, DB= 0.2339, CH= 49007.0901\n",
      "Training epoch 1453, recon_loss:0.878140, zinb_loss:0.478383, cluster_loss:0.159814\n",
      "Clustering   1453: ASW= 0.8287, DB= 0.2309, CH= 50365.1428\n",
      "Training epoch 1454, recon_loss:0.878014, zinb_loss:0.478081, cluster_loss:0.160226\n",
      "Clustering   1454: ASW= 0.8257, DB= 0.2341, CH= 48773.0752\n",
      "Training epoch 1455, recon_loss:0.878586, zinb_loss:0.478409, cluster_loss:0.159897\n",
      "Clustering   1455: ASW= 0.8283, DB= 0.2302, CH= 50391.3314\n",
      "Training epoch 1456, recon_loss:0.878406, zinb_loss:0.478080, cluster_loss:0.160452\n",
      "Clustering   1456: ASW= 0.8258, DB= 0.2343, CH= 48576.0119\n",
      "Training epoch 1457, recon_loss:0.879009, zinb_loss:0.478428, cluster_loss:0.159943\n",
      "Clustering   1457: ASW= 0.8280, DB= 0.2303, CH= 50386.4043\n",
      "Training epoch 1458, recon_loss:0.878601, zinb_loss:0.478076, cluster_loss:0.160493\n",
      "Clustering   1458: ASW= 0.8260, DB= 0.2343, CH= 48563.5865\n",
      "Training epoch 1459, recon_loss:0.879120, zinb_loss:0.478418, cluster_loss:0.159854\n",
      "Clustering   1459: ASW= 0.8278, DB= 0.2303, CH= 50403.4616\n",
      "Training epoch 1460, recon_loss:0.878637, zinb_loss:0.478069, cluster_loss:0.160342\n",
      "Clustering   1460: ASW= 0.8263, DB= 0.2342, CH= 48742.0308\n",
      "Training epoch 1461, recon_loss:0.879071, zinb_loss:0.478375, cluster_loss:0.159719\n",
      "Clustering   1461: ASW= 0.8278, DB= 0.2302, CH= 50428.0064\n",
      "Training epoch 1462, recon_loss:0.878779, zinb_loss:0.478077, cluster_loss:0.160188\n",
      "Clustering   1462: ASW= 0.8266, DB= 0.2341, CH= 48997.6167\n",
      "Training epoch 1463, recon_loss:0.879079, zinb_loss:0.478337, cluster_loss:0.159630\n",
      "Clustering   1463: ASW= 0.8278, DB= 0.2303, CH= 50387.3895\n",
      "Training epoch 1464, recon_loss:0.878946, zinb_loss:0.478091, cluster_loss:0.160052\n",
      "Clustering   1464: ASW= 0.8268, DB= 0.2340, CH= 49250.8842\n",
      "Training epoch 1465, recon_loss:0.879108, zinb_loss:0.478312, cluster_loss:0.159601\n",
      "Clustering   1465: ASW= 0.8278, DB= 0.2304, CH= 50293.8498\n",
      "Training epoch 1466, recon_loss:0.879067, zinb_loss:0.478109, cluster_loss:0.159943\n",
      "Clustering   1466: ASW= 0.8268, DB= 0.2338, CH= 49458.5914\n",
      "Training epoch 1467, recon_loss:0.879104, zinb_loss:0.478292, cluster_loss:0.159607\n",
      "Clustering   1467: ASW= 0.8279, DB= 0.2304, CH= 50153.9941\n",
      "Training epoch 1468, recon_loss:0.879046, zinb_loss:0.478129, cluster_loss:0.159839\n",
      "Clustering   1468: ASW= 0.8268, DB= 0.2336, CH= 49642.2874\n",
      "Training epoch 1469, recon_loss:0.879045, zinb_loss:0.478271, cluster_loss:0.159628\n",
      "Clustering   1469: ASW= 0.8280, DB= 0.2304, CH= 50013.2687\n",
      "Training epoch 1470, recon_loss:0.878988, zinb_loss:0.478151, cluster_loss:0.159765\n",
      "Clustering   1470: ASW= 0.8268, DB= 0.2333, CH= 49798.9579\n",
      "Training epoch 1471, recon_loss:0.878998, zinb_loss:0.478259, cluster_loss:0.159675\n",
      "Clustering   1471: ASW= 0.8281, DB= 0.2308, CH= 49888.7624\n",
      "Training epoch 1472, recon_loss:0.878927, zinb_loss:0.478169, cluster_loss:0.159708\n",
      "Clustering   1472: ASW= 0.8268, DB= 0.2333, CH= 49950.1548\n",
      "Training epoch 1473, recon_loss:0.878927, zinb_loss:0.478248, cluster_loss:0.159730\n",
      "Clustering   1473: ASW= 0.8282, DB= 0.2312, CH= 49766.9132\n",
      "Training epoch 1474, recon_loss:0.878838, zinb_loss:0.478182, cluster_loss:0.159687\n",
      "Clustering   1474: ASW= 0.8267, DB= 0.2331, CH= 50076.4734\n",
      "Training epoch 1475, recon_loss:0.878828, zinb_loss:0.478234, cluster_loss:0.159790\n",
      "Clustering   1475: ASW= 0.8285, DB= 0.2312, CH= 49702.2344\n",
      "Training epoch 1476, recon_loss:0.878679, zinb_loss:0.478180, cluster_loss:0.159678\n",
      "Clustering   1476: ASW= 0.8267, DB= 0.2329, CH= 50177.4595\n",
      "Training epoch 1477, recon_loss:0.878658, zinb_loss:0.478215, cluster_loss:0.159833\n",
      "Clustering   1477: ASW= 0.8286, DB= 0.2312, CH= 49659.8808\n",
      "Training epoch 1478, recon_loss:0.878428, zinb_loss:0.478167, cluster_loss:0.159648\n",
      "Clustering   1478: ASW= 0.8267, DB= 0.2328, CH= 50244.5868\n",
      "Training epoch 1479, recon_loss:0.878444, zinb_loss:0.478196, cluster_loss:0.159856\n",
      "Clustering   1479: ASW= 0.8287, DB= 0.2313, CH= 49636.5014\n",
      "Training epoch 1480, recon_loss:0.878197, zinb_loss:0.478158, cluster_loss:0.159612\n",
      "Clustering   1480: ASW= 0.8266, DB= 0.2336, CH= 50322.6034\n",
      "Training epoch 1481, recon_loss:0.878240, zinb_loss:0.478181, cluster_loss:0.159868\n",
      "Clustering   1481: ASW= 0.8288, DB= 0.2314, CH= 49614.2485\n",
      "Training epoch 1482, recon_loss:0.878010, zinb_loss:0.478149, cluster_loss:0.159583\n",
      "Clustering   1482: ASW= 0.8267, DB= 0.2335, CH= 50377.9094\n",
      "Training epoch 1483, recon_loss:0.878064, zinb_loss:0.478169, cluster_loss:0.159870\n",
      "Clustering   1483: ASW= 0.8288, DB= 0.2315, CH= 49585.6770\n",
      "Training epoch 1484, recon_loss:0.877846, zinb_loss:0.478143, cluster_loss:0.159545\n",
      "Clustering   1484: ASW= 0.8267, DB= 0.2334, CH= 50426.8492\n",
      "Training epoch 1485, recon_loss:0.877896, zinb_loss:0.478151, cluster_loss:0.159869\n",
      "Clustering   1485: ASW= 0.8288, DB= 0.2315, CH= 49518.0535\n",
      "Training epoch 1486, recon_loss:0.877762, zinb_loss:0.478131, cluster_loss:0.159545\n",
      "Clustering   1486: ASW= 0.8267, DB= 0.2333, CH= 50430.5670\n",
      "Training epoch 1487, recon_loss:0.877982, zinb_loss:0.478177, cluster_loss:0.159853\n",
      "Clustering   1487: ASW= 0.8288, DB= 0.2315, CH= 49545.9425\n",
      "Training epoch 1488, recon_loss:0.877996, zinb_loss:0.478172, cluster_loss:0.159536\n",
      "Clustering   1488: ASW= 0.8267, DB= 0.2331, CH= 50517.6398\n",
      "Training epoch 1489, recon_loss:0.878028, zinb_loss:0.478169, cluster_loss:0.159843\n",
      "Clustering   1489: ASW= 0.8287, DB= 0.2317, CH= 49448.2034\n",
      "Training epoch 1490, recon_loss:0.878010, zinb_loss:0.478167, cluster_loss:0.159558\n",
      "Clustering   1490: ASW= 0.8267, DB= 0.2330, CH= 50493.6791\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training epoch 1491, recon_loss:0.878312, zinb_loss:0.478214, cluster_loss:0.159841\n",
      "Clustering   1491: ASW= 0.8289, DB= 0.2316, CH= 49539.4429\n",
      "Training epoch 1492, recon_loss:0.878331, zinb_loss:0.478218, cluster_loss:0.159550\n",
      "Clustering   1492: ASW= 0.8268, DB= 0.2327, CH= 50663.2418\n",
      "Training epoch 1493, recon_loss:0.878567, zinb_loss:0.478246, cluster_loss:0.159875\n",
      "Clustering   1493: ASW= 0.8289, DB= 0.2318, CH= 49482.4189\n",
      "Training epoch 1494, recon_loss:0.878417, zinb_loss:0.478236, cluster_loss:0.159548\n",
      "Clustering   1494: ASW= 0.8269, DB= 0.2326, CH= 50807.6424\n",
      "Training epoch 1495, recon_loss:0.878352, zinb_loss:0.478218, cluster_loss:0.159842\n",
      "Clustering   1495: ASW= 0.8288, DB= 0.2319, CH= 49363.7355\n",
      "Training epoch 1496, recon_loss:0.878903, zinb_loss:0.478291, cluster_loss:0.159716\n",
      "Clustering   1496: ASW= 0.8270, DB= 0.2326, CH= 50958.1860\n",
      "Training epoch 1497, recon_loss:0.878495, zinb_loss:0.478095, cluster_loss:0.159950\n",
      "Clustering   1497: ASW= 0.8273, DB= 0.2335, CH= 48615.3355\n",
      "Training epoch 1498, recon_loss:0.877826, zinb_loss:0.478045, cluster_loss:0.159509\n",
      "Clustering   1498: ASW= 0.8269, DB= 0.2330, CH= 50692.0775\n",
      "Training epoch 1499, recon_loss:0.877505, zinb_loss:0.478026, cluster_loss:0.159781\n",
      "Clustering   1499: ASW= 0.8284, DB= 0.2318, CH= 49208.7321\n",
      "Training epoch 1500, recon_loss:0.877490, zinb_loss:0.478004, cluster_loss:0.159540\n",
      "Clustering   1500: ASW= 0.8270, DB= 0.2327, CH= 50814.2169\n",
      "Training epoch 1501, recon_loss:0.877782, zinb_loss:0.478029, cluster_loss:0.160063\n",
      "Clustering   1501: ASW= 0.8282, DB= 0.2326, CH= 49157.1059\n",
      "Training epoch 1502, recon_loss:0.878534, zinb_loss:0.478043, cluster_loss:0.159971\n",
      "Clustering   1502: ASW= 0.8269, DB= 0.2322, CH= 50864.7005\n",
      "Training epoch 1503, recon_loss:0.879617, zinb_loss:0.478107, cluster_loss:0.161025\n",
      "Clustering   1503: ASW= 0.8275, DB= 0.2339, CH= 48658.2832\n",
      "Training epoch 1504, recon_loss:0.879628, zinb_loss:0.478139, cluster_loss:0.160194\n",
      "Clustering   1504: ASW= 0.8270, DB= 0.2318, CH= 50807.7259\n",
      "Training epoch 1505, recon_loss:0.879276, zinb_loss:0.478066, cluster_loss:0.160909\n",
      "Clustering   1505: ASW= 0.8273, DB= 0.2343, CH= 48753.3473\n",
      "Training epoch 1506, recon_loss:0.879329, zinb_loss:0.478220, cluster_loss:0.160038\n",
      "Clustering   1506: ASW= 0.8275, DB= 0.2310, CH= 50782.9887\n",
      "Training epoch 1507, recon_loss:0.878368, zinb_loss:0.478014, cluster_loss:0.160452\n",
      "Clustering   1507: ASW= 0.8273, DB= 0.2340, CH= 49045.5035\n",
      "Training epoch 1508, recon_loss:0.878738, zinb_loss:0.478246, cluster_loss:0.159831\n",
      "Clustering   1508: ASW= 0.8277, DB= 0.2308, CH= 50762.2465\n",
      "Training epoch 1509, recon_loss:0.877767, zinb_loss:0.477997, cluster_loss:0.160158\n",
      "Clustering   1509: ASW= 0.8274, DB= 0.2337, CH= 49271.8591\n",
      "Training epoch 1510, recon_loss:0.878345, zinb_loss:0.478236, cluster_loss:0.159675\n",
      "Clustering   1510: ASW= 0.8277, DB= 0.2309, CH= 50784.6093\n",
      "Training epoch 1511, recon_loss:0.877410, zinb_loss:0.478002, cluster_loss:0.159978\n",
      "Clustering   1511: ASW= 0.8276, DB= 0.2334, CH= 49445.5747\n",
      "Training epoch 1512, recon_loss:0.878175, zinb_loss:0.478229, cluster_loss:0.159586\n",
      "Clustering   1512: ASW= 0.8276, DB= 0.2311, CH= 50803.1748\n",
      "Training epoch 1513, recon_loss:0.877328, zinb_loss:0.478032, cluster_loss:0.159888\n",
      "Clustering   1513: ASW= 0.8277, DB= 0.2334, CH= 49554.8172\n",
      "Training epoch 1514, recon_loss:0.878349, zinb_loss:0.478249, cluster_loss:0.159606\n",
      "Clustering   1514: ASW= 0.8275, DB= 0.2316, CH= 50820.1487\n",
      "Training epoch 1515, recon_loss:0.877564, zinb_loss:0.478092, cluster_loss:0.159895\n",
      "Clustering   1515: ASW= 0.8277, DB= 0.2331, CH= 49564.3885\n",
      "Training epoch 1516, recon_loss:0.878928, zinb_loss:0.478285, cluster_loss:0.159741\n",
      "Clustering   1516: ASW= 0.8274, DB= 0.2320, CH= 50811.7476\n",
      "Training epoch 1517, recon_loss:0.878158, zinb_loss:0.478169, cluster_loss:0.159998\n",
      "Clustering   1517: ASW= 0.8276, DB= 0.2325, CH= 49453.1994\n",
      "Training epoch 1518, recon_loss:0.879658, zinb_loss:0.478302, cluster_loss:0.159999\n",
      "Clustering   1518: ASW= 0.8273, DB= 0.2330, CH= 50672.0875\n",
      "Training epoch 1519, recon_loss:0.878785, zinb_loss:0.478229, cluster_loss:0.160128\n",
      "Clustering   1519: ASW= 0.8274, DB= 0.2321, CH= 49268.9023\n",
      "Training epoch 1520, recon_loss:0.879906, zinb_loss:0.478234, cluster_loss:0.160100\n",
      "Clustering   1520: ASW= 0.8273, DB= 0.2337, CH= 50493.0867\n",
      "Training epoch 1521, recon_loss:0.878715, zinb_loss:0.478203, cluster_loss:0.160028\n",
      "Clustering   1521: ASW= 0.8274, DB= 0.2314, CH= 49428.8126\n",
      "Training epoch 1522, recon_loss:0.879454, zinb_loss:0.478109, cluster_loss:0.160044\n",
      "Clustering   1522: ASW= 0.8275, DB= 0.2343, CH= 50348.8099\n",
      "Training epoch 1523, recon_loss:0.878354, zinb_loss:0.478135, cluster_loss:0.159841\n",
      "Clustering   1523: ASW= 0.8276, DB= 0.2307, CH= 49755.6100\n",
      "Training epoch 1524, recon_loss:0.878906, zinb_loss:0.477981, cluster_loss:0.160029\n",
      "Clustering   1524: ASW= 0.8277, DB= 0.2338, CH= 50217.7074\n",
      "Training epoch 1525, recon_loss:0.878072, zinb_loss:0.478075, cluster_loss:0.159770\n",
      "Clustering   1525: ASW= 0.8275, DB= 0.2304, CH= 49919.2569\n",
      "Training epoch 1526, recon_loss:0.878461, zinb_loss:0.477901, cluster_loss:0.160069\n",
      "Clustering   1526: ASW= 0.8277, DB= 0.2339, CH= 50094.1566\n",
      "Training epoch 1527, recon_loss:0.877772, zinb_loss:0.478032, cluster_loss:0.159754\n",
      "Clustering   1527: ASW= 0.8273, DB= 0.2303, CH= 49970.3646\n",
      "Training epoch 1528, recon_loss:0.878000, zinb_loss:0.477862, cluster_loss:0.160108\n",
      "Clustering   1528: ASW= 0.8277, DB= 0.2341, CH= 49971.6131\n",
      "Training epoch 1529, recon_loss:0.877397, zinb_loss:0.478003, cluster_loss:0.159736\n",
      "Clustering   1529: ASW= 0.8270, DB= 0.2301, CH= 49927.0956\n",
      "Training epoch 1530, recon_loss:0.877550, zinb_loss:0.477856, cluster_loss:0.160083\n",
      "Clustering   1530: ASW= 0.8277, DB= 0.2341, CH= 49903.7873\n",
      "Training epoch 1531, recon_loss:0.877067, zinb_loss:0.477992, cluster_loss:0.159695\n",
      "Clustering   1531: ASW= 0.8268, DB= 0.2301, CH= 49880.5621\n",
      "Training epoch 1532, recon_loss:0.877246, zinb_loss:0.477889, cluster_loss:0.160015\n",
      "Clustering   1532: ASW= 0.8278, DB= 0.2336, CH= 49902.3157\n",
      "Training epoch 1533, recon_loss:0.876943, zinb_loss:0.478009, cluster_loss:0.159654\n",
      "Clustering   1533: ASW= 0.8266, DB= 0.2303, CH= 49835.5708\n",
      "Training epoch 1534, recon_loss:0.877202, zinb_loss:0.477951, cluster_loss:0.159968\n",
      "Clustering   1534: ASW= 0.8279, DB= 0.2333, CH= 49904.4194\n",
      "Training epoch 1535, recon_loss:0.877083, zinb_loss:0.478051, cluster_loss:0.159640\n",
      "Clustering   1535: ASW= 0.8264, DB= 0.2306, CH= 49800.6706\n",
      "Training epoch 1536, recon_loss:0.877373, zinb_loss:0.478017, cluster_loss:0.159957\n",
      "Clustering   1536: ASW= 0.8280, DB= 0.2329, CH= 49899.0798\n",
      "Training epoch 1537, recon_loss:0.877423, zinb_loss:0.478110, cluster_loss:0.159654\n",
      "Clustering   1537: ASW= 0.8264, DB= 0.2308, CH= 49820.6146\n",
      "Training epoch 1538, recon_loss:0.877742, zinb_loss:0.478082, cluster_loss:0.159992\n",
      "Clustering   1538: ASW= 0.8281, DB= 0.2325, CH= 49902.0199\n",
      "Training epoch 1539, recon_loss:0.877938, zinb_loss:0.478183, cluster_loss:0.159714\n",
      "Clustering   1539: ASW= 0.8266, DB= 0.2310, CH= 49895.9533\n",
      "Training epoch 1540, recon_loss:0.878266, zinb_loss:0.478145, cluster_loss:0.160071\n",
      "Clustering   1540: ASW= 0.8281, DB= 0.2321, CH= 49918.4028\n",
      "Training epoch 1541, recon_loss:0.878422, zinb_loss:0.478239, cluster_loss:0.159784\n",
      "Clustering   1541: ASW= 0.8269, DB= 0.2313, CH= 50005.6608\n",
      "Training epoch 1542, recon_loss:0.878608, zinb_loss:0.478183, cluster_loss:0.160103\n",
      "Clustering   1542: ASW= 0.8282, DB= 0.2317, CH= 49978.9611\n",
      "Training epoch 1543, recon_loss:0.878451, zinb_loss:0.478242, cluster_loss:0.159770\n",
      "Clustering   1543: ASW= 0.8273, DB= 0.2313, CH= 50139.6246\n",
      "Training epoch 1544, recon_loss:0.878443, zinb_loss:0.478162, cluster_loss:0.160037\n",
      "Clustering   1544: ASW= 0.8282, DB= 0.2312, CH= 50083.2355\n",
      "Training epoch 1545, recon_loss:0.878039, zinb_loss:0.478191, cluster_loss:0.159666\n",
      "Clustering   1545: ASW= 0.8277, DB= 0.2311, CH= 50238.1215\n",
      "Training epoch 1546, recon_loss:0.877951, zinb_loss:0.478109, cluster_loss:0.159905\n",
      "Clustering   1546: ASW= 0.8282, DB= 0.2308, CH= 50217.1591\n",
      "Training epoch 1547, recon_loss:0.877464, zinb_loss:0.478127, cluster_loss:0.159558\n",
      "Clustering   1547: ASW= 0.8280, DB= 0.2311, CH= 50268.6479\n",
      "Training epoch 1548, recon_loss:0.877465, zinb_loss:0.478068, cluster_loss:0.159817\n",
      "Clustering   1548: ASW= 0.8282, DB= 0.2306, CH= 50331.8253\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training epoch 1549, recon_loss:0.877027, zinb_loss:0.478080, cluster_loss:0.159548\n",
      "Clustering   1549: ASW= 0.8281, DB= 0.2311, CH= 50151.5677\n",
      "Training epoch 1550, recon_loss:0.877188, zinb_loss:0.478055, cluster_loss:0.159866\n",
      "Clustering   1550: ASW= 0.8282, DB= 0.2305, CH= 50398.4480\n",
      "Training epoch 1551, recon_loss:0.876904, zinb_loss:0.478059, cluster_loss:0.159716\n",
      "Clustering   1551: ASW= 0.8281, DB= 0.2312, CH= 49867.5930\n",
      "Training epoch 1552, recon_loss:0.877226, zinb_loss:0.478060, cluster_loss:0.159987\n",
      "Clustering   1552: ASW= 0.8282, DB= 0.2304, CH= 50412.0697\n",
      "Training epoch 1553, recon_loss:0.877127, zinb_loss:0.478060, cluster_loss:0.159876\n",
      "Clustering   1553: ASW= 0.8283, DB= 0.2314, CH= 49783.1421\n",
      "Training epoch 1554, recon_loss:0.877601, zinb_loss:0.478070, cluster_loss:0.160047\n",
      "Clustering   1554: ASW= 0.8282, DB= 0.2310, CH= 50454.9857\n",
      "Training epoch 1555, recon_loss:0.877668, zinb_loss:0.478072, cluster_loss:0.159868\n",
      "Clustering   1555: ASW= 0.8286, DB= 0.2314, CH= 49994.5745\n",
      "Training epoch 1556, recon_loss:0.878335, zinb_loss:0.478101, cluster_loss:0.160095\n",
      "Clustering   1556: ASW= 0.8282, DB= 0.2308, CH= 50521.0084\n",
      "Training epoch 1557, recon_loss:0.878272, zinb_loss:0.478087, cluster_loss:0.159847\n",
      "Clustering   1557: ASW= 0.8287, DB= 0.2313, CH= 50115.0659\n",
      "Training epoch 1558, recon_loss:0.878799, zinb_loss:0.478120, cluster_loss:0.160200\n",
      "Clustering   1558: ASW= 0.8280, DB= 0.2309, CH= 50510.4888\n",
      "Training epoch 1559, recon_loss:0.878340, zinb_loss:0.478048, cluster_loss:0.159801\n",
      "Clustering   1559: ASW= 0.8287, DB= 0.2317, CH= 50146.1807\n",
      "Training epoch 1560, recon_loss:0.878735, zinb_loss:0.478125, cluster_loss:0.160092\n",
      "Clustering   1560: ASW= 0.8281, DB= 0.2308, CH= 50621.7903\n",
      "Training epoch 1561, recon_loss:0.878677, zinb_loss:0.478042, cluster_loss:0.159929\n",
      "Clustering   1561: ASW= 0.8284, DB= 0.2318, CH= 50010.5682\n",
      "Training epoch 1562, recon_loss:0.879110, zinb_loss:0.478158, cluster_loss:0.160204\n",
      "Clustering   1562: ASW= 0.8282, DB= 0.2304, CH= 50581.7795\n",
      "Training epoch 1563, recon_loss:0.878672, zinb_loss:0.478010, cluster_loss:0.160023\n",
      "Clustering   1563: ASW= 0.8281, DB= 0.2322, CH= 49996.5902\n",
      "Training epoch 1564, recon_loss:0.878948, zinb_loss:0.478168, cluster_loss:0.160142\n",
      "Clustering   1564: ASW= 0.8282, DB= 0.2309, CH= 50599.3270\n",
      "Training epoch 1565, recon_loss:0.878268, zinb_loss:0.477990, cluster_loss:0.159933\n",
      "Clustering   1565: ASW= 0.8281, DB= 0.2321, CH= 50005.0752\n",
      "Training epoch 1566, recon_loss:0.878403, zinb_loss:0.478146, cluster_loss:0.159893\n",
      "Clustering   1566: ASW= 0.8282, DB= 0.2311, CH= 50669.1170\n",
      "Training epoch 1567, recon_loss:0.877786, zinb_loss:0.477983, cluster_loss:0.159733\n",
      "Clustering   1567: ASW= 0.8282, DB= 0.2319, CH= 50057.1448\n",
      "Training epoch 1568, recon_loss:0.877958, zinb_loss:0.478131, cluster_loss:0.159658\n",
      "Clustering   1568: ASW= 0.8281, DB= 0.2314, CH= 50716.0549\n",
      "Training epoch 1569, recon_loss:0.877542, zinb_loss:0.477989, cluster_loss:0.159569\n",
      "Clustering   1569: ASW= 0.8284, DB= 0.2316, CH= 50101.5619\n",
      "Training epoch 1570, recon_loss:0.877814, zinb_loss:0.478143, cluster_loss:0.159527\n",
      "Clustering   1570: ASW= 0.8281, DB= 0.2318, CH= 50722.9115\n",
      "Training epoch 1571, recon_loss:0.877543, zinb_loss:0.478029, cluster_loss:0.159507\n",
      "Clustering   1571: ASW= 0.8284, DB= 0.2313, CH= 50123.7745\n",
      "Training epoch 1572, recon_loss:0.877842, zinb_loss:0.478167, cluster_loss:0.159495\n",
      "Clustering   1572: ASW= 0.8280, DB= 0.2321, CH= 50656.4964\n",
      "Training epoch 1573, recon_loss:0.877636, zinb_loss:0.478075, cluster_loss:0.159489\n",
      "Clustering   1573: ASW= 0.8284, DB= 0.2310, CH= 50170.6679\n",
      "Training epoch 1574, recon_loss:0.877907, zinb_loss:0.478183, cluster_loss:0.159514\n",
      "Clustering   1574: ASW= 0.8278, DB= 0.2324, CH= 50547.1179\n",
      "Training epoch 1575, recon_loss:0.877737, zinb_loss:0.478107, cluster_loss:0.159489\n",
      "Clustering   1575: ASW= 0.8284, DB= 0.2311, CH= 50281.8847\n",
      "Training epoch 1576, recon_loss:0.877978, zinb_loss:0.478179, cluster_loss:0.159599\n",
      "Clustering   1576: ASW= 0.8277, DB= 0.2325, CH= 50400.3773\n",
      "Training epoch 1577, recon_loss:0.877890, zinb_loss:0.478125, cluster_loss:0.159527\n",
      "Clustering   1577: ASW= 0.8283, DB= 0.2310, CH= 50483.8350\n",
      "Training epoch 1578, recon_loss:0.878158, zinb_loss:0.478151, cluster_loss:0.159807\n",
      "Clustering   1578: ASW= 0.8275, DB= 0.2318, CH= 50143.4998\n",
      "Training epoch 1579, recon_loss:0.878250, zinb_loss:0.478139, cluster_loss:0.159662\n",
      "Clustering   1579: ASW= 0.8282, DB= 0.2307, CH= 50710.7086\n",
      "Training epoch 1580, recon_loss:0.878441, zinb_loss:0.478093, cluster_loss:0.160125\n",
      "Clustering   1580: ASW= 0.8272, DB= 0.2324, CH= 49847.2248\n",
      "Training epoch 1581, recon_loss:0.878799, zinb_loss:0.478141, cluster_loss:0.159908\n",
      "Clustering   1581: ASW= 0.8282, DB= 0.2306, CH= 50951.2099\n",
      "Training epoch 1582, recon_loss:0.878512, zinb_loss:0.477997, cluster_loss:0.160415\n",
      "Clustering   1582: ASW= 0.8268, DB= 0.2329, CH= 49531.7902\n",
      "Training epoch 1583, recon_loss:0.879033, zinb_loss:0.478122, cluster_loss:0.160087\n",
      "Clustering   1583: ASW= 0.8283, DB= 0.2300, CH= 51219.1887\n",
      "Training epoch 1584, recon_loss:0.878202, zinb_loss:0.477888, cluster_loss:0.160542\n",
      "Clustering   1584: ASW= 0.8266, DB= 0.2331, CH= 49296.8829\n",
      "Training epoch 1585, recon_loss:0.878760, zinb_loss:0.478078, cluster_loss:0.160107\n",
      "Clustering   1585: ASW= 0.8284, DB= 0.2301, CH= 51388.4884\n",
      "Training epoch 1586, recon_loss:0.877799, zinb_loss:0.477796, cluster_loss:0.160517\n",
      "Clustering   1586: ASW= 0.8267, DB= 0.2329, CH= 49252.0005\n",
      "Training epoch 1587, recon_loss:0.878409, zinb_loss:0.478032, cluster_loss:0.160045\n",
      "Clustering   1587: ASW= 0.8288, DB= 0.2297, CH= 51541.1517\n",
      "Training epoch 1588, recon_loss:0.877451, zinb_loss:0.477732, cluster_loss:0.160403\n",
      "Clustering   1588: ASW= 0.8269, DB= 0.2323, CH= 49347.7949\n",
      "Training epoch 1589, recon_loss:0.878092, zinb_loss:0.477996, cluster_loss:0.159931\n",
      "Clustering   1589: ASW= 0.8293, DB= 0.2295, CH= 51718.4797\n",
      "Training epoch 1590, recon_loss:0.877178, zinb_loss:0.477697, cluster_loss:0.160250\n",
      "Clustering   1590: ASW= 0.8270, DB= 0.2319, CH= 49462.5436\n",
      "Training epoch 1591, recon_loss:0.877913, zinb_loss:0.477984, cluster_loss:0.159861\n",
      "Clustering   1591: ASW= 0.8297, DB= 0.2299, CH= 51822.6275\n",
      "Training epoch 1592, recon_loss:0.877066, zinb_loss:0.477692, cluster_loss:0.160129\n",
      "Clustering   1592: ASW= 0.8270, DB= 0.2326, CH= 49508.2050\n",
      "Training epoch 1593, recon_loss:0.877952, zinb_loss:0.478001, cluster_loss:0.159934\n",
      "Clustering   1593: ASW= 0.8298, DB= 0.2303, CH= 51792.2197\n",
      "Training epoch 1594, recon_loss:0.877246, zinb_loss:0.477726, cluster_loss:0.160156\n",
      "Clustering   1594: ASW= 0.8267, DB= 0.2328, CH= 49254.8898\n",
      "Training epoch 1595, recon_loss:0.878191, zinb_loss:0.478042, cluster_loss:0.160158\n",
      "Clustering   1595: ASW= 0.8296, DB= 0.2315, CH= 51499.1382\n",
      "Training epoch 1596, recon_loss:0.877468, zinb_loss:0.477796, cluster_loss:0.160227\n",
      "Clustering   1596: ASW= 0.8265, DB= 0.2327, CH= 48908.3699\n",
      "Training epoch 1597, recon_loss:0.878045, zinb_loss:0.478054, cluster_loss:0.160135\n",
      "Clustering   1597: ASW= 0.8296, DB= 0.2317, CH= 51306.2050\n",
      "Training epoch 1598, recon_loss:0.877125, zinb_loss:0.477842, cluster_loss:0.159909\n",
      "Clustering   1598: ASW= 0.8270, DB= 0.2317, CH= 49341.8084\n",
      "Training epoch 1599, recon_loss:0.877630, zinb_loss:0.478040, cluster_loss:0.159873\n",
      "Clustering   1599: ASW= 0.8298, DB= 0.2318, CH= 51346.7727\n",
      "Training epoch 1600, recon_loss:0.876823, zinb_loss:0.477876, cluster_loss:0.159606\n",
      "Clustering   1600: ASW= 0.8276, DB= 0.2311, CH= 49923.3256\n",
      "Training epoch 1601, recon_loss:0.877409, zinb_loss:0.478035, cluster_loss:0.159713\n",
      "Clustering   1601: ASW= 0.8299, DB= 0.2320, CH= 51356.1466\n",
      "Training epoch 1602, recon_loss:0.876772, zinb_loss:0.477905, cluster_loss:0.159463\n",
      "Clustering   1602: ASW= 0.8280, DB= 0.2305, CH= 50292.5030\n",
      "Training epoch 1603, recon_loss:0.877364, zinb_loss:0.478020, cluster_loss:0.159657\n",
      "Clustering   1603: ASW= 0.8298, DB= 0.2315, CH= 51296.9414\n",
      "Training epoch 1604, recon_loss:0.876857, zinb_loss:0.477930, cluster_loss:0.159409\n",
      "Clustering   1604: ASW= 0.8283, DB= 0.2299, CH= 50535.0473\n",
      "Training epoch 1605, recon_loss:0.877396, zinb_loss:0.477991, cluster_loss:0.159673\n",
      "Clustering   1605: ASW= 0.8296, DB= 0.2319, CH= 51167.4591\n",
      "Training epoch 1606, recon_loss:0.876982, zinb_loss:0.477934, cluster_loss:0.159411\n",
      "Clustering   1606: ASW= 0.8286, DB= 0.2295, CH= 50782.8978\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training epoch 1607, recon_loss:0.877446, zinb_loss:0.477938, cluster_loss:0.159731\n",
      "Clustering   1607: ASW= 0.8294, DB= 0.2323, CH= 51003.1188\n",
      "Training epoch 1608, recon_loss:0.877056, zinb_loss:0.477926, cluster_loss:0.159422\n",
      "Clustering   1608: ASW= 0.8288, DB= 0.2290, CH= 50999.6867\n",
      "Training epoch 1609, recon_loss:0.877366, zinb_loss:0.477863, cluster_loss:0.159773\n",
      "Clustering   1609: ASW= 0.8292, DB= 0.2326, CH= 50819.9348\n",
      "Training epoch 1610, recon_loss:0.877078, zinb_loss:0.477915, cluster_loss:0.159435\n",
      "Clustering   1610: ASW= 0.8289, DB= 0.2285, CH= 51188.6817\n",
      "Training epoch 1611, recon_loss:0.877266, zinb_loss:0.477786, cluster_loss:0.159821\n",
      "Clustering   1611: ASW= 0.8290, DB= 0.2333, CH= 50615.4946\n",
      "Training epoch 1612, recon_loss:0.877148, zinb_loss:0.477916, cluster_loss:0.159481\n",
      "Clustering   1612: ASW= 0.8289, DB= 0.2283, CH= 51354.4634\n",
      "Training epoch 1613, recon_loss:0.877231, zinb_loss:0.477727, cluster_loss:0.159912\n",
      "Clustering   1613: ASW= 0.8286, DB= 0.2339, CH= 50384.0385\n",
      "Training epoch 1614, recon_loss:0.877293, zinb_loss:0.477930, cluster_loss:0.159572\n",
      "Clustering   1614: ASW= 0.8291, DB= 0.2278, CH= 51443.0312\n",
      "Training epoch 1615, recon_loss:0.877254, zinb_loss:0.477685, cluster_loss:0.159990\n",
      "Clustering   1615: ASW= 0.8283, DB= 0.2344, CH= 50168.5606\n",
      "Training epoch 1616, recon_loss:0.877392, zinb_loss:0.477945, cluster_loss:0.159615\n",
      "Clustering   1616: ASW= 0.8292, DB= 0.2276, CH= 51474.7583\n",
      "Training epoch 1617, recon_loss:0.877193, zinb_loss:0.477657, cluster_loss:0.160005\n",
      "Clustering   1617: ASW= 0.8280, DB= 0.2338, CH= 50068.6813\n",
      "Training epoch 1618, recon_loss:0.877404, zinb_loss:0.477956, cluster_loss:0.159594\n",
      "Clustering   1618: ASW= 0.8294, DB= 0.2274, CH= 51459.9642\n",
      "Training epoch 1619, recon_loss:0.877169, zinb_loss:0.477643, cluster_loss:0.159963\n",
      "Clustering   1619: ASW= 0.8278, DB= 0.2339, CH= 50037.5068\n",
      "Training epoch 1620, recon_loss:0.877493, zinb_loss:0.477978, cluster_loss:0.159544\n",
      "Clustering   1620: ASW= 0.8297, DB= 0.2273, CH= 51398.5790\n",
      "Training epoch 1621, recon_loss:0.877198, zinb_loss:0.477644, cluster_loss:0.159889\n",
      "Clustering   1621: ASW= 0.8278, DB= 0.2335, CH= 50139.9435\n",
      "Training epoch 1622, recon_loss:0.877541, zinb_loss:0.478004, cluster_loss:0.159516\n",
      "Clustering   1622: ASW= 0.8300, DB= 0.2272, CH= 51331.8690\n",
      "Training epoch 1623, recon_loss:0.877244, zinb_loss:0.477650, cluster_loss:0.159823\n",
      "Clustering   1623: ASW= 0.8279, DB= 0.2329, CH= 50305.6318\n",
      "Training epoch 1624, recon_loss:0.877641, zinb_loss:0.478028, cluster_loss:0.159525\n",
      "Clustering   1624: ASW= 0.8304, DB= 0.2274, CH= 51297.3608\n",
      "Training epoch 1625, recon_loss:0.877539, zinb_loss:0.477688, cluster_loss:0.159800\n",
      "Clustering   1625: ASW= 0.8280, DB= 0.2323, CH= 50511.3133\n",
      "Training epoch 1626, recon_loss:0.877807, zinb_loss:0.478034, cluster_loss:0.159598\n",
      "Clustering   1626: ASW= 0.8306, DB= 0.2276, CH= 51272.1398\n",
      "Training epoch 1627, recon_loss:0.877576, zinb_loss:0.477693, cluster_loss:0.159724\n",
      "Clustering   1627: ASW= 0.8281, DB= 0.2320, CH= 50650.4784\n",
      "Training epoch 1628, recon_loss:0.877644, zinb_loss:0.477985, cluster_loss:0.159535\n",
      "Clustering   1628: ASW= 0.8307, DB= 0.2278, CH= 51290.7186\n",
      "Training epoch 1629, recon_loss:0.877535, zinb_loss:0.477718, cluster_loss:0.159607\n",
      "Clustering   1629: ASW= 0.8283, DB= 0.2315, CH= 50823.8955\n",
      "Training epoch 1630, recon_loss:0.877543, zinb_loss:0.477950, cluster_loss:0.159552\n",
      "Clustering   1630: ASW= 0.8307, DB= 0.2280, CH= 51328.5773\n",
      "Training epoch 1631, recon_loss:0.877480, zinb_loss:0.477707, cluster_loss:0.159549\n",
      "Clustering   1631: ASW= 0.8282, DB= 0.2313, CH= 50865.8296\n",
      "Training epoch 1632, recon_loss:0.877457, zinb_loss:0.477905, cluster_loss:0.159527\n",
      "Clustering   1632: ASW= 0.8307, DB= 0.2283, CH= 51342.9361\n",
      "Training epoch 1633, recon_loss:0.877410, zinb_loss:0.477717, cluster_loss:0.159516\n",
      "Clustering   1633: ASW= 0.8281, DB= 0.2313, CH= 50885.7367\n",
      "Training epoch 1634, recon_loss:0.877666, zinb_loss:0.477922, cluster_loss:0.159487\n",
      "Clustering   1634: ASW= 0.8308, DB= 0.2284, CH= 51354.6917\n",
      "Training epoch 1635, recon_loss:0.877700, zinb_loss:0.477782, cluster_loss:0.159509\n",
      "Clustering   1635: ASW= 0.8282, DB= 0.2311, CH= 50933.8599\n",
      "Training epoch 1636, recon_loss:0.877893, zinb_loss:0.477951, cluster_loss:0.159574\n",
      "Clustering   1636: ASW= 0.8306, DB= 0.2287, CH= 51304.5532\n",
      "Training epoch 1637, recon_loss:0.877837, zinb_loss:0.477821, cluster_loss:0.159572\n",
      "Clustering   1637: ASW= 0.8281, DB= 0.2311, CH= 50855.1971\n",
      "Training epoch 1638, recon_loss:0.877901, zinb_loss:0.477961, cluster_loss:0.159630\n",
      "Clustering   1638: ASW= 0.8303, DB= 0.2290, CH= 51154.4906\n",
      "Training epoch 1639, recon_loss:0.877666, zinb_loss:0.477857, cluster_loss:0.159610\n",
      "Clustering   1639: ASW= 0.8280, DB= 0.2311, CH= 50710.4597\n",
      "Training epoch 1640, recon_loss:0.877679, zinb_loss:0.477970, cluster_loss:0.159666\n",
      "Clustering   1640: ASW= 0.8298, DB= 0.2294, CH= 50895.1757\n",
      "Training epoch 1641, recon_loss:0.877416, zinb_loss:0.477882, cluster_loss:0.159665\n",
      "Clustering   1641: ASW= 0.8278, DB= 0.2306, CH= 50487.1036\n",
      "Training epoch 1642, recon_loss:0.877399, zinb_loss:0.477980, cluster_loss:0.159683\n",
      "Clustering   1642: ASW= 0.8295, DB= 0.2298, CH= 50724.1770\n",
      "Training epoch 1643, recon_loss:0.877058, zinb_loss:0.477890, cluster_loss:0.159681\n",
      "Clustering   1643: ASW= 0.8277, DB= 0.2315, CH= 50338.3651\n",
      "Training epoch 1644, recon_loss:0.877066, zinb_loss:0.477983, cluster_loss:0.159614\n",
      "Clustering   1644: ASW= 0.8294, DB= 0.2298, CH= 50837.1388\n",
      "Training epoch 1645, recon_loss:0.876711, zinb_loss:0.477878, cluster_loss:0.159644\n",
      "Clustering   1645: ASW= 0.8278, DB= 0.2313, CH= 50349.9151\n",
      "Training epoch 1646, recon_loss:0.876843, zinb_loss:0.477980, cluster_loss:0.159558\n",
      "Clustering   1646: ASW= 0.8293, DB= 0.2299, CH= 51033.6439\n",
      "Training epoch 1647, recon_loss:0.876549, zinb_loss:0.477864, cluster_loss:0.159634\n",
      "Clustering   1647: ASW= 0.8278, DB= 0.2312, CH= 50415.9550\n",
      "Training epoch 1648, recon_loss:0.876905, zinb_loss:0.477983, cluster_loss:0.159595\n",
      "Clustering   1648: ASW= 0.8293, DB= 0.2302, CH= 51178.6519\n",
      "Training epoch 1649, recon_loss:0.876707, zinb_loss:0.477857, cluster_loss:0.159685\n",
      "Clustering   1649: ASW= 0.8279, DB= 0.2312, CH= 50470.2603\n",
      "Training epoch 1650, recon_loss:0.877293, zinb_loss:0.477987, cluster_loss:0.159699\n",
      "Clustering   1650: ASW= 0.8292, DB= 0.2304, CH= 51266.8578\n",
      "Training epoch 1651, recon_loss:0.877233, zinb_loss:0.477858, cluster_loss:0.159779\n",
      "Clustering   1651: ASW= 0.8281, DB= 0.2311, CH= 50555.6732\n",
      "Training epoch 1652, recon_loss:0.877957, zinb_loss:0.477987, cluster_loss:0.159845\n",
      "Clustering   1652: ASW= 0.8291, DB= 0.2306, CH= 51318.6221\n",
      "Training epoch 1653, recon_loss:0.877847, zinb_loss:0.477862, cluster_loss:0.159861\n",
      "Clustering   1653: ASW= 0.8284, DB= 0.2308, CH= 50672.0427\n",
      "Training epoch 1654, recon_loss:0.878461, zinb_loss:0.477961, cluster_loss:0.159894\n",
      "Clustering   1654: ASW= 0.8289, DB= 0.2309, CH= 51371.1317\n",
      "Training epoch 1655, recon_loss:0.877994, zinb_loss:0.477846, cluster_loss:0.159793\n",
      "Clustering   1655: ASW= 0.8289, DB= 0.2303, CH= 50855.4811\n",
      "Training epoch 1656, recon_loss:0.878230, zinb_loss:0.477900, cluster_loss:0.159761\n",
      "Clustering   1656: ASW= 0.8289, DB= 0.2310, CH= 51436.6578\n",
      "Training epoch 1657, recon_loss:0.877568, zinb_loss:0.477802, cluster_loss:0.159610\n",
      "Clustering   1657: ASW= 0.8294, DB= 0.2298, CH= 51054.8430\n",
      "Training epoch 1658, recon_loss:0.877740, zinb_loss:0.477818, cluster_loss:0.159527\n",
      "Clustering   1658: ASW= 0.8289, DB= 0.2309, CH= 51532.8054\n",
      "Training epoch 1659, recon_loss:0.877140, zinb_loss:0.477752, cluster_loss:0.159427\n",
      "Clustering   1659: ASW= 0.8297, DB= 0.2299, CH= 51230.6404\n",
      "Training epoch 1660, recon_loss:0.877369, zinb_loss:0.477790, cluster_loss:0.159349\n",
      "Clustering   1660: ASW= 0.8290, DB= 0.2308, CH= 51633.7817\n",
      "Training epoch 1661, recon_loss:0.876798, zinb_loss:0.477693, cluster_loss:0.159306\n",
      "Clustering   1661: ASW= 0.8298, DB= 0.2303, CH= 51258.5690\n",
      "Training epoch 1662, recon_loss:0.877125, zinb_loss:0.477728, cluster_loss:0.159255\n",
      "Clustering   1662: ASW= 0.8290, DB= 0.2308, CH= 51647.1997\n",
      "Training epoch 1663, recon_loss:0.876918, zinb_loss:0.477673, cluster_loss:0.159331\n",
      "Clustering   1663: ASW= 0.8299, DB= 0.2303, CH= 51250.3842\n",
      "Training epoch 1664, recon_loss:0.877389, zinb_loss:0.477783, cluster_loss:0.159243\n",
      "Clustering   1664: ASW= 0.8290, DB= 0.2304, CH= 51570.3992\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training epoch 1665, recon_loss:0.877109, zinb_loss:0.477674, cluster_loss:0.159404\n",
      "Clustering   1665: ASW= 0.8297, DB= 0.2296, CH= 51056.4048\n",
      "Training epoch 1666, recon_loss:0.877670, zinb_loss:0.477864, cluster_loss:0.159348\n",
      "Clustering   1666: ASW= 0.8288, DB= 0.2305, CH= 51238.7315\n",
      "Training epoch 1667, recon_loss:0.877215, zinb_loss:0.477682, cluster_loss:0.159513\n",
      "Clustering   1667: ASW= 0.8293, DB= 0.2299, CH= 50752.3111\n",
      "Training epoch 1668, recon_loss:0.877704, zinb_loss:0.477910, cluster_loss:0.159377\n",
      "Clustering   1668: ASW= 0.8287, DB= 0.2304, CH= 51023.5320\n",
      "Training epoch 1669, recon_loss:0.877226, zinb_loss:0.477703, cluster_loss:0.159561\n",
      "Clustering   1669: ASW= 0.8292, DB= 0.2299, CH= 50700.3340\n",
      "Training epoch 1670, recon_loss:0.877552, zinb_loss:0.477907, cluster_loss:0.159368\n",
      "Clustering   1670: ASW= 0.8287, DB= 0.2302, CH= 51070.5436\n",
      "Training epoch 1671, recon_loss:0.877539, zinb_loss:0.477757, cluster_loss:0.159855\n",
      "Clustering   1671: ASW= 0.8291, DB= 0.2296, CH= 50600.9842\n",
      "Training epoch 1672, recon_loss:0.877856, zinb_loss:0.477963, cluster_loss:0.159749\n",
      "Clustering   1672: ASW= 0.8283, DB= 0.2304, CH= 50674.9039\n",
      "Training epoch 1673, recon_loss:0.877808, zinb_loss:0.477807, cluster_loss:0.160232\n",
      "Clustering   1673: ASW= 0.8289, DB= 0.2296, CH= 50386.2347\n",
      "Training epoch 1674, recon_loss:0.877738, zinb_loss:0.477942, cluster_loss:0.159979\n",
      "Clustering   1674: ASW= 0.8283, DB= 0.2305, CH= 50509.0958\n",
      "Training epoch 1675, recon_loss:0.877361, zinb_loss:0.477773, cluster_loss:0.160295\n",
      "Clustering   1675: ASW= 0.8288, DB= 0.2294, CH= 50308.4148\n",
      "Training epoch 1676, recon_loss:0.877076, zinb_loss:0.477860, cluster_loss:0.159812\n",
      "Clustering   1676: ASW= 0.8288, DB= 0.2302, CH= 50772.8787\n",
      "Training epoch 1677, recon_loss:0.876765, zinb_loss:0.477708, cluster_loss:0.160143\n",
      "Clustering   1677: ASW= 0.8288, DB= 0.2292, CH= 50396.9109\n",
      "Training epoch 1678, recon_loss:0.876594, zinb_loss:0.477805, cluster_loss:0.159622\n",
      "Clustering   1678: ASW= 0.8292, DB= 0.2299, CH= 50995.0601\n",
      "Training epoch 1679, recon_loss:0.876407, zinb_loss:0.477668, cluster_loss:0.159972\n",
      "Clustering   1679: ASW= 0.8289, DB= 0.2291, CH= 50519.8012\n",
      "Training epoch 1680, recon_loss:0.876296, zinb_loss:0.477768, cluster_loss:0.159490\n",
      "Clustering   1680: ASW= 0.8295, DB= 0.2297, CH= 51169.0962\n",
      "Training epoch 1681, recon_loss:0.876313, zinb_loss:0.477661, cluster_loss:0.159859\n",
      "Clustering   1681: ASW= 0.8290, DB= 0.2291, CH= 50650.2154\n",
      "Training epoch 1682, recon_loss:0.876286, zinb_loss:0.477767, cluster_loss:0.159432\n",
      "Clustering   1682: ASW= 0.8297, DB= 0.2296, CH= 51232.5066\n",
      "Training epoch 1683, recon_loss:0.876367, zinb_loss:0.477665, cluster_loss:0.159792\n",
      "Clustering   1683: ASW= 0.8289, DB= 0.2290, CH= 50749.0863\n",
      "Training epoch 1684, recon_loss:0.876362, zinb_loss:0.477764, cluster_loss:0.159387\n",
      "Clustering   1684: ASW= 0.8299, DB= 0.2295, CH= 51306.6920\n",
      "Training epoch 1685, recon_loss:0.876868, zinb_loss:0.477736, cluster_loss:0.159805\n",
      "Clustering   1685: ASW= 0.8289, DB= 0.2289, CH= 50922.9171\n",
      "Training epoch 1686, recon_loss:0.876633, zinb_loss:0.477763, cluster_loss:0.159392\n",
      "Clustering   1686: ASW= 0.8292, DB= 0.2301, CH= 50819.2126\n",
      "Training epoch 1687, recon_loss:0.876255, zinb_loss:0.477661, cluster_loss:0.159657\n",
      "Clustering   1687: ASW= 0.8287, DB= 0.2294, CH= 50693.4674\n",
      "Training epoch 1688, recon_loss:0.875863, zinb_loss:0.477697, cluster_loss:0.159186\n",
      "Clustering   1688: ASW= 0.8297, DB= 0.2294, CH= 51301.7403\n",
      "Training epoch 1689, recon_loss:0.875858, zinb_loss:0.477619, cluster_loss:0.159484\n",
      "Clustering   1689: ASW= 0.8288, DB= 0.2291, CH= 50908.8277\n",
      "Training epoch 1690, recon_loss:0.875808, zinb_loss:0.477691, cluster_loss:0.159136\n",
      "Clustering   1690: ASW= 0.8300, DB= 0.2290, CH= 51352.1767\n",
      "Training epoch 1691, recon_loss:0.875805, zinb_loss:0.477612, cluster_loss:0.159465\n",
      "Clustering   1691: ASW= 0.8288, DB= 0.2291, CH= 51041.1752\n",
      "Training epoch 1692, recon_loss:0.875971, zinb_loss:0.477686, cluster_loss:0.159268\n",
      "Clustering   1692: ASW= 0.8302, DB= 0.2292, CH= 51353.4209\n",
      "Training epoch 1693, recon_loss:0.876136, zinb_loss:0.477643, cluster_loss:0.159559\n",
      "Clustering   1693: ASW= 0.8287, DB= 0.2291, CH= 51230.4572\n",
      "Training epoch 1694, recon_loss:0.876389, zinb_loss:0.477693, cluster_loss:0.159512\n",
      "Clustering   1694: ASW= 0.8303, DB= 0.2295, CH= 51133.1960\n",
      "Training epoch 1695, recon_loss:0.876791, zinb_loss:0.477705, cluster_loss:0.159708\n",
      "Clustering   1695: ASW= 0.8285, DB= 0.2289, CH= 51451.4540\n",
      "Training epoch 1696, recon_loss:0.877197, zinb_loss:0.477755, cluster_loss:0.159934\n",
      "Clustering   1696: ASW= 0.8305, DB= 0.2301, CH= 50632.6800\n",
      "Training epoch 1697, recon_loss:0.877674, zinb_loss:0.477827, cluster_loss:0.159812\n",
      "Clustering   1697: ASW= 0.8283, DB= 0.2295, CH= 51731.1759\n",
      "Training epoch 1698, recon_loss:0.877603, zinb_loss:0.477807, cluster_loss:0.160334\n",
      "Clustering   1698: ASW= 0.8305, DB= 0.2309, CH= 50230.1199\n",
      "Training epoch 1699, recon_loss:0.877663, zinb_loss:0.477894, cluster_loss:0.159718\n",
      "Clustering   1699: ASW= 0.8284, DB= 0.2293, CH= 52052.0598\n",
      "Training epoch 1700, recon_loss:0.876688, zinb_loss:0.477735, cluster_loss:0.160140\n",
      "Clustering   1700: ASW= 0.8306, DB= 0.2306, CH= 50117.7339\n",
      "Training epoch 1701, recon_loss:0.876875, zinb_loss:0.477866, cluster_loss:0.159471\n",
      "Clustering   1701: ASW= 0.8285, DB= 0.2293, CH= 52278.7013\n",
      "Training epoch 1702, recon_loss:0.876008, zinb_loss:0.477651, cluster_loss:0.160144\n",
      "Clustering   1702: ASW= 0.8305, DB= 0.2308, CH= 49965.8538\n",
      "Training epoch 1703, recon_loss:0.876442, zinb_loss:0.477833, cluster_loss:0.159424\n",
      "Clustering   1703: ASW= 0.8285, DB= 0.2292, CH= 52341.8976\n",
      "Training epoch 1704, recon_loss:0.875951, zinb_loss:0.477669, cluster_loss:0.160287\n",
      "Clustering   1704: ASW= 0.8303, DB= 0.2312, CH= 49762.4369\n",
      "Training epoch 1705, recon_loss:0.876437, zinb_loss:0.477859, cluster_loss:0.159423\n",
      "Clustering   1705: ASW= 0.8286, DB= 0.2292, CH= 52347.8740\n",
      "Training epoch 1706, recon_loss:0.876086, zinb_loss:0.477692, cluster_loss:0.160381\n",
      "Clustering   1706: ASW= 0.8300, DB= 0.2314, CH= 49664.3302\n",
      "Training epoch 1707, recon_loss:0.876489, zinb_loss:0.477863, cluster_loss:0.159409\n",
      "Clustering   1707: ASW= 0.8287, DB= 0.2290, CH= 52315.5334\n",
      "Training epoch 1708, recon_loss:0.876108, zinb_loss:0.477725, cluster_loss:0.160310\n",
      "Clustering   1708: ASW= 0.8299, DB= 0.2316, CH= 49812.1474\n",
      "Training epoch 1709, recon_loss:0.876357, zinb_loss:0.477879, cluster_loss:0.159328\n",
      "Clustering   1709: ASW= 0.8289, DB= 0.2288, CH= 52365.3662\n",
      "Training epoch 1710, recon_loss:0.875953, zinb_loss:0.477717, cluster_loss:0.160162\n",
      "Clustering   1710: ASW= 0.8296, DB= 0.2307, CH= 49937.5547\n",
      "Training epoch 1711, recon_loss:0.876192, zinb_loss:0.477896, cluster_loss:0.159268\n",
      "Clustering   1711: ASW= 0.8290, DB= 0.2287, CH= 52353.2797\n",
      "Training epoch 1712, recon_loss:0.875764, zinb_loss:0.477733, cluster_loss:0.160055\n",
      "Clustering   1712: ASW= 0.8293, DB= 0.2308, CH= 50031.9548\n",
      "Training epoch 1713, recon_loss:0.876032, zinb_loss:0.477930, cluster_loss:0.159284\n",
      "Clustering   1713: ASW= 0.8290, DB= 0.2288, CH= 52236.6393\n",
      "Training epoch 1714, recon_loss:0.875659, zinb_loss:0.477757, cluster_loss:0.160011\n",
      "Clustering   1714: ASW= 0.8290, DB= 0.2308, CH= 50026.9554\n",
      "Training epoch 1715, recon_loss:0.875980, zinb_loss:0.477964, cluster_loss:0.159357\n",
      "Clustering   1715: ASW= 0.8288, DB= 0.2290, CH= 52006.2141\n",
      "Training epoch 1716, recon_loss:0.875579, zinb_loss:0.477779, cluster_loss:0.159969\n",
      "Clustering   1716: ASW= 0.8286, DB= 0.2308, CH= 50011.7407\n",
      "Training epoch 1717, recon_loss:0.875968, zinb_loss:0.477982, cluster_loss:0.159417\n",
      "Clustering   1717: ASW= 0.8286, DB= 0.2292, CH= 51791.0225\n",
      "Training epoch 1718, recon_loss:0.875541, zinb_loss:0.477785, cluster_loss:0.159874\n",
      "Clustering   1718: ASW= 0.8284, DB= 0.2304, CH= 50058.5261\n",
      "Training epoch 1719, recon_loss:0.876022, zinb_loss:0.477980, cluster_loss:0.159409\n",
      "Clustering   1719: ASW= 0.8288, DB= 0.2294, CH= 51722.1090\n",
      "Training epoch 1720, recon_loss:0.875599, zinb_loss:0.477780, cluster_loss:0.159772\n",
      "Clustering   1720: ASW= 0.8284, DB= 0.2302, CH= 50148.2601\n",
      "Training epoch 1721, recon_loss:0.876229, zinb_loss:0.477973, cluster_loss:0.159421\n",
      "Clustering   1721: ASW= 0.8290, DB= 0.2295, CH= 51753.7259\n",
      "Training epoch 1722, recon_loss:0.875843, zinb_loss:0.477762, cluster_loss:0.159748\n",
      "Clustering   1722: ASW= 0.8283, DB= 0.2300, CH= 50169.9044\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training epoch 1723, recon_loss:0.876729, zinb_loss:0.477968, cluster_loss:0.159534\n",
      "Clustering   1723: ASW= 0.8292, DB= 0.2296, CH= 51791.3014\n",
      "Training epoch 1724, recon_loss:0.876322, zinb_loss:0.477738, cluster_loss:0.159820\n",
      "Clustering   1724: ASW= 0.8282, DB= 0.2299, CH= 50052.7341\n",
      "Training epoch 1725, recon_loss:0.877453, zinb_loss:0.477962, cluster_loss:0.159741\n",
      "Clustering   1725: ASW= 0.8293, DB= 0.2299, CH= 51806.9469\n",
      "Training epoch 1726, recon_loss:0.876738, zinb_loss:0.477719, cluster_loss:0.159893\n",
      "Clustering   1726: ASW= 0.8281, DB= 0.2308, CH= 49951.4678\n",
      "Training epoch 1727, recon_loss:0.877635, zinb_loss:0.477890, cluster_loss:0.159704\n",
      "Clustering   1727: ASW= 0.8294, DB= 0.2301, CH= 51879.9646\n",
      "Training epoch 1728, recon_loss:0.876536, zinb_loss:0.477693, cluster_loss:0.159688\n",
      "Clustering   1728: ASW= 0.8285, DB= 0.2302, CH= 50163.2641\n",
      "Training epoch 1729, recon_loss:0.877253, zinb_loss:0.477788, cluster_loss:0.159443\n",
      "Clustering   1729: ASW= 0.8296, DB= 0.2299, CH= 51994.0726\n",
      "Training epoch 1730, recon_loss:0.876266, zinb_loss:0.477659, cluster_loss:0.159481\n",
      "Clustering   1730: ASW= 0.8290, DB= 0.2296, CH= 50488.3490\n",
      "Training epoch 1731, recon_loss:0.877024, zinb_loss:0.477720, cluster_loss:0.159336\n",
      "Clustering   1731: ASW= 0.8296, DB= 0.2303, CH= 52013.5280\n",
      "Training epoch 1732, recon_loss:0.876317, zinb_loss:0.477670, cluster_loss:0.159440\n",
      "Clustering   1732: ASW= 0.8293, DB= 0.2291, CH= 50683.3323\n",
      "Training epoch 1733, recon_loss:0.877245, zinb_loss:0.477691, cluster_loss:0.159398\n",
      "Clustering   1733: ASW= 0.8296, DB= 0.2297, CH= 51998.9344\n",
      "Training epoch 1734, recon_loss:0.876735, zinb_loss:0.477708, cluster_loss:0.159537\n",
      "Clustering   1734: ASW= 0.8296, DB= 0.2287, CH= 50804.6107\n",
      "Training epoch 1735, recon_loss:0.877657, zinb_loss:0.477697, cluster_loss:0.159574\n",
      "Clustering   1735: ASW= 0.8295, DB= 0.2300, CH= 51943.5473\n",
      "Training epoch 1736, recon_loss:0.877238, zinb_loss:0.477758, cluster_loss:0.159667\n",
      "Clustering   1736: ASW= 0.8297, DB= 0.2290, CH= 50835.4491\n",
      "Training epoch 1737, recon_loss:0.877930, zinb_loss:0.477701, cluster_loss:0.159685\n",
      "Clustering   1737: ASW= 0.8295, DB= 0.2301, CH= 51846.6259\n",
      "Training epoch 1738, recon_loss:0.877414, zinb_loss:0.477790, cluster_loss:0.159718\n",
      "Clustering   1738: ASW= 0.8297, DB= 0.2292, CH= 50830.6386\n",
      "Training epoch 1739, recon_loss:0.877863, zinb_loss:0.477710, cluster_loss:0.159707\n",
      "Clustering   1739: ASW= 0.8295, DB= 0.2299, CH= 51765.0749\n",
      "Training epoch 1740, recon_loss:0.877340, zinb_loss:0.477818, cluster_loss:0.159687\n",
      "Clustering   1740: ASW= 0.8296, DB= 0.2293, CH= 50822.8784\n",
      "Training epoch 1741, recon_loss:0.877559, zinb_loss:0.477721, cluster_loss:0.159646\n",
      "Clustering   1741: ASW= 0.8296, DB= 0.2294, CH= 51741.0188\n",
      "Training epoch 1742, recon_loss:0.877148, zinb_loss:0.477837, cluster_loss:0.159582\n",
      "Clustering   1742: ASW= 0.8294, DB= 0.2291, CH= 50903.2819\n",
      "Training epoch 1743, recon_loss:0.877165, zinb_loss:0.477742, cluster_loss:0.159536\n",
      "Clustering   1743: ASW= 0.8298, DB= 0.2288, CH= 51769.2949\n",
      "Training epoch 1744, recon_loss:0.876941, zinb_loss:0.477858, cluster_loss:0.159451\n",
      "Clustering   1744: ASW= 0.8292, DB= 0.2293, CH= 50987.1153\n",
      "Training epoch 1745, recon_loss:0.876910, zinb_loss:0.477801, cluster_loss:0.159448\n",
      "Clustering   1745: ASW= 0.8301, DB= 0.2284, CH= 51840.8659\n",
      "Training epoch 1746, recon_loss:0.876879, zinb_loss:0.477904, cluster_loss:0.159369\n",
      "Clustering   1746: ASW= 0.8289, DB= 0.2296, CH= 50985.4225\n",
      "Training epoch 1747, recon_loss:0.876852, zinb_loss:0.477903, cluster_loss:0.159413\n",
      "Clustering   1747: ASW= 0.8305, DB= 0.2280, CH= 51934.0871\n",
      "Training epoch 1748, recon_loss:0.876939, zinb_loss:0.477960, cluster_loss:0.159350\n",
      "Clustering   1748: ASW= 0.8285, DB= 0.2300, CH= 50930.8957\n",
      "Training epoch 1749, recon_loss:0.876924, zinb_loss:0.477987, cluster_loss:0.159394\n",
      "Clustering   1749: ASW= 0.8308, DB= 0.2276, CH= 52041.4364\n",
      "Training epoch 1750, recon_loss:0.876970, zinb_loss:0.477984, cluster_loss:0.159339\n",
      "Clustering   1750: ASW= 0.8283, DB= 0.2302, CH= 50887.7188\n",
      "Training epoch 1751, recon_loss:0.877049, zinb_loss:0.478052, cluster_loss:0.159440\n",
      "Clustering   1751: ASW= 0.8310, DB= 0.2284, CH= 52114.8565\n",
      "Training epoch 1752, recon_loss:0.876999, zinb_loss:0.477945, cluster_loss:0.159342\n",
      "Clustering   1752: ASW= 0.8281, DB= 0.2306, CH= 50855.0705\n",
      "Training epoch 1753, recon_loss:0.877102, zinb_loss:0.478024, cluster_loss:0.159423\n",
      "Clustering   1753: ASW= 0.8313, DB= 0.2284, CH= 52156.9419\n",
      "Training epoch 1754, recon_loss:0.876862, zinb_loss:0.477855, cluster_loss:0.159337\n",
      "Clustering   1754: ASW= 0.8280, DB= 0.2308, CH= 50883.2783\n",
      "Training epoch 1755, recon_loss:0.877085, zinb_loss:0.477976, cluster_loss:0.159409\n",
      "Clustering   1755: ASW= 0.8314, DB= 0.2289, CH= 52098.3841\n",
      "Training epoch 1756, recon_loss:0.876779, zinb_loss:0.477759, cluster_loss:0.159317\n",
      "Clustering   1756: ASW= 0.8282, DB= 0.2298, CH= 50920.0503\n",
      "Training epoch 1757, recon_loss:0.877089, zinb_loss:0.477887, cluster_loss:0.159409\n",
      "Clustering   1757: ASW= 0.8313, DB= 0.2290, CH= 52037.6751\n",
      "Training epoch 1758, recon_loss:0.876562, zinb_loss:0.477627, cluster_loss:0.159301\n",
      "Clustering   1758: ASW= 0.8284, DB= 0.2293, CH= 50987.1157\n",
      "Training epoch 1759, recon_loss:0.876735, zinb_loss:0.477797, cluster_loss:0.159289\n",
      "Clustering   1759: ASW= 0.8315, DB= 0.2291, CH= 52135.0981\n",
      "Training epoch 1760, recon_loss:0.876133, zinb_loss:0.477527, cluster_loss:0.159230\n",
      "Clustering   1760: ASW= 0.8286, DB= 0.2291, CH= 51113.0741\n",
      "Training epoch 1761, recon_loss:0.876351, zinb_loss:0.477718, cluster_loss:0.159201\n",
      "Clustering   1761: ASW= 0.8315, DB= 0.2288, CH= 52209.4205\n",
      "Training epoch 1762, recon_loss:0.875761, zinb_loss:0.477435, cluster_loss:0.159214\n",
      "Clustering   1762: ASW= 0.8287, DB= 0.2292, CH= 51206.7485\n",
      "Training epoch 1763, recon_loss:0.876010, zinb_loss:0.477683, cluster_loss:0.159145\n",
      "Clustering   1763: ASW= 0.8317, DB= 0.2286, CH= 52294.6927\n",
      "Training epoch 1764, recon_loss:0.875655, zinb_loss:0.477402, cluster_loss:0.159277\n",
      "Clustering   1764: ASW= 0.8288, DB= 0.2292, CH= 51286.7244\n",
      "Training epoch 1765, recon_loss:0.875993, zinb_loss:0.477684, cluster_loss:0.159197\n",
      "Clustering   1765: ASW= 0.8318, DB= 0.2283, CH= 52232.6743\n",
      "Training epoch 1766, recon_loss:0.875731, zinb_loss:0.477393, cluster_loss:0.159409\n",
      "Clustering   1766: ASW= 0.8288, DB= 0.2294, CH= 51279.6010\n",
      "Training epoch 1767, recon_loss:0.876053, zinb_loss:0.477699, cluster_loss:0.159283\n",
      "Clustering   1767: ASW= 0.8317, DB= 0.2279, CH= 52063.0327\n",
      "Training epoch 1768, recon_loss:0.875735, zinb_loss:0.477405, cluster_loss:0.159474\n",
      "Clustering   1768: ASW= 0.8289, DB= 0.2296, CH= 51223.5305\n",
      "Training epoch 1769, recon_loss:0.875943, zinb_loss:0.477698, cluster_loss:0.159268\n",
      "Clustering   1769: ASW= 0.8317, DB= 0.2277, CH= 52016.2938\n",
      "Training epoch 1770, recon_loss:0.875608, zinb_loss:0.477412, cluster_loss:0.159456\n",
      "Clustering   1770: ASW= 0.8289, DB= 0.2298, CH= 51177.1952\n",
      "Training epoch 1771, recon_loss:0.875844, zinb_loss:0.477704, cluster_loss:0.159235\n",
      "Clustering   1771: ASW= 0.8316, DB= 0.2273, CH= 52077.0636\n",
      "Training epoch 1772, recon_loss:0.875603, zinb_loss:0.477435, cluster_loss:0.159447\n",
      "Clustering   1772: ASW= 0.8289, DB= 0.2300, CH= 51148.8279\n",
      "Training epoch 1773, recon_loss:0.875934, zinb_loss:0.477731, cluster_loss:0.159261\n",
      "Clustering   1773: ASW= 0.8316, DB= 0.2270, CH= 52142.4367\n",
      "Training epoch 1774, recon_loss:0.875763, zinb_loss:0.477465, cluster_loss:0.159510\n",
      "Clustering   1774: ASW= 0.8288, DB= 0.2303, CH= 51081.9311\n",
      "Training epoch 1775, recon_loss:0.876136, zinb_loss:0.477755, cluster_loss:0.159366\n",
      "Clustering   1775: ASW= 0.8313, DB= 0.2269, CH= 52106.4245\n",
      "Training epoch 1776, recon_loss:0.875927, zinb_loss:0.477498, cluster_loss:0.159581\n",
      "Clustering   1776: ASW= 0.8287, DB= 0.2306, CH= 51003.5295\n",
      "Training epoch 1777, recon_loss:0.876264, zinb_loss:0.477765, cluster_loss:0.159471\n",
      "Clustering   1777: ASW= 0.8310, DB= 0.2270, CH= 51991.0833\n",
      "Training epoch 1778, recon_loss:0.875943, zinb_loss:0.477516, cluster_loss:0.159598\n",
      "Clustering   1778: ASW= 0.8285, DB= 0.2310, CH= 50923.0701\n",
      "Training epoch 1779, recon_loss:0.876206, zinb_loss:0.477740, cluster_loss:0.159478\n",
      "Clustering   1779: ASW= 0.8307, DB= 0.2271, CH= 51858.6854\n",
      "Training epoch 1780, recon_loss:0.875782, zinb_loss:0.477520, cluster_loss:0.159521\n",
      "Clustering   1780: ASW= 0.8285, DB= 0.2311, CH= 50891.6446\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training epoch 1781, recon_loss:0.875983, zinb_loss:0.477690, cluster_loss:0.159355\n",
      "Clustering   1781: ASW= 0.8304, DB= 0.2269, CH= 51783.5805\n",
      "Training epoch 1782, recon_loss:0.875604, zinb_loss:0.477511, cluster_loss:0.159432\n",
      "Clustering   1782: ASW= 0.8284, DB= 0.2311, CH= 50852.3771\n",
      "Training epoch 1783, recon_loss:0.875875, zinb_loss:0.477645, cluster_loss:0.159240\n",
      "Clustering   1783: ASW= 0.8301, DB= 0.2271, CH= 51673.1143\n",
      "Training epoch 1784, recon_loss:0.875658, zinb_loss:0.477514, cluster_loss:0.159363\n",
      "Clustering   1784: ASW= 0.8285, DB= 0.2310, CH= 50851.5525\n",
      "Training epoch 1785, recon_loss:0.876045, zinb_loss:0.477632, cluster_loss:0.159156\n",
      "Clustering   1785: ASW= 0.8298, DB= 0.2272, CH= 51604.3400\n",
      "Training epoch 1786, recon_loss:0.875883, zinb_loss:0.477527, cluster_loss:0.159403\n",
      "Clustering   1786: ASW= 0.8286, DB= 0.2312, CH= 50811.8615\n",
      "Training epoch 1787, recon_loss:0.876391, zinb_loss:0.477645, cluster_loss:0.159173\n",
      "Clustering   1787: ASW= 0.8295, DB= 0.2274, CH= 51514.8544\n",
      "Training epoch 1788, recon_loss:0.876314, zinb_loss:0.477550, cluster_loss:0.159499\n",
      "Clustering   1788: ASW= 0.8288, DB= 0.2312, CH= 50819.9250\n",
      "Training epoch 1789, recon_loss:0.876875, zinb_loss:0.477675, cluster_loss:0.159238\n",
      "Clustering   1789: ASW= 0.8294, DB= 0.2274, CH= 51525.3989\n",
      "Training epoch 1790, recon_loss:0.876768, zinb_loss:0.477554, cluster_loss:0.159704\n",
      "Clustering   1790: ASW= 0.8291, DB= 0.2315, CH= 50834.2374\n",
      "Training epoch 1791, recon_loss:0.877173, zinb_loss:0.477670, cluster_loss:0.159345\n",
      "Clustering   1791: ASW= 0.8293, DB= 0.2276, CH= 51540.5851\n",
      "Training epoch 1792, recon_loss:0.877003, zinb_loss:0.477530, cluster_loss:0.159871\n",
      "Clustering   1792: ASW= 0.8295, DB= 0.2313, CH= 50968.8452\n",
      "Training epoch 1793, recon_loss:0.877136, zinb_loss:0.477627, cluster_loss:0.159386\n",
      "Clustering   1793: ASW= 0.8293, DB= 0.2274, CH= 51603.1275\n",
      "Training epoch 1794, recon_loss:0.876837, zinb_loss:0.477469, cluster_loss:0.159950\n",
      "Clustering   1794: ASW= 0.8298, DB= 0.2311, CH= 51117.2000\n",
      "Training epoch 1795, recon_loss:0.876811, zinb_loss:0.477568, cluster_loss:0.159363\n",
      "Clustering   1795: ASW= 0.8295, DB= 0.2273, CH= 51674.9456\n",
      "Training epoch 1796, recon_loss:0.876459, zinb_loss:0.477405, cluster_loss:0.159909\n",
      "Clustering   1796: ASW= 0.8301, DB= 0.2308, CH= 51288.3428\n",
      "Training epoch 1797, recon_loss:0.876480, zinb_loss:0.477533, cluster_loss:0.159301\n",
      "Clustering   1797: ASW= 0.8298, DB= 0.2269, CH= 51750.5992\n",
      "Training epoch 1798, recon_loss:0.876107, zinb_loss:0.477366, cluster_loss:0.159807\n",
      "Clustering   1798: ASW= 0.8302, DB= 0.2305, CH= 51467.5051\n",
      "Training epoch 1799, recon_loss:0.876164, zinb_loss:0.477529, cluster_loss:0.159233\n",
      "Clustering   1799: ASW= 0.8301, DB= 0.2268, CH= 51777.5805\n",
      "Training epoch 1800, recon_loss:0.875854, zinb_loss:0.477356, cluster_loss:0.159685\n",
      "Clustering   1800: ASW= 0.8302, DB= 0.2304, CH= 51625.6433\n",
      "Training epoch 1801, recon_loss:0.876012, zinb_loss:0.477531, cluster_loss:0.159215\n",
      "Clustering   1801: ASW= 0.8303, DB= 0.2267, CH= 51796.4285\n",
      "Training epoch 1802, recon_loss:0.875785, zinb_loss:0.477363, cluster_loss:0.159603\n",
      "Clustering   1802: ASW= 0.8302, DB= 0.2303, CH= 51791.1119\n",
      "Training epoch 1803, recon_loss:0.875920, zinb_loss:0.477541, cluster_loss:0.159209\n",
      "Clustering   1803: ASW= 0.8304, DB= 0.2264, CH= 51747.8417\n",
      "Training epoch 1804, recon_loss:0.875705, zinb_loss:0.477360, cluster_loss:0.159485\n",
      "Clustering   1804: ASW= 0.8302, DB= 0.2299, CH= 51895.4260\n",
      "Training epoch 1805, recon_loss:0.875775, zinb_loss:0.477534, cluster_loss:0.159138\n",
      "Clustering   1805: ASW= 0.8305, DB= 0.2265, CH= 51813.5051\n",
      "Training epoch 1806, recon_loss:0.875665, zinb_loss:0.477353, cluster_loss:0.159367\n",
      "Clustering   1806: ASW= 0.8303, DB= 0.2296, CH= 51982.8971\n",
      "Training epoch 1807, recon_loss:0.875753, zinb_loss:0.477543, cluster_loss:0.159064\n",
      "Clustering   1807: ASW= 0.8305, DB= 0.2264, CH= 51845.0710\n",
      "Training epoch 1808, recon_loss:0.875786, zinb_loss:0.477359, cluster_loss:0.159294\n",
      "Clustering   1808: ASW= 0.8303, DB= 0.2295, CH= 51993.8040\n",
      "Training epoch 1809, recon_loss:0.875902, zinb_loss:0.477562, cluster_loss:0.159016\n",
      "Clustering   1809: ASW= 0.8304, DB= 0.2266, CH= 51918.4022\n",
      "Training epoch 1810, recon_loss:0.876014, zinb_loss:0.477377, cluster_loss:0.159280\n",
      "Clustering   1810: ASW= 0.8304, DB= 0.2294, CH= 51977.2546\n",
      "Training epoch 1811, recon_loss:0.876029, zinb_loss:0.477584, cluster_loss:0.159010\n",
      "Clustering   1811: ASW= 0.8302, DB= 0.2269, CH= 51898.8278\n",
      "Training epoch 1812, recon_loss:0.876109, zinb_loss:0.477393, cluster_loss:0.159286\n",
      "Clustering   1812: ASW= 0.8304, DB= 0.2292, CH= 51924.8998\n",
      "Training epoch 1813, recon_loss:0.876029, zinb_loss:0.477587, cluster_loss:0.159028\n",
      "Clustering   1813: ASW= 0.8299, DB= 0.2273, CH= 51882.2357\n",
      "Training epoch 1814, recon_loss:0.876128, zinb_loss:0.477409, cluster_loss:0.159329\n",
      "Clustering   1814: ASW= 0.8304, DB= 0.2291, CH= 51874.0135\n",
      "Training epoch 1815, recon_loss:0.875991, zinb_loss:0.477580, cluster_loss:0.159092\n",
      "Clustering   1815: ASW= 0.8296, DB= 0.2279, CH= 51744.1882\n",
      "Training epoch 1816, recon_loss:0.876128, zinb_loss:0.477424, cluster_loss:0.159397\n",
      "Clustering   1816: ASW= 0.8304, DB= 0.2289, CH= 51839.0012\n",
      "Training epoch 1817, recon_loss:0.875975, zinb_loss:0.477563, cluster_loss:0.159200\n",
      "Clustering   1817: ASW= 0.8292, DB= 0.2286, CH= 51569.0394\n",
      "Training epoch 1818, recon_loss:0.876131, zinb_loss:0.477438, cluster_loss:0.159458\n",
      "Clustering   1818: ASW= 0.8305, DB= 0.2287, CH= 51852.4378\n",
      "Training epoch 1819, recon_loss:0.875913, zinb_loss:0.477546, cluster_loss:0.159281\n",
      "Clustering   1819: ASW= 0.8290, DB= 0.2291, CH= 51471.4452\n",
      "Training epoch 1820, recon_loss:0.876063, zinb_loss:0.477444, cluster_loss:0.159445\n",
      "Clustering   1820: ASW= 0.8307, DB= 0.2284, CH= 51908.6948\n",
      "Training epoch 1821, recon_loss:0.875760, zinb_loss:0.477530, cluster_loss:0.159250\n",
      "Clustering   1821: ASW= 0.8292, DB= 0.2292, CH= 51561.5530\n",
      "Training epoch 1822, recon_loss:0.875933, zinb_loss:0.477449, cluster_loss:0.159340\n",
      "Clustering   1822: ASW= 0.8309, DB= 0.2279, CH= 52018.6153\n",
      "Training epoch 1823, recon_loss:0.875563, zinb_loss:0.477527, cluster_loss:0.159136\n",
      "Clustering   1823: ASW= 0.8296, DB= 0.2290, CH= 51766.2309\n",
      "Training epoch 1824, recon_loss:0.875783, zinb_loss:0.477455, cluster_loss:0.159225\n",
      "Clustering   1824: ASW= 0.8311, DB= 0.2274, CH= 52133.9223\n",
      "Training epoch 1825, recon_loss:0.875542, zinb_loss:0.477527, cluster_loss:0.159103\n",
      "Clustering   1825: ASW= 0.8298, DB= 0.2288, CH= 51939.0132\n",
      "Training epoch 1826, recon_loss:0.875839, zinb_loss:0.477490, cluster_loss:0.159132\n",
      "Clustering   1826: ASW= 0.8312, DB= 0.2273, CH= 52176.6258\n",
      "Training epoch 1827, recon_loss:0.875399, zinb_loss:0.477549, cluster_loss:0.158963\n",
      "Clustering   1827: ASW= 0.8300, DB= 0.2285, CH= 52054.1961\n",
      "Training epoch 1828, recon_loss:0.875946, zinb_loss:0.477526, cluster_loss:0.159149\n",
      "Clustering   1828: ASW= 0.8311, DB= 0.2273, CH= 52201.5509\n",
      "Training epoch 1829, recon_loss:0.875699, zinb_loss:0.477590, cluster_loss:0.159012\n",
      "Clustering   1829: ASW= 0.8301, DB= 0.2284, CH= 52076.2177\n",
      "Training epoch 1830, recon_loss:0.876175, zinb_loss:0.477583, cluster_loss:0.159184\n",
      "Clustering   1830: ASW= 0.8313, DB= 0.2272, CH= 52259.7496\n",
      "Training epoch 1831, recon_loss:0.875823, zinb_loss:0.477617, cluster_loss:0.159049\n",
      "Clustering   1831: ASW= 0.8300, DB= 0.2285, CH= 52030.2084\n",
      "Training epoch 1832, recon_loss:0.876342, zinb_loss:0.477666, cluster_loss:0.159235\n",
      "Clustering   1832: ASW= 0.8315, DB= 0.2270, CH= 52336.8533\n",
      "Training epoch 1833, recon_loss:0.875771, zinb_loss:0.477634, cluster_loss:0.159079\n",
      "Clustering   1833: ASW= 0.8299, DB= 0.2287, CH= 51943.2785\n",
      "Training epoch 1834, recon_loss:0.876349, zinb_loss:0.477731, cluster_loss:0.159301\n",
      "Clustering   1834: ASW= 0.8317, DB= 0.2271, CH= 52415.8082\n",
      "Training epoch 1835, recon_loss:0.875648, zinb_loss:0.477642, cluster_loss:0.159104\n",
      "Clustering   1835: ASW= 0.8297, DB= 0.2288, CH= 51812.1946\n",
      "Training epoch 1836, recon_loss:0.876342, zinb_loss:0.477786, cluster_loss:0.159369\n",
      "Clustering   1836: ASW= 0.8319, DB= 0.2268, CH= 52575.0725\n",
      "Training epoch 1837, recon_loss:0.875574, zinb_loss:0.477633, cluster_loss:0.159170\n",
      "Clustering   1837: ASW= 0.8296, DB= 0.2290, CH= 51575.7228\n",
      "Training epoch 1838, recon_loss:0.876504, zinb_loss:0.477822, cluster_loss:0.159517\n",
      "Clustering   1838: ASW= 0.8320, DB= 0.2264, CH= 52742.3615\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training epoch 1839, recon_loss:0.875793, zinb_loss:0.477602, cluster_loss:0.159348\n",
      "Clustering   1839: ASW= 0.8295, DB= 0.2293, CH= 51220.7542\n",
      "Training epoch 1840, recon_loss:0.877018, zinb_loss:0.477836, cluster_loss:0.159769\n",
      "Clustering   1840: ASW= 0.8321, DB= 0.2257, CH= 52893.6760\n",
      "Training epoch 1841, recon_loss:0.876303, zinb_loss:0.477531, cluster_loss:0.159698\n",
      "Clustering   1841: ASW= 0.8292, DB= 0.2298, CH= 50673.8809\n",
      "Training epoch 1842, recon_loss:0.877476, zinb_loss:0.477780, cluster_loss:0.159935\n",
      "Clustering   1842: ASW= 0.8319, DB= 0.2253, CH= 52846.7049\n",
      "Training epoch 1843, recon_loss:0.876379, zinb_loss:0.477440, cluster_loss:0.159737\n",
      "Clustering   1843: ASW= 0.8293, DB= 0.2301, CH= 50583.8590\n",
      "Training epoch 1844, recon_loss:0.877542, zinb_loss:0.477726, cluster_loss:0.159855\n",
      "Clustering   1844: ASW= 0.8316, DB= 0.2260, CH= 52813.3930\n",
      "Training epoch 1845, recon_loss:0.876418, zinb_loss:0.477397, cluster_loss:0.159701\n",
      "Clustering   1845: ASW= 0.8295, DB= 0.2305, CH= 50657.0951\n",
      "Training epoch 1846, recon_loss:0.877299, zinb_loss:0.477665, cluster_loss:0.159771\n",
      "Clustering   1846: ASW= 0.8312, DB= 0.2265, CH= 52720.4733\n",
      "Training epoch 1847, recon_loss:0.876485, zinb_loss:0.477371, cluster_loss:0.159681\n",
      "Clustering   1847: ASW= 0.8297, DB= 0.2306, CH= 50687.1059\n",
      "Training epoch 1848, recon_loss:0.877083, zinb_loss:0.477632, cluster_loss:0.159649\n",
      "Clustering   1848: ASW= 0.8310, DB= 0.2263, CH= 52652.0314\n",
      "Training epoch 1849, recon_loss:0.876039, zinb_loss:0.477389, cluster_loss:0.159627\n",
      "Clustering   1849: ASW= 0.8299, DB= 0.2305, CH= 50858.8325\n",
      "Training epoch 1850, recon_loss:0.876613, zinb_loss:0.477631, cluster_loss:0.159572\n",
      "Clustering   1850: ASW= 0.8308, DB= 0.2265, CH= 52585.8394\n",
      "Training epoch 1851, recon_loss:0.876155, zinb_loss:0.477374, cluster_loss:0.159791\n",
      "Clustering   1851: ASW= 0.8299, DB= 0.2309, CH= 50729.9787\n",
      "Training epoch 1852, recon_loss:0.876344, zinb_loss:0.477600, cluster_loss:0.159513\n",
      "Clustering   1852: ASW= 0.8308, DB= 0.2264, CH= 52454.3756\n",
      "Training epoch 1853, recon_loss:0.875695, zinb_loss:0.477364, cluster_loss:0.159476\n",
      "Clustering   1853: ASW= 0.8302, DB= 0.2301, CH= 51116.5029\n",
      "Training epoch 1854, recon_loss:0.875864, zinb_loss:0.477542, cluster_loss:0.159338\n",
      "Clustering   1854: ASW= 0.8308, DB= 0.2265, CH= 52374.6763\n",
      "Training epoch 1855, recon_loss:0.875396, zinb_loss:0.477392, cluster_loss:0.159299\n",
      "Clustering   1855: ASW= 0.8304, DB= 0.2296, CH= 51488.8013\n",
      "Training epoch 1856, recon_loss:0.875614, zinb_loss:0.477525, cluster_loss:0.159307\n",
      "Clustering   1856: ASW= 0.8309, DB= 0.2268, CH= 52269.0138\n",
      "Training epoch 1857, recon_loss:0.875573, zinb_loss:0.477435, cluster_loss:0.159251\n",
      "Clustering   1857: ASW= 0.8306, DB= 0.2293, CH= 51754.1472\n",
      "Training epoch 1858, recon_loss:0.875747, zinb_loss:0.477515, cluster_loss:0.159426\n",
      "Clustering   1858: ASW= 0.8309, DB= 0.2262, CH= 52100.6374\n",
      "Training epoch 1859, recon_loss:0.875826, zinb_loss:0.477498, cluster_loss:0.159310\n",
      "Clustering   1859: ASW= 0.8306, DB= 0.2289, CH= 51993.2963\n",
      "Training epoch 1860, recon_loss:0.875865, zinb_loss:0.477497, cluster_loss:0.159591\n",
      "Clustering   1860: ASW= 0.8307, DB= 0.2267, CH= 51885.7237\n",
      "Training epoch 1861, recon_loss:0.875955, zinb_loss:0.477554, cluster_loss:0.159338\n",
      "Clustering   1861: ASW= 0.8307, DB= 0.2285, CH= 52215.6208\n",
      "Training epoch 1862, recon_loss:0.875868, zinb_loss:0.477466, cluster_loss:0.159714\n",
      "Clustering   1862: ASW= 0.8303, DB= 0.2275, CH= 51628.4621\n",
      "Training epoch 1863, recon_loss:0.875958, zinb_loss:0.477603, cluster_loss:0.159345\n",
      "Clustering   1863: ASW= 0.8307, DB= 0.2281, CH= 52385.8816\n",
      "Training epoch 1864, recon_loss:0.875822, zinb_loss:0.477436, cluster_loss:0.159797\n",
      "Clustering   1864: ASW= 0.8299, DB= 0.2283, CH= 51373.8385\n",
      "Training epoch 1865, recon_loss:0.875989, zinb_loss:0.477640, cluster_loss:0.159359\n",
      "Clustering   1865: ASW= 0.8308, DB= 0.2278, CH= 52523.2298\n",
      "Training epoch 1866, recon_loss:0.875818, zinb_loss:0.477418, cluster_loss:0.159883\n",
      "Clustering   1866: ASW= 0.8295, DB= 0.2291, CH= 51140.0850\n",
      "Training epoch 1867, recon_loss:0.876014, zinb_loss:0.477667, cluster_loss:0.159359\n",
      "Clustering   1867: ASW= 0.8309, DB= 0.2271, CH= 52652.7043\n",
      "Training epoch 1868, recon_loss:0.875622, zinb_loss:0.477398, cluster_loss:0.159879\n",
      "Clustering   1868: ASW= 0.8293, DB= 0.2294, CH= 51022.7348\n",
      "Training epoch 1869, recon_loss:0.875717, zinb_loss:0.477653, cluster_loss:0.159272\n",
      "Clustering   1869: ASW= 0.8312, DB= 0.2266, CH= 52777.4804\n",
      "Training epoch 1870, recon_loss:0.875188, zinb_loss:0.477363, cluster_loss:0.159747\n",
      "Clustering   1870: ASW= 0.8293, DB= 0.2292, CH= 51081.7113\n",
      "Training epoch 1871, recon_loss:0.875338, zinb_loss:0.477618, cluster_loss:0.159153\n",
      "Clustering   1871: ASW= 0.8316, DB= 0.2262, CH= 52861.9791\n",
      "Training epoch 1872, recon_loss:0.874902, zinb_loss:0.477326, cluster_loss:0.159627\n",
      "Clustering   1872: ASW= 0.8294, DB= 0.2289, CH= 51208.9758\n",
      "Training epoch 1873, recon_loss:0.875157, zinb_loss:0.477581, cluster_loss:0.159075\n",
      "Clustering   1873: ASW= 0.8319, DB= 0.2260, CH= 52895.3320\n",
      "Training epoch 1874, recon_loss:0.874908, zinb_loss:0.477292, cluster_loss:0.159534\n",
      "Clustering   1874: ASW= 0.8295, DB= 0.2288, CH= 51340.3731\n",
      "Training epoch 1875, recon_loss:0.875299, zinb_loss:0.477545, cluster_loss:0.159039\n",
      "Clustering   1875: ASW= 0.8322, DB= 0.2258, CH= 52890.9445\n",
      "Training epoch 1876, recon_loss:0.875068, zinb_loss:0.477263, cluster_loss:0.159450\n",
      "Clustering   1876: ASW= 0.8297, DB= 0.2285, CH= 51509.5675\n",
      "Training epoch 1877, recon_loss:0.875490, zinb_loss:0.477492, cluster_loss:0.159036\n",
      "Clustering   1877: ASW= 0.8324, DB= 0.2258, CH= 52894.0183\n",
      "Training epoch 1878, recon_loss:0.875184, zinb_loss:0.477229, cluster_loss:0.159401\n",
      "Clustering   1878: ASW= 0.8297, DB= 0.2286, CH= 51630.3858\n",
      "Training epoch 1879, recon_loss:0.875584, zinb_loss:0.477449, cluster_loss:0.159000\n",
      "Clustering   1879: ASW= 0.8325, DB= 0.2262, CH= 52870.5049\n",
      "Training epoch 1880, recon_loss:0.875271, zinb_loss:0.477239, cluster_loss:0.159301\n",
      "Clustering   1880: ASW= 0.8300, DB= 0.2281, CH= 51846.9191\n",
      "Training epoch 1881, recon_loss:0.875698, zinb_loss:0.477421, cluster_loss:0.159070\n",
      "Clustering   1881: ASW= 0.8325, DB= 0.2266, CH= 52871.6929\n",
      "Training epoch 1882, recon_loss:0.875525, zinb_loss:0.477267, cluster_loss:0.159329\n",
      "Clustering   1882: ASW= 0.8301, DB= 0.2278, CH= 51965.0718\n",
      "Training epoch 1883, recon_loss:0.875746, zinb_loss:0.477398, cluster_loss:0.159168\n",
      "Clustering   1883: ASW= 0.8325, DB= 0.2270, CH= 52893.8806\n",
      "Training epoch 1884, recon_loss:0.875736, zinb_loss:0.477329, cluster_loss:0.159347\n",
      "Clustering   1884: ASW= 0.8302, DB= 0.2275, CH= 52032.1523\n",
      "Training epoch 1885, recon_loss:0.875734, zinb_loss:0.477379, cluster_loss:0.159293\n",
      "Clustering   1885: ASW= 0.8324, DB= 0.2277, CH= 52814.6364\n",
      "Training epoch 1886, recon_loss:0.876275, zinb_loss:0.477459, cluster_loss:0.159595\n",
      "Clustering   1886: ASW= 0.8298, DB= 0.2286, CH= 51861.2960\n",
      "Training epoch 1887, recon_loss:0.875730, zinb_loss:0.477391, cluster_loss:0.159381\n",
      "Clustering   1887: ASW= 0.8320, DB= 0.2274, CH= 52476.1623\n",
      "Training epoch 1888, recon_loss:0.876265, zinb_loss:0.477558, cluster_loss:0.159592\n",
      "Clustering   1888: ASW= 0.8295, DB= 0.2281, CH= 51556.9450\n",
      "Training epoch 1889, recon_loss:0.875837, zinb_loss:0.477390, cluster_loss:0.159619\n",
      "Clustering   1889: ASW= 0.8314, DB= 0.2280, CH= 52291.4257\n",
      "Training epoch 1890, recon_loss:0.876633, zinb_loss:0.477627, cluster_loss:0.159754\n",
      "Clustering   1890: ASW= 0.8299, DB= 0.2273, CH= 51550.2304\n",
      "Training epoch 1891, recon_loss:0.875709, zinb_loss:0.477356, cluster_loss:0.159635\n",
      "Clustering   1891: ASW= 0.8311, DB= 0.2287, CH= 52314.6311\n",
      "Training epoch 1892, recon_loss:0.876332, zinb_loss:0.477573, cluster_loss:0.159536\n",
      "Clustering   1892: ASW= 0.8304, DB= 0.2265, CH= 51825.6698\n",
      "Training epoch 1893, recon_loss:0.875370, zinb_loss:0.477317, cluster_loss:0.159488\n",
      "Clustering   1893: ASW= 0.8310, DB= 0.2287, CH= 52404.4956\n",
      "Training epoch 1894, recon_loss:0.875766, zinb_loss:0.477489, cluster_loss:0.159253\n",
      "Clustering   1894: ASW= 0.8307, DB= 0.2260, CH= 52071.3786\n",
      "Training epoch 1895, recon_loss:0.875255, zinb_loss:0.477282, cluster_loss:0.159331\n",
      "Clustering   1895: ASW= 0.8310, DB= 0.2286, CH= 52496.9222\n",
      "Training epoch 1896, recon_loss:0.875489, zinb_loss:0.477435, cluster_loss:0.159077\n",
      "Clustering   1896: ASW= 0.8309, DB= 0.2259, CH= 52127.3590\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training epoch 1897, recon_loss:0.875363, zinb_loss:0.477278, cluster_loss:0.159250\n",
      "Clustering   1897: ASW= 0.8309, DB= 0.2285, CH= 52553.9522\n",
      "Training epoch 1898, recon_loss:0.875615, zinb_loss:0.477399, cluster_loss:0.158988\n",
      "Clustering   1898: ASW= 0.8307, DB= 0.2261, CH= 51986.8285\n",
      "Training epoch 1899, recon_loss:0.875624, zinb_loss:0.477285, cluster_loss:0.159216\n",
      "Clustering   1899: ASW= 0.8307, DB= 0.2286, CH= 52531.4253\n",
      "Training epoch 1900, recon_loss:0.875656, zinb_loss:0.477357, cluster_loss:0.158958\n",
      "Clustering   1900: ASW= 0.8304, DB= 0.2264, CH= 51711.1394\n",
      "Training epoch 1901, recon_loss:0.875682, zinb_loss:0.477289, cluster_loss:0.159167\n",
      "Clustering   1901: ASW= 0.8307, DB= 0.2285, CH= 52495.3004\n",
      "Training epoch 1902, recon_loss:0.875396, zinb_loss:0.477298, cluster_loss:0.158924\n",
      "Clustering   1902: ASW= 0.8300, DB= 0.2268, CH= 51373.7462\n",
      "Training epoch 1903, recon_loss:0.875425, zinb_loss:0.477283, cluster_loss:0.159117\n",
      "Clustering   1903: ASW= 0.8307, DB= 0.2283, CH= 52434.2546\n",
      "Training epoch 1904, recon_loss:0.875018, zinb_loss:0.477233, cluster_loss:0.158937\n",
      "Clustering   1904: ASW= 0.8297, DB= 0.2272, CH= 51014.5359\n",
      "Training epoch 1905, recon_loss:0.875158, zinb_loss:0.477284, cluster_loss:0.159133\n",
      "Clustering   1905: ASW= 0.8308, DB= 0.2284, CH= 52341.1083\n",
      "Training epoch 1906, recon_loss:0.874743, zinb_loss:0.477192, cluster_loss:0.158981\n",
      "Clustering   1906: ASW= 0.8296, DB= 0.2273, CH= 50856.9391\n",
      "Training epoch 1907, recon_loss:0.875063, zinb_loss:0.477292, cluster_loss:0.159151\n",
      "Clustering   1907: ASW= 0.8311, DB= 0.2280, CH= 52427.4033\n",
      "Training epoch 1908, recon_loss:0.874663, zinb_loss:0.477192, cluster_loss:0.158942\n",
      "Clustering   1908: ASW= 0.8300, DB= 0.2270, CH= 51098.7879\n",
      "Training epoch 1909, recon_loss:0.875100, zinb_loss:0.477302, cluster_loss:0.159086\n",
      "Clustering   1909: ASW= 0.8316, DB= 0.2275, CH= 52712.0974\n",
      "Training epoch 1910, recon_loss:0.874853, zinb_loss:0.477228, cluster_loss:0.158866\n",
      "Clustering   1910: ASW= 0.8304, DB= 0.2266, CH= 51492.7172\n",
      "Training epoch 1911, recon_loss:0.875435, zinb_loss:0.477339, cluster_loss:0.159070\n",
      "Clustering   1911: ASW= 0.8318, DB= 0.2274, CH= 52944.9036\n",
      "Training epoch 1912, recon_loss:0.875309, zinb_loss:0.477281, cluster_loss:0.158912\n",
      "Clustering   1912: ASW= 0.8308, DB= 0.2265, CH= 51740.8222\n",
      "Training epoch 1913, recon_loss:0.875902, zinb_loss:0.477396, cluster_loss:0.159116\n",
      "Clustering   1913: ASW= 0.8317, DB= 0.2275, CH= 53067.7751\n",
      "Training epoch 1914, recon_loss:0.875700, zinb_loss:0.477336, cluster_loss:0.159014\n",
      "Clustering   1914: ASW= 0.8312, DB= 0.2262, CH= 51873.6715\n",
      "Training epoch 1915, recon_loss:0.876147, zinb_loss:0.477446, cluster_loss:0.159135\n",
      "Clustering   1915: ASW= 0.8315, DB= 0.2276, CH= 53125.4228\n",
      "Training epoch 1916, recon_loss:0.875698, zinb_loss:0.477366, cluster_loss:0.159077\n",
      "Clustering   1916: ASW= 0.8315, DB= 0.2261, CH= 52007.5539\n",
      "Training epoch 1917, recon_loss:0.876060, zinb_loss:0.477467, cluster_loss:0.159085\n",
      "Clustering   1917: ASW= 0.8313, DB= 0.2277, CH= 53188.5128\n",
      "Training epoch 1918, recon_loss:0.875458, zinb_loss:0.477374, cluster_loss:0.159108\n",
      "Clustering   1918: ASW= 0.8319, DB= 0.2260, CH= 52105.9142\n",
      "Training epoch 1919, recon_loss:0.875815, zinb_loss:0.477465, cluster_loss:0.158997\n",
      "Clustering   1919: ASW= 0.8311, DB= 0.2278, CH= 53287.2314\n",
      "Training epoch 1920, recon_loss:0.875159, zinb_loss:0.477367, cluster_loss:0.159131\n",
      "Clustering   1920: ASW= 0.8323, DB= 0.2260, CH= 52162.0977\n",
      "Training epoch 1921, recon_loss:0.875532, zinb_loss:0.477447, cluster_loss:0.158896\n",
      "Clustering   1921: ASW= 0.8310, DB= 0.2277, CH= 53426.6943\n",
      "Training epoch 1922, recon_loss:0.874905, zinb_loss:0.477351, cluster_loss:0.159145\n",
      "Clustering   1922: ASW= 0.8326, DB= 0.2256, CH= 52173.6516\n",
      "Training epoch 1923, recon_loss:0.875207, zinb_loss:0.477411, cluster_loss:0.158797\n",
      "Clustering   1923: ASW= 0.8309, DB= 0.2274, CH= 53548.3985\n",
      "Training epoch 1924, recon_loss:0.874664, zinb_loss:0.477324, cluster_loss:0.159126\n",
      "Clustering   1924: ASW= 0.8327, DB= 0.2259, CH= 52175.4376\n",
      "Training epoch 1925, recon_loss:0.874945, zinb_loss:0.477383, cluster_loss:0.158713\n",
      "Clustering   1925: ASW= 0.8309, DB= 0.2271, CH= 53653.8240\n",
      "Training epoch 1926, recon_loss:0.874516, zinb_loss:0.477304, cluster_loss:0.159121\n",
      "Clustering   1926: ASW= 0.8328, DB= 0.2262, CH= 52123.3513\n",
      "Training epoch 1927, recon_loss:0.874835, zinb_loss:0.477373, cluster_loss:0.158696\n",
      "Clustering   1927: ASW= 0.8309, DB= 0.2268, CH= 53719.9885\n",
      "Training epoch 1928, recon_loss:0.874535, zinb_loss:0.477290, cluster_loss:0.159181\n",
      "Clustering   1928: ASW= 0.8327, DB= 0.2266, CH= 51979.1377\n",
      "Training epoch 1929, recon_loss:0.874953, zinb_loss:0.477387, cluster_loss:0.158768\n",
      "Clustering   1929: ASW= 0.8307, DB= 0.2273, CH= 53744.4561\n",
      "Training epoch 1930, recon_loss:0.874774, zinb_loss:0.477265, cluster_loss:0.159377\n",
      "Clustering   1930: ASW= 0.8324, DB= 0.2273, CH= 51645.9515\n",
      "Training epoch 1931, recon_loss:0.875382, zinb_loss:0.477414, cluster_loss:0.159022\n",
      "Clustering   1931: ASW= 0.8304, DB= 0.2272, CH= 53596.8944\n",
      "Training epoch 1932, recon_loss:0.875340, zinb_loss:0.477236, cluster_loss:0.159775\n",
      "Clustering   1932: ASW= 0.8318, DB= 0.2277, CH= 51067.4036\n",
      "Training epoch 1933, recon_loss:0.875920, zinb_loss:0.477431, cluster_loss:0.159341\n",
      "Clustering   1933: ASW= 0.8300, DB= 0.2271, CH= 53235.6202\n",
      "Training epoch 1934, recon_loss:0.875646, zinb_loss:0.477175, cluster_loss:0.159998\n",
      "Clustering   1934: ASW= 0.8310, DB= 0.2287, CH= 50639.7725\n",
      "Training epoch 1935, recon_loss:0.876065, zinb_loss:0.477396, cluster_loss:0.159375\n",
      "Clustering   1935: ASW= 0.8300, DB= 0.2269, CH= 53046.7002\n",
      "Training epoch 1936, recon_loss:0.875591, zinb_loss:0.477164, cluster_loss:0.159847\n",
      "Clustering   1936: ASW= 0.8308, DB= 0.2289, CH= 50824.2501\n",
      "Training epoch 1937, recon_loss:0.875764, zinb_loss:0.477402, cluster_loss:0.159205\n",
      "Clustering   1937: ASW= 0.8305, DB= 0.2267, CH= 53172.6411\n",
      "Training epoch 1938, recon_loss:0.875399, zinb_loss:0.477178, cluster_loss:0.159694\n",
      "Clustering   1938: ASW= 0.8306, DB= 0.2289, CH= 50980.3109\n",
      "Training epoch 1939, recon_loss:0.875555, zinb_loss:0.477440, cluster_loss:0.159078\n",
      "Clustering   1939: ASW= 0.8309, DB= 0.2263, CH= 53270.6012\n",
      "Training epoch 1940, recon_loss:0.875006, zinb_loss:0.477249, cluster_loss:0.159508\n",
      "Clustering   1940: ASW= 0.8307, DB= 0.2286, CH= 51297.2888\n",
      "Training epoch 1941, recon_loss:0.875102, zinb_loss:0.477525, cluster_loss:0.158994\n",
      "Clustering   1941: ASW= 0.8311, DB= 0.2263, CH= 53317.0673\n",
      "Training epoch 1942, recon_loss:0.874720, zinb_loss:0.477324, cluster_loss:0.159442\n",
      "Clustering   1942: ASW= 0.8305, DB= 0.2284, CH= 51373.2742\n",
      "Training epoch 1943, recon_loss:0.875028, zinb_loss:0.477634, cluster_loss:0.159006\n",
      "Clustering   1943: ASW= 0.8313, DB= 0.2263, CH= 53274.7643\n",
      "Training epoch 1944, recon_loss:0.874682, zinb_loss:0.477437, cluster_loss:0.159455\n",
      "Clustering   1944: ASW= 0.8303, DB= 0.2283, CH= 51447.4665\n",
      "Training epoch 1945, recon_loss:0.875129, zinb_loss:0.477752, cluster_loss:0.159085\n",
      "Clustering   1945: ASW= 0.8313, DB= 0.2266, CH= 53158.9509\n",
      "Training epoch 1946, recon_loss:0.874828, zinb_loss:0.477565, cluster_loss:0.159529\n",
      "Clustering   1946: ASW= 0.8301, DB= 0.2282, CH= 51450.9211\n",
      "Training epoch 1947, recon_loss:0.875522, zinb_loss:0.477879, cluster_loss:0.159203\n",
      "Clustering   1947: ASW= 0.8311, DB= 0.2269, CH= 53039.0264\n",
      "Training epoch 1948, recon_loss:0.875246, zinb_loss:0.477701, cluster_loss:0.159602\n",
      "Clustering   1948: ASW= 0.8301, DB= 0.2280, CH= 51469.5466\n",
      "Training epoch 1949, recon_loss:0.876153, zinb_loss:0.477973, cluster_loss:0.159303\n",
      "Clustering   1949: ASW= 0.8310, DB= 0.2274, CH= 52957.0116\n",
      "Training epoch 1950, recon_loss:0.875807, zinb_loss:0.477806, cluster_loss:0.159628\n",
      "Clustering   1950: ASW= 0.8302, DB= 0.2274, CH= 51539.0202\n",
      "Training epoch 1951, recon_loss:0.876698, zinb_loss:0.478020, cluster_loss:0.159360\n",
      "Clustering   1951: ASW= 0.8309, DB= 0.2279, CH= 52919.0658\n",
      "Training epoch 1952, recon_loss:0.876154, zinb_loss:0.477868, cluster_loss:0.159558\n",
      "Clustering   1952: ASW= 0.8305, DB= 0.2269, CH= 51713.9611\n",
      "Training epoch 1953, recon_loss:0.876792, zinb_loss:0.477972, cluster_loss:0.159362\n",
      "Clustering   1953: ASW= 0.8309, DB= 0.2282, CH= 52920.9512\n",
      "Training epoch 1954, recon_loss:0.876085, zinb_loss:0.477834, cluster_loss:0.159427\n",
      "Clustering   1954: ASW= 0.8309, DB= 0.2273, CH= 51868.0820\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training epoch 1955, recon_loss:0.876453, zinb_loss:0.477851, cluster_loss:0.159310\n",
      "Clustering   1955: ASW= 0.8309, DB= 0.2273, CH= 52881.5070\n",
      "Training epoch 1956, recon_loss:0.875810, zinb_loss:0.477746, cluster_loss:0.159268\n",
      "Clustering   1956: ASW= 0.8313, DB= 0.2267, CH= 52060.4979\n",
      "Training epoch 1957, recon_loss:0.876020, zinb_loss:0.477673, cluster_loss:0.159219\n",
      "Clustering   1957: ASW= 0.8310, DB= 0.2272, CH= 52842.4263\n",
      "Training epoch 1958, recon_loss:0.875565, zinb_loss:0.477629, cluster_loss:0.159075\n",
      "Clustering   1958: ASW= 0.8318, DB= 0.2265, CH= 52411.8357\n",
      "Training epoch 1959, recon_loss:0.875738, zinb_loss:0.477493, cluster_loss:0.159212\n",
      "Clustering   1959: ASW= 0.8310, DB= 0.2271, CH= 52747.9891\n",
      "Training epoch 1960, recon_loss:0.875698, zinb_loss:0.477533, cluster_loss:0.159060\n",
      "Clustering   1960: ASW= 0.8322, DB= 0.2264, CH= 52708.0458\n",
      "Training epoch 1961, recon_loss:0.875974, zinb_loss:0.477354, cluster_loss:0.159440\n",
      "Clustering   1961: ASW= 0.8309, DB= 0.2273, CH= 52557.0110\n",
      "Training epoch 1962, recon_loss:0.876159, zinb_loss:0.477467, cluster_loss:0.159249\n",
      "Clustering   1962: ASW= 0.8324, DB= 0.2262, CH= 52816.9195\n",
      "Training epoch 1963, recon_loss:0.876267, zinb_loss:0.477241, cluster_loss:0.159725\n",
      "Clustering   1963: ASW= 0.8307, DB= 0.2275, CH= 52390.9229\n",
      "Training epoch 1964, recon_loss:0.876278, zinb_loss:0.477377, cluster_loss:0.159449\n",
      "Clustering   1964: ASW= 0.8324, DB= 0.2260, CH= 52733.2854\n",
      "Training epoch 1965, recon_loss:0.875980, zinb_loss:0.477134, cluster_loss:0.159827\n",
      "Clustering   1965: ASW= 0.8306, DB= 0.2275, CH= 52330.1871\n",
      "Training epoch 1966, recon_loss:0.875874, zinb_loss:0.477293, cluster_loss:0.159471\n",
      "Clustering   1966: ASW= 0.8325, DB= 0.2261, CH= 52600.6936\n",
      "Training epoch 1967, recon_loss:0.875344, zinb_loss:0.477078, cluster_loss:0.159664\n",
      "Clustering   1967: ASW= 0.8307, DB= 0.2273, CH= 52410.4387\n",
      "Training epoch 1968, recon_loss:0.875206, zinb_loss:0.477237, cluster_loss:0.159308\n",
      "Clustering   1968: ASW= 0.8325, DB= 0.2262, CH= 52535.3439\n",
      "Training epoch 1969, recon_loss:0.874771, zinb_loss:0.477079, cluster_loss:0.159390\n",
      "Clustering   1969: ASW= 0.8309, DB= 0.2268, CH= 52581.9604\n",
      "Training epoch 1970, recon_loss:0.874628, zinb_loss:0.477200, cluster_loss:0.159146\n",
      "Clustering   1970: ASW= 0.8324, DB= 0.2264, CH= 52434.9507\n",
      "Training epoch 1971, recon_loss:0.874507, zinb_loss:0.477113, cluster_loss:0.159214\n",
      "Clustering   1971: ASW= 0.8310, DB= 0.2264, CH= 52783.4862\n",
      "Training epoch 1972, recon_loss:0.874394, zinb_loss:0.477197, cluster_loss:0.159062\n",
      "Clustering   1972: ASW= 0.8322, DB= 0.2264, CH= 52233.7939\n",
      "Training epoch 1973, recon_loss:0.874357, zinb_loss:0.477138, cluster_loss:0.159100\n",
      "Clustering   1973: ASW= 0.8312, DB= 0.2260, CH= 52939.1144\n",
      "Training epoch 1974, recon_loss:0.874353, zinb_loss:0.477189, cluster_loss:0.159116\n",
      "Clustering   1974: ASW= 0.8320, DB= 0.2271, CH= 52093.3605\n",
      "Training epoch 1975, recon_loss:0.874578, zinb_loss:0.477204, cluster_loss:0.159131\n",
      "Clustering   1975: ASW= 0.8314, DB= 0.2253, CH= 53161.7542\n",
      "Training epoch 1976, recon_loss:0.874487, zinb_loss:0.477186, cluster_loss:0.159227\n",
      "Clustering   1976: ASW= 0.8316, DB= 0.2281, CH= 51844.4933\n",
      "Training epoch 1977, recon_loss:0.874733, zinb_loss:0.477242, cluster_loss:0.159140\n",
      "Clustering   1977: ASW= 0.8316, DB= 0.2249, CH= 53347.3212\n",
      "Training epoch 1978, recon_loss:0.874403, zinb_loss:0.477152, cluster_loss:0.159271\n",
      "Clustering   1978: ASW= 0.8313, DB= 0.2286, CH= 51642.1734\n",
      "Training epoch 1979, recon_loss:0.874761, zinb_loss:0.477248, cluster_loss:0.159114\n",
      "Clustering   1979: ASW= 0.8317, DB= 0.2247, CH= 53436.6326\n",
      "Training epoch 1980, recon_loss:0.874344, zinb_loss:0.477139, cluster_loss:0.159231\n",
      "Clustering   1980: ASW= 0.8312, DB= 0.2285, CH= 51608.2537\n",
      "Training epoch 1981, recon_loss:0.874825, zinb_loss:0.477271, cluster_loss:0.159021\n",
      "Clustering   1981: ASW= 0.8320, DB= 0.2243, CH= 53601.9121\n",
      "Training epoch 1982, recon_loss:0.874183, zinb_loss:0.477128, cluster_loss:0.159166\n",
      "Clustering   1982: ASW= 0.8310, DB= 0.2286, CH= 51665.0395\n",
      "Training epoch 1983, recon_loss:0.874967, zinb_loss:0.477324, cluster_loss:0.159027\n",
      "Clustering   1983: ASW= 0.8321, DB= 0.2241, CH= 53670.0096\n",
      "Training epoch 1984, recon_loss:0.874272, zinb_loss:0.477129, cluster_loss:0.159148\n",
      "Clustering   1984: ASW= 0.8308, DB= 0.2287, CH= 51680.2780\n",
      "Training epoch 1985, recon_loss:0.875192, zinb_loss:0.477377, cluster_loss:0.159017\n",
      "Clustering   1985: ASW= 0.8322, DB= 0.2239, CH= 53711.6483\n",
      "Training epoch 1986, recon_loss:0.874380, zinb_loss:0.477164, cluster_loss:0.159079\n",
      "Clustering   1986: ASW= 0.8304, DB= 0.2286, CH= 51542.2352\n",
      "Training epoch 1987, recon_loss:0.875447, zinb_loss:0.477446, cluster_loss:0.159008\n",
      "Clustering   1987: ASW= 0.8325, DB= 0.2239, CH= 53667.0528\n",
      "Training epoch 1988, recon_loss:0.874540, zinb_loss:0.477207, cluster_loss:0.159091\n",
      "Clustering   1988: ASW= 0.8301, DB= 0.2284, CH= 51435.7323\n",
      "Training epoch 1989, recon_loss:0.875742, zinb_loss:0.477511, cluster_loss:0.159071\n",
      "Clustering   1989: ASW= 0.8326, DB= 0.2242, CH= 53537.2950\n",
      "Training epoch 1990, recon_loss:0.874707, zinb_loss:0.477244, cluster_loss:0.159123\n",
      "Clustering   1990: ASW= 0.8298, DB= 0.2287, CH= 51368.5212\n",
      "Training epoch 1991, recon_loss:0.875864, zinb_loss:0.477552, cluster_loss:0.159106\n",
      "Clustering   1991: ASW= 0.8327, DB= 0.2257, CH= 53419.3997\n",
      "Training epoch 1992, recon_loss:0.874780, zinb_loss:0.477268, cluster_loss:0.159097\n",
      "Clustering   1992: ASW= 0.8297, DB= 0.2286, CH= 51480.3135\n",
      "Training epoch 1993, recon_loss:0.875905, zinb_loss:0.477568, cluster_loss:0.159106\n",
      "Clustering   1993: ASW= 0.8330, DB= 0.2258, CH= 53414.1313\n",
      "Training epoch 1994, recon_loss:0.874796, zinb_loss:0.477282, cluster_loss:0.159056\n",
      "Clustering   1994: ASW= 0.8298, DB= 0.2284, CH= 51716.1455\n",
      "Training epoch 1995, recon_loss:0.875886, zinb_loss:0.477580, cluster_loss:0.159086\n",
      "Clustering   1995: ASW= 0.8334, DB= 0.2258, CH= 53507.2131\n",
      "Training epoch 1996, recon_loss:0.874846, zinb_loss:0.477294, cluster_loss:0.159039\n",
      "Clustering   1996: ASW= 0.8299, DB= 0.2273, CH= 51914.7519\n",
      "Training epoch 1997, recon_loss:0.875814, zinb_loss:0.477592, cluster_loss:0.159064\n",
      "Clustering   1997: ASW= 0.8336, DB= 0.2259, CH= 53607.0182\n",
      "Training epoch 1998, recon_loss:0.874871, zinb_loss:0.477305, cluster_loss:0.159039\n",
      "Clustering   1998: ASW= 0.8300, DB= 0.2271, CH= 52114.2896\n",
      "Training epoch 1999, recon_loss:0.875595, zinb_loss:0.477597, cluster_loss:0.159037\n",
      "Clustering   1999: ASW= 0.8337, DB= 0.2260, CH= 53657.4944\n",
      "Training epoch 2000, recon_loss:0.874697, zinb_loss:0.477298, cluster_loss:0.159016\n",
      "Clustering   2000: ASW= 0.8302, DB= 0.2268, CH= 52273.0578\n",
      "Final Result : ASW= 0.8302, DB= 0.2268, CH= 52273.0578\n"
     ]
    }
   ],
   "source": [
    "y_pred, final_latent = model.fit(y=y, n_clusters=-1, num_epochs=2000, file='GSE158013',pretrain_latent=pretrain_latent)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "9d57af13",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "np.savetxt(\"../results/GSE158013_pred.csv\", y_pred, delimiter=\",\")\n",
    "np.savetxt(\"../results/GSE158013_embedding.csv\", final_latent.cpu().detach().numpy(), delimiter=\",\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "3ee7936f",
   "metadata": {},
   "outputs": [],
   "source": [
    "library(anndata)\n",
    "library(reticulate)\n",
    "library(dplyr)\n",
    "library(tidyr)\n",
    "library(Seurat)\n",
    "library(ggplot2)\n",
    "library(reticulate)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "f0f4bfb0",
   "metadata": {},
   "outputs": [],
   "source": [
    "set.seed(0)\n",
    "\n",
    "use_python(\"/home/zhouzeming/anaconda3/bin\")\n",
    "ad <- import(\"anndata\")\n",
    "rna <- ad$read_h5ad('../datasets/GSE158013/GSE158013_rna.h5ad')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "fe3c9a6b",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning message:\n",
      "“Data is of class matrix. Coercing to dgCMatrix.”\n",
      "Normalizing layer: counts\n",
      "\n",
      "Finding variable features for layer counts\n",
      "\n",
      "Centering and scaling data matrix\n",
      "\n",
      "Warning message:\n",
      "“The following arguments are not used: label”\n",
      "10:51:01 UMAP embedding parameters a = 0.9922 b = 1.112\n",
      "\n",
      "10:51:01 Read 7084 rows and found 32 numeric columns\n",
      "\n",
      "10:51:01 Using Annoy for neighbor search, n_neighbors = 30\n",
      "\n",
      "10:51:01 Building Annoy index with metric = cosine, n_trees = 50\n",
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      "\n",
      "10:51:01 Writing NN index file to temp file /tmp/RtmpIKgN4h/file2ee48a17268e3e\n",
      "\n",
      "10:51:01 Searching Annoy index using 1 thread, search_k = 3000\n",
      "\n",
      "10:51:04 Annoy recall = 100%\n",
      "\n",
      "10:51:04 Commencing smooth kNN distance calibration using 1 thread\n",
      " with target n_neighbors = 30\n",
      "\n",
      "10:51:04 Initializing from normalized Laplacian + noise (using RSpectra)\n",
      "\n",
      "10:51:05 Commencing optimization for 500 epochs, with 274822 positive edges\n",
      "\n",
      "10:51:05 Using rng type: pcg\n",
      "\n",
      "10:51:12 Optimization finished\n",
      "\n",
      "Calculating cluster 0\n",
      "\n",
      "Calculating cluster 1\n",
      "\n",
      "Calculating cluster 2\n",
      "\n",
      "Calculating cluster 3\n",
      "\n",
      "Calculating cluster 4\n",
      "\n",
      "Calculating cluster 5\n",
      "\n",
      "Calculating cluster 6\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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nq5KJhyEs51+UuscordlHG4p4Q0b0B1CUwWBVOcNS/FMeEkvAoAAEAvQ7CDAaa2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Wq8tLVN7Z+6qux2jlf/1QevZLWfwaxGop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\ne/Zs1KhRx44dTS18pktmZiaUFZZlZ86caR76Vi6zPHz4sH379gRBnD179k0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XfiSVFTiDMaMwR6X/OrjW/75tSVhVAsBTl+1ev//W48dP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      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "mycolors <- c('B cell'='#E5D2DD', 'Naive CD4 T cell'='#53A85F','T follicular helper cell'='#F1BB72',\n",
    "              'CD8 T cell'='#F3B1A0','Natural killer cell'='#D6E7A3', 'CD8+ MAIT'='#57C3F3', 'Neutrophil lineage'='#E95C59')\n",
    "\n",
    "scRNA <- CreateSeuratObject(counts = t(rna$X),project =\"scRNA\",min.cells = 3)\n",
    "label <- read.csv(\"../results/GSE158013_pred.csv\",header = F)\n",
    "scRNA@meta.data$clusters <- label\n",
    "Idents(scRNA) <- as.factor(label$V1)\n",
    "scRNA <- NormalizeData(scRNA, normalization.method = \"LogNormalize\")\n",
    "scRNA <- FindVariableFeatures(scRNA, selection.method = \"vst\", nfeatures = 2000) \n",
    "scRNA <- ScaleData(scRNA, features = rownames(scRNA))\n",
    "variable.genes <- head(VariableFeatures(scRNA), 2000)\n",
    "\n",
    "# 数据聚类降维\n",
    "mds<- read.csv('../results/GSE158013_embedding.csv',row.names=1)\n",
    "mds <- as.matrix(mds)\n",
    "colnames(mds) <- paste0(\"MDS_\", 1:32)\n",
    "scRNA[[\"mds\"]] <- CreateDimReducObject(embeddings = mds, key = \"MDS_\", assay = DefaultAssay(scRNA))\n",
    "scRNA <- RunUMAP(scRNA,dims = 1:32,label=T,reduction = \"mds\")\n",
    "\n",
    "# 寻找标记基因\n",
    "scRNA.markers <- FindAllMarkers(scRNA, \n",
    "                                ident.col = \"clusters\",\n",
    "                                only.pos = T,  \n",
    "                                min.pct = 0.25, \n",
    "                                logfc.threshold = 0.25,\n",
    "                                features = variable.genes)\n",
    "top.markers <- scRNA.markers %>% group_by(cluster) %>% top_n(n = 10, wt = avg_log2FC)\n",
    "\n",
    "# 细胞注释\n",
    "new.cluster.ids <- c(\"B cell\", \"Naive CD4 T cell\", \"T follicular helper cell\", \"CD8 T cell\", \"Natural killer cell\", \"CD8+ MAIT\", \"Neutrophil lineage\")\n",
    "names(new.cluster.ids) <- levels(scRNA)\n",
    "scRNA <- RenameIdents(scRNA, new.cluster.ids)\n",
    "Joint_umap <-DimPlot(scRNA, reduction = \"umap\", label = TRUE, cols = mycolors)\n",
    "\n",
    "Joint_umap"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "R",
   "language": "R",
   "name": "ir"
  },
  "language_info": {
   "codemirror_mode": "r",
   "file_extension": ".r",
   "mimetype": "text/x-r-source",
   "name": "R",
   "pygments_lexer": "r",
   "version": "4.4.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}