Tutorial:Batch dataset¶
In this tutorial, we will show how to use scMAGCA for batch correction. As an example, we use a mouse Glioblastoma (GBM) dataset ‘GSE163120’ containing 24559 cells. Specifically, the dataset contains two omicsc and two batches, with ADT containing 174 features and RNA containing 12411 features.
Loading package¶
[1]:
import numpy as np
import pandas as pd
import torch
import scanpy as sc
import random
import warnings
from scipy.sparse import csr_matrix
from scipy.io import mmread
from sklearn.preprocessing import OneHotEncoder
warnings.filterwarnings("ignore")
/home/zhouzeming/anaconda3/lib/python3.9/site-packages/setuptools_scm/_integration/setuptools.py:30: RuntimeWarning:
ERROR: setuptools==58.0.4 is used in combination with setuptools_scm>=8.x
Your build configuration is incomplete and previously worked by accident!
setuptools_scm requires setuptools>=61
Suggested workaround if applicable:
- migrating from the deprecated setup_requires mechanism to pep517/518
and using a pyproject.toml to declare build dependencies
which are reliably pre-installed before running the build tools
warnings.warn(
<frozen importlib._bootstrap>:228: RuntimeWarning: scipy._lib.messagestream.MessageStream size changed, may indicate binary incompatibility. Expected 56 from C header, got 64 from PyObject
[2]:
from scMAGCA.preprocess import read_dataset, preprocess_dataset
from scMAGCA.utils import *
from scMAGCA.scMAGCA_batch import scMultiCluster
[3]:
# set seed
random.seed(3407)
np.random.seed(3407)
torch.manual_seed(3407)
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.enabled = False
torch.backends.cudnn.benchmark = False
Reading dataset¶
We fully took into account the batch information of the batch dataset when correcting for batch effects.
The required input files include:
x1: protein abundance matrix (data format is csv file) : ADT.csv;
x2: Gene expression matrix (data format is mtx file) : matrix.mtx;
Real label (‘cell_label’ column in csv file) : GSE163120_label.csv;
Batch information (‘batch_id’ column in csv file) : GSE163120_label.csv.
To ensure reproducibility of the results, please read the above data as follows:
[4]:
x1 = np.array(pd.read_csv('../datasets/GSE163120/ADT.csv',index_col=0).T)
x2 = csr_matrix(mmread('../datasets/GSE163120/matrix.mtx').T).toarray()
y = np.array(pd.read_csv('../datasets/GSE163120/GSE163120_label.csv', index_col=0)['cell_label'])
b = np.array(pd.read_csv('../datasets/GSE163120/GSE163120_label.csv', index_col=0)['batch_id'])
enc = OneHotEncoder()
enc.fit(b.reshape(-1, 1))
B = enc.transform(b.reshape(-1, 1)).toarray()
[5]:
x1,x2,y,b
[5]:
(array([[ 0, 0, 0, ..., 0, 0, 0],
[24, 0, 0, ..., 0, 0, 0],
[19, 8, 0, ..., 0, 0, 0],
...,
[ 4, 0, 3, ..., 3, 0, 0],
[ 2, 1, 0, ..., 2, 0, 0],
[ 8, 1, 0, ..., 2, 0, 0]]),
array([[0, 0, 0, ..., 0, 0, 0],
[2, 2, 7, ..., 3, 0, 0],
[0, 0, 1, ..., 0, 0, 0],
...,
[0, 0, 1, ..., 4, 1, 0],
[0, 1, 0, ..., 0, 1, 0],
[3, 0, 4, ..., 2, 0, 0]]),
array([ 5, 11, 17, ..., 1, 3, 0]),
array([0, 0, 0, ..., 1, 1, 1]))
Due to the small number of features in ADT omics data and the large gap between the feature dimensions of RNA omics, for RNA+ADT data, we only select high-expression features for RNA omics (the default number of chosen genes is 2000).
[6]:
importantGenes = geneSelection(x2, n=2000)
x2 = x2[:, importantGenes]
Chosen offset: 0.29
[7]:
adata1 = sc.AnnData(x1)
adata1 = read_dataset(adata1, copy=True)
adata1 = preprocess_dataset(adata1, normalize_input=True, logtrans_input=True)
### Autoencoder: Successfully preprocessed 174 features and 24559 cells.
[8]:
adata1
[8]:
AnnData object with n_obs × n_vars = 24559 × 174
obs: 'DCA_split', 'size_factors'
var: 'mean', 'std'
uns: 'log1p'
[9]:
adata2 = sc.AnnData(x2)
adata2 = read_dataset(adata2, copy=True)
adata2 = preprocess_dataset(adata2, normalize_input=True, logtrans_input=True)
### Autoencoder: Successfully preprocessed 2000 features and 24559 cells.
[10]:
adata2
[10]:
AnnData object with n_obs × n_vars = 24559 × 2000
obs: 'DCA_split', 'size_factors'
var: 'mean', 'std'
uns: 'log1p'
Training the model¶
[11]:
model = scMultiCluster(input_dim1=adata1.n_vars, input_dim2=adata2.n_vars, n_batch=2,
alpha=0.2,beta=0.8,gama=0.01,device='cuda').to('cuda')
Note: When correcting datasets with batch effects, the actual number of batches need to be provided.
[12]:
model
[12]:
scMultiCluster(
(encoder): Encoder(
(stacked_gnn): ModuleList(
(0): GCNConv(2176, 1024)
(1): GCNConv(1024, 256)
(2): GCNConv(256, 64)
(3): GCNConv(64, 8)
)
(stacked_bns): ModuleList(
(0): BatchNorm1d(1024, eps=1e-05, momentum=0.01, affine=True, track_running_stats=True)
(1): BatchNorm1d(256, eps=1e-05, momentum=0.01, affine=True, track_running_stats=True)
(2): BatchNorm1d(64, eps=1e-05, momentum=0.01, affine=True, track_running_stats=True)
(3): BatchNorm1d(8, eps=1e-05, momentum=0.01, affine=True, track_running_stats=True)
)
(stacked_prelus): ModuleList(
(0-3): 4 x PReLU(num_parameters=1)
)
)
(decoder): Sequential(
(0): Linear(in_features=10, out_features=512, bias=True)
(1): BatchNorm1d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): PReLU(num_parameters=1)
(3): Linear(in_features=512, out_features=1024, bias=True)
(4): BatchNorm1d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(5): PReLU(num_parameters=1)
(6): Linear(in_features=1024, out_features=2176, bias=True)
)
(dec_mean): Sequential(
(0): Linear(in_features=10, out_features=256, bias=True)
(1): Linear(in_features=256, out_features=2174, bias=True)
(2): MeanAct()
)
(dec_disp): Sequential(
(0): Linear(in_features=10, out_features=256, bias=True)
(1): Linear(in_features=256, out_features=2174, bias=True)
(2): DispAct()
)
(dec_pi): Sequential(
(0): Linear(in_features=10, out_features=256, bias=True)
(1): Linear(in_features=256, out_features=2174, bias=True)
(2): Sigmoid()
)
(zinb_loss): ZINBLoss()
)
[13]:
pretrain_latent = model.pretrain_autoencoder(
X1=adata1.X, X2=adata2.X, X1_raw=adata1.raw.X, X2_raw=adata2.raw.X,
B=B, epochs=400, file='GSE163120')
Pretraining stage
Pretrain epoch 1, recon_loss:1.174770, zinb_loss:2.021940, adversial_loss:1.448147
Pretrain epoch 2, recon_loss:1.151908, zinb_loss:1.944988, adversial_loss:1.447797
Pretrain epoch 3, recon_loss:0.941301, zinb_loss:1.855513, adversial_loss:1.440014
Pretrain epoch 4, recon_loss:0.850893, zinb_loss:1.770818, adversial_loss:1.436623
Pretrain epoch 5, recon_loss:0.814821, zinb_loss:1.691993, adversial_loss:1.434692
Pretrain epoch 6, recon_loss:0.807255, zinb_loss:1.620129, adversial_loss:1.431878
Pretrain epoch 7, recon_loss:0.797138, zinb_loss:1.555519, adversial_loss:1.428869
Pretrain epoch 8, recon_loss:0.784488, zinb_loss:1.499163, adversial_loss:1.427543
Pretrain epoch 9, recon_loss:0.773013, zinb_loss:1.449482, adversial_loss:1.426432
Pretrain epoch 10, recon_loss:0.764595, zinb_loss:1.405877, adversial_loss:1.425571
Pretrain epoch 11, recon_loss:0.757382, zinb_loss:1.367658, adversial_loss:1.425086
Pretrain epoch 12, recon_loss:0.750995, zinb_loss:1.334295, adversial_loss:1.424672
Pretrain epoch 13, recon_loss:0.746195, zinb_loss:1.305338, adversial_loss:1.424173
Pretrain epoch 14, recon_loss:0.742152, zinb_loss:1.280044, adversial_loss:1.423640
Pretrain epoch 15, recon_loss:0.738033, zinb_loss:1.257793, adversial_loss:1.423142
Pretrain epoch 16, recon_loss:0.733943, zinb_loss:1.238180, adversial_loss:1.422661
Pretrain epoch 17, recon_loss:0.730133, zinb_loss:1.220824, adversial_loss:1.422139
Pretrain epoch 18, recon_loss:0.726655, zinb_loss:1.205376, adversial_loss:1.421629
Pretrain epoch 19, recon_loss:0.723586, zinb_loss:1.191563, adversial_loss:1.421198
Pretrain epoch 20, recon_loss:0.720818, zinb_loss:1.179186, adversial_loss:1.420892
Pretrain epoch 21, recon_loss:0.717997, zinb_loss:1.168057, adversial_loss:1.420760
Pretrain epoch 22, recon_loss:0.715006, zinb_loss:1.158000, adversial_loss:1.420806
Pretrain epoch 23, recon_loss:0.712047, zinb_loss:1.148849, adversial_loss:1.420975
Pretrain epoch 24, recon_loss:0.709203, zinb_loss:1.140463, adversial_loss:1.421224
Pretrain epoch 25, recon_loss:0.706365, zinb_loss:1.132722, adversial_loss:1.421513
Pretrain epoch 26, recon_loss:0.703534, zinb_loss:1.125538, adversial_loss:1.421808
Pretrain epoch 27, recon_loss:0.700850, zinb_loss:1.118840, adversial_loss:1.422088
Pretrain epoch 28, recon_loss:0.698339, zinb_loss:1.112568, adversial_loss:1.422369
Pretrain epoch 29, recon_loss:0.695930, zinb_loss:1.106679, adversial_loss:1.422658
Pretrain epoch 30, recon_loss:0.693613, zinb_loss:1.101140, adversial_loss:1.422967
Pretrain epoch 31, recon_loss:0.691440, zinb_loss:1.095925, adversial_loss:1.423300
Pretrain epoch 32, recon_loss:0.689396, zinb_loss:1.091016, adversial_loss:1.423637
Pretrain epoch 33, recon_loss:0.687417, zinb_loss:1.086394, adversial_loss:1.423953
Pretrain epoch 34, recon_loss:0.685456, zinb_loss:1.082040, adversial_loss:1.424239
Pretrain epoch 35, recon_loss:0.683512, zinb_loss:1.077929, adversial_loss:1.424494
Pretrain epoch 36, recon_loss:0.681597, zinb_loss:1.074035, adversial_loss:1.424728
Pretrain epoch 37, recon_loss:0.679721, zinb_loss:1.070337, adversial_loss:1.424954
Pretrain epoch 38, recon_loss:0.677887, zinb_loss:1.066815, adversial_loss:1.425178
Pretrain epoch 39, recon_loss:0.676109, zinb_loss:1.063457, adversial_loss:1.425403
Pretrain epoch 40, recon_loss:0.674432, zinb_loss:1.060254, adversial_loss:1.425619
Pretrain epoch 41, recon_loss:0.672862, zinb_loss:1.057196, adversial_loss:1.425813
Pretrain epoch 42, recon_loss:0.671350, zinb_loss:1.054272, adversial_loss:1.425982
Pretrain epoch 43, recon_loss:0.669858, zinb_loss:1.051471, adversial_loss:1.426140
Pretrain epoch 44, recon_loss:0.668399, zinb_loss:1.048784, adversial_loss:1.426301
Pretrain epoch 45, recon_loss:0.667004, zinb_loss:1.046203, adversial_loss:1.426463
Pretrain epoch 46, recon_loss:0.665683, zinb_loss:1.043719, adversial_loss:1.426597
Pretrain epoch 47, recon_loss:0.664430, zinb_loss:1.041326, adversial_loss:1.426684
Pretrain epoch 48, recon_loss:0.663227, zinb_loss:1.039021, adversial_loss:1.426740
Pretrain epoch 49, recon_loss:0.662064, zinb_loss:1.036797, adversial_loss:1.426804
Pretrain epoch 50, recon_loss:0.660941, zinb_loss:1.034654, adversial_loss:1.426893
Pretrain epoch 51, recon_loss:0.659869, zinb_loss:1.032589, adversial_loss:1.426964
Pretrain epoch 52, recon_loss:0.658836, zinb_loss:1.030594, adversial_loss:1.426992
Pretrain epoch 53, recon_loss:0.657831, zinb_loss:1.028666, adversial_loss:1.427025
Pretrain epoch 54, recon_loss:0.656849, zinb_loss:1.026804, adversial_loss:1.427068
Pretrain epoch 55, recon_loss:0.655881, zinb_loss:1.025000, adversial_loss:1.427068
Pretrain epoch 56, recon_loss:0.654931, zinb_loss:1.023251, adversial_loss:1.427027
Pretrain epoch 57, recon_loss:0.654003, zinb_loss:1.021553, adversial_loss:1.427018
Pretrain epoch 58, recon_loss:0.653108, zinb_loss:1.019905, adversial_loss:1.426997
Pretrain epoch 59, recon_loss:0.652237, zinb_loss:1.018307, adversial_loss:1.426944
Pretrain epoch 60, recon_loss:0.651356, zinb_loss:1.016760, adversial_loss:1.426977
Pretrain epoch 61, recon_loss:0.650486, zinb_loss:1.015258, adversial_loss:1.427012
Pretrain epoch 62, recon_loss:0.649628, zinb_loss:1.013798, adversial_loss:1.427090
Pretrain epoch 63, recon_loss:0.648782, zinb_loss:1.012383, adversial_loss:1.427128
Pretrain epoch 64, recon_loss:0.647940, zinb_loss:1.011008, adversial_loss:1.427177
Pretrain epoch 65, recon_loss:0.647124, zinb_loss:1.009664, adversial_loss:1.427304
Pretrain epoch 66, recon_loss:0.646323, zinb_loss:1.008361, adversial_loss:1.427408
Pretrain epoch 67, recon_loss:0.645497, zinb_loss:1.007101, adversial_loss:1.427519
Pretrain epoch 68, recon_loss:0.644712, zinb_loss:1.005887, adversial_loss:1.427447
Pretrain epoch 69, recon_loss:0.644069, zinb_loss:1.004913, adversial_loss:1.428004
Pretrain epoch 70, recon_loss:0.645123, zinb_loss:1.005683, adversial_loss:1.426616
Pretrain epoch 71, recon_loss:0.646951, zinb_loss:1.006878, adversial_loss:1.428822
Pretrain epoch 72, recon_loss:0.642654, zinb_loss:1.001845, adversial_loss:1.426637
Pretrain epoch 73, recon_loss:0.645500, zinb_loss:1.003279, adversial_loss:1.425650
Pretrain epoch 74, recon_loss:0.641194, zinb_loss:0.999893, adversial_loss:1.427640
Pretrain epoch 75, recon_loss:0.643866, zinb_loss:0.999886, adversial_loss:1.429787
Pretrain epoch 76, recon_loss:0.640801, zinb_loss:0.998426, adversial_loss:1.428794
Pretrain epoch 77, recon_loss:0.640501, zinb_loss:0.997216, adversial_loss:1.426821
Pretrain epoch 78, recon_loss:0.640470, zinb_loss:0.996680, adversial_loss:1.426498
Pretrain epoch 79, recon_loss:0.638483, zinb_loss:0.995274, adversial_loss:1.427804
Pretrain epoch 80, recon_loss:0.639101, zinb_loss:0.994437, adversial_loss:1.429055
Pretrain epoch 81, recon_loss:0.637388, zinb_loss:0.993527, adversial_loss:1.428898
Pretrain epoch 82, recon_loss:0.637050, zinb_loss:0.992565, adversial_loss:1.427955
Pretrain epoch 83, recon_loss:0.636603, zinb_loss:0.991751, adversial_loss:1.427494
Pretrain epoch 84, recon_loss:0.635113, zinb_loss:0.990830, adversial_loss:1.427936
Pretrain epoch 85, recon_loss:0.635495, zinb_loss:0.990029, adversial_loss:1.428646
Pretrain epoch 86, recon_loss:0.634227, zinb_loss:0.989183, adversial_loss:1.428837
Pretrain epoch 87, recon_loss:0.633442, zinb_loss:0.988351, adversial_loss:1.428606
Pretrain epoch 88, recon_loss:0.633570, zinb_loss:0.987673, adversial_loss:1.428558
Pretrain epoch 89, recon_loss:0.632033, zinb_loss:0.986806, adversial_loss:1.428848
Pretrain epoch 90, recon_loss:0.632087, zinb_loss:0.986128, adversial_loss:1.429096
Pretrain epoch 91, recon_loss:0.631432, zinb_loss:0.985385, adversial_loss:1.428928
Pretrain epoch 92, recon_loss:0.630449, zinb_loss:0.984674, adversial_loss:1.428694
Pretrain epoch 93, recon_loss:0.630361, zinb_loss:0.984015, adversial_loss:1.428874
Pretrain epoch 94, recon_loss:0.629597, zinb_loss:0.983287, adversial_loss:1.429363
Pretrain epoch 95, recon_loss:0.629118, zinb_loss:0.982636, adversial_loss:1.429528
Pretrain epoch 96, recon_loss:0.628685, zinb_loss:0.981999, adversial_loss:1.429178
Pretrain epoch 97, recon_loss:0.628071, zinb_loss:0.981370, adversial_loss:1.428863
Pretrain epoch 98, recon_loss:0.627664, zinb_loss:0.980727, adversial_loss:1.429028
Pretrain epoch 99, recon_loss:0.627247, zinb_loss:0.980134, adversial_loss:1.429293
Pretrain epoch 100, recon_loss:0.626712, zinb_loss:0.979490, adversial_loss:1.429212
Pretrain epoch 101, recon_loss:0.626367, zinb_loss:0.978948, adversial_loss:1.429038
Pretrain epoch 102, recon_loss:0.625858, zinb_loss:0.978325, adversial_loss:1.429015
Pretrain epoch 103, recon_loss:0.625516, zinb_loss:0.977792, adversial_loss:1.429042
Pretrain epoch 104, recon_loss:0.625104, zinb_loss:0.977240, adversial_loss:1.429012
Pretrain epoch 105, recon_loss:0.624742, zinb_loss:0.976691, adversial_loss:1.428981
Pretrain epoch 106, recon_loss:0.624316, zinb_loss:0.976169, adversial_loss:1.429068
Pretrain epoch 107, recon_loss:0.623914, zinb_loss:0.975606, adversial_loss:1.428961
Pretrain epoch 108, recon_loss:0.623572, zinb_loss:0.975113, adversial_loss:1.428916
Pretrain epoch 109, recon_loss:0.623133, zinb_loss:0.974610, adversial_loss:1.429048
Pretrain epoch 110, recon_loss:0.622807, zinb_loss:0.974098, adversial_loss:1.429110
Pretrain epoch 111, recon_loss:0.622475, zinb_loss:0.973645, adversial_loss:1.429105
Pretrain epoch 112, recon_loss:0.622167, zinb_loss:0.973212, adversial_loss:1.429200
Pretrain epoch 113, recon_loss:0.622034, zinb_loss:0.972862, adversial_loss:1.429024
Pretrain epoch 114, recon_loss:0.622099, zinb_loss:0.972703, adversial_loss:1.429111
Pretrain epoch 115, recon_loss:0.621434, zinb_loss:0.971904, adversial_loss:1.429097
Pretrain epoch 116, recon_loss:0.621009, zinb_loss:0.971451, adversial_loss:1.429049
Pretrain epoch 117, recon_loss:0.620695, zinb_loss:0.971125, adversial_loss:1.429136
Pretrain epoch 118, recon_loss:0.620237, zinb_loss:0.970577, adversial_loss:1.429183
Pretrain epoch 119, recon_loss:0.620105, zinb_loss:0.970281, adversial_loss:1.429277
Pretrain epoch 120, recon_loss:0.619490, zinb_loss:0.969755, adversial_loss:1.429142
Pretrain epoch 121, recon_loss:0.619160, zinb_loss:0.969379, adversial_loss:1.429138
Pretrain epoch 122, recon_loss:0.619090, zinb_loss:0.969102, adversial_loss:1.429344
Pretrain epoch 123, recon_loss:0.618618, zinb_loss:0.968682, adversial_loss:1.429099
Pretrain epoch 124, recon_loss:0.618526, zinb_loss:0.968558, adversial_loss:1.429225
Pretrain epoch 125, recon_loss:0.618637, zinb_loss:0.968575, adversial_loss:1.428929
Pretrain epoch 126, recon_loss:0.619092, zinb_loss:0.969307, adversial_loss:1.429116
Pretrain epoch 127, recon_loss:0.618681, zinb_loss:0.968614, adversial_loss:1.428772
Pretrain epoch 128, recon_loss:0.617303, zinb_loss:0.967060, adversial_loss:1.428879
Pretrain epoch 129, recon_loss:0.617263, zinb_loss:0.967078, adversial_loss:1.429295
Pretrain epoch 130, recon_loss:0.617240, zinb_loss:0.966846, adversial_loss:1.428835
Pretrain epoch 131, recon_loss:0.616269, zinb_loss:0.965958, adversial_loss:1.429287
Pretrain epoch 132, recon_loss:0.616477, zinb_loss:0.966108, adversial_loss:1.429424
Pretrain epoch 133, recon_loss:0.615766, zinb_loss:0.965359, adversial_loss:1.428878
Pretrain epoch 134, recon_loss:0.615684, zinb_loss:0.965282, adversial_loss:1.429186
Pretrain epoch 135, recon_loss:0.615411, zinb_loss:0.964948, adversial_loss:1.429230
Pretrain epoch 136, recon_loss:0.614962, zinb_loss:0.964457, adversial_loss:1.429154
Pretrain epoch 137, recon_loss:0.614913, zinb_loss:0.964325, adversial_loss:1.428897
Pretrain epoch 138, recon_loss:0.614463, zinb_loss:0.963821, adversial_loss:1.429244
Pretrain epoch 139, recon_loss:0.614149, zinb_loss:0.963603, adversial_loss:1.428917
Pretrain epoch 140, recon_loss:0.614027, zinb_loss:0.963349, adversial_loss:1.428762
Pretrain epoch 141, recon_loss:0.613575, zinb_loss:0.962941, adversial_loss:1.429289
Pretrain epoch 142, recon_loss:0.613386, zinb_loss:0.962810, adversial_loss:1.428784
Pretrain epoch 143, recon_loss:0.613120, zinb_loss:0.962629, adversial_loss:1.428834
Pretrain epoch 144, recon_loss:0.612992, zinb_loss:0.962514, adversial_loss:1.428753
Pretrain epoch 145, recon_loss:0.613211, zinb_loss:0.962841, adversial_loss:1.429576
Pretrain epoch 146, recon_loss:0.614520, zinb_loss:0.963558, adversial_loss:1.427821
Pretrain epoch 147, recon_loss:0.613984, zinb_loss:0.963325, adversial_loss:1.429116
Pretrain epoch 148, recon_loss:0.612539, zinb_loss:0.961804, adversial_loss:1.428339
Pretrain epoch 149, recon_loss:0.612393, zinb_loss:0.961236, adversial_loss:1.428280
Pretrain epoch 150, recon_loss:0.612142, zinb_loss:0.961799, adversial_loss:1.429263
Pretrain epoch 151, recon_loss:0.611880, zinb_loss:0.961015, adversial_loss:1.428813
Pretrain epoch 152, recon_loss:0.611173, zinb_loss:0.960480, adversial_loss:1.428246
Pretrain epoch 153, recon_loss:0.611500, zinb_loss:0.960880, adversial_loss:1.428657
Pretrain epoch 154, recon_loss:0.610817, zinb_loss:0.959982, adversial_loss:1.428742
Pretrain epoch 155, recon_loss:0.610532, zinb_loss:0.959872, adversial_loss:1.428401
Pretrain epoch 156, recon_loss:0.610524, zinb_loss:0.959882, adversial_loss:1.428544
Pretrain epoch 157, recon_loss:0.609901, zinb_loss:0.959174, adversial_loss:1.428756
Pretrain epoch 158, recon_loss:0.609964, zinb_loss:0.959239, adversial_loss:1.428768
Pretrain epoch 159, recon_loss:0.609522, zinb_loss:0.958955, adversial_loss:1.428555
Pretrain epoch 160, recon_loss:0.609137, zinb_loss:0.958543, adversial_loss:1.428527
Pretrain epoch 161, recon_loss:0.609137, zinb_loss:0.958564, adversial_loss:1.428753
Pretrain epoch 162, recon_loss:0.608743, zinb_loss:0.958210, adversial_loss:1.428579
Pretrain epoch 163, recon_loss:0.608385, zinb_loss:0.957912, adversial_loss:1.428407
Pretrain epoch 164, recon_loss:0.608262, zinb_loss:0.957818, adversial_loss:1.428603
Pretrain epoch 165, recon_loss:0.607998, zinb_loss:0.957516, adversial_loss:1.428690
Pretrain epoch 166, recon_loss:0.607641, zinb_loss:0.957273, adversial_loss:1.428346
Pretrain epoch 167, recon_loss:0.607503, zinb_loss:0.957133, adversial_loss:1.428531
Pretrain epoch 168, recon_loss:0.607305, zinb_loss:0.956954, adversial_loss:1.428707
Pretrain epoch 169, recon_loss:0.606972, zinb_loss:0.956723, adversial_loss:1.428190
Pretrain epoch 170, recon_loss:0.606809, zinb_loss:0.956597, adversial_loss:1.428576
Pretrain epoch 171, recon_loss:0.606891, zinb_loss:0.956632, adversial_loss:1.428575
Pretrain epoch 172, recon_loss:0.607303, zinb_loss:0.957199, adversial_loss:1.428157
Pretrain epoch 173, recon_loss:0.608943, zinb_loss:0.958271, adversial_loss:1.428259
Pretrain epoch 174, recon_loss:0.609508, zinb_loss:0.958879, adversial_loss:1.428585
Pretrain epoch 175, recon_loss:0.606720, zinb_loss:0.956581, adversial_loss:1.427546
Pretrain epoch 176, recon_loss:0.606695, zinb_loss:0.956393, adversial_loss:1.428298
Pretrain epoch 177, recon_loss:0.607467, zinb_loss:0.956780, adversial_loss:1.428735
Pretrain epoch 178, recon_loss:0.605802, zinb_loss:0.955732, adversial_loss:1.427329
Pretrain epoch 179, recon_loss:0.606636, zinb_loss:0.955996, adversial_loss:1.427505
Pretrain epoch 180, recon_loss:0.605689, zinb_loss:0.955446, adversial_loss:1.428727
Pretrain epoch 181, recon_loss:0.605730, zinb_loss:0.955299, adversial_loss:1.427825
Pretrain epoch 182, recon_loss:0.605198, zinb_loss:0.955160, adversial_loss:1.426990
Pretrain epoch 183, recon_loss:0.605235, zinb_loss:0.954859, adversial_loss:1.428034
Pretrain epoch 184, recon_loss:0.604594, zinb_loss:0.954622, adversial_loss:1.428196
Pretrain epoch 185, recon_loss:0.604399, zinb_loss:0.954554, adversial_loss:1.427356
Pretrain epoch 186, recon_loss:0.604010, zinb_loss:0.954214, adversial_loss:1.427431
Pretrain epoch 187, recon_loss:0.603821, zinb_loss:0.954215, adversial_loss:1.428051
Pretrain epoch 188, recon_loss:0.603584, zinb_loss:0.953911, adversial_loss:1.427611
Pretrain epoch 189, recon_loss:0.603187, zinb_loss:0.953732, adversial_loss:1.427278
Pretrain epoch 190, recon_loss:0.603220, zinb_loss:0.953670, adversial_loss:1.427715
Pretrain epoch 191, recon_loss:0.602728, zinb_loss:0.953320, adversial_loss:1.427637
Pretrain epoch 192, recon_loss:0.602864, zinb_loss:0.953345, adversial_loss:1.427393
Pretrain epoch 193, recon_loss:0.602380, zinb_loss:0.953087, adversial_loss:1.427703
Pretrain epoch 194, recon_loss:0.602317, zinb_loss:0.953047, adversial_loss:1.427688
Pretrain epoch 195, recon_loss:0.601962, zinb_loss:0.953053, adversial_loss:1.427253
Pretrain epoch 196, recon_loss:0.602363, zinb_loss:0.953086, adversial_loss:1.427564
Pretrain epoch 197, recon_loss:0.603229, zinb_loss:0.953886, adversial_loss:1.427807
Pretrain epoch 198, recon_loss:0.603240, zinb_loss:0.955095, adversial_loss:1.427271
Pretrain epoch 199, recon_loss:0.602859, zinb_loss:0.955135, adversial_loss:1.427088
Pretrain epoch 200, recon_loss:0.601353, zinb_loss:0.952706, adversial_loss:1.428152
Pretrain epoch 201, recon_loss:0.601788, zinb_loss:0.952935, adversial_loss:1.427828
Pretrain epoch 202, recon_loss:0.602279, zinb_loss:0.953297, adversial_loss:1.427325
Pretrain epoch 203, recon_loss:0.600736, zinb_loss:0.951970, adversial_loss:1.427600
Pretrain epoch 204, recon_loss:0.601353, zinb_loss:0.952870, adversial_loss:1.427555
Pretrain epoch 205, recon_loss:0.600841, zinb_loss:0.951904, adversial_loss:1.427374
Pretrain epoch 206, recon_loss:0.600565, zinb_loss:0.952214, adversial_loss:1.427428
Pretrain epoch 207, recon_loss:0.600366, zinb_loss:0.951833, adversial_loss:1.427639
Pretrain epoch 208, recon_loss:0.600176, zinb_loss:0.951553, adversial_loss:1.427284
Pretrain epoch 209, recon_loss:0.599820, zinb_loss:0.951557, adversial_loss:1.426997
Pretrain epoch 210, recon_loss:0.599880, zinb_loss:0.951233, adversial_loss:1.427256
Pretrain epoch 211, recon_loss:0.599644, zinb_loss:0.951163, adversial_loss:1.427038
Pretrain epoch 212, recon_loss:0.599370, zinb_loss:0.950934, adversial_loss:1.426819
Pretrain epoch 213, recon_loss:0.599154, zinb_loss:0.950865, adversial_loss:1.427049
Pretrain epoch 214, recon_loss:0.599040, zinb_loss:0.950633, adversial_loss:1.427195
Pretrain epoch 215, recon_loss:0.598855, zinb_loss:0.950526, adversial_loss:1.426654
Pretrain epoch 216, recon_loss:0.598662, zinb_loss:0.950392, adversial_loss:1.426881
Pretrain epoch 217, recon_loss:0.598230, zinb_loss:0.950218, adversial_loss:1.427045
Pretrain epoch 218, recon_loss:0.598321, zinb_loss:0.950143, adversial_loss:1.426833
Pretrain epoch 219, recon_loss:0.597963, zinb_loss:0.950006, adversial_loss:1.426752
Pretrain epoch 220, recon_loss:0.597819, zinb_loss:0.949891, adversial_loss:1.426944
Pretrain epoch 221, recon_loss:0.597657, zinb_loss:0.949787, adversial_loss:1.427035
Pretrain epoch 222, recon_loss:0.597443, zinb_loss:0.949698, adversial_loss:1.426747
Pretrain epoch 223, recon_loss:0.597206, zinb_loss:0.949627, adversial_loss:1.426669
Pretrain epoch 224, recon_loss:0.597469, zinb_loss:0.949890, adversial_loss:1.427063
Pretrain epoch 225, recon_loss:0.599332, zinb_loss:0.951508, adversial_loss:1.426097
Pretrain epoch 226, recon_loss:0.605253, zinb_loss:0.958174, adversial_loss:1.427295
Pretrain epoch 227, recon_loss:0.608280, zinb_loss:0.959103, adversial_loss:1.424130
Pretrain epoch 228, recon_loss:0.599972, zinb_loss:0.950836, adversial_loss:1.425049
Pretrain epoch 229, recon_loss:0.606478, zinb_loss:0.954529, adversial_loss:1.425959
Pretrain epoch 230, recon_loss:0.601315, zinb_loss:0.951071, adversial_loss:1.426224
Pretrain epoch 231, recon_loss:0.604429, zinb_loss:0.952158, adversial_loss:1.426145
Pretrain epoch 232, recon_loss:0.601641, zinb_loss:0.951250, adversial_loss:1.426066
Pretrain epoch 233, recon_loss:0.601722, zinb_loss:0.950813, adversial_loss:1.425947
Pretrain epoch 234, recon_loss:0.601142, zinb_loss:0.951202, adversial_loss:1.425873
Pretrain epoch 235, recon_loss:0.600673, zinb_loss:0.950270, adversial_loss:1.425840
Pretrain epoch 236, recon_loss:0.601137, zinb_loss:0.950210, adversial_loss:1.425662
Pretrain epoch 237, recon_loss:0.599812, zinb_loss:0.950077, adversial_loss:1.425620
Pretrain epoch 238, recon_loss:0.599699, zinb_loss:0.949669, adversial_loss:1.425650
Pretrain epoch 239, recon_loss:0.599563, zinb_loss:0.949625, adversial_loss:1.425446
Pretrain epoch 240, recon_loss:0.598390, zinb_loss:0.949360, adversial_loss:1.425284
Pretrain epoch 241, recon_loss:0.598522, zinb_loss:0.949196, adversial_loss:1.425510
Pretrain epoch 242, recon_loss:0.598058, zinb_loss:0.949014, adversial_loss:1.425793
Pretrain epoch 243, recon_loss:0.597337, zinb_loss:0.948837, adversial_loss:1.425403
Pretrain epoch 244, recon_loss:0.597191, zinb_loss:0.948783, adversial_loss:1.425131
Pretrain epoch 245, recon_loss:0.596562, zinb_loss:0.948609, adversial_loss:1.425381
Pretrain epoch 246, recon_loss:0.596425, zinb_loss:0.948481, adversial_loss:1.425480
Pretrain epoch 247, recon_loss:0.595926, zinb_loss:0.948392, adversial_loss:1.425278
Pretrain epoch 248, recon_loss:0.595740, zinb_loss:0.948239, adversial_loss:1.425449
Pretrain epoch 249, recon_loss:0.595363, zinb_loss:0.948177, adversial_loss:1.425799
Pretrain epoch 250, recon_loss:0.595102, zinb_loss:0.948007, adversial_loss:1.425575
Pretrain epoch 251, recon_loss:0.594927, zinb_loss:0.947932, adversial_loss:1.425204
Pretrain epoch 252, recon_loss:0.594572, zinb_loss:0.947832, adversial_loss:1.425196
Pretrain epoch 253, recon_loss:0.594208, zinb_loss:0.947716, adversial_loss:1.425335
Pretrain epoch 254, recon_loss:0.594147, zinb_loss:0.947630, adversial_loss:1.425483
Pretrain epoch 255, recon_loss:0.593790, zinb_loss:0.947518, adversial_loss:1.425484
Pretrain epoch 256, recon_loss:0.593520, zinb_loss:0.947431, adversial_loss:1.425133
Pretrain epoch 257, recon_loss:0.593419, zinb_loss:0.947331, adversial_loss:1.425059
Pretrain epoch 258, recon_loss:0.593114, zinb_loss:0.947230, adversial_loss:1.425240
Pretrain epoch 259, recon_loss:0.592929, zinb_loss:0.947156, adversial_loss:1.425238
Pretrain epoch 260, recon_loss:0.592723, zinb_loss:0.947053, adversial_loss:1.425262
Pretrain epoch 261, recon_loss:0.592524, zinb_loss:0.946957, adversial_loss:1.425203
Pretrain epoch 262, recon_loss:0.592345, zinb_loss:0.946880, adversial_loss:1.425052
Pretrain epoch 263, recon_loss:0.592196, zinb_loss:0.946789, adversial_loss:1.425057
Pretrain epoch 264, recon_loss:0.591969, zinb_loss:0.946705, adversial_loss:1.425004
Pretrain epoch 265, recon_loss:0.591802, zinb_loss:0.946618, adversial_loss:1.425002
Pretrain epoch 266, recon_loss:0.591626, zinb_loss:0.946531, adversial_loss:1.424854
Pretrain epoch 267, recon_loss:0.591460, zinb_loss:0.946453, adversial_loss:1.424752
Pretrain epoch 268, recon_loss:0.591321, zinb_loss:0.946374, adversial_loss:1.424782
Pretrain epoch 269, recon_loss:0.591128, zinb_loss:0.946296, adversial_loss:1.424794
Pretrain epoch 270, recon_loss:0.591044, zinb_loss:0.946219, adversial_loss:1.424881
Pretrain epoch 271, recon_loss:0.590914, zinb_loss:0.946169, adversial_loss:1.424607
Pretrain epoch 272, recon_loss:0.590952, zinb_loss:0.946151, adversial_loss:1.424812
Pretrain epoch 273, recon_loss:0.591415, zinb_loss:0.946322, adversial_loss:1.424409
Pretrain epoch 274, recon_loss:0.592091, zinb_loss:0.946455, adversial_loss:1.424971
Pretrain epoch 275, recon_loss:0.591637, zinb_loss:0.946825, adversial_loss:1.424388
Pretrain epoch 276, recon_loss:0.591432, zinb_loss:0.947589, adversial_loss:1.424958
Pretrain epoch 277, recon_loss:0.593588, zinb_loss:0.950002, adversial_loss:1.424491
Pretrain epoch 278, recon_loss:0.593222, zinb_loss:0.949670, adversial_loss:1.425065
Pretrain epoch 279, recon_loss:0.591369, zinb_loss:0.946412, adversial_loss:1.424347
Pretrain epoch 280, recon_loss:0.591747, zinb_loss:0.947250, adversial_loss:1.424495
Pretrain epoch 281, recon_loss:0.591976, zinb_loss:0.947268, adversial_loss:1.424966
Pretrain epoch 282, recon_loss:0.591191, zinb_loss:0.946159, adversial_loss:1.424046
Pretrain epoch 283, recon_loss:0.591449, zinb_loss:0.946912, adversial_loss:1.423779
Pretrain epoch 284, recon_loss:0.590953, zinb_loss:0.945963, adversial_loss:1.424882
Pretrain epoch 285, recon_loss:0.590662, zinb_loss:0.946328, adversial_loss:1.424976
Pretrain epoch 286, recon_loss:0.590715, zinb_loss:0.945890, adversial_loss:1.423669
Pretrain epoch 287, recon_loss:0.590290, zinb_loss:0.945944, adversial_loss:1.423691
Pretrain epoch 288, recon_loss:0.590042, zinb_loss:0.945648, adversial_loss:1.424490
Pretrain epoch 289, recon_loss:0.589951, zinb_loss:0.945687, adversial_loss:1.424091
Pretrain epoch 290, recon_loss:0.589625, zinb_loss:0.945495, adversial_loss:1.423323
Pretrain epoch 291, recon_loss:0.589453, zinb_loss:0.945375, adversial_loss:1.423556
Pretrain epoch 292, recon_loss:0.589453, zinb_loss:0.945355, adversial_loss:1.424193
Pretrain epoch 293, recon_loss:0.588965, zinb_loss:0.945079, adversial_loss:1.423679
Pretrain epoch 294, recon_loss:0.589114, zinb_loss:0.945218, adversial_loss:1.423072
Pretrain epoch 295, recon_loss:0.588595, zinb_loss:0.944889, adversial_loss:1.423803
Pretrain epoch 296, recon_loss:0.588636, zinb_loss:0.944955, adversial_loss:1.423836
Pretrain epoch 297, recon_loss:0.588296, zinb_loss:0.944849, adversial_loss:1.423315
Pretrain epoch 298, recon_loss:0.588316, zinb_loss:0.944757, adversial_loss:1.423798
Pretrain epoch 299, recon_loss:0.587997, zinb_loss:0.944757, adversial_loss:1.423531
Pretrain epoch 300, recon_loss:0.587803, zinb_loss:0.944592, adversial_loss:1.423436
Pretrain epoch 301, recon_loss:0.587743, zinb_loss:0.944545, adversial_loss:1.423621
Pretrain epoch 302, recon_loss:0.587472, zinb_loss:0.944469, adversial_loss:1.423401
Pretrain epoch 303, recon_loss:0.587392, zinb_loss:0.944377, adversial_loss:1.423245
Pretrain epoch 304, recon_loss:0.587130, zinb_loss:0.944332, adversial_loss:1.423414
Pretrain epoch 305, recon_loss:0.587189, zinb_loss:0.944271, adversial_loss:1.423377
Pretrain epoch 306, recon_loss:0.586892, zinb_loss:0.944210, adversial_loss:1.423191
Pretrain epoch 307, recon_loss:0.586698, zinb_loss:0.944218, adversial_loss:1.423472
Pretrain epoch 308, recon_loss:0.586839, zinb_loss:0.944312, adversial_loss:1.422914
Pretrain epoch 309, recon_loss:0.587066, zinb_loss:0.944669, adversial_loss:1.423441
Pretrain epoch 310, recon_loss:0.588263, zinb_loss:0.945739, adversial_loss:1.422480
Pretrain epoch 311, recon_loss:0.590677, zinb_loss:0.947965, adversial_loss:1.423993
Pretrain epoch 312, recon_loss:0.590389, zinb_loss:0.947794, adversial_loss:1.422032
Pretrain epoch 313, recon_loss:0.587383, zinb_loss:0.944717, adversial_loss:1.423186
Pretrain epoch 314, recon_loss:0.587864, zinb_loss:0.945086, adversial_loss:1.423807
Pretrain epoch 315, recon_loss:0.588613, zinb_loss:0.945402, adversial_loss:1.422340
Pretrain epoch 316, recon_loss:0.586939, zinb_loss:0.944250, adversial_loss:1.422483
Pretrain epoch 317, recon_loss:0.587853, zinb_loss:0.944800, adversial_loss:1.423511
Pretrain epoch 318, recon_loss:0.586910, zinb_loss:0.944164, adversial_loss:1.423070
Pretrain epoch 319, recon_loss:0.587304, zinb_loss:0.944364, adversial_loss:1.422066
Pretrain epoch 320, recon_loss:0.586568, zinb_loss:0.943986, adversial_loss:1.422565
Pretrain epoch 321, recon_loss:0.586992, zinb_loss:0.944140, adversial_loss:1.423091
Pretrain epoch 322, recon_loss:0.586294, zinb_loss:0.943814, adversial_loss:1.422346
Pretrain epoch 323, recon_loss:0.586181, zinb_loss:0.943861, adversial_loss:1.422142
Pretrain epoch 324, recon_loss:0.586141, zinb_loss:0.943737, adversial_loss:1.422673
Pretrain epoch 325, recon_loss:0.585637, zinb_loss:0.943591, adversial_loss:1.422521
Pretrain epoch 326, recon_loss:0.585669, zinb_loss:0.943602, adversial_loss:1.421944
Pretrain epoch 327, recon_loss:0.585300, zinb_loss:0.943453, adversial_loss:1.422092
Pretrain epoch 328, recon_loss:0.585294, zinb_loss:0.943389, adversial_loss:1.422398
Pretrain epoch 329, recon_loss:0.584881, zinb_loss:0.943300, adversial_loss:1.422248
Pretrain epoch 330, recon_loss:0.585024, zinb_loss:0.943301, adversial_loss:1.422163
Pretrain epoch 331, recon_loss:0.584343, zinb_loss:0.943160, adversial_loss:1.422036
Pretrain epoch 332, recon_loss:0.584618, zinb_loss:0.943164, adversial_loss:1.422024
Pretrain epoch 333, recon_loss:0.584046, zinb_loss:0.943058, adversial_loss:1.422158
Pretrain epoch 334, recon_loss:0.584154, zinb_loss:0.943039, adversial_loss:1.421857
Pretrain epoch 335, recon_loss:0.583842, zinb_loss:0.942957, adversial_loss:1.421720
Pretrain epoch 336, recon_loss:0.583707, zinb_loss:0.942890, adversial_loss:1.422023
Pretrain epoch 337, recon_loss:0.583644, zinb_loss:0.942867, adversial_loss:1.421784
Pretrain epoch 338, recon_loss:0.583390, zinb_loss:0.942781, adversial_loss:1.421498
Pretrain epoch 339, recon_loss:0.583468, zinb_loss:0.942748, adversial_loss:1.421943
Pretrain epoch 340, recon_loss:0.583190, zinb_loss:0.942672, adversial_loss:1.421609
Pretrain epoch 341, recon_loss:0.583118, zinb_loss:0.942679, adversial_loss:1.421522
Pretrain epoch 342, recon_loss:0.583196, zinb_loss:0.942692, adversial_loss:1.421640
Pretrain epoch 343, recon_loss:0.583504, zinb_loss:0.942836, adversial_loss:1.421503
Pretrain epoch 344, recon_loss:0.584756, zinb_loss:0.943194, adversial_loss:1.421440
Pretrain epoch 345, recon_loss:0.585259, zinb_loss:0.943688, adversial_loss:1.421374
Pretrain epoch 346, recon_loss:0.585178, zinb_loss:0.944037, adversial_loss:1.421304
Pretrain epoch 347, recon_loss:0.583679, zinb_loss:0.943874, adversial_loss:1.421407
Pretrain epoch 348, recon_loss:0.584861, zinb_loss:0.944298, adversial_loss:1.421194
Pretrain epoch 349, recon_loss:0.586685, zinb_loss:0.944888, adversial_loss:1.421857
Pretrain epoch 350, recon_loss:0.585146, zinb_loss:0.944214, adversial_loss:1.420706
Pretrain epoch 351, recon_loss:0.583222, zinb_loss:0.942731, adversial_loss:1.421321
Pretrain epoch 352, recon_loss:0.584606, zinb_loss:0.943340, adversial_loss:1.421256
Pretrain epoch 353, recon_loss:0.583529, zinb_loss:0.943080, adversial_loss:1.420614
Pretrain epoch 354, recon_loss:0.583561, zinb_loss:0.942839, adversial_loss:1.421177
Pretrain epoch 355, recon_loss:0.583848, zinb_loss:0.942995, adversial_loss:1.421145
Pretrain epoch 356, recon_loss:0.582299, zinb_loss:0.942536, adversial_loss:1.420783
Pretrain epoch 357, recon_loss:0.583882, zinb_loss:0.942727, adversial_loss:1.420773
Pretrain epoch 358, recon_loss:0.581920, zinb_loss:0.942356, adversial_loss:1.420544
Pretrain epoch 359, recon_loss:0.582930, zinb_loss:0.942428, adversial_loss:1.420674
Pretrain epoch 360, recon_loss:0.582263, zinb_loss:0.942330, adversial_loss:1.420741
Pretrain epoch 361, recon_loss:0.581675, zinb_loss:0.942141, adversial_loss:1.420511
Pretrain epoch 362, recon_loss:0.582157, zinb_loss:0.942253, adversial_loss:1.420473
Pretrain epoch 363, recon_loss:0.581211, zinb_loss:0.942099, adversial_loss:1.420307
Pretrain epoch 364, recon_loss:0.581602, zinb_loss:0.942146, adversial_loss:1.420399
Pretrain epoch 365, recon_loss:0.580901, zinb_loss:0.941898, adversial_loss:1.420458
Pretrain epoch 366, recon_loss:0.581039, zinb_loss:0.941902, adversial_loss:1.420304
Pretrain epoch 367, recon_loss:0.580787, zinb_loss:0.941995, adversial_loss:1.420470
Pretrain epoch 368, recon_loss:0.580621, zinb_loss:0.941834, adversial_loss:1.420494
Pretrain epoch 369, recon_loss:0.580609, zinb_loss:0.941738, adversial_loss:1.420355
Pretrain epoch 370, recon_loss:0.580194, zinb_loss:0.941658, adversial_loss:1.420252
Pretrain epoch 371, recon_loss:0.580262, zinb_loss:0.941619, adversial_loss:1.420218
Pretrain epoch 372, recon_loss:0.580156, zinb_loss:0.941618, adversial_loss:1.420169
Pretrain epoch 373, recon_loss:0.580015, zinb_loss:0.941595, adversial_loss:1.420110
Pretrain epoch 374, recon_loss:0.579838, zinb_loss:0.941561, adversial_loss:1.420260
Pretrain epoch 375, recon_loss:0.579670, zinb_loss:0.941490, adversial_loss:1.419937
Pretrain epoch 376, recon_loss:0.579518, zinb_loss:0.941404, adversial_loss:1.420126
Pretrain epoch 377, recon_loss:0.579423, zinb_loss:0.941341, adversial_loss:1.419921
Pretrain epoch 378, recon_loss:0.579356, zinb_loss:0.941316, adversial_loss:1.419799
Pretrain epoch 379, recon_loss:0.579203, zinb_loss:0.941284, adversial_loss:1.419973
Pretrain epoch 380, recon_loss:0.579167, zinb_loss:0.941332, adversial_loss:1.419929
Pretrain epoch 381, recon_loss:0.579386, zinb_loss:0.941511, adversial_loss:1.419563
Pretrain epoch 382, recon_loss:0.580304, zinb_loss:0.942061, adversial_loss:1.419789
Pretrain epoch 383, recon_loss:0.582138, zinb_loss:0.943379, adversial_loss:1.419681
Pretrain epoch 384, recon_loss:0.583550, zinb_loss:0.945045, adversial_loss:1.419542
Pretrain epoch 385, recon_loss:0.582927, zinb_loss:0.944927, adversial_loss:1.419255
Pretrain epoch 386, recon_loss:0.581570, zinb_loss:0.942347, adversial_loss:1.420436
Pretrain epoch 387, recon_loss:0.582110, zinb_loss:0.942766, adversial_loss:1.419017
Pretrain epoch 388, recon_loss:0.581604, zinb_loss:0.942581, adversial_loss:1.419060
Pretrain epoch 389, recon_loss:0.580717, zinb_loss:0.941900, adversial_loss:1.420170
Pretrain epoch 390, recon_loss:0.581997, zinb_loss:0.942281, adversial_loss:1.418559
Pretrain epoch 391, recon_loss:0.580230, zinb_loss:0.941709, adversial_loss:1.418235
Pretrain epoch 392, recon_loss:0.581017, zinb_loss:0.941724, adversial_loss:1.419802
Pretrain epoch 393, recon_loss:0.580346, zinb_loss:0.941516, adversial_loss:1.419295
Pretrain epoch 394, recon_loss:0.579912, zinb_loss:0.941409, adversial_loss:1.418395
Pretrain epoch 395, recon_loss:0.580094, zinb_loss:0.941453, adversial_loss:1.419263
Pretrain epoch 396, recon_loss:0.579287, zinb_loss:0.941191, adversial_loss:1.419363
Pretrain epoch 397, recon_loss:0.579749, zinb_loss:0.941160, adversial_loss:1.418412
Pretrain epoch 398, recon_loss:0.578888, zinb_loss:0.941031, adversial_loss:1.418818
Pretrain epoch 399, recon_loss:0.579094, zinb_loss:0.941045, adversial_loss:1.419081
Pretrain epoch 400, recon_loss:0.578716, zinb_loss:0.940966, adversial_loss:1.418836
[14]:
y_pred, final_latent = model.fit(y=y, n_clusters=23, num_epochs=2000, file='GSE163120')
Clustering stage
Initializing cluster centers with kmeans.
Initializing k-means: AMI= 0.7055, NMI= 0.7065, ARI= 0.4759, ACC= 0.6091
Training epoch 1, recon_loss:0.578283, zinb_loss:0.940858, cluster_loss:0.267897
Clustering 1: AMI= 0.7055, NMI= 0.7065, ARI= 0.4759, ACC= 0.6091
0.0
Training epoch 2, recon_loss:0.604183, zinb_loss:0.950158, cluster_loss:0.267520
Clustering 2: AMI= 0.7053, NMI= 0.7063, ARI= 0.4967, ACC= 0.6196
0.09955617085386213
Training epoch 3, recon_loss:0.624748, zinb_loss:0.975262, cluster_loss:0.251129
Clustering 3: AMI= 0.6985, NMI= 0.6995, ARI= 0.4479, ACC= 0.5841
0.19475548678692128
Training epoch 4, recon_loss:0.641964, zinb_loss:1.007354, cluster_loss:0.248802
Clustering 4: AMI= 0.6996, NMI= 0.7006, ARI= 0.4984, ACC= 0.6370
0.3092959811067226
Training epoch 5, recon_loss:0.620443, zinb_loss:1.014138, cluster_loss:0.258289
Clustering 5: AMI= 0.7008, NMI= 0.7018, ARI= 0.4810, ACC= 0.6155
0.273382466712814
Training epoch 6, recon_loss:0.627468, zinb_loss:1.000923, cluster_loss:0.267297
Clustering 6: AMI= 0.7166, NMI= 0.7176, ARI= 0.5168, ACC= 0.6404
0.1743963516429822
Training epoch 7, recon_loss:0.629639, zinb_loss:0.981455, cluster_loss:0.253169
Clustering 7: AMI= 0.6973, NMI= 0.6983, ARI= 0.4921, ACC= 0.6150
0.16759640050490654
Training epoch 8, recon_loss:0.610951, zinb_loss:0.963163, cluster_loss:0.273688
Clustering 8: AMI= 0.7118, NMI= 0.7127, ARI= 0.5102, ACC= 0.6205
0.11107944134533165
Training epoch 9, recon_loss:0.606674, zinb_loss:0.955225, cluster_loss:0.274060
Clustering 9: AMI= 0.7030, NMI= 0.7040, ARI= 0.4945, ACC= 0.6177
0.10362799788264994
Training epoch 10, recon_loss:0.603629, zinb_loss:0.955168, cluster_loss:0.281613
Clustering 10: AMI= 0.7131, NMI= 0.7140, ARI= 0.5062, ACC= 0.6242
0.09866036890752881
Training epoch 11, recon_loss:0.605655, zinb_loss:0.955295, cluster_loss:0.277240
Clustering 11: AMI= 0.7034, NMI= 0.7044, ARI= 0.4917, ACC= 0.6151
0.09454782360845311
Training epoch 12, recon_loss:0.603617, zinb_loss:0.954015, cluster_loss:0.284091
Clustering 12: AMI= 0.7128, NMI= 0.7137, ARI= 0.5038, ACC= 0.6228
0.09186041776945315
Training epoch 13, recon_loss:0.602957, zinb_loss:0.953503, cluster_loss:0.282067
Clustering 13: AMI= 0.7056, NMI= 0.7066, ARI= 0.4964, ACC= 0.6156
0.0831874261981351
Training epoch 14, recon_loss:0.602049, zinb_loss:0.952359, cluster_loss:0.286583
Clustering 14: AMI= 0.7135, NMI= 0.7144, ARI= 0.5025, ACC= 0.6210
0.07850482511502911
Training epoch 15, recon_loss:0.601373, zinb_loss:0.951753, cluster_loss:0.285459
Clustering 15: AMI= 0.7082, NMI= 0.7092, ARI= 0.5006, ACC= 0.6167
0.06954680565169592
Training epoch 16, recon_loss:0.601026, zinb_loss:0.951113, cluster_loss:0.288606
Clustering 16: AMI= 0.7140, NMI= 0.7149, ARI= 0.5009, ACC= 0.6193
0.06571928824463537
Training epoch 17, recon_loss:0.600724, zinb_loss:0.950717, cluster_loss:0.287691
Clustering 17: AMI= 0.7102, NMI= 0.7112, ARI= 0.5031, ACC= 0.6165
0.0593265198094385
Training epoch 18, recon_loss:0.601057, zinb_loss:0.950548, cluster_loss:0.289826
Clustering 18: AMI= 0.7137, NMI= 0.7146, ARI= 0.4987, ACC= 0.6173
0.059367238079726374
Training epoch 19, recon_loss:0.601084, zinb_loss:0.950544, cluster_loss:0.288866
Clustering 19: AMI= 0.7123, NMI= 0.7133, ARI= 0.5068, ACC= 0.6179
0.05814568997109003
Training epoch 20, recon_loss:0.602567, zinb_loss:0.950983, cluster_loss:0.289988
Clustering 20: AMI= 0.7124, NMI= 0.7134, ARI= 0.4946, ACC= 0.6140
0.06376481127081722
Training epoch 21, recon_loss:0.602428, zinb_loss:0.951537, cluster_loss:0.289339
Clustering 21: AMI= 0.7145, NMI= 0.7154, ARI= 0.5100, ACC= 0.6204
0.0651085141903172
Training epoch 22, recon_loss:0.603932, zinb_loss:0.952431, cluster_loss:0.289908
Clustering 22: AMI= 0.7117, NMI= 0.7126, ARI= 0.4923, ACC= 0.6114
0.07166415570666558
Training epoch 23, recon_loss:0.602905, zinb_loss:0.952970, cluster_loss:0.290755
Clustering 23: AMI= 0.7161, NMI= 0.7170, ARI= 0.5114, ACC= 0.6222
0.0703204527871656
Training epoch 24, recon_loss:0.602801, zinb_loss:0.953425, cluster_loss:0.291247
Clustering 24: AMI= 0.7117, NMI= 0.7126, ARI= 0.4924, ACC= 0.6100
0.07166415570666558
Training epoch 25, recon_loss:0.602533, zinb_loss:0.953915, cluster_loss:0.292682
Clustering 25: AMI= 0.7168, NMI= 0.7177, ARI= 0.5112, ACC= 0.6235
0.06873244024593836
Training epoch 26, recon_loss:0.602246, zinb_loss:0.953963, cluster_loss:0.292619
Clustering 26: AMI= 0.7116, NMI= 0.7125, ARI= 0.4930, ACC= 0.6091
0.06747017386701414
Training epoch 27, recon_loss:0.602707, zinb_loss:0.954565, cluster_loss:0.294073
Clustering 27: AMI= 0.7166, NMI= 0.7175, ARI= 0.5095, ACC= 0.6234
0.06421271224398388
Training epoch 28, recon_loss:0.602441, zinb_loss:0.954300, cluster_loss:0.293615
Clustering 28: AMI= 0.7115, NMI= 0.7125, ARI= 0.4956, ACC= 0.6099
0.060833095810089985
Training epoch 29, recon_loss:0.603012, zinb_loss:0.954816, cluster_loss:0.295198
Clustering 29: AMI= 0.7164, NMI= 0.7173, ARI= 0.5078, ACC= 0.6223
0.05724988802475671
Training epoch 30, recon_loss:0.602607, zinb_loss:0.954239, cluster_loss:0.294651
Clustering 30: AMI= 0.7116, NMI= 0.7125, ARI= 0.4970, ACC= 0.6099
0.05334093407712041
Training epoch 31, recon_loss:0.602986, zinb_loss:0.954600, cluster_loss:0.296351
Clustering 31: AMI= 0.7166, NMI= 0.7175, ARI= 0.5077, ACC= 0.6223
0.05040921861639318
Training epoch 32, recon_loss:0.602366, zinb_loss:0.953835, cluster_loss:0.295951
Clustering 32: AMI= 0.7120, NMI= 0.7129, ARI= 0.4981, ACC= 0.6107
0.046215236776741726
Training epoch 33, recon_loss:0.602762, zinb_loss:0.954243, cluster_loss:0.297542
Clustering 33: AMI= 0.7166, NMI= 0.7176, ARI= 0.5072, ACC= 0.6218
0.04299849342399935
Training epoch 34, recon_loss:0.602057, zinb_loss:0.953480, cluster_loss:0.297270
Clustering 34: AMI= 0.7125, NMI= 0.7135, ARI= 0.4988, ACC= 0.6109
0.04063683374730241
Training epoch 35, recon_loss:0.602738, zinb_loss:0.954083, cluster_loss:0.298615
Clustering 35: AMI= 0.7164, NMI= 0.7174, ARI= 0.5074, ACC= 0.6219
0.03941528563866607
Training epoch 36, recon_loss:0.601913, zinb_loss:0.953373, cluster_loss:0.298413
Clustering 36: AMI= 0.7129, NMI= 0.7138, ARI= 0.4989, ACC= 0.6107
0.03888594812492365
Training epoch 37, recon_loss:0.603167, zinb_loss:0.954282, cluster_loss:0.299491
Clustering 37: AMI= 0.7164, NMI= 0.7173, ARI= 0.5077, ACC= 0.6219
0.04006677796327212
Training epoch 38, recon_loss:0.601935, zinb_loss:0.953599, cluster_loss:0.299274
Clustering 38: AMI= 0.7130, NMI= 0.7139, ARI= 0.4980, ACC= 0.6098
0.04193981839651452
Training epoch 39, recon_loss:0.604018, zinb_loss:0.954999, cluster_loss:0.300035
Clustering 39: AMI= 0.7169, NMI= 0.7178, ARI= 0.5091, ACC= 0.6228
0.04584877234415082
Training epoch 40, recon_loss:0.602190, zinb_loss:0.954309, cluster_loss:0.299712
Clustering 40: AMI= 0.7131, NMI= 0.7141, ARI= 0.4965, ACC= 0.6088
0.052241540779347694
Training epoch 41, recon_loss:0.605564, zinb_loss:0.956435, cluster_loss:0.299894
Clustering 41: AMI= 0.7168, NMI= 0.7177, ARI= 0.5106, ACC= 0.6243
0.06132171505354453
Training epoch 42, recon_loss:0.603153, zinb_loss:0.955624, cluster_loss:0.299327
Clustering 42: AMI= 0.7134, NMI= 0.7143, ARI= 0.4946, ACC= 0.6065
0.07158271916608983
Training epoch 43, recon_loss:0.607563, zinb_loss:0.957940, cluster_loss:0.298822
Clustering 43: AMI= 0.7173, NMI= 0.7182, ARI= 0.5127, ACC= 0.6259
0.08021499246712
Training epoch 44, recon_loss:0.604288, zinb_loss:0.956329, cluster_loss:0.298775
Clustering 44: AMI= 0.7127, NMI= 0.7137, ARI= 0.4921, ACC= 0.6047
0.08664847917260475
Training epoch 45, recon_loss:0.607811, zinb_loss:0.957405, cluster_loss:0.298476
Clustering 45: AMI= 0.7167, NMI= 0.7177, ARI= 0.5121, ACC= 0.6249
0.08640416955087749
Training epoch 46, recon_loss:0.603954, zinb_loss:0.955212, cluster_loss:0.299695
Clustering 46: AMI= 0.7127, NMI= 0.7137, ARI= 0.4922, ACC= 0.6059
0.08269880695468057
Training epoch 47, recon_loss:0.607041, zinb_loss:0.955818, cluster_loss:0.299224
Clustering 47: AMI= 0.7166, NMI= 0.7175, ARI= 0.5109, ACC= 0.6228
0.07732399527668064
Training epoch 48, recon_loss:0.603899, zinb_loss:0.953994, cluster_loss:0.300955
Clustering 48: AMI= 0.7137, NMI= 0.7147, ARI= 0.4943, ACC= 0.6086
0.07170487397695346
Training epoch 49, recon_loss:0.606360, zinb_loss:0.954578, cluster_loss:0.300085
Clustering 49: AMI= 0.7166, NMI= 0.7176, ARI= 0.5099, ACC= 0.6214
0.06726658251557474
Training epoch 50, recon_loss:0.603710, zinb_loss:0.953259, cluster_loss:0.301935
Clustering 50: AMI= 0.7147, NMI= 0.7156, ARI= 0.4966, ACC= 0.6110
0.06107740543181726
Training epoch 51, recon_loss:0.606071, zinb_loss:0.953811, cluster_loss:0.300916
Clustering 51: AMI= 0.7160, NMI= 0.7169, ARI= 0.5091, ACC= 0.6202
0.05680198705159005
Training epoch 52, recon_loss:0.603947, zinb_loss:0.952961, cluster_loss:0.302709
Clustering 52: AMI= 0.7154, NMI= 0.7163, ARI= 0.4980, ACC= 0.6125
0.053707398509711304
Training epoch 53, recon_loss:0.606021, zinb_loss:0.953407, cluster_loss:0.301580
Clustering 53: AMI= 0.7161, NMI= 0.7170, ARI= 0.5082, ACC= 0.6192
0.05126430229243862
Training epoch 54, recon_loss:0.604311, zinb_loss:0.952948, cluster_loss:0.303314
Clustering 54: AMI= 0.7154, NMI= 0.7163, ARI= 0.4985, ACC= 0.6134
0.04833258683171139
Training epoch 55, recon_loss:0.606182, zinb_loss:0.953258, cluster_loss:0.302095
Clustering 55: AMI= 0.7164, NMI= 0.7173, ARI= 0.5081, ACC= 0.6183
0.047111038723075045
Training epoch 56, recon_loss:0.604760, zinb_loss:0.953142, cluster_loss:0.303795
Clustering 56: AMI= 0.7159, NMI= 0.7168, ARI= 0.4998, ACC= 0.6144
0.04548230791155992
Training epoch 57, recon_loss:0.606341, zinb_loss:0.953292, cluster_loss:0.302517
Clustering 57: AMI= 0.7163, NMI= 0.7172, ARI= 0.5072, ACC= 0.6170
0.04430147807321145
Training epoch 58, recon_loss:0.605274, zinb_loss:0.953513, cluster_loss:0.304217
Clustering 58: AMI= 0.7164, NMI= 0.7173, ARI= 0.5015, ACC= 0.6158
0.04206197320737815
Training epoch 59, recon_loss:0.606405, zinb_loss:0.953505, cluster_loss:0.302888
Clustering 59: AMI= 0.7157, NMI= 0.7167, ARI= 0.5058, ACC= 0.6157
0.04092186163931756
Training epoch 60, recon_loss:0.605611, zinb_loss:0.954064, cluster_loss:0.304581
Clustering 60: AMI= 0.7174, NMI= 0.7184, ARI= 0.5040, ACC= 0.6176
0.03937456736837819
Training epoch 61, recon_loss:0.606343, zinb_loss:0.953926, cluster_loss:0.303256
Clustering 61: AMI= 0.7154, NMI= 0.7164, ARI= 0.5039, ACC= 0.6141
0.03847876542204487
Training epoch 62, recon_loss:0.606012, zinb_loss:0.954873, cluster_loss:0.304893
Clustering 62: AMI= 0.7183, NMI= 0.7192, ARI= 0.5064, ACC= 0.6194
0.03676859806995399
Training epoch 63, recon_loss:0.606218, zinb_loss:0.954626, cluster_loss:0.303585
Clustering 63: AMI= 0.7153, NMI= 0.7162, ARI= 0.5024, ACC= 0.6128
0.03595423266419642
Training epoch 64, recon_loss:0.606223, zinb_loss:0.956046, cluster_loss:0.305074
Clustering 64: AMI= 0.7190, NMI= 0.7200, ARI= 0.5089, ACC= 0.6214
0.036646443259090354
Training epoch 65, recon_loss:0.606071, zinb_loss:0.955746, cluster_loss:0.303877
Clustering 65: AMI= 0.7145, NMI= 0.7155, ARI= 0.4999, ACC= 0.6103
0.040270369314711514
Training epoch 66, recon_loss:0.606696, zinb_loss:0.957720, cluster_loss:0.305082
Clustering 66: AMI= 0.7206, NMI= 0.7215, ARI= 0.5126, ACC= 0.6242
0.04593020888472658
Training epoch 67, recon_loss:0.606087, zinb_loss:0.957220, cluster_loss:0.304076
Clustering 67: AMI= 0.7134, NMI= 0.7143, ARI= 0.4964, ACC= 0.6072
0.052200822509059816
Training epoch 68, recon_loss:0.607118, zinb_loss:0.959537, cluster_loss:0.304932
Clustering 68: AMI= 0.7217, NMI= 0.7226, ARI= 0.5150, ACC= 0.6264
0.0591229284579991
Training epoch 69, recon_loss:0.606211, zinb_loss:0.958407, cluster_loss:0.304311
Clustering 69: AMI= 0.7135, NMI= 0.7144, ARI= 0.4948, ACC= 0.6059
0.06523066900118082
Training epoch 70, recon_loss:0.607683, zinb_loss:0.960386, cluster_loss:0.304953
Clustering 70: AMI= 0.7220, NMI= 0.7229, ARI= 0.5161, ACC= 0.6269
0.06893603159737774
Training epoch 71, recon_loss:0.606178, zinb_loss:0.958425, cluster_loss:0.304747
Clustering 71: AMI= 0.7138, NMI= 0.7148, ARI= 0.4947, ACC= 0.6052
0.07109409992263528
Training epoch 72, recon_loss:0.607365, zinb_loss:0.959681, cluster_loss:0.305366
Clustering 72: AMI= 0.7214, NMI= 0.7223, ARI= 0.5155, ACC= 0.6267
0.07023901624658985
Training epoch 73, recon_loss:0.605811, zinb_loss:0.957490, cluster_loss:0.305452
Clustering 73: AMI= 0.7143, NMI= 0.7153, ARI= 0.4951, ACC= 0.6056
0.06836597581334745
Training epoch 74, recon_loss:0.607197, zinb_loss:0.958380, cluster_loss:0.306015
Clustering 74: AMI= 0.7205, NMI= 0.7214, ARI= 0.5141, ACC= 0.6255
0.0647420497577263
Training epoch 75, recon_loss:0.605477, zinb_loss:0.956487, cluster_loss:0.306114
Clustering 75: AMI= 0.7152, NMI= 0.7161, ARI= 0.4972, ACC= 0.6073
0.059937293863756666
Training epoch 76, recon_loss:0.606875, zinb_loss:0.957310, cluster_loss:0.306613
Clustering 76: AMI= 0.7196, NMI= 0.7205, ARI= 0.5121, ACC= 0.6237
0.056191212997271874
Training epoch 77, recon_loss:0.605363, zinb_loss:0.955818, cluster_loss:0.306662
Clustering 77: AMI= 0.7158, NMI= 0.7167, ARI= 0.4983, ACC= 0.6083
0.052404413860499204
Training epoch 78, recon_loss:0.606800, zinb_loss:0.956598, cluster_loss:0.307085
Clustering 78: AMI= 0.7193, NMI= 0.7202, ARI= 0.5109, ACC= 0.6231
0.049146952237468955
Training epoch 79, recon_loss:0.605479, zinb_loss:0.955451, cluster_loss:0.307041
Clustering 79: AMI= 0.7164, NMI= 0.7173, ARI= 0.4998, ACC= 0.6097
0.04572661753328719
Training epoch 80, recon_loss:0.606916, zinb_loss:0.956171, cluster_loss:0.307393
Clustering 80: AMI= 0.7190, NMI= 0.7199, ARI= 0.5100, ACC= 0.6222
0.04385357710004479
Training epoch 81, recon_loss:0.605828, zinb_loss:0.955307, cluster_loss:0.307235
Clustering 81: AMI= 0.7169, NMI= 0.7179, ARI= 0.5012, ACC= 0.6110
0.04169550877478725
Training epoch 82, recon_loss:0.607365, zinb_loss:0.955926, cluster_loss:0.307506
Clustering 82: AMI= 0.7184, NMI= 0.7193, ARI= 0.5087, ACC= 0.6215
0.040433242395863024
Training epoch 83, recon_loss:0.606410, zinb_loss:0.955293, cluster_loss:0.307216
Clustering 83: AMI= 0.7177, NMI= 0.7186, ARI= 0.5031, ACC= 0.6121
0.03949672217924183
Training epoch 84, recon_loss:0.607947, zinb_loss:0.955779, cluster_loss:0.307419
Clustering 84: AMI= 0.7178, NMI= 0.7187, ARI= 0.5070, ACC= 0.6206
0.03876379331406002
Training epoch 85, recon_loss:0.607013, zinb_loss:0.955298, cluster_loss:0.307125
Clustering 85: AMI= 0.7175, NMI= 0.7184, ARI= 0.5034, ACC= 0.6119
0.03860092023290851
Training epoch 86, recon_loss:0.608465, zinb_loss:0.955660, cluster_loss:0.307329
Clustering 86: AMI= 0.7172, NMI= 0.7181, ARI= 0.5054, ACC= 0.6190
0.037949427908302455
Training epoch 87, recon_loss:0.607330, zinb_loss:0.955233, cluster_loss:0.307181
Clustering 87: AMI= 0.7172, NMI= 0.7182, ARI= 0.5035, ACC= 0.6122
0.03676859806995399
Training epoch 88, recon_loss:0.608503, zinb_loss:0.955554, cluster_loss:0.307461
Clustering 88: AMI= 0.7170, NMI= 0.7180, ARI= 0.5046, ACC= 0.6184
0.03623926055621157
Training epoch 89, recon_loss:0.607349, zinb_loss:0.955141, cluster_loss:0.307450
Clustering 89: AMI= 0.7172, NMI= 0.7181, ARI= 0.5039, ACC= 0.6125
0.03481412109613584
Training epoch 90, recon_loss:0.608406, zinb_loss:0.955515, cluster_loss:0.307786
Clustering 90: AMI= 0.7174, NMI= 0.7183, ARI= 0.5046, ACC= 0.6183
0.03383688260922676
Training epoch 91, recon_loss:0.607378, zinb_loss:0.955110, cluster_loss:0.307764
Clustering 91: AMI= 0.7175, NMI= 0.7185, ARI= 0.5048, ACC= 0.6129
0.03298179893318132
Training epoch 92, recon_loss:0.608392, zinb_loss:0.955573, cluster_loss:0.308129
Clustering 92: AMI= 0.7175, NMI= 0.7184, ARI= 0.5046, ACC= 0.6183
0.03176025082454497
Training epoch 93, recon_loss:0.607579, zinb_loss:0.955176, cluster_loss:0.308014
Clustering 93: AMI= 0.7177, NMI= 0.7187, ARI= 0.5054, ACC= 0.6131
0.03049798444562075
Training epoch 94, recon_loss:0.608668, zinb_loss:0.955735, cluster_loss:0.308408
Clustering 94: AMI= 0.7178, NMI= 0.7188, ARI= 0.5048, ACC= 0.6184
0.030253674823893482
Training epoch 95, recon_loss:0.608084, zinb_loss:0.955347, cluster_loss:0.308127
Clustering 95: AMI= 0.7175, NMI= 0.7184, ARI= 0.5058, ACC= 0.6131
0.03171953255425709
Training epoch 96, recon_loss:0.608954, zinb_loss:0.955956, cluster_loss:0.308523
Clustering 96: AMI= 0.7180, NMI= 0.7189, ARI= 0.5047, ACC= 0.6183
0.033429699906347976
Training epoch 97, recon_loss:0.609026, zinb_loss:0.955630, cluster_loss:0.308012
Clustering 97: AMI= 0.7178, NMI= 0.7187, ARI= 0.5068, ACC= 0.6135
0.037094344232257014
Training epoch 98, recon_loss:0.609937, zinb_loss:0.956232, cluster_loss:0.308301
Clustering 98: AMI= 0.7184, NMI= 0.7193, ARI= 0.5041, ACC= 0.6179
0.04193981839651452
Training epoch 99, recon_loss:0.610543, zinb_loss:0.955965, cluster_loss:0.307413
Clustering 99: AMI= 0.7180, NMI= 0.7189, ARI= 0.5077, ACC= 0.6133
0.04694816564192353
Training epoch 100, recon_loss:0.610926, zinb_loss:0.956363, cluster_loss:0.307656
Clustering 100: AMI= 0.7185, NMI= 0.7194, ARI= 0.5028, ACC= 0.6168
0.05317806099596889
Training epoch 101, recon_loss:0.611473, zinb_loss:0.955983, cluster_loss:0.306782
Clustering 101: AMI= 0.7186, NMI= 0.7196, ARI= 0.5095, ACC= 0.6139
0.05765707072763549
Training epoch 102, recon_loss:0.610925, zinb_loss:0.956001, cluster_loss:0.307378
Clustering 102: AMI= 0.7186, NMI= 0.7196, ARI= 0.5016, ACC= 0.6158
0.06148458813469604
Training epoch 103, recon_loss:0.611021, zinb_loss:0.955536, cluster_loss:0.306828
Clustering 103: AMI= 0.7189, NMI= 0.7199, ARI= 0.5098, ACC= 0.6147
0.061688179486135426
Training epoch 104, recon_loss:0.610129, zinb_loss:0.955389, cluster_loss:0.307910
Clustering 104: AMI= 0.7183, NMI= 0.7192, ARI= 0.5007, ACC= 0.6148
0.06160674294555967
Training epoch 105, recon_loss:0.610252, zinb_loss:0.955133, cluster_loss:0.307241
Clustering 105: AMI= 0.7192, NMI= 0.7201, ARI= 0.5100, ACC= 0.6152
0.05973370251231728
Training epoch 106, recon_loss:0.609513, zinb_loss:0.954981, cluster_loss:0.308655
Clustering 106: AMI= 0.7183, NMI= 0.7192, ARI= 0.5011, ACC= 0.6146
0.05737204283562034
Training epoch 107, recon_loss:0.609748, zinb_loss:0.954945, cluster_loss:0.307676
Clustering 107: AMI= 0.7195, NMI= 0.7204, ARI= 0.5103, ACC= 0.6163
0.05480679180748402
Training epoch 108, recon_loss:0.609195, zinb_loss:0.954816, cluster_loss:0.309277
Clustering 108: AMI= 0.7187, NMI= 0.7196, ARI= 0.5016, ACC= 0.6148
0.0520379494279083
Training epoch 109, recon_loss:0.609469, zinb_loss:0.954896, cluster_loss:0.308078
Clustering 109: AMI= 0.7193, NMI= 0.7202, ARI= 0.5100, ACC= 0.6166
0.05069424650840832
Training epoch 110, recon_loss:0.609041, zinb_loss:0.954789, cluster_loss:0.309755
Clustering 110: AMI= 0.7186, NMI= 0.7195, ARI= 0.5014, ACC= 0.6144
0.04853617818315078
Training epoch 111, recon_loss:0.609301, zinb_loss:0.954911, cluster_loss:0.308449
Clustering 111: AMI= 0.7194, NMI= 0.7203, ARI= 0.5099, ACC= 0.6167
0.046378109857893236
Training epoch 112, recon_loss:0.608973, zinb_loss:0.954819, cluster_loss:0.310133
Clustering 112: AMI= 0.7190, NMI= 0.7200, ARI= 0.5024, ACC= 0.6149
0.04397573191090842
Training epoch 113, recon_loss:0.609188, zinb_loss:0.954963, cluster_loss:0.308795
Clustering 113: AMI= 0.7191, NMI= 0.7201, ARI= 0.5098, ACC= 0.6166
0.04169550877478725
Training epoch 114, recon_loss:0.608921, zinb_loss:0.954877, cluster_loss:0.310452
Clustering 114: AMI= 0.7193, NMI= 0.7202, ARI= 0.5027, ACC= 0.6149
0.04035180585528727
Training epoch 115, recon_loss:0.609100, zinb_loss:0.955044, cluster_loss:0.309130
Clustering 115: AMI= 0.7196, NMI= 0.7205, ARI= 0.5102, ACC= 0.6172
0.03835661061118124
Training epoch 116, recon_loss:0.608898, zinb_loss:0.954950, cluster_loss:0.310736
Clustering 116: AMI= 0.7195, NMI= 0.7204, ARI= 0.5026, ACC= 0.6147
0.037216499043120646
Training epoch 117, recon_loss:0.609052, zinb_loss:0.955168, cluster_loss:0.309444
Clustering 117: AMI= 0.7194, NMI= 0.7203, ARI= 0.5101, ACC= 0.6175
0.03542489515045401
Training epoch 118, recon_loss:0.608858, zinb_loss:0.955048, cluster_loss:0.310988
Clustering 118: AMI= 0.7196, NMI= 0.7205, ARI= 0.5029, ACC= 0.6148
0.0346919662852722
Training epoch 119, recon_loss:0.609045, zinb_loss:0.955368, cluster_loss:0.309739
Clustering 119: AMI= 0.7202, NMI= 0.7211, ARI= 0.5110, ACC= 0.6184
0.033266826825196466
Training epoch 120, recon_loss:0.608846, zinb_loss:0.955208, cluster_loss:0.311205
Clustering 120: AMI= 0.7196, NMI= 0.7206, ARI= 0.5026, ACC= 0.6146
0.03298179893318132
Training epoch 121, recon_loss:0.609099, zinb_loss:0.955705, cluster_loss:0.310003
Clustering 121: AMI= 0.7201, NMI= 0.7210, ARI= 0.5112, ACC= 0.6189
0.03200456044627224
Training epoch 122, recon_loss:0.608880, zinb_loss:0.955495, cluster_loss:0.311375
Clustering 122: AMI= 0.7193, NMI= 0.7202, ARI= 0.5015, ACC= 0.6134
0.03119019504051468
Training epoch 123, recon_loss:0.609257, zinb_loss:0.956287, cluster_loss:0.310224
Clustering 123: AMI= 0.7207, NMI= 0.7216, ARI= 0.5121, ACC= 0.6201
0.031108758499938924
Training epoch 124, recon_loss:0.608811, zinb_loss:0.956018, cluster_loss:0.311457
Clustering 124: AMI= 0.7190, NMI= 0.7200, ARI= 0.5009, ACC= 0.6127
0.031678814283969216
Training epoch 125, recon_loss:0.609496, zinb_loss:0.957270, cluster_loss:0.310420
Clustering 125: AMI= 0.7215, NMI= 0.7224, ARI= 0.5138, ACC= 0.6219
0.03424406531210554
Training epoch 126, recon_loss:0.608865, zinb_loss:0.956917, cluster_loss:0.311414
Clustering 126: AMI= 0.7184, NMI= 0.7193, ARI= 0.4998, ACC= 0.6112
0.037094344232257014
Training epoch 127, recon_loss:0.609908, zinb_loss:0.958780, cluster_loss:0.310547
Clustering 127: AMI= 0.7219, NMI= 0.7228, ARI= 0.5150, ACC= 0.6231
0.04116617126104483
Training epoch 128, recon_loss:0.608848, zinb_loss:0.958213, cluster_loss:0.311159
Clustering 128: AMI= 0.7173, NMI= 0.7182, ARI= 0.4979, ACC= 0.6086
0.04548230791155992
Training epoch 129, recon_loss:0.610377, zinb_loss:0.960672, cluster_loss:0.310622
Clustering 129: AMI= 0.7226, NMI= 0.7235, ARI= 0.5162, ACC= 0.6251
0.0511014292112871
Training epoch 130, recon_loss:0.608994, zinb_loss:0.959572, cluster_loss:0.310676
Clustering 130: AMI= 0.7170, NMI= 0.7179, ARI= 0.4972, ACC= 0.6067
0.05610977645669612
Training epoch 131, recon_loss:0.610778, zinb_loss:0.962282, cluster_loss:0.310602
Clustering 131: AMI= 0.7232, NMI= 0.7241, ARI= 0.5171, ACC= 0.6267
0.06124027851296877
Training epoch 132, recon_loss:0.608930, zinb_loss:0.960295, cluster_loss:0.310067
Clustering 132: AMI= 0.7163, NMI= 0.7173, ARI= 0.4962, ACC= 0.6045
0.06543426035262022
Training epoch 133, recon_loss:0.610875, zinb_loss:0.962723, cluster_loss:0.310613
Clustering 133: AMI= 0.7234, NMI= 0.7243, ARI= 0.5168, ACC= 0.6269
0.06690011808298384
Training epoch 134, recon_loss:0.609190, zinb_loss:0.959977, cluster_loss:0.309716
Clustering 134: AMI= 0.7160, NMI= 0.7170, ARI= 0.4963, ACC= 0.6043
0.06779592002931716
Training epoch 135, recon_loss:0.610738, zinb_loss:0.961877, cluster_loss:0.310704
Clustering 135: AMI= 0.7231, NMI= 0.7241, ARI= 0.5160, ACC= 0.6260
0.06571928824463537
Training epoch 136, recon_loss:0.609241, zinb_loss:0.958911, cluster_loss:0.309813
Clustering 136: AMI= 0.7161, NMI= 0.7171, ARI= 0.4976, ACC= 0.6061
0.06327619202736268
Training epoch 137, recon_loss:0.610444, zinb_loss:0.960439, cluster_loss:0.310977
Clustering 137: AMI= 0.7228, NMI= 0.7237, ARI= 0.5149, ACC= 0.6249
0.059937293863756666
Training epoch 138, recon_loss:0.609179, zinb_loss:0.957765, cluster_loss:0.310201
Clustering 138: AMI= 0.7169, NMI= 0.7179, ARI= 0.4991, ACC= 0.6079
0.05562115721324158
Training epoch 139, recon_loss:0.610097, zinb_loss:0.959116, cluster_loss:0.311345
Clustering 139: AMI= 0.7225, NMI= 0.7234, ARI= 0.5137, ACC= 0.6234
0.05118286575186286
Training epoch 140, recon_loss:0.609124, zinb_loss:0.956890, cluster_loss:0.310629
Clustering 140: AMI= 0.7172, NMI= 0.7181, ARI= 0.5001, ACC= 0.6093
0.04698888391221141
Training epoch 141, recon_loss:0.609852, zinb_loss:0.958147, cluster_loss:0.311705
Clustering 141: AMI= 0.7223, NMI= 0.7232, ARI= 0.5135, ACC= 0.6228
0.042876338613135716
Training epoch 142, recon_loss:0.609198, zinb_loss:0.956339, cluster_loss:0.310944
Clustering 142: AMI= 0.7176, NMI= 0.7185, ARI= 0.5013, ACC= 0.6106
0.03856020196262063
Training epoch 143, recon_loss:0.609799, zinb_loss:0.957498, cluster_loss:0.312017
Clustering 143: AMI= 0.7215, NMI= 0.7224, ARI= 0.5111, ACC= 0.6211
0.035180585528726736
Training epoch 144, recon_loss:0.609554, zinb_loss:0.956044, cluster_loss:0.311054
Clustering 144: AMI= 0.7180, NMI= 0.7190, ARI= 0.5032, ACC= 0.6117
0.03094588541878741
Training epoch 145, recon_loss:0.609971, zinb_loss:0.957072, cluster_loss:0.312209
Clustering 145: AMI= 0.7211, NMI= 0.7220, ARI= 0.5089, ACC= 0.6194
0.02964290076957531
Training epoch 146, recon_loss:0.610114, zinb_loss:0.955937, cluster_loss:0.310882
Clustering 146: AMI= 0.7182, NMI= 0.7191, ARI= 0.5045, ACC= 0.6124
0.030579420986196506
Training epoch 147, recon_loss:0.610440, zinb_loss:0.956775, cluster_loss:0.312253
Clustering 147: AMI= 0.7207, NMI= 0.7216, ARI= 0.5071, ACC= 0.6184
0.032574616230302535
Training epoch 148, recon_loss:0.610913, zinb_loss:0.955947, cluster_loss:0.310460
Clustering 148: AMI= 0.7186, NMI= 0.7195, ARI= 0.5060, ACC= 0.6133
0.03465124801498432
Training epoch 149, recon_loss:0.610900, zinb_loss:0.956508, cluster_loss:0.312201
Clustering 149: AMI= 0.7205, NMI= 0.7214, ARI= 0.5055, ACC= 0.6172
0.03717578077283277
Training epoch 150, recon_loss:0.611440, zinb_loss:0.955965, cluster_loss:0.310135
Clustering 150: AMI= 0.7192, NMI= 0.7202, ARI= 0.5075, ACC= 0.6146
0.040148214503847875
Training epoch 151, recon_loss:0.611062, zinb_loss:0.956211, cluster_loss:0.312320
Clustering 151: AMI= 0.7203, NMI= 0.7212, ARI= 0.5042, ACC= 0.6163
0.04181766358565088
Training epoch 152, recon_loss:0.611245, zinb_loss:0.955898, cluster_loss:0.310225
Clustering 152: AMI= 0.7199, NMI= 0.7209, ARI= 0.5087, ACC= 0.6153
0.04324280304572662
Training epoch 153, recon_loss:0.610994, zinb_loss:0.955925, cluster_loss:0.312706
Clustering 153: AMI= 0.7201, NMI= 0.7210, ARI= 0.5032, ACC= 0.6153
0.044667942505802354
Training epoch 154, recon_loss:0.610966, zinb_loss:0.955846, cluster_loss:0.310612
Clustering 154: AMI= 0.7207, NMI= 0.7216, ARI= 0.5097, ACC= 0.6163
0.04474937904637811
Training epoch 155, recon_loss:0.610202, zinb_loss:0.955712, cluster_loss:0.313120
Clustering 155: AMI= 0.7199, NMI= 0.7208, ARI= 0.5023, ACC= 0.6144
0.04474937904637811
Training epoch 156, recon_loss:0.610614, zinb_loss:0.955914, cluster_loss:0.311131
Clustering 156: AMI= 0.7214, NMI= 0.7223, ARI= 0.5111, ACC= 0.6175
0.04360926747831752
Training epoch 157, recon_loss:0.609841, zinb_loss:0.955665, cluster_loss:0.313457
Clustering 157: AMI= 0.7197, NMI= 0.7206, ARI= 0.5014, ACC= 0.6135
0.043487112667453885
Training epoch 158, recon_loss:0.610411, zinb_loss:0.956125, cluster_loss:0.311547
Clustering 158: AMI= 0.7219, NMI= 0.7228, ARI= 0.5118, ACC= 0.6186
0.0430799299645751
Training epoch 159, recon_loss:0.609405, zinb_loss:0.955768, cluster_loss:0.313694
Clustering 159: AMI= 0.7194, NMI= 0.7203, ARI= 0.5005, ACC= 0.6127
0.04369070401889328
Training epoch 160, recon_loss:0.610223, zinb_loss:0.956502, cluster_loss:0.311885
Clustering 160: AMI= 0.7222, NMI= 0.7231, ARI= 0.5125, ACC= 0.6197
0.044627224235514476
Training epoch 161, recon_loss:0.609372, zinb_loss:0.956075, cluster_loss:0.313859
Clustering 161: AMI= 0.7192, NMI= 0.7202, ARI= 0.5001, ACC= 0.6121
0.04617451850645385
Training epoch 162, recon_loss:0.610223, zinb_loss:0.957075, cluster_loss:0.312080
Clustering 162: AMI= 0.7222, NMI= 0.7231, ARI= 0.5129, ACC= 0.6208
0.04796612239912049
Training epoch 163, recon_loss:0.609072, zinb_loss:0.956555, cluster_loss:0.313909
Clustering 163: AMI= 0.7193, NMI= 0.7202, ARI= 0.4996, ACC= 0.6111
0.04971700802149925
Training epoch 164, recon_loss:0.610119, zinb_loss:0.957808, cluster_loss:0.312279
Clustering 164: AMI= 0.7225, NMI= 0.7234, ARI= 0.5134, ACC= 0.6214
0.05118286575186286
Training epoch 165, recon_loss:0.609228, zinb_loss:0.957182, cluster_loss:0.313900
Clustering 165: AMI= 0.7189, NMI= 0.7198, ARI= 0.4988, ACC= 0.6101
0.05313734272568101
Training epoch 166, recon_loss:0.610111, zinb_loss:0.958602, cluster_loss:0.312395
Clustering 166: AMI= 0.7230, NMI= 0.7239, ARI= 0.5142, ACC= 0.6225
0.05460320045604463
Training epoch 167, recon_loss:0.608803, zinb_loss:0.957754, cluster_loss:0.313814
Clustering 167: AMI= 0.7189, NMI= 0.7198, ARI= 0.4988, ACC= 0.6097
0.05525469278065068
Training epoch 168, recon_loss:0.609962, zinb_loss:0.959217, cluster_loss:0.312639
Clustering 168: AMI= 0.7229, NMI= 0.7238, ARI= 0.5139, ACC= 0.6226
0.05570259375381734
Training epoch 169, recon_loss:0.608813, zinb_loss:0.958138, cluster_loss:0.313739
Clustering 169: AMI= 0.7190, NMI= 0.7199, ARI= 0.4993, ACC= 0.6096
0.05501038315892341
Training epoch 170, recon_loss:0.609773, zinb_loss:0.959547, cluster_loss:0.312875
Clustering 170: AMI= 0.7227, NMI= 0.7236, ARI= 0.5139, ACC= 0.6230
0.05476607353719614
Training epoch 171, recon_loss:0.608661, zinb_loss:0.958230, cluster_loss:0.313695
Clustering 171: AMI= 0.7189, NMI= 0.7199, ARI= 0.4997, ACC= 0.6094
0.05374811677999919
Training epoch 172, recon_loss:0.609632, zinb_loss:0.959549, cluster_loss:0.313167
Clustering 172: AMI= 0.7228, NMI= 0.7237, ARI= 0.5138, ACC= 0.6230
0.05277087829309011
Training epoch 173, recon_loss:0.608520, zinb_loss:0.958090, cluster_loss:0.313686
Clustering 173: AMI= 0.7192, NMI= 0.7201, ARI= 0.5007, ACC= 0.6098
0.05138645710330225
Training epoch 174, recon_loss:0.609622, zinb_loss:0.959365, cluster_loss:0.313484
Clustering 174: AMI= 0.7226, NMI= 0.7235, ARI= 0.5131, ACC= 0.6228
0.04963557148092349
Training epoch 175, recon_loss:0.608410, zinb_loss:0.957842, cluster_loss:0.313674
Clustering 175: AMI= 0.7194, NMI= 0.7203, ARI= 0.5018, ACC= 0.6106
0.048169713750559874
Training epoch 176, recon_loss:0.610205, zinb_loss:0.959123, cluster_loss:0.313816
Clustering 176: AMI= 0.7225, NMI= 0.7234, ARI= 0.5127, ACC= 0.6224
0.04576733580357507
Training epoch 177, recon_loss:0.608655, zinb_loss:0.957586, cluster_loss:0.313504
Clustering 177: AMI= 0.7194, NMI= 0.7203, ARI= 0.5029, ACC= 0.6109
0.04336495785659025
Training epoch 178, recon_loss:0.611623, zinb_loss:0.958914, cluster_loss:0.314109
Clustering 178: AMI= 0.7224, NMI= 0.7233, ARI= 0.5112, ACC= 0.6216
0.0414104808827721
Training epoch 179, recon_loss:0.610148, zinb_loss:0.957474, cluster_loss:0.313181
Clustering 179: AMI= 0.7193, NMI= 0.7202, ARI= 0.5038, ACC= 0.6111
0.03892666639521153
Training epoch 180, recon_loss:0.611013, zinb_loss:0.958622, cluster_loss:0.314233
Clustering 180: AMI= 0.7221, NMI= 0.7230, ARI= 0.5100, ACC= 0.6209
0.03754224520542367
Training epoch 181, recon_loss:0.609433, zinb_loss:0.957331, cluster_loss:0.313341
Clustering 181: AMI= 0.7194, NMI= 0.7203, ARI= 0.5040, ACC= 0.6113
0.03717578077283277
Training epoch 182, recon_loss:0.611130, zinb_loss:0.958281, cluster_loss:0.314487
Clustering 182: AMI= 0.7220, NMI= 0.7229, ARI= 0.5099, ACC= 0.6207
0.03550633169102976
Training epoch 183, recon_loss:0.609504, zinb_loss:0.957085, cluster_loss:0.313458
Clustering 183: AMI= 0.7194, NMI= 0.7204, ARI= 0.5042, ACC= 0.6116
0.03351113644692374
Training epoch 184, recon_loss:0.610534, zinb_loss:0.957986, cluster_loss:0.314667
Clustering 184: AMI= 0.7217, NMI= 0.7227, ARI= 0.5095, ACC= 0.6205
0.032411743149151025
Training epoch 185, recon_loss:0.609007, zinb_loss:0.956890, cluster_loss:0.313731
Clustering 185: AMI= 0.7195, NMI= 0.7204, ARI= 0.5041, ACC= 0.6118
0.03159737774339346
Training epoch 186, recon_loss:0.610299, zinb_loss:0.957778, cluster_loss:0.314860
Clustering 186: AMI= 0.7219, NMI= 0.7228, ARI= 0.5094, ACC= 0.6204
0.030457266175332873
Training epoch 187, recon_loss:0.608846, zinb_loss:0.956754, cluster_loss:0.313935
Clustering 187: AMI= 0.7196, NMI= 0.7205, ARI= 0.5044, ACC= 0.6122
0.02915428152612077
Training epoch 188, recon_loss:0.610122, zinb_loss:0.957683, cluster_loss:0.314996
Clustering 188: AMI= 0.7218, NMI= 0.7228, ARI= 0.5096, ACC= 0.6204
0.028665662282666232
Training epoch 189, recon_loss:0.608788, zinb_loss:0.956705, cluster_loss:0.314092
Clustering 189: AMI= 0.7198, NMI= 0.7207, ARI= 0.5045, ACC= 0.6124
0.028217761309499573
Training epoch 190, recon_loss:0.610123, zinb_loss:0.957676, cluster_loss:0.315081
Clustering 190: AMI= 0.7221, NMI= 0.7230, ARI= 0.5096, ACC= 0.6203
0.02833991612036321
Training epoch 191, recon_loss:0.608894, zinb_loss:0.956726, cluster_loss:0.314171
Clustering 191: AMI= 0.7197, NMI= 0.7206, ARI= 0.5044, ACC= 0.6123
0.028217761309499573
Training epoch 192, recon_loss:0.610287, zinb_loss:0.957739, cluster_loss:0.315100
Clustering 192: AMI= 0.7222, NMI= 0.7231, ARI= 0.5096, ACC= 0.6202
0.028584225742090477
Training epoch 193, recon_loss:0.609166, zinb_loss:0.956805, cluster_loss:0.314158
Clustering 193: AMI= 0.7198, NMI= 0.7207, ARI= 0.5043, ACC= 0.6119
0.028584225742090477
Training epoch 194, recon_loss:0.610449, zinb_loss:0.957822, cluster_loss:0.315036
Clustering 194: AMI= 0.7223, NMI= 0.7232, ARI= 0.5096, ACC= 0.6202
0.029235718066696528
Training epoch 195, recon_loss:0.609537, zinb_loss:0.956902, cluster_loss:0.314095
Clustering 195: AMI= 0.7195, NMI= 0.7205, ARI= 0.5041, ACC= 0.6116
0.029724337310151065
Training epoch 196, recon_loss:0.610897, zinb_loss:0.957871, cluster_loss:0.314917
Clustering 196: AMI= 0.7222, NMI= 0.7231, ARI= 0.5091, ACC= 0.6199
0.030905167148499533
Training epoch 197, recon_loss:0.610099, zinb_loss:0.956976, cluster_loss:0.313954
Clustering 197: AMI= 0.7196, NMI= 0.7205, ARI= 0.5041, ACC= 0.6116
0.031027321959363165
Training epoch 198, recon_loss:0.610682, zinb_loss:0.957800, cluster_loss:0.314737
Clustering 198: AMI= 0.7223, NMI= 0.7232, ARI= 0.5091, ACC= 0.6196
0.0311494767702268
Training epoch 199, recon_loss:0.610417, zinb_loss:0.956985, cluster_loss:0.313953
Clustering 199: AMI= 0.7195, NMI= 0.7204, ARI= 0.5042, ACC= 0.6116
0.03155665947310558
Training epoch 200, recon_loss:0.611210, zinb_loss:0.957638, cluster_loss:0.314636
Clustering 200: AMI= 0.7225, NMI= 0.7235, ARI= 0.5091, ACC= 0.6192
0.03131234985137831
Training epoch 201, recon_loss:0.610836, zinb_loss:0.956913, cluster_loss:0.313926
Clustering 201: AMI= 0.7196, NMI= 0.7206, ARI= 0.5047, ACC= 0.6120
0.030986603689075288
Training epoch 202, recon_loss:0.610744, zinb_loss:0.957363, cluster_loss:0.314636
Clustering 202: AMI= 0.7226, NMI= 0.7235, ARI= 0.5091, ACC= 0.6190
0.030375829634757115
Training epoch 203, recon_loss:0.610859, zinb_loss:0.956804, cluster_loss:0.314162
Clustering 203: AMI= 0.7201, NMI= 0.7210, ARI= 0.5051, ACC= 0.6126
0.02956146422899955
Training epoch 204, recon_loss:0.611062, zinb_loss:0.957097, cluster_loss:0.314771
Clustering 204: AMI= 0.7224, NMI= 0.7233, ARI= 0.5090, ACC= 0.6188
0.0289099719043935
Training epoch 205, recon_loss:0.610963, zinb_loss:0.956711, cluster_loss:0.314350
Clustering 205: AMI= 0.7203, NMI= 0.7212, ARI= 0.5056, ACC= 0.6129
0.028299197850075328
Training epoch 206, recon_loss:0.610374, zinb_loss:0.956868, cluster_loss:0.314935
Clustering 206: AMI= 0.7225, NMI= 0.7234, ARI= 0.5091, ACC= 0.6192
0.027892015147196546
Training epoch 207, recon_loss:0.610833, zinb_loss:0.956670, cluster_loss:0.314713
Clustering 207: AMI= 0.7204, NMI= 0.7213, ARI= 0.5055, ACC= 0.6130
0.027118368011726863
Training epoch 208, recon_loss:0.611048, zinb_loss:0.956745, cluster_loss:0.315123
Clustering 208: AMI= 0.7224, NMI= 0.7233, ARI= 0.5085, ACC= 0.6186
0.026344720876257176
Training epoch 209, recon_loss:0.611142, zinb_loss:0.956708, cluster_loss:0.314809
Clustering 209: AMI= 0.7202, NMI= 0.7211, ARI= 0.5057, ACC= 0.6131
0.025896819903090517
Training epoch 210, recon_loss:0.610380, zinb_loss:0.956690, cluster_loss:0.315211
Clustering 210: AMI= 0.7221, NMI= 0.7230, ARI= 0.5078, ACC= 0.6180
0.02626328433568142
Training epoch 211, recon_loss:0.611125, zinb_loss:0.956820, cluster_loss:0.315070
Clustering 211: AMI= 0.7199, NMI= 0.7208, ARI= 0.5052, ACC= 0.6129
0.02642615741683293
Training epoch 212, recon_loss:0.611538, zinb_loss:0.956747, cluster_loss:0.315299
Clustering 212: AMI= 0.7222, NMI= 0.7231, ARI= 0.5073, ACC= 0.6179
0.0276069872551814
Training epoch 213, recon_loss:0.611816, zinb_loss:0.956997, cluster_loss:0.314945
Clustering 213: AMI= 0.7203, NMI= 0.7212, ARI= 0.5058, ACC= 0.6124
0.02850278920151472
Training epoch 214, recon_loss:0.610891, zinb_loss:0.956852, cluster_loss:0.315233
Clustering 214: AMI= 0.7219, NMI= 0.7228, ARI= 0.5067, ACC= 0.6175
0.029927928661590456
Training epoch 215, recon_loss:0.611641, zinb_loss:0.957161, cluster_loss:0.315076
Clustering 215: AMI= 0.7200, NMI= 0.7209, ARI= 0.5051, ACC= 0.6116
0.03180096909483285
Training epoch 216, recon_loss:0.612018, zinb_loss:0.956984, cluster_loss:0.315229
Clustering 216: AMI= 0.7222, NMI= 0.7231, ARI= 0.5068, ACC= 0.6176
0.03367400952807525
Training epoch 217, recon_loss:0.612204, zinb_loss:0.957281, cluster_loss:0.314864
Clustering 217: AMI= 0.7205, NMI= 0.7214, ARI= 0.5060, ACC= 0.6118
0.03607638747506006
Training epoch 218, recon_loss:0.611100, zinb_loss:0.957062, cluster_loss:0.315160
Clustering 218: AMI= 0.7220, NMI= 0.7229, ARI= 0.5065, ACC= 0.6178
0.03774583655686307
Training epoch 219, recon_loss:0.611481, zinb_loss:0.957245, cluster_loss:0.315038
Clustering 219: AMI= 0.7205, NMI= 0.7214, ARI= 0.5053, ACC= 0.6109
0.03847876542204487
Training epoch 220, recon_loss:0.611518, zinb_loss:0.957060, cluster_loss:0.315289
Clustering 220: AMI= 0.7219, NMI= 0.7228, ARI= 0.5062, ACC= 0.6178
0.039089539476363046
Training epoch 221, recon_loss:0.611382, zinb_loss:0.957115, cluster_loss:0.314988
Clustering 221: AMI= 0.7205, NMI= 0.7214, ARI= 0.5054, ACC= 0.6109
0.03945600390895395
Training epoch 222, recon_loss:0.610697, zinb_loss:0.957030, cluster_loss:0.315394
Clustering 222: AMI= 0.7221, NMI= 0.7230, ARI= 0.5064, ACC= 0.6177
0.03994462315240849
Training epoch 223, recon_loss:0.610627, zinb_loss:0.956958, cluster_loss:0.315195
Clustering 223: AMI= 0.7203, NMI= 0.7212, ARI= 0.5051, ACC= 0.6109
0.040148214503847875
Training epoch 224, recon_loss:0.611064, zinb_loss:0.957010, cluster_loss:0.315591
Clustering 224: AMI= 0.7218, NMI= 0.7227, ARI= 0.5065, ACC= 0.6176
0.039985341422696365
Training epoch 225, recon_loss:0.610524, zinb_loss:0.956813, cluster_loss:0.315186
Clustering 225: AMI= 0.7204, NMI= 0.7213, ARI= 0.5052, ACC= 0.6113
0.03982246834154485
Training epoch 226, recon_loss:0.610389, zinb_loss:0.957008, cluster_loss:0.315705
Clustering 226: AMI= 0.7219, NMI= 0.7228, ARI= 0.5068, ACC= 0.6177
0.040270369314711514
Training epoch 227, recon_loss:0.609957, zinb_loss:0.956723, cluster_loss:0.315373
Clustering 227: AMI= 0.7206, NMI= 0.7216, ARI= 0.5053, ACC= 0.6116
0.04006677796327212
Training epoch 228, recon_loss:0.611048, zinb_loss:0.957057, cluster_loss:0.315885
Clustering 228: AMI= 0.7219, NMI= 0.7228, ARI= 0.5071, ACC= 0.6178
0.03978175007125697
Training epoch 229, recon_loss:0.610076, zinb_loss:0.956653, cluster_loss:0.315313
Clustering 229: AMI= 0.7208, NMI= 0.7217, ARI= 0.5054, ACC= 0.6118
0.04002605969298424
Training epoch 230, recon_loss:0.610408, zinb_loss:0.957107, cluster_loss:0.315973
Clustering 230: AMI= 0.7220, NMI= 0.7230, ARI= 0.5073, ACC= 0.6176
0.03982246834154485
Training epoch 231, recon_loss:0.609650, zinb_loss:0.956651, cluster_loss:0.315515
Clustering 231: AMI= 0.7208, NMI= 0.7217, ARI= 0.5052, ACC= 0.6118
0.03937456736837819
Training epoch 232, recon_loss:0.611161, zinb_loss:0.957212, cluster_loss:0.316144
Clustering 232: AMI= 0.7224, NMI= 0.7233, ARI= 0.5080, ACC= 0.6178
0.03876379331406002
Training epoch 233, recon_loss:0.609798, zinb_loss:0.956637, cluster_loss:0.315479
Clustering 233: AMI= 0.7209, NMI= 0.7218, ARI= 0.5052, ACC= 0.6119
0.03815301925974185
Training epoch 234, recon_loss:0.610485, zinb_loss:0.957318, cluster_loss:0.316237
Clustering 234: AMI= 0.7225, NMI= 0.7235, ARI= 0.5084, ACC= 0.6182
0.0378679913677267
Training epoch 235, recon_loss:0.609417, zinb_loss:0.956715, cluster_loss:0.315751
Clustering 235: AMI= 0.7206, NMI= 0.7215, ARI= 0.5045, ACC= 0.6116
0.0378679913677267
Training epoch 236, recon_loss:0.611155, zinb_loss:0.957497, cluster_loss:0.316398
Clustering 236: AMI= 0.7228, NMI= 0.7237, ARI= 0.5089, ACC= 0.6183
0.03737937212427216
Training epoch 237, recon_loss:0.609453, zinb_loss:0.956778, cluster_loss:0.315803
Clustering 237: AMI= 0.7207, NMI= 0.7216, ARI= 0.5046, ACC= 0.6118
0.0365650067185146
Training epoch 238, recon_loss:0.610534, zinb_loss:0.957710, cluster_loss:0.316471
Clustering 238: AMI= 0.7229, NMI= 0.7238, ARI= 0.5092, ACC= 0.6181
0.036483570177938844
Training epoch 239, recon_loss:0.609130, zinb_loss:0.956963, cluster_loss:0.316109
Clustering 239: AMI= 0.7204, NMI= 0.7213, ARI= 0.5034, ACC= 0.6111
0.03583207785333279
Training epoch 240, recon_loss:0.611087, zinb_loss:0.958045, cluster_loss:0.316533
Clustering 240: AMI= 0.7229, NMI= 0.7238, ARI= 0.5098, ACC= 0.6183
0.03615782401563582
Training epoch 241, recon_loss:0.609130, zinb_loss:0.957183, cluster_loss:0.316235
Clustering 241: AMI= 0.7204, NMI= 0.7213, ARI= 0.5028, ACC= 0.6110
0.03701290769168126
Training epoch 242, recon_loss:0.610920, zinb_loss:0.958479, cluster_loss:0.316449
Clustering 242: AMI= 0.7233, NMI= 0.7242, ARI= 0.5108, ACC= 0.6187
0.03803086444887821
Training epoch 243, recon_loss:0.609061, zinb_loss:0.957554, cluster_loss:0.316484
Clustering 243: AMI= 0.7201, NMI= 0.7210, ARI= 0.5017, ACC= 0.6105
0.03978175007125697
Training epoch 244, recon_loss:0.611231, zinb_loss:0.959043, cluster_loss:0.316175
Clustering 244: AMI= 0.7237, NMI= 0.7246, ARI= 0.5119, ACC= 0.6191
0.041736227045075125
Training epoch 245, recon_loss:0.609307, zinb_loss:0.958027, cluster_loss:0.316634
Clustering 245: AMI= 0.7201, NMI= 0.7210, ARI= 0.5001, ACC= 0.6096
0.044220041532635694
Training epoch 246, recon_loss:0.611599, zinb_loss:0.959640, cluster_loss:0.315659
Clustering 246: AMI= 0.7239, NMI= 0.7248, ARI= 0.5127, ACC= 0.6188
0.046826010831059896
Training epoch 247, recon_loss:0.609914, zinb_loss:0.958498, cluster_loss:0.316718
Clustering 247: AMI= 0.7197, NMI= 0.7206, ARI= 0.4984, ACC= 0.6091
0.05065352823812044
Training epoch 248, recon_loss:0.611967, zinb_loss:0.959978, cluster_loss:0.315127
Clustering 248: AMI= 0.7244, NMI= 0.7253, ARI= 0.5135, ACC= 0.6192
0.054236736023453726
Training epoch 249, recon_loss:0.610779, zinb_loss:0.958652, cluster_loss:0.316854
Clustering 249: AMI= 0.7197, NMI= 0.7206, ARI= 0.4967, ACC= 0.6078
0.05753491591677186
Training epoch 250, recon_loss:0.611834, zinb_loss:0.959774, cluster_loss:0.314930
Clustering 250: AMI= 0.7243, NMI= 0.7252, ARI= 0.5136, ACC= 0.6194
0.05924508326886274
Training epoch 251, recon_loss:0.611626, zinb_loss:0.958403, cluster_loss:0.317142
Clustering 251: AMI= 0.7198, NMI= 0.7207, ARI= 0.4964, ACC= 0.6074
0.05900077364713547
Training epoch 252, recon_loss:0.612315, zinb_loss:0.959255, cluster_loss:0.314994
Clustering 252: AMI= 0.7246, NMI= 0.7255, ARI= 0.5144, ACC= 0.6198
0.0587564640254082
Training epoch 253, recon_loss:0.610826, zinb_loss:0.957910, cluster_loss:0.317369
Clustering 253: AMI= 0.7200, NMI= 0.7209, ARI= 0.4968, ACC= 0.6079
0.05692414186245368
Training epoch 254, recon_loss:0.611784, zinb_loss:0.958609, cluster_loss:0.315468
Clustering 254: AMI= 0.7245, NMI= 0.7254, ARI= 0.5140, ACC= 0.6192
0.053096624455393135
Training epoch 255, recon_loss:0.611447, zinb_loss:0.957485, cluster_loss:0.317661
Clustering 255: AMI= 0.7201, NMI= 0.7210, ARI= 0.4974, ACC= 0.6084
0.0507349647786962
Training epoch 256, recon_loss:0.612136, zinb_loss:0.958159, cluster_loss:0.315665
Clustering 256: AMI= 0.7245, NMI= 0.7254, ARI= 0.5138, ACC= 0.6192
0.04910623396718108
Training epoch 257, recon_loss:0.610752, zinb_loss:0.957149, cluster_loss:0.317833
Clustering 257: AMI= 0.7203, NMI= 0.7212, ARI= 0.4982, ACC= 0.6089
0.046215236776741726
Training epoch 258, recon_loss:0.611266, zinb_loss:0.957733, cluster_loss:0.316081
Clustering 258: AMI= 0.7243, NMI= 0.7252, ARI= 0.5132, ACC= 0.6188
0.04360926747831752
Training epoch 259, recon_loss:0.611017, zinb_loss:0.956915, cluster_loss:0.318093
Clustering 259: AMI= 0.7205, NMI= 0.7214, ARI= 0.4986, ACC= 0.6091
0.041573353963923615
Training epoch 260, recon_loss:0.611265, zinb_loss:0.957447, cluster_loss:0.316262
Clustering 260: AMI= 0.7244, NMI= 0.7253, ARI= 0.5129, ACC= 0.6186
0.04035180585528727
Training epoch 261, recon_loss:0.610602, zinb_loss:0.956774, cluster_loss:0.318258
Clustering 261: AMI= 0.7207, NMI= 0.7216, ARI= 0.4994, ACC= 0.6097
0.03831589234089336
Training epoch 262, recon_loss:0.610795, zinb_loss:0.957201, cluster_loss:0.316515
Clustering 262: AMI= 0.7240, NMI= 0.7249, ARI= 0.5122, ACC= 0.6180
0.036483570177938844
Training epoch 263, recon_loss:0.610954, zinb_loss:0.956705, cluster_loss:0.318463
Clustering 263: AMI= 0.7209, NMI= 0.7218, ARI= 0.5000, ACC= 0.6103
0.03534345860987825
Training epoch 264, recon_loss:0.610889, zinb_loss:0.957022, cluster_loss:0.316581
Clustering 264: AMI= 0.7239, NMI= 0.7248, ARI= 0.5117, ACC= 0.6174
0.03497699417728735
Training epoch 265, recon_loss:0.610788, zinb_loss:0.956690, cluster_loss:0.318596
Clustering 265: AMI= 0.7211, NMI= 0.7220, ARI= 0.5008, ACC= 0.6110
0.03383688260922676
Training epoch 266, recon_loss:0.610726, zinb_loss:0.956858, cluster_loss:0.316674
Clustering 266: AMI= 0.7237, NMI= 0.7246, ARI= 0.5107, ACC= 0.6166
0.03290036239260556
Training epoch 267, recon_loss:0.611726, zinb_loss:0.956776, cluster_loss:0.318750
Clustering 267: AMI= 0.7214, NMI= 0.7224, ARI= 0.5013, ACC= 0.6114
0.03265605277087829
Training epoch 268, recon_loss:0.611284, zinb_loss:0.956750, cluster_loss:0.316496
Clustering 268: AMI= 0.7235, NMI= 0.7244, ARI= 0.5103, ACC= 0.6159
0.03420334704181766
Training epoch 269, recon_loss:0.611562, zinb_loss:0.956892, cluster_loss:0.318766
Clustering 269: AMI= 0.7216, NMI= 0.7226, ARI= 0.5015, ACC= 0.6119
0.03473268455556008
Training epoch 270, recon_loss:0.611361, zinb_loss:0.956671, cluster_loss:0.316436
Clustering 270: AMI= 0.7229, NMI= 0.7238, ARI= 0.5090, ACC= 0.6148
0.034447656663544934
Training epoch 271, recon_loss:0.612953, zinb_loss:0.957145, cluster_loss:0.318779
Clustering 271: AMI= 0.7220, NMI= 0.7229, ARI= 0.5025, ACC= 0.6131
0.03440693839325706
Training epoch 272, recon_loss:0.612090, zinb_loss:0.956669, cluster_loss:0.316079
Clustering 272: AMI= 0.7226, NMI= 0.7235, ARI= 0.5082, ACC= 0.6136
0.03587279612362067
Training epoch 273, recon_loss:0.612922, zinb_loss:0.957401, cluster_loss:0.318655
Clustering 273: AMI= 0.7225, NMI= 0.7234, ARI= 0.5032, ACC= 0.6139
0.035913514393908545
Training epoch 274, recon_loss:0.612248, zinb_loss:0.956731, cluster_loss:0.316001
Clustering 274: AMI= 0.7219, NMI= 0.7228, ARI= 0.5065, ACC= 0.6121
0.035017712447575226
Training epoch 275, recon_loss:0.613842, zinb_loss:0.957757, cluster_loss:0.318557
Clustering 275: AMI= 0.7229, NMI= 0.7239, ARI= 0.5041, ACC= 0.6149
0.03497699417728735
Training epoch 276, recon_loss:0.612292, zinb_loss:0.956879, cluster_loss:0.315902
Clustering 276: AMI= 0.7213, NMI= 0.7222, ARI= 0.5051, ACC= 0.6108
0.03632069709678733
Training epoch 277, recon_loss:0.613942, zinb_loss:0.958190, cluster_loss:0.318473
Clustering 277: AMI= 0.7234, NMI= 0.7243, ARI= 0.5056, ACC= 0.6161
0.036361415367075205
Training epoch 278, recon_loss:0.612190, zinb_loss:0.957184, cluster_loss:0.315993
Clustering 278: AMI= 0.7206, NMI= 0.7216, ARI= 0.5035, ACC= 0.6091
0.036035669204772185
Training epoch 279, recon_loss:0.613957, zinb_loss:0.958771, cluster_loss:0.318374
Clustering 279: AMI= 0.7237, NMI= 0.7246, ARI= 0.5064, ACC= 0.6168
0.03615782401563582
Training epoch 280, recon_loss:0.612087, zinb_loss:0.957675, cluster_loss:0.316170
Clustering 280: AMI= 0.7199, NMI= 0.7209, ARI= 0.5022, ACC= 0.6079
0.03550633169102976
Training epoch 281, recon_loss:0.613718, zinb_loss:0.959457, cluster_loss:0.318248
Clustering 281: AMI= 0.7239, NMI= 0.7248, ARI= 0.5074, ACC= 0.6178
0.035180585528726736
Training epoch 282, recon_loss:0.612444, zinb_loss:0.958217, cluster_loss:0.316455
Clustering 282: AMI= 0.7197, NMI= 0.7206, ARI= 0.5012, ACC= 0.6071
0.03542489515045401
Training epoch 283, recon_loss:0.613497, zinb_loss:0.960026, cluster_loss:0.318047
Clustering 283: AMI= 0.7245, NMI= 0.7254, ARI= 0.5092, ACC= 0.6188
0.03595423266419642
Training epoch 284, recon_loss:0.612869, zinb_loss:0.958562, cluster_loss:0.316830
Clustering 284: AMI= 0.7192, NMI= 0.7201, ARI= 0.4994, ACC= 0.6058
0.03668716152937823
Training epoch 285, recon_loss:0.613922, zinb_loss:0.960248, cluster_loss:0.317901
Clustering 285: AMI= 0.7245, NMI= 0.7254, ARI= 0.5100, ACC= 0.6189
0.036646443259090354
Training epoch 286, recon_loss:0.611754, zinb_loss:0.958487, cluster_loss:0.317166
Clustering 286: AMI= 0.7195, NMI= 0.7204, ARI= 0.4990, ACC= 0.6057
0.03627997882649945
Training epoch 287, recon_loss:0.612856, zinb_loss:0.960026, cluster_loss:0.318170
Clustering 287: AMI= 0.7244, NMI= 0.7253, ARI= 0.5102, ACC= 0.6188
0.035465613420741886
Training epoch 288, recon_loss:0.612063, zinb_loss:0.958245, cluster_loss:0.317611
Clustering 288: AMI= 0.7193, NMI= 0.7202, ARI= 0.4985, ACC= 0.6057
0.03404047396066615
Training epoch 289, recon_loss:0.612836, zinb_loss:0.959754, cluster_loss:0.318178
Clustering 289: AMI= 0.7251, NMI= 0.7260, ARI= 0.5112, ACC= 0.6195
0.03379616433893888
Training epoch 290, recon_loss:0.610673, zinb_loss:0.957988, cluster_loss:0.317946
Clustering 290: AMI= 0.7195, NMI= 0.7204, ARI= 0.4984, ACC= 0.6060
0.033144672014332834
Training epoch 291, recon_loss:0.611592, zinb_loss:0.959410, cluster_loss:0.318513
Clustering 291: AMI= 0.7246, NMI= 0.7255, ARI= 0.5104, ACC= 0.6190
0.03155665947310558
Training epoch 292, recon_loss:0.611073, zinb_loss:0.957819, cluster_loss:0.318326
Clustering 292: AMI= 0.7198, NMI= 0.7208, ARI= 0.4988, ACC= 0.6065
0.0311494767702268
Training epoch 293, recon_loss:0.611577, zinb_loss:0.959245, cluster_loss:0.318491
Clustering 293: AMI= 0.7247, NMI= 0.7256, ARI= 0.5107, ACC= 0.6189
0.032411743149151025
Training epoch 294, recon_loss:0.610007, zinb_loss:0.957731, cluster_loss:0.318561
Clustering 294: AMI= 0.7197, NMI= 0.7206, ARI= 0.4984, ACC= 0.6066
0.03322610855490859
Training epoch 295, recon_loss:0.610830, zinb_loss:0.959112, cluster_loss:0.318701
Clustering 295: AMI= 0.7247, NMI= 0.7257, ARI= 0.5105, ACC= 0.6187
0.033022517203469194
Training epoch 296, recon_loss:0.610481, zinb_loss:0.957729, cluster_loss:0.318830
Clustering 296: AMI= 0.7197, NMI= 0.7207, ARI= 0.4983, ACC= 0.6064
0.033266826825196466
Training epoch 297, recon_loss:0.610803, zinb_loss:0.959094, cluster_loss:0.318614
Clustering 297: AMI= 0.7249, NMI= 0.7259, ARI= 0.5111, ACC= 0.6191
0.035017712447575226
Training epoch 298, recon_loss:0.609772, zinb_loss:0.957766, cluster_loss:0.319009
Clustering 298: AMI= 0.7198, NMI= 0.7208, ARI= 0.4982, ACC= 0.6066
0.0359949509344843
Training epoch 299, recon_loss:0.610423, zinb_loss:0.959071, cluster_loss:0.318714
Clustering 299: AMI= 0.7247, NMI= 0.7256, ARI= 0.5104, ACC= 0.6187
0.03652428844822672
Training epoch 300, recon_loss:0.610202, zinb_loss:0.957832, cluster_loss:0.319221
Clustering 300: AMI= 0.7199, NMI= 0.7208, ARI= 0.4980, ACC= 0.6066
0.037094344232257014
Training epoch 301, recon_loss:0.610351, zinb_loss:0.959083, cluster_loss:0.318619
Clustering 301: AMI= 0.7249, NMI= 0.7258, ARI= 0.5110, ACC= 0.6187
0.03921169428722668
Training epoch 302, recon_loss:0.609917, zinb_loss:0.957906, cluster_loss:0.319405
Clustering 302: AMI= 0.7200, NMI= 0.7209, ARI= 0.4978, ACC= 0.6064
0.03986318661183273
Training epoch 303, recon_loss:0.610193, zinb_loss:0.959053, cluster_loss:0.318616
Clustering 303: AMI= 0.7249, NMI= 0.7258, ARI= 0.5112, ACC= 0.6183
0.04075898855816605
Training epoch 304, recon_loss:0.610027, zinb_loss:0.957945, cluster_loss:0.319588
Clustering 304: AMI= 0.7199, NMI= 0.7208, ARI= 0.4974, ACC= 0.6064
0.04149191742334785
Training epoch 305, recon_loss:0.610167, zinb_loss:0.959012, cluster_loss:0.318546
Clustering 305: AMI= 0.7248, NMI= 0.7257, ARI= 0.5112, ACC= 0.6179
0.042428437639969056
Training epoch 306, recon_loss:0.610041, zinb_loss:0.957981, cluster_loss:0.319746
Clustering 306: AMI= 0.7198, NMI= 0.7208, ARI= 0.4971, ACC= 0.6065
0.0430799299645751
Training epoch 307, recon_loss:0.610249, zinb_loss:0.958946, cluster_loss:0.318443
Clustering 307: AMI= 0.7244, NMI= 0.7253, ARI= 0.5112, ACC= 0.6172
0.04381285882975691
Training epoch 308, recon_loss:0.610238, zinb_loss:0.957989, cluster_loss:0.319844
Clustering 308: AMI= 0.7202, NMI= 0.7211, ARI= 0.4976, ACC= 0.6075
0.04352783093774176
Training epoch 309, recon_loss:0.610498, zinb_loss:0.958849, cluster_loss:0.318279
Clustering 309: AMI= 0.7245, NMI= 0.7254, ARI= 0.5112, ACC= 0.6168
0.044627224235514476
Training epoch 310, recon_loss:0.610532, zinb_loss:0.957971, cluster_loss:0.319858
Clustering 310: AMI= 0.7203, NMI= 0.7212, ARI= 0.4975, ACC= 0.6080
0.04442363288407508
Training epoch 311, recon_loss:0.610876, zinb_loss:0.958708, cluster_loss:0.318116
Clustering 311: AMI= 0.7241, NMI= 0.7250, ARI= 0.5108, ACC= 0.6160
0.04503440693839326
Training epoch 312, recon_loss:0.610903, zinb_loss:0.957921, cluster_loss:0.319823
Clustering 312: AMI= 0.7204, NMI= 0.7214, ARI= 0.4979, ACC= 0.6084
0.04417932326234782
Training epoch 313, recon_loss:0.611190, zinb_loss:0.958524, cluster_loss:0.318068
Clustering 313: AMI= 0.7239, NMI= 0.7249, ARI= 0.5105, ACC= 0.6156
0.04409788672177206
Training epoch 314, recon_loss:0.611121, zinb_loss:0.957837, cluster_loss:0.319803
Clustering 314: AMI= 0.7208, NMI= 0.7217, ARI= 0.4987, ACC= 0.6091
0.04356854920802964
Training epoch 315, recon_loss:0.611329, zinb_loss:0.958325, cluster_loss:0.318202
Clustering 315: AMI= 0.7238, NMI= 0.7247, ARI= 0.5101, ACC= 0.6152
0.042876338613135716
Training epoch 316, recon_loss:0.611147, zinb_loss:0.957738, cluster_loss:0.319823
Clustering 316: AMI= 0.7211, NMI= 0.7221, ARI= 0.4994, ACC= 0.6096
0.041451199153059975
Training epoch 317, recon_loss:0.611515, zinb_loss:0.958182, cluster_loss:0.318472
Clustering 317: AMI= 0.7240, NMI= 0.7249, ARI= 0.5098, ACC= 0.6152
0.04022965104442363
Training epoch 318, recon_loss:0.611127, zinb_loss:0.957676, cluster_loss:0.319813
Clustering 318: AMI= 0.7211, NMI= 0.7221, ARI= 0.5000, ACC= 0.6099
0.03961887699010546
Training epoch 319, recon_loss:0.612779, zinb_loss:0.958178, cluster_loss:0.318818
Clustering 319: AMI= 0.7236, NMI= 0.7246, ARI= 0.5086, ACC= 0.6148
0.03860092023290851
Training epoch 320, recon_loss:0.612381, zinb_loss:0.957683, cluster_loss:0.319465
Clustering 320: AMI= 0.7213, NMI= 0.7222, ARI= 0.5012, ACC= 0.6099
0.03717578077283277
Training epoch 321, recon_loss:0.613076, zinb_loss:0.958275, cluster_loss:0.319104
Clustering 321: AMI= 0.7231, NMI= 0.7240, ARI= 0.5073, ACC= 0.6148
0.036646443259090354
Training epoch 322, recon_loss:0.612368, zinb_loss:0.957777, cluster_loss:0.319393
Clustering 322: AMI= 0.7209, NMI= 0.7218, ARI= 0.5014, ACC= 0.6094
0.03762368174599943
Training epoch 323, recon_loss:0.613093, zinb_loss:0.958305, cluster_loss:0.319334
Clustering 323: AMI= 0.7231, NMI= 0.7240, ARI= 0.5067, ACC= 0.6148
0.03729793558369641
Training epoch 324, recon_loss:0.612041, zinb_loss:0.957765, cluster_loss:0.319397
Clustering 324: AMI= 0.7209, NMI= 0.7218, ARI= 0.5018, ACC= 0.6093
0.03676859806995399
Training epoch 325, recon_loss:0.612438, zinb_loss:0.958273, cluster_loss:0.319564
Clustering 325: AMI= 0.7233, NMI= 0.7242, ARI= 0.5069, ACC= 0.6154
0.03558776823160552
Training epoch 326, recon_loss:0.611284, zinb_loss:0.957687, cluster_loss:0.319556
Clustering 326: AMI= 0.7209, NMI= 0.7218, ARI= 0.5017, ACC= 0.6092
0.03436622012296918
Training epoch 327, recon_loss:0.612005, zinb_loss:0.958243, cluster_loss:0.319805
Clustering 327: AMI= 0.7232, NMI= 0.7241, ARI= 0.5068, ACC= 0.6157
0.032696771041166174
Training epoch 328, recon_loss:0.610905, zinb_loss:0.957617, cluster_loss:0.319659
Clustering 328: AMI= 0.7209, NMI= 0.7218, ARI= 0.5021, ACC= 0.6093
0.03159737774339346
Training epoch 329, recon_loss:0.611669, zinb_loss:0.958264, cluster_loss:0.320018
Clustering 329: AMI= 0.7234, NMI= 0.7243, ARI= 0.5070, ACC= 0.6160
0.030660857526772264
Training epoch 330, recon_loss:0.610605, zinb_loss:0.957593, cluster_loss:0.319734
Clustering 330: AMI= 0.7208, NMI= 0.7217, ARI= 0.5020, ACC= 0.6089
0.029968646931878333
Training epoch 331, recon_loss:0.611662, zinb_loss:0.958342, cluster_loss:0.320225
Clustering 331: AMI= 0.7234, NMI= 0.7243, ARI= 0.5069, ACC= 0.6163
0.028747098823241987
Training epoch 332, recon_loss:0.610599, zinb_loss:0.957616, cluster_loss:0.319706
Clustering 332: AMI= 0.7211, NMI= 0.7221, ARI= 0.5029, ACC= 0.6091
0.027199804552302618
Training epoch 333, recon_loss:0.611596, zinb_loss:0.958463, cluster_loss:0.320402
Clustering 333: AMI= 0.7233, NMI= 0.7242, ARI= 0.5066, ACC= 0.6162
0.026385439146545054
Training epoch 334, recon_loss:0.610610, zinb_loss:0.957692, cluster_loss:0.319632
Clustering 334: AMI= 0.7211, NMI= 0.7221, ARI= 0.5035, ACC= 0.6092
0.02577466509222688
Training epoch 335, recon_loss:0.612173, zinb_loss:0.958631, cluster_loss:0.320577
Clustering 335: AMI= 0.7229, NMI= 0.7239, ARI= 0.5056, ACC= 0.6158
0.02557107374078749
Training epoch 336, recon_loss:0.611137, zinb_loss:0.957796, cluster_loss:0.319358
Clustering 336: AMI= 0.7211, NMI= 0.7220, ARI= 0.5040, ACC= 0.6085
0.025978256443666272
Training epoch 337, recon_loss:0.612008, zinb_loss:0.958765, cluster_loss:0.320675
Clustering 337: AMI= 0.7229, NMI= 0.7238, ARI= 0.5053, ACC= 0.6161
0.026996213200863227
Training epoch 338, recon_loss:0.611274, zinb_loss:0.957919, cluster_loss:0.319123
Clustering 338: AMI= 0.7210, NMI= 0.7219, ARI= 0.5039, ACC= 0.6080
0.027729142066045036
Training epoch 339, recon_loss:0.613444, zinb_loss:0.958895, cluster_loss:0.320782
Clustering 339: AMI= 0.7231, NMI= 0.7240, ARI= 0.5050, ACC= 0.6161
0.028584225742090477
Training epoch 340, recon_loss:0.612424, zinb_loss:0.957956, cluster_loss:0.318562
Clustering 340: AMI= 0.7215, NMI= 0.7224, ARI= 0.5052, ACC= 0.6077
0.03094588541878741
Training epoch 341, recon_loss:0.612770, zinb_loss:0.958796, cluster_loss:0.320738
Clustering 341: AMI= 0.7232, NMI= 0.7241, ARI= 0.5047, ACC= 0.6161
0.032085996986848
Training epoch 342, recon_loss:0.612094, zinb_loss:0.957869, cluster_loss:0.318480
Clustering 342: AMI= 0.7216, NMI= 0.7225, ARI= 0.5054, ACC= 0.6076
0.03265605277087829
Training epoch 343, recon_loss:0.613712, zinb_loss:0.958603, cluster_loss:0.320841
Clustering 343: AMI= 0.7228, NMI= 0.7238, ARI= 0.5040, ACC= 0.6155
0.03261533450059041
Training epoch 344, recon_loss:0.612384, zinb_loss:0.957605, cluster_loss:0.318338
Clustering 344: AMI= 0.7218, NMI= 0.7227, ARI= 0.5060, ACC= 0.6081
0.03318539028462071
Training epoch 345, recon_loss:0.612434, zinb_loss:0.958220, cluster_loss:0.320913
Clustering 345: AMI= 0.7228, NMI= 0.7237, ARI= 0.5037, ACC= 0.6150
0.033103953744044956
Training epoch 346, recon_loss:0.611541, zinb_loss:0.957326, cluster_loss:0.318711
Clustering 346: AMI= 0.7221, NMI= 0.7230, ARI= 0.5059, ACC= 0.6085
0.03233030660857527
Training epoch 347, recon_loss:0.613046, zinb_loss:0.957942, cluster_loss:0.321143
Clustering 347: AMI= 0.7227, NMI= 0.7236, ARI= 0.5034, ACC= 0.6143
0.03135306812166619
Training epoch 348, recon_loss:0.611477, zinb_loss:0.957053, cluster_loss:0.318812
Clustering 348: AMI= 0.7225, NMI= 0.7234, ARI= 0.5064, ACC= 0.6094
0.030457266175332873
Training epoch 349, recon_loss:0.611730, zinb_loss:0.957630, cluster_loss:0.321281
Clustering 349: AMI= 0.7226, NMI= 0.7235, ARI= 0.5030, ACC= 0.6137
0.03029439309418136
Training epoch 350, recon_loss:0.610809, zinb_loss:0.956875, cluster_loss:0.319229
Clustering 350: AMI= 0.7225, NMI= 0.7234, ARI= 0.5060, ACC= 0.6096
0.02911356325583289
Training epoch 351, recon_loss:0.612379, zinb_loss:0.957475, cluster_loss:0.321506
Clustering 351: AMI= 0.7224, NMI= 0.7233, ARI= 0.5027, ACC= 0.6132
0.028177043039211695
Training epoch 352, recon_loss:0.610779, zinb_loss:0.956726, cluster_loss:0.319305
Clustering 352: AMI= 0.7224, NMI= 0.7233, ARI= 0.5061, ACC= 0.6099
0.02805488822834806
Training epoch 353, recon_loss:0.611509, zinb_loss:0.957317, cluster_loss:0.321642
Clustering 353: AMI= 0.7223, NMI= 0.7232, ARI= 0.5024, ACC= 0.6127
0.0276069872551814
Training epoch 354, recon_loss:0.610455, zinb_loss:0.956652, cluster_loss:0.319579
Clustering 354: AMI= 0.7223, NMI= 0.7232, ARI= 0.5057, ACC= 0.6099
0.026874058389999594
Training epoch 355, recon_loss:0.612342, zinb_loss:0.957281, cluster_loss:0.321821
Clustering 355: AMI= 0.7222, NMI= 0.7231, ARI= 0.5018, ACC= 0.6120
0.026344720876257176
Training epoch 356, recon_loss:0.610653, zinb_loss:0.956608, cluster_loss:0.319521
Clustering 356: AMI= 0.7227, NMI= 0.7236, ARI= 0.5062, ACC= 0.6105
0.026344720876257176
Training epoch 357, recon_loss:0.611452, zinb_loss:0.957240, cluster_loss:0.321905
Clustering 357: AMI= 0.7222, NMI= 0.7231, ARI= 0.5014, ACC= 0.6112
0.02605969298424203
Training epoch 358, recon_loss:0.610492, zinb_loss:0.956654, cluster_loss:0.319702
Clustering 358: AMI= 0.7227, NMI= 0.7236, ARI= 0.5057, ACC= 0.6106
0.025896819903090517
Training epoch 359, recon_loss:0.612407, zinb_loss:0.957365, cluster_loss:0.322009
Clustering 359: AMI= 0.7220, NMI= 0.7229, ARI= 0.5010, ACC= 0.6106
0.027281241092878373
Training epoch 360, recon_loss:0.610777, zinb_loss:0.956807, cluster_loss:0.319509
Clustering 360: AMI= 0.7229, NMI= 0.7238, ARI= 0.5062, ACC= 0.6113
0.03009080174274197
Training epoch 361, recon_loss:0.612403, zinb_loss:0.957606, cluster_loss:0.322023
Clustering 361: AMI= 0.7216, NMI= 0.7225, ARI= 0.4999, ACC= 0.6095
0.032411743149151025
Training epoch 362, recon_loss:0.611058, zinb_loss:0.957151, cluster_loss:0.319383
Clustering 362: AMI= 0.7226, NMI= 0.7235, ARI= 0.5060, ACC= 0.6115
0.035058430717863104
Training epoch 363, recon_loss:0.612987, zinb_loss:0.958042, cluster_loss:0.321949
Clustering 363: AMI= 0.7214, NMI= 0.7223, ARI= 0.4991, ACC= 0.6085
0.03770511828657518
Training epoch 364, recon_loss:0.611493, zinb_loss:0.957756, cluster_loss:0.319154
Clustering 364: AMI= 0.7229, NMI= 0.7238, ARI= 0.5070, ACC= 0.6128
0.041288326071908465
Training epoch 365, recon_loss:0.613134, zinb_loss:0.958645, cluster_loss:0.321781
Clustering 365: AMI= 0.7214, NMI= 0.7223, ARI= 0.4990, ACC= 0.6081
0.042876338613135716
Training epoch 366, recon_loss:0.611712, zinb_loss:0.958525, cluster_loss:0.319042
Clustering 366: AMI= 0.7229, NMI= 0.7239, ARI= 0.5077, ACC= 0.6139
0.04527871656012052
Training epoch 367, recon_loss:0.613043, zinb_loss:0.959238, cluster_loss:0.321576
Clustering 367: AMI= 0.7216, NMI= 0.7225, ARI= 0.4991, ACC= 0.6080
0.046378109857893236
Training epoch 368, recon_loss:0.611668, zinb_loss:0.959203, cluster_loss:0.319072
Clustering 368: AMI= 0.7230, NMI= 0.7240, ARI= 0.5083, ACC= 0.6148
0.04796612239912049
Training epoch 369, recon_loss:0.612697, zinb_loss:0.959594, cluster_loss:0.321421
Clustering 369: AMI= 0.7220, NMI= 0.7229, ARI= 0.4996, ACC= 0.6082
0.04788468585854473
Training epoch 370, recon_loss:0.611386, zinb_loss:0.959551, cluster_loss:0.319248
Clustering 370: AMI= 0.7234, NMI= 0.7243, ARI= 0.5087, ACC= 0.6156
0.04768109450710534
Training epoch 371, recon_loss:0.612053, zinb_loss:0.959600, cluster_loss:0.321334
Clustering 371: AMI= 0.7217, NMI= 0.7226, ARI= 0.4996, ACC= 0.6085
0.04764037623681746
Training epoch 372, recon_loss:0.611225, zinb_loss:0.959562, cluster_loss:0.319555
Clustering 372: AMI= 0.7234, NMI= 0.7243, ARI= 0.5085, ACC= 0.6159
0.04674457429048414
Training epoch 373, recon_loss:0.611413, zinb_loss:0.959377, cluster_loss:0.321242
Clustering 373: AMI= 0.7217, NMI= 0.7227, ARI= 0.5000, ACC= 0.6083
0.04564518099271143
Training epoch 374, recon_loss:0.612190, zinb_loss:0.959421, cluster_loss:0.319923
Clustering 374: AMI= 0.7232, NMI= 0.7241, ARI= 0.5083, ACC= 0.6163
0.044464351154362966
Training epoch 375, recon_loss:0.612061, zinb_loss:0.959098, cluster_loss:0.320931
Clustering 375: AMI= 0.7217, NMI= 0.7226, ARI= 0.5011, ACC= 0.6085
0.0440164501811963
Training epoch 376, recon_loss:0.611402, zinb_loss:0.959152, cluster_loss:0.320217
Clustering 376: AMI= 0.7228, NMI= 0.7237, ARI= 0.5073, ACC= 0.6161
0.04409788672177206
Training epoch 377, recon_loss:0.611220, zinb_loss:0.958810, cluster_loss:0.321036
Clustering 377: AMI= 0.7215, NMI= 0.7224, ARI= 0.5012, ACC= 0.6082
0.043772140559469035
Training epoch 378, recon_loss:0.612332, zinb_loss:0.958850, cluster_loss:0.320504
Clustering 378: AMI= 0.7228, NMI= 0.7237, ARI= 0.5070, ACC= 0.6157
0.042021254937090274
Training epoch 379, recon_loss:0.611890, zinb_loss:0.958516, cluster_loss:0.320842
Clustering 379: AMI= 0.7215, NMI= 0.7224, ARI= 0.5016, ACC= 0.6082
0.040840425098741806
Training epoch 380, recon_loss:0.610939, zinb_loss:0.958527, cluster_loss:0.320723
Clustering 380: AMI= 0.7222, NMI= 0.7232, ARI= 0.5056, ACC= 0.6147
0.039089539476363046
Training epoch 381, recon_loss:0.610773, zinb_loss:0.958277, cluster_loss:0.321095
Clustering 381: AMI= 0.7216, NMI= 0.7225, ARI= 0.5017, ACC= 0.6082
0.03856020196262063
Training epoch 382, recon_loss:0.611623, zinb_loss:0.958267, cluster_loss:0.320985
Clustering 382: AMI= 0.7223, NMI= 0.7232, ARI= 0.5055, ACC= 0.6147
0.03570992304246916
Training epoch 383, recon_loss:0.611231, zinb_loss:0.958059, cluster_loss:0.320987
Clustering 383: AMI= 0.7216, NMI= 0.7225, ARI= 0.5021, ACC= 0.6083
0.0339590374200904
Training epoch 384, recon_loss:0.610612, zinb_loss:0.958060, cluster_loss:0.321153
Clustering 384: AMI= 0.7222, NMI= 0.7232, ARI= 0.5048, ACC= 0.6144
0.033103953744044956
Training epoch 385, recon_loss:0.610612, zinb_loss:0.957897, cluster_loss:0.321195
Clustering 385: AMI= 0.7213, NMI= 0.7223, ARI= 0.5018, ACC= 0.6081
0.03249317968972678
Training epoch 386, recon_loss:0.611287, zinb_loss:0.957878, cluster_loss:0.321346
Clustering 386: AMI= 0.7221, NMI= 0.7230, ARI= 0.5045, ACC= 0.6141
0.031515941202817706
Training epoch 387, recon_loss:0.611006, zinb_loss:0.957729, cluster_loss:0.321118
Clustering 387: AMI= 0.7219, NMI= 0.7228, ARI= 0.5029, ACC= 0.6089
0.03049798444562075
Training epoch 388, recon_loss:0.610489, zinb_loss:0.957716, cluster_loss:0.321491
Clustering 388: AMI= 0.7221, NMI= 0.7230, ARI= 0.5043, ACC= 0.6141
0.03000936520216621
Training epoch 389, recon_loss:0.610534, zinb_loss:0.957594, cluster_loss:0.321332
Clustering 389: AMI= 0.7217, NMI= 0.7227, ARI= 0.5026, ACC= 0.6086
0.029480027688423796
Training epoch 390, recon_loss:0.611218, zinb_loss:0.957561, cluster_loss:0.321651
Clustering 390: AMI= 0.7218, NMI= 0.7227, ARI= 0.5040, ACC= 0.6136
0.029072844985545014
Training epoch 391, recon_loss:0.611045, zinb_loss:0.957472, cluster_loss:0.321251
Clustering 391: AMI= 0.7216, NMI= 0.7226, ARI= 0.5033, ACC= 0.6091
0.028787817093529868
Training epoch 392, recon_loss:0.610319, zinb_loss:0.957408, cluster_loss:0.321749
Clustering 392: AMI= 0.7220, NMI= 0.7229, ARI= 0.5039, ACC= 0.6134
0.0289099719043935
Training epoch 393, recon_loss:0.610565, zinb_loss:0.957379, cluster_loss:0.321485
Clustering 393: AMI= 0.7216, NMI= 0.7226, ARI= 0.5033, ACC= 0.6091
0.029032126715257137
Training epoch 394, recon_loss:0.611208, zinb_loss:0.957261, cluster_loss:0.321855
Clustering 394: AMI= 0.7223, NMI= 0.7232, ARI= 0.5038, ACC= 0.6129
0.02870638055295411
Training epoch 395, recon_loss:0.611272, zinb_loss:0.957302, cluster_loss:0.321345
Clustering 395: AMI= 0.7218, NMI= 0.7228, ARI= 0.5039, ACC= 0.6095
0.0289099719043935
Training epoch 396, recon_loss:0.610405, zinb_loss:0.957117, cluster_loss:0.321881
Clustering 396: AMI= 0.7219, NMI= 0.7228, ARI= 0.5031, ACC= 0.6123
0.02919499979640865
Training epoch 397, recon_loss:0.610923, zinb_loss:0.957248, cluster_loss:0.321498
Clustering 397: AMI= 0.7216, NMI= 0.7226, ARI= 0.5039, ACC= 0.6095
0.02935787287756016
Training epoch 398, recon_loss:0.611492, zinb_loss:0.956972, cluster_loss:0.321903
Clustering 398: AMI= 0.7217, NMI= 0.7226, ARI= 0.5024, ACC= 0.6113
0.02911356325583289
Training epoch 399, recon_loss:0.611919, zinb_loss:0.957258, cluster_loss:0.321228
Clustering 399: AMI= 0.7218, NMI= 0.7227, ARI= 0.5044, ACC= 0.6102
0.029520745958711674
Training epoch 400, recon_loss:0.610812, zinb_loss:0.956875, cluster_loss:0.321805
Clustering 400: AMI= 0.7213, NMI= 0.7222, ARI= 0.5015, ACC= 0.6104
0.03009080174274197
Training epoch 401, recon_loss:0.611748, zinb_loss:0.957325, cluster_loss:0.321240
Clustering 401: AMI= 0.7222, NMI= 0.7231, ARI= 0.5047, ACC= 0.6109
0.030131520013029846
Training epoch 402, recon_loss:0.612115, zinb_loss:0.956860, cluster_loss:0.321695
Clustering 402: AMI= 0.7214, NMI= 0.7224, ARI= 0.5012, ACC= 0.6099
0.029887210391302578
Training epoch 403, recon_loss:0.612885, zinb_loss:0.957579, cluster_loss:0.320801
Clustering 403: AMI= 0.7223, NMI= 0.7232, ARI= 0.5054, ACC= 0.6113
0.0298464921210147
Training epoch 404, recon_loss:0.611599, zinb_loss:0.957057, cluster_loss:0.321466
Clustering 404: AMI= 0.7213, NMI= 0.7222, ARI= 0.5005, ACC= 0.6087
0.030701575797060142
Training epoch 405, recon_loss:0.612743, zinb_loss:0.957893, cluster_loss:0.320753
Clustering 405: AMI= 0.7222, NMI= 0.7231, ARI= 0.5051, ACC= 0.6114
0.03053870271590863
Training epoch 406, recon_loss:0.612582, zinb_loss:0.957354, cluster_loss:0.321315
Clustering 406: AMI= 0.7210, NMI= 0.7219, ARI= 0.4996, ACC= 0.6078
0.03049798444562075
Training epoch 407, recon_loss:0.613140, zinb_loss:0.958314, cluster_loss:0.320480
Clustering 407: AMI= 0.7227, NMI= 0.7236, ARI= 0.5061, ACC= 0.6126
0.029765055580438942
Training epoch 408, recon_loss:0.612347, zinb_loss:0.957737, cluster_loss:0.321242
Clustering 408: AMI= 0.7207, NMI= 0.7216, ARI= 0.4990, ACC= 0.6070
0.029724337310151065
Training epoch 409, recon_loss:0.612787, zinb_loss:0.958571, cluster_loss:0.320596
Clustering 409: AMI= 0.7229, NMI= 0.7239, ARI= 0.5063, ACC= 0.6132
0.02833991612036321
Training epoch 410, recon_loss:0.612681, zinb_loss:0.957941, cluster_loss:0.321315
Clustering 410: AMI= 0.7206, NMI= 0.7215, ARI= 0.4989, ACC= 0.6065
0.028095606498635937
Training epoch 411, recon_loss:0.612647, zinb_loss:0.958743, cluster_loss:0.320666
Clustering 411: AMI= 0.7232, NMI= 0.7241, ARI= 0.5067, ACC= 0.6139
0.028624944012378355
Training epoch 412, recon_loss:0.611901, zinb_loss:0.958040, cluster_loss:0.321463
Clustering 412: AMI= 0.7201, NMI= 0.7210, ARI= 0.4984, ACC= 0.6054
0.028624944012378355
Training epoch 413, recon_loss:0.612109, zinb_loss:0.958791, cluster_loss:0.320991
Clustering 413: AMI= 0.7231, NMI= 0.7240, ARI= 0.5066, ACC= 0.6142
0.028543507471802596
Training epoch 414, recon_loss:0.612273, zinb_loss:0.958039, cluster_loss:0.321650
Clustering 414: AMI= 0.7200, NMI= 0.7209, ARI= 0.4984, ACC= 0.6054
0.028014169958060182
Training epoch 415, recon_loss:0.612047, zinb_loss:0.958805, cluster_loss:0.321121
Clustering 415: AMI= 0.7233, NMI= 0.7242, ARI= 0.5071, ACC= 0.6148
0.028543507471802596
Training epoch 416, recon_loss:0.611124, zinb_loss:0.957996, cluster_loss:0.321843
Clustering 416: AMI= 0.7200, NMI= 0.7209, ARI= 0.4983, ACC= 0.6054
0.02846207093122684
Training epoch 417, recon_loss:0.611489, zinb_loss:0.958785, cluster_loss:0.321511
Clustering 417: AMI= 0.7229, NMI= 0.7238, ARI= 0.5063, ACC= 0.6146
0.027769860336332913
Training epoch 418, recon_loss:0.611729, zinb_loss:0.957937, cluster_loss:0.322041
Clustering 418: AMI= 0.7200, NMI= 0.7209, ARI= 0.4984, ACC= 0.6053
0.027484832444317764
Training epoch 419, recon_loss:0.611693, zinb_loss:0.958809, cluster_loss:0.321563
Clustering 419: AMI= 0.7234, NMI= 0.7243, ARI= 0.5073, ACC= 0.6152
0.028624944012378355
Training epoch 420, recon_loss:0.610701, zinb_loss:0.957900, cluster_loss:0.322214
Clustering 420: AMI= 0.7197, NMI= 0.7206, ARI= 0.4979, ACC= 0.6049
0.029276436336984405
Training epoch 421, recon_loss:0.611211, zinb_loss:0.958833, cluster_loss:0.321861
Clustering 421: AMI= 0.7233, NMI= 0.7242, ARI= 0.5065, ACC= 0.6146
0.028950690174681378
Training epoch 422, recon_loss:0.611436, zinb_loss:0.957880, cluster_loss:0.322373
Clustering 422: AMI= 0.7199, NMI= 0.7208, ARI= 0.4981, ACC= 0.6053
0.02960218249928743
Training epoch 423, recon_loss:0.611850, zinb_loss:0.958967, cluster_loss:0.321785
Clustering 423: AMI= 0.7236, NMI= 0.7246, ARI= 0.5072, ACC= 0.6149
0.030905167148499533
Training epoch 424, recon_loss:0.610583, zinb_loss:0.957922, cluster_loss:0.322403
Clustering 424: AMI= 0.7201, NMI= 0.7210, ARI= 0.4980, ACC= 0.6052
0.030864448878211652
Training epoch 425, recon_loss:0.611445, zinb_loss:0.959120, cluster_loss:0.321937
Clustering 425: AMI= 0.7237, NMI= 0.7246, ARI= 0.5069, ACC= 0.6145
0.030701575797060142
Training epoch 426, recon_loss:0.611854, zinb_loss:0.958023, cluster_loss:0.322406
Clustering 426: AMI= 0.7198, NMI= 0.7208, ARI= 0.4976, ACC= 0.6050
0.03147522293252983
Training epoch 427, recon_loss:0.612569, zinb_loss:0.959346, cluster_loss:0.321661
Clustering 427: AMI= 0.7238, NMI= 0.7247, ARI= 0.5075, ACC= 0.6150
0.033266826825196466
Training epoch 428, recon_loss:0.610698, zinb_loss:0.958133, cluster_loss:0.322243
Clustering 428: AMI= 0.7198, NMI= 0.7207, ARI= 0.4974, ACC= 0.6050
0.034162628771529785
Training epoch 429, recon_loss:0.611660, zinb_loss:0.959434, cluster_loss:0.321847
Clustering 429: AMI= 0.7234, NMI= 0.7243, ARI= 0.5064, ACC= 0.6140
0.03290036239260556
Training epoch 430, recon_loss:0.611642, zinb_loss:0.958203, cluster_loss:0.322239
Clustering 430: AMI= 0.7196, NMI= 0.7205, ARI= 0.4973, ACC= 0.6049
0.03253389796001466
Training epoch 431, recon_loss:0.612234, zinb_loss:0.959476, cluster_loss:0.321760
Clustering 431: AMI= 0.7234, NMI= 0.7243, ARI= 0.5067, ACC= 0.6141
0.033551854717211615
Training epoch 432, recon_loss:0.610446, zinb_loss:0.958211, cluster_loss:0.322194
Clustering 432: AMI= 0.7200, NMI= 0.7209, ARI= 0.4975, ACC= 0.6054
0.03322610855490859
Training epoch 433, recon_loss:0.611379, zinb_loss:0.959386, cluster_loss:0.322108
Clustering 433: AMI= 0.7232, NMI= 0.7241, ARI= 0.5057, ACC= 0.6135
0.03155665947310558
Training epoch 434, recon_loss:0.611210, zinb_loss:0.958182, cluster_loss:0.322315
Clustering 434: AMI= 0.7200, NMI= 0.7209, ARI= 0.4979, ACC= 0.6057
0.031108758499938924
Training epoch 435, recon_loss:0.611736, zinb_loss:0.959324, cluster_loss:0.322129
Clustering 435: AMI= 0.7232, NMI= 0.7241, ARI= 0.5060, ACC= 0.6138
0.03171953255425709
Training epoch 436, recon_loss:0.610164, zinb_loss:0.958169, cluster_loss:0.322370
Clustering 436: AMI= 0.7199, NMI= 0.7208, ARI= 0.4977, ACC= 0.6058
0.031230913310802556
Training epoch 437, recon_loss:0.611165, zinb_loss:0.959238, cluster_loss:0.322427
Clustering 437: AMI= 0.7229, NMI= 0.7238, ARI= 0.5056, ACC= 0.6132
0.030457266175332873
Training epoch 438, recon_loss:0.610908, zinb_loss:0.958170, cluster_loss:0.322513
Clustering 438: AMI= 0.7200, NMI= 0.7209, ARI= 0.4982, ACC= 0.6062
0.02964290076957531
Training epoch 439, recon_loss:0.611489, zinb_loss:0.959213, cluster_loss:0.322360
Clustering 439: AMI= 0.7230, NMI= 0.7239, ARI= 0.5058, ACC= 0.6133
0.03005008347245409
Training epoch 440, recon_loss:0.610241, zinb_loss:0.958231, cluster_loss:0.322594
Clustering 440: AMI= 0.7200, NMI= 0.7209, ARI= 0.4982, ACC= 0.6065
0.0302129565536056
Training epoch 441, recon_loss:0.611201, zinb_loss:0.959169, cluster_loss:0.322454
Clustering 441: AMI= 0.7230, NMI= 0.7239, ARI= 0.5057, ACC= 0.6129
0.030131520013029846
Training epoch 442, recon_loss:0.611160, zinb_loss:0.958301, cluster_loss:0.322722
Clustering 442: AMI= 0.7202, NMI= 0.7211, ARI= 0.4982, ACC= 0.6070
0.03053870271590863
Training epoch 443, recon_loss:0.611732, zinb_loss:0.959163, cluster_loss:0.322201
Clustering 443: AMI= 0.7232, NMI= 0.7241, ARI= 0.5064, ACC= 0.6128
0.03294108066289344
Training epoch 444, recon_loss:0.610673, zinb_loss:0.958408, cluster_loss:0.322777
Clustering 444: AMI= 0.7202, NMI= 0.7212, ARI= 0.4979, ACC= 0.6073
0.0339590374200904
Training epoch 445, recon_loss:0.611603, zinb_loss:0.959104, cluster_loss:0.322074
Clustering 445: AMI= 0.7233, NMI= 0.7242, ARI= 0.5062, ACC= 0.6119
0.03554704996131764
Training epoch 446, recon_loss:0.612006, zinb_loss:0.958517, cluster_loss:0.322888
Clustering 446: AMI= 0.7202, NMI= 0.7211, ARI= 0.4976, ACC= 0.6078
0.03672787979966611
Training epoch 447, recon_loss:0.612444, zinb_loss:0.959059, cluster_loss:0.321608
Clustering 447: AMI= 0.7234, NMI= 0.7243, ARI= 0.5071, ACC= 0.6116
0.04018893277413575
Training epoch 448, recon_loss:0.611403, zinb_loss:0.958569, cluster_loss:0.322913
Clustering 448: AMI= 0.7205, NMI= 0.7214, ARI= 0.4977, ACC= 0.6085
0.042021254937090274
Training epoch 449, recon_loss:0.612113, zinb_loss:0.958876, cluster_loss:0.321510
Clustering 449: AMI= 0.7233, NMI= 0.7242, ARI= 0.5071, ACC= 0.6117
0.043324239586302375
Training epoch 450, recon_loss:0.612564, zinb_loss:0.958531, cluster_loss:0.323083
Clustering 450: AMI= 0.7206, NMI= 0.7215, ARI= 0.4977, ACC= 0.6087
0.0436499857486054
Training epoch 451, recon_loss:0.612618, zinb_loss:0.958686, cluster_loss:0.321244
Clustering 451: AMI= 0.7231, NMI= 0.7240, ARI= 0.5072, ACC= 0.6120
0.04487153385724174
Training epoch 452, recon_loss:0.611423, zinb_loss:0.958379, cluster_loss:0.323205
Clustering 452: AMI= 0.7209, NMI= 0.7218, ARI= 0.4978, ACC= 0.6087
0.04487153385724174
Training epoch 453, recon_loss:0.611831, zinb_loss:0.958377, cluster_loss:0.321471
Clustering 453: AMI= 0.7229, NMI= 0.7238, ARI= 0.5068, ACC= 0.6116
0.043935013640620545
Training epoch 454, recon_loss:0.612297, zinb_loss:0.958243, cluster_loss:0.323456
Clustering 454: AMI= 0.7209, NMI= 0.7218, ARI= 0.4978, ACC= 0.6085
0.0432835213160145
Training epoch 455, recon_loss:0.612180, zinb_loss:0.958163, cluster_loss:0.321392
Clustering 455: AMI= 0.7228, NMI= 0.7237, ARI= 0.5069, ACC= 0.6119
0.04336495785659025
Training epoch 456, recon_loss:0.611243, zinb_loss:0.958098, cluster_loss:0.323584
Clustering 456: AMI= 0.7209, NMI= 0.7218, ARI= 0.4977, ACC= 0.6085
0.04250987418054481
Training epoch 457, recon_loss:0.611568, zinb_loss:0.957929, cluster_loss:0.321634
Clustering 457: AMI= 0.7227, NMI= 0.7236, ARI= 0.5068, ACC= 0.6119
0.04149191742334785
Training epoch 458, recon_loss:0.612598, zinb_loss:0.958062, cluster_loss:0.323773
Clustering 458: AMI= 0.7209, NMI= 0.7218, ARI= 0.4976, ACC= 0.6085
0.041003298179893316
Training epoch 459, recon_loss:0.612461, zinb_loss:0.957809, cluster_loss:0.321431
Clustering 459: AMI= 0.7228, NMI= 0.7237, ARI= 0.5070, ACC= 0.6121
0.04132904434219634
Training epoch 460, recon_loss:0.611491, zinb_loss:0.957973, cluster_loss:0.323793
Clustering 460: AMI= 0.7210, NMI= 0.7219, ARI= 0.4979, ACC= 0.6085
0.04039252412557515
Training epoch 461, recon_loss:0.611746, zinb_loss:0.957653, cluster_loss:0.321656
Clustering 461: AMI= 0.7224, NMI= 0.7233, ARI= 0.5060, ACC= 0.6112
0.03819373753002973
Training epoch 462, recon_loss:0.612743, zinb_loss:0.957983, cluster_loss:0.323925
Clustering 462: AMI= 0.7210, NMI= 0.7219, ARI= 0.4978, ACC= 0.6082
0.037664400016287305
Training epoch 463, recon_loss:0.612377, zinb_loss:0.957569, cluster_loss:0.321524
Clustering 463: AMI= 0.7222, NMI= 0.7232, ARI= 0.5059, ACC= 0.6112
0.03746080866484792
Training epoch 464, recon_loss:0.611789, zinb_loss:0.957922, cluster_loss:0.323942
Clustering 464: AMI= 0.7208, NMI= 0.7217, ARI= 0.4978, ACC= 0.6081
0.03623926055621157
Training epoch 465, recon_loss:0.611542, zinb_loss:0.957455, cluster_loss:0.321824
Clustering 465: AMI= 0.7221, NMI= 0.7230, ARI= 0.5054, ACC= 0.6108
0.033755446068651
Training epoch 466, recon_loss:0.612862, zinb_loss:0.957950, cluster_loss:0.324093
Clustering 466: AMI= 0.7210, NMI= 0.7219, ARI= 0.4983, ACC= 0.6082
0.03249317968972678
Training epoch 467, recon_loss:0.611930, zinb_loss:0.957389, cluster_loss:0.321774
Clustering 467: AMI= 0.7220, NMI= 0.7229, ARI= 0.5053, ACC= 0.6107
0.032248870067999515
Training epoch 468, recon_loss:0.611627, zinb_loss:0.957895, cluster_loss:0.324144
Clustering 468: AMI= 0.7208, NMI= 0.7218, ARI= 0.4984, ACC= 0.6082
0.030864448878211652
Training epoch 469, recon_loss:0.611020, zinb_loss:0.957322, cluster_loss:0.322138
Clustering 469: AMI= 0.7220, NMI= 0.7230, ARI= 0.5042, ACC= 0.6099
0.028624944012378355
Training epoch 470, recon_loss:0.612589, zinb_loss:0.957969, cluster_loss:0.324309
Clustering 470: AMI= 0.7209, NMI= 0.7218, ARI= 0.4987, ACC= 0.6083
0.02833991612036321
Training epoch 471, recon_loss:0.611303, zinb_loss:0.957298, cluster_loss:0.322109
Clustering 471: AMI= 0.7220, NMI= 0.7229, ARI= 0.5045, ACC= 0.6100
0.028584225742090477
Training epoch 472, recon_loss:0.611849, zinb_loss:0.957999, cluster_loss:0.324385
Clustering 472: AMI= 0.7211, NMI= 0.7220, ARI= 0.4993, ACC= 0.6087
0.027892015147196546
Training epoch 473, recon_loss:0.610809, zinb_loss:0.957292, cluster_loss:0.322352
Clustering 473: AMI= 0.7217, NMI= 0.7226, ARI= 0.5036, ACC= 0.6092
0.026792621849423836
Training epoch 474, recon_loss:0.612848, zinb_loss:0.958121, cluster_loss:0.324499
Clustering 474: AMI= 0.7210, NMI= 0.7220, ARI= 0.4991, ACC= 0.6085
0.026792621849423836
Training epoch 475, recon_loss:0.611250, zinb_loss:0.957302, cluster_loss:0.322294
Clustering 475: AMI= 0.7218, NMI= 0.7227, ARI= 0.5034, ACC= 0.6088
0.02675190357913596
Training epoch 476, recon_loss:0.612049, zinb_loss:0.958157, cluster_loss:0.324497
Clustering 476: AMI= 0.7210, NMI= 0.7219, ARI= 0.4992, ACC= 0.6087
0.02671118530884808
Training epoch 477, recon_loss:0.610837, zinb_loss:0.957326, cluster_loss:0.322525
Clustering 477: AMI= 0.7218, NMI= 0.7227, ARI= 0.5029, ACC= 0.6082
0.026100411254529908
Training epoch 478, recon_loss:0.613139, zinb_loss:0.958317, cluster_loss:0.324517
Clustering 478: AMI= 0.7212, NMI= 0.7221, ARI= 0.4995, ACC= 0.6090
0.026792621849423836
Training epoch 479, recon_loss:0.611337, zinb_loss:0.957358, cluster_loss:0.322418
Clustering 479: AMI= 0.7217, NMI= 0.7226, ARI= 0.5027, ACC= 0.6080
0.02736267763345413
Training epoch 480, recon_loss:0.612758, zinb_loss:0.958397, cluster_loss:0.324429
Clustering 480: AMI= 0.7211, NMI= 0.7221, ARI= 0.4996, ACC= 0.6096
0.02740339590374201
Training epoch 481, recon_loss:0.611188, zinb_loss:0.957408, cluster_loss:0.322532
Clustering 481: AMI= 0.7219, NMI= 0.7228, ARI= 0.5025, ACC= 0.6072
0.027036931471151104
Training epoch 482, recon_loss:0.613756, zinb_loss:0.958558, cluster_loss:0.324323
Clustering 482: AMI= 0.7217, NMI= 0.7226, ARI= 0.5006, ACC= 0.6106
0.02785129687690867
Training epoch 483, recon_loss:0.611801, zinb_loss:0.957476, cluster_loss:0.322425
Clustering 483: AMI= 0.7221, NMI= 0.7230, ARI= 0.5026, ACC= 0.6068
0.027932733417484427
Training epoch 484, recon_loss:0.613028, zinb_loss:0.958633, cluster_loss:0.324116
Clustering 484: AMI= 0.7215, NMI= 0.7225, ARI= 0.5007, ACC= 0.6109
0.02715908628201474
Training epoch 485, recon_loss:0.611448, zinb_loss:0.957603, cluster_loss:0.322630
Clustering 485: AMI= 0.7220, NMI= 0.7230, ARI= 0.5025, ACC= 0.6061
0.026670467038560203
Training epoch 486, recon_loss:0.614037, zinb_loss:0.958854, cluster_loss:0.323985
Clustering 486: AMI= 0.7216, NMI= 0.7225, ARI= 0.5012, ACC= 0.6115
0.027525550714605645
Training epoch 487, recon_loss:0.611953, zinb_loss:0.957744, cluster_loss:0.322563
Clustering 487: AMI= 0.7222, NMI= 0.7231, ARI= 0.5029, ACC= 0.6061
0.0276069872551814
Training epoch 488, recon_loss:0.612882, zinb_loss:0.959026, cluster_loss:0.323803
Clustering 488: AMI= 0.7219, NMI= 0.7228, ARI= 0.5025, ACC= 0.6132
0.027566268984893522
Training epoch 489, recon_loss:0.611263, zinb_loss:0.958013, cluster_loss:0.322888
Clustering 489: AMI= 0.7218, NMI= 0.7227, ARI= 0.5015, ACC= 0.6048
0.027688423795757155
Training epoch 490, recon_loss:0.613970, zinb_loss:0.959410, cluster_loss:0.323741
Clustering 490: AMI= 0.7222, NMI= 0.7231, ARI= 0.5036, ACC= 0.6143
0.02956146422899955
Training epoch 491, recon_loss:0.611936, zinb_loss:0.958305, cluster_loss:0.322792
Clustering 491: AMI= 0.7219, NMI= 0.7228, ARI= 0.5017, ACC= 0.6048
0.03009080174274197
Training epoch 492, recon_loss:0.612386, zinb_loss:0.959759, cluster_loss:0.323574
Clustering 492: AMI= 0.7224, NMI= 0.7233, ARI= 0.5050, ACC= 0.6158
0.03159737774339346
Training epoch 493, recon_loss:0.610810, zinb_loss:0.958695, cluster_loss:0.323152
Clustering 493: AMI= 0.7216, NMI= 0.7225, ARI= 0.5003, ACC= 0.6036
0.03359257298749949
Training epoch 494, recon_loss:0.613314, zinb_loss:0.960083, cluster_loss:0.323587
Clustering 494: AMI= 0.7228, NMI= 0.7237, ARI= 0.5059, ACC= 0.6163
0.034895557636711594
Training epoch 495, recon_loss:0.611379, zinb_loss:0.958902, cluster_loss:0.323116
Clustering 495: AMI= 0.7217, NMI= 0.7226, ARI= 0.5003, ACC= 0.6036
0.03534345860987825
Training epoch 496, recon_loss:0.611543, zinb_loss:0.960155, cluster_loss:0.323584
Clustering 496: AMI= 0.7227, NMI= 0.7236, ARI= 0.5061, ACC= 0.6165
0.03587279612362067
Training epoch 497, recon_loss:0.610183, zinb_loss:0.959026, cluster_loss:0.323593
Clustering 497: AMI= 0.7211, NMI= 0.7220, ARI= 0.4990, ACC= 0.6030
0.03672787979966611
Training epoch 498, recon_loss:0.612101, zinb_loss:0.960083, cluster_loss:0.323769
Clustering 498: AMI= 0.7229, NMI= 0.7238, ARI= 0.5064, ACC= 0.6161
0.03615782401563582
Training epoch 499, recon_loss:0.610582, zinb_loss:0.959012, cluster_loss:0.323700
Clustering 499: AMI= 0.7213, NMI= 0.7222, ARI= 0.4995, ACC= 0.6035
0.03513986725843886
Training epoch 500, recon_loss:0.610632, zinb_loss:0.959935, cluster_loss:0.323855
Clustering 500: AMI= 0.7228, NMI= 0.7237, ARI= 0.5060, ACC= 0.6156
0.035017712447575226
Training epoch 501, recon_loss:0.609753, zinb_loss:0.959041, cluster_loss:0.324164
Clustering 501: AMI= 0.7213, NMI= 0.7222, ARI= 0.4986, ACC= 0.6034
0.03579135958304491
Training epoch 502, recon_loss:0.611369, zinb_loss:0.959805, cluster_loss:0.324007
Clustering 502: AMI= 0.7230, NMI= 0.7239, ARI= 0.5061, ACC= 0.6151
0.03513986725843886
Training epoch 503, recon_loss:0.610373, zinb_loss:0.959073, cluster_loss:0.324251
Clustering 503: AMI= 0.7216, NMI= 0.7225, ARI= 0.4990, ACC= 0.6040
0.0339590374200904
Training epoch 504, recon_loss:0.610228, zinb_loss:0.959714, cluster_loss:0.324017
Clustering 504: AMI= 0.7229, NMI= 0.7239, ARI= 0.5059, ACC= 0.6145
0.034162628771529785
Training epoch 505, recon_loss:0.609907, zinb_loss:0.959196, cluster_loss:0.324654
Clustering 505: AMI= 0.7216, NMI= 0.7226, ARI= 0.4984, ACC= 0.6040
0.035058430717863104
Training epoch 506, recon_loss:0.611250, zinb_loss:0.959667, cluster_loss:0.324030
Clustering 506: AMI= 0.7232, NMI= 0.7241, ARI= 0.5059, ACC= 0.6141
0.03509914898815098
Training epoch 507, recon_loss:0.610916, zinb_loss:0.959327, cluster_loss:0.324674
Clustering 507: AMI= 0.7215, NMI= 0.7224, ARI= 0.4984, ACC= 0.6044
0.034610529744696444
Training epoch 508, recon_loss:0.610532, zinb_loss:0.959631, cluster_loss:0.323891
Clustering 508: AMI= 0.7234, NMI= 0.7243, ARI= 0.5058, ACC= 0.6131
0.03566920477218128
Training epoch 509, recon_loss:0.610912, zinb_loss:0.959494, cluster_loss:0.324980
Clustering 509: AMI= 0.7215, NMI= 0.7224, ARI= 0.4979, ACC= 0.6045
0.03672787979966611
Training epoch 510, recon_loss:0.612099, zinb_loss:0.959555, cluster_loss:0.323749
Clustering 510: AMI= 0.7233, NMI= 0.7242, ARI= 0.5056, ACC= 0.6119
0.037216499043120646
Training epoch 511, recon_loss:0.612418, zinb_loss:0.959537, cluster_loss:0.324855
Clustering 511: AMI= 0.7215, NMI= 0.7225, ARI= 0.4982, ACC= 0.6052
0.03615782401563582
Training epoch 512, recon_loss:0.611465, zinb_loss:0.959360, cluster_loss:0.323539
Clustering 512: AMI= 0.7234, NMI= 0.7243, ARI= 0.5054, ACC= 0.6112
0.036646443259090354
Training epoch 513, recon_loss:0.612337, zinb_loss:0.959466, cluster_loss:0.325047
Clustering 513: AMI= 0.7215, NMI= 0.7225, ARI= 0.4977, ACC= 0.6057
0.03652428844822672
Training epoch 514, recon_loss:0.612839, zinb_loss:0.959046, cluster_loss:0.323468
Clustering 514: AMI= 0.7232, NMI= 0.7241, ARI= 0.5050, ACC= 0.6103
0.03611710574534794
Training epoch 515, recon_loss:0.613283, zinb_loss:0.959188, cluster_loss:0.324842
Clustering 515: AMI= 0.7216, NMI= 0.7225, ARI= 0.4984, ACC= 0.6061
0.03440693839325706
Training epoch 516, recon_loss:0.611738, zinb_loss:0.958667, cluster_loss:0.323520
Clustering 516: AMI= 0.7229, NMI= 0.7239, ARI= 0.5042, ACC= 0.6096
0.03334826336577222
Training epoch 517, recon_loss:0.612460, zinb_loss:0.958875, cluster_loss:0.325023
Clustering 517: AMI= 0.7215, NMI= 0.7225, ARI= 0.4983, ACC= 0.6064
0.032574616230302535
Training epoch 518, recon_loss:0.612473, zinb_loss:0.958323, cluster_loss:0.323760
Clustering 518: AMI= 0.7226, NMI= 0.7235, ARI= 0.5036, ACC= 0.6087
0.030335111364469237
Training epoch 519, recon_loss:0.612658, zinb_loss:0.958549, cluster_loss:0.324920
Clustering 519: AMI= 0.7218, NMI= 0.7227, ARI= 0.4992, ACC= 0.6071
0.028217761309499573
Training epoch 520, recon_loss:0.611098, zinb_loss:0.958054, cluster_loss:0.324026
Clustering 520: AMI= 0.7226, NMI= 0.7235, ARI= 0.5034, ACC= 0.6087
0.026874058389999594
Training epoch 521, recon_loss:0.611668, zinb_loss:0.958321, cluster_loss:0.325169
Clustering 521: AMI= 0.7219, NMI= 0.7228, ARI= 0.4993, ACC= 0.6071
0.02622256606539354
Training epoch 522, recon_loss:0.611938, zinb_loss:0.957872, cluster_loss:0.324349
Clustering 522: AMI= 0.7225, NMI= 0.7234, ARI= 0.5031, ACC= 0.6086
0.02455311698359054
Training epoch 523, recon_loss:0.612010, zinb_loss:0.958142, cluster_loss:0.325060
Clustering 523: AMI= 0.7218, NMI= 0.7228, ARI= 0.4997, ACC= 0.6071
0.02251720346919663
Training epoch 524, recon_loss:0.610650, zinb_loss:0.957750, cluster_loss:0.324580
Clustering 524: AMI= 0.7223, NMI= 0.7232, ARI= 0.5028, ACC= 0.6089
0.02153996498228755
Training epoch 525, recon_loss:0.611210, zinb_loss:0.958048, cluster_loss:0.325280
Clustering 525: AMI= 0.7219, NMI= 0.7228, ARI= 0.4996, ACC= 0.6069
0.021254937090272406
Training epoch 526, recon_loss:0.611766, zinb_loss:0.957664, cluster_loss:0.324847
Clustering 526: AMI= 0.7222, NMI= 0.7231, ARI= 0.5028, ACC= 0.6089
0.020440571684514842
Training epoch 527, recon_loss:0.611918, zinb_loss:0.957989, cluster_loss:0.325101
Clustering 527: AMI= 0.7223, NMI= 0.7232, ARI= 0.5011, ACC= 0.6078
0.01917830530559062
Training epoch 528, recon_loss:0.610603, zinb_loss:0.957622, cluster_loss:0.324991
Clustering 528: AMI= 0.7217, NMI= 0.7227, ARI= 0.5022, ACC= 0.6087
0.019015432224439105
Training epoch 529, recon_loss:0.611241, zinb_loss:0.957983, cluster_loss:0.325263
Clustering 529: AMI= 0.7222, NMI= 0.7231, ARI= 0.5005, ACC= 0.6071
0.01913758703530274
Training epoch 530, recon_loss:0.611938, zinb_loss:0.957581, cluster_loss:0.325182
Clustering 530: AMI= 0.7216, NMI= 0.7225, ARI= 0.5018, ACC= 0.6085
0.01848609471069669
Training epoch 531, recon_loss:0.612234, zinb_loss:0.958006, cluster_loss:0.325017
Clustering 531: AMI= 0.7223, NMI= 0.7233, ARI= 0.5012, ACC= 0.6071
0.018608249521560323
Training epoch 532, recon_loss:0.610788, zinb_loss:0.957583, cluster_loss:0.325237
Clustering 532: AMI= 0.7215, NMI= 0.7225, ARI= 0.5017, ACC= 0.6084
0.01917830530559062
Training epoch 533, recon_loss:0.611508, zinb_loss:0.958053, cluster_loss:0.325151
Clustering 533: AMI= 0.7223, NMI= 0.7232, ARI= 0.5010, ACC= 0.6066
0.019666924549045155
Training epoch 534, recon_loss:0.612179, zinb_loss:0.957570, cluster_loss:0.325377
Clustering 534: AMI= 0.7213, NMI= 0.7222, ARI= 0.5011, ACC= 0.6078
0.019748361089620914
Training epoch 535, recon_loss:0.612565, zinb_loss:0.958138, cluster_loss:0.324890
Clustering 535: AMI= 0.7226, NMI= 0.7235, ARI= 0.5017, ACC= 0.6070
0.019992670711348182
Training epoch 536, recon_loss:0.610903, zinb_loss:0.957617, cluster_loss:0.325367
Clustering 536: AMI= 0.7210, NMI= 0.7219, ARI= 0.5007, ACC= 0.6073
0.02092919092796938
Training epoch 537, recon_loss:0.611676, zinb_loss:0.958243, cluster_loss:0.325050
Clustering 537: AMI= 0.7225, NMI= 0.7234, ARI= 0.5014, ACC= 0.6067
0.020644163035954233
Training epoch 538, recon_loss:0.612104, zinb_loss:0.957654, cluster_loss:0.325452
Clustering 538: AMI= 0.7209, NMI= 0.7218, ARI= 0.5001, ACC= 0.6067
0.020807036117105746
Training epoch 539, recon_loss:0.612585, zinb_loss:0.958429, cluster_loss:0.324806
Clustering 539: AMI= 0.7229, NMI= 0.7238, ARI= 0.5025, ACC= 0.6074
0.022395048658332993
Training epoch 540, recon_loss:0.610973, zinb_loss:0.957798, cluster_loss:0.325353
Clustering 540: AMI= 0.7208, NMI= 0.7217, ARI= 0.5001, ACC= 0.6070
0.02272079482063602
Training epoch 541, recon_loss:0.611836, zinb_loss:0.958667, cluster_loss:0.324936
Clustering 541: AMI= 0.7233, NMI= 0.7242, ARI= 0.5028, ACC= 0.6077
0.023209414064090557
Training epoch 542, recon_loss:0.612293, zinb_loss:0.957978, cluster_loss:0.325313
Clustering 542: AMI= 0.7207, NMI= 0.7217, ARI= 0.4997, ACC= 0.6066
0.023046540982939043
Training epoch 543, recon_loss:0.612824, zinb_loss:0.959028, cluster_loss:0.324648
Clustering 543: AMI= 0.7238, NMI= 0.7247, ARI= 0.5038, ACC= 0.6085
0.02402377946984812
Training epoch 544, recon_loss:0.611188, zinb_loss:0.958269, cluster_loss:0.325068
Clustering 544: AMI= 0.7208, NMI= 0.7217, ARI= 0.4990, ACC= 0.6058
0.024349525632151148
Training epoch 545, recon_loss:0.612156, zinb_loss:0.959383, cluster_loss:0.324789
Clustering 545: AMI= 0.7238, NMI= 0.7247, ARI= 0.5037, ACC= 0.6087
0.023942342929272366
Training epoch 546, recon_loss:0.612355, zinb_loss:0.958541, cluster_loss:0.324933
Clustering 546: AMI= 0.7207, NMI= 0.7216, ARI= 0.4985, ACC= 0.6049
0.023901624658984485
Training epoch 547, recon_loss:0.612775, zinb_loss:0.959756, cluster_loss:0.324609
Clustering 547: AMI= 0.7241, NMI= 0.7251, ARI= 0.5045, ACC= 0.6095
0.024390243902439025
Training epoch 548, recon_loss:0.611289, zinb_loss:0.958807, cluster_loss:0.324751
Clustering 548: AMI= 0.7207, NMI= 0.7216, ARI= 0.4979, ACC= 0.6039
0.023942342929272366
Training epoch 549, recon_loss:0.612170, zinb_loss:0.960000, cluster_loss:0.324893
Clustering 549: AMI= 0.7238, NMI= 0.7247, ARI= 0.5042, ACC= 0.6098
0.023087259253226924
Training epoch 550, recon_loss:0.612167, zinb_loss:0.958985, cluster_loss:0.324779
Clustering 550: AMI= 0.7209, NMI= 0.7218, ARI= 0.4980, ACC= 0.6035
0.023290850604666315
Training epoch 551, recon_loss:0.612366, zinb_loss:0.960213, cluster_loss:0.324885
Clustering 551: AMI= 0.7237, NMI= 0.7246, ARI= 0.5045, ACC= 0.6104
0.023657315037257216
Training epoch 552, recon_loss:0.610912, zinb_loss:0.959127, cluster_loss:0.324842
Clustering 552: AMI= 0.7211, NMI= 0.7220, ARI= 0.4979, ACC= 0.6030
0.023005822712651166
Training epoch 553, recon_loss:0.611750, zinb_loss:0.960332, cluster_loss:0.325256
Clustering 553: AMI= 0.7237, NMI= 0.7246, ARI= 0.5045, ACC= 0.6108
0.02251720346919663
Training epoch 554, recon_loss:0.611670, zinb_loss:0.959200, cluster_loss:0.325007
Clustering 554: AMI= 0.7211, NMI= 0.7220, ARI= 0.4979, ACC= 0.6028
0.023413005415529948
Training epoch 555, recon_loss:0.611856, zinb_loss:0.960460, cluster_loss:0.325251
Clustering 555: AMI= 0.7238, NMI= 0.7247, ARI= 0.5053, ACC= 0.6119
0.024960299686469317
Training epoch 556, recon_loss:0.610556, zinb_loss:0.959287, cluster_loss:0.325155
Clustering 556: AMI= 0.7211, NMI= 0.7220, ARI= 0.4979, ACC= 0.6025
0.02491958141618144
Training epoch 557, recon_loss:0.611324, zinb_loss:0.960525, cluster_loss:0.325540
Clustering 557: AMI= 0.7237, NMI= 0.7246, ARI= 0.5052, ACC= 0.6122
0.02471599006474205
Training epoch 558, recon_loss:0.611445, zinb_loss:0.959318, cluster_loss:0.325332
Clustering 558: AMI= 0.7213, NMI= 0.7222, ARI= 0.4982, ACC= 0.6024
0.025733946821939004
Training epoch 559, recon_loss:0.611625, zinb_loss:0.960616, cluster_loss:0.325451
Clustering 559: AMI= 0.7242, NMI= 0.7251, ARI= 0.5064, ACC= 0.6132
0.027932733417484427
Training epoch 560, recon_loss:0.610263, zinb_loss:0.959363, cluster_loss:0.325444
Clustering 560: AMI= 0.7215, NMI= 0.7224, ARI= 0.4980, ACC= 0.6021
0.028869253634105623
Training epoch 561, recon_loss:0.611086, zinb_loss:0.960616, cluster_loss:0.325683
Clustering 561: AMI= 0.7241, NMI= 0.7250, ARI= 0.5064, ACC= 0.6136
0.02911356325583289
Training epoch 562, recon_loss:0.611106, zinb_loss:0.959332, cluster_loss:0.325570
Clustering 562: AMI= 0.7216, NMI= 0.7225, ARI= 0.4980, ACC= 0.6018
0.030335111364469237
Training epoch 563, recon_loss:0.611318, zinb_loss:0.960616, cluster_loss:0.325544
Clustering 563: AMI= 0.7244, NMI= 0.7254, ARI= 0.5072, ACC= 0.6146
0.03233030660857527
Training epoch 564, recon_loss:0.610461, zinb_loss:0.959354, cluster_loss:0.325693
Clustering 564: AMI= 0.7218, NMI= 0.7227, ARI= 0.4976, ACC= 0.6015
0.03294108066289344
Training epoch 565, recon_loss:0.610901, zinb_loss:0.960492, cluster_loss:0.325648
Clustering 565: AMI= 0.7242, NMI= 0.7251, ARI= 0.5069, ACC= 0.6142
0.032859644122317684
Training epoch 566, recon_loss:0.611412, zinb_loss:0.959299, cluster_loss:0.325850
Clustering 566: AMI= 0.7217, NMI= 0.7226, ARI= 0.4974, ACC= 0.6015
0.033999755690378275
Training epoch 567, recon_loss:0.611414, zinb_loss:0.960352, cluster_loss:0.325500
Clustering 567: AMI= 0.7244, NMI= 0.7254, ARI= 0.5075, ACC= 0.6143
0.035180585528726736
Training epoch 568, recon_loss:0.610102, zinb_loss:0.959183, cluster_loss:0.325945
Clustering 568: AMI= 0.7216, NMI= 0.7225, ARI= 0.4972, ACC= 0.6014
0.034895557636711594
Training epoch 569, recon_loss:0.610706, zinb_loss:0.960113, cluster_loss:0.325752
Clustering 569: AMI= 0.7242, NMI= 0.7251, ARI= 0.5066, ACC= 0.6133
0.03334826336577222
Training epoch 570, recon_loss:0.611138, zinb_loss:0.959065, cluster_loss:0.326118
Clustering 570: AMI= 0.7215, NMI= 0.7224, ARI= 0.4973, ACC= 0.6015
0.03379616433893888
Training epoch 571, recon_loss:0.611150, zinb_loss:0.959932, cluster_loss:0.325590
Clustering 571: AMI= 0.7241, NMI= 0.7251, ARI= 0.5070, ACC= 0.6134
0.034854839366423716
Training epoch 572, recon_loss:0.610339, zinb_loss:0.959012, cluster_loss:0.326245
Clustering 572: AMI= 0.7215, NMI= 0.7224, ARI= 0.4972, ACC= 0.6017
0.035017712447575226
Training epoch 573, recon_loss:0.610729, zinb_loss:0.959700, cluster_loss:0.325709
Clustering 573: AMI= 0.7239, NMI= 0.7248, ARI= 0.5062, ACC= 0.6125
0.03412191050124191
Training epoch 574, recon_loss:0.611890, zinb_loss:0.958994, cluster_loss:0.326424
Clustering 574: AMI= 0.7216, NMI= 0.7225, ARI= 0.4975, ACC= 0.6023
0.03408119223095403
Training epoch 575, recon_loss:0.611792, zinb_loss:0.959539, cluster_loss:0.325379
Clustering 575: AMI= 0.7241, NMI= 0.7250, ARI= 0.5062, ACC= 0.6122
0.035180585528726736
Training epoch 576, recon_loss:0.610745, zinb_loss:0.958973, cluster_loss:0.326462
Clustering 576: AMI= 0.7216, NMI= 0.7225, ARI= 0.4974, ACC= 0.6027
0.03481412109613584
Training epoch 577, recon_loss:0.611211, zinb_loss:0.959297, cluster_loss:0.325456
Clustering 577: AMI= 0.7236, NMI= 0.7245, ARI= 0.5053, ACC= 0.6109
0.0339590374200904
Training epoch 578, recon_loss:0.612637, zinb_loss:0.959019, cluster_loss:0.326579
Clustering 578: AMI= 0.7220, NMI= 0.7229, ARI= 0.4979, ACC= 0.6035
0.0333889816360601
Training epoch 579, recon_loss:0.612503, zinb_loss:0.959133, cluster_loss:0.324963
Clustering 579: AMI= 0.7237, NMI= 0.7246, ARI= 0.5053, ACC= 0.6102
0.035058430717863104
Training epoch 580, recon_loss:0.611996, zinb_loss:0.959068, cluster_loss:0.326474
Clustering 580: AMI= 0.7221, NMI= 0.7230, ARI= 0.4981, ACC= 0.6042
0.035750641312757035
Training epoch 581, recon_loss:0.612218, zinb_loss:0.958875, cluster_loss:0.324901
Clustering 581: AMI= 0.7235, NMI= 0.7244, ARI= 0.5046, ACC= 0.6091
0.0365650067185146
Training epoch 582, recon_loss:0.613952, zinb_loss:0.959163, cluster_loss:0.326452
Clustering 582: AMI= 0.7224, NMI= 0.7233, ARI= 0.4988, ACC= 0.6052
0.03742009039456004
Training epoch 583, recon_loss:0.613304, zinb_loss:0.958679, cluster_loss:0.324485
Clustering 583: AMI= 0.7238, NMI= 0.7247, ARI= 0.5054, ACC= 0.6093
0.03937456736837819
Training epoch 584, recon_loss:0.612764, zinb_loss:0.959113, cluster_loss:0.326334
Clustering 584: AMI= 0.7224, NMI= 0.7233, ARI= 0.4991, ACC= 0.6063
0.04002605969298424
Training epoch 585, recon_loss:0.612335, zinb_loss:0.958373, cluster_loss:0.324767
Clustering 585: AMI= 0.7235, NMI= 0.7244, ARI= 0.5044, ACC= 0.6080
0.03900810293578729
Training epoch 586, recon_loss:0.613786, zinb_loss:0.959079, cluster_loss:0.326462
Clustering 586: AMI= 0.7223, NMI= 0.7232, ARI= 0.4991, ACC= 0.6065
0.03892666639521153
Training epoch 587, recon_loss:0.612502, zinb_loss:0.958153, cluster_loss:0.324776
Clustering 587: AMI= 0.7234, NMI= 0.7243, ARI= 0.5046, ACC= 0.6079
0.038641638503196386
Training epoch 588, recon_loss:0.612265, zinb_loss:0.958920, cluster_loss:0.326548
Clustering 588: AMI= 0.7223, NMI= 0.7233, ARI= 0.4992, ACC= 0.6066
0.037949427908302455
Training epoch 589, recon_loss:0.611437, zinb_loss:0.957920, cluster_loss:0.325267
Clustering 589: AMI= 0.7233, NMI= 0.7242, ARI= 0.5040, ACC= 0.6073
0.034895557636711594
Training epoch 590, recon_loss:0.612691, zinb_loss:0.958847, cluster_loss:0.326727
Clustering 590: AMI= 0.7224, NMI= 0.7233, ARI= 0.4995, ACC= 0.6069
0.033999755690378275
Training epoch 591, recon_loss:0.611297, zinb_loss:0.957767, cluster_loss:0.325408
Clustering 591: AMI= 0.7232, NMI= 0.7241, ARI= 0.5038, ACC= 0.6071
0.033144672014332834
Training epoch 592, recon_loss:0.612462, zinb_loss:0.958781, cluster_loss:0.326866
Clustering 592: AMI= 0.7225, NMI= 0.7234, ARI= 0.4996, ACC= 0.6069
0.03228958833828739
Training epoch 593, recon_loss:0.611059, zinb_loss:0.957626, cluster_loss:0.325615
Clustering 593: AMI= 0.7230, NMI= 0.7239, ARI= 0.5038, ACC= 0.6069
0.030335111364469237
Training epoch 594, recon_loss:0.612400, zinb_loss:0.958701, cluster_loss:0.326958
Clustering 594: AMI= 0.7223, NMI= 0.7232, ARI= 0.5000, ACC= 0.6070
0.029683619039863187
Training epoch 595, recon_loss:0.611010, zinb_loss:0.957526, cluster_loss:0.325799
Clustering 595: AMI= 0.7230, NMI= 0.7240, ARI= 0.5038, ACC= 0.6069
0.02899140844496926
Training epoch 596, recon_loss:0.612373, zinb_loss:0.958645, cluster_loss:0.326990
Clustering 596: AMI= 0.7224, NMI= 0.7233, ARI= 0.5004, ACC= 0.6073
0.028380634390651086
Training epoch 597, recon_loss:0.611010, zinb_loss:0.957461, cluster_loss:0.325951
Clustering 597: AMI= 0.7233, NMI= 0.7242, ARI= 0.5038, ACC= 0.6065
0.02740339590374201
Training epoch 598, recon_loss:0.612617, zinb_loss:0.958632, cluster_loss:0.326941
Clustering 598: AMI= 0.7224, NMI= 0.7233, ARI= 0.5008, ACC= 0.6077
0.027484832444317764
Training epoch 599, recon_loss:0.611217, zinb_loss:0.957445, cluster_loss:0.326012
Clustering 599: AMI= 0.7231, NMI= 0.7241, ARI= 0.5033, ACC= 0.6060
0.02671118530884808
Training epoch 600, recon_loss:0.612507, zinb_loss:0.958639, cluster_loss:0.326811
Clustering 600: AMI= 0.7226, NMI= 0.7235, ARI= 0.5012, ACC= 0.6081
0.026385439146545054
Training epoch 601, recon_loss:0.611145, zinb_loss:0.957476, cluster_loss:0.326109
Clustering 601: AMI= 0.7227, NMI= 0.7237, ARI= 0.5026, ACC= 0.6049
0.026385439146545054
Training epoch 602, recon_loss:0.613439, zinb_loss:0.958714, cluster_loss:0.326657
Clustering 602: AMI= 0.7225, NMI= 0.7234, ARI= 0.5015, ACC= 0.6085
0.025815383362514762
Training epoch 603, recon_loss:0.611768, zinb_loss:0.957527, cluster_loss:0.325969
Clustering 603: AMI= 0.7226, NMI= 0.7236, ARI= 0.5026, ACC= 0.6043
0.025489637200211735
Training epoch 604, recon_loss:0.612319, zinb_loss:0.958708, cluster_loss:0.326452
Clustering 604: AMI= 0.7225, NMI= 0.7234, ARI= 0.5017, ACC= 0.6087
0.024797426605317807
Training epoch 605, recon_loss:0.611027, zinb_loss:0.957626, cluster_loss:0.326203
Clustering 605: AMI= 0.7222, NMI= 0.7231, ARI= 0.5013, ACC= 0.6037
0.02402377946984812
Training epoch 606, recon_loss:0.613183, zinb_loss:0.958778, cluster_loss:0.326439
Clustering 606: AMI= 0.7226, NMI= 0.7236, ARI= 0.5022, ACC= 0.6088
0.02272079482063602
Training epoch 607, recon_loss:0.611402, zinb_loss:0.957682, cluster_loss:0.326106
Clustering 607: AMI= 0.7226, NMI= 0.7235, ARI= 0.5017, ACC= 0.6041
0.022313612117757238
Training epoch 608, recon_loss:0.611842, zinb_loss:0.958793, cluster_loss:0.326423
Clustering 608: AMI= 0.7226, NMI= 0.7235, ARI= 0.5024, ACC= 0.6091
0.021662119793151188
Training epoch 609, recon_loss:0.610599, zinb_loss:0.957795, cluster_loss:0.326422
Clustering 609: AMI= 0.7223, NMI= 0.7232, ARI= 0.5006, ACC= 0.6031
0.02158068325257543
Training epoch 610, recon_loss:0.612557, zinb_loss:0.958881, cluster_loss:0.326563
Clustering 610: AMI= 0.7227, NMI= 0.7236, ARI= 0.5031, ACC= 0.6094
0.021010627468545137
Training epoch 611, recon_loss:0.610848, zinb_loss:0.957866, cluster_loss:0.326388
Clustering 611: AMI= 0.7223, NMI= 0.7232, ARI= 0.5008, ACC= 0.6031
0.02076631784681787
Training epoch 612, recon_loss:0.611238, zinb_loss:0.958953, cluster_loss:0.326627
Clustering 612: AMI= 0.7226, NMI= 0.7235, ARI= 0.5032, ACC= 0.6095
0.021214218819984528
Training epoch 613, recon_loss:0.610166, zinb_loss:0.958025, cluster_loss:0.326709
Clustering 613: AMI= 0.7222, NMI= 0.7231, ARI= 0.4998, ACC= 0.6022
0.0218249928743027
Training epoch 614, recon_loss:0.611976, zinb_loss:0.959094, cluster_loss:0.326766
Clustering 614: AMI= 0.7227, NMI= 0.7237, ARI= 0.5037, ACC= 0.6100
0.021906429414878456
Training epoch 615, recon_loss:0.610451, zinb_loss:0.958153, cluster_loss:0.326654
Clustering 615: AMI= 0.7221, NMI= 0.7231, ARI= 0.5001, ACC= 0.6023
0.02198786595545421
Training epoch 616, recon_loss:0.611214, zinb_loss:0.959269, cluster_loss:0.326833
Clustering 616: AMI= 0.7230, NMI= 0.7239, ARI= 0.5040, ACC= 0.6102
0.022395048658332993
Training epoch 617, recon_loss:0.610005, zinb_loss:0.958373, cluster_loss:0.326845
Clustering 617: AMI= 0.7219, NMI= 0.7228, ARI= 0.4996, ACC= 0.6016
0.02316869579380268
Training epoch 618, recon_loss:0.612333, zinb_loss:0.959503, cluster_loss:0.326937
Clustering 618: AMI= 0.7230, NMI= 0.7239, ARI= 0.5042, ACC= 0.6107
0.024105216010423876
Training epoch 619, recon_loss:0.610875, zinb_loss:0.958621, cluster_loss:0.326707
Clustering 619: AMI= 0.7220, NMI= 0.7229, ARI= 0.4999, ACC= 0.6016
0.024349525632151148
Training epoch 620, recon_loss:0.610831, zinb_loss:0.959706, cluster_loss:0.326894
Clustering 620: AMI= 0.7233, NMI= 0.7242, ARI= 0.5047, ACC= 0.6113
0.025733946821939004
Training epoch 621, recon_loss:0.610062, zinb_loss:0.958964, cluster_loss:0.327015
Clustering 621: AMI= 0.7219, NMI= 0.7228, ARI= 0.4992, ACC= 0.6009
0.027281241092878373
Training epoch 622, recon_loss:0.612200, zinb_loss:0.959960, cluster_loss:0.326971
Clustering 622: AMI= 0.7234, NMI= 0.7244, ARI= 0.5053, ACC= 0.6119
0.027769860336332913
Training epoch 623, recon_loss:0.611082, zinb_loss:0.959248, cluster_loss:0.326826
Clustering 623: AMI= 0.7220, NMI= 0.7229, ARI= 0.4996, ACC= 0.6007
0.027769860336332913
Training epoch 624, recon_loss:0.610971, zinb_loss:0.960161, cluster_loss:0.326897
Clustering 624: AMI= 0.7235, NMI= 0.7244, ARI= 0.5055, ACC= 0.6121
0.028380634390651086
Training epoch 625, recon_loss:0.610429, zinb_loss:0.959564, cluster_loss:0.327070
Clustering 625: AMI= 0.7219, NMI= 0.7228, ARI= 0.4990, ACC= 0.6002
0.029887210391302578
Training epoch 626, recon_loss:0.612555, zinb_loss:0.960313, cluster_loss:0.326950
Clustering 626: AMI= 0.7236, NMI= 0.7245, ARI= 0.5061, ACC= 0.6124
0.030620139256484383
Training epoch 627, recon_loss:0.611834, zinb_loss:0.959776, cluster_loss:0.326822
Clustering 627: AMI= 0.7220, NMI= 0.7229, ARI= 0.4993, ACC= 0.5999
0.03131234985137831
Training epoch 628, recon_loss:0.611180, zinb_loss:0.960318, cluster_loss:0.326833
Clustering 628: AMI= 0.7235, NMI= 0.7244, ARI= 0.5062, ACC= 0.6127
0.032126715257135875
Training epoch 629, recon_loss:0.611102, zinb_loss:0.959951, cluster_loss:0.327083
Clustering 629: AMI= 0.7217, NMI= 0.7226, ARI= 0.4986, ACC= 0.5997
0.03379616433893888
Training epoch 630, recon_loss:0.612641, zinb_loss:0.960226, cluster_loss:0.326843
Clustering 630: AMI= 0.7238, NMI= 0.7248, ARI= 0.5067, ACC= 0.6128
0.034610529744696444
Training epoch 631, recon_loss:0.612589, zinb_loss:0.960007, cluster_loss:0.326819
Clustering 631: AMI= 0.7221, NMI= 0.7230, ARI= 0.4997, ACC= 0.6006
0.035017712447575226
Training epoch 632, recon_loss:0.611549, zinb_loss:0.960032, cluster_loss:0.326664
Clustering 632: AMI= 0.7239, NMI= 0.7248, ARI= 0.5064, ACC= 0.6125
0.03607638747506006
Training epoch 633, recon_loss:0.612157, zinb_loss:0.960030, cluster_loss:0.327013
Clustering 633: AMI= 0.7217, NMI= 0.7226, ARI= 0.4989, ACC= 0.6000
0.03807158271916609
Training epoch 634, recon_loss:0.613139, zinb_loss:0.959816, cluster_loss:0.326581
Clustering 634: AMI= 0.7241, NMI= 0.7250, ARI= 0.5064, ACC= 0.6119
0.038804511584347896
Training epoch 635, recon_loss:0.613807, zinb_loss:0.960022, cluster_loss:0.326766
Clustering 635: AMI= 0.7218, NMI= 0.7227, ARI= 0.4992, ACC= 0.6004
0.039252412557514556
Training epoch 636, recon_loss:0.612150, zinb_loss:0.959616, cluster_loss:0.326359
Clustering 636: AMI= 0.7241, NMI= 0.7250, ARI= 0.5061, ACC= 0.6111
0.040677552017590296
Training epoch 637, recon_loss:0.613291, zinb_loss:0.959962, cluster_loss:0.326967
Clustering 637: AMI= 0.7214, NMI= 0.7223, ARI= 0.4985, ACC= 0.6004
0.04222484628852966
Training epoch 638, recon_loss:0.613540, zinb_loss:0.959439, cluster_loss:0.326301
Clustering 638: AMI= 0.7242, NMI= 0.7251, ARI= 0.5064, ACC= 0.6109
0.04316136650515086
Training epoch 639, recon_loss:0.614430, zinb_loss:0.959906, cluster_loss:0.326734
Clustering 639: AMI= 0.7212, NMI= 0.7222, ARI= 0.4988, ACC= 0.6006
0.04336495785659025
Training epoch 640, recon_loss:0.612437, zinb_loss:0.959321, cluster_loss:0.326198
Clustering 640: AMI= 0.7242, NMI= 0.7251, ARI= 0.5060, ACC= 0.6102
0.04413860499205994
Training epoch 641, recon_loss:0.613573, zinb_loss:0.959769, cluster_loss:0.326870
Clustering 641: AMI= 0.7210, NMI= 0.7219, ARI= 0.4982, ACC= 0.6007
0.04397573191090842
Training epoch 642, recon_loss:0.613317, zinb_loss:0.959186, cluster_loss:0.326254
Clustering 642: AMI= 0.7238, NMI= 0.7247, ARI= 0.5055, ACC= 0.6098
0.0440164501811963
Training epoch 643, recon_loss:0.613999, zinb_loss:0.959654, cluster_loss:0.326681
Clustering 643: AMI= 0.7209, NMI= 0.7219, ARI= 0.4986, ACC= 0.6013
0.0432835213160145
Training epoch 644, recon_loss:0.612225, zinb_loss:0.959101, cluster_loss:0.326301
Clustering 644: AMI= 0.7239, NMI= 0.7248, ARI= 0.5053, ACC= 0.6091
0.043039211694287226
Training epoch 645, recon_loss:0.613049, zinb_loss:0.959441, cluster_loss:0.326890
Clustering 645: AMI= 0.7211, NMI= 0.7220, ARI= 0.4986, ACC= 0.6017
0.041776945315363
Training epoch 646, recon_loss:0.612810, zinb_loss:0.958959, cluster_loss:0.326489
Clustering 646: AMI= 0.7238, NMI= 0.7247, ARI= 0.5050, ACC= 0.6087
0.0404739606661509
Training epoch 647, recon_loss:0.613028, zinb_loss:0.959248, cluster_loss:0.326871
Clustering 647: AMI= 0.7211, NMI= 0.7220, ARI= 0.4989, ACC= 0.6019
0.03929313082780243
Training epoch 648, recon_loss:0.611491, zinb_loss:0.958852, cluster_loss:0.326693
Clustering 648: AMI= 0.7236, NMI= 0.7245, ARI= 0.5040, ACC= 0.6077
0.038234455800317604
Training epoch 649, recon_loss:0.611999, zinb_loss:0.959020, cluster_loss:0.327241
Clustering 649: AMI= 0.7212, NMI= 0.7221, ARI= 0.4989, ACC= 0.6024
0.036483570177938844
Training epoch 650, recon_loss:0.611629, zinb_loss:0.958713, cluster_loss:0.326917
Clustering 650: AMI= 0.7234, NMI= 0.7243, ARI= 0.5038, ACC= 0.6071
0.03477340282584796
Training epoch 651, recon_loss:0.611713, zinb_loss:0.958877, cluster_loss:0.327382
Clustering 651: AMI= 0.7211, NMI= 0.7220, ARI= 0.4988, ACC= 0.6028
0.032859644122317684
Training epoch 652, recon_loss:0.611937, zinb_loss:0.958700, cluster_loss:0.327164
Clustering 652: AMI= 0.7236, NMI= 0.7245, ARI= 0.5033, ACC= 0.6063
0.03220815179771163
Training epoch 653, recon_loss:0.611684, zinb_loss:0.958698, cluster_loss:0.327497
Clustering 653: AMI= 0.7214, NMI= 0.7223, ARI= 0.4993, ACC= 0.6031
0.031068040229651043
Training epoch 654, recon_loss:0.610590, zinb_loss:0.958588, cluster_loss:0.327323
Clustering 654: AMI= 0.7236, NMI= 0.7245, ARI= 0.5031, ACC= 0.6061
0.030457266175332873
Training epoch 655, recon_loss:0.610982, zinb_loss:0.958620, cluster_loss:0.327888
Clustering 655: AMI= 0.7214, NMI= 0.7223, ARI= 0.4990, ACC= 0.6032
0.028665662282666232
Training epoch 656, recon_loss:0.611002, zinb_loss:0.958525, cluster_loss:0.327448
Clustering 656: AMI= 0.7237, NMI= 0.7246, ARI= 0.5034, ACC= 0.6062
0.02805488822834806
Training epoch 657, recon_loss:0.610951, zinb_loss:0.958580, cluster_loss:0.327980
Clustering 657: AMI= 0.7215, NMI= 0.7224, ARI= 0.4993, ACC= 0.6034
0.02736267763345413
Training epoch 658, recon_loss:0.611439, zinb_loss:0.958565, cluster_loss:0.327585
Clustering 658: AMI= 0.7237, NMI= 0.7246, ARI= 0.5032, ACC= 0.6058
0.02715908628201474
Training epoch 659, recon_loss:0.611143, zinb_loss:0.958489, cluster_loss:0.328023
Clustering 659: AMI= 0.7216, NMI= 0.7225, ARI= 0.4996, ACC= 0.6036
0.026792621849423836
Training epoch 660, recon_loss:0.610792, zinb_loss:0.958525, cluster_loss:0.327694
Clustering 660: AMI= 0.7235, NMI= 0.7244, ARI= 0.5029, ACC= 0.6058
0.02622256606539354
Training epoch 661, recon_loss:0.610788, zinb_loss:0.958477, cluster_loss:0.328229
Clustering 661: AMI= 0.7216, NMI= 0.7226, ARI= 0.4993, ACC= 0.6033
0.025693228551651126
Training epoch 662, recon_loss:0.611275, zinb_loss:0.958501, cluster_loss:0.327786
Clustering 662: AMI= 0.7236, NMI= 0.7245, ARI= 0.5030, ACC= 0.6060
0.02577466509222688
Training epoch 663, recon_loss:0.611111, zinb_loss:0.958480, cluster_loss:0.328246
Clustering 663: AMI= 0.7218, NMI= 0.7227, ARI= 0.4996, ACC= 0.6034
0.025693228551651126
Training epoch 664, recon_loss:0.610959, zinb_loss:0.958503, cluster_loss:0.327862
Clustering 664: AMI= 0.7235, NMI= 0.7244, ARI= 0.5029, ACC= 0.6058
0.02577466509222688
Training epoch 665, recon_loss:0.610946, zinb_loss:0.958480, cluster_loss:0.328348
Clustering 665: AMI= 0.7218, NMI= 0.7228, ARI= 0.4994, ACC= 0.6032
0.0253674823893481
Training epoch 666, recon_loss:0.611344, zinb_loss:0.958499, cluster_loss:0.327917
Clustering 666: AMI= 0.7236, NMI= 0.7245, ARI= 0.5029, ACC= 0.6060
0.025815383362514762
Training epoch 667, recon_loss:0.611333, zinb_loss:0.958556, cluster_loss:0.328321
Clustering 667: AMI= 0.7219, NMI= 0.7228, ARI= 0.4994, ACC= 0.6029
0.02577466509222688
Training epoch 668, recon_loss:0.611165, zinb_loss:0.958542, cluster_loss:0.327911
Clustering 668: AMI= 0.7233, NMI= 0.7242, ARI= 0.5027, ACC= 0.6058
0.02622256606539354
Training epoch 669, recon_loss:0.611426, zinb_loss:0.958696, cluster_loss:0.328324
Clustering 669: AMI= 0.7218, NMI= 0.7228, ARI= 0.4991, ACC= 0.6026
0.026466875687120812
Training epoch 670, recon_loss:0.611905, zinb_loss:0.958645, cluster_loss:0.327848
Clustering 670: AMI= 0.7232, NMI= 0.7241, ARI= 0.5026, ACC= 0.6058
0.026833340119711713
Training epoch 671, recon_loss:0.612252, zinb_loss:0.958981, cluster_loss:0.328111
Clustering 671: AMI= 0.7219, NMI= 0.7228, ARI= 0.4992, ACC= 0.6027
0.027932733417484427
Training epoch 672, recon_loss:0.611258, zinb_loss:0.958822, cluster_loss:0.327635
Clustering 672: AMI= 0.7229, NMI= 0.7238, ARI= 0.5024, ACC= 0.6056
0.029072844985545014
Training epoch 673, recon_loss:0.612292, zinb_loss:0.959432, cluster_loss:0.328056
Clustering 673: AMI= 0.7221, NMI= 0.7230, ARI= 0.4991, ACC= 0.6026
0.029927928661590456
Training epoch 674, recon_loss:0.612783, zinb_loss:0.959129, cluster_loss:0.327408
Clustering 674: AMI= 0.7225, NMI= 0.7234, ARI= 0.5021, ACC= 0.6049
0.030335111364469237
Training epoch 675, recon_loss:0.613632, zinb_loss:0.960027, cluster_loss:0.327577
Clustering 675: AMI= 0.7225, NMI= 0.7234, ARI= 0.4999, ACC= 0.6029
0.03094588541878741
Training epoch 676, recon_loss:0.611784, zinb_loss:0.959477, cluster_loss:0.327140
Clustering 676: AMI= 0.7223, NMI= 0.7232, ARI= 0.5013, ACC= 0.6042
0.03159737774339346
Training epoch 677, recon_loss:0.613192, zinb_loss:0.960430, cluster_loss:0.327558
Clustering 677: AMI= 0.7228, NMI= 0.7237, ARI= 0.4999, ACC= 0.6029
0.03119019504051468
Training epoch 678, recon_loss:0.612662, zinb_loss:0.959616, cluster_loss:0.327114
Clustering 678: AMI= 0.7222, NMI= 0.7231, ARI= 0.5010, ACC= 0.6034
0.030375829634757115
Training epoch 679, recon_loss:0.613503, zinb_loss:0.960635, cluster_loss:0.327328
Clustering 679: AMI= 0.7231, NMI= 0.7240, ARI= 0.5007, ACC= 0.6035
0.029072844985545014
Training epoch 680, recon_loss:0.611528, zinb_loss:0.959613, cluster_loss:0.327237
Clustering 680: AMI= 0.7220, NMI= 0.7229, ARI= 0.5001, ACC= 0.6027
0.02736267763345413
Training epoch 681, recon_loss:0.612511, zinb_loss:0.960534, cluster_loss:0.327529
Clustering 681: AMI= 0.7228, NMI= 0.7238, ARI= 0.5007, ACC= 0.6037
0.02577466509222688
Training epoch 682, recon_loss:0.611968, zinb_loss:0.959452, cluster_loss:0.327508
Clustering 682: AMI= 0.7219, NMI= 0.7229, ARI= 0.4996, ACC= 0.6024
0.02455311698359054
Training epoch 683, recon_loss:0.612561, zinb_loss:0.960405, cluster_loss:0.327499
Clustering 683: AMI= 0.7231, NMI= 0.7240, ARI= 0.5017, ACC= 0.6043
0.023087259253226924
Training epoch 684, recon_loss:0.610406, zinb_loss:0.959305, cluster_loss:0.327736
Clustering 684: AMI= 0.7219, NMI= 0.7228, ARI= 0.4990, ACC= 0.6017
0.02162140152286331
Training epoch 685, recon_loss:0.611444, zinb_loss:0.960250, cluster_loss:0.327824
Clustering 685: AMI= 0.7228, NMI= 0.7238, ARI= 0.5014, ACC= 0.6041
0.020114825522211815
Training epoch 686, recon_loss:0.611277, zinb_loss:0.959198, cluster_loss:0.327987
Clustering 686: AMI= 0.7219, NMI= 0.7228, ARI= 0.4984, ACC= 0.6013
0.0195854880084694
Training epoch 687, recon_loss:0.611918, zinb_loss:0.960234, cluster_loss:0.327701
Clustering 687: AMI= 0.7233, NMI= 0.7242, ARI= 0.5028, ACC= 0.6053
0.019056150494726982
Training epoch 688, recon_loss:0.610174, zinb_loss:0.959222, cluster_loss:0.328106
Clustering 688: AMI= 0.7217, NMI= 0.7226, ARI= 0.4974, ACC= 0.6008
0.018567531251272446
Training epoch 689, recon_loss:0.611169, zinb_loss:0.960233, cluster_loss:0.327884
Clustering 689: AMI= 0.7234, NMI= 0.7244, ARI= 0.5033, ACC= 0.6058
0.018526812980984568
Training epoch 690, recon_loss:0.611733, zinb_loss:0.959257, cluster_loss:0.328190
Clustering 690: AMI= 0.7215, NMI= 0.7224, ARI= 0.4969, ACC= 0.6002
0.0195854880084694
Training epoch 691, recon_loss:0.612430, zinb_loss:0.960350, cluster_loss:0.327572
Clustering 691: AMI= 0.7234, NMI= 0.7243, ARI= 0.5040, ACC= 0.6062
0.021906429414878456
Training epoch 692, recon_loss:0.610890, zinb_loss:0.959354, cluster_loss:0.328107
Clustering 692: AMI= 0.7214, NMI= 0.7223, ARI= 0.4964, ACC= 0.5998
0.02292438617207541
Training epoch 693, recon_loss:0.611840, zinb_loss:0.960354, cluster_loss:0.327638
Clustering 693: AMI= 0.7235, NMI= 0.7244, ARI= 0.5041, ACC= 0.6065
0.02357587849668146
Training epoch 694, recon_loss:0.612612, zinb_loss:0.959352, cluster_loss:0.328089
Clustering 694: AMI= 0.7216, NMI= 0.7226, ARI= 0.4963, ACC= 0.5993
0.024105216010423876
Training epoch 695, recon_loss:0.612997, zinb_loss:0.960333, cluster_loss:0.327329
Clustering 695: AMI= 0.7239, NMI= 0.7249, ARI= 0.5054, ACC= 0.6076
0.026141129524817786
Training epoch 696, recon_loss:0.611638, zinb_loss:0.959312, cluster_loss:0.328046
Clustering 696: AMI= 0.7216, NMI= 0.7225, ARI= 0.4960, ACC= 0.5991
0.02642615741683293
Training epoch 697, recon_loss:0.612102, zinb_loss:0.960130, cluster_loss:0.327519
Clustering 697: AMI= 0.7239, NMI= 0.7248, ARI= 0.5054, ACC= 0.6080
0.02650759395740869
Training epoch 698, recon_loss:0.612867, zinb_loss:0.959179, cluster_loss:0.328197
Clustering 698: AMI= 0.7216, NMI= 0.7225, ARI= 0.4960, ACC= 0.5990
0.026670467038560203
Training epoch 699, recon_loss:0.612567, zinb_loss:0.959925, cluster_loss:0.327424
Clustering 699: AMI= 0.7240, NMI= 0.7249, ARI= 0.5060, ACC= 0.6087
0.02781057860662079
Training epoch 700, recon_loss:0.611323, zinb_loss:0.959051, cluster_loss:0.328336
Clustering 700: AMI= 0.7218, NMI= 0.7227, ARI= 0.4959, ACC= 0.5989
0.027892015147196546
Training epoch 701, recon_loss:0.611380, zinb_loss:0.959668, cluster_loss:0.327785
Clustering 701: AMI= 0.7240, NMI= 0.7249, ARI= 0.5057, ACC= 0.6085
0.026833340119711713
Training epoch 702, recon_loss:0.612098, zinb_loss:0.958932, cluster_loss:0.328566
Clustering 702: AMI= 0.7217, NMI= 0.7227, ARI= 0.4964, ACC= 0.5995
0.02650759395740869
Training epoch 703, recon_loss:0.611521, zinb_loss:0.959502, cluster_loss:0.327786
Clustering 703: AMI= 0.7240, NMI= 0.7249, ARI= 0.5063, ACC= 0.6092
0.027892015147196546
Training epoch 704, recon_loss:0.610872, zinb_loss:0.958896, cluster_loss:0.328744
Clustering 704: AMI= 0.7219, NMI= 0.7228, ARI= 0.4968, ACC= 0.5998
0.02785129687690867
Training epoch 705, recon_loss:0.610689, zinb_loss:0.959365, cluster_loss:0.328082
Clustering 705: AMI= 0.7237, NMI= 0.7247, ARI= 0.5059, ACC= 0.6088
0.026833340119711713
Training epoch 706, recon_loss:0.611779, zinb_loss:0.958897, cluster_loss:0.328943
Clustering 706: AMI= 0.7218, NMI= 0.7227, ARI= 0.4967, ACC= 0.5996
0.027077649741438985
Training epoch 707, recon_loss:0.611058, zinb_loss:0.959315, cluster_loss:0.328009
Clustering 707: AMI= 0.7238, NMI= 0.7248, ARI= 0.5067, ACC= 0.6095
0.0289099719043935
Training epoch 708, recon_loss:0.610605, zinb_loss:0.958961, cluster_loss:0.329093
Clustering 708: AMI= 0.7218, NMI= 0.7227, ARI= 0.4964, ACC= 0.5995
0.029235718066696528
Training epoch 709, recon_loss:0.610294, zinb_loss:0.959307, cluster_loss:0.328243
Clustering 709: AMI= 0.7237, NMI= 0.7246, ARI= 0.5064, ACC= 0.6094
0.0289099719043935
Training epoch 710, recon_loss:0.611540, zinb_loss:0.959078, cluster_loss:0.329258
Clustering 710: AMI= 0.7220, NMI= 0.7229, ARI= 0.4964, ACC= 0.5994
0.029520745958711674
Training epoch 711, recon_loss:0.610719, zinb_loss:0.959390, cluster_loss:0.328084
Clustering 711: AMI= 0.7239, NMI= 0.7248, ARI= 0.5072, ACC= 0.6096
0.031515941202817706
Training epoch 712, recon_loss:0.610779, zinb_loss:0.959295, cluster_loss:0.329385
Clustering 712: AMI= 0.7222, NMI= 0.7231, ARI= 0.4962, ACC= 0.5992
0.03261533450059041
Training epoch 713, recon_loss:0.610166, zinb_loss:0.959503, cluster_loss:0.328164
Clustering 713: AMI= 0.7240, NMI= 0.7249, ARI= 0.5073, ACC= 0.6102
0.0333889816360601
Training epoch 714, recon_loss:0.612272, zinb_loss:0.959613, cluster_loss:0.329526
Clustering 714: AMI= 0.7221, NMI= 0.7230, ARI= 0.4958, ACC= 0.5990
0.03481412109613584
Training epoch 715, recon_loss:0.611179, zinb_loss:0.959734, cluster_loss:0.327797
Clustering 715: AMI= 0.7242, NMI= 0.7251, ARI= 0.5081, ACC= 0.6105
0.037664400016287305
Training epoch 716, recon_loss:0.610910, zinb_loss:0.959908, cluster_loss:0.329527
Clustering 716: AMI= 0.7219, NMI= 0.7228, ARI= 0.4956, ACC= 0.5988
0.038845229854635774
Training epoch 717, recon_loss:0.610423, zinb_loss:0.959909, cluster_loss:0.327858
Clustering 717: AMI= 0.7239, NMI= 0.7248, ARI= 0.5074, ACC= 0.6100
0.03941528563866607
Training epoch 718, recon_loss:0.612135, zinb_loss:0.960279, cluster_loss:0.329560
Clustering 718: AMI= 0.7222, NMI= 0.7232, ARI= 0.4954, ACC= 0.5989
0.04181766358565088
Training epoch 719, recon_loss:0.611016, zinb_loss:0.960163, cluster_loss:0.327403
Clustering 719: AMI= 0.7237, NMI= 0.7246, ARI= 0.5076, ACC= 0.6103
0.044790097316665986
Training epoch 720, recon_loss:0.612519, zinb_loss:0.960639, cluster_loss:0.329499
Clustering 720: AMI= 0.7220, NMI= 0.7229, ARI= 0.4952, ACC= 0.5992
0.04556374445213567
Training epoch 721, recon_loss:0.611351, zinb_loss:0.960239, cluster_loss:0.327033
Clustering 721: AMI= 0.7236, NMI= 0.7245, ARI= 0.5074, ACC= 0.6102
0.04674457429048414
Training epoch 722, recon_loss:0.613217, zinb_loss:0.960783, cluster_loss:0.329397
Clustering 722: AMI= 0.7220, NMI= 0.7230, ARI= 0.4955, ACC= 0.5997
0.04601164542530233
Training epoch 723, recon_loss:0.611898, zinb_loss:0.960159, cluster_loss:0.326653
Clustering 723: AMI= 0.7233, NMI= 0.7242, ARI= 0.5063, ACC= 0.6091
0.04572661753328719
Training epoch 724, recon_loss:0.613039, zinb_loss:0.960682, cluster_loss:0.329280
Clustering 724: AMI= 0.7222, NMI= 0.7231, ARI= 0.4964, ACC= 0.6011
0.04385357710004479
Training epoch 725, recon_loss:0.611742, zinb_loss:0.959888, cluster_loss:0.326611
Clustering 725: AMI= 0.7233, NMI= 0.7242, ARI= 0.5056, ACC= 0.6079
0.04255059245083269
Training epoch 726, recon_loss:0.613463, zinb_loss:0.960483, cluster_loss:0.329289
Clustering 726: AMI= 0.7220, NMI= 0.7229, ARI= 0.4968, ACC= 0.6019
0.0414104808827721
Training epoch 727, recon_loss:0.611777, zinb_loss:0.959599, cluster_loss:0.326637
Clustering 727: AMI= 0.7233, NMI= 0.7243, ARI= 0.5050, ACC= 0.6072
0.03990390488212061
Training epoch 728, recon_loss:0.612746, zinb_loss:0.960190, cluster_loss:0.329366
Clustering 728: AMI= 0.7220, NMI= 0.7230, ARI= 0.4975, ACC= 0.6028
0.03835661061118124
Training epoch 729, recon_loss:0.611194, zinb_loss:0.959285, cluster_loss:0.326950
Clustering 729: AMI= 0.7232, NMI= 0.7241, ARI= 0.5044, ACC= 0.6060
0.03672787979966611
Training epoch 730, recon_loss:0.612796, zinb_loss:0.959943, cluster_loss:0.329518
Clustering 730: AMI= 0.7218, NMI= 0.7227, ARI= 0.4977, ACC= 0.6032
0.036361415367075205
Training epoch 731, recon_loss:0.610998, zinb_loss:0.959068, cluster_loss:0.327156
Clustering 731: AMI= 0.7233, NMI= 0.7242, ARI= 0.5044, ACC= 0.6056
0.0356284865018934
Training epoch 732, recon_loss:0.612124, zinb_loss:0.959709, cluster_loss:0.329657
Clustering 732: AMI= 0.7218, NMI= 0.7228, ARI= 0.4985, ACC= 0.6039
0.03465124801498432
Training epoch 733, recon_loss:0.610510, zinb_loss:0.958872, cluster_loss:0.327461
Clustering 733: AMI= 0.7232, NMI= 0.7241, ARI= 0.5041, ACC= 0.6050
0.03351113644692374
Training epoch 734, recon_loss:0.612062, zinb_loss:0.959533, cluster_loss:0.329799
Clustering 734: AMI= 0.7219, NMI= 0.7228, ARI= 0.4988, ACC= 0.6042
0.032574616230302535
Training epoch 735, recon_loss:0.610415, zinb_loss:0.958753, cluster_loss:0.327648
Clustering 735: AMI= 0.7230, NMI= 0.7239, ARI= 0.5037, ACC= 0.6048
0.031678814283969216
Training epoch 736, recon_loss:0.611538, zinb_loss:0.959378, cluster_loss:0.329913
Clustering 736: AMI= 0.7218, NMI= 0.7227, ARI= 0.4989, ACC= 0.6041
0.031027321959363165
Training epoch 737, recon_loss:0.610109, zinb_loss:0.958658, cluster_loss:0.327875
Clustering 737: AMI= 0.7231, NMI= 0.7240, ARI= 0.5036, ACC= 0.6046
0.030131520013029846
Training epoch 738, recon_loss:0.611531, zinb_loss:0.959270, cluster_loss:0.330021
Clustering 738: AMI= 0.7218, NMI= 0.7227, ARI= 0.4990, ACC= 0.6043
0.02956146422899955
Training epoch 739, recon_loss:0.610175, zinb_loss:0.958613, cluster_loss:0.327990
Clustering 739: AMI= 0.7231, NMI= 0.7240, ARI= 0.5035, ACC= 0.6044
0.02935787287756016
Training epoch 740, recon_loss:0.611165, zinb_loss:0.959173, cluster_loss:0.330102
Clustering 740: AMI= 0.7220, NMI= 0.7229, ARI= 0.4994, ACC= 0.6048
0.0289099719043935
Training epoch 741, recon_loss:0.610063, zinb_loss:0.958578, cluster_loss:0.328143
Clustering 741: AMI= 0.7233, NMI= 0.7242, ARI= 0.5037, ACC= 0.6041
0.028747098823241987
Training epoch 742, recon_loss:0.611503, zinb_loss:0.959106, cluster_loss:0.330186
Clustering 742: AMI= 0.7219, NMI= 0.7228, ARI= 0.4996, ACC= 0.6049
0.02850278920151472
Training epoch 743, recon_loss:0.610575, zinb_loss:0.958579, cluster_loss:0.328159
Clustering 743: AMI= 0.7236, NMI= 0.7246, ARI= 0.5043, ACC= 0.6043
0.028869253634105623
Training epoch 744, recon_loss:0.610477, zinb_loss:0.958985, cluster_loss:0.330191
Clustering 744: AMI= 0.7222, NMI= 0.7231, ARI= 0.5000, ACC= 0.6053
0.028380634390651086
Training epoch 745, recon_loss:0.610142, zinb_loss:0.958589, cluster_loss:0.328409
Clustering 745: AMI= 0.7236, NMI= 0.7245, ARI= 0.5039, ACC= 0.6039
0.027769860336332913
Training epoch 746, recon_loss:0.612200, zinb_loss:0.958942, cluster_loss:0.330258
Clustering 746: AMI= 0.7221, NMI= 0.7230, ARI= 0.5001, ACC= 0.6058
0.02825847957978745
Training epoch 747, recon_loss:0.612059, zinb_loss:0.958667, cluster_loss:0.328149
Clustering 747: AMI= 0.7237, NMI= 0.7246, ARI= 0.5044, ACC= 0.6039
0.029887210391302578
Training epoch 748, recon_loss:0.610669, zinb_loss:0.958814, cluster_loss:0.330105
Clustering 748: AMI= 0.7222, NMI= 0.7232, ARI= 0.5004, ACC= 0.6062
0.029968646931878333
Training epoch 749, recon_loss:0.611164, zinb_loss:0.958713, cluster_loss:0.328430
Clustering 749: AMI= 0.7236, NMI= 0.7246, ARI= 0.5033, ACC= 0.6028
0.028584225742090477
Training epoch 750, recon_loss:0.612951, zinb_loss:0.958742, cluster_loss:0.330065
Clustering 750: AMI= 0.7227, NMI= 0.7236, ARI= 0.5010, ACC= 0.6067
0.028177043039211695
Training epoch 751, recon_loss:0.613898, zinb_loss:0.958876, cluster_loss:0.328043
Clustering 751: AMI= 0.7236, NMI= 0.7246, ARI= 0.5033, ACC= 0.6019
0.02964290076957531
Training epoch 752, recon_loss:0.611830, zinb_loss:0.958722, cluster_loss:0.329697
Clustering 752: AMI= 0.7224, NMI= 0.7233, ARI= 0.5009, ACC= 0.6069
0.029724337310151065
Training epoch 753, recon_loss:0.612514, zinb_loss:0.958992, cluster_loss:0.328242
Clustering 753: AMI= 0.7233, NMI= 0.7242, ARI= 0.5019, ACC= 0.6004
0.0276069872551814
Training epoch 754, recon_loss:0.613450, zinb_loss:0.958743, cluster_loss:0.329542
Clustering 754: AMI= 0.7228, NMI= 0.7237, ARI= 0.5024, ACC= 0.6082
0.027077649741438985
Training epoch 755, recon_loss:0.614279, zinb_loss:0.959188, cluster_loss:0.328027
Clustering 755: AMI= 0.7229, NMI= 0.7238, ARI= 0.5018, ACC= 0.6003
0.027769860336332913
Training epoch 756, recon_loss:0.611688, zinb_loss:0.958918, cluster_loss:0.329240
Clustering 756: AMI= 0.7231, NMI= 0.7240, ARI= 0.5036, ACC= 0.6090
0.028136324768923818
Training epoch 757, recon_loss:0.612162, zinb_loss:0.959388, cluster_loss:0.328479
Clustering 757: AMI= 0.7225, NMI= 0.7234, ARI= 0.5000, ACC= 0.5990
0.02825847957978745
Training epoch 758, recon_loss:0.612424, zinb_loss:0.959151, cluster_loss:0.329244
Clustering 758: AMI= 0.7234, NMI= 0.7244, ARI= 0.5049, ACC= 0.6098
0.028299197850075328
Training epoch 759, recon_loss:0.612920, zinb_loss:0.959683, cluster_loss:0.328540
Clustering 759: AMI= 0.7223, NMI= 0.7232, ARI= 0.4996, ACC= 0.5990
0.028828535363817746
Training epoch 760, recon_loss:0.610924, zinb_loss:0.959569, cluster_loss:0.329077
Clustering 760: AMI= 0.7234, NMI= 0.7243, ARI= 0.5053, ACC= 0.6100
0.0298464921210147
Training epoch 761, recon_loss:0.611519, zinb_loss:0.960035, cluster_loss:0.328924
Clustering 761: AMI= 0.7219, NMI= 0.7228, ARI= 0.4982, ACC= 0.5981
0.030620139256484383
Training epoch 762, recon_loss:0.611856, zinb_loss:0.959981, cluster_loss:0.329052
Clustering 762: AMI= 0.7235, NMI= 0.7244, ARI= 0.5058, ACC= 0.6102
0.031068040229651043
Training epoch 763, recon_loss:0.612215, zinb_loss:0.960390, cluster_loss:0.328899
Clustering 763: AMI= 0.7218, NMI= 0.7228, ARI= 0.4985, ACC= 0.5982
0.03204527871656012
Training epoch 764, recon_loss:0.610686, zinb_loss:0.960372, cluster_loss:0.328923
Clustering 764: AMI= 0.7236, NMI= 0.7245, ARI= 0.5059, ACC= 0.6104
0.03351113644692374
Training epoch 765, recon_loss:0.611243, zinb_loss:0.960615, cluster_loss:0.329160
Clustering 765: AMI= 0.7220, NMI= 0.7229, ARI= 0.4982, ACC= 0.5981
0.0339590374200904
Training epoch 766, recon_loss:0.611679, zinb_loss:0.960576, cluster_loss:0.328946
Clustering 766: AMI= 0.7238, NMI= 0.7247, ARI= 0.5064, ACC= 0.6109
0.0339590374200904
Training epoch 767, recon_loss:0.611845, zinb_loss:0.960700, cluster_loss:0.329071
Clustering 767: AMI= 0.7218, NMI= 0.7227, ARI= 0.4983, ACC= 0.5983
0.033551854717211615
Training epoch 768, recon_loss:0.610493, zinb_loss:0.960633, cluster_loss:0.328925
Clustering 768: AMI= 0.7236, NMI= 0.7245, ARI= 0.5060, ACC= 0.6105
0.033266826825196466
Training epoch 769, recon_loss:0.610993, zinb_loss:0.960606, cluster_loss:0.329291
Clustering 769: AMI= 0.7218, NMI= 0.7227, ARI= 0.4983, ACC= 0.5987
0.03281892585202981
Training epoch 770, recon_loss:0.611592, zinb_loss:0.960550, cluster_loss:0.329035
Clustering 770: AMI= 0.7234, NMI= 0.7244, ARI= 0.5055, ACC= 0.6101
0.03155665947310558
Training epoch 771, recon_loss:0.611599, zinb_loss:0.960445, cluster_loss:0.329146
Clustering 771: AMI= 0.7219, NMI= 0.7228, ARI= 0.4990, ACC= 0.5993
0.030335111364469237
Training epoch 772, recon_loss:0.610479, zinb_loss:0.960406, cluster_loss:0.329071
Clustering 772: AMI= 0.7233, NMI= 0.7242, ARI= 0.5046, ACC= 0.6094
0.02935787287756016
Training epoch 773, recon_loss:0.610947, zinb_loss:0.960182, cluster_loss:0.329299
Clustering 773: AMI= 0.7218, NMI= 0.7227, ARI= 0.4990, ACC= 0.5993
0.028747098823241987
Training epoch 774, recon_loss:0.611481, zinb_loss:0.960237, cluster_loss:0.329168
Clustering 774: AMI= 0.7234, NMI= 0.7243, ARI= 0.5043, ACC= 0.6093
0.027973451687772304
Training epoch 775, recon_loss:0.611420, zinb_loss:0.959906, cluster_loss:0.329074
Clustering 775: AMI= 0.7222, NMI= 0.7231, ARI= 0.5006, ACC= 0.6002
0.027036931471151104
Training epoch 776, recon_loss:0.611515, zinb_loss:0.960099, cluster_loss:0.329198
Clustering 776: AMI= 0.7231, NMI= 0.7240, ARI= 0.5030, ACC= 0.6084
0.026833340119711713
Training epoch 777, recon_loss:0.611344, zinb_loss:0.959599, cluster_loss:0.328973
Clustering 777: AMI= 0.7224, NMI= 0.7233, ARI= 0.5012, ACC= 0.6003
0.027566268984893522
Training epoch 778, recon_loss:0.612500, zinb_loss:0.959978, cluster_loss:0.329248
Clustering 778: AMI= 0.7228, NMI= 0.7237, ARI= 0.5023, ACC= 0.6078
0.027688423795757155
Training epoch 779, recon_loss:0.611854, zinb_loss:0.959319, cluster_loss:0.328671
Clustering 779: AMI= 0.7226, NMI= 0.7235, ARI= 0.5022, ACC= 0.6006
0.02781057860662079
Training epoch 780, recon_loss:0.612013, zinb_loss:0.959812, cluster_loss:0.329235
Clustering 780: AMI= 0.7226, NMI= 0.7235, ARI= 0.5014, ACC= 0.6070
0.027769860336332913
Training epoch 781, recon_loss:0.611347, zinb_loss:0.959082, cluster_loss:0.328706
Clustering 781: AMI= 0.7229, NMI= 0.7238, ARI= 0.5025, ACC= 0.6010
0.027647705525469277
Training epoch 782, recon_loss:0.613026, zinb_loss:0.959728, cluster_loss:0.329353
Clustering 782: AMI= 0.7225, NMI= 0.7234, ARI= 0.5010, ACC= 0.6068
0.027240522822590495
Training epoch 783, recon_loss:0.611567, zinb_loss:0.958872, cluster_loss:0.328517
Clustering 783: AMI= 0.7228, NMI= 0.7238, ARI= 0.5028, ACC= 0.6015
0.02695549493057535
Training epoch 784, recon_loss:0.611766, zinb_loss:0.959579, cluster_loss:0.329437
Clustering 784: AMI= 0.7224, NMI= 0.7233, ARI= 0.5000, ACC= 0.6056
0.026100411254529908
Training epoch 785, recon_loss:0.610812, zinb_loss:0.958714, cluster_loss:0.328758
Clustering 785: AMI= 0.7231, NMI= 0.7240, ARI= 0.5027, ACC= 0.6014
0.02565251028136325
Training epoch 786, recon_loss:0.612360, zinb_loss:0.959524, cluster_loss:0.329620
Clustering 786: AMI= 0.7222, NMI= 0.7231, ARI= 0.4996, ACC= 0.6048
0.024593835253878416
Training epoch 787, recon_loss:0.610678, zinb_loss:0.958608, cluster_loss:0.328740
Clustering 787: AMI= 0.7232, NMI= 0.7241, ARI= 0.5032, ACC= 0.6020
0.02455311698359054
Training epoch 788, recon_loss:0.612137, zinb_loss:0.959507, cluster_loss:0.329784
Clustering 788: AMI= 0.7223, NMI= 0.7232, ARI= 0.4995, ACC= 0.6045
0.023901624658984485
Training epoch 789, recon_loss:0.610463, zinb_loss:0.958511, cluster_loss:0.328824
Clustering 789: AMI= 0.7233, NMI= 0.7243, ARI= 0.5032, ACC= 0.6021
0.02357587849668146
Training epoch 790, recon_loss:0.612088, zinb_loss:0.959498, cluster_loss:0.329934
Clustering 790: AMI= 0.7222, NMI= 0.7231, ARI= 0.4991, ACC= 0.6039
0.023046540982939043
Training epoch 791, recon_loss:0.610388, zinb_loss:0.958476, cluster_loss:0.328908
Clustering 791: AMI= 0.7233, NMI= 0.7242, ARI= 0.5033, ACC= 0.6023
0.022802231361211775
Training epoch 792, recon_loss:0.611866, zinb_loss:0.959511, cluster_loss:0.330045
Clustering 792: AMI= 0.7224, NMI= 0.7233, ARI= 0.4991, ACC= 0.6038
0.02247648519890875
Training epoch 793, recon_loss:0.610223, zinb_loss:0.958478, cluster_loss:0.329022
Clustering 793: AMI= 0.7233, NMI= 0.7242, ARI= 0.5033, ACC= 0.6020
0.022557921739484506
Training epoch 794, recon_loss:0.612171, zinb_loss:0.959576, cluster_loss:0.330133
Clustering 794: AMI= 0.7224, NMI= 0.7234, ARI= 0.4989, ACC= 0.6036
0.022232175577181483
Training epoch 795, recon_loss:0.610507, zinb_loss:0.958509, cluster_loss:0.329040
Clustering 795: AMI= 0.7232, NMI= 0.7241, ARI= 0.5035, ACC= 0.6024
0.022150739036605725
Training epoch 796, recon_loss:0.611815, zinb_loss:0.959642, cluster_loss:0.330156
Clustering 796: AMI= 0.7223, NMI= 0.7233, ARI= 0.4985, ACC= 0.6032
0.021743556333726943
Training epoch 797, recon_loss:0.610287, zinb_loss:0.958578, cluster_loss:0.329160
Clustering 797: AMI= 0.7233, NMI= 0.7242, ARI= 0.5037, ACC= 0.6026
0.021377091901136038
Training epoch 798, recon_loss:0.612861, zinb_loss:0.959781, cluster_loss:0.330162
Clustering 798: AMI= 0.7223, NMI= 0.7232, ARI= 0.4986, ACC= 0.6030
0.021214218819984528
Training epoch 799, recon_loss:0.611265, zinb_loss:0.958678, cluster_loss:0.329039
Clustering 799: AMI= 0.7235, NMI= 0.7244, ARI= 0.5040, ACC= 0.6030
0.021051345738833015
Training epoch 800, recon_loss:0.611929, zinb_loss:0.959847, cluster_loss:0.330011
Clustering 800: AMI= 0.7224, NMI= 0.7233, ARI= 0.4989, ACC= 0.6030
0.020196262062787573
Training epoch 801, recon_loss:0.610707, zinb_loss:0.958819, cluster_loss:0.329228
Clustering 801: AMI= 0.7231, NMI= 0.7240, ARI= 0.5030, ACC= 0.6023
0.018567531251272446
Training epoch 802, recon_loss:0.613347, zinb_loss:0.960002, cluster_loss:0.329913
Clustering 802: AMI= 0.7224, NMI= 0.7233, ARI= 0.4991, ACC= 0.6028
0.017508856223787613
Training epoch 803, recon_loss:0.611844, zinb_loss:0.958947, cluster_loss:0.328967
Clustering 803: AMI= 0.7229, NMI= 0.7238, ARI= 0.5025, ACC= 0.6022
0.01718311006148459
Training epoch 804, recon_loss:0.612660, zinb_loss:0.960006, cluster_loss:0.329748
Clustering 804: AMI= 0.7225, NMI= 0.7234, ARI= 0.4991, ACC= 0.6026
0.016653772547742172
Training epoch 805, recon_loss:0.611269, zinb_loss:0.959083, cluster_loss:0.329048
Clustering 805: AMI= 0.7225, NMI= 0.7235, ARI= 0.5016, ACC= 0.6019
0.015432224439105826
Training epoch 806, recon_loss:0.613909, zinb_loss:0.960081, cluster_loss:0.329627
Clustering 806: AMI= 0.7224, NMI= 0.7233, ARI= 0.4993, ACC= 0.6025
0.014984323465939167
Training epoch 807, recon_loss:0.612263, zinb_loss:0.959204, cluster_loss:0.328822
Clustering 807: AMI= 0.7228, NMI= 0.7237, ARI= 0.5016, ACC= 0.6020
0.014740013844211898
Training epoch 808, recon_loss:0.612030, zinb_loss:0.959992, cluster_loss:0.329450
Clustering 808: AMI= 0.7221, NMI= 0.7230, ARI= 0.4993, ACC= 0.6023
0.01449570422248463
Training epoch 809, recon_loss:0.610946, zinb_loss:0.959328, cluster_loss:0.329140
Clustering 809: AMI= 0.7225, NMI= 0.7235, ARI= 0.5007, ACC= 0.6017
0.014414267681908873
Training epoch 810, recon_loss:0.612461, zinb_loss:0.960052, cluster_loss:0.329412
Clustering 810: AMI= 0.7224, NMI= 0.7233, ARI= 0.5000, ACC= 0.6024
0.014862168655075532
Training epoch 811, recon_loss:0.610942, zinb_loss:0.959408, cluster_loss:0.329093
Clustering 811: AMI= 0.7224, NMI= 0.7234, ARI= 0.5007, ACC= 0.6018
0.015310069628242192
Training epoch 812, recon_loss:0.612471, zinb_loss:0.960055, cluster_loss:0.329404
Clustering 812: AMI= 0.7224, NMI= 0.7233, ARI= 0.5002, ACC= 0.6024
0.015432224439105826
Training epoch 813, recon_loss:0.610961, zinb_loss:0.959451, cluster_loss:0.329065
Clustering 813: AMI= 0.7225, NMI= 0.7235, ARI= 0.5005, ACC= 0.6016
0.015513660979681583
Training epoch 814, recon_loss:0.611969, zinb_loss:0.959949, cluster_loss:0.329394
Clustering 814: AMI= 0.7223, NMI= 0.7232, ARI= 0.5002, ACC= 0.6021
0.015391506168817948
Training epoch 815, recon_loss:0.610706, zinb_loss:0.959463, cluster_loss:0.329147
Clustering 815: AMI= 0.7225, NMI= 0.7234, ARI= 0.5001, ACC= 0.6015
0.015554379249969462
Training epoch 816, recon_loss:0.612151, zinb_loss:0.959825, cluster_loss:0.329420
Clustering 816: AMI= 0.7222, NMI= 0.7231, ARI= 0.5007, ACC= 0.6022
0.015513660979681583
Training epoch 817, recon_loss:0.610955, zinb_loss:0.959436, cluster_loss:0.329135
Clustering 817: AMI= 0.7226, NMI= 0.7235, ARI= 0.5000, ACC= 0.6013
0.015228633087666437
Training epoch 818, recon_loss:0.611550, zinb_loss:0.959633, cluster_loss:0.329433
Clustering 818: AMI= 0.7225, NMI= 0.7234, ARI= 0.5012, ACC= 0.6027
0.015065760006514923
Training epoch 819, recon_loss:0.610729, zinb_loss:0.959402, cluster_loss:0.329274
Clustering 819: AMI= 0.7225, NMI= 0.7234, ARI= 0.4995, ACC= 0.6009
0.014984323465939167
Training epoch 820, recon_loss:0.612150, zinb_loss:0.959478, cluster_loss:0.329481
Clustering 820: AMI= 0.7224, NMI= 0.7233, ARI= 0.5016, ACC= 0.6029
0.015187914817378557
Training epoch 821, recon_loss:0.611224, zinb_loss:0.959331, cluster_loss:0.329240
Clustering 821: AMI= 0.7225, NMI= 0.7235, ARI= 0.4997, ACC= 0.6010
0.014821450384787655
Training epoch 822, recon_loss:0.611139, zinb_loss:0.959251, cluster_loss:0.329505
Clustering 822: AMI= 0.7224, NMI= 0.7233, ARI= 0.5016, ACC= 0.6027
0.014658577303636141
Training epoch 823, recon_loss:0.610722, zinb_loss:0.959272, cluster_loss:0.329491
Clustering 823: AMI= 0.7224, NMI= 0.7233, ARI= 0.4991, ACC= 0.6007
0.015025041736227046
Training epoch 824, recon_loss:0.611836, zinb_loss:0.959110, cluster_loss:0.329572
Clustering 824: AMI= 0.7225, NMI= 0.7234, ARI= 0.5018, ACC= 0.6027
0.01514719654709068
Training epoch 825, recon_loss:0.611122, zinb_loss:0.959192, cluster_loss:0.329474
Clustering 825: AMI= 0.7224, NMI= 0.7233, ARI= 0.4994, ACC= 0.6010
0.01490288692536341
Training epoch 826, recon_loss:0.610882, zinb_loss:0.958929, cluster_loss:0.329611
Clustering 826: AMI= 0.7223, NMI= 0.7232, ARI= 0.5015, ACC= 0.6024
0.01469929557392402
Training epoch 827, recon_loss:0.610555, zinb_loss:0.959139, cluster_loss:0.329730
Clustering 827: AMI= 0.7224, NMI= 0.7233, ARI= 0.4990, ACC= 0.6007
0.014943605195651289
Training epoch 828, recon_loss:0.611734, zinb_loss:0.958828, cluster_loss:0.329678
Clustering 828: AMI= 0.7223, NMI= 0.7232, ARI= 0.5016, ACC= 0.6022
0.014943605195651289
Training epoch 829, recon_loss:0.611179, zinb_loss:0.959097, cluster_loss:0.329712
Clustering 829: AMI= 0.7223, NMI= 0.7232, ARI= 0.4994, ACC= 0.6008
0.013966366708742213
Training epoch 830, recon_loss:0.610175, zinb_loss:0.958671, cluster_loss:0.329651
Clustering 830: AMI= 0.7224, NMI= 0.7233, ARI= 0.5017, ACC= 0.6023
0.01449570422248463
Training epoch 831, recon_loss:0.610459, zinb_loss:0.959110, cluster_loss:0.330093
Clustering 831: AMI= 0.7222, NMI= 0.7231, ARI= 0.4987, ACC= 0.6004
0.01559509752025734
Training epoch 832, recon_loss:0.610927, zinb_loss:0.958613, cluster_loss:0.329673
Clustering 832: AMI= 0.7222, NMI= 0.7232, ARI= 0.5019, ACC= 0.6022
0.016083716763711876
Training epoch 833, recon_loss:0.610754, zinb_loss:0.959115, cluster_loss:0.330086
Clustering 833: AMI= 0.7222, NMI= 0.7231, ARI= 0.4989, ACC= 0.6006
0.01579868887169673
Training epoch 834, recon_loss:0.610837, zinb_loss:0.958569, cluster_loss:0.329643
Clustering 834: AMI= 0.7223, NMI= 0.7232, ARI= 0.5020, ACC= 0.6023
0.016042998493424
Training epoch 835, recon_loss:0.610827, zinb_loss:0.959158, cluster_loss:0.330195
Clustering 835: AMI= 0.7221, NMI= 0.7231, ARI= 0.4989, ACC= 0.6007
0.016816645628893685
Training epoch 836, recon_loss:0.611254, zinb_loss:0.958544, cluster_loss:0.329562
Clustering 836: AMI= 0.7224, NMI= 0.7233, ARI= 0.5022, ACC= 0.6025
0.017590292764363368
Training epoch 837, recon_loss:0.611343, zinb_loss:0.959253, cluster_loss:0.330244
Clustering 837: AMI= 0.7220, NMI= 0.7229, ARI= 0.4985, ACC= 0.6005
0.018526812980984568
Training epoch 838, recon_loss:0.610474, zinb_loss:0.958522, cluster_loss:0.329393
Clustering 838: AMI= 0.7225, NMI= 0.7234, ARI= 0.5025, ACC= 0.6026
0.019219023575878496
Training epoch 839, recon_loss:0.611027, zinb_loss:0.959392, cluster_loss:0.330501
Clustering 839: AMI= 0.7219, NMI= 0.7228, ARI= 0.4981, ACC= 0.6004
0.019911234170772424
Training epoch 840, recon_loss:0.611801, zinb_loss:0.958586, cluster_loss:0.329221
Clustering 840: AMI= 0.7228, NMI= 0.7237, ARI= 0.5032, ACC= 0.6029
0.021662119793151188
Training epoch 841, recon_loss:0.612148, zinb_loss:0.959538, cluster_loss:0.330404
Clustering 841: AMI= 0.7219, NMI= 0.7228, ARI= 0.4977, ACC= 0.6001
0.022395048658332993
Training epoch 842, recon_loss:0.610595, zinb_loss:0.958672, cluster_loss:0.328962
Clustering 842: AMI= 0.7227, NMI= 0.7236, ARI= 0.5036, ACC= 0.6031
0.024186652550999634
Training epoch 843, recon_loss:0.611585, zinb_loss:0.959698, cluster_loss:0.330691
Clustering 843: AMI= 0.7215, NMI= 0.7225, ARI= 0.4967, ACC= 0.5994
0.02605969298424203
Training epoch 844, recon_loss:0.611796, zinb_loss:0.958823, cluster_loss:0.328788
Clustering 844: AMI= 0.7229, NMI= 0.7238, ARI= 0.5040, ACC= 0.6038
0.0276069872551814
Training epoch 845, recon_loss:0.612320, zinb_loss:0.959830, cluster_loss:0.330560
Clustering 845: AMI= 0.7216, NMI= 0.7225, ARI= 0.4967, ACC= 0.5990
0.028747098823241987
Training epoch 846, recon_loss:0.611025, zinb_loss:0.959043, cluster_loss:0.328636
Clustering 846: AMI= 0.7229, NMI= 0.7238, ARI= 0.5043, ACC= 0.6048
0.03074229406734802
Training epoch 847, recon_loss:0.611805, zinb_loss:0.959948, cluster_loss:0.330701
Clustering 847: AMI= 0.7216, NMI= 0.7225, ARI= 0.4963, ACC= 0.5985
0.03290036239260556
Training epoch 848, recon_loss:0.611897, zinb_loss:0.959276, cluster_loss:0.328547
Clustering 848: AMI= 0.7230, NMI= 0.7239, ARI= 0.5044, ACC= 0.6056
0.03477340282584796
Training epoch 849, recon_loss:0.612395, zinb_loss:0.960066, cluster_loss:0.330529
Clustering 849: AMI= 0.7214, NMI= 0.7223, ARI= 0.4963, ACC= 0.5983
0.0352620220693025
Training epoch 850, recon_loss:0.610840, zinb_loss:0.959477, cluster_loss:0.328502
Clustering 850: AMI= 0.7230, NMI= 0.7239, ARI= 0.5047, ACC= 0.6063
0.03685003461052974
Training epoch 851, recon_loss:0.611636, zinb_loss:0.960118, cluster_loss:0.330665
Clustering 851: AMI= 0.7214, NMI= 0.7224, ARI= 0.4956, ACC= 0.5976
0.038641638503196386
Training epoch 852, recon_loss:0.611372, zinb_loss:0.959598, cluster_loss:0.328585
Clustering 852: AMI= 0.7232, NMI= 0.7241, ARI= 0.5049, ACC= 0.6069
0.039537440449529705
Training epoch 853, recon_loss:0.611864, zinb_loss:0.960161, cluster_loss:0.330538
Clustering 853: AMI= 0.7214, NMI= 0.7223, ARI= 0.4961, ACC= 0.5977
0.038967384665499406
Training epoch 854, recon_loss:0.610982, zinb_loss:0.959681, cluster_loss:0.328773
Clustering 854: AMI= 0.7235, NMI= 0.7244, ARI= 0.5052, ACC= 0.6071
0.03921169428722668
Training epoch 855, recon_loss:0.611362, zinb_loss:0.960096, cluster_loss:0.330581
Clustering 855: AMI= 0.7213, NMI= 0.7222, ARI= 0.4960, ACC= 0.5975
0.0391709760169388
Training epoch 856, recon_loss:0.611143, zinb_loss:0.959677, cluster_loss:0.329032
Clustering 856: AMI= 0.7234, NMI= 0.7243, ARI= 0.5052, ACC= 0.6069
0.03860092023290851
Training epoch 857, recon_loss:0.611339, zinb_loss:0.960030, cluster_loss:0.330563
Clustering 857: AMI= 0.7212, NMI= 0.7221, ARI= 0.4964, ACC= 0.5977
0.03713506250254489
Training epoch 858, recon_loss:0.610535, zinb_loss:0.959647, cluster_loss:0.329306
Clustering 858: AMI= 0.7232, NMI= 0.7241, ARI= 0.5047, ACC= 0.6065
0.0359949509344843
Training epoch 859, recon_loss:0.610763, zinb_loss:0.959941, cluster_loss:0.330661
Clustering 859: AMI= 0.7211, NMI= 0.7220, ARI= 0.4965, ACC= 0.5977
0.03558776823160552
Training epoch 860, recon_loss:0.610844, zinb_loss:0.959634, cluster_loss:0.329587
Clustering 860: AMI= 0.7232, NMI= 0.7242, ARI= 0.5046, ACC= 0.6066
0.03497699417728735
Training epoch 861, recon_loss:0.610909, zinb_loss:0.959889, cluster_loss:0.330620
Clustering 861: AMI= 0.7212, NMI= 0.7221, ARI= 0.4969, ACC= 0.5977
0.03367400952807525
Training epoch 862, recon_loss:0.610157, zinb_loss:0.959632, cluster_loss:0.329825
Clustering 862: AMI= 0.7229, NMI= 0.7238, ARI= 0.5041, ACC= 0.6065
0.032859644122317684
Training epoch 863, recon_loss:0.610353, zinb_loss:0.959833, cluster_loss:0.330714
Clustering 863: AMI= 0.7213, NMI= 0.7222, ARI= 0.4969, ACC= 0.5975
0.03233030660857527
Training epoch 864, recon_loss:0.610901, zinb_loss:0.959675, cluster_loss:0.330059
Clustering 864: AMI= 0.7229, NMI= 0.7238, ARI= 0.5039, ACC= 0.6065
0.03135306812166619
Training epoch 865, recon_loss:0.610920, zinb_loss:0.959828, cluster_loss:0.330578
Clustering 865: AMI= 0.7215, NMI= 0.7224, ARI= 0.4975, ACC= 0.5978
0.03029439309418136
Training epoch 866, recon_loss:0.609779, zinb_loss:0.959725, cluster_loss:0.330202
Clustering 866: AMI= 0.7228, NMI= 0.7237, ARI= 0.5036, ACC= 0.6064
0.02964290076957531
Training epoch 867, recon_loss:0.610319, zinb_loss:0.959830, cluster_loss:0.330694
Clustering 867: AMI= 0.7213, NMI= 0.7222, ARI= 0.4971, ACC= 0.5973
0.03009080174274197
Training epoch 868, recon_loss:0.610878, zinb_loss:0.959855, cluster_loss:0.330340
Clustering 868: AMI= 0.7228, NMI= 0.7238, ARI= 0.5036, ACC= 0.6066
0.029765055580438942
Training epoch 869, recon_loss:0.611084, zinb_loss:0.959876, cluster_loss:0.330411
Clustering 869: AMI= 0.7212, NMI= 0.7221, ARI= 0.4975, ACC= 0.5972
0.02915428152612077
Training epoch 870, recon_loss:0.610315, zinb_loss:0.960023, cluster_loss:0.330368
Clustering 870: AMI= 0.7231, NMI= 0.7240, ARI= 0.5040, ACC= 0.6072
0.02943930941813592
Training epoch 871, recon_loss:0.610797, zinb_loss:0.959883, cluster_loss:0.330301
Clustering 871: AMI= 0.7216, NMI= 0.7225, ARI= 0.4976, ACC= 0.5970
0.02964290076957531
Training epoch 872, recon_loss:0.611596, zinb_loss:0.960235, cluster_loss:0.330316
Clustering 872: AMI= 0.7232, NMI= 0.7241, ARI= 0.5044, ACC= 0.6076
0.029683619039863187
Training epoch 873, recon_loss:0.611808, zinb_loss:0.959901, cluster_loss:0.329840
Clustering 873: AMI= 0.7217, NMI= 0.7227, ARI= 0.4979, ACC= 0.5969
0.02960218249928743
Training epoch 874, recon_loss:0.611277, zinb_loss:0.960404, cluster_loss:0.330121
Clustering 874: AMI= 0.7234, NMI= 0.7243, ARI= 0.5045, ACC= 0.6078
0.029683619039863187
Training epoch 875, recon_loss:0.611630, zinb_loss:0.959863, cluster_loss:0.329638
Clustering 875: AMI= 0.7215, NMI= 0.7224, ARI= 0.4975, ACC= 0.5966
0.029480027688423796
Training epoch 876, recon_loss:0.612189, zinb_loss:0.960557, cluster_loss:0.329956
Clustering 876: AMI= 0.7232, NMI= 0.7242, ARI= 0.5044, ACC= 0.6077
0.02919499979640865
Training epoch 877, recon_loss:0.611849, zinb_loss:0.959843, cluster_loss:0.329393
Clustering 877: AMI= 0.7212, NMI= 0.7221, ARI= 0.4977, ACC= 0.5969
0.028421352660938964
Training epoch 878, recon_loss:0.611842, zinb_loss:0.960616, cluster_loss:0.329880
Clustering 878: AMI= 0.7232, NMI= 0.7241, ARI= 0.5040, ACC= 0.6073
0.027566268984893522
Training epoch 879, recon_loss:0.611220, zinb_loss:0.959778, cluster_loss:0.329491
Clustering 879: AMI= 0.7214, NMI= 0.7223, ARI= 0.4977, ACC= 0.5970
0.02675190357913596
Training epoch 880, recon_loss:0.611577, zinb_loss:0.960621, cluster_loss:0.329910
Clustering 880: AMI= 0.7231, NMI= 0.7241, ARI= 0.5039, ACC= 0.6071
0.025530355470499613
Training epoch 881, recon_loss:0.610742, zinb_loss:0.959741, cluster_loss:0.329668
Clustering 881: AMI= 0.7214, NMI= 0.7223, ARI= 0.4981, ACC= 0.5975
0.023983061199560243
Training epoch 882, recon_loss:0.611111, zinb_loss:0.960610, cluster_loss:0.329984
Clustering 882: AMI= 0.7231, NMI= 0.7240, ARI= 0.5038, ACC= 0.6070
0.023005822712651166
Training epoch 883, recon_loss:0.610211, zinb_loss:0.959709, cluster_loss:0.329890
Clustering 883: AMI= 0.7216, NMI= 0.7225, ARI= 0.4982, ACC= 0.5977
0.022313612117757238
Training epoch 884, recon_loss:0.610820, zinb_loss:0.960601, cluster_loss:0.330062
Clustering 884: AMI= 0.7230, NMI= 0.7239, ARI= 0.5033, ACC= 0.6065
0.02198786595545421
Training epoch 885, recon_loss:0.609858, zinb_loss:0.959684, cluster_loss:0.330064
Clustering 885: AMI= 0.7219, NMI= 0.7228, ARI= 0.4985, ACC= 0.5980
0.02162140152286331
Training epoch 886, recon_loss:0.610593, zinb_loss:0.960587, cluster_loss:0.330141
Clustering 886: AMI= 0.7230, NMI= 0.7239, ARI= 0.5031, ACC= 0.6066
0.020807036117105746
Training epoch 887, recon_loss:0.609589, zinb_loss:0.959648, cluster_loss:0.330217
Clustering 887: AMI= 0.7218, NMI= 0.7227, ARI= 0.4985, ACC= 0.5980
0.020562726495378478
Training epoch 888, recon_loss:0.610436, zinb_loss:0.960567, cluster_loss:0.330216
Clustering 888: AMI= 0.7232, NMI= 0.7241, ARI= 0.5031, ACC= 0.6063
0.020359135143939087
Training epoch 889, recon_loss:0.609412, zinb_loss:0.959613, cluster_loss:0.330343
Clustering 889: AMI= 0.7218, NMI= 0.7228, ARI= 0.4987, ACC= 0.5982
0.02003338898163606
Training epoch 890, recon_loss:0.610306, zinb_loss:0.960546, cluster_loss:0.330285
Clustering 890: AMI= 0.7230, NMI= 0.7239, ARI= 0.5027, ACC= 0.6058
0.019748361089620914
Training epoch 891, recon_loss:0.609290, zinb_loss:0.959579, cluster_loss:0.330445
Clustering 891: AMI= 0.7220, NMI= 0.7229, ARI= 0.4988, ACC= 0.5984
0.019666924549045155
Training epoch 892, recon_loss:0.610268, zinb_loss:0.960522, cluster_loss:0.330344
Clustering 892: AMI= 0.7228, NMI= 0.7237, ARI= 0.5023, ACC= 0.6056
0.019219023575878496
Training epoch 893, recon_loss:0.609280, zinb_loss:0.959548, cluster_loss:0.330509
Clustering 893: AMI= 0.7219, NMI= 0.7229, ARI= 0.4988, ACC= 0.5983
0.01893399568386335
Training epoch 894, recon_loss:0.610189, zinb_loss:0.960493, cluster_loss:0.330390
Clustering 894: AMI= 0.7228, NMI= 0.7237, ARI= 0.5021, ACC= 0.6054
0.018811840872999714
Training epoch 895, recon_loss:0.609238, zinb_loss:0.959522, cluster_loss:0.330565
Clustering 895: AMI= 0.7219, NMI= 0.7228, ARI= 0.4988, ACC= 0.5983
0.01868968606213608
Training epoch 896, recon_loss:0.610383, zinb_loss:0.960473, cluster_loss:0.330424
Clustering 896: AMI= 0.7226, NMI= 0.7236, ARI= 0.5018, ACC= 0.6049
0.018445376440408813
Training epoch 897, recon_loss:0.609445, zinb_loss:0.959496, cluster_loss:0.330555
Clustering 897: AMI= 0.7220, NMI= 0.7229, ARI= 0.4991, ACC= 0.5986
0.018160348548393664
Training epoch 898, recon_loss:0.610157, zinb_loss:0.960417, cluster_loss:0.330431
Clustering 898: AMI= 0.7225, NMI= 0.7234, ARI= 0.5014, ACC= 0.6045
0.017956757196954273
Training epoch 899, recon_loss:0.609305, zinb_loss:0.959470, cluster_loss:0.330595
Clustering 899: AMI= 0.7221, NMI= 0.7230, ARI= 0.4992, ACC= 0.5987
0.017916038926666395
Training epoch 900, recon_loss:0.610703, zinb_loss:0.960379, cluster_loss:0.330437
Clustering 900: AMI= 0.7224, NMI= 0.7233, ARI= 0.5014, ACC= 0.6046
0.017875320656378518
Training epoch 901, recon_loss:0.609824, zinb_loss:0.959429, cluster_loss:0.330488
Clustering 901: AMI= 0.7222, NMI= 0.7231, ARI= 0.4994, ACC= 0.5988
0.017427419683211858
Training epoch 902, recon_loss:0.610202, zinb_loss:0.960259, cluster_loss:0.330412
Clustering 902: AMI= 0.7223, NMI= 0.7232, ARI= 0.5012, ACC= 0.6044
0.01718311006148459
Training epoch 903, recon_loss:0.609476, zinb_loss:0.959375, cluster_loss:0.330526
Clustering 903: AMI= 0.7221, NMI= 0.7231, ARI= 0.4993, ACC= 0.5986
0.01783460238609064
Training epoch 904, recon_loss:0.611165, zinb_loss:0.960179, cluster_loss:0.330428
Clustering 904: AMI= 0.7222, NMI= 0.7232, ARI= 0.5011, ACC= 0.6039
0.01718311006148459
Training epoch 905, recon_loss:0.610335, zinb_loss:0.959287, cluster_loss:0.330320
Clustering 905: AMI= 0.7224, NMI= 0.7233, ARI= 0.4998, ACC= 0.5986
0.01718311006148459
Training epoch 906, recon_loss:0.609930, zinb_loss:0.959952, cluster_loss:0.330386
Clustering 906: AMI= 0.7219, NMI= 0.7228, ARI= 0.5005, ACC= 0.6035
0.016653772547742172
Training epoch 907, recon_loss:0.609692, zinb_loss:0.959219, cluster_loss:0.330470
Clustering 907: AMI= 0.7224, NMI= 0.7233, ARI= 0.4995, ACC= 0.5984
0.01714239179119671
Training epoch 908, recon_loss:0.610851, zinb_loss:0.959834, cluster_loss:0.330449
Clustering 908: AMI= 0.7218, NMI= 0.7227, ARI= 0.5004, ACC= 0.6031
0.017020236980333076
Training epoch 909, recon_loss:0.610271, zinb_loss:0.959122, cluster_loss:0.330266
Clustering 909: AMI= 0.7223, NMI= 0.7233, ARI= 0.4999, ACC= 0.5987
0.01649089946659066
Training epoch 910, recon_loss:0.610613, zinb_loss:0.959645, cluster_loss:0.330494
Clustering 910: AMI= 0.7217, NMI= 0.7226, ARI= 0.5000, ACC= 0.6028
0.016368744655727026
Training epoch 911, recon_loss:0.610182, zinb_loss:0.959001, cluster_loss:0.330190
Clustering 911: AMI= 0.7224, NMI= 0.7233, ARI= 0.4999, ACC= 0.5987
0.0169795187100452
Training epoch 912, recon_loss:0.611318, zinb_loss:0.959463, cluster_loss:0.330557
Clustering 912: AMI= 0.7213, NMI= 0.7222, ARI= 0.4995, ACC= 0.6028
0.017427419683211858
Training epoch 913, recon_loss:0.610955, zinb_loss:0.958914, cluster_loss:0.329976
Clustering 913: AMI= 0.7225, NMI= 0.7234, ARI= 0.5001, ACC= 0.5984
0.01779388411580276
Training epoch 914, recon_loss:0.610329, zinb_loss:0.959216, cluster_loss:0.330522
Clustering 914: AMI= 0.7212, NMI= 0.7221, ARI= 0.4995, ACC= 0.6029
0.01783460238609064
Training epoch 915, recon_loss:0.610574, zinb_loss:0.958884, cluster_loss:0.330040
Clustering 915: AMI= 0.7226, NMI= 0.7235, ARI= 0.5000, ACC= 0.5981
0.018323221629545177
Training epoch 916, recon_loss:0.611846, zinb_loss:0.959088, cluster_loss:0.330589
Clustering 916: AMI= 0.7213, NMI= 0.7222, ARI= 0.4998, ACC= 0.6031
0.01820106681868154
Training epoch 917, recon_loss:0.611720, zinb_loss:0.958902, cluster_loss:0.329741
Clustering 917: AMI= 0.7227, NMI= 0.7236, ARI= 0.5002, ACC= 0.5983
0.019259741846166373
Training epoch 918, recon_loss:0.610403, zinb_loss:0.958894, cluster_loss:0.330509
Clustering 918: AMI= 0.7210, NMI= 0.7219, ARI= 0.4995, ACC= 0.6030
0.019870515900484546
Training epoch 919, recon_loss:0.611045, zinb_loss:0.958896, cluster_loss:0.329919
Clustering 919: AMI= 0.7224, NMI= 0.7233, ARI= 0.4997, ACC= 0.5978
0.02027769860336333
Training epoch 920, recon_loss:0.611644, zinb_loss:0.958785, cluster_loss:0.330637
Clustering 920: AMI= 0.7211, NMI= 0.7220, ARI= 0.4999, ACC= 0.6032
0.020522008225090597
Training epoch 921, recon_loss:0.611639, zinb_loss:0.958894, cluster_loss:0.329809
Clustering 921: AMI= 0.7225, NMI= 0.7234, ARI= 0.5000, ACC= 0.5980
0.020847754387393624
Training epoch 922, recon_loss:0.610456, zinb_loss:0.958657, cluster_loss:0.330685
Clustering 922: AMI= 0.7211, NMI= 0.7220, ARI= 0.4995, ACC= 0.6031
0.02133637363084816
Training epoch 923, recon_loss:0.611003, zinb_loss:0.958873, cluster_loss:0.330051
Clustering 923: AMI= 0.7226, NMI= 0.7235, ARI= 0.4996, ACC= 0.5977
0.02092919092796938
Training epoch 924, recon_loss:0.611707, zinb_loss:0.958571, cluster_loss:0.330849
Clustering 924: AMI= 0.7210, NMI= 0.7219, ARI= 0.4994, ACC= 0.6029
0.020603444765666355
Training epoch 925, recon_loss:0.611763, zinb_loss:0.958878, cluster_loss:0.329967
Clustering 925: AMI= 0.7227, NMI= 0.7236, ARI= 0.5004, ACC= 0.5986
0.020359135143939087
Training epoch 926, recon_loss:0.609998, zinb_loss:0.958475, cluster_loss:0.330892
Clustering 926: AMI= 0.7211, NMI= 0.7220, ARI= 0.4992, ACC= 0.6023
0.019463333197605764
Training epoch 927, recon_loss:0.610707, zinb_loss:0.958883, cluster_loss:0.330260
Clustering 927: AMI= 0.7226, NMI= 0.7235, ARI= 0.4998, ACC= 0.5985
0.01913758703530274
Training epoch 928, recon_loss:0.611397, zinb_loss:0.958440, cluster_loss:0.331071
Clustering 928: AMI= 0.7212, NMI= 0.7221, ARI= 0.4993, ACC= 0.6021
0.019015432224439105
Training epoch 929, recon_loss:0.611650, zinb_loss:0.958963, cluster_loss:0.330141
Clustering 929: AMI= 0.7227, NMI= 0.7236, ARI= 0.5005, ACC= 0.5993
0.01868968606213608
Training epoch 930, recon_loss:0.609803, zinb_loss:0.958439, cluster_loss:0.331092
Clustering 930: AMI= 0.7211, NMI= 0.7220, ARI= 0.4986, ACC= 0.6012
0.018119630278105786
Training epoch 931, recon_loss:0.610625, zinb_loss:0.959051, cluster_loss:0.330355
Clustering 931: AMI= 0.7225, NMI= 0.7235, ARI= 0.5003, ACC= 0.5996
0.01779388411580276
Training epoch 932, recon_loss:0.611353, zinb_loss:0.958491, cluster_loss:0.331228
Clustering 932: AMI= 0.7214, NMI= 0.7223, ARI= 0.4988, ACC= 0.6012
0.018119630278105786
Training epoch 933, recon_loss:0.611872, zinb_loss:0.959256, cluster_loss:0.330144
Clustering 933: AMI= 0.7227, NMI= 0.7236, ARI= 0.5010, ACC= 0.6006
0.01848609471069669
Training epoch 934, recon_loss:0.609937, zinb_loss:0.958614, cluster_loss:0.331200
Clustering 934: AMI= 0.7212, NMI= 0.7222, ARI= 0.4983, ACC= 0.6007
0.018526812980984568
Training epoch 935, recon_loss:0.610813, zinb_loss:0.959456, cluster_loss:0.330255
Clustering 935: AMI= 0.7225, NMI= 0.7234, ARI= 0.5007, ACC= 0.6008
0.018526812980984568
Training epoch 936, recon_loss:0.611578, zinb_loss:0.958788, cluster_loss:0.331299
Clustering 936: AMI= 0.7214, NMI= 0.7223, ARI= 0.4981, ACC= 0.6001
0.018567531251272446
Training epoch 937, recon_loss:0.612222, zinb_loss:0.959794, cluster_loss:0.329982
Clustering 937: AMI= 0.7226, NMI= 0.7235, ARI= 0.5017, ACC= 0.6021
0.018771122602711836
Training epoch 938, recon_loss:0.610073, zinb_loss:0.959029, cluster_loss:0.331226
Clustering 938: AMI= 0.7214, NMI= 0.7223, ARI= 0.4978, ACC= 0.5997
0.019341178386742132
Training epoch 939, recon_loss:0.610889, zinb_loss:0.960041, cluster_loss:0.330087
Clustering 939: AMI= 0.7227, NMI= 0.7236, ARI= 0.5017, ACC= 0.6026
0.019259741846166373
Training epoch 940, recon_loss:0.611733, zinb_loss:0.959270, cluster_loss:0.331318
Clustering 940: AMI= 0.7213, NMI= 0.7222, ARI= 0.4977, ACC= 0.5991
0.01978907935990879
Training epoch 941, recon_loss:0.612162, zinb_loss:0.960381, cluster_loss:0.329842
Clustering 941: AMI= 0.7227, NMI= 0.7236, ARI= 0.5023, ACC= 0.6034
0.020725599576529988
Training epoch 942, recon_loss:0.609817, zinb_loss:0.959507, cluster_loss:0.331247
Clustering 942: AMI= 0.7210, NMI= 0.7220, ARI= 0.4971, ACC= 0.5986
0.021702838063439065
Training epoch 943, recon_loss:0.610471, zinb_loss:0.960504, cluster_loss:0.330064
Clustering 943: AMI= 0.7227, NMI= 0.7236, ARI= 0.5022, ACC= 0.6037
0.02153996498228755
Training epoch 944, recon_loss:0.610975, zinb_loss:0.959639, cluster_loss:0.331381
Clustering 944: AMI= 0.7211, NMI= 0.7221, ARI= 0.4971, ACC= 0.5981
0.0218249928743027
Training epoch 945, recon_loss:0.611204, zinb_loss:0.960678, cluster_loss:0.329957
Clustering 945: AMI= 0.7231, NMI= 0.7240, ARI= 0.5032, ACC= 0.6048
0.023290850604666315
Training epoch 946, recon_loss:0.609296, zinb_loss:0.959770, cluster_loss:0.331375
Clustering 946: AMI= 0.7209, NMI= 0.7218, ARI= 0.4969, ACC= 0.5980
0.024105216010423876
Training epoch 947, recon_loss:0.609848, zinb_loss:0.960677, cluster_loss:0.330206
Clustering 947: AMI= 0.7230, NMI= 0.7239, ARI= 0.5029, ACC= 0.6048
0.02402377946984812
Training epoch 948, recon_loss:0.610390, zinb_loss:0.959846, cluster_loss:0.331515
Clustering 948: AMI= 0.7210, NMI= 0.7220, ARI= 0.4971, ACC= 0.5981
0.024349525632151148
Training epoch 949, recon_loss:0.610456, zinb_loss:0.960762, cluster_loss:0.330124
Clustering 949: AMI= 0.7230, NMI= 0.7240, ARI= 0.5033, ACC= 0.6052
0.025245327578484467
Training epoch 950, recon_loss:0.608940, zinb_loss:0.959930, cluster_loss:0.331518
Clustering 950: AMI= 0.7210, NMI= 0.7219, ARI= 0.4968, ACC= 0.5979
0.02605969298424203
Training epoch 951, recon_loss:0.609384, zinb_loss:0.960702, cluster_loss:0.330353
Clustering 951: AMI= 0.7228, NMI= 0.7238, ARI= 0.5028, ACC= 0.6048
0.025530355470499613
Training epoch 952, recon_loss:0.609941, zinb_loss:0.959997, cluster_loss:0.331632
Clustering 952: AMI= 0.7212, NMI= 0.7221, ARI= 0.4970, ACC= 0.5979
0.025693228551651126
Training epoch 953, recon_loss:0.609894, zinb_loss:0.960742, cluster_loss:0.330247
Clustering 953: AMI= 0.7226, NMI= 0.7236, ARI= 0.5028, ACC= 0.6048
0.026833340119711713
Training epoch 954, recon_loss:0.609117, zinb_loss:0.960131, cluster_loss:0.331649
Clustering 954: AMI= 0.7211, NMI= 0.7220, ARI= 0.4966, ACC= 0.5978
0.0276069872551814
Training epoch 955, recon_loss:0.609257, zinb_loss:0.960701, cluster_loss:0.330359
Clustering 955: AMI= 0.7228, NMI= 0.7237, ARI= 0.5029, ACC= 0.6049
0.027647705525469277
Training epoch 956, recon_loss:0.610283, zinb_loss:0.960289, cluster_loss:0.331701
Clustering 956: AMI= 0.7214, NMI= 0.7223, ARI= 0.4968, ACC= 0.5981
0.028217761309499573
Training epoch 957, recon_loss:0.610035, zinb_loss:0.960739, cluster_loss:0.330126
Clustering 957: AMI= 0.7229, NMI= 0.7238, ARI= 0.5034, ACC= 0.6053
0.02939859114784804
Training epoch 958, recon_loss:0.609369, zinb_loss:0.960462, cluster_loss:0.331623
Clustering 958: AMI= 0.7215, NMI= 0.7224, ARI= 0.4966, ACC= 0.5983
0.0302129565536056
Training epoch 959, recon_loss:0.609568, zinb_loss:0.960653, cluster_loss:0.330129
Clustering 959: AMI= 0.7230, NMI= 0.7239, ARI= 0.5031, ACC= 0.6052
0.030620139256484383
Training epoch 960, recon_loss:0.610644, zinb_loss:0.960693, cluster_loss:0.331534
Clustering 960: AMI= 0.7215, NMI= 0.7225, ARI= 0.4967, ACC= 0.5984
0.031515941202817706
Training epoch 961, recon_loss:0.610185, zinb_loss:0.960650, cluster_loss:0.329716
Clustering 961: AMI= 0.7230, NMI= 0.7239, ARI= 0.5032, ACC= 0.6049
0.03294108066289344
Training epoch 962, recon_loss:0.611436, zinb_loss:0.960941, cluster_loss:0.331334
Clustering 962: AMI= 0.7215, NMI= 0.7224, ARI= 0.4966, ACC= 0.5988
0.033877600879514636
Training epoch 963, recon_loss:0.610592, zinb_loss:0.960481, cluster_loss:0.329387
Clustering 963: AMI= 0.7226, NMI= 0.7236, ARI= 0.5029, ACC= 0.6042
0.03456981147440857
Training epoch 964, recon_loss:0.611869, zinb_loss:0.960985, cluster_loss:0.331134
Clustering 964: AMI= 0.7214, NMI= 0.7223, ARI= 0.4965, ACC= 0.5994
0.03473268455556008
Training epoch 965, recon_loss:0.610613, zinb_loss:0.960195, cluster_loss:0.329287
Clustering 965: AMI= 0.7227, NMI= 0.7236, ARI= 0.5029, ACC= 0.6038
0.034447656663544934
Training epoch 966, recon_loss:0.611821, zinb_loss:0.960821, cluster_loss:0.331055
Clustering 966: AMI= 0.7213, NMI= 0.7222, ARI= 0.4968, ACC= 0.5997
0.03408119223095403
Training epoch 967, recon_loss:0.610288, zinb_loss:0.959847, cluster_loss:0.329402
Clustering 967: AMI= 0.7227, NMI= 0.7236, ARI= 0.5029, ACC= 0.6034
0.033266826825196466
Training epoch 968, recon_loss:0.611671, zinb_loss:0.960551, cluster_loss:0.331113
Clustering 968: AMI= 0.7213, NMI= 0.7222, ARI= 0.4968, ACC= 0.5998
0.032126715257135875
Training epoch 969, recon_loss:0.609994, zinb_loss:0.959516, cluster_loss:0.329609
Clustering 969: AMI= 0.7226, NMI= 0.7235, ARI= 0.5027, ACC= 0.6033
0.03135306812166619
Training epoch 970, recon_loss:0.610943, zinb_loss:0.960237, cluster_loss:0.331224
Clustering 970: AMI= 0.7215, NMI= 0.7224, ARI= 0.4972, ACC= 0.6003
0.030335111364469237
Training epoch 971, recon_loss:0.609405, zinb_loss:0.959237, cluster_loss:0.329913
Clustering 971: AMI= 0.7225, NMI= 0.7234, ARI= 0.5024, ACC= 0.6031
0.02899140844496926
Training epoch 972, recon_loss:0.610865, zinb_loss:0.959987, cluster_loss:0.331363
Clustering 972: AMI= 0.7214, NMI= 0.7223, ARI= 0.4972, ACC= 0.6001
0.027769860336332913
Training epoch 973, recon_loss:0.609349, zinb_loss:0.959041, cluster_loss:0.330101
Clustering 973: AMI= 0.7226, NMI= 0.7236, ARI= 0.5023, ACC= 0.6028
0.026996213200863227
Training epoch 974, recon_loss:0.610223, zinb_loss:0.959749, cluster_loss:0.331459
Clustering 974: AMI= 0.7212, NMI= 0.7222, ARI= 0.4973, ACC= 0.6001
0.02585610163280264
Training epoch 975, recon_loss:0.608933, zinb_loss:0.958888, cluster_loss:0.330341
Clustering 975: AMI= 0.7224, NMI= 0.7233, ARI= 0.5018, ACC= 0.6023
0.02447168044301478
Training epoch 976, recon_loss:0.610644, zinb_loss:0.959613, cluster_loss:0.331554
Clustering 976: AMI= 0.7213, NMI= 0.7222, ARI= 0.4973, ACC= 0.6002
0.02402377946984812
Training epoch 977, recon_loss:0.609367, zinb_loss:0.958801, cluster_loss:0.330394
Clustering 977: AMI= 0.7225, NMI= 0.7234, ARI= 0.5021, ACC= 0.6023
0.023860906388696607
Training epoch 978, recon_loss:0.609604, zinb_loss:0.959413, cluster_loss:0.331558
Clustering 978: AMI= 0.7214, NMI= 0.7223, ARI= 0.4975, ACC= 0.6004
0.023413005415529948
Training epoch 979, recon_loss:0.608881, zinb_loss:0.958721, cluster_loss:0.330639
Clustering 979: AMI= 0.7224, NMI= 0.7234, ARI= 0.5016, ACC= 0.6017
0.022680076550348142
Training epoch 980, recon_loss:0.610788, zinb_loss:0.959348, cluster_loss:0.331591
Clustering 980: AMI= 0.7215, NMI= 0.7224, ARI= 0.4978, ACC= 0.6006
0.022232175577181483
Training epoch 981, recon_loss:0.609959, zinb_loss:0.958700, cluster_loss:0.330500
Clustering 981: AMI= 0.7227, NMI= 0.7237, ARI= 0.5020, ACC= 0.6018
0.023127977523514802
Training epoch 982, recon_loss:0.609656, zinb_loss:0.959146, cluster_loss:0.331452
Clustering 982: AMI= 0.7213, NMI= 0.7222, ARI= 0.4976, ACC= 0.6006
0.022883667901787533
Training epoch 983, recon_loss:0.609618, zinb_loss:0.958661, cluster_loss:0.330727
Clustering 983: AMI= 0.7227, NMI= 0.7236, ARI= 0.5017, ACC= 0.6015
0.02251720346919663
Training epoch 984, recon_loss:0.610345, zinb_loss:0.959015, cluster_loss:0.331336
Clustering 984: AMI= 0.7212, NMI= 0.7221, ARI= 0.4978, ACC= 0.6007
0.022232175577181483
Training epoch 985, recon_loss:0.609989, zinb_loss:0.958681, cluster_loss:0.330677
Clustering 985: AMI= 0.7226, NMI= 0.7235, ARI= 0.5017, ACC= 0.6013
0.022557921739484506
Training epoch 986, recon_loss:0.611669, zinb_loss:0.958906, cluster_loss:0.331235
Clustering 986: AMI= 0.7212, NMI= 0.7221, ARI= 0.4981, ACC= 0.6010
0.021743556333726943
Training epoch 987, recon_loss:0.611288, zinb_loss:0.958659, cluster_loss:0.330559
Clustering 987: AMI= 0.7226, NMI= 0.7236, ARI= 0.5019, ACC= 0.6012
0.02141781017142392
Training epoch 988, recon_loss:0.610336, zinb_loss:0.958699, cluster_loss:0.331047
Clustering 988: AMI= 0.7212, NMI= 0.7221, ARI= 0.4984, ACC= 0.6015
0.021173500549696647
Training epoch 989, recon_loss:0.610446, zinb_loss:0.958669, cluster_loss:0.330837
Clustering 989: AMI= 0.7221, NMI= 0.7230, ARI= 0.5005, ACC= 0.6002
0.019300460116454254
Training epoch 990, recon_loss:0.611673, zinb_loss:0.958598, cluster_loss:0.331036
Clustering 990: AMI= 0.7214, NMI= 0.7223, ARI= 0.4990, ACC= 0.6022
0.01885255914328759
Training epoch 991, recon_loss:0.611709, zinb_loss:0.958750, cluster_loss:0.330696
Clustering 991: AMI= 0.7219, NMI= 0.7228, ARI= 0.5006, ACC= 0.6004
0.019015432224439105
Training epoch 992, recon_loss:0.610490, zinb_loss:0.958523, cluster_loss:0.330916
Clustering 992: AMI= 0.7216, NMI= 0.7225, ARI= 0.4992, ACC= 0.6024
0.018526812980984568
Training epoch 993, recon_loss:0.610951, zinb_loss:0.958777, cluster_loss:0.330862
Clustering 993: AMI= 0.7216, NMI= 0.7225, ARI= 0.4998, ACC= 0.5997
0.018526812980984568
Training epoch 994, recon_loss:0.611697, zinb_loss:0.958499, cluster_loss:0.330948
Clustering 994: AMI= 0.7218, NMI= 0.7228, ARI= 0.5001, ACC= 0.6030
0.019219023575878496
Training epoch 995, recon_loss:0.612222, zinb_loss:0.958881, cluster_loss:0.330688
Clustering 995: AMI= 0.7212, NMI= 0.7221, ARI= 0.4994, ACC= 0.5991
0.020603444765666355
Training epoch 996, recon_loss:0.610555, zinb_loss:0.958512, cluster_loss:0.330856
Clustering 996: AMI= 0.7219, NMI= 0.7228, ARI= 0.5002, ACC= 0.6031
0.022191457306893602
Training epoch 997, recon_loss:0.611274, zinb_loss:0.958945, cluster_loss:0.330811
Clustering 997: AMI= 0.7209, NMI= 0.7219, ARI= 0.4989, ACC= 0.5988
0.02316869579380268
Training epoch 998, recon_loss:0.611928, zinb_loss:0.958566, cluster_loss:0.330886
Clustering 998: AMI= 0.7222, NMI= 0.7231, ARI= 0.5010, ACC= 0.6037
0.025001017956757198
Training epoch 999, recon_loss:0.612632, zinb_loss:0.959115, cluster_loss:0.330605
Clustering 999: AMI= 0.7209, NMI= 0.7218, ARI= 0.4985, ACC= 0.5979
0.02622256606539354
Training epoch 1000, recon_loss:0.610550, zinb_loss:0.958667, cluster_loss:0.330761
Clustering 1000: AMI= 0.7223, NMI= 0.7232, ARI= 0.5014, ACC= 0.6041
0.027444114174029886
Training epoch 1001, recon_loss:0.611369, zinb_loss:0.959248, cluster_loss:0.330734
Clustering 1001: AMI= 0.7208, NMI= 0.7218, ARI= 0.4980, ACC= 0.5977
0.02850278920151472
Training epoch 1002, recon_loss:0.611737, zinb_loss:0.958785, cluster_loss:0.330776
Clustering 1002: AMI= 0.7223, NMI= 0.7232, ARI= 0.5020, ACC= 0.6045
0.029235718066696528
Training epoch 1003, recon_loss:0.612395, zinb_loss:0.959488, cluster_loss:0.330589
Clustering 1003: AMI= 0.7209, NMI= 0.7218, ARI= 0.4982, ACC= 0.5975
0.030660857526772264
Training epoch 1004, recon_loss:0.610386, zinb_loss:0.958958, cluster_loss:0.330664
Clustering 1004: AMI= 0.7224, NMI= 0.7233, ARI= 0.5022, ACC= 0.6046
0.03184168736512073
Training epoch 1005, recon_loss:0.611206, zinb_loss:0.959672, cluster_loss:0.330779
Clustering 1005: AMI= 0.7207, NMI= 0.7217, ARI= 0.4975, ACC= 0.5969
0.03233030660857527
Training epoch 1006, recon_loss:0.611148, zinb_loss:0.959109, cluster_loss:0.330708
Clustering 1006: AMI= 0.7225, NMI= 0.7234, ARI= 0.5029, ACC= 0.6051
0.032126715257135875
Training epoch 1007, recon_loss:0.611767, zinb_loss:0.959929, cluster_loss:0.330736
Clustering 1007: AMI= 0.7207, NMI= 0.7217, ARI= 0.4973, ACC= 0.5967
0.032411743149151025
Training epoch 1008, recon_loss:0.610263, zinb_loss:0.959319, cluster_loss:0.330695
Clustering 1008: AMI= 0.7224, NMI= 0.7233, ARI= 0.5031, ACC= 0.6054
0.03322610855490859
Training epoch 1009, recon_loss:0.610988, zinb_loss:0.960105, cluster_loss:0.330909
Clustering 1009: AMI= 0.7207, NMI= 0.7216, ARI= 0.4966, ACC= 0.5963
0.03322610855490859
Training epoch 1010, recon_loss:0.611094, zinb_loss:0.959499, cluster_loss:0.330757
Clustering 1010: AMI= 0.7224, NMI= 0.7234, ARI= 0.5033, ACC= 0.6054
0.03322610855490859
Training epoch 1011, recon_loss:0.611598, zinb_loss:0.960302, cluster_loss:0.330878
Clustering 1011: AMI= 0.7210, NMI= 0.7219, ARI= 0.4967, ACC= 0.5966
0.03318539028462071
Training epoch 1012, recon_loss:0.609700, zinb_loss:0.959646, cluster_loss:0.330756
Clustering 1012: AMI= 0.7226, NMI= 0.7235, ARI= 0.5038, ACC= 0.6060
0.032859644122317684
Training epoch 1013, recon_loss:0.610706, zinb_loss:0.960382, cluster_loss:0.331131
Clustering 1013: AMI= 0.7209, NMI= 0.7218, ARI= 0.4958, ACC= 0.5963
0.03290036239260556
Training epoch 1014, recon_loss:0.610280, zinb_loss:0.959770, cluster_loss:0.330830
Clustering 1014: AMI= 0.7229, NMI= 0.7238, ARI= 0.5045, ACC= 0.6062
0.03322610855490859
Training epoch 1015, recon_loss:0.610915, zinb_loss:0.960468, cluster_loss:0.331103
Clustering 1015: AMI= 0.7209, NMI= 0.7218, ARI= 0.4957, ACC= 0.5963
0.03306323547375707
Training epoch 1016, recon_loss:0.610314, zinb_loss:0.959927, cluster_loss:0.330886
Clustering 1016: AMI= 0.7225, NMI= 0.7235, ARI= 0.5039, ACC= 0.6056
0.03351113644692374
Training epoch 1017, recon_loss:0.610775, zinb_loss:0.960445, cluster_loss:0.331110
Clustering 1017: AMI= 0.7210, NMI= 0.7219, ARI= 0.4959, ACC= 0.5963
0.03383688260922676
Training epoch 1018, recon_loss:0.610120, zinb_loss:0.960012, cluster_loss:0.330942
Clustering 1018: AMI= 0.7228, NMI= 0.7238, ARI= 0.5045, ACC= 0.6062
0.03383688260922676
Training epoch 1019, recon_loss:0.610544, zinb_loss:0.960415, cluster_loss:0.331142
Clustering 1019: AMI= 0.7211, NMI= 0.7220, ARI= 0.4958, ACC= 0.5962
0.03351113644692374
Training epoch 1020, recon_loss:0.610024, zinb_loss:0.960078, cluster_loss:0.330986
Clustering 1020: AMI= 0.7229, NMI= 0.7238, ARI= 0.5049, ACC= 0.6067
0.03359257298749949
Training epoch 1021, recon_loss:0.610374, zinb_loss:0.960362, cluster_loss:0.331154
Clustering 1021: AMI= 0.7210, NMI= 0.7219, ARI= 0.4958, ACC= 0.5960
0.03318539028462071
Training epoch 1022, recon_loss:0.609922, zinb_loss:0.960128, cluster_loss:0.331022
Clustering 1022: AMI= 0.7228, NMI= 0.7237, ARI= 0.5046, ACC= 0.6065
0.03253389796001466
Training epoch 1023, recon_loss:0.610174, zinb_loss:0.960295, cluster_loss:0.331168
Clustering 1023: AMI= 0.7211, NMI= 0.7220, ARI= 0.4960, ACC= 0.5961
0.03192312390569649
Training epoch 1024, recon_loss:0.610047, zinb_loss:0.960174, cluster_loss:0.331076
Clustering 1024: AMI= 0.7226, NMI= 0.7236, ARI= 0.5045, ACC= 0.6066
0.03176025082454497
Training epoch 1025, recon_loss:0.610129, zinb_loss:0.960210, cluster_loss:0.331148
Clustering 1025: AMI= 0.7211, NMI= 0.7220, ARI= 0.4964, ACC= 0.5964
0.030905167148499533
Training epoch 1026, recon_loss:0.609685, zinb_loss:0.960179, cluster_loss:0.331135
Clustering 1026: AMI= 0.7226, NMI= 0.7235, ARI= 0.5042, ACC= 0.6066
0.0302129565536056
Training epoch 1027, recon_loss:0.609715, zinb_loss:0.960115, cluster_loss:0.331208
Clustering 1027: AMI= 0.7210, NMI= 0.7220, ARI= 0.4965, ACC= 0.5964
0.02960218249928743
Training epoch 1028, recon_loss:0.609891, zinb_loss:0.960198, cluster_loss:0.331216
Clustering 1028: AMI= 0.7225, NMI= 0.7235, ARI= 0.5040, ACC= 0.6067
0.028299197850075328
Training epoch 1029, recon_loss:0.609767, zinb_loss:0.960030, cluster_loss:0.331190
Clustering 1029: AMI= 0.7211, NMI= 0.7221, ARI= 0.4969, ACC= 0.5965
0.02715908628201474
Training epoch 1030, recon_loss:0.609444, zinb_loss:0.960193, cluster_loss:0.331282
Clustering 1030: AMI= 0.7223, NMI= 0.7232, ARI= 0.5035, ACC= 0.6064
0.02601897471395415
Training epoch 1031, recon_loss:0.609316, zinb_loss:0.959935, cluster_loss:0.331256
Clustering 1031: AMI= 0.7212, NMI= 0.7222, ARI= 0.4970, ACC= 0.5965
0.02540820065963598
Training epoch 1032, recon_loss:0.609840, zinb_loss:0.960213, cluster_loss:0.331378
Clustering 1032: AMI= 0.7226, NMI= 0.7235, ARI= 0.5032, ACC= 0.6063
0.024512398713302658
Training epoch 1033, recon_loss:0.609563, zinb_loss:0.959862, cluster_loss:0.331197
Clustering 1033: AMI= 0.7214, NMI= 0.7223, ARI= 0.4977, ACC= 0.5968
0.023290850604666315
Training epoch 1034, recon_loss:0.609265, zinb_loss:0.960209, cluster_loss:0.331438
Clustering 1034: AMI= 0.7224, NMI= 0.7233, ARI= 0.5027, ACC= 0.6060
0.02206930249602997
Training epoch 1035, recon_loss:0.609066, zinb_loss:0.959772, cluster_loss:0.331246
Clustering 1035: AMI= 0.7215, NMI= 0.7224, ARI= 0.4978, ACC= 0.5968
0.021295655360560283
Training epoch 1036, recon_loss:0.610241, zinb_loss:0.960268, cluster_loss:0.331532
Clustering 1036: AMI= 0.7221, NMI= 0.7230, ARI= 0.5020, ACC= 0.6058
0.020359135143939087
Training epoch 1037, recon_loss:0.609801, zinb_loss:0.959692, cluster_loss:0.331065
Clustering 1037: AMI= 0.7218, NMI= 0.7227, ARI= 0.4987, ACC= 0.5968
0.02003338898163606
Training epoch 1038, recon_loss:0.609121, zinb_loss:0.960238, cluster_loss:0.331528
Clustering 1038: AMI= 0.7217, NMI= 0.7227, ARI= 0.5013, ACC= 0.6057
0.019666924549045155
Training epoch 1039, recon_loss:0.609182, zinb_loss:0.959606, cluster_loss:0.331159
Clustering 1039: AMI= 0.7221, NMI= 0.7230, ARI= 0.4992, ACC= 0.5969
0.019707642819333036
Training epoch 1040, recon_loss:0.610048, zinb_loss:0.960313, cluster_loss:0.331600
Clustering 1040: AMI= 0.7219, NMI= 0.7228, ARI= 0.5012, ACC= 0.6056
0.018771122602711836
Training epoch 1041, recon_loss:0.609575, zinb_loss:0.959528, cluster_loss:0.330943
Clustering 1041: AMI= 0.7221, NMI= 0.7230, ARI= 0.4998, ACC= 0.5974
0.018445376440408813
Training epoch 1042, recon_loss:0.610517, zinb_loss:0.960369, cluster_loss:0.331621
Clustering 1042: AMI= 0.7218, NMI= 0.7227, ARI= 0.5005, ACC= 0.6053
0.0182825033592573
Training epoch 1043, recon_loss:0.609898, zinb_loss:0.959401, cluster_loss:0.330744
Clustering 1043: AMI= 0.7220, NMI= 0.7229, ARI= 0.5002, ACC= 0.5976
0.019056150494726982
Training epoch 1044, recon_loss:0.610806, zinb_loss:0.960372, cluster_loss:0.331617
Clustering 1044: AMI= 0.7215, NMI= 0.7224, ARI= 0.4996, ACC= 0.6048
0.01938189665703001
Training epoch 1045, recon_loss:0.610042, zinb_loss:0.959294, cluster_loss:0.330601
Clustering 1045: AMI= 0.7221, NMI= 0.7230, ARI= 0.5008, ACC= 0.5981
0.019992670711348182
Training epoch 1046, recon_loss:0.610959, zinb_loss:0.960343, cluster_loss:0.331605
Clustering 1046: AMI= 0.7214, NMI= 0.7223, ARI= 0.4993, ACC= 0.6045
0.02068488130624211
Training epoch 1047, recon_loss:0.610050, zinb_loss:0.959209, cluster_loss:0.330521
Clustering 1047: AMI= 0.7219, NMI= 0.7229, ARI= 0.5007, ACC= 0.5983
0.02092919092796938
Training epoch 1048, recon_loss:0.611002, zinb_loss:0.960297, cluster_loss:0.331617
Clustering 1048: AMI= 0.7215, NMI= 0.7224, ARI= 0.4991, ACC= 0.6041
0.02096990919825726
Training epoch 1049, recon_loss:0.609971, zinb_loss:0.959155, cluster_loss:0.330520
Clustering 1049: AMI= 0.7220, NMI= 0.7229, ARI= 0.5008, ACC= 0.5986
0.0208884726576815
Training epoch 1050, recon_loss:0.610953, zinb_loss:0.960247, cluster_loss:0.331665
Clustering 1050: AMI= 0.7215, NMI= 0.7224, ARI= 0.4990, ACC= 0.6037
0.02096990919825726
Training epoch 1051, recon_loss:0.609837, zinb_loss:0.959137, cluster_loss:0.330592
Clustering 1051: AMI= 0.7221, NMI= 0.7230, ARI= 0.5008, ACC= 0.5986
0.021010627468545137
Training epoch 1052, recon_loss:0.610898, zinb_loss:0.960209, cluster_loss:0.331721
Clustering 1052: AMI= 0.7215, NMI= 0.7224, ARI= 0.4989, ACC= 0.6031
0.020847754387393624
Training epoch 1053, recon_loss:0.609748, zinb_loss:0.959162, cluster_loss:0.330699
Clustering 1053: AMI= 0.7221, NMI= 0.7230, ARI= 0.5008, ACC= 0.5986
0.020725599576529988
Training epoch 1054, recon_loss:0.610878, zinb_loss:0.960203, cluster_loss:0.331748
Clustering 1054: AMI= 0.7216, NMI= 0.7225, ARI= 0.4989, ACC= 0.6028
0.020074107251923937
Training epoch 1055, recon_loss:0.609700, zinb_loss:0.959232, cluster_loss:0.330817
Clustering 1055: AMI= 0.7220, NMI= 0.7229, ARI= 0.5008, ACC= 0.5988
0.019504051467893645
Training epoch 1056, recon_loss:0.610896, zinb_loss:0.960227, cluster_loss:0.331733
Clustering 1056: AMI= 0.7215, NMI= 0.7224, ARI= 0.4987, ACC= 0.6022
0.019056150494726982
Training epoch 1057, recon_loss:0.609686, zinb_loss:0.959334, cluster_loss:0.330936
Clustering 1057: AMI= 0.7218, NMI= 0.7227, ARI= 0.5004, ACC= 0.5987
0.018771122602711836
Training epoch 1058, recon_loss:0.610928, zinb_loss:0.960271, cluster_loss:0.331668
Clustering 1058: AMI= 0.7214, NMI= 0.7223, ARI= 0.4985, ACC= 0.6017
0.018567531251272446
Training epoch 1059, recon_loss:0.609675, zinb_loss:0.959451, cluster_loss:0.331051
Clustering 1059: AMI= 0.7218, NMI= 0.7228, ARI= 0.5004, ACC= 0.5989
0.018567531251272446
Training epoch 1060, recon_loss:0.610933, zinb_loss:0.960323, cluster_loss:0.331556
Clustering 1060: AMI= 0.7214, NMI= 0.7223, ARI= 0.4981, ACC= 0.6008
0.018404658170120932
Training epoch 1061, recon_loss:0.609643, zinb_loss:0.959558, cluster_loss:0.331159
Clustering 1061: AMI= 0.7217, NMI= 0.7227, ARI= 0.5003, ACC= 0.5993
0.01803819373753003
Training epoch 1062, recon_loss:0.610887, zinb_loss:0.960362, cluster_loss:0.331412
Clustering 1062: AMI= 0.7216, NMI= 0.7225, ARI= 0.4985, ACC= 0.6007
0.01783460238609064
Training epoch 1063, recon_loss:0.609577, zinb_loss:0.959632, cluster_loss:0.331265
Clustering 1063: AMI= 0.7218, NMI= 0.7227, ARI= 0.5004, ACC= 0.5997
0.01799747546724215
Training epoch 1064, recon_loss:0.610833, zinb_loss:0.960369, cluster_loss:0.331243
Clustering 1064: AMI= 0.7216, NMI= 0.7225, ARI= 0.4987, ACC= 0.6004
0.018119630278105786
Training epoch 1065, recon_loss:0.609534, zinb_loss:0.959659, cluster_loss:0.331352
Clustering 1065: AMI= 0.7218, NMI= 0.7227, ARI= 0.5002, ACC= 0.6003
0.01779388411580276
Training epoch 1066, recon_loss:0.610720, zinb_loss:0.960324, cluster_loss:0.331073
Clustering 1066: AMI= 0.7219, NMI= 0.7228, ARI= 0.4993, ACC= 0.6004
0.01763101103465125
Training epoch 1067, recon_loss:0.609434, zinb_loss:0.959629, cluster_loss:0.331456
Clustering 1067: AMI= 0.7216, NMI= 0.7225, ARI= 0.5000, ACC= 0.6007
0.018119630278105786
Training epoch 1068, recon_loss:0.610629, zinb_loss:0.960223, cluster_loss:0.330936
Clustering 1068: AMI= 0.7222, NMI= 0.7232, ARI= 0.4998, ACC= 0.6003
0.019015432224439105
Training epoch 1069, recon_loss:0.609353, zinb_loss:0.959542, cluster_loss:0.331559
Clustering 1069: AMI= 0.7214, NMI= 0.7223, ARI= 0.4997, ACC= 0.6007
0.01917830530559062
Training epoch 1070, recon_loss:0.610293, zinb_loss:0.960052, cluster_loss:0.330861
Clustering 1070: AMI= 0.7227, NMI= 0.7236, ARI= 0.5006, ACC= 0.6005
0.019422614927317887
Training epoch 1071, recon_loss:0.609111, zinb_loss:0.959421, cluster_loss:0.331703
Clustering 1071: AMI= 0.7212, NMI= 0.7221, ARI= 0.4992, ACC= 0.6006
0.01978907935990879
Training epoch 1072, recon_loss:0.610211, zinb_loss:0.959854, cluster_loss:0.330849
Clustering 1072: AMI= 0.7226, NMI= 0.7236, ARI= 0.5007, ACC= 0.6003
0.01938189665703001
Training epoch 1073, recon_loss:0.609071, zinb_loss:0.959290, cluster_loss:0.331820
Clustering 1073: AMI= 0.7212, NMI= 0.7221, ARI= 0.4991, ACC= 0.6007
0.019300460116454254
Training epoch 1074, recon_loss:0.609683, zinb_loss:0.959626, cluster_loss:0.330864
Clustering 1074: AMI= 0.7228, NMI= 0.7237, ARI= 0.5008, ACC= 0.6004
0.018893277413575472
Training epoch 1075, recon_loss:0.608750, zinb_loss:0.959192, cluster_loss:0.332005
Clustering 1075: AMI= 0.7211, NMI= 0.7220, ARI= 0.4985, ACC= 0.6004
0.01873040433242396
Training epoch 1076, recon_loss:0.609713, zinb_loss:0.959454, cluster_loss:0.330879
Clustering 1076: AMI= 0.7229, NMI= 0.7238, ARI= 0.5009, ACC= 0.6006
0.018771122602711836
Training epoch 1077, recon_loss:0.608875, zinb_loss:0.959139, cluster_loss:0.332122
Clustering 1077: AMI= 0.7210, NMI= 0.7220, ARI= 0.4983, ACC= 0.6004
0.0195854880084694
Training epoch 1078, recon_loss:0.609290, zinb_loss:0.959302, cluster_loss:0.330851
Clustering 1078: AMI= 0.7228, NMI= 0.7237, ARI= 0.5010, ACC= 0.6008
0.020725599576529988
Training epoch 1079, recon_loss:0.608699, zinb_loss:0.959154, cluster_loss:0.332293
Clustering 1079: AMI= 0.7210, NMI= 0.7219, ARI= 0.4981, ACC= 0.6003
0.02153996498228755
Training epoch 1080, recon_loss:0.609536, zinb_loss:0.959230, cluster_loss:0.330769
Clustering 1080: AMI= 0.7227, NMI= 0.7236, ARI= 0.5014, ACC= 0.6013
0.022150739036605725
Training epoch 1081, recon_loss:0.609139, zinb_loss:0.959252, cluster_loss:0.332348
Clustering 1081: AMI= 0.7210, NMI= 0.7220, ARI= 0.4976, ACC= 0.6001
0.02357587849668146
Training epoch 1082, recon_loss:0.609185, zinb_loss:0.959180, cluster_loss:0.330605
Clustering 1082: AMI= 0.7221, NMI= 0.7230, ARI= 0.5014, ACC= 0.6017
0.02557107374078749
Training epoch 1083, recon_loss:0.609159, zinb_loss:0.959435, cluster_loss:0.332436
Clustering 1083: AMI= 0.7212, NMI= 0.7221, ARI= 0.4970, ACC= 0.5994
0.027281241092878373
Training epoch 1084, recon_loss:0.609690, zinb_loss:0.959197, cluster_loss:0.330419
Clustering 1084: AMI= 0.7223, NMI= 0.7232, ARI= 0.5020, ACC= 0.6024
0.02919499979640865
Training epoch 1085, recon_loss:0.609933, zinb_loss:0.959716, cluster_loss:0.332361
Clustering 1085: AMI= 0.7211, NMI= 0.7220, ARI= 0.4965, ACC= 0.5988
0.030579420986196506
Training epoch 1086, recon_loss:0.609500, zinb_loss:0.959237, cluster_loss:0.330254
Clustering 1086: AMI= 0.7222, NMI= 0.7231, ARI= 0.5023, ACC= 0.6028
0.03228958833828739
Training epoch 1087, recon_loss:0.610142, zinb_loss:0.960031, cluster_loss:0.332363
Clustering 1087: AMI= 0.7211, NMI= 0.7221, ARI= 0.4960, ACC= 0.5984
0.033103953744044956
Training epoch 1088, recon_loss:0.610153, zinb_loss:0.959342, cluster_loss:0.330158
Clustering 1088: AMI= 0.7220, NMI= 0.7229, ARI= 0.5024, ACC= 0.6032
0.034447656663544934
Training epoch 1089, recon_loss:0.610947, zinb_loss:0.960362, cluster_loss:0.332239
Clustering 1089: AMI= 0.7211, NMI= 0.7221, ARI= 0.4959, ACC= 0.5978
0.03509914898815098
Training epoch 1090, recon_loss:0.609900, zinb_loss:0.959421, cluster_loss:0.330148
Clustering 1090: AMI= 0.7219, NMI= 0.7229, ARI= 0.5024, ACC= 0.6033
0.03570992304246916
Training epoch 1091, recon_loss:0.610913, zinb_loss:0.960573, cluster_loss:0.332240
Clustering 1091: AMI= 0.7210, NMI= 0.7220, ARI= 0.4957, ACC= 0.5975
0.03587279612362067
Training epoch 1092, recon_loss:0.610522, zinb_loss:0.959479, cluster_loss:0.330229
Clustering 1092: AMI= 0.7219, NMI= 0.7228, ARI= 0.5022, ACC= 0.6032
0.03607638747506006
Training epoch 1093, recon_loss:0.611422, zinb_loss:0.960695, cluster_loss:0.332107
Clustering 1093: AMI= 0.7212, NMI= 0.7221, ARI= 0.4961, ACC= 0.5976
0.035058430717863104
Training epoch 1094, recon_loss:0.609921, zinb_loss:0.959442, cluster_loss:0.330340
Clustering 1094: AMI= 0.7218, NMI= 0.7227, ARI= 0.5019, ACC= 0.6026
0.03424406531210554
Training epoch 1095, recon_loss:0.610902, zinb_loss:0.960611, cluster_loss:0.332104
Clustering 1095: AMI= 0.7214, NMI= 0.7223, ARI= 0.4962, ACC= 0.5977
0.03367400952807525
Training epoch 1096, recon_loss:0.610453, zinb_loss:0.959350, cluster_loss:0.330530
Clustering 1096: AMI= 0.7215, NMI= 0.7224, ARI= 0.5014, ACC= 0.6027
0.03265605277087829
Training epoch 1097, recon_loss:0.611143, zinb_loss:0.960459, cluster_loss:0.331938
Clustering 1097: AMI= 0.7215, NMI= 0.7224, ARI= 0.4970, ACC= 0.5980
0.03094588541878741
Training epoch 1098, recon_loss:0.609789, zinb_loss:0.959195, cluster_loss:0.330759
Clustering 1098: AMI= 0.7214, NMI= 0.7223, ARI= 0.5013, ACC= 0.6028
0.02915428152612077
Training epoch 1099, recon_loss:0.610558, zinb_loss:0.960185, cluster_loss:0.331920
Clustering 1099: AMI= 0.7215, NMI= 0.7224, ARI= 0.4972, ACC= 0.5977
0.027729142066045036
Training epoch 1100, recon_loss:0.610259, zinb_loss:0.959033, cluster_loss:0.331030
Clustering 1100: AMI= 0.7214, NMI= 0.7223, ARI= 0.5012, ACC= 0.6032
0.0263040026059693
Training epoch 1101, recon_loss:0.610745, zinb_loss:0.959915, cluster_loss:0.331786
Clustering 1101: AMI= 0.7215, NMI= 0.7225, ARI= 0.4979, ACC= 0.5980
0.024960299686469317
Training epoch 1102, recon_loss:0.609636, zinb_loss:0.958857, cluster_loss:0.331255
Clustering 1102: AMI= 0.7214, NMI= 0.7224, ARI= 0.5010, ACC= 0.6033
0.02357587849668146
Training epoch 1103, recon_loss:0.610193, zinb_loss:0.959632, cluster_loss:0.331784
Clustering 1103: AMI= 0.7215, NMI= 0.7224, ARI= 0.4983, ACC= 0.5981
0.022395048658332993
Training epoch 1104, recon_loss:0.610253, zinb_loss:0.958717, cluster_loss:0.331478
Clustering 1104: AMI= 0.7213, NMI= 0.7222, ARI= 0.5006, ACC= 0.6032
0.021295655360560283
Training epoch 1105, recon_loss:0.610539, zinb_loss:0.959412, cluster_loss:0.331647
Clustering 1105: AMI= 0.7215, NMI= 0.7224, ARI= 0.4986, ACC= 0.5979
0.020399853414226964
Training epoch 1106, recon_loss:0.609489, zinb_loss:0.958589, cluster_loss:0.331633
Clustering 1106: AMI= 0.7213, NMI= 0.7222, ARI= 0.5000, ACC= 0.6028
0.020074107251923937
Training epoch 1107, recon_loss:0.609924, zinb_loss:0.959207, cluster_loss:0.331660
Clustering 1107: AMI= 0.7220, NMI= 0.7229, ARI= 0.4991, ACC= 0.5979
0.020440571684514842
Training epoch 1108, recon_loss:0.610339, zinb_loss:0.958519, cluster_loss:0.331806
Clustering 1108: AMI= 0.7212, NMI= 0.7221, ARI= 0.4997, ACC= 0.6025
0.019992670711348182
Training epoch 1109, recon_loss:0.610485, zinb_loss:0.959085, cluster_loss:0.331511
Clustering 1109: AMI= 0.7220, NMI= 0.7229, ARI= 0.4996, ACC= 0.5981
0.019748361089620914
Training epoch 1110, recon_loss:0.609143, zinb_loss:0.958465, cluster_loss:0.331904
Clustering 1110: AMI= 0.7210, NMI= 0.7219, ARI= 0.4989, ACC= 0.6019
0.019259741846166373
Training epoch 1111, recon_loss:0.609667, zinb_loss:0.958957, cluster_loss:0.331599
Clustering 1111: AMI= 0.7219, NMI= 0.7228, ARI= 0.4995, ACC= 0.5981
0.018526812980984568
Training epoch 1112, recon_loss:0.610311, zinb_loss:0.958470, cluster_loss:0.332087
Clustering 1112: AMI= 0.7211, NMI= 0.7220, ARI= 0.4986, ACC= 0.6015
0.017590292764363368
Training epoch 1113, recon_loss:0.610368, zinb_loss:0.958921, cluster_loss:0.331419
Clustering 1113: AMI= 0.7218, NMI= 0.7227, ARI= 0.4995, ACC= 0.5978
0.016857363899181563
Training epoch 1114, recon_loss:0.609005, zinb_loss:0.958512, cluster_loss:0.332162
Clustering 1114: AMI= 0.7209, NMI= 0.7218, ARI= 0.4980, ACC= 0.6012
0.016775927358605808
Training epoch 1115, recon_loss:0.609502, zinb_loss:0.958874, cluster_loss:0.331498
Clustering 1115: AMI= 0.7220, NMI= 0.7229, ARI= 0.4996, ACC= 0.5977
0.01645018119630278
Training epoch 1116, recon_loss:0.610343, zinb_loss:0.958618, cluster_loss:0.332346
Clustering 1116: AMI= 0.7208, NMI= 0.7217, ARI= 0.4978, ACC= 0.6010
0.016328026385439145
Training epoch 1117, recon_loss:0.610356, zinb_loss:0.958949, cluster_loss:0.331265
Clustering 1117: AMI= 0.7220, NMI= 0.7229, ARI= 0.5005, ACC= 0.5985
0.016816645628893685
Training epoch 1118, recon_loss:0.609073, zinb_loss:0.958790, cluster_loss:0.332406
Clustering 1118: AMI= 0.7208, NMI= 0.7217, ARI= 0.4971, ACC= 0.6002
0.017468137953499736
Training epoch 1119, recon_loss:0.609426, zinb_loss:0.959028, cluster_loss:0.331308
Clustering 1119: AMI= 0.7221, NMI= 0.7230, ARI= 0.5006, ACC= 0.5990
0.018404658170120932
Training epoch 1120, recon_loss:0.610583, zinb_loss:0.959033, cluster_loss:0.332574
Clustering 1120: AMI= 0.7206, NMI= 0.7215, ARI= 0.4967, ACC= 0.5999
0.01978907935990879
Training epoch 1121, recon_loss:0.610432, zinb_loss:0.959269, cluster_loss:0.331019
Clustering 1121: AMI= 0.7222, NMI= 0.7231, ARI= 0.5019, ACC= 0.6002
0.022110020766317847
Training epoch 1122, recon_loss:0.609140, zinb_loss:0.959335, cluster_loss:0.332592
Clustering 1122: AMI= 0.7204, NMI= 0.7214, ARI= 0.4962, ACC= 0.5994
0.023657315037257216
Training epoch 1123, recon_loss:0.609268, zinb_loss:0.959469, cluster_loss:0.331066
Clustering 1123: AMI= 0.7224, NMI= 0.7233, ARI= 0.5022, ACC= 0.6008
0.023698033307545094
Training epoch 1124, recon_loss:0.610600, zinb_loss:0.959650, cluster_loss:0.332738
Clustering 1124: AMI= 0.7207, NMI= 0.7216, ARI= 0.4964, ACC= 0.5995
0.025001017956757198
Training epoch 1125, recon_loss:0.610153, zinb_loss:0.959809, cluster_loss:0.330818
Clustering 1125: AMI= 0.7224, NMI= 0.7234, ARI= 0.5033, ACC= 0.6024
0.027036931471151104
Training epoch 1126, recon_loss:0.609038, zinb_loss:0.959953, cluster_loss:0.332721
Clustering 1126: AMI= 0.7205, NMI= 0.7214, ARI= 0.4957, ACC= 0.5987
0.02785129687690867
Training epoch 1127, recon_loss:0.608965, zinb_loss:0.960000, cluster_loss:0.330938
Clustering 1127: AMI= 0.7227, NMI= 0.7236, ARI= 0.5036, ACC= 0.6033
0.02805488822834806
Training epoch 1128, recon_loss:0.610319, zinb_loss:0.960194, cluster_loss:0.332824
Clustering 1128: AMI= 0.7203, NMI= 0.7213, ARI= 0.4953, ACC= 0.5979
0.029480027688423796
Training epoch 1129, recon_loss:0.609666, zinb_loss:0.960289, cluster_loss:0.330764
Clustering 1129: AMI= 0.7228, NMI= 0.7237, ARI= 0.5044, ACC= 0.6046
0.03180096909483285
Training epoch 1130, recon_loss:0.608910, zinb_loss:0.960374, cluster_loss:0.332760
Clustering 1130: AMI= 0.7206, NMI= 0.7215, ARI= 0.4952, ACC= 0.5976
0.03249317968972678
Training epoch 1131, recon_loss:0.608793, zinb_loss:0.960359, cluster_loss:0.330940
Clustering 1131: AMI= 0.7227, NMI= 0.7237, ARI= 0.5039, ACC= 0.6044
0.03228958833828739
Training epoch 1132, recon_loss:0.609986, zinb_loss:0.960471, cluster_loss:0.332770
Clustering 1132: AMI= 0.7207, NMI= 0.7216, ARI= 0.4956, ACC= 0.5976
0.03237102487886315
Training epoch 1133, recon_loss:0.609352, zinb_loss:0.960510, cluster_loss:0.330812
Clustering 1133: AMI= 0.7230, NMI= 0.7239, ARI= 0.5044, ACC= 0.6051
0.03359257298749949
Training epoch 1134, recon_loss:0.609255, zinb_loss:0.960522, cluster_loss:0.332669
Clustering 1134: AMI= 0.7207, NMI= 0.7217, ARI= 0.4956, ACC= 0.5972
0.03383688260922676
Training epoch 1135, recon_loss:0.608772, zinb_loss:0.960452, cluster_loss:0.330963
Clustering 1135: AMI= 0.7225, NMI= 0.7234, ARI= 0.5036, ACC= 0.6050
0.03306323547375707
Training epoch 1136, recon_loss:0.610303, zinb_loss:0.960485, cluster_loss:0.332595
Clustering 1136: AMI= 0.7208, NMI= 0.7217, ARI= 0.4959, ACC= 0.5971
0.03277820758174193
Training epoch 1137, recon_loss:0.609423, zinb_loss:0.960442, cluster_loss:0.330952
Clustering 1137: AMI= 0.7227, NMI= 0.7237, ARI= 0.5039, ACC= 0.6057
0.03322610855490859
Training epoch 1138, recon_loss:0.608917, zinb_loss:0.960339, cluster_loss:0.332464
Clustering 1138: AMI= 0.7208, NMI= 0.7218, ARI= 0.4962, ACC= 0.5970
0.032574616230302535
Training epoch 1139, recon_loss:0.608584, zinb_loss:0.960221, cluster_loss:0.331329
Clustering 1139: AMI= 0.7226, NMI= 0.7235, ARI= 0.5035, ACC= 0.6057
0.030783012337635897
Training epoch 1140, recon_loss:0.609371, zinb_loss:0.960167, cluster_loss:0.332452
Clustering 1140: AMI= 0.7209, NMI= 0.7218, ARI= 0.4963, ACC= 0.5970
0.029927928661590456
Training epoch 1141, recon_loss:0.608468, zinb_loss:0.960104, cluster_loss:0.331461
Clustering 1141: AMI= 0.7225, NMI= 0.7234, ARI= 0.5032, ACC= 0.6055
0.029235718066696528
Training epoch 1142, recon_loss:0.609715, zinb_loss:0.960066, cluster_loss:0.332452
Clustering 1142: AMI= 0.7208, NMI= 0.7217, ARI= 0.4962, ACC= 0.5969
0.028421352660938964
Training epoch 1143, recon_loss:0.608628, zinb_loss:0.959942, cluster_loss:0.331586
Clustering 1143: AMI= 0.7225, NMI= 0.7234, ARI= 0.5032, ACC= 0.6054
0.027525550714605645
Training epoch 1144, recon_loss:0.609189, zinb_loss:0.959914, cluster_loss:0.332419
Clustering 1144: AMI= 0.7208, NMI= 0.7217, ARI= 0.4963, ACC= 0.5969
0.026874058389999594
Training epoch 1145, recon_loss:0.608169, zinb_loss:0.959790, cluster_loss:0.331824
Clustering 1145: AMI= 0.7223, NMI= 0.7232, ARI= 0.5028, ACC= 0.6051
0.02605969298424203
Training epoch 1146, recon_loss:0.609837, zinb_loss:0.959790, cluster_loss:0.332364
Clustering 1146: AMI= 0.7206, NMI= 0.7215, ARI= 0.4964, ACC= 0.5968
0.0253674823893481
Training epoch 1147, recon_loss:0.608624, zinb_loss:0.959709, cluster_loss:0.331899
Clustering 1147: AMI= 0.7224, NMI= 0.7233, ARI= 0.5028, ACC= 0.6053
0.025001017956757198
Training epoch 1148, recon_loss:0.608866, zinb_loss:0.959628, cluster_loss:0.332217
Clustering 1148: AMI= 0.7205, NMI= 0.7214, ARI= 0.4965, ACC= 0.5966
0.024186652550999634
Training epoch 1149, recon_loss:0.608103, zinb_loss:0.959605, cluster_loss:0.332139
Clustering 1149: AMI= 0.7223, NMI= 0.7232, ARI= 0.5027, ACC= 0.6054
0.023860906388696607
Training epoch 1150, recon_loss:0.610165, zinb_loss:0.959554, cluster_loss:0.332071
Clustering 1150: AMI= 0.7207, NMI= 0.7217, ARI= 0.4969, ACC= 0.5966
0.023494441956105706
Training epoch 1151, recon_loss:0.608921, zinb_loss:0.959568, cluster_loss:0.332046
Clustering 1151: AMI= 0.7224, NMI= 0.7233, ARI= 0.5031, ACC= 0.6058
0.023983061199560243
Training epoch 1152, recon_loss:0.609141, zinb_loss:0.959414, cluster_loss:0.331870
Clustering 1152: AMI= 0.7209, NMI= 0.7218, ARI= 0.4970, ACC= 0.5964
0.02361659676696934
Training epoch 1153, recon_loss:0.608486, zinb_loss:0.959468, cluster_loss:0.332216
Clustering 1153: AMI= 0.7222, NMI= 0.7232, ARI= 0.5028, ACC= 0.6056
0.023127977523514802
Training epoch 1154, recon_loss:0.609932, zinb_loss:0.959352, cluster_loss:0.331754
Clustering 1154: AMI= 0.7207, NMI= 0.7216, ARI= 0.4970, ACC= 0.5961
0.023005822712651166
Training epoch 1155, recon_loss:0.608708, zinb_loss:0.959388, cluster_loss:0.332143
Clustering 1155: AMI= 0.7222, NMI= 0.7231, ARI= 0.5028, ACC= 0.6056
0.023250132334378434
Training epoch 1156, recon_loss:0.610612, zinb_loss:0.959309, cluster_loss:0.331723
Clustering 1156: AMI= 0.7206, NMI= 0.7215, ARI= 0.4968, ACC= 0.5958
0.023494441956105706
Training epoch 1157, recon_loss:0.609137, zinb_loss:0.959259, cluster_loss:0.332073
Clustering 1157: AMI= 0.7224, NMI= 0.7233, ARI= 0.5028, ACC= 0.6055
0.023250132334378434
Training epoch 1158, recon_loss:0.609814, zinb_loss:0.959209, cluster_loss:0.331759
Clustering 1158: AMI= 0.7206, NMI= 0.7215, ARI= 0.4968, ACC= 0.5962
0.022761513090923897
Training epoch 1159, recon_loss:0.608614, zinb_loss:0.959148, cluster_loss:0.332197
Clustering 1159: AMI= 0.7223, NMI= 0.7232, ARI= 0.5025, ACC= 0.6053
0.022028584225742092
Training epoch 1160, recon_loss:0.610936, zinb_loss:0.959203, cluster_loss:0.331851
Clustering 1160: AMI= 0.7205, NMI= 0.7215, ARI= 0.4969, ACC= 0.5965
0.021662119793151188
Training epoch 1161, recon_loss:0.609322, zinb_loss:0.959069, cluster_loss:0.332056
Clustering 1161: AMI= 0.7223, NMI= 0.7232, ARI= 0.5027, ACC= 0.6050
0.0218249928743027
Training epoch 1162, recon_loss:0.609847, zinb_loss:0.959140, cluster_loss:0.331939
Clustering 1162: AMI= 0.7203, NMI= 0.7212, ARI= 0.4961, ACC= 0.5964
0.021947147685166334
Training epoch 1163, recon_loss:0.609021, zinb_loss:0.959008, cluster_loss:0.332153
Clustering 1163: AMI= 0.7223, NMI= 0.7232, ARI= 0.5026, ACC= 0.6046
0.022191457306893602
Training epoch 1164, recon_loss:0.610594, zinb_loss:0.959185, cluster_loss:0.332007
Clustering 1164: AMI= 0.7202, NMI= 0.7212, ARI= 0.4961, ACC= 0.5967
0.022598640009772384
Training epoch 1165, recon_loss:0.609410, zinb_loss:0.958985, cluster_loss:0.331991
Clustering 1165: AMI= 0.7224, NMI= 0.7233, ARI= 0.5027, ACC= 0.6043
0.023535160226393584
Training epoch 1166, recon_loss:0.611669, zinb_loss:0.959248, cluster_loss:0.332043
Clustering 1166: AMI= 0.7201, NMI= 0.7210, ARI= 0.4955, ACC= 0.5970
0.02467527179445417
Training epoch 1167, recon_loss:0.609941, zinb_loss:0.958929, cluster_loss:0.331734
Clustering 1167: AMI= 0.7224, NMI= 0.7234, ARI= 0.5025, ACC= 0.6037
0.025163891037908708
Training epoch 1168, recon_loss:0.611889, zinb_loss:0.959254, cluster_loss:0.332082
Clustering 1168: AMI= 0.7202, NMI= 0.7211, ARI= 0.4954, ACC= 0.5973
0.02605969298424203
Training epoch 1169, recon_loss:0.610051, zinb_loss:0.958862, cluster_loss:0.331586
Clustering 1169: AMI= 0.7223, NMI= 0.7232, ARI= 0.5023, ACC= 0.6031
0.025489637200211735
Training epoch 1170, recon_loss:0.611450, zinb_loss:0.959211, cluster_loss:0.332125
Clustering 1170: AMI= 0.7201, NMI= 0.7210, ARI= 0.4953, ACC= 0.5977
0.025286045848772344
Training epoch 1171, recon_loss:0.609678, zinb_loss:0.958818, cluster_loss:0.331585
Clustering 1171: AMI= 0.7223, NMI= 0.7233, ARI= 0.5020, ACC= 0.6021
0.02447168044301478
Training epoch 1172, recon_loss:0.611406, zinb_loss:0.959211, cluster_loss:0.332193
Clustering 1172: AMI= 0.7201, NMI= 0.7210, ARI= 0.4956, ACC= 0.5984
0.023657315037257216
Training epoch 1173, recon_loss:0.609611, zinb_loss:0.958808, cluster_loss:0.331581
Clustering 1173: AMI= 0.7223, NMI= 0.7232, ARI= 0.5016, ACC= 0.6015
0.023005822712651166
Training epoch 1174, recon_loss:0.610566, zinb_loss:0.959220, cluster_loss:0.332246
Clustering 1174: AMI= 0.7203, NMI= 0.7212, ARI= 0.4958, ACC= 0.5988
0.02247648519890875
Training epoch 1175, recon_loss:0.609125, zinb_loss:0.958873, cluster_loss:0.331701
Clustering 1175: AMI= 0.7221, NMI= 0.7230, ARI= 0.5012, ACC= 0.6007
0.021743556333726943
Training epoch 1176, recon_loss:0.610807, zinb_loss:0.959369, cluster_loss:0.332285
Clustering 1176: AMI= 0.7204, NMI= 0.7213, ARI= 0.4963, ACC= 0.5995
0.020562726495378478
Training epoch 1177, recon_loss:0.609514, zinb_loss:0.959029, cluster_loss:0.331685
Clustering 1177: AMI= 0.7221, NMI= 0.7230, ARI= 0.5008, ACC= 0.5999
0.019504051467893645
Training epoch 1178, recon_loss:0.609762, zinb_loss:0.959545, cluster_loss:0.332236
Clustering 1178: AMI= 0.7203, NMI= 0.7212, ARI= 0.4968, ACC= 0.6008
0.01783460238609064
Training epoch 1179, recon_loss:0.608985, zinb_loss:0.959308, cluster_loss:0.331813
Clustering 1179: AMI= 0.7219, NMI= 0.7228, ARI= 0.5001, ACC= 0.5988
0.017468137953499736
Training epoch 1180, recon_loss:0.611066, zinb_loss:0.959961, cluster_loss:0.332174
Clustering 1180: AMI= 0.7203, NMI= 0.7213, ARI= 0.4974, ACC= 0.6015
0.018241785088969422
Training epoch 1181, recon_loss:0.610271, zinb_loss:0.959677, cluster_loss:0.331623
Clustering 1181: AMI= 0.7220, NMI= 0.7229, ARI= 0.5002, ACC= 0.5983
0.019219023575878496
Training epoch 1182, recon_loss:0.609301, zinb_loss:0.960231, cluster_loss:0.331953
Clustering 1182: AMI= 0.7206, NMI= 0.7215, ARI= 0.4980, ACC= 0.6025
0.01938189665703001
Training epoch 1183, recon_loss:0.609301, zinb_loss:0.960028, cluster_loss:0.331803
Clustering 1183: AMI= 0.7219, NMI= 0.7228, ARI= 0.4995, ACC= 0.5977
0.019096868765014863
Training epoch 1184, recon_loss:0.609741, zinb_loss:0.960633, cluster_loss:0.331877
Clustering 1184: AMI= 0.7207, NMI= 0.7216, ARI= 0.4995, ACC= 0.6041
0.019992670711348182
Training epoch 1185, recon_loss:0.609245, zinb_loss:0.960241, cluster_loss:0.331711
Clustering 1185: AMI= 0.7218, NMI= 0.7228, ARI= 0.4993, ACC= 0.5973
0.019951952441060305
Training epoch 1186, recon_loss:0.610663, zinb_loss:0.960809, cluster_loss:0.331836
Clustering 1186: AMI= 0.7207, NMI= 0.7216, ARI= 0.4998, ACC= 0.6046
0.020522008225090597
Training epoch 1187, recon_loss:0.610109, zinb_loss:0.960182, cluster_loss:0.331549
Clustering 1187: AMI= 0.7216, NMI= 0.7225, ARI= 0.4994, ACC= 0.5968
0.021295655360560283
Training epoch 1188, recon_loss:0.609019, zinb_loss:0.960602, cluster_loss:0.331761
Clustering 1188: AMI= 0.7211, NMI= 0.7220, ARI= 0.5003, ACC= 0.6050
0.0218249928743027
Training epoch 1189, recon_loss:0.608909, zinb_loss:0.959969, cluster_loss:0.331779
Clustering 1189: AMI= 0.7214, NMI= 0.7223, ARI= 0.4990, ACC= 0.5966
0.022191457306893602
Training epoch 1190, recon_loss:0.609276, zinb_loss:0.960419, cluster_loss:0.331841
Clustering 1190: AMI= 0.7210, NMI= 0.7219, ARI= 0.5009, ACC= 0.6055
0.022313612117757238
Training epoch 1191, recon_loss:0.608938, zinb_loss:0.959752, cluster_loss:0.331778
Clustering 1191: AMI= 0.7212, NMI= 0.7222, ARI= 0.4990, ACC= 0.5967
0.022232175577181483
Training epoch 1192, recon_loss:0.609664, zinb_loss:0.960154, cluster_loss:0.331901
Clustering 1192: AMI= 0.7211, NMI= 0.7220, ARI= 0.5007, ACC= 0.6054
0.02251720346919663
Training epoch 1193, recon_loss:0.609358, zinb_loss:0.959516, cluster_loss:0.331773
Clustering 1193: AMI= 0.7214, NMI= 0.7223, ARI= 0.4992, ACC= 0.5968
0.023127977523514802
Training epoch 1194, recon_loss:0.608401, zinb_loss:0.959834, cluster_loss:0.331911
Clustering 1194: AMI= 0.7215, NMI= 0.7225, ARI= 0.5010, ACC= 0.6056
0.02251720346919663
Training epoch 1195, recon_loss:0.608336, zinb_loss:0.959314, cluster_loss:0.332016
Clustering 1195: AMI= 0.7214, NMI= 0.7223, ARI= 0.4988, ACC= 0.5970
0.0218249928743027
Training epoch 1196, recon_loss:0.609680, zinb_loss:0.959669, cluster_loss:0.332020
Clustering 1196: AMI= 0.7218, NMI= 0.7227, ARI= 0.5013, ACC= 0.6054
0.021051345738833015
Training epoch 1197, recon_loss:0.609490, zinb_loss:0.959237, cluster_loss:0.331967
Clustering 1197: AMI= 0.7212, NMI= 0.7222, ARI= 0.4989, ACC= 0.5972
0.02141781017142392
Training epoch 1198, recon_loss:0.607895, zinb_loss:0.959426, cluster_loss:0.331971
Clustering 1198: AMI= 0.7217, NMI= 0.7226, ARI= 0.5012, ACC= 0.6052
0.021906429414878456
Training epoch 1199, recon_loss:0.608166, zinb_loss:0.959133, cluster_loss:0.332250
Clustering 1199: AMI= 0.7212, NMI= 0.7221, ARI= 0.4985, ACC= 0.5970
0.02198786595545421
Training epoch 1200, recon_loss:0.608864, zinb_loss:0.959337, cluster_loss:0.332079
Clustering 1200: AMI= 0.7219, NMI= 0.7228, ARI= 0.5016, ACC= 0.6051
0.02186571114459058
Training epoch 1201, recon_loss:0.608906, zinb_loss:0.959171, cluster_loss:0.332264
Clustering 1201: AMI= 0.7210, NMI= 0.7219, ARI= 0.4983, ACC= 0.5974
0.021743556333726943
Training epoch 1202, recon_loss:0.608088, zinb_loss:0.959243, cluster_loss:0.332046
Clustering 1202: AMI= 0.7218, NMI= 0.7227, ARI= 0.5012, ACC= 0.6047
0.02178427460401482
Training epoch 1203, recon_loss:0.608398, zinb_loss:0.959182, cluster_loss:0.332424
Clustering 1203: AMI= 0.7210, NMI= 0.7219, ARI= 0.4981, ACC= 0.5974
0.022191457306893602
Training epoch 1204, recon_loss:0.609128, zinb_loss:0.959220, cluster_loss:0.332050
Clustering 1204: AMI= 0.7218, NMI= 0.7227, ARI= 0.5014, ACC= 0.6045
0.022354330388045116
Training epoch 1205, recon_loss:0.609281, zinb_loss:0.959297, cluster_loss:0.332405
Clustering 1205: AMI= 0.7209, NMI= 0.7218, ARI= 0.4981, ACC= 0.5975
0.022842949631499652
Training epoch 1206, recon_loss:0.608120, zinb_loss:0.959151, cluster_loss:0.331953
Clustering 1206: AMI= 0.7219, NMI= 0.7229, ARI= 0.5014, ACC= 0.6044
0.02361659676696934
Training epoch 1207, recon_loss:0.608624, zinb_loss:0.959374, cluster_loss:0.332605
Clustering 1207: AMI= 0.7208, NMI= 0.7218, ARI= 0.4979, ACC= 0.5974
0.023657315037257216
Training epoch 1208, recon_loss:0.609221, zinb_loss:0.959167, cluster_loss:0.331932
Clustering 1208: AMI= 0.7220, NMI= 0.7229, ARI= 0.5016, ACC= 0.6044
0.02382018811840873
Training epoch 1209, recon_loss:0.609355, zinb_loss:0.959555, cluster_loss:0.332574
Clustering 1209: AMI= 0.7208, NMI= 0.7218, ARI= 0.4979, ACC= 0.5975
0.024349525632151148
Training epoch 1210, recon_loss:0.608070, zinb_loss:0.959139, cluster_loss:0.331846
Clustering 1210: AMI= 0.7221, NMI= 0.7230, ARI= 0.5016, ACC= 0.6041
0.025082454497332953
Training epoch 1211, recon_loss:0.608611, zinb_loss:0.959668, cluster_loss:0.332764
Clustering 1211: AMI= 0.7211, NMI= 0.7220, ARI= 0.4977, ACC= 0.5974
0.025448918929923858
Training epoch 1212, recon_loss:0.608957, zinb_loss:0.959172, cluster_loss:0.331827
Clustering 1212: AMI= 0.7220, NMI= 0.7230, ARI= 0.5018, ACC= 0.6041
0.02605969298424203
Training epoch 1213, recon_loss:0.609161, zinb_loss:0.959895, cluster_loss:0.332728
Clustering 1213: AMI= 0.7209, NMI= 0.7218, ARI= 0.4972, ACC= 0.5975
0.02650759395740869
Training epoch 1214, recon_loss:0.608263, zinb_loss:0.959194, cluster_loss:0.331744
Clustering 1214: AMI= 0.7221, NMI= 0.7230, ARI= 0.5018, ACC= 0.6040
0.027199804552302618
Training epoch 1215, recon_loss:0.608752, zinb_loss:0.960052, cluster_loss:0.332797
Clustering 1215: AMI= 0.7208, NMI= 0.7218, ARI= 0.4969, ACC= 0.5975
0.028584225742090477
Training epoch 1216, recon_loss:0.608861, zinb_loss:0.959239, cluster_loss:0.331671
Clustering 1216: AMI= 0.7220, NMI= 0.7229, ARI= 0.5019, ACC= 0.6039
0.02939859114784804
Training epoch 1217, recon_loss:0.609334, zinb_loss:0.960264, cluster_loss:0.332693
Clustering 1217: AMI= 0.7208, NMI= 0.7218, ARI= 0.4966, ACC= 0.5976
0.0302129565536056
Training epoch 1218, recon_loss:0.608633, zinb_loss:0.959254, cluster_loss:0.331577
Clustering 1218: AMI= 0.7221, NMI= 0.7230, ARI= 0.5020, ACC= 0.6037
0.030823730607923774
Training epoch 1219, recon_loss:0.609293, zinb_loss:0.960366, cluster_loss:0.332640
Clustering 1219: AMI= 0.7207, NMI= 0.7216, ARI= 0.4961, ACC= 0.5973
0.03131234985137831
Training epoch 1220, recon_loss:0.609153, zinb_loss:0.959237, cluster_loss:0.331537
Clustering 1220: AMI= 0.7219, NMI= 0.7229, ARI= 0.5022, ACC= 0.6038
0.031108758499938924
Training epoch 1221, recon_loss:0.609764, zinb_loss:0.960419, cluster_loss:0.332538
Clustering 1221: AMI= 0.7207, NMI= 0.7216, ARI= 0.4962, ACC= 0.5976
0.03119019504051468
Training epoch 1222, recon_loss:0.608791, zinb_loss:0.959165, cluster_loss:0.331540
Clustering 1222: AMI= 0.7220, NMI= 0.7229, ARI= 0.5023, ACC= 0.6036
0.031230913310802556
Training epoch 1223, recon_loss:0.609500, zinb_loss:0.960356, cluster_loss:0.332560
Clustering 1223: AMI= 0.7205, NMI= 0.7214, ARI= 0.4960, ACC= 0.5977
0.031678814283969216
Training epoch 1224, recon_loss:0.609244, zinb_loss:0.959079, cluster_loss:0.331605
Clustering 1224: AMI= 0.7221, NMI= 0.7231, ARI= 0.5024, ACC= 0.6035
0.031068040229651043
Training epoch 1225, recon_loss:0.609812, zinb_loss:0.960263, cluster_loss:0.332528
Clustering 1225: AMI= 0.7207, NMI= 0.7216, ARI= 0.4962, ACC= 0.5977
0.030620139256484383
Training epoch 1226, recon_loss:0.609034, zinb_loss:0.958995, cluster_loss:0.331673
Clustering 1226: AMI= 0.7219, NMI= 0.7228, ARI= 0.5017, ACC= 0.6028
0.030335111364469237
Training epoch 1227, recon_loss:0.609582, zinb_loss:0.960109, cluster_loss:0.332566
Clustering 1227: AMI= 0.7205, NMI= 0.7214, ARI= 0.4961, ACC= 0.5977
0.02964290076957531
Training epoch 1228, recon_loss:0.609399, zinb_loss:0.958926, cluster_loss:0.331773
Clustering 1228: AMI= 0.7216, NMI= 0.7225, ARI= 0.5016, ACC= 0.6028
0.028543507471802596
Training epoch 1229, recon_loss:0.609791, zinb_loss:0.959984, cluster_loss:0.332558
Clustering 1229: AMI= 0.7207, NMI= 0.7216, ARI= 0.4965, ACC= 0.5980
0.027281241092878373
Training epoch 1230, recon_loss:0.609043, zinb_loss:0.958864, cluster_loss:0.331880
Clustering 1230: AMI= 0.7213, NMI= 0.7223, ARI= 0.5011, ACC= 0.6026
0.026670467038560203
Training epoch 1231, recon_loss:0.609423, zinb_loss:0.959855, cluster_loss:0.332635
Clustering 1231: AMI= 0.7207, NMI= 0.7216, ARI= 0.4966, ACC= 0.5981
0.026100411254529908
Training epoch 1232, recon_loss:0.609588, zinb_loss:0.958841, cluster_loss:0.332021
Clustering 1232: AMI= 0.7212, NMI= 0.7221, ARI= 0.5009, ACC= 0.6025
0.025041736227045076
Training epoch 1233, recon_loss:0.609797, zinb_loss:0.959783, cluster_loss:0.332621
Clustering 1233: AMI= 0.7206, NMI= 0.7216, ARI= 0.4966, ACC= 0.5977
0.024064497740136
Training epoch 1234, recon_loss:0.608797, zinb_loss:0.958830, cluster_loss:0.332119
Clustering 1234: AMI= 0.7213, NMI= 0.7222, ARI= 0.5007, ACC= 0.6025
0.023657315037257216
Training epoch 1235, recon_loss:0.609155, zinb_loss:0.959731, cluster_loss:0.332740
Clustering 1235: AMI= 0.7206, NMI= 0.7215, ARI= 0.4965, ACC= 0.5976
0.02337228714524207
Training epoch 1236, recon_loss:0.609454, zinb_loss:0.958888, cluster_loss:0.332244
Clustering 1236: AMI= 0.7214, NMI= 0.7223, ARI= 0.5008, ACC= 0.6025
0.022842949631499652
Training epoch 1237, recon_loss:0.609721, zinb_loss:0.959796, cluster_loss:0.332695
Clustering 1237: AMI= 0.7205, NMI= 0.7214, ARI= 0.4963, ACC= 0.5973
0.022842949631499652
Training epoch 1238, recon_loss:0.608638, zinb_loss:0.958986, cluster_loss:0.332277
Clustering 1238: AMI= 0.7216, NMI= 0.7225, ARI= 0.5009, ACC= 0.6024
0.02272079482063602
Training epoch 1239, recon_loss:0.609174, zinb_loss:0.959883, cluster_loss:0.332781
Clustering 1239: AMI= 0.7204, NMI= 0.7213, ARI= 0.4961, ACC= 0.5972
0.02337228714524207
Training epoch 1240, recon_loss:0.609702, zinb_loss:0.959180, cluster_loss:0.332296
Clustering 1240: AMI= 0.7215, NMI= 0.7225, ARI= 0.5011, ACC= 0.6026
0.02402377946984812
Training epoch 1241, recon_loss:0.610257, zinb_loss:0.960075, cluster_loss:0.332611
Clustering 1241: AMI= 0.7204, NMI= 0.7214, ARI= 0.4959, ACC= 0.5968
0.024593835253878416
Training epoch 1242, recon_loss:0.608778, zinb_loss:0.959359, cluster_loss:0.332170
Clustering 1242: AMI= 0.7217, NMI= 0.7226, ARI= 0.5014, ACC= 0.6027
0.02565251028136325
Training epoch 1243, recon_loss:0.609780, zinb_loss:0.960206, cluster_loss:0.332619
Clustering 1243: AMI= 0.7204, NMI= 0.7213, ARI= 0.4956, ACC= 0.5968
0.026833340119711713
Training epoch 1244, recon_loss:0.610004, zinb_loss:0.959547, cluster_loss:0.332071
Clustering 1244: AMI= 0.7217, NMI= 0.7226, ARI= 0.5022, ACC= 0.6034
0.028177043039211695
Training epoch 1245, recon_loss:0.610758, zinb_loss:0.960283, cluster_loss:0.332337
Clustering 1245: AMI= 0.7203, NMI= 0.7212, ARI= 0.4955, ACC= 0.5964
0.028217761309499573
Training epoch 1246, recon_loss:0.609538, zinb_loss:0.959567, cluster_loss:0.331952
Clustering 1246: AMI= 0.7218, NMI= 0.7227, ARI= 0.5025, ACC= 0.6037
0.028665662282666232
Training epoch 1247, recon_loss:0.610171, zinb_loss:0.960124, cluster_loss:0.332265
Clustering 1247: AMI= 0.7200, NMI= 0.7209, ARI= 0.4952, ACC= 0.5961
0.028747098823241987
Training epoch 1248, recon_loss:0.610350, zinb_loss:0.959499, cluster_loss:0.331966
Clustering 1248: AMI= 0.7217, NMI= 0.7226, ARI= 0.5027, ACC= 0.6039
0.028299197850075328
Training epoch 1249, recon_loss:0.610675, zinb_loss:0.959895, cluster_loss:0.332117
Clustering 1249: AMI= 0.7198, NMI= 0.7208, ARI= 0.4954, ACC= 0.5962
0.027525550714605645
Training epoch 1250, recon_loss:0.608753, zinb_loss:0.959297, cluster_loss:0.331984
Clustering 1250: AMI= 0.7216, NMI= 0.7226, ARI= 0.5025, ACC= 0.6038
0.026914776660287472
Training epoch 1251, recon_loss:0.609330, zinb_loss:0.959619, cluster_loss:0.332313
Clustering 1251: AMI= 0.7198, NMI= 0.7208, ARI= 0.4954, ACC= 0.5963
0.026589030497984445
Training epoch 1252, recon_loss:0.609363, zinb_loss:0.959204, cluster_loss:0.332134
Clustering 1252: AMI= 0.7219, NMI= 0.7228, ARI= 0.5025, ACC= 0.6039
0.025693228551651126
Training epoch 1253, recon_loss:0.609570, zinb_loss:0.959431, cluster_loss:0.332269
Clustering 1253: AMI= 0.7201, NMI= 0.7210, ARI= 0.4962, ACC= 0.5968
0.02455311698359054
Training epoch 1254, recon_loss:0.608866, zinb_loss:0.959120, cluster_loss:0.332233
Clustering 1254: AMI= 0.7217, NMI= 0.7227, ARI= 0.5021, ACC= 0.6037
0.02382018811840873
Training epoch 1255, recon_loss:0.609042, zinb_loss:0.959237, cluster_loss:0.332328
Clustering 1255: AMI= 0.7202, NMI= 0.7211, ARI= 0.4965, ACC= 0.5970
0.023494441956105706
Training epoch 1256, recon_loss:0.609472, zinb_loss:0.959104, cluster_loss:0.332352
Clustering 1256: AMI= 0.7218, NMI= 0.7227, ARI= 0.5020, ACC= 0.6038
0.023005822712651166
Training epoch 1257, recon_loss:0.609532, zinb_loss:0.959112, cluster_loss:0.332267
Clustering 1257: AMI= 0.7200, NMI= 0.7210, ARI= 0.4966, ACC= 0.5970
0.02243576692862087
Training epoch 1258, recon_loss:0.608643, zinb_loss:0.959053, cluster_loss:0.332391
Clustering 1258: AMI= 0.7217, NMI= 0.7226, ARI= 0.5017, ACC= 0.6038
0.021743556333726943
Training epoch 1259, recon_loss:0.608786, zinb_loss:0.959002, cluster_loss:0.332369
Clustering 1259: AMI= 0.7200, NMI= 0.7210, ARI= 0.4966, ACC= 0.5968
0.021173500549696647
Training epoch 1260, recon_loss:0.609825, zinb_loss:0.959117, cluster_loss:0.332476
Clustering 1260: AMI= 0.7216, NMI= 0.7225, ARI= 0.5015, ACC= 0.6037
0.02076631784681787
Training epoch 1261, recon_loss:0.609785, zinb_loss:0.958942, cluster_loss:0.332206
Clustering 1261: AMI= 0.7201, NMI= 0.7210, ARI= 0.4967, ACC= 0.5970
0.020399853414226964
Training epoch 1262, recon_loss:0.608491, zinb_loss:0.959066, cluster_loss:0.332457
Clustering 1262: AMI= 0.7215, NMI= 0.7225, ARI= 0.5014, ACC= 0.6038
0.020522008225090597
Training epoch 1263, recon_loss:0.608731, zinb_loss:0.958852, cluster_loss:0.332368
Clustering 1263: AMI= 0.7202, NMI= 0.7211, ARI= 0.4966, ACC= 0.5970
0.019911234170772424
Training epoch 1264, recon_loss:0.609479, zinb_loss:0.959153, cluster_loss:0.332560
Clustering 1264: AMI= 0.7215, NMI= 0.7224, ARI= 0.5012, ACC= 0.6034
0.019300460116454254
Training epoch 1265, recon_loss:0.609432, zinb_loss:0.958825, cluster_loss:0.332235
Clustering 1265: AMI= 0.7202, NMI= 0.7211, ARI= 0.4969, ACC= 0.5971
0.01938189665703001
Training epoch 1266, recon_loss:0.608989, zinb_loss:0.959144, cluster_loss:0.332590
Clustering 1266: AMI= 0.7214, NMI= 0.7223, ARI= 0.5010, ACC= 0.6036
0.019341178386742132
Training epoch 1267, recon_loss:0.608900, zinb_loss:0.958753, cluster_loss:0.332275
Clustering 1267: AMI= 0.7203, NMI= 0.7213, ARI= 0.4973, ACC= 0.5973
0.019300460116454254
Training epoch 1268, recon_loss:0.609846, zinb_loss:0.959189, cluster_loss:0.332664
Clustering 1268: AMI= 0.7213, NMI= 0.7222, ARI= 0.5009, ACC= 0.6034
0.019015432224439105
Training epoch 1269, recon_loss:0.609563, zinb_loss:0.958713, cluster_loss:0.332187
Clustering 1269: AMI= 0.7205, NMI= 0.7214, ARI= 0.4977, ACC= 0.5976
0.018771122602711836
Training epoch 1270, recon_loss:0.608372, zinb_loss:0.959100, cluster_loss:0.332655
Clustering 1270: AMI= 0.7212, NMI= 0.7221, ARI= 0.5005, ACC= 0.6031
0.01799747546724215
Training epoch 1271, recon_loss:0.608409, zinb_loss:0.958653, cluster_loss:0.332433
Clustering 1271: AMI= 0.7204, NMI= 0.7213, ARI= 0.4975, ACC= 0.5977
0.017020236980333076
Training epoch 1272, recon_loss:0.609182, zinb_loss:0.959179, cluster_loss:0.332745
Clustering 1272: AMI= 0.7213, NMI= 0.7222, ARI= 0.5007, ACC= 0.6034
0.01649089946659066
Training epoch 1273, recon_loss:0.608848, zinb_loss:0.958660, cluster_loss:0.332417
Clustering 1273: AMI= 0.7203, NMI= 0.7212, ARI= 0.4977, ACC= 0.5977
0.016083716763711876
Training epoch 1274, recon_loss:0.608525, zinb_loss:0.959191, cluster_loss:0.332759
Clustering 1274: AMI= 0.7211, NMI= 0.7220, ARI= 0.5000, ACC= 0.6027
0.015310069628242192
Training epoch 1275, recon_loss:0.608166, zinb_loss:0.958665, cluster_loss:0.332587
Clustering 1275: AMI= 0.7204, NMI= 0.7213, ARI= 0.4977, ACC= 0.5976
0.014740013844211898
Training epoch 1276, recon_loss:0.609246, zinb_loss:0.959346, cluster_loss:0.332772
Clustering 1276: AMI= 0.7212, NMI= 0.7221, ARI= 0.5000, ACC= 0.6026
0.014210676330469482
Training epoch 1277, recon_loss:0.608658, zinb_loss:0.958772, cluster_loss:0.332623
Clustering 1277: AMI= 0.7206, NMI= 0.7216, ARI= 0.4980, ACC= 0.5979
0.014007084979030091
Training epoch 1278, recon_loss:0.607971, zinb_loss:0.959479, cluster_loss:0.332657
Clustering 1278: AMI= 0.7210, NMI= 0.7220, ARI= 0.4998, ACC= 0.6026
0.013559184005863431
Training epoch 1279, recon_loss:0.607707, zinb_loss:0.958985, cluster_loss:0.332910
Clustering 1279: AMI= 0.7207, NMI= 0.7216, ARI= 0.4979, ACC= 0.5979
0.013722057087014943
Training epoch 1280, recon_loss:0.608893, zinb_loss:0.959941, cluster_loss:0.332506
Clustering 1280: AMI= 0.7214, NMI= 0.7223, ARI= 0.5002, ACC= 0.6029
0.014943605195651289
Training epoch 1281, recon_loss:0.608192, zinb_loss:0.959417, cluster_loss:0.332918
Clustering 1281: AMI= 0.7207, NMI= 0.7217, ARI= 0.4979, ACC= 0.5981
0.015717252331120975
Training epoch 1282, recon_loss:0.608470, zinb_loss:0.960490, cluster_loss:0.332186
Clustering 1282: AMI= 0.7215, NMI= 0.7224, ARI= 0.5003, ACC= 0.6032
0.017264546602060345
Training epoch 1283, recon_loss:0.607915, zinb_loss:0.959982, cluster_loss:0.333033
Clustering 1283: AMI= 0.7207, NMI= 0.7216, ARI= 0.4977, ACC= 0.5979
0.0195854880084694
Training epoch 1284, recon_loss:0.609229, zinb_loss:0.961201, cluster_loss:0.331803
Clustering 1284: AMI= 0.7215, NMI= 0.7224, ARI= 0.5007, ACC= 0.6033
0.02227289384746936
Training epoch 1285, recon_loss:0.608332, zinb_loss:0.960533, cluster_loss:0.332969
Clustering 1285: AMI= 0.7208, NMI= 0.7217, ARI= 0.4981, ACC= 0.5985
0.024756708335029926
Training epoch 1286, recon_loss:0.608745, zinb_loss:0.961606, cluster_loss:0.331453
Clustering 1286: AMI= 0.7217, NMI= 0.7226, ARI= 0.5014, ACC= 0.6039
0.02585610163280264
Training epoch 1287, recon_loss:0.608059, zinb_loss:0.960757, cluster_loss:0.333014
Clustering 1287: AMI= 0.7211, NMI= 0.7220, ARI= 0.4982, ACC= 0.5990
0.02671118530884808
Training epoch 1288, recon_loss:0.609013, zinb_loss:0.961639, cluster_loss:0.331300
Clustering 1288: AMI= 0.7219, NMI= 0.7228, ARI= 0.5017, ACC= 0.6041
0.02785129687690867
Training epoch 1289, recon_loss:0.608018, zinb_loss:0.960655, cluster_loss:0.333000
Clustering 1289: AMI= 0.7211, NMI= 0.7220, ARI= 0.4983, ACC= 0.5990
0.028217761309499573
Training epoch 1290, recon_loss:0.608430, zinb_loss:0.961262, cluster_loss:0.331342
Clustering 1290: AMI= 0.7217, NMI= 0.7226, ARI= 0.5014, ACC= 0.6038
0.027932733417484427
Training epoch 1291, recon_loss:0.607707, zinb_loss:0.960346, cluster_loss:0.333128
Clustering 1291: AMI= 0.7210, NMI= 0.7219, ARI= 0.4981, ACC= 0.5990
0.027525550714605645
Training epoch 1292, recon_loss:0.608447, zinb_loss:0.960817, cluster_loss:0.331466
Clustering 1292: AMI= 0.7219, NMI= 0.7229, ARI= 0.5017, ACC= 0.6037
0.027036931471151104
Training epoch 1293, recon_loss:0.607682, zinb_loss:0.960037, cluster_loss:0.333223
Clustering 1293: AMI= 0.7209, NMI= 0.7218, ARI= 0.4980, ACC= 0.5991
0.026670467038560203
Training epoch 1294, recon_loss:0.608053, zinb_loss:0.960364, cluster_loss:0.331605
Clustering 1294: AMI= 0.7218, NMI= 0.7227, ARI= 0.5013, ACC= 0.6031
0.02605969298424203
Training epoch 1295, recon_loss:0.607578, zinb_loss:0.959771, cluster_loss:0.333363
Clustering 1295: AMI= 0.7209, NMI= 0.7218, ARI= 0.4980, ACC= 0.5994
0.02585610163280264
Training epoch 1296, recon_loss:0.608236, zinb_loss:0.960011, cluster_loss:0.331725
Clustering 1296: AMI= 0.7219, NMI= 0.7228, ARI= 0.5012, ACC= 0.6028
0.024960299686469317
Training epoch 1297, recon_loss:0.607789, zinb_loss:0.959576, cluster_loss:0.333434
Clustering 1297: AMI= 0.7207, NMI= 0.7217, ARI= 0.4977, ACC= 0.5992
0.024878863145893562
Training epoch 1298, recon_loss:0.608076, zinb_loss:0.959694, cluster_loss:0.331808
Clustering 1298: AMI= 0.7219, NMI= 0.7228, ARI= 0.5012, ACC= 0.6027
0.02467527179445417
Training epoch 1299, recon_loss:0.607870, zinb_loss:0.959431, cluster_loss:0.333530
Clustering 1299: AMI= 0.7207, NMI= 0.7216, ARI= 0.4975, ACC= 0.5995
0.02455311698359054
Training epoch 1300, recon_loss:0.608238, zinb_loss:0.959453, cluster_loss:0.331865
Clustering 1300: AMI= 0.7217, NMI= 0.7226, ARI= 0.5010, ACC= 0.6024
0.02402377946984812
Training epoch 1301, recon_loss:0.608138, zinb_loss:0.959347, cluster_loss:0.333585
Clustering 1301: AMI= 0.7206, NMI= 0.7215, ARI= 0.4973, ACC= 0.5995
0.024145934280711757
Training epoch 1302, recon_loss:0.608323, zinb_loss:0.959256, cluster_loss:0.331898
Clustering 1302: AMI= 0.7217, NMI= 0.7227, ARI= 0.5010, ACC= 0.6022
0.024390243902439025
Training epoch 1303, recon_loss:0.608375, zinb_loss:0.959306, cluster_loss:0.333625
Clustering 1303: AMI= 0.7206, NMI= 0.7215, ARI= 0.4970, ACC= 0.5994
0.024838144875605685
Training epoch 1304, recon_loss:0.608550, zinb_loss:0.959109, cluster_loss:0.331922
Clustering 1304: AMI= 0.7218, NMI= 0.7227, ARI= 0.5011, ACC= 0.6021
0.025163891037908708
Training epoch 1305, recon_loss:0.608705, zinb_loss:0.959319, cluster_loss:0.333635
Clustering 1305: AMI= 0.7205, NMI= 0.7214, ARI= 0.4968, ACC= 0.5995
0.02557107374078749
Training epoch 1306, recon_loss:0.608463, zinb_loss:0.958998, cluster_loss:0.331929
Clustering 1306: AMI= 0.7219, NMI= 0.7228, ARI= 0.5013, ACC= 0.6020
0.02626328433568142
Training epoch 1307, recon_loss:0.608797, zinb_loss:0.959381, cluster_loss:0.333653
Clustering 1307: AMI= 0.7205, NMI= 0.7214, ARI= 0.4968, ACC= 0.5997
0.02671118530884808
Training epoch 1308, recon_loss:0.608812, zinb_loss:0.958955, cluster_loss:0.331918
Clustering 1308: AMI= 0.7219, NMI= 0.7228, ARI= 0.5015, ACC= 0.6019
0.027199804552302618
Training epoch 1309, recon_loss:0.609204, zinb_loss:0.959506, cluster_loss:0.333587
Clustering 1309: AMI= 0.7207, NMI= 0.7216, ARI= 0.4968, ACC= 0.5999
0.027525550714605645
Training epoch 1310, recon_loss:0.608846, zinb_loss:0.958954, cluster_loss:0.331905
Clustering 1310: AMI= 0.7216, NMI= 0.7225, ARI= 0.5011, ACC= 0.6017
0.027892015147196546
Training epoch 1311, recon_loss:0.609329, zinb_loss:0.959640, cluster_loss:0.333517
Clustering 1311: AMI= 0.7203, NMI= 0.7212, ARI= 0.4964, ACC= 0.5996
0.028217761309499573
Training epoch 1312, recon_loss:0.609243, zinb_loss:0.958997, cluster_loss:0.331916
Clustering 1312: AMI= 0.7216, NMI= 0.7226, ARI= 0.5010, ACC= 0.6014
0.028421352660938964
Training epoch 1313, recon_loss:0.609705, zinb_loss:0.959788, cluster_loss:0.333378
Clustering 1313: AMI= 0.7201, NMI= 0.7210, ARI= 0.4962, ACC= 0.5995
0.028177043039211695
Training epoch 1314, recon_loss:0.608921, zinb_loss:0.959041, cluster_loss:0.331966
Clustering 1314: AMI= 0.7216, NMI= 0.7225, ARI= 0.5009, ACC= 0.6014
0.028624944012378355
Training epoch 1315, recon_loss:0.609462, zinb_loss:0.959897, cluster_loss:0.333293
Clustering 1315: AMI= 0.7200, NMI= 0.7209, ARI= 0.4961, ACC= 0.5995
0.028136324768923818
Training epoch 1316, recon_loss:0.609293, zinb_loss:0.959102, cluster_loss:0.332077
Clustering 1316: AMI= 0.7215, NMI= 0.7225, ARI= 0.5009, ACC= 0.6013
0.027892015147196546
Training epoch 1317, recon_loss:0.609771, zinb_loss:0.959982, cluster_loss:0.333126
Clustering 1317: AMI= 0.7198, NMI= 0.7208, ARI= 0.4961, ACC= 0.5993
0.027688423795757155
Training epoch 1318, recon_loss:0.608712, zinb_loss:0.959144, cluster_loss:0.332236
Clustering 1318: AMI= 0.7216, NMI= 0.7225, ARI= 0.5008, ACC= 0.6012
0.027118368011726863
Training epoch 1319, recon_loss:0.609243, zinb_loss:0.959980, cluster_loss:0.333026
Clustering 1319: AMI= 0.7198, NMI= 0.7207, ARI= 0.4963, ACC= 0.5992
0.026344720876257176
Training epoch 1320, recon_loss:0.609288, zinb_loss:0.959199, cluster_loss:0.332439
Clustering 1320: AMI= 0.7213, NMI= 0.7222, ARI= 0.5002, ACC= 0.6007
0.02585610163280264
Training epoch 1321, recon_loss:0.609782, zinb_loss:0.959974, cluster_loss:0.332774
Clustering 1321: AMI= 0.7198, NMI= 0.7208, ARI= 0.4965, ACC= 0.5994
0.025082454497332953
Training epoch 1322, recon_loss:0.608222, zinb_loss:0.959230, cluster_loss:0.332613
Clustering 1322: AMI= 0.7213, NMI= 0.7222, ARI= 0.5000, ACC= 0.6008
0.024878863145893562
Training epoch 1323, recon_loss:0.608797, zinb_loss:0.959888, cluster_loss:0.332684
Clustering 1323: AMI= 0.7196, NMI= 0.7205, ARI= 0.4964, ACC= 0.5993
0.024593835253878416
Training epoch 1324, recon_loss:0.609228, zinb_loss:0.959290, cluster_loss:0.332852
Clustering 1324: AMI= 0.7211, NMI= 0.7221, ARI= 0.4995, ACC= 0.6004
0.023860906388696607
Training epoch 1325, recon_loss:0.609616, zinb_loss:0.959867, cluster_loss:0.332413
Clustering 1325: AMI= 0.7197, NMI= 0.7206, ARI= 0.4971, ACC= 0.5997
0.023698033307545094
Training epoch 1326, recon_loss:0.607635, zinb_loss:0.959324, cluster_loss:0.333024
Clustering 1326: AMI= 0.7212, NMI= 0.7221, ARI= 0.4993, ACC= 0.6006
0.023005822712651166
Training epoch 1327, recon_loss:0.608238, zinb_loss:0.959755, cluster_loss:0.332483
Clustering 1327: AMI= 0.7199, NMI= 0.7208, ARI= 0.4974, ACC= 0.5998
0.022150739036605725
Training epoch 1328, recon_loss:0.608104, zinb_loss:0.959371, cluster_loss:0.333250
Clustering 1328: AMI= 0.7212, NMI= 0.7221, ARI= 0.4992, ACC= 0.6006
0.021295655360560283
Training epoch 1329, recon_loss:0.608435, zinb_loss:0.959727, cluster_loss:0.332318
Clustering 1329: AMI= 0.7198, NMI= 0.7207, ARI= 0.4982, ACC= 0.6004
0.021662119793151188
Training epoch 1330, recon_loss:0.608364, zinb_loss:0.959492, cluster_loss:0.333398
Clustering 1330: AMI= 0.7210, NMI= 0.7220, ARI= 0.4985, ACC= 0.6002
0.022557921739484506
Training epoch 1331, recon_loss:0.608483, zinb_loss:0.959691, cluster_loss:0.332146
Clustering 1331: AMI= 0.7200, NMI= 0.7210, ARI= 0.4991, ACC= 0.6007
0.023535160226393584
Training epoch 1332, recon_loss:0.607964, zinb_loss:0.959563, cluster_loss:0.333482
Clustering 1332: AMI= 0.7209, NMI= 0.7218, ARI= 0.4977, ACC= 0.6001
0.023901624658984485
Training epoch 1333, recon_loss:0.608092, zinb_loss:0.959619, cluster_loss:0.332086
Clustering 1333: AMI= 0.7201, NMI= 0.7210, ARI= 0.4988, ACC= 0.6004
0.02426808909157539
Training epoch 1334, recon_loss:0.608564, zinb_loss:0.959640, cluster_loss:0.333526
Clustering 1334: AMI= 0.7207, NMI= 0.7216, ARI= 0.4973, ACC= 0.6000
0.024512398713302658
Training epoch 1335, recon_loss:0.608497, zinb_loss:0.959565, cluster_loss:0.331923
Clustering 1335: AMI= 0.7204, NMI= 0.7214, ARI= 0.4994, ACC= 0.6005
0.025489637200211735
Training epoch 1336, recon_loss:0.608048, zinb_loss:0.959667, cluster_loss:0.333475
Clustering 1336: AMI= 0.7206, NMI= 0.7215, ARI= 0.4970, ACC= 0.5999
0.026181847795105663
Training epoch 1337, recon_loss:0.608060, zinb_loss:0.959429, cluster_loss:0.331898
Clustering 1337: AMI= 0.7207, NMI= 0.7217, ARI= 0.4996, ACC= 0.6006
0.02626328433568142
Training epoch 1338, recon_loss:0.608661, zinb_loss:0.959698, cluster_loss:0.333446
Clustering 1338: AMI= 0.7204, NMI= 0.7213, ARI= 0.4967, ACC= 0.6000
0.026344720876257176
Training epoch 1339, recon_loss:0.608401, zinb_loss:0.959334, cluster_loss:0.331801
Clustering 1339: AMI= 0.7208, NMI= 0.7217, ARI= 0.5000, ACC= 0.6010
0.026792621849423836
Training epoch 1340, recon_loss:0.608291, zinb_loss:0.959710, cluster_loss:0.333379
Clustering 1340: AMI= 0.7202, NMI= 0.7212, ARI= 0.4967, ACC= 0.6001
0.027281241092878373
Training epoch 1341, recon_loss:0.608097, zinb_loss:0.959219, cluster_loss:0.331837
Clustering 1341: AMI= 0.7208, NMI= 0.7217, ARI= 0.5002, ACC= 0.6012
0.027566268984893522
Training epoch 1342, recon_loss:0.608829, zinb_loss:0.959769, cluster_loss:0.333369
Clustering 1342: AMI= 0.7204, NMI= 0.7213, ARI= 0.4968, ACC= 0.6003
0.027444114174029886
Training epoch 1343, recon_loss:0.608350, zinb_loss:0.959213, cluster_loss:0.331835
Clustering 1343: AMI= 0.7209, NMI= 0.7218, ARI= 0.5007, ACC= 0.6021
0.027281241092878373
Training epoch 1344, recon_loss:0.608501, zinb_loss:0.959853, cluster_loss:0.333340
Clustering 1344: AMI= 0.7202, NMI= 0.7212, ARI= 0.4964, ACC= 0.5998
0.027240522822590495
Training epoch 1345, recon_loss:0.608153, zinb_loss:0.959266, cluster_loss:0.331925
Clustering 1345: AMI= 0.7210, NMI= 0.7219, ARI= 0.5012, ACC= 0.6028
0.027199804552302618
Training epoch 1346, recon_loss:0.609064, zinb_loss:0.960054, cluster_loss:0.333353
Clustering 1346: AMI= 0.7201, NMI= 0.7211, ARI= 0.4959, ACC= 0.5991
0.027036931471151104
Training epoch 1347, recon_loss:0.608515, zinb_loss:0.959488, cluster_loss:0.331944
Clustering 1347: AMI= 0.7210, NMI= 0.7219, ARI= 0.5015, ACC= 0.6034
0.027892015147196546
Training epoch 1348, recon_loss:0.608811, zinb_loss:0.960314, cluster_loss:0.333319
Clustering 1348: AMI= 0.7200, NMI= 0.7209, ARI= 0.4958, ACC= 0.5988
0.028217761309499573
Training epoch 1349, recon_loss:0.608484, zinb_loss:0.959792, cluster_loss:0.332018
Clustering 1349: AMI= 0.7209, NMI= 0.7219, ARI= 0.5016, ACC= 0.6039
0.028584225742090477
Training epoch 1350, recon_loss:0.609421, zinb_loss:0.960684, cluster_loss:0.333282
Clustering 1350: AMI= 0.7199, NMI= 0.7208, ARI= 0.4954, ACC= 0.5981
0.029480027688423796
Training epoch 1351, recon_loss:0.608945, zinb_loss:0.960221, cluster_loss:0.332000
Clustering 1351: AMI= 0.7212, NMI= 0.7221, ARI= 0.5019, ACC= 0.6043
0.030375829634757115
Training epoch 1352, recon_loss:0.609311, zinb_loss:0.961008, cluster_loss:0.333196
Clustering 1352: AMI= 0.7200, NMI= 0.7209, ARI= 0.4953, ACC= 0.5974
0.03163809601368134
Training epoch 1353, recon_loss:0.608977, zinb_loss:0.960575, cluster_loss:0.332029
Clustering 1353: AMI= 0.7213, NMI= 0.7223, ARI= 0.5022, ACC= 0.6049
0.032248870067999515
Training epoch 1354, recon_loss:0.609695, zinb_loss:0.961203, cluster_loss:0.333139
Clustering 1354: AMI= 0.7200, NMI= 0.7209, ARI= 0.4952, ACC= 0.5970
0.032696771041166174
Training epoch 1355, recon_loss:0.609232, zinb_loss:0.960812, cluster_loss:0.332047
Clustering 1355: AMI= 0.7213, NMI= 0.7222, ARI= 0.5021, ACC= 0.6048
0.03277820758174193
Training epoch 1356, recon_loss:0.609215, zinb_loss:0.961139, cluster_loss:0.333083
Clustering 1356: AMI= 0.7196, NMI= 0.7205, ARI= 0.4949, ACC= 0.5963
0.03318539028462071
Training epoch 1357, recon_loss:0.609020, zinb_loss:0.960824, cluster_loss:0.332143
Clustering 1357: AMI= 0.7215, NMI= 0.7224, ARI= 0.5020, ACC= 0.6049
0.03273748931145405
Training epoch 1358, recon_loss:0.609429, zinb_loss:0.960903, cluster_loss:0.333078
Clustering 1358: AMI= 0.7198, NMI= 0.7207, ARI= 0.4952, ACC= 0.5964
0.032126715257135875
Training epoch 1359, recon_loss:0.609209, zinb_loss:0.960728, cluster_loss:0.332195
Clustering 1359: AMI= 0.7216, NMI= 0.7225, ARI= 0.5021, ACC= 0.6049
0.03163809601368134
Training epoch 1360, recon_loss:0.609096, zinb_loss:0.960520, cluster_loss:0.333059
Clustering 1360: AMI= 0.7195, NMI= 0.7204, ARI= 0.4953, ACC= 0.5965
0.031108758499938924
Training epoch 1361, recon_loss:0.609073, zinb_loss:0.960504, cluster_loss:0.332297
Clustering 1361: AMI= 0.7214, NMI= 0.7223, ARI= 0.5018, ACC= 0.6047
0.02980577385072682
Training epoch 1362, recon_loss:0.609437, zinb_loss:0.960117, cluster_loss:0.333047
Clustering 1362: AMI= 0.7194, NMI= 0.7203, ARI= 0.4954, ACC= 0.5964
0.028869253634105623
Training epoch 1363, recon_loss:0.609406, zinb_loss:0.960272, cluster_loss:0.332350
Clustering 1363: AMI= 0.7212, NMI= 0.7221, ARI= 0.5016, ACC= 0.6043
0.028014169958060182
Training epoch 1364, recon_loss:0.609037, zinb_loss:0.959677, cluster_loss:0.332996
Clustering 1364: AMI= 0.7194, NMI= 0.7204, ARI= 0.4958, ACC= 0.5973
0.026792621849423836
Training epoch 1365, recon_loss:0.609155, zinb_loss:0.959978, cluster_loss:0.332448
Clustering 1365: AMI= 0.7210, NMI= 0.7219, ARI= 0.5007, ACC= 0.6032
0.0253674823893481
Training epoch 1366, recon_loss:0.609652, zinb_loss:0.959330, cluster_loss:0.332991
Clustering 1366: AMI= 0.7195, NMI= 0.7204, ARI= 0.4962, ACC= 0.5980
0.024105216010423876
Training epoch 1367, recon_loss:0.609606, zinb_loss:0.959745, cluster_loss:0.332435
Clustering 1367: AMI= 0.7209, NMI= 0.7218, ARI= 0.5001, ACC= 0.6023
0.02247648519890875
Training epoch 1368, recon_loss:0.608894, zinb_loss:0.958987, cluster_loss:0.332955
Clustering 1368: AMI= 0.7194, NMI= 0.7203, ARI= 0.4964, ACC= 0.5985
0.020847754387393624
Training epoch 1369, recon_loss:0.608959, zinb_loss:0.959463, cluster_loss:0.332509
Clustering 1369: AMI= 0.7208, NMI= 0.7217, ARI= 0.4995, ACC= 0.6013
0.019300460116454254
Training epoch 1370, recon_loss:0.609403, zinb_loss:0.958779, cluster_loss:0.333045
Clustering 1370: AMI= 0.7194, NMI= 0.7203, ARI= 0.4966, ACC= 0.5988
0.01754957449407549
Training epoch 1371, recon_loss:0.609173, zinb_loss:0.959274, cluster_loss:0.332498
Clustering 1371: AMI= 0.7208, NMI= 0.7218, ARI= 0.4994, ACC= 0.6010
0.01645018119630278
Training epoch 1372, recon_loss:0.608428, zinb_loss:0.958589, cluster_loss:0.333129
Clustering 1372: AMI= 0.7194, NMI= 0.7203, ARI= 0.4966, ACC= 0.5996
0.01490288692536341
Training epoch 1373, recon_loss:0.608404, zinb_loss:0.959074, cluster_loss:0.332614
Clustering 1373: AMI= 0.7208, NMI= 0.7217, ARI= 0.4992, ACC= 0.6005
0.013966366708742213
Training epoch 1374, recon_loss:0.608800, zinb_loss:0.958515, cluster_loss:0.333310
Clustering 1374: AMI= 0.7195, NMI= 0.7204, ARI= 0.4969, ACC= 0.6002
0.01311128303269677
Training epoch 1375, recon_loss:0.608586, zinb_loss:0.958985, cluster_loss:0.332636
Clustering 1375: AMI= 0.7208, NMI= 0.7217, ARI= 0.4993, ACC= 0.6000
0.01311128303269677
Training epoch 1376, recon_loss:0.608093, zinb_loss:0.958448, cluster_loss:0.333427
Clustering 1376: AMI= 0.7194, NMI= 0.7204, ARI= 0.4969, ACC= 0.6004
0.012744818600105868
Training epoch 1377, recon_loss:0.608107, zinb_loss:0.958896, cluster_loss:0.332733
Clustering 1377: AMI= 0.7207, NMI= 0.7217, ARI= 0.4991, ACC= 0.5996
0.012581945518954354
Training epoch 1378, recon_loss:0.608493, zinb_loss:0.958454, cluster_loss:0.333574
Clustering 1378: AMI= 0.7194, NMI= 0.7203, ARI= 0.4970, ACC= 0.6007
0.012785536870393745
Training epoch 1379, recon_loss:0.608381, zinb_loss:0.958882, cluster_loss:0.332743
Clustering 1379: AMI= 0.7207, NMI= 0.7217, ARI= 0.4991, ACC= 0.5992
0.013396310924711918
Training epoch 1380, recon_loss:0.608152, zinb_loss:0.958459, cluster_loss:0.333647
Clustering 1380: AMI= 0.7194, NMI= 0.7203, ARI= 0.4968, ACC= 0.6008
0.013599902276151309
Training epoch 1381, recon_loss:0.608182, zinb_loss:0.958878, cluster_loss:0.332789
Clustering 1381: AMI= 0.7207, NMI= 0.7216, ARI= 0.4986, ACC= 0.5982
0.014373549411620994
Training epoch 1382, recon_loss:0.608621, zinb_loss:0.958526, cluster_loss:0.333715
Clustering 1382: AMI= 0.7195, NMI= 0.7204, ARI= 0.4971, ACC= 0.6015
0.01469929557392402
Training epoch 1383, recon_loss:0.608563, zinb_loss:0.958937, cluster_loss:0.332771
Clustering 1383: AMI= 0.7207, NMI= 0.7216, ARI= 0.4986, ACC= 0.5979
0.015432224439105826
Training epoch 1384, recon_loss:0.608235, zinb_loss:0.958602, cluster_loss:0.333701
Clustering 1384: AMI= 0.7196, NMI= 0.7205, ARI= 0.4976, ACC= 0.6022
0.01600228022313612
Training epoch 1385, recon_loss:0.608342, zinb_loss:0.959019, cluster_loss:0.332809
Clustering 1385: AMI= 0.7207, NMI= 0.7216, ARI= 0.4983, ACC= 0.5973
0.016042998493424
Training epoch 1386, recon_loss:0.608834, zinb_loss:0.958780, cluster_loss:0.333681
Clustering 1386: AMI= 0.7197, NMI= 0.7207, ARI= 0.4981, ACC= 0.6031
0.016816645628893685
Training epoch 1387, recon_loss:0.608804, zinb_loss:0.959191, cluster_loss:0.332772
Clustering 1387: AMI= 0.7206, NMI= 0.7215, ARI= 0.4981, ACC= 0.5969
0.017020236980333076
Training epoch 1388, recon_loss:0.608437, zinb_loss:0.958993, cluster_loss:0.333556
Clustering 1388: AMI= 0.7198, NMI= 0.7208, ARI= 0.4985, ACC= 0.6038
0.017956757196954273
Training epoch 1389, recon_loss:0.608555, zinb_loss:0.959390, cluster_loss:0.332784
Clustering 1389: AMI= 0.7204, NMI= 0.7213, ARI= 0.4978, ACC= 0.5962
0.018160348548393664
Training epoch 1390, recon_loss:0.609077, zinb_loss:0.959327, cluster_loss:0.333418
Clustering 1390: AMI= 0.7199, NMI= 0.7208, ARI= 0.4993, ACC= 0.6048
0.018811840872999714
Training epoch 1391, recon_loss:0.608954, zinb_loss:0.959662, cluster_loss:0.332711
Clustering 1391: AMI= 0.7202, NMI= 0.7211, ARI= 0.4974, ACC= 0.5954
0.018893277413575472
Training epoch 1392, recon_loss:0.608487, zinb_loss:0.959654, cluster_loss:0.333186
Clustering 1392: AMI= 0.7202, NMI= 0.7211, ARI= 0.4999, ACC= 0.6057
0.019504051467893645
Training epoch 1393, recon_loss:0.608480, zinb_loss:0.959866, cluster_loss:0.332716
Clustering 1393: AMI= 0.7201, NMI= 0.7210, ARI= 0.4973, ACC= 0.5951
0.020359135143939087
Training epoch 1394, recon_loss:0.608964, zinb_loss:0.960000, cluster_loss:0.333008
Clustering 1394: AMI= 0.7204, NMI= 0.7213, ARI= 0.5003, ACC= 0.6062
0.020725599576529988
Training epoch 1395, recon_loss:0.608581, zinb_loss:0.960024, cluster_loss:0.332661
Clustering 1395: AMI= 0.7201, NMI= 0.7211, ARI= 0.4976, ACC= 0.5951
0.021377091901136038
Training epoch 1396, recon_loss:0.608302, zinb_loss:0.960194, cluster_loss:0.332823
Clustering 1396: AMI= 0.7205, NMI= 0.7215, ARI= 0.5007, ACC= 0.6067
0.022395048658332993
Training epoch 1397, recon_loss:0.607963, zinb_loss:0.960024, cluster_loss:0.332733
Clustering 1397: AMI= 0.7200, NMI= 0.7209, ARI= 0.4974, ACC= 0.5952
0.022680076550348142
Training epoch 1398, recon_loss:0.608444, zinb_loss:0.960312, cluster_loss:0.332760
Clustering 1398: AMI= 0.7204, NMI= 0.7214, ARI= 0.5008, ACC= 0.6067
0.023453723685817825
Training epoch 1399, recon_loss:0.607860, zinb_loss:0.960000, cluster_loss:0.332796
Clustering 1399: AMI= 0.7198, NMI= 0.7207, ARI= 0.4972, ACC= 0.5955
0.023290850604666315
Training epoch 1400, recon_loss:0.607839, zinb_loss:0.960330, cluster_loss:0.332713
Clustering 1400: AMI= 0.7205, NMI= 0.7214, ARI= 0.5009, ACC= 0.6065
0.023494441956105706
Training epoch 1401, recon_loss:0.607381, zinb_loss:0.959911, cluster_loss:0.332931
Clustering 1401: AMI= 0.7198, NMI= 0.7207, ARI= 0.4970, ACC= 0.5956
0.023331568874954193
Training epoch 1402, recon_loss:0.608067, zinb_loss:0.960354, cluster_loss:0.332730
Clustering 1402: AMI= 0.7206, NMI= 0.7215, ARI= 0.5009, ACC= 0.6064
0.023494441956105706
Training epoch 1403, recon_loss:0.607445, zinb_loss:0.959843, cluster_loss:0.332987
Clustering 1403: AMI= 0.7197, NMI= 0.7207, ARI= 0.4968, ACC= 0.5959
0.023209414064090557
Training epoch 1404, recon_loss:0.607790, zinb_loss:0.960325, cluster_loss:0.332720
Clustering 1404: AMI= 0.7205, NMI= 0.7214, ARI= 0.5009, ACC= 0.6061
0.02316869579380268
Training epoch 1405, recon_loss:0.607267, zinb_loss:0.959748, cluster_loss:0.333057
Clustering 1405: AMI= 0.7199, NMI= 0.7208, ARI= 0.4970, ACC= 0.5963
0.022842949631499652
Training epoch 1406, recon_loss:0.608228, zinb_loss:0.960305, cluster_loss:0.332739
Clustering 1406: AMI= 0.7209, NMI= 0.7218, ARI= 0.5013, ACC= 0.6059
0.02263935828006026
Training epoch 1407, recon_loss:0.607551, zinb_loss:0.959650, cluster_loss:0.333041
Clustering 1407: AMI= 0.7197, NMI= 0.7206, ARI= 0.4967, ACC= 0.5966
0.022680076550348142
Training epoch 1408, recon_loss:0.608040, zinb_loss:0.960209, cluster_loss:0.332728
Clustering 1408: AMI= 0.7208, NMI= 0.7218, ARI= 0.5011, ACC= 0.6052
0.02272079482063602
Training epoch 1409, recon_loss:0.607448, zinb_loss:0.959530, cluster_loss:0.333055
Clustering 1409: AMI= 0.7195, NMI= 0.7204, ARI= 0.4965, ACC= 0.5969
0.022883667901787533
Training epoch 1410, recon_loss:0.608474, zinb_loss:0.960114, cluster_loss:0.332739
Clustering 1410: AMI= 0.7210, NMI= 0.7219, ARI= 0.5011, ACC= 0.6050
0.022680076550348142
Training epoch 1411, recon_loss:0.607766, zinb_loss:0.959422, cluster_loss:0.333011
Clustering 1411: AMI= 0.7195, NMI= 0.7205, ARI= 0.4965, ACC= 0.5972
0.02251720346919663
Training epoch 1412, recon_loss:0.608172, zinb_loss:0.959952, cluster_loss:0.332724
Clustering 1412: AMI= 0.7210, NMI= 0.7219, ARI= 0.5010, ACC= 0.6045
0.022232175577181483
Training epoch 1413, recon_loss:0.607618, zinb_loss:0.959311, cluster_loss:0.333046
Clustering 1413: AMI= 0.7194, NMI= 0.7203, ARI= 0.4965, ACC= 0.5973
0.02247648519890875
Training epoch 1414, recon_loss:0.608541, zinb_loss:0.959822, cluster_loss:0.332750
Clustering 1414: AMI= 0.7210, NMI= 0.7219, ARI= 0.5009, ACC= 0.6043
0.022150739036605725
Training epoch 1415, recon_loss:0.607895, zinb_loss:0.959233, cluster_loss:0.333039
Clustering 1415: AMI= 0.7194, NMI= 0.7204, ARI= 0.4967, ACC= 0.5976
0.022232175577181483
Training epoch 1416, recon_loss:0.608129, zinb_loss:0.959655, cluster_loss:0.332751
Clustering 1416: AMI= 0.7209, NMI= 0.7218, ARI= 0.5007, ACC= 0.6040
0.02272079482063602
Training epoch 1417, recon_loss:0.607697, zinb_loss:0.959172, cluster_loss:0.333110
Clustering 1417: AMI= 0.7194, NMI= 0.7203, ARI= 0.4966, ACC= 0.5978
0.022802231361211775
Training epoch 1418, recon_loss:0.608319, zinb_loss:0.959542, cluster_loss:0.332785
Clustering 1418: AMI= 0.7211, NMI= 0.7220, ARI= 0.5011, ACC= 0.6042
0.023250132334378434
Training epoch 1419, recon_loss:0.607894, zinb_loss:0.959161, cluster_loss:0.333143
Clustering 1419: AMI= 0.7195, NMI= 0.7204, ARI= 0.4966, ACC= 0.5979
0.02357587849668146
Training epoch 1420, recon_loss:0.608097, zinb_loss:0.959413, cluster_loss:0.332790
Clustering 1420: AMI= 0.7213, NMI= 0.7222, ARI= 0.5014, ACC= 0.6043
0.024145934280711757
Training epoch 1421, recon_loss:0.607870, zinb_loss:0.959184, cluster_loss:0.333213
Clustering 1421: AMI= 0.7194, NMI= 0.7203, ARI= 0.4963, ACC= 0.5978
0.024634553524166294
Training epoch 1422, recon_loss:0.607975, zinb_loss:0.959328, cluster_loss:0.332792
Clustering 1422: AMI= 0.7214, NMI= 0.7223, ARI= 0.5019, ACC= 0.6048
0.025815383362514762
Training epoch 1423, recon_loss:0.607976, zinb_loss:0.959276, cluster_loss:0.333276
Clustering 1423: AMI= 0.7192, NMI= 0.7202, ARI= 0.4956, ACC= 0.5972
0.026589030497984445
Training epoch 1424, recon_loss:0.608015, zinb_loss:0.959283, cluster_loss:0.332755
Clustering 1424: AMI= 0.7215, NMI= 0.7224, ARI= 0.5024, ACC= 0.6052
0.026996213200863227
Training epoch 1425, recon_loss:0.608227, zinb_loss:0.959426, cluster_loss:0.333312
Clustering 1425: AMI= 0.7191, NMI= 0.7201, ARI= 0.4952, ACC= 0.5972
0.027892015147196546
Training epoch 1426, recon_loss:0.608041, zinb_loss:0.959265, cluster_loss:0.332674
Clustering 1426: AMI= 0.7215, NMI= 0.7224, ARI= 0.5027, ACC= 0.6055
0.02911356325583289
Training epoch 1427, recon_loss:0.608489, zinb_loss:0.959613, cluster_loss:0.333332
Clustering 1427: AMI= 0.7190, NMI= 0.7199, ARI= 0.4946, ACC= 0.5967
0.030375829634757115
Training epoch 1428, recon_loss:0.608139, zinb_loss:0.959270, cluster_loss:0.332583
Clustering 1428: AMI= 0.7213, NMI= 0.7222, ARI= 0.5026, ACC= 0.6054
0.03143450466224195
Training epoch 1429, recon_loss:0.608760, zinb_loss:0.959809, cluster_loss:0.333331
Clustering 1429: AMI= 0.7188, NMI= 0.7197, ARI= 0.4942, ACC= 0.5966
0.03290036239260556
Training epoch 1430, recon_loss:0.608195, zinb_loss:0.959273, cluster_loss:0.332512
Clustering 1430: AMI= 0.7211, NMI= 0.7220, ARI= 0.5023, ACC= 0.6049
0.033470418176635854
Training epoch 1431, recon_loss:0.608887, zinb_loss:0.959959, cluster_loss:0.333346
Clustering 1431: AMI= 0.7188, NMI= 0.7198, ARI= 0.4939, ACC= 0.5965
0.034162628771529785
Training epoch 1432, recon_loss:0.608211, zinb_loss:0.959248, cluster_loss:0.332515
Clustering 1432: AMI= 0.7210, NMI= 0.7219, ARI= 0.5023, ACC= 0.6048
0.03424406531210554
Training epoch 1433, recon_loss:0.608868, zinb_loss:0.960035, cluster_loss:0.333386
Clustering 1433: AMI= 0.7189, NMI= 0.7198, ARI= 0.4940, ACC= 0.5967
0.03436622012296918
Training epoch 1434, recon_loss:0.608019, zinb_loss:0.959180, cluster_loss:0.332616
Clustering 1434: AMI= 0.7209, NMI= 0.7219, ARI= 0.5020, ACC= 0.6042
0.033714727798363125
Training epoch 1435, recon_loss:0.608671, zinb_loss:0.960032, cluster_loss:0.333453
Clustering 1435: AMI= 0.7191, NMI= 0.7200, ARI= 0.4942, ACC= 0.5969
0.033144672014332834
Training epoch 1436, recon_loss:0.607844, zinb_loss:0.959089, cluster_loss:0.332778
Clustering 1436: AMI= 0.7210, NMI= 0.7219, ARI= 0.5017, ACC= 0.6037
0.032248870067999515
Training epoch 1437, recon_loss:0.608502, zinb_loss:0.959991, cluster_loss:0.333523
Clustering 1437: AMI= 0.7192, NMI= 0.7201, ARI= 0.4947, ACC= 0.5973
0.03131234985137831
Training epoch 1438, recon_loss:0.607608, zinb_loss:0.958988, cluster_loss:0.332953
Clustering 1438: AMI= 0.7207, NMI= 0.7216, ARI= 0.5011, ACC= 0.6032
0.03029439309418136
Training epoch 1439, recon_loss:0.608373, zinb_loss:0.959957, cluster_loss:0.333576
Clustering 1439: AMI= 0.7193, NMI= 0.7202, ARI= 0.4951, ACC= 0.5978
0.02939859114784804
Training epoch 1440, recon_loss:0.607712, zinb_loss:0.958911, cluster_loss:0.333112
Clustering 1440: AMI= 0.7206, NMI= 0.7216, ARI= 0.5006, ACC= 0.6027
0.028869253634105623
Training epoch 1441, recon_loss:0.608503, zinb_loss:0.959932, cluster_loss:0.333571
Clustering 1441: AMI= 0.7194, NMI= 0.7203, ARI= 0.4956, ACC= 0.5978
0.02781057860662079
Training epoch 1442, recon_loss:0.607560, zinb_loss:0.958836, cluster_loss:0.333231
Clustering 1442: AMI= 0.7207, NMI= 0.7217, ARI= 0.5005, ACC= 0.6028
0.02695549493057535
Training epoch 1443, recon_loss:0.608532, zinb_loss:0.959927, cluster_loss:0.333545
Clustering 1443: AMI= 0.7196, NMI= 0.7205, ARI= 0.4962, ACC= 0.5983
0.025815383362514762
Training epoch 1444, recon_loss:0.607957, zinb_loss:0.958794, cluster_loss:0.333327
Clustering 1444: AMI= 0.7205, NMI= 0.7214, ARI= 0.4999, ACC= 0.6024
0.025001017956757198
Training epoch 1445, recon_loss:0.608907, zinb_loss:0.959919, cluster_loss:0.333430
Clustering 1445: AMI= 0.7192, NMI= 0.7201, ARI= 0.4964, ACC= 0.5984
0.023738751577832975
Training epoch 1446, recon_loss:0.608127, zinb_loss:0.958754, cluster_loss:0.333377
Clustering 1446: AMI= 0.7203, NMI= 0.7212, ARI= 0.4996, ACC= 0.6021
0.022761513090923897
Training epoch 1447, recon_loss:0.609028, zinb_loss:0.959878, cluster_loss:0.333296
Clustering 1447: AMI= 0.7195, NMI= 0.7204, ARI= 0.4971, ACC= 0.5986
0.02178427460401482
Training epoch 1448, recon_loss:0.608713, zinb_loss:0.958751, cluster_loss:0.333423
Clustering 1448: AMI= 0.7204, NMI= 0.7213, ARI= 0.4990, ACC= 0.6017
0.02048128995480272
Training epoch 1449, recon_loss:0.609297, zinb_loss:0.959794, cluster_loss:0.333130
Clustering 1449: AMI= 0.7196, NMI= 0.7205, ARI= 0.4979, ACC= 0.5993
0.019748361089620914
Training epoch 1450, recon_loss:0.608689, zinb_loss:0.958740, cluster_loss:0.333435
Clustering 1450: AMI= 0.7202, NMI= 0.7211, ARI= 0.4983, ACC= 0.6012
0.018323221629545177
Training epoch 1451, recon_loss:0.609042, zinb_loss:0.959688, cluster_loss:0.333040
Clustering 1451: AMI= 0.7200, NMI= 0.7209, ARI= 0.4985, ACC= 0.5996
0.01710167352090883
Training epoch 1452, recon_loss:0.609051, zinb_loss:0.958763, cluster_loss:0.333452
Clustering 1452: AMI= 0.7201, NMI= 0.7210, ARI= 0.4981, ACC= 0.6009
0.016042998493424
Training epoch 1453, recon_loss:0.609048, zinb_loss:0.959591, cluster_loss:0.332955
Clustering 1453: AMI= 0.7200, NMI= 0.7209, ARI= 0.4993, ACC= 0.6003
0.014821450384787655
Training epoch 1454, recon_loss:0.608903, zinb_loss:0.958783, cluster_loss:0.333443
Clustering 1454: AMI= 0.7202, NMI= 0.7211, ARI= 0.4978, ACC= 0.6007
0.014373549411620994
Training epoch 1455, recon_loss:0.608736, zinb_loss:0.959493, cluster_loss:0.332928
Clustering 1455: AMI= 0.7202, NMI= 0.7211, ARI= 0.4996, ACC= 0.6006
0.014088521519605848
Training epoch 1456, recon_loss:0.608992, zinb_loss:0.958812, cluster_loss:0.333439
Clustering 1456: AMI= 0.7202, NMI= 0.7211, ARI= 0.4977, ACC= 0.6006
0.013844211897878577
Training epoch 1457, recon_loss:0.608628, zinb_loss:0.959410, cluster_loss:0.332906
Clustering 1457: AMI= 0.7203, NMI= 0.7212, ARI= 0.4999, ACC= 0.6006
0.014169958060181604
Training epoch 1458, recon_loss:0.608811, zinb_loss:0.958826, cluster_loss:0.333435
Clustering 1458: AMI= 0.7200, NMI= 0.7209, ARI= 0.4973, ACC= 0.6004
0.013925648438454334
Training epoch 1459, recon_loss:0.608420, zinb_loss:0.959330, cluster_loss:0.332932
Clustering 1459: AMI= 0.7203, NMI= 0.7212, ARI= 0.5000, ACC= 0.6006
0.013884930168166457
Training epoch 1460, recon_loss:0.608782, zinb_loss:0.958824, cluster_loss:0.333453
Clustering 1460: AMI= 0.7199, NMI= 0.7208, ARI= 0.4969, ACC= 0.6002
0.013844211897878577
Training epoch 1461, recon_loss:0.608339, zinb_loss:0.959260, cluster_loss:0.332985
Clustering 1461: AMI= 0.7205, NMI= 0.7214, ARI= 0.5000, ACC= 0.6004
0.013925648438454334
Training epoch 1462, recon_loss:0.608640, zinb_loss:0.958799, cluster_loss:0.333479
Clustering 1462: AMI= 0.7199, NMI= 0.7208, ARI= 0.4969, ACC= 0.6004
0.013722057087014943
Training epoch 1463, recon_loss:0.608207, zinb_loss:0.959186, cluster_loss:0.333076
Clustering 1463: AMI= 0.7204, NMI= 0.7214, ARI= 0.5000, ACC= 0.6003
0.014007084979030091
Training epoch 1464, recon_loss:0.608501, zinb_loss:0.958767, cluster_loss:0.333504
Clustering 1464: AMI= 0.7201, NMI= 0.7210, ARI= 0.4971, ACC= 0.6004
0.013844211897878577
Training epoch 1465, recon_loss:0.608134, zinb_loss:0.959118, cluster_loss:0.333166
Clustering 1465: AMI= 0.7200, NMI= 0.7210, ARI= 0.4995, ACC= 0.5998
0.01335559265442404
Training epoch 1466, recon_loss:0.608402, zinb_loss:0.958729, cluster_loss:0.333517
Clustering 1466: AMI= 0.7201, NMI= 0.7210, ARI= 0.4971, ACC= 0.6007
0.013681338816727066
Training epoch 1467, recon_loss:0.608120, zinb_loss:0.959049, cluster_loss:0.333263
Clustering 1467: AMI= 0.7199, NMI= 0.7208, ARI= 0.4990, ACC= 0.5997
0.013314874384136161
Training epoch 1468, recon_loss:0.608356, zinb_loss:0.958696, cluster_loss:0.333517
Clustering 1468: AMI= 0.7202, NMI= 0.7211, ARI= 0.4973, ACC= 0.6009
0.013192719573272527
Training epoch 1469, recon_loss:0.608147, zinb_loss:0.958980, cluster_loss:0.333333
Clustering 1469: AMI= 0.7200, NMI= 0.7209, ARI= 0.4992, ACC= 0.5999
0.012948409951545259
Training epoch 1470, recon_loss:0.608174, zinb_loss:0.958648, cluster_loss:0.333483
Clustering 1470: AMI= 0.7200, NMI= 0.7209, ARI= 0.4976, ACC= 0.6011
0.013396310924711918
Training epoch 1471, recon_loss:0.608090, zinb_loss:0.958897, cluster_loss:0.333424
Clustering 1471: AMI= 0.7202, NMI= 0.7211, ARI= 0.4990, ACC= 0.5995
0.014088521519605848
Training epoch 1472, recon_loss:0.608157, zinb_loss:0.958622, cluster_loss:0.333425
Clustering 1472: AMI= 0.7202, NMI= 0.7212, ARI= 0.4979, ACC= 0.6013
0.014821450384787655
Training epoch 1473, recon_loss:0.608156, zinb_loss:0.958825, cluster_loss:0.333487
Clustering 1473: AMI= 0.7197, NMI= 0.7207, ARI= 0.4984, ACC= 0.5993
0.015269351357954314
Training epoch 1474, recon_loss:0.608100, zinb_loss:0.958603, cluster_loss:0.333299
Clustering 1474: AMI= 0.7204, NMI= 0.7214, ARI= 0.4981, ACC= 0.6013
0.016735209088317927
Training epoch 1475, recon_loss:0.608211, zinb_loss:0.958765, cluster_loss:0.333534
Clustering 1475: AMI= 0.7197, NMI= 0.7206, ARI= 0.4981, ACC= 0.5995
0.017712447575227004
Training epoch 1476, recon_loss:0.608097, zinb_loss:0.958629, cluster_loss:0.333097
Clustering 1476: AMI= 0.7206, NMI= 0.7215, ARI= 0.4986, ACC= 0.6015
0.019422614927317887
Training epoch 1477, recon_loss:0.608308, zinb_loss:0.958778, cluster_loss:0.333532
Clustering 1477: AMI= 0.7195, NMI= 0.7204, ARI= 0.4973, ACC= 0.5988
0.0208884726576815
Training epoch 1478, recon_loss:0.608209, zinb_loss:0.958759, cluster_loss:0.332785
Clustering 1478: AMI= 0.7205, NMI= 0.7214, ARI= 0.4991, ACC= 0.6019
0.02296510444236329
Training epoch 1479, recon_loss:0.608443, zinb_loss:0.958940, cluster_loss:0.333476
Clustering 1479: AMI= 0.7193, NMI= 0.7203, ARI= 0.4967, ACC= 0.5984
0.024186652550999634
Training epoch 1480, recon_loss:0.608288, zinb_loss:0.959072, cluster_loss:0.332424
Clustering 1480: AMI= 0.7202, NMI= 0.7212, ARI= 0.4993, ACC= 0.6023
0.026181847795105663
Training epoch 1481, recon_loss:0.608474, zinb_loss:0.959308, cluster_loss:0.333398
Clustering 1481: AMI= 0.7196, NMI= 0.7205, ARI= 0.4965, ACC= 0.5983
0.027729142066045036
Training epoch 1482, recon_loss:0.608314, zinb_loss:0.959540, cluster_loss:0.332132
Clustering 1482: AMI= 0.7204, NMI= 0.7214, ARI= 0.4996, ACC= 0.6026
0.02870638055295411
Training epoch 1483, recon_loss:0.608297, zinb_loss:0.959743, cluster_loss:0.333350
Clustering 1483: AMI= 0.7198, NMI= 0.7208, ARI= 0.4966, ACC= 0.5983
0.02956146422899955
Training epoch 1484, recon_loss:0.608165, zinb_loss:0.959942, cluster_loss:0.332051
Clustering 1484: AMI= 0.7204, NMI= 0.7213, ARI= 0.4998, ACC= 0.6027
0.02943930941813592
Training epoch 1485, recon_loss:0.607902, zinb_loss:0.960022, cluster_loss:0.333413
Clustering 1485: AMI= 0.7199, NMI= 0.7208, ARI= 0.4969, ACC= 0.5986
0.02870638055295411
Training epoch 1486, recon_loss:0.607847, zinb_loss:0.960117, cluster_loss:0.332185
Clustering 1486: AMI= 0.7204, NMI= 0.7213, ARI= 0.4997, ACC= 0.6028
0.0276069872551814
Training epoch 1487, recon_loss:0.607436, zinb_loss:0.960076, cluster_loss:0.333540
Clustering 1487: AMI= 0.7204, NMI= 0.7213, ARI= 0.4975, ACC= 0.5990
0.02650759395740869
Training epoch 1488, recon_loss:0.607514, zinb_loss:0.960092, cluster_loss:0.332418
Clustering 1488: AMI= 0.7204, NMI= 0.7213, ARI= 0.4996, ACC= 0.6030
0.02532676411906022
Training epoch 1489, recon_loss:0.607049, zinb_loss:0.960007, cluster_loss:0.333685
Clustering 1489: AMI= 0.7202, NMI= 0.7211, ARI= 0.4975, ACC= 0.5991
0.023698033307545094
Training epoch 1490, recon_loss:0.607304, zinb_loss:0.959987, cluster_loss:0.332672
Clustering 1490: AMI= 0.7201, NMI= 0.7210, ARI= 0.4992, ACC= 0.6027
0.022313612117757238
Training epoch 1491, recon_loss:0.606829, zinb_loss:0.959904, cluster_loss:0.333800
Clustering 1491: AMI= 0.7202, NMI= 0.7211, ARI= 0.4976, ACC= 0.5991
0.0208884726576815
Training epoch 1492, recon_loss:0.607259, zinb_loss:0.959871, cluster_loss:0.332906
Clustering 1492: AMI= 0.7200, NMI= 0.7209, ARI= 0.4988, ACC= 0.6024
0.019544769738181523
Training epoch 1493, recon_loss:0.606786, zinb_loss:0.959808, cluster_loss:0.333854
Clustering 1493: AMI= 0.7201, NMI= 0.7211, ARI= 0.4976, ACC= 0.5990
0.018608249521560323
Training epoch 1494, recon_loss:0.607413, zinb_loss:0.959778, cluster_loss:0.333113
Clustering 1494: AMI= 0.7197, NMI= 0.7206, ARI= 0.4981, ACC= 0.6021
0.01738670141292398
Training epoch 1495, recon_loss:0.606947, zinb_loss:0.959722, cluster_loss:0.333829
Clustering 1495: AMI= 0.7202, NMI= 0.7211, ARI= 0.4977, ACC= 0.5988
0.016572336007166417
Training epoch 1496, recon_loss:0.607549, zinb_loss:0.959703, cluster_loss:0.333281
Clustering 1496: AMI= 0.7195, NMI= 0.7204, ARI= 0.4975, ACC= 0.6020
0.015839407141984608
Training epoch 1497, recon_loss:0.607159, zinb_loss:0.959656, cluster_loss:0.333733
Clustering 1497: AMI= 0.7205, NMI= 0.7215, ARI= 0.4981, ACC= 0.5986
0.01559509752025734
Training epoch 1498, recon_loss:0.608075, zinb_loss:0.959657, cluster_loss:0.333412
Clustering 1498: AMI= 0.7195, NMI= 0.7204, ARI= 0.4974, ACC= 0.6021
0.014984323465939167
Training epoch 1499, recon_loss:0.607664, zinb_loss:0.959609, cluster_loss:0.333517
Clustering 1499: AMI= 0.7206, NMI= 0.7215, ARI= 0.4983, ACC= 0.5986
0.014169958060181604
Training epoch 1500, recon_loss:0.608307, zinb_loss:0.959605, cluster_loss:0.333476
Clustering 1500: AMI= 0.7192, NMI= 0.7202, ARI= 0.4970, ACC= 0.6017
0.013884930168166457
Training epoch 1501, recon_loss:0.607977, zinb_loss:0.959574, cluster_loss:0.333260
Clustering 1501: AMI= 0.7205, NMI= 0.7214, ARI= 0.4985, ACC= 0.5988
0.014292112871045239
Training epoch 1502, recon_loss:0.608844, zinb_loss:0.959581, cluster_loss:0.333482
Clustering 1502: AMI= 0.7192, NMI= 0.7201, ARI= 0.4968, ACC= 0.6017
0.01449570422248463
Training epoch 1503, recon_loss:0.608369, zinb_loss:0.959532, cluster_loss:0.332963
Clustering 1503: AMI= 0.7205, NMI= 0.7214, ARI= 0.4987, ACC= 0.5986
0.015757970601408853
Training epoch 1504, recon_loss:0.608963, zinb_loss:0.959536, cluster_loss:0.333446
Clustering 1504: AMI= 0.7191, NMI= 0.7200, ARI= 0.4969, ACC= 0.6019
0.016531617736878536
Training epoch 1505, recon_loss:0.608396, zinb_loss:0.959466, cluster_loss:0.332745
Clustering 1505: AMI= 0.7202, NMI= 0.7211, ARI= 0.4983, ACC= 0.5980
0.01783460238609064
Training epoch 1506, recon_loss:0.608976, zinb_loss:0.959494, cluster_loss:0.333451
Clustering 1506: AMI= 0.7193, NMI= 0.7202, ARI= 0.4971, ACC= 0.6022
0.01885255914328759
Training epoch 1507, recon_loss:0.608362, zinb_loss:0.959375, cluster_loss:0.332569
Clustering 1507: AMI= 0.7202, NMI= 0.7211, ARI= 0.4983, ACC= 0.5981
0.020155543792499696
Training epoch 1508, recon_loss:0.608819, zinb_loss:0.959433, cluster_loss:0.333489
Clustering 1508: AMI= 0.7194, NMI= 0.7203, ARI= 0.4973, ACC= 0.6023
0.020847754387393624
Training epoch 1509, recon_loss:0.608223, zinb_loss:0.959260, cluster_loss:0.332459
Clustering 1509: AMI= 0.7200, NMI= 0.7210, ARI= 0.4982, ACC= 0.5982
0.021947147685166334
Training epoch 1510, recon_loss:0.608614, zinb_loss:0.959375, cluster_loss:0.333571
Clustering 1510: AMI= 0.7193, NMI= 0.7202, ARI= 0.4973, ACC= 0.6021
0.022598640009772384
Training epoch 1511, recon_loss:0.608064, zinb_loss:0.959155, cluster_loss:0.332409
Clustering 1511: AMI= 0.7201, NMI= 0.7211, ARI= 0.4984, ACC= 0.5984
0.02263935828006026
Training epoch 1512, recon_loss:0.608461, zinb_loss:0.959325, cluster_loss:0.333679
Clustering 1512: AMI= 0.7192, NMI= 0.7202, ARI= 0.4969, ACC= 0.6019
0.023535160226393584
Training epoch 1513, recon_loss:0.607920, zinb_loss:0.959069, cluster_loss:0.332399
Clustering 1513: AMI= 0.7201, NMI= 0.7210, ARI= 0.4987, ACC= 0.5991
0.02382018811840873
Training epoch 1514, recon_loss:0.608356, zinb_loss:0.959302, cluster_loss:0.333805
Clustering 1514: AMI= 0.7193, NMI= 0.7202, ARI= 0.4965, ACC= 0.6013
0.02402377946984812
Training epoch 1515, recon_loss:0.607783, zinb_loss:0.959032, cluster_loss:0.332412
Clustering 1515: AMI= 0.7201, NMI= 0.7210, ARI= 0.4990, ACC= 0.5997
0.02402377946984812
Training epoch 1516, recon_loss:0.608271, zinb_loss:0.959331, cluster_loss:0.333923
Clustering 1516: AMI= 0.7193, NMI= 0.7202, ARI= 0.4959, ACC= 0.6007
0.024512398713302658
Training epoch 1517, recon_loss:0.607678, zinb_loss:0.959068, cluster_loss:0.332433
Clustering 1517: AMI= 0.7203, NMI= 0.7212, ARI= 0.4994, ACC= 0.6004
0.02426808909157539
Training epoch 1518, recon_loss:0.608249, zinb_loss:0.959446, cluster_loss:0.334028
Clustering 1518: AMI= 0.7193, NMI= 0.7203, ARI= 0.4957, ACC= 0.6005
0.024430962172726903
Training epoch 1519, recon_loss:0.607615, zinb_loss:0.959189, cluster_loss:0.332452
Clustering 1519: AMI= 0.7203, NMI= 0.7212, ARI= 0.4996, ACC= 0.6009
0.025001017956757198
Training epoch 1520, recon_loss:0.608242, zinb_loss:0.959657, cluster_loss:0.334098
Clustering 1520: AMI= 0.7190, NMI= 0.7199, ARI= 0.4949, ACC= 0.5996
0.025286045848772344
Training epoch 1521, recon_loss:0.607568, zinb_loss:0.959400, cluster_loss:0.332471
Clustering 1521: AMI= 0.7203, NMI= 0.7213, ARI= 0.5001, ACC= 0.6019
0.02622256606539354
Training epoch 1522, recon_loss:0.608359, zinb_loss:0.959958, cluster_loss:0.334139
Clustering 1522: AMI= 0.7190, NMI= 0.7199, ARI= 0.4945, ACC= 0.5990
0.027199804552302618
Training epoch 1523, recon_loss:0.607604, zinb_loss:0.959673, cluster_loss:0.332486
Clustering 1523: AMI= 0.7204, NMI= 0.7213, ARI= 0.5004, ACC= 0.6024
0.02833991612036321
Training epoch 1524, recon_loss:0.608426, zinb_loss:0.960296, cluster_loss:0.334126
Clustering 1524: AMI= 0.7189, NMI= 0.7198, ARI= 0.4944, ACC= 0.5986
0.02915428152612077
Training epoch 1525, recon_loss:0.607629, zinb_loss:0.959951, cluster_loss:0.332524
Clustering 1525: AMI= 0.7207, NMI= 0.7216, ARI= 0.5012, ACC= 0.6034
0.030375829634757115
Training epoch 1526, recon_loss:0.608786, zinb_loss:0.960607, cluster_loss:0.334083
Clustering 1526: AMI= 0.7189, NMI= 0.7198, ARI= 0.4941, ACC= 0.5983
0.030986603689075288
Training epoch 1527, recon_loss:0.607841, zinb_loss:0.960173, cluster_loss:0.332556
Clustering 1527: AMI= 0.7208, NMI= 0.7218, ARI= 0.5016, ACC= 0.6037
0.031515941202817706
Training epoch 1528, recon_loss:0.608799, zinb_loss:0.960801, cluster_loss:0.333992
Clustering 1528: AMI= 0.7188, NMI= 0.7197, ARI= 0.4940, ACC= 0.5978
0.03143450466224195
Training epoch 1529, recon_loss:0.607878, zinb_loss:0.960271, cluster_loss:0.332620
Clustering 1529: AMI= 0.7208, NMI= 0.7218, ARI= 0.5020, ACC= 0.6043
0.03143450466224195
Training epoch 1530, recon_loss:0.608983, zinb_loss:0.960837, cluster_loss:0.333932
Clustering 1530: AMI= 0.7187, NMI= 0.7196, ARI= 0.4943, ACC= 0.5978
0.030864448878211652
Training epoch 1531, recon_loss:0.607930, zinb_loss:0.960215, cluster_loss:0.332726
Clustering 1531: AMI= 0.7209, NMI= 0.7218, ARI= 0.5021, ACC= 0.6045
0.029968646931878333
Training epoch 1532, recon_loss:0.608523, zinb_loss:0.960707, cluster_loss:0.333899
Clustering 1532: AMI= 0.7188, NMI= 0.7197, ARI= 0.4944, ACC= 0.5977
0.02956146422899955
Training epoch 1533, recon_loss:0.607618, zinb_loss:0.960117, cluster_loss:0.332851
Clustering 1533: AMI= 0.7206, NMI= 0.7215, ARI= 0.5018, ACC= 0.6043
0.0289099719043935
Training epoch 1534, recon_loss:0.608371, zinb_loss:0.960546, cluster_loss:0.333925
Clustering 1534: AMI= 0.7188, NMI= 0.7198, ARI= 0.4945, ACC= 0.5977
0.028380634390651086
Training epoch 1535, recon_loss:0.607447, zinb_loss:0.959953, cluster_loss:0.332985
Clustering 1535: AMI= 0.7205, NMI= 0.7214, ARI= 0.5016, ACC= 0.6042
0.027892015147196546
Training epoch 1536, recon_loss:0.608425, zinb_loss:0.960369, cluster_loss:0.333979
Clustering 1536: AMI= 0.7189, NMI= 0.7198, ARI= 0.4945, ACC= 0.5975
0.027688423795757155
Training epoch 1537, recon_loss:0.607513, zinb_loss:0.959788, cluster_loss:0.333027
Clustering 1537: AMI= 0.7203, NMI= 0.7212, ARI= 0.5016, ACC= 0.6042
0.027688423795757155
Training epoch 1538, recon_loss:0.608391, zinb_loss:0.960191, cluster_loss:0.334021
Clustering 1538: AMI= 0.7189, NMI= 0.7199, ARI= 0.4945, ACC= 0.5976
0.027240522822590495
Training epoch 1539, recon_loss:0.607583, zinb_loss:0.959596, cluster_loss:0.333054
Clustering 1539: AMI= 0.7202, NMI= 0.7211, ARI= 0.5017, ACC= 0.6039
0.02622256606539354
Training epoch 1540, recon_loss:0.608443, zinb_loss:0.960045, cluster_loss:0.334045
Clustering 1540: AMI= 0.7189, NMI= 0.7198, ARI= 0.4945, ACC= 0.5979
0.02585610163280264
Training epoch 1541, recon_loss:0.607745, zinb_loss:0.959400, cluster_loss:0.332992
Clustering 1541: AMI= 0.7202, NMI= 0.7212, ARI= 0.5019, ACC= 0.6038
0.02561179201107537
Training epoch 1542, recon_loss:0.608658, zinb_loss:0.959905, cluster_loss:0.334030
Clustering 1542: AMI= 0.7191, NMI= 0.7201, ARI= 0.4947, ACC= 0.5981
0.0253674823893481
Training epoch 1543, recon_loss:0.607969, zinb_loss:0.959186, cluster_loss:0.332925
Clustering 1543: AMI= 0.7200, NMI= 0.7209, ARI= 0.5018, ACC= 0.6036
0.025163891037908708
Training epoch 1544, recon_loss:0.608847, zinb_loss:0.959751, cluster_loss:0.334002
Clustering 1544: AMI= 0.7194, NMI= 0.7203, ARI= 0.4951, ACC= 0.5986
0.02447168044301478
Training epoch 1545, recon_loss:0.608129, zinb_loss:0.958958, cluster_loss:0.332877
Clustering 1545: AMI= 0.7198, NMI= 0.7207, ARI= 0.5016, ACC= 0.6032
0.023983061199560243
Training epoch 1546, recon_loss:0.608747, zinb_loss:0.959566, cluster_loss:0.333996
Clustering 1546: AMI= 0.7195, NMI= 0.7204, ARI= 0.4953, ACC= 0.5991
0.023046540982939043
Training epoch 1547, recon_loss:0.608019, zinb_loss:0.958760, cluster_loss:0.332906
Clustering 1547: AMI= 0.7200, NMI= 0.7209, ARI= 0.5013, ACC= 0.6028
0.02141781017142392
Training epoch 1548, recon_loss:0.608556, zinb_loss:0.959407, cluster_loss:0.334003
Clustering 1548: AMI= 0.7197, NMI= 0.7206, ARI= 0.4955, ACC= 0.5994
0.020807036117105746
Training epoch 1549, recon_loss:0.607865, zinb_loss:0.958596, cluster_loss:0.332994
Clustering 1549: AMI= 0.7199, NMI= 0.7208, ARI= 0.5009, ACC= 0.6023
0.01938189665703001
Training epoch 1550, recon_loss:0.608295, zinb_loss:0.959279, cluster_loss:0.334008
Clustering 1550: AMI= 0.7195, NMI= 0.7204, ARI= 0.4955, ACC= 0.5997
0.01873040433242396
Training epoch 1551, recon_loss:0.607677, zinb_loss:0.958495, cluster_loss:0.333093
Clustering 1551: AMI= 0.7198, NMI= 0.7207, ARI= 0.5005, ACC= 0.6020
0.01803819373753003
Training epoch 1552, recon_loss:0.608181, zinb_loss:0.959229, cluster_loss:0.333982
Clustering 1552: AMI= 0.7194, NMI= 0.7204, ARI= 0.4955, ACC= 0.5997
0.017060955250620954
Training epoch 1553, recon_loss:0.607609, zinb_loss:0.958455, cluster_loss:0.333149
Clustering 1553: AMI= 0.7196, NMI= 0.7205, ARI= 0.4996, ACC= 0.6008
0.016368744655727026
Training epoch 1554, recon_loss:0.608127, zinb_loss:0.959255, cluster_loss:0.333908
Clustering 1554: AMI= 0.7194, NMI= 0.7203, ARI= 0.4958, ACC= 0.6001
0.015920843682560366
Training epoch 1555, recon_loss:0.607584, zinb_loss:0.958479, cluster_loss:0.333149
Clustering 1555: AMI= 0.7199, NMI= 0.7208, ARI= 0.4993, ACC= 0.6000
0.015554379249969462
Training epoch 1556, recon_loss:0.608123, zinb_loss:0.959351, cluster_loss:0.333777
Clustering 1556: AMI= 0.7193, NMI= 0.7202, ARI= 0.4961, ACC= 0.6005
0.015513660979681583
Training epoch 1557, recon_loss:0.607568, zinb_loss:0.958570, cluster_loss:0.333113
Clustering 1557: AMI= 0.7197, NMI= 0.7206, ARI= 0.4986, ACC= 0.5992
0.015676534060833094
Training epoch 1558, recon_loss:0.608094, zinb_loss:0.959501, cluster_loss:0.333603
Clustering 1558: AMI= 0.7191, NMI= 0.7200, ARI= 0.4964, ACC= 0.6010
0.015310069628242192
Training epoch 1559, recon_loss:0.607488, zinb_loss:0.958726, cluster_loss:0.333097
Clustering 1559: AMI= 0.7197, NMI= 0.7206, ARI= 0.4982, ACC= 0.5985
0.015391506168817948
Training epoch 1560, recon_loss:0.607997, zinb_loss:0.959683, cluster_loss:0.333430
Clustering 1560: AMI= 0.7194, NMI= 0.7203, ARI= 0.4971, ACC= 0.6021
0.015432224439105826
Training epoch 1561, recon_loss:0.607345, zinb_loss:0.958944, cluster_loss:0.333142
Clustering 1561: AMI= 0.7195, NMI= 0.7205, ARI= 0.4976, ACC= 0.5976
0.015432224439105826
Training epoch 1562, recon_loss:0.607895, zinb_loss:0.959889, cluster_loss:0.333296
Clustering 1562: AMI= 0.7195, NMI= 0.7204, ARI= 0.4980, ACC= 0.6032
0.01579868887169673
Training epoch 1563, recon_loss:0.607267, zinb_loss:0.959220, cluster_loss:0.333259
Clustering 1563: AMI= 0.7198, NMI= 0.7207, ARI= 0.4974, ACC= 0.5970
0.016572336007166417
Training epoch 1564, recon_loss:0.607824, zinb_loss:0.960106, cluster_loss:0.333206
Clustering 1564: AMI= 0.7198, NMI= 0.7208, ARI= 0.4991, ACC= 0.6044
0.01714239179119671
Training epoch 1565, recon_loss:0.607303, zinb_loss:0.959532, cluster_loss:0.333400
Clustering 1565: AMI= 0.7196, NMI= 0.7205, ARI= 0.4972, ACC= 0.5967
0.018363939899833055
Training epoch 1566, recon_loss:0.607685, zinb_loss:0.960314, cluster_loss:0.333142
Clustering 1566: AMI= 0.7203, NMI= 0.7212, ARI= 0.5000, ACC= 0.6054
0.019504051467893645
Training epoch 1567, recon_loss:0.607348, zinb_loss:0.959846, cluster_loss:0.333537
Clustering 1567: AMI= 0.7194, NMI= 0.7203, ARI= 0.4967, ACC= 0.5961
0.020399853414226964
Training epoch 1568, recon_loss:0.607330, zinb_loss:0.960478, cluster_loss:0.333101
Clustering 1568: AMI= 0.7204, NMI= 0.7214, ARI= 0.5013, ACC= 0.6065
0.02153996498228755
Training epoch 1569, recon_loss:0.607265, zinb_loss:0.960119, cluster_loss:0.333669
Clustering 1569: AMI= 0.7194, NMI= 0.7203, ARI= 0.4963, ACC= 0.5959
0.023331568874954193
Training epoch 1570, recon_loss:0.607236, zinb_loss:0.960596, cluster_loss:0.333108
Clustering 1570: AMI= 0.7208, NMI= 0.7217, ARI= 0.5025, ACC= 0.6075
0.024960299686469317
Training epoch 1571, recon_loss:0.607349, zinb_loss:0.960313, cluster_loss:0.333748
Clustering 1571: AMI= 0.7192, NMI= 0.7202, ARI= 0.4957, ACC= 0.5956
0.02626328433568142
Training epoch 1572, recon_loss:0.607144, zinb_loss:0.960622, cluster_loss:0.333149
Clustering 1572: AMI= 0.7208, NMI= 0.7217, ARI= 0.5029, ACC= 0.6076
0.027240522822590495
Training epoch 1573, recon_loss:0.607403, zinb_loss:0.960418, cluster_loss:0.333816
Clustering 1573: AMI= 0.7190, NMI= 0.7199, ARI= 0.4954, ACC= 0.5956
0.028380634390651086
Training epoch 1574, recon_loss:0.607089, zinb_loss:0.960567, cluster_loss:0.333215
Clustering 1574: AMI= 0.7208, NMI= 0.7217, ARI= 0.5029, ACC= 0.6076
0.028828535363817746
Training epoch 1575, recon_loss:0.607453, zinb_loss:0.960433, cluster_loss:0.333874
Clustering 1575: AMI= 0.7188, NMI= 0.7197, ARI= 0.4949, ACC= 0.5952
0.029480027688423796
Training epoch 1576, recon_loss:0.607082, zinb_loss:0.960446, cluster_loss:0.333297
Clustering 1576: AMI= 0.7208, NMI= 0.7218, ARI= 0.5027, ACC= 0.6072
0.029520745958711674
Training epoch 1577, recon_loss:0.607503, zinb_loss:0.960374, cluster_loss:0.333917
Clustering 1577: AMI= 0.7186, NMI= 0.7195, ARI= 0.4945, ACC= 0.5953
0.02960218249928743
Training epoch 1578, recon_loss:0.607267, zinb_loss:0.960291, cluster_loss:0.333391
Clustering 1578: AMI= 0.7210, NMI= 0.7219, ARI= 0.5026, ACC= 0.6071
0.02980577385072682
Training epoch 1579, recon_loss:0.607635, zinb_loss:0.960247, cluster_loss:0.333928
Clustering 1579: AMI= 0.7186, NMI= 0.7196, ARI= 0.4944, ACC= 0.5953
0.03005008347245409
Training epoch 1580, recon_loss:0.607299, zinb_loss:0.960094, cluster_loss:0.333483
Clustering 1580: AMI= 0.7211, NMI= 0.7220, ARI= 0.5026, ACC= 0.6066
0.030335111364469237
Training epoch 1581, recon_loss:0.607652, zinb_loss:0.960078, cluster_loss:0.333932
Clustering 1581: AMI= 0.7185, NMI= 0.7195, ARI= 0.4943, ACC= 0.5954
0.03029439309418136
Training epoch 1582, recon_loss:0.607518, zinb_loss:0.959890, cluster_loss:0.333581
Clustering 1582: AMI= 0.7211, NMI= 0.7220, ARI= 0.5025, ACC= 0.6062
0.029968646931878333
Training epoch 1583, recon_loss:0.607802, zinb_loss:0.959896, cluster_loss:0.333902
Clustering 1583: AMI= 0.7186, NMI= 0.7195, ARI= 0.4943, ACC= 0.5956
0.03009080174274197
Training epoch 1584, recon_loss:0.607594, zinb_loss:0.959673, cluster_loss:0.333662
Clustering 1584: AMI= 0.7210, NMI= 0.7219, ARI= 0.5023, ACC= 0.6059
0.02960218249928743
Training epoch 1585, recon_loss:0.607866, zinb_loss:0.959725, cluster_loss:0.333859
Clustering 1585: AMI= 0.7187, NMI= 0.7196, ARI= 0.4943, ACC= 0.5959
0.02956146422899955
Training epoch 1586, recon_loss:0.607857, zinb_loss:0.959467, cluster_loss:0.333729
Clustering 1586: AMI= 0.7209, NMI= 0.7219, ARI= 0.5020, ACC= 0.6055
0.02943930941813592
Training epoch 1587, recon_loss:0.608077, zinb_loss:0.959565, cluster_loss:0.333786
Clustering 1587: AMI= 0.7184, NMI= 0.7194, ARI= 0.4940, ACC= 0.5960
0.02956146422899955
Training epoch 1588, recon_loss:0.607972, zinb_loss:0.959256, cluster_loss:0.333775
Clustering 1588: AMI= 0.7210, NMI= 0.7219, ARI= 0.5021, ACC= 0.6053
0.02911356325583289
Training epoch 1589, recon_loss:0.608140, zinb_loss:0.959418, cluster_loss:0.333713
Clustering 1589: AMI= 0.7185, NMI= 0.7194, ARI= 0.4940, ACC= 0.5963
0.0289099719043935
Training epoch 1590, recon_loss:0.608074, zinb_loss:0.959057, cluster_loss:0.333828
Clustering 1590: AMI= 0.7207, NMI= 0.7217, ARI= 0.5018, ACC= 0.6048
0.02833991612036321
Training epoch 1591, recon_loss:0.608192, zinb_loss:0.959291, cluster_loss:0.333652
Clustering 1591: AMI= 0.7183, NMI= 0.7192, ARI= 0.4940, ACC= 0.5966
0.027932733417484427
Training epoch 1592, recon_loss:0.608065, zinb_loss:0.958881, cluster_loss:0.333903
Clustering 1592: AMI= 0.7205, NMI= 0.7214, ARI= 0.5011, ACC= 0.6041
0.027118368011726863
Training epoch 1593, recon_loss:0.608119, zinb_loss:0.959188, cluster_loss:0.333630
Clustering 1593: AMI= 0.7181, NMI= 0.7191, ARI= 0.4942, ACC= 0.5969
0.026466875687120812
Training epoch 1594, recon_loss:0.607983, zinb_loss:0.958758, cluster_loss:0.334013
Clustering 1594: AMI= 0.7201, NMI= 0.7210, ARI= 0.5002, ACC= 0.6030
0.025489637200211735
Training epoch 1595, recon_loss:0.608020, zinb_loss:0.959125, cluster_loss:0.333634
Clustering 1595: AMI= 0.7183, NMI= 0.7192, ARI= 0.4947, ACC= 0.5975
0.024960299686469317
Training epoch 1596, recon_loss:0.607889, zinb_loss:0.958706, cluster_loss:0.334143
Clustering 1596: AMI= 0.7201, NMI= 0.7210, ARI= 0.4999, ACC= 0.6028
0.023779469848120852
Training epoch 1597, recon_loss:0.607975, zinb_loss:0.959154, cluster_loss:0.333635
Clustering 1597: AMI= 0.7185, NMI= 0.7194, ARI= 0.4953, ACC= 0.5978
0.023046540982939043
Training epoch 1598, recon_loss:0.607963, zinb_loss:0.958763, cluster_loss:0.334279
Clustering 1598: AMI= 0.7202, NMI= 0.7211, ARI= 0.4998, ACC= 0.6027
0.02243576692862087
Training epoch 1599, recon_loss:0.608146, zinb_loss:0.959293, cluster_loss:0.333563
Clustering 1599: AMI= 0.7189, NMI= 0.7198, ARI= 0.4957, ACC= 0.5986
0.021906429414878456
Training epoch 1600, recon_loss:0.608106, zinb_loss:0.958939, cluster_loss:0.334359
Clustering 1600: AMI= 0.7203, NMI= 0.7212, ARI= 0.4998, ACC= 0.6023
0.02096990919825726
Training epoch 1601, recon_loss:0.608426, zinb_loss:0.959583, cluster_loss:0.333445
Clustering 1601: AMI= 0.7188, NMI= 0.7197, ARI= 0.4958, ACC= 0.5985
0.021051345738833015
Training epoch 1602, recon_loss:0.608380, zinb_loss:0.959264, cluster_loss:0.334388
Clustering 1602: AMI= 0.7201, NMI= 0.7210, ARI= 0.4990, ACC= 0.6016
0.021254937090272406
Training epoch 1603, recon_loss:0.608784, zinb_loss:0.960008, cluster_loss:0.333252
Clustering 1603: AMI= 0.7192, NMI= 0.7202, ARI= 0.4964, ACC= 0.5988
0.02158068325257543
Training epoch 1604, recon_loss:0.608795, zinb_loss:0.959670, cluster_loss:0.334341
Clustering 1604: AMI= 0.7199, NMI= 0.7209, ARI= 0.4988, ACC= 0.6014
0.02206930249602997
Training epoch 1605, recon_loss:0.609118, zinb_loss:0.960465, cluster_loss:0.333084
Clustering 1605: AMI= 0.7195, NMI= 0.7205, ARI= 0.4970, ACC= 0.5995
0.022191457306893602
Training epoch 1606, recon_loss:0.608830, zinb_loss:0.960015, cluster_loss:0.334258
Clustering 1606: AMI= 0.7197, NMI= 0.7206, ARI= 0.4983, ACC= 0.6010
0.023087259253226924
Training epoch 1607, recon_loss:0.608993, zinb_loss:0.960759, cluster_loss:0.332988
Clustering 1607: AMI= 0.7198, NMI= 0.7208, ARI= 0.4975, ACC= 0.6001
0.02292438617207541
Training epoch 1608, recon_loss:0.608585, zinb_loss:0.960145, cluster_loss:0.334192
Clustering 1608: AMI= 0.7198, NMI= 0.7207, ARI= 0.4983, ACC= 0.6008
0.022842949631499652
Training epoch 1609, recon_loss:0.608532, zinb_loss:0.960796, cluster_loss:0.333049
Clustering 1609: AMI= 0.7201, NMI= 0.7210, ARI= 0.4978, ACC= 0.6008
0.022110020766317847
Training epoch 1610, recon_loss:0.608281, zinb_loss:0.960074, cluster_loss:0.334151
Clustering 1610: AMI= 0.7196, NMI= 0.7206, ARI= 0.4982, ACC= 0.6007
0.021377091901136038
Training epoch 1611, recon_loss:0.608064, zinb_loss:0.960644, cluster_loss:0.333181
Clustering 1611: AMI= 0.7202, NMI= 0.7211, ARI= 0.4982, ACC= 0.6015
0.020644163035954233
Training epoch 1612, recon_loss:0.607623, zinb_loss:0.959892, cluster_loss:0.334161
Clustering 1612: AMI= 0.7197, NMI= 0.7206, ARI= 0.4983, ACC= 0.6002
0.019544769738181523
Training epoch 1613, recon_loss:0.607441, zinb_loss:0.960459, cluster_loss:0.333375
Clustering 1613: AMI= 0.7202, NMI= 0.7211, ARI= 0.4982, ACC= 0.6018
0.01873040433242396
Training epoch 1614, recon_loss:0.607199, zinb_loss:0.959694, cluster_loss:0.334149
Clustering 1614: AMI= 0.7198, NMI= 0.7208, ARI= 0.4985, ACC= 0.6002
0.017590292764363368
Training epoch 1615, recon_loss:0.607092, zinb_loss:0.960277, cluster_loss:0.333560
Clustering 1615: AMI= 0.7199, NMI= 0.7209, ARI= 0.4977, ACC= 0.6017
0.01649089946659066
Training epoch 1616, recon_loss:0.606985, zinb_loss:0.959525, cluster_loss:0.334119
Clustering 1616: AMI= 0.7198, NMI= 0.7207, ARI= 0.4986, ACC= 0.6000
0.016205871574575512
Training epoch 1617, recon_loss:0.606938, zinb_loss:0.960150, cluster_loss:0.333704
Clustering 1617: AMI= 0.7197, NMI= 0.7206, ARI= 0.4975, ACC= 0.6019
0.015676534060833094
Training epoch 1618, recon_loss:0.606894, zinb_loss:0.959383, cluster_loss:0.334031
Clustering 1618: AMI= 0.7198, NMI= 0.7208, ARI= 0.4988, ACC= 0.5999
0.01559509752025734
Training epoch 1619, recon_loss:0.606913, zinb_loss:0.960059, cluster_loss:0.333811
Clustering 1619: AMI= 0.7197, NMI= 0.7206, ARI= 0.4974, ACC= 0.6021
0.014984323465939167
Training epoch 1620, recon_loss:0.606958, zinb_loss:0.959265, cluster_loss:0.333886
Clustering 1620: AMI= 0.7198, NMI= 0.7208, ARI= 0.4985, ACC= 0.5996
0.01490288692536341
Training epoch 1621, recon_loss:0.607037, zinb_loss:0.960003, cluster_loss:0.333852
Clustering 1621: AMI= 0.7196, NMI= 0.7205, ARI= 0.4971, ACC= 0.6020
0.015025041736227046
Training epoch 1622, recon_loss:0.607051, zinb_loss:0.959160, cluster_loss:0.333710
Clustering 1622: AMI= 0.7201, NMI= 0.7210, ARI= 0.4992, ACC= 0.5998
0.014740013844211898
Training epoch 1623, recon_loss:0.607164, zinb_loss:0.959946, cluster_loss:0.333859
Clustering 1623: AMI= 0.7193, NMI= 0.7203, ARI= 0.4967, ACC= 0.6017
0.014373549411620994
Training epoch 1624, recon_loss:0.607101, zinb_loss:0.959059, cluster_loss:0.333549
Clustering 1624: AMI= 0.7201, NMI= 0.7210, ARI= 0.4991, ACC= 0.5996
0.0138034936275907
Training epoch 1625, recon_loss:0.607205, zinb_loss:0.959865, cluster_loss:0.333856
Clustering 1625: AMI= 0.7191, NMI= 0.7200, ARI= 0.4962, ACC= 0.6013
0.013762775357302822
Training epoch 1626, recon_loss:0.607031, zinb_loss:0.958953, cluster_loss:0.333430
Clustering 1626: AMI= 0.7202, NMI= 0.7211, ARI= 0.4994, ACC= 0.5996
0.013844211897878577
Training epoch 1627, recon_loss:0.607097, zinb_loss:0.959757, cluster_loss:0.333867
Clustering 1627: AMI= 0.7191, NMI= 0.7200, ARI= 0.4960, ACC= 0.6012
0.013844211897878577
Training epoch 1628, recon_loss:0.606838, zinb_loss:0.958851, cluster_loss:0.333399
Clustering 1628: AMI= 0.7202, NMI= 0.7212, ARI= 0.4994, ACC= 0.5996
0.0138034936275907
Training epoch 1629, recon_loss:0.606865, zinb_loss:0.959636, cluster_loss:0.333906
Clustering 1629: AMI= 0.7190, NMI= 0.7199, ARI= 0.4958, ACC= 0.6009
0.013762775357302822
Training epoch 1630, recon_loss:0.606567, zinb_loss:0.958767, cluster_loss:0.333448
Clustering 1630: AMI= 0.7201, NMI= 0.7211, ARI= 0.4991, ACC= 0.5996
0.013314874384136161
Training epoch 1631, recon_loss:0.606597, zinb_loss:0.959529, cluster_loss:0.333959
Clustering 1631: AMI= 0.7190, NMI= 0.7199, ARI= 0.4960, ACC= 0.6009
0.013070564762408893
Training epoch 1632, recon_loss:0.606303, zinb_loss:0.958704, cluster_loss:0.333530
Clustering 1632: AMI= 0.7201, NMI= 0.7210, ARI= 0.4989, ACC= 0.5995
0.012256199356651329
Training epoch 1633, recon_loss:0.606372, zinb_loss:0.959454, cluster_loss:0.334012
Clustering 1633: AMI= 0.7192, NMI= 0.7202, ARI= 0.4966, ACC= 0.6013
0.011930453194348304
Training epoch 1634, recon_loss:0.606089, zinb_loss:0.958662, cluster_loss:0.333612
Clustering 1634: AMI= 0.7201, NMI= 0.7210, ARI= 0.4985, ACC= 0.5992
0.011726861842908913
Training epoch 1635, recon_loss:0.606254, zinb_loss:0.959424, cluster_loss:0.334036
Clustering 1635: AMI= 0.7191, NMI= 0.7201, ARI= 0.4967, ACC= 0.6015
0.01180829838348467
Training epoch 1636, recon_loss:0.605960, zinb_loss:0.958644, cluster_loss:0.333684
Clustering 1636: AMI= 0.7199, NMI= 0.7208, ARI= 0.4983, ACC= 0.5990
0.01180829838348467
Training epoch 1637, recon_loss:0.606283, zinb_loss:0.959443, cluster_loss:0.334028
Clustering 1637: AMI= 0.7193, NMI= 0.7202, ARI= 0.4971, ACC= 0.6015
0.011441833950893767
Training epoch 1638, recon_loss:0.605971, zinb_loss:0.958657, cluster_loss:0.333742
Clustering 1638: AMI= 0.7198, NMI= 0.7207, ARI= 0.4980, ACC= 0.5990
0.011156806058878619
Training epoch 1639, recon_loss:0.606538, zinb_loss:0.959509, cluster_loss:0.333974
Clustering 1639: AMI= 0.7194, NMI= 0.7203, ARI= 0.4975, ACC= 0.6017
0.011278960869742253
Training epoch 1640, recon_loss:0.606225, zinb_loss:0.958700, cluster_loss:0.333776
Clustering 1640: AMI= 0.7198, NMI= 0.7207, ARI= 0.4978, ACC= 0.5990
0.011238242599454376
Training epoch 1641, recon_loss:0.607005, zinb_loss:0.959585, cluster_loss:0.333887
Clustering 1641: AMI= 0.7198, NMI= 0.7207, ARI= 0.4983, ACC= 0.6020
0.01136039741031801
Training epoch 1642, recon_loss:0.606661, zinb_loss:0.958771, cluster_loss:0.333815
Clustering 1642: AMI= 0.7198, NMI= 0.7207, ARI= 0.4978, ACC= 0.5988
0.011563988761757401
Training epoch 1643, recon_loss:0.607419, zinb_loss:0.959617, cluster_loss:0.333804
Clustering 1643: AMI= 0.7199, NMI= 0.7208, ARI= 0.4989, ACC= 0.6023
0.011726861842908913
Training epoch 1644, recon_loss:0.606972, zinb_loss:0.958856, cluster_loss:0.333894
Clustering 1644: AMI= 0.7196, NMI= 0.7206, ARI= 0.4976, ACC= 0.5987
0.012174762816075574
Training epoch 1645, recon_loss:0.607590, zinb_loss:0.959590, cluster_loss:0.333765
Clustering 1645: AMI= 0.7201, NMI= 0.7210, ARI= 0.4994, ACC= 0.6027
0.012581945518954354
Training epoch 1646, recon_loss:0.607100, zinb_loss:0.958927, cluster_loss:0.334006
Clustering 1646: AMI= 0.7196, NMI= 0.7206, ARI= 0.4973, ACC= 0.5985
0.013477747465287675
Training epoch 1647, recon_loss:0.607581, zinb_loss:0.959542, cluster_loss:0.333758
Clustering 1647: AMI= 0.7202, NMI= 0.7211, ARI= 0.5000, ACC= 0.6035
0.014780732114499776
Training epoch 1648, recon_loss:0.607081, zinb_loss:0.959014, cluster_loss:0.334166
Clustering 1648: AMI= 0.7195, NMI= 0.7204, ARI= 0.4970, ACC= 0.5987
0.015635815790545217
Training epoch 1649, recon_loss:0.607482, zinb_loss:0.959511, cluster_loss:0.333772
Clustering 1649: AMI= 0.7202, NMI= 0.7211, ARI= 0.5001, ACC= 0.6036
0.016042998493424
Training epoch 1650, recon_loss:0.607144, zinb_loss:0.959086, cluster_loss:0.334273
Clustering 1650: AMI= 0.7192, NMI= 0.7201, ARI= 0.4963, ACC= 0.5981
0.017020236980333076
Training epoch 1651, recon_loss:0.607331, zinb_loss:0.959496, cluster_loss:0.333779
Clustering 1651: AMI= 0.7202, NMI= 0.7211, ARI= 0.5006, ACC= 0.6041
0.01807891200781791
Training epoch 1652, recon_loss:0.607027, zinb_loss:0.959228, cluster_loss:0.334422
Clustering 1652: AMI= 0.7189, NMI= 0.7199, ARI= 0.4959, ACC= 0.5979
0.019300460116454254
Training epoch 1653, recon_loss:0.607164, zinb_loss:0.959525, cluster_loss:0.333753
Clustering 1653: AMI= 0.7203, NMI= 0.7212, ARI= 0.5009, ACC= 0.6045
0.020603444765666355
Training epoch 1654, recon_loss:0.607055, zinb_loss:0.959406, cluster_loss:0.334536
Clustering 1654: AMI= 0.7189, NMI= 0.7198, ARI= 0.4956, ACC= 0.5978
0.02162140152286331
Training epoch 1655, recon_loss:0.607113, zinb_loss:0.959604, cluster_loss:0.333698
Clustering 1655: AMI= 0.7206, NMI= 0.7215, ARI= 0.5014, ACC= 0.6053
0.023250132334378434
Training epoch 1656, recon_loss:0.607153, zinb_loss:0.959651, cluster_loss:0.334623
Clustering 1656: AMI= 0.7186, NMI= 0.7196, ARI= 0.4945, ACC= 0.5971
0.025041736227045076
Training epoch 1657, recon_loss:0.607076, zinb_loss:0.959718, cluster_loss:0.333613
Clustering 1657: AMI= 0.7208, NMI= 0.7217, ARI= 0.5022, ACC= 0.6058
0.02675190357913596
Training epoch 1658, recon_loss:0.607297, zinb_loss:0.959953, cluster_loss:0.334671
Clustering 1658: AMI= 0.7184, NMI= 0.7193, ARI= 0.4940, ACC= 0.5966
0.028421352660938964
Training epoch 1659, recon_loss:0.607014, zinb_loss:0.959865, cluster_loss:0.333503
Clustering 1659: AMI= 0.7209, NMI= 0.7218, ARI= 0.5027, ACC= 0.6064
0.02964290076957531
Training epoch 1660, recon_loss:0.607395, zinb_loss:0.960267, cluster_loss:0.334691
Clustering 1660: AMI= 0.7184, NMI= 0.7193, ARI= 0.4938, ACC= 0.5966
0.03135306812166619
Training epoch 1661, recon_loss:0.606914, zinb_loss:0.960017, cluster_loss:0.333414
Clustering 1661: AMI= 0.7206, NMI= 0.7215, ARI= 0.5019, ACC= 0.6059
0.03237102487886315
Training epoch 1662, recon_loss:0.607454, zinb_loss:0.960568, cluster_loss:0.334699
Clustering 1662: AMI= 0.7183, NMI= 0.7192, ARI= 0.4935, ACC= 0.5964
0.03367400952807525
Training epoch 1663, recon_loss:0.606763, zinb_loss:0.960149, cluster_loss:0.333342
Clustering 1663: AMI= 0.7204, NMI= 0.7214, ARI= 0.5018, ACC= 0.6056
0.03448837493383281
Training epoch 1664, recon_loss:0.607468, zinb_loss:0.960812, cluster_loss:0.334709
Clustering 1664: AMI= 0.7184, NMI= 0.7193, ARI= 0.4936, ACC= 0.5966
0.035017712447575226
Training epoch 1665, recon_loss:0.606610, zinb_loss:0.960234, cluster_loss:0.333294
Clustering 1665: AMI= 0.7205, NMI= 0.7214, ARI= 0.5013, ACC= 0.6049
0.03550633169102976
Training epoch 1666, recon_loss:0.607540, zinb_loss:0.961000, cluster_loss:0.334726
Clustering 1666: AMI= 0.7185, NMI= 0.7194, ARI= 0.4939, ACC= 0.5969
0.03554704996131764
Training epoch 1667, recon_loss:0.606587, zinb_loss:0.960271, cluster_loss:0.333239
Clustering 1667: AMI= 0.7202, NMI= 0.7211, ARI= 0.5007, ACC= 0.6042
0.03542489515045401
Training epoch 1668, recon_loss:0.607708, zinb_loss:0.961123, cluster_loss:0.334725
Clustering 1668: AMI= 0.7188, NMI= 0.7197, ARI= 0.4944, ACC= 0.5975
0.03538417688016613
Training epoch 1669, recon_loss:0.606774, zinb_loss:0.960248, cluster_loss:0.333182
Clustering 1669: AMI= 0.7201, NMI= 0.7210, ARI= 0.5004, ACC= 0.6037
0.035465613420741886
Training epoch 1670, recon_loss:0.607967, zinb_loss:0.961158, cluster_loss:0.334675
Clustering 1670: AMI= 0.7186, NMI= 0.7195, ARI= 0.4942, ACC= 0.5975
0.03497699417728735
Training epoch 1671, recon_loss:0.607159, zinb_loss:0.960150, cluster_loss:0.333116
Clustering 1671: AMI= 0.7198, NMI= 0.7207, ARI= 0.4999, ACC= 0.6029
0.034895557636711594
Training epoch 1672, recon_loss:0.608174, zinb_loss:0.961053, cluster_loss:0.334573
Clustering 1672: AMI= 0.7189, NMI= 0.7199, ARI= 0.4951, ACC= 0.5984
0.0339590374200904
Training epoch 1673, recon_loss:0.607453, zinb_loss:0.959946, cluster_loss:0.333088
Clustering 1673: AMI= 0.7198, NMI= 0.7208, ARI= 0.4998, ACC= 0.6028
0.03318539028462071
Training epoch 1674, recon_loss:0.608132, zinb_loss:0.960803, cluster_loss:0.334483
Clustering 1674: AMI= 0.7191, NMI= 0.7201, ARI= 0.4955, ACC= 0.5987
0.03204527871656012
Training epoch 1675, recon_loss:0.607469, zinb_loss:0.959675, cluster_loss:0.333166
Clustering 1675: AMI= 0.7198, NMI= 0.7208, ARI= 0.4998, ACC= 0.6028
0.030864448878211652
Training epoch 1676, recon_loss:0.607840, zinb_loss:0.960501, cluster_loss:0.334454
Clustering 1676: AMI= 0.7193, NMI= 0.7202, ARI= 0.4958, ACC= 0.5990
0.030253674823893482
Training epoch 1677, recon_loss:0.607321, zinb_loss:0.959424, cluster_loss:0.333325
Clustering 1677: AMI= 0.7197, NMI= 0.7207, ARI= 0.4995, ACC= 0.6026
0.02915428152612077
Training epoch 1678, recon_loss:0.607540, zinb_loss:0.960231, cluster_loss:0.334465
Clustering 1678: AMI= 0.7196, NMI= 0.7205, ARI= 0.4961, ACC= 0.5993
0.028095606498635937
Training epoch 1679, recon_loss:0.607230, zinb_loss:0.959226, cluster_loss:0.333504
Clustering 1679: AMI= 0.7198, NMI= 0.7207, ARI= 0.4993, ACC= 0.6026
0.026914776660287472
Training epoch 1680, recon_loss:0.607357, zinb_loss:0.960018, cluster_loss:0.334480
Clustering 1680: AMI= 0.7197, NMI= 0.7206, ARI= 0.4961, ACC= 0.5992
0.026141129524817786
Training epoch 1681, recon_loss:0.607382, zinb_loss:0.959096, cluster_loss:0.333673
Clustering 1681: AMI= 0.7199, NMI= 0.7209, ARI= 0.4995, ACC= 0.6028
0.024838144875605685
Training epoch 1682, recon_loss:0.607416, zinb_loss:0.959844, cluster_loss:0.334458
Clustering 1682: AMI= 0.7196, NMI= 0.7205, ARI= 0.4962, ACC= 0.5990
0.024145934280711757
Training epoch 1683, recon_loss:0.607936, zinb_loss:0.959019, cluster_loss:0.333821
Clustering 1683: AMI= 0.7203, NMI= 0.7212, ARI= 0.4997, ACC= 0.6032
0.023127977523514802
Training epoch 1684, recon_loss:0.607858, zinb_loss:0.959684, cluster_loss:0.334358
Clustering 1684: AMI= 0.7194, NMI= 0.7203, ARI= 0.4960, ACC= 0.5987
0.022842949631499652
Training epoch 1685, recon_loss:0.608214, zinb_loss:0.958967, cluster_loss:0.333941
Clustering 1685: AMI= 0.7202, NMI= 0.7211, ARI= 0.4994, ACC= 0.6029
0.021254937090272406
Training epoch 1686, recon_loss:0.608003, zinb_loss:0.959587, cluster_loss:0.334261
Clustering 1686: AMI= 0.7191, NMI= 0.7201, ARI= 0.4960, ACC= 0.5985
0.02068488130624211
Training epoch 1687, recon_loss:0.608220, zinb_loss:0.958947, cluster_loss:0.334041
Clustering 1687: AMI= 0.7201, NMI= 0.7211, ARI= 0.4995, ACC= 0.6031
0.019748361089620914
Training epoch 1688, recon_loss:0.607935, zinb_loss:0.959503, cluster_loss:0.334171
Clustering 1688: AMI= 0.7190, NMI= 0.7199, ARI= 0.4960, ACC= 0.5984
0.019219023575878496
Training epoch 1689, recon_loss:0.608167, zinb_loss:0.958957, cluster_loss:0.334129
Clustering 1689: AMI= 0.7201, NMI= 0.7211, ARI= 0.4994, ACC= 0.6029
0.018771122602711836
Training epoch 1690, recon_loss:0.607744, zinb_loss:0.959455, cluster_loss:0.334142
Clustering 1690: AMI= 0.7190, NMI= 0.7199, ARI= 0.4958, ACC= 0.5979
0.018404658170120932
Training epoch 1691, recon_loss:0.608103, zinb_loss:0.959016, cluster_loss:0.334201
Clustering 1691: AMI= 0.7202, NMI= 0.7212, ARI= 0.4993, ACC= 0.6030
0.017712447575227004
Training epoch 1692, recon_loss:0.607673, zinb_loss:0.959397, cluster_loss:0.334105
Clustering 1692: AMI= 0.7188, NMI= 0.7198, ARI= 0.4956, ACC= 0.5975
0.01669449081803005
Training epoch 1693, recon_loss:0.608055, zinb_loss:0.959088, cluster_loss:0.334268
Clustering 1693: AMI= 0.7200, NMI= 0.7209, ARI= 0.4989, ACC= 0.6028
0.016775927358605808
Training epoch 1694, recon_loss:0.607590, zinb_loss:0.959339, cluster_loss:0.334094
Clustering 1694: AMI= 0.7187, NMI= 0.7197, ARI= 0.4957, ACC= 0.5977
0.016328026385439145
Training epoch 1695, recon_loss:0.608018, zinb_loss:0.959168, cluster_loss:0.334332
Clustering 1695: AMI= 0.7203, NMI= 0.7212, ARI= 0.4989, ACC= 0.6030
0.01689808216946944
Training epoch 1696, recon_loss:0.607539, zinb_loss:0.959275, cluster_loss:0.334076
Clustering 1696: AMI= 0.7187, NMI= 0.7197, ARI= 0.4957, ACC= 0.5976
0.016287308115151267
Training epoch 1697, recon_loss:0.607985, zinb_loss:0.959237, cluster_loss:0.334397
Clustering 1697: AMI= 0.7201, NMI= 0.7211, ARI= 0.4989, ACC= 0.6031
0.01669449081803005
Training epoch 1698, recon_loss:0.607516, zinb_loss:0.959226, cluster_loss:0.334056
Clustering 1698: AMI= 0.7187, NMI= 0.7196, ARI= 0.4959, ACC= 0.5977
0.01669449081803005
Training epoch 1699, recon_loss:0.607954, zinb_loss:0.959317, cluster_loss:0.334448
Clustering 1699: AMI= 0.7202, NMI= 0.7211, ARI= 0.4989, ACC= 0.6033
0.016816645628893685
Training epoch 1700, recon_loss:0.607496, zinb_loss:0.959197, cluster_loss:0.334030
Clustering 1700: AMI= 0.7189, NMI= 0.7199, ARI= 0.4965, ACC= 0.5981
0.017264546602060345
Training epoch 1701, recon_loss:0.607946, zinb_loss:0.959415, cluster_loss:0.334488
Clustering 1701: AMI= 0.7200, NMI= 0.7209, ARI= 0.4986, ACC= 0.6031
0.017508856223787613
Training epoch 1702, recon_loss:0.607540, zinb_loss:0.959196, cluster_loss:0.333984
Clustering 1702: AMI= 0.7192, NMI= 0.7201, ARI= 0.4972, ACC= 0.5986
0.01714239179119671
Training epoch 1703, recon_loss:0.608060, zinb_loss:0.959537, cluster_loss:0.334502
Clustering 1703: AMI= 0.7199, NMI= 0.7209, ARI= 0.4985, ACC= 0.6032
0.017060955250620954
Training epoch 1704, recon_loss:0.607678, zinb_loss:0.959224, cluster_loss:0.333906
Clustering 1704: AMI= 0.7192, NMI= 0.7201, ARI= 0.4979, ACC= 0.5990
0.0173459831426361
Training epoch 1705, recon_loss:0.608231, zinb_loss:0.959659, cluster_loss:0.334465
Clustering 1705: AMI= 0.7199, NMI= 0.7208, ARI= 0.4984, ACC= 0.6030
0.01779388411580276
Training epoch 1706, recon_loss:0.607900, zinb_loss:0.959269, cluster_loss:0.333806
Clustering 1706: AMI= 0.7195, NMI= 0.7204, ARI= 0.4984, ACC= 0.5995
0.017956757196954273
Training epoch 1707, recon_loss:0.608417, zinb_loss:0.959732, cluster_loss:0.334377
Clustering 1707: AMI= 0.7195, NMI= 0.7204, ARI= 0.4977, ACC= 0.6024
0.018241785088969422
Training epoch 1708, recon_loss:0.608087, zinb_loss:0.959293, cluster_loss:0.333723
Clustering 1708: AMI= 0.7197, NMI= 0.7206, ARI= 0.4986, ACC= 0.5999
0.018119630278105786
Training epoch 1709, recon_loss:0.608539, zinb_loss:0.959739, cluster_loss:0.334256
Clustering 1709: AMI= 0.7193, NMI= 0.7202, ARI= 0.4971, ACC= 0.6020
0.018567531251272446
Training epoch 1710, recon_loss:0.608202, zinb_loss:0.959256, cluster_loss:0.333639
Clustering 1710: AMI= 0.7198, NMI= 0.7207, ARI= 0.4991, ACC= 0.6006
0.018363939899833055
Training epoch 1711, recon_loss:0.608503, zinb_loss:0.959643, cluster_loss:0.334123
Clustering 1711: AMI= 0.7191, NMI= 0.7200, ARI= 0.4968, ACC= 0.6017
0.018404658170120932
Training epoch 1712, recon_loss:0.608025, zinb_loss:0.959160, cluster_loss:0.333675
Clustering 1712: AMI= 0.7198, NMI= 0.7207, ARI= 0.4992, ACC= 0.6006
0.018526812980984568
Training epoch 1713, recon_loss:0.608255, zinb_loss:0.959506, cluster_loss:0.334051
Clustering 1713: AMI= 0.7187, NMI= 0.7196, ARI= 0.4961, ACC= 0.6008
0.017956757196954273
Training epoch 1714, recon_loss:0.607712, zinb_loss:0.959018, cluster_loss:0.333720
Clustering 1714: AMI= 0.7197, NMI= 0.7207, ARI= 0.4994, ACC= 0.6009
0.01803819373753003
Training epoch 1715, recon_loss:0.607788, zinb_loss:0.959325, cluster_loss:0.334046
Clustering 1715: AMI= 0.7185, NMI= 0.7195, ARI= 0.4958, ACC= 0.6005
0.017427419683211858
Training epoch 1716, recon_loss:0.607213, zinb_loss:0.958897, cluster_loss:0.333873
Clustering 1716: AMI= 0.7197, NMI= 0.7206, ARI= 0.4995, ACC= 0.6009
0.01718311006148459
Training epoch 1717, recon_loss:0.607421, zinb_loss:0.959172, cluster_loss:0.334099
Clustering 1717: AMI= 0.7186, NMI= 0.7196, ARI= 0.4958, ACC= 0.6004
0.01649089946659066
Training epoch 1718, recon_loss:0.606915, zinb_loss:0.958781, cluster_loss:0.333994
Clustering 1718: AMI= 0.7197, NMI= 0.7207, ARI= 0.4996, ACC= 0.6011
0.016613054277454294
Training epoch 1719, recon_loss:0.607083, zinb_loss:0.959038, cluster_loss:0.334182
Clustering 1719: AMI= 0.7185, NMI= 0.7195, ARI= 0.4956, ACC= 0.6002
0.01649089946659066
Training epoch 1720, recon_loss:0.606654, zinb_loss:0.958717, cluster_loss:0.334148
Clustering 1720: AMI= 0.7197, NMI= 0.7206, ARI= 0.4994, ACC= 0.6010
0.01645018119630278
Training epoch 1721, recon_loss:0.606961, zinb_loss:0.958940, cluster_loss:0.334255
Clustering 1721: AMI= 0.7186, NMI= 0.7195, ARI= 0.4955, ACC= 0.5998
0.016775927358605808
Training epoch 1722, recon_loss:0.606626, zinb_loss:0.958668, cluster_loss:0.334240
Clustering 1722: AMI= 0.7198, NMI= 0.7207, ARI= 0.4995, ACC= 0.6012
0.01738670141292398
Training epoch 1723, recon_loss:0.606930, zinb_loss:0.958865, cluster_loss:0.334326
Clustering 1723: AMI= 0.7186, NMI= 0.7196, ARI= 0.4955, ACC= 0.5998
0.017427419683211858
Training epoch 1724, recon_loss:0.606659, zinb_loss:0.958657, cluster_loss:0.334324
Clustering 1724: AMI= 0.7200, NMI= 0.7209, ARI= 0.4995, ACC= 0.6015
0.017427419683211858
Training epoch 1725, recon_loss:0.607061, zinb_loss:0.958819, cluster_loss:0.334369
Clustering 1725: AMI= 0.7186, NMI= 0.7196, ARI= 0.4953, ACC= 0.5995
0.017590292764363368
Training epoch 1726, recon_loss:0.606863, zinb_loss:0.958665, cluster_loss:0.334353
Clustering 1726: AMI= 0.7201, NMI= 0.7210, ARI= 0.4998, ACC= 0.6020
0.018363939899833055
Training epoch 1727, recon_loss:0.607222, zinb_loss:0.958804, cluster_loss:0.334420
Clustering 1727: AMI= 0.7187, NMI= 0.7196, ARI= 0.4952, ACC= 0.5992
0.018404658170120932
Training epoch 1728, recon_loss:0.607053, zinb_loss:0.958708, cluster_loss:0.334372
Clustering 1728: AMI= 0.7201, NMI= 0.7210, ARI= 0.4997, ACC= 0.6022
0.01873040433242396
Training epoch 1729, recon_loss:0.607500, zinb_loss:0.958802, cluster_loss:0.334450
Clustering 1729: AMI= 0.7189, NMI= 0.7198, ARI= 0.4952, ACC= 0.5993
0.01873040433242396
Training epoch 1730, recon_loss:0.607351, zinb_loss:0.958764, cluster_loss:0.334337
Clustering 1730: AMI= 0.7200, NMI= 0.7209, ARI= 0.4998, ACC= 0.6025
0.019219023575878496
Training epoch 1731, recon_loss:0.607693, zinb_loss:0.958848, cluster_loss:0.334509
Clustering 1731: AMI= 0.7189, NMI= 0.7198, ARI= 0.4951, ACC= 0.5991
0.019626206278757278
Training epoch 1732, recon_loss:0.607535, zinb_loss:0.958887, cluster_loss:0.334325
Clustering 1732: AMI= 0.7199, NMI= 0.7209, ARI= 0.5000, ACC= 0.6026
0.019992670711348182
Training epoch 1733, recon_loss:0.607825, zinb_loss:0.958911, cluster_loss:0.334542
Clustering 1733: AMI= 0.7188, NMI= 0.7197, ARI= 0.4951, ACC= 0.5988
0.02027769860336333
Training epoch 1734, recon_loss:0.607604, zinb_loss:0.959033, cluster_loss:0.334260
Clustering 1734: AMI= 0.7197, NMI= 0.7207, ARI= 0.5000, ACC= 0.6028
0.02076631784681787
Training epoch 1735, recon_loss:0.607858, zinb_loss:0.959068, cluster_loss:0.334614
Clustering 1735: AMI= 0.7188, NMI= 0.7197, ARI= 0.4950, ACC= 0.5987
0.020725599576529988
Training epoch 1736, recon_loss:0.607644, zinb_loss:0.959252, cluster_loss:0.334225
Clustering 1736: AMI= 0.7196, NMI= 0.7206, ARI= 0.5001, ACC= 0.6030
0.021092064009120892
Training epoch 1737, recon_loss:0.607647, zinb_loss:0.959219, cluster_loss:0.334644
Clustering 1737: AMI= 0.7190, NMI= 0.7199, ARI= 0.4954, ACC= 0.5987
0.021458528441711797
Training epoch 1738, recon_loss:0.607448, zinb_loss:0.959509, cluster_loss:0.334187
Clustering 1738: AMI= 0.7197, NMI= 0.7207, ARI= 0.5002, ACC= 0.6033
0.021702838063439065
Training epoch 1739, recon_loss:0.607592, zinb_loss:0.959449, cluster_loss:0.334684
Clustering 1739: AMI= 0.7192, NMI= 0.7201, ARI= 0.4957, ACC= 0.5988
0.022191457306893602
Training epoch 1740, recon_loss:0.607330, zinb_loss:0.959772, cluster_loss:0.334129
Clustering 1740: AMI= 0.7196, NMI= 0.7206, ARI= 0.5002, ACC= 0.6034
0.022883667901787533
Training epoch 1741, recon_loss:0.607509, zinb_loss:0.959689, cluster_loss:0.334720
Clustering 1741: AMI= 0.7193, NMI= 0.7203, ARI= 0.4958, ACC= 0.5989
0.023331568874954193
Training epoch 1742, recon_loss:0.607210, zinb_loss:0.960006, cluster_loss:0.334072
Clustering 1742: AMI= 0.7196, NMI= 0.7206, ARI= 0.5005, ACC= 0.6038
0.023738751577832975
Training epoch 1743, recon_loss:0.607473, zinb_loss:0.959883, cluster_loss:0.334736
Clustering 1743: AMI= 0.7194, NMI= 0.7203, ARI= 0.4960, ACC= 0.5990
0.023535160226393584
Training epoch 1744, recon_loss:0.607073, zinb_loss:0.960187, cluster_loss:0.334029
Clustering 1744: AMI= 0.7195, NMI= 0.7205, ARI= 0.5006, ACC= 0.6042
0.023413005415529948
Training epoch 1745, recon_loss:0.607387, zinb_loss:0.960014, cluster_loss:0.334728
Clustering 1745: AMI= 0.7194, NMI= 0.7203, ARI= 0.4959, ACC= 0.5988
0.023005822712651166
Training epoch 1746, recon_loss:0.606911, zinb_loss:0.960262, cluster_loss:0.333945
Clustering 1746: AMI= 0.7198, NMI= 0.7207, ARI= 0.5007, ACC= 0.6044
0.02247648519890875
Training epoch 1747, recon_loss:0.607351, zinb_loss:0.960074, cluster_loss:0.334690
Clustering 1747: AMI= 0.7195, NMI= 0.7204, ARI= 0.4962, ACC= 0.5986
0.022028584225742092
Training epoch 1748, recon_loss:0.606827, zinb_loss:0.960214, cluster_loss:0.333875
Clustering 1748: AMI= 0.7197, NMI= 0.7206, ARI= 0.5004, ACC= 0.6043
0.021254937090272406
Training epoch 1749, recon_loss:0.607304, zinb_loss:0.960031, cluster_loss:0.334625
Clustering 1749: AMI= 0.7196, NMI= 0.7206, ARI= 0.4967, ACC= 0.5986
0.020318416873651206
Training epoch 1750, recon_loss:0.606667, zinb_loss:0.960073, cluster_loss:0.333784
Clustering 1750: AMI= 0.7195, NMI= 0.7204, ARI= 0.5000, ACC= 0.6044
0.019870515900484546
Training epoch 1751, recon_loss:0.607144, zinb_loss:0.959922, cluster_loss:0.334531
Clustering 1751: AMI= 0.7197, NMI= 0.7206, ARI= 0.4969, ACC= 0.5986
0.01913758703530274
Training epoch 1752, recon_loss:0.606563, zinb_loss:0.959858, cluster_loss:0.333762
Clustering 1752: AMI= 0.7193, NMI= 0.7202, ARI= 0.4993, ACC= 0.6041
0.018893277413575472
Training epoch 1753, recon_loss:0.606937, zinb_loss:0.959785, cluster_loss:0.334485
Clustering 1753: AMI= 0.7199, NMI= 0.7208, ARI= 0.4975, ACC= 0.5988
0.018119630278105786
Training epoch 1754, recon_loss:0.606439, zinb_loss:0.959652, cluster_loss:0.333812
Clustering 1754: AMI= 0.7191, NMI= 0.7200, ARI= 0.4987, ACC= 0.6037
0.017916038926666395
Training epoch 1755, recon_loss:0.606725, zinb_loss:0.959681, cluster_loss:0.334494
Clustering 1755: AMI= 0.7198, NMI= 0.7208, ARI= 0.4976, ACC= 0.5990
0.017468137953499736
Training epoch 1756, recon_loss:0.606375, zinb_loss:0.959492, cluster_loss:0.333890
Clustering 1756: AMI= 0.7190, NMI= 0.7200, ARI= 0.4983, ACC= 0.6032
0.01714239179119671
Training epoch 1757, recon_loss:0.606575, zinb_loss:0.959634, cluster_loss:0.334535
Clustering 1757: AMI= 0.7198, NMI= 0.7208, ARI= 0.4979, ACC= 0.5992
0.017671729304939127
Training epoch 1758, recon_loss:0.606295, zinb_loss:0.959394, cluster_loss:0.334005
Clustering 1758: AMI= 0.7192, NMI= 0.7201, ARI= 0.4986, ACC= 0.6037
0.01807891200781791
Training epoch 1759, recon_loss:0.606463, zinb_loss:0.959635, cluster_loss:0.334616
Clustering 1759: AMI= 0.7199, NMI= 0.7208, ARI= 0.4978, ACC= 0.5992
0.018648967791848204
Training epoch 1760, recon_loss:0.606222, zinb_loss:0.959374, cluster_loss:0.334087
Clustering 1760: AMI= 0.7194, NMI= 0.7204, ARI= 0.4989, ACC= 0.6042
0.019341178386742132
Training epoch 1761, recon_loss:0.606292, zinb_loss:0.959685, cluster_loss:0.334696
Clustering 1761: AMI= 0.7197, NMI= 0.7207, ARI= 0.4973, ACC= 0.5985
0.020196262062787573
Training epoch 1762, recon_loss:0.606089, zinb_loss:0.959435, cluster_loss:0.334165
Clustering 1762: AMI= 0.7197, NMI= 0.7206, ARI= 0.4991, ACC= 0.6045
0.0208884726576815
Training epoch 1763, recon_loss:0.606165, zinb_loss:0.959794, cluster_loss:0.334781
Clustering 1763: AMI= 0.7196, NMI= 0.7206, ARI= 0.4970, ACC= 0.5981
0.021743556333726943
Training epoch 1764, recon_loss:0.606018, zinb_loss:0.959558, cluster_loss:0.334207
Clustering 1764: AMI= 0.7196, NMI= 0.7205, ARI= 0.4993, ACC= 0.6047
0.022191457306893602
Training epoch 1765, recon_loss:0.606119, zinb_loss:0.959969, cluster_loss:0.334855
Clustering 1765: AMI= 0.7193, NMI= 0.7203, ARI= 0.4963, ACC= 0.5977
0.022883667901787533
Training epoch 1766, recon_loss:0.606039, zinb_loss:0.959744, cluster_loss:0.334207
Clustering 1766: AMI= 0.7200, NMI= 0.7209, ARI= 0.5000, ACC= 0.6052
0.024308807361863267
Training epoch 1767, recon_loss:0.606170, zinb_loss:0.960202, cluster_loss:0.334914
Clustering 1767: AMI= 0.7191, NMI= 0.7200, ARI= 0.4961, ACC= 0.5975
0.025163891037908708
Training epoch 1768, recon_loss:0.606149, zinb_loss:0.959958, cluster_loss:0.334167
Clustering 1768: AMI= 0.7201, NMI= 0.7210, ARI= 0.5002, ACC= 0.6054
0.02622256606539354
Training epoch 1769, recon_loss:0.606313, zinb_loss:0.960461, cluster_loss:0.334954
Clustering 1769: AMI= 0.7189, NMI= 0.7198, ARI= 0.4955, ACC= 0.5973
0.027647705525469277
Training epoch 1770, recon_loss:0.606399, zinb_loss:0.960173, cluster_loss:0.334090
Clustering 1770: AMI= 0.7202, NMI= 0.7211, ARI= 0.5005, ACC= 0.6055
0.029235718066696528
Training epoch 1771, recon_loss:0.606568, zinb_loss:0.960687, cluster_loss:0.334948
Clustering 1771: AMI= 0.7186, NMI= 0.7196, ARI= 0.4952, ACC= 0.5967
0.030375829634757115
Training epoch 1772, recon_loss:0.606900, zinb_loss:0.960347, cluster_loss:0.333973
Clustering 1772: AMI= 0.7202, NMI= 0.7211, ARI= 0.5001, ACC= 0.6053
0.031108758499938924
Training epoch 1773, recon_loss:0.607030, zinb_loss:0.960864, cluster_loss:0.334893
Clustering 1773: AMI= 0.7186, NMI= 0.7195, ARI= 0.4950, ACC= 0.5968
0.0324524614194389
Training epoch 1774, recon_loss:0.607388, zinb_loss:0.960410, cluster_loss:0.333799
Clustering 1774: AMI= 0.7202, NMI= 0.7211, ARI= 0.4998, ACC= 0.6046
0.033429699906347976
Training epoch 1775, recon_loss:0.607480, zinb_loss:0.960924, cluster_loss:0.334801
Clustering 1775: AMI= 0.7186, NMI= 0.7195, ARI= 0.4950, ACC= 0.5975
0.03404047396066615
Training epoch 1776, recon_loss:0.607827, zinb_loss:0.960321, cluster_loss:0.333601
Clustering 1776: AMI= 0.7202, NMI= 0.7211, ARI= 0.4991, ACC= 0.6038
0.03440693839325706
Training epoch 1777, recon_loss:0.607776, zinb_loss:0.960791, cluster_loss:0.334701
Clustering 1777: AMI= 0.7186, NMI= 0.7196, ARI= 0.4954, ACC= 0.5984
0.0346919662852722
Training epoch 1778, recon_loss:0.607682, zinb_loss:0.960039, cluster_loss:0.333477
Clustering 1778: AMI= 0.7202, NMI= 0.7211, ARI= 0.4989, ACC= 0.6030
0.034325501852681295
Training epoch 1779, recon_loss:0.607523, zinb_loss:0.960501, cluster_loss:0.334691
Clustering 1779: AMI= 0.7184, NMI= 0.7194, ARI= 0.4955, ACC= 0.5990
0.033714727798363125
Training epoch 1780, recon_loss:0.607247, zinb_loss:0.959692, cluster_loss:0.333465
Clustering 1780: AMI= 0.7202, NMI= 0.7211, ARI= 0.4989, ACC= 0.6026
0.03237102487886315
Training epoch 1781, recon_loss:0.607050, zinb_loss:0.960118, cluster_loss:0.334746
Clustering 1781: AMI= 0.7186, NMI= 0.7195, ARI= 0.4959, ACC= 0.5995
0.03159737774339346
Training epoch 1782, recon_loss:0.606755, zinb_loss:0.959372, cluster_loss:0.333563
Clustering 1782: AMI= 0.7201, NMI= 0.7210, ARI= 0.4989, ACC= 0.6022
0.030701575797060142
Training epoch 1783, recon_loss:0.606571, zinb_loss:0.959756, cluster_loss:0.334836
Clustering 1783: AMI= 0.7187, NMI= 0.7197, ARI= 0.4960, ACC= 0.5998
0.02915428152612077
Training epoch 1784, recon_loss:0.606345, zinb_loss:0.959132, cluster_loss:0.333699
Clustering 1784: AMI= 0.7199, NMI= 0.7208, ARI= 0.4986, ACC= 0.6017
0.02785129687690867
Training epoch 1785, recon_loss:0.606178, zinb_loss:0.959453, cluster_loss:0.334922
Clustering 1785: AMI= 0.7187, NMI= 0.7197, ARI= 0.4962, ACC= 0.6002
0.026548312227696567
Training epoch 1786, recon_loss:0.606079, zinb_loss:0.958974, cluster_loss:0.333833
Clustering 1786: AMI= 0.7198, NMI= 0.7208, ARI= 0.4985, ACC= 0.6014
0.025693228551651126
Training epoch 1787, recon_loss:0.605921, zinb_loss:0.959228, cluster_loss:0.334991
Clustering 1787: AMI= 0.7188, NMI= 0.7197, ARI= 0.4962, ACC= 0.6001
0.024512398713302658
Training epoch 1788, recon_loss:0.605934, zinb_loss:0.958885, cluster_loss:0.333936
Clustering 1788: AMI= 0.7199, NMI= 0.7208, ARI= 0.4983, ACC= 0.6010
0.02382018811840873
Training epoch 1789, recon_loss:0.605769, zinb_loss:0.959050, cluster_loss:0.335038
Clustering 1789: AMI= 0.7186, NMI= 0.7196, ARI= 0.4965, ACC= 0.6004
0.022761513090923897
Training epoch 1790, recon_loss:0.605881, zinb_loss:0.958845, cluster_loss:0.334015
Clustering 1790: AMI= 0.7198, NMI= 0.7208, ARI= 0.4980, ACC= 0.6007
0.021662119793151188
Training epoch 1791, recon_loss:0.605695, zinb_loss:0.958910, cluster_loss:0.335061
Clustering 1791: AMI= 0.7188, NMI= 0.7198, ARI= 0.4968, ACC= 0.6006
0.02076631784681787
Training epoch 1792, recon_loss:0.605901, zinb_loss:0.958846, cluster_loss:0.334059
Clustering 1792: AMI= 0.7197, NMI= 0.7207, ARI= 0.4979, ACC= 0.6006
0.020114825522211815
Training epoch 1793, recon_loss:0.605677, zinb_loss:0.958801, cluster_loss:0.335064
Clustering 1793: AMI= 0.7191, NMI= 0.7200, ARI= 0.4972, ACC= 0.6008
0.019666924549045155
Training epoch 1794, recon_loss:0.605973, zinb_loss:0.958892, cluster_loss:0.334078
Clustering 1794: AMI= 0.7196, NMI= 0.7205, ARI= 0.4976, ACC= 0.6002
0.019056150494726982
Training epoch 1795, recon_loss:0.605714, zinb_loss:0.958728, cluster_loss:0.335039
Clustering 1795: AMI= 0.7194, NMI= 0.7203, ARI= 0.4977, ACC= 0.6011
0.01783460238609064
Training epoch 1796, recon_loss:0.606098, zinb_loss:0.958993, cluster_loss:0.334064
Clustering 1796: AMI= 0.7197, NMI= 0.7206, ARI= 0.4973, ACC= 0.5999
0.017468137953499736
Training epoch 1797, recon_loss:0.605802, zinb_loss:0.958701, cluster_loss:0.334980
Clustering 1797: AMI= 0.7193, NMI= 0.7202, ARI= 0.4976, ACC= 0.6010
0.0169795187100452
Training epoch 1798, recon_loss:0.606281, zinb_loss:0.959169, cluster_loss:0.334018
Clustering 1798: AMI= 0.7194, NMI= 0.7204, ARI= 0.4970, ACC= 0.5996
0.01669449081803005
Training epoch 1799, recon_loss:0.605934, zinb_loss:0.958740, cluster_loss:0.334871
Clustering 1799: AMI= 0.7194, NMI= 0.7203, ARI= 0.4981, ACC= 0.6013
0.015880125412272485
Training epoch 1800, recon_loss:0.606487, zinb_loss:0.959430, cluster_loss:0.333952
Clustering 1800: AMI= 0.7193, NMI= 0.7202, ARI= 0.4966, ACC= 0.5994
0.015310069628242192
Training epoch 1801, recon_loss:0.606050, zinb_loss:0.958859, cluster_loss:0.334720
Clustering 1801: AMI= 0.7197, NMI= 0.7206, ARI= 0.4986, ACC= 0.6017
0.015350787898530071
Training epoch 1802, recon_loss:0.606652, zinb_loss:0.959755, cluster_loss:0.333898
Clustering 1802: AMI= 0.7194, NMI= 0.7204, ARI= 0.4967, ACC= 0.5993
0.015228633087666437
Training epoch 1803, recon_loss:0.606180, zinb_loss:0.959041, cluster_loss:0.334550
Clustering 1803: AMI= 0.7201, NMI= 0.7210, ARI= 0.4997, ACC= 0.6023
0.01449570422248463
Training epoch 1804, recon_loss:0.606860, zinb_loss:0.960100, cluster_loss:0.333878
Clustering 1804: AMI= 0.7192, NMI= 0.7202, ARI= 0.4962, ACC= 0.5989
0.015513660979681583
Training epoch 1805, recon_loss:0.606502, zinb_loss:0.959242, cluster_loss:0.334379
Clustering 1805: AMI= 0.7202, NMI= 0.7211, ARI= 0.5004, ACC= 0.6029
0.016938800439757318
Training epoch 1806, recon_loss:0.607275, zinb_loss:0.960381, cluster_loss:0.333903
Clustering 1806: AMI= 0.7189, NMI= 0.7199, ARI= 0.4958, ACC= 0.5990
0.018323221629545177
Training epoch 1807, recon_loss:0.607320, zinb_loss:0.959392, cluster_loss:0.334247
Clustering 1807: AMI= 0.7198, NMI= 0.7208, ARI= 0.5008, ACC= 0.6035
0.019341178386742132
Training epoch 1808, recon_loss:0.608071, zinb_loss:0.960508, cluster_loss:0.333964
Clustering 1808: AMI= 0.7188, NMI= 0.7197, ARI= 0.4953, ACC= 0.5987
0.021051345738833015
Training epoch 1809, recon_loss:0.608486, zinb_loss:0.959440, cluster_loss:0.334188
Clustering 1809: AMI= 0.7201, NMI= 0.7210, ARI= 0.5013, ACC= 0.6040
0.0218249928743027
Training epoch 1810, recon_loss:0.608709, zinb_loss:0.960428, cluster_loss:0.334057
Clustering 1810: AMI= 0.7185, NMI= 0.7194, ARI= 0.4950, ACC= 0.5984
0.02296510444236329
Training epoch 1811, recon_loss:0.608256, zinb_loss:0.959342, cluster_loss:0.334227
Clustering 1811: AMI= 0.7200, NMI= 0.7209, ARI= 0.5013, ACC= 0.6041
0.023046540982939043
Training epoch 1812, recon_loss:0.608267, zinb_loss:0.960259, cluster_loss:0.334254
Clustering 1812: AMI= 0.7187, NMI= 0.7197, ARI= 0.4948, ACC= 0.5984
0.023331568874954193
Training epoch 1813, recon_loss:0.608249, zinb_loss:0.959208, cluster_loss:0.334327
Clustering 1813: AMI= 0.7199, NMI= 0.7208, ARI= 0.5014, ACC= 0.6041
0.02316869579380268
Training epoch 1814, recon_loss:0.608367, zinb_loss:0.959990, cluster_loss:0.334351
Clustering 1814: AMI= 0.7185, NMI= 0.7194, ARI= 0.4944, ACC= 0.5982
0.023290850604666315
Training epoch 1815, recon_loss:0.607845, zinb_loss:0.959054, cluster_loss:0.334431
Clustering 1815: AMI= 0.7200, NMI= 0.7209, ARI= 0.5014, ACC= 0.6041
0.023046540982939043
Training epoch 1816, recon_loss:0.607789, zinb_loss:0.959752, cluster_loss:0.334486
Clustering 1816: AMI= 0.7187, NMI= 0.7197, ARI= 0.4948, ACC= 0.5984
0.022598640009772384
Training epoch 1817, recon_loss:0.607431, zinb_loss:0.958950, cluster_loss:0.334531
Clustering 1817: AMI= 0.7199, NMI= 0.7209, ARI= 0.5011, ACC= 0.6037
0.02206930249602997
Training epoch 1818, recon_loss:0.607571, zinb_loss:0.959549, cluster_loss:0.334568
Clustering 1818: AMI= 0.7186, NMI= 0.7196, ARI= 0.4950, ACC= 0.5984
0.02133637363084816
Training epoch 1819, recon_loss:0.607299, zinb_loss:0.958889, cluster_loss:0.334633
Clustering 1819: AMI= 0.7199, NMI= 0.7208, ARI= 0.5009, ACC= 0.6033
0.02092919092796938
Training epoch 1820, recon_loss:0.607481, zinb_loss:0.959392, cluster_loss:0.334559
Clustering 1820: AMI= 0.7188, NMI= 0.7198, ARI= 0.4951, ACC= 0.5985
0.020807036117105746
Training epoch 1821, recon_loss:0.607238, zinb_loss:0.958860, cluster_loss:0.334702
Clustering 1821: AMI= 0.7197, NMI= 0.7206, ARI= 0.5006, ACC= 0.6030
0.020562726495378478
Training epoch 1822, recon_loss:0.607378, zinb_loss:0.959287, cluster_loss:0.334546
Clustering 1822: AMI= 0.7187, NMI= 0.7196, ARI= 0.4949, ACC= 0.5983
0.020359135143939087
Training epoch 1823, recon_loss:0.607242, zinb_loss:0.958873, cluster_loss:0.334739
Clustering 1823: AMI= 0.7197, NMI= 0.7206, ARI= 0.5005, ACC= 0.6032
0.020114825522211815
Training epoch 1824, recon_loss:0.607344, zinb_loss:0.959195, cluster_loss:0.334496
Clustering 1824: AMI= 0.7187, NMI= 0.7197, ARI= 0.4953, ACC= 0.5982
0.019463333197605764
Training epoch 1825, recon_loss:0.607199, zinb_loss:0.958891, cluster_loss:0.334753
Clustering 1825: AMI= 0.7199, NMI= 0.7208, ARI= 0.5004, ACC= 0.6033
0.01913758703530274
Training epoch 1826, recon_loss:0.607212, zinb_loss:0.959131, cluster_loss:0.334415
Clustering 1826: AMI= 0.7190, NMI= 0.7199, ARI= 0.4957, ACC= 0.5982
0.01885255914328759
Training epoch 1827, recon_loss:0.607110, zinb_loss:0.958914, cluster_loss:0.334726
Clustering 1827: AMI= 0.7198, NMI= 0.7207, ARI= 0.5003, ACC= 0.6037
0.018771122602711836
Training epoch 1828, recon_loss:0.607041, zinb_loss:0.959091, cluster_loss:0.334352
Clustering 1828: AMI= 0.7191, NMI= 0.7200, ARI= 0.4960, ACC= 0.5983
0.018323221629545177
Training epoch 1829, recon_loss:0.606971, zinb_loss:0.958974, cluster_loss:0.334693
Clustering 1829: AMI= 0.7197, NMI= 0.7206, ARI= 0.4999, ACC= 0.6035
0.01820106681868154
Training epoch 1830, recon_loss:0.606839, zinb_loss:0.959086, cluster_loss:0.334282
Clustering 1830: AMI= 0.7193, NMI= 0.7202, ARI= 0.4967, ACC= 0.5987
0.01775316584551488
Training epoch 1831, recon_loss:0.606852, zinb_loss:0.959069, cluster_loss:0.334638
Clustering 1831: AMI= 0.7195, NMI= 0.7205, ARI= 0.4998, ACC= 0.6038
0.017956757196954273
Training epoch 1832, recon_loss:0.606681, zinb_loss:0.959114, cluster_loss:0.334167
Clustering 1832: AMI= 0.7193, NMI= 0.7202, ARI= 0.4972, ACC= 0.5987
0.01783460238609064
Training epoch 1833, recon_loss:0.606872, zinb_loss:0.959223, cluster_loss:0.334550
Clustering 1833: AMI= 0.7196, NMI= 0.7205, ARI= 0.4996, ACC= 0.6040
0.018404658170120932
Training epoch 1834, recon_loss:0.606723, zinb_loss:0.959192, cluster_loss:0.334027
Clustering 1834: AMI= 0.7195, NMI= 0.7204, ARI= 0.4975, ACC= 0.5983
0.019422614927317887
Training epoch 1835, recon_loss:0.607051, zinb_loss:0.959456, cluster_loss:0.334434
Clustering 1835: AMI= 0.7194, NMI= 0.7203, ARI= 0.4990, ACC= 0.6042
0.02068488130624211
Training epoch 1836, recon_loss:0.606956, zinb_loss:0.959314, cluster_loss:0.333832
Clustering 1836: AMI= 0.7194, NMI= 0.7203, ARI= 0.4976, ACC= 0.5979
0.021743556333726943
Training epoch 1837, recon_loss:0.607364, zinb_loss:0.959717, cluster_loss:0.334286
Clustering 1837: AMI= 0.7194, NMI= 0.7203, ARI= 0.4989, ACC= 0.6046
0.02227289384746936
Training epoch 1838, recon_loss:0.607255, zinb_loss:0.959398, cluster_loss:0.333606
Clustering 1838: AMI= 0.7194, NMI= 0.7204, ARI= 0.4978, ACC= 0.5974
0.023494441956105706
Training epoch 1839, recon_loss:0.607461, zinb_loss:0.959882, cluster_loss:0.334191
Clustering 1839: AMI= 0.7195, NMI= 0.7205, ARI= 0.4989, ACC= 0.6050
0.023535160226393584
Training epoch 1840, recon_loss:0.607179, zinb_loss:0.959446, cluster_loss:0.333621
Clustering 1840: AMI= 0.7192, NMI= 0.7201, ARI= 0.4975, ACC= 0.5969
0.023494441956105706
Training epoch 1841, recon_loss:0.607080, zinb_loss:0.959957, cluster_loss:0.334239
Clustering 1841: AMI= 0.7196, NMI= 0.7205, ARI= 0.4990, ACC= 0.6054
0.02272079482063602
Training epoch 1842, recon_loss:0.606731, zinb_loss:0.959438, cluster_loss:0.333818
Clustering 1842: AMI= 0.7192, NMI= 0.7202, ARI= 0.4975, ACC= 0.5970
0.0218249928743027
Training epoch 1843, recon_loss:0.606539, zinb_loss:0.959964, cluster_loss:0.334357
Clustering 1843: AMI= 0.7196, NMI= 0.7206, ARI= 0.4995, ACC= 0.6056
0.021010627468545137
Training epoch 1844, recon_loss:0.606223, zinb_loss:0.959418, cluster_loss:0.334081
Clustering 1844: AMI= 0.7192, NMI= 0.7202, ARI= 0.4972, ACC= 0.5968
0.019422614927317887
Training epoch 1845, recon_loss:0.606021, zinb_loss:0.959947, cluster_loss:0.334483
Clustering 1845: AMI= 0.7196, NMI= 0.7206, ARI= 0.4997, ACC= 0.6056
0.018811840872999714
Training epoch 1846, recon_loss:0.605818, zinb_loss:0.959424, cluster_loss:0.334320
Clustering 1846: AMI= 0.7190, NMI= 0.7199, ARI= 0.4970, ACC= 0.5967
0.01763101103465125
Training epoch 1847, recon_loss:0.605640, zinb_loss:0.959948, cluster_loss:0.334588
Clustering 1847: AMI= 0.7197, NMI= 0.7206, ARI= 0.4996, ACC= 0.6053
0.016938800439757318
Training epoch 1848, recon_loss:0.605549, zinb_loss:0.959475, cluster_loss:0.334524
Clustering 1848: AMI= 0.7190, NMI= 0.7199, ARI= 0.4968, ACC= 0.5968
0.01669449081803005
Training epoch 1849, recon_loss:0.605360, zinb_loss:0.959984, cluster_loss:0.334658
Clustering 1849: AMI= 0.7195, NMI= 0.7204, ARI= 0.4998, ACC= 0.6053
0.016613054277454294
Training epoch 1850, recon_loss:0.605397, zinb_loss:0.959583, cluster_loss:0.334696
Clustering 1850: AMI= 0.7190, NMI= 0.7199, ARI= 0.4968, ACC= 0.5972
0.016613054277454294
Training epoch 1851, recon_loss:0.605141, zinb_loss:0.960058, cluster_loss:0.334677
Clustering 1851: AMI= 0.7197, NMI= 0.7206, ARI= 0.5000, ACC= 0.6056
0.0169795187100452
Training epoch 1852, recon_loss:0.605368, zinb_loss:0.959766, cluster_loss:0.334840
Clustering 1852: AMI= 0.7191, NMI= 0.7200, ARI= 0.4966, ACC= 0.5973
0.017468137953499736
Training epoch 1853, recon_loss:0.604977, zinb_loss:0.960185, cluster_loss:0.334642
Clustering 1853: AMI= 0.7200, NMI= 0.7209, ARI= 0.5003, ACC= 0.6057
0.018363939899833055
Training epoch 1854, recon_loss:0.605527, zinb_loss:0.960063, cluster_loss:0.334969
Clustering 1854: AMI= 0.7189, NMI= 0.7199, ARI= 0.4962, ACC= 0.5973
0.0195854880084694
Training epoch 1855, recon_loss:0.604998, zinb_loss:0.960387, cluster_loss:0.334537
Clustering 1855: AMI= 0.7202, NMI= 0.7211, ARI= 0.5010, ACC= 0.6063
0.02068488130624211
Training epoch 1856, recon_loss:0.605717, zinb_loss:0.960432, cluster_loss:0.335070
Clustering 1856: AMI= 0.7190, NMI= 0.7199, ARI= 0.4958, ACC= 0.5973
0.02227289384746936
Training epoch 1857, recon_loss:0.605095, zinb_loss:0.960617, cluster_loss:0.334447
Clustering 1857: AMI= 0.7202, NMI= 0.7211, ARI= 0.5016, ACC= 0.6065
0.02402377946984812
Training epoch 1858, recon_loss:0.605902, zinb_loss:0.960699, cluster_loss:0.335121
Clustering 1858: AMI= 0.7188, NMI= 0.7198, ARI= 0.4953, ACC= 0.5971
0.025448918929923858
Training epoch 1859, recon_loss:0.605263, zinb_loss:0.960783, cluster_loss:0.334367
Clustering 1859: AMI= 0.7204, NMI= 0.7213, ARI= 0.5022, ACC= 0.6065
0.026385439146545054
Training epoch 1860, recon_loss:0.606260, zinb_loss:0.960889, cluster_loss:0.335172
Clustering 1860: AMI= 0.7188, NMI= 0.7197, ARI= 0.4950, ACC= 0.5973
0.027444114174029886
Training epoch 1861, recon_loss:0.605583, zinb_loss:0.960872, cluster_loss:0.334256
Clustering 1861: AMI= 0.7205, NMI= 0.7215, ARI= 0.5022, ACC= 0.6060
0.027932733417484427
Training epoch 1862, recon_loss:0.606770, zinb_loss:0.960967, cluster_loss:0.335185
Clustering 1862: AMI= 0.7190, NMI= 0.7199, ARI= 0.4953, ACC= 0.5981
0.027892015147196546
Training epoch 1863, recon_loss:0.606170, zinb_loss:0.960838, cluster_loss:0.334154
Clustering 1863: AMI= 0.7203, NMI= 0.7212, ARI= 0.5019, ACC= 0.6050
0.028217761309499573
Training epoch 1864, recon_loss:0.607202, zinb_loss:0.960921, cluster_loss:0.335198
Clustering 1864: AMI= 0.7189, NMI= 0.7199, ARI= 0.4953, ACC= 0.5984
0.02850278920151472
Training epoch 1865, recon_loss:0.606419, zinb_loss:0.960747, cluster_loss:0.334037
Clustering 1865: AMI= 0.7205, NMI= 0.7214, ARI= 0.5021, ACC= 0.6048
0.029072844985545014
Training epoch 1866, recon_loss:0.607195, zinb_loss:0.960729, cluster_loss:0.335172
Clustering 1866: AMI= 0.7190, NMI= 0.7199, ARI= 0.4953, ACC= 0.5989
0.028828535363817746
Training epoch 1867, recon_loss:0.606642, zinb_loss:0.960493, cluster_loss:0.334012
Clustering 1867: AMI= 0.7206, NMI= 0.7215, ARI= 0.5020, ACC= 0.6043
0.028380634390651086
Training epoch 1868, recon_loss:0.607282, zinb_loss:0.960474, cluster_loss:0.335169
Clustering 1868: AMI= 0.7189, NMI= 0.7198, ARI= 0.4954, ACC= 0.5993
0.028014169958060182
Training epoch 1869, recon_loss:0.606828, zinb_loss:0.960189, cluster_loss:0.333993
Clustering 1869: AMI= 0.7204, NMI= 0.7213, ARI= 0.5016, ACC= 0.6038
0.02736267763345413
Training epoch 1870, recon_loss:0.607353, zinb_loss:0.960199, cluster_loss:0.335175
Clustering 1870: AMI= 0.7188, NMI= 0.7197, ARI= 0.4953, ACC= 0.5994
0.027199804552302618
Training epoch 1871, recon_loss:0.607007, zinb_loss:0.959902, cluster_loss:0.333942
Clustering 1871: AMI= 0.7202, NMI= 0.7211, ARI= 0.5012, ACC= 0.6033
0.026100411254529908
Training epoch 1872, recon_loss:0.607614, zinb_loss:0.959973, cluster_loss:0.335135
Clustering 1872: AMI= 0.7188, NMI= 0.7197, ARI= 0.4950, ACC= 0.5995
0.02577466509222688
Training epoch 1873, recon_loss:0.607287, zinb_loss:0.959629, cluster_loss:0.333876
Clustering 1873: AMI= 0.7204, NMI= 0.7213, ARI= 0.5014, ACC= 0.6033
0.024960299686469317
Training epoch 1874, recon_loss:0.607803, zinb_loss:0.959776, cluster_loss:0.335079
Clustering 1874: AMI= 0.7186, NMI= 0.7196, ARI= 0.4945, ACC= 0.5995
0.02471599006474205
Training epoch 1875, recon_loss:0.607372, zinb_loss:0.959389, cluster_loss:0.333823
Clustering 1875: AMI= 0.7203, NMI= 0.7212, ARI= 0.5010, ACC= 0.6032
0.024593835253878416
Training epoch 1876, recon_loss:0.607736, zinb_loss:0.959605, cluster_loss:0.335014
Clustering 1876: AMI= 0.7186, NMI= 0.7195, ARI= 0.4944, ACC= 0.5998
0.024512398713302658
Training epoch 1877, recon_loss:0.607232, zinb_loss:0.959190, cluster_loss:0.333817
Clustering 1877: AMI= 0.7202, NMI= 0.7212, ARI= 0.5010, ACC= 0.6032
0.024186652550999634
Training epoch 1878, recon_loss:0.607479, zinb_loss:0.959451, cluster_loss:0.334976
Clustering 1878: AMI= 0.7184, NMI= 0.7193, ARI= 0.4943, ACC= 0.5997
0.02382018811840873
Training epoch 1879, recon_loss:0.606948, zinb_loss:0.959024, cluster_loss:0.333881
Clustering 1879: AMI= 0.7202, NMI= 0.7212, ARI= 0.5006, ACC= 0.6028
0.023087259253226924
Training epoch 1880, recon_loss:0.607104, zinb_loss:0.959308, cluster_loss:0.334969
Clustering 1880: AMI= 0.7184, NMI= 0.7194, ARI= 0.4943, ACC= 0.5997
0.022598640009772384
Training epoch 1881, recon_loss:0.606576, zinb_loss:0.958887, cluster_loss:0.334022
Clustering 1881: AMI= 0.7202, NMI= 0.7211, ARI= 0.5004, ACC= 0.6026
0.022313612117757238
Training epoch 1882, recon_loss:0.606698, zinb_loss:0.959189, cluster_loss:0.334999
Clustering 1882: AMI= 0.7184, NMI= 0.7193, ARI= 0.4941, ACC= 0.5995
0.02178427460401482
Training epoch 1883, recon_loss:0.606221, zinb_loss:0.958786, cluster_loss:0.334191
Clustering 1883: AMI= 0.7202, NMI= 0.7211, ARI= 0.5004, ACC= 0.6026
0.021377091901136038
Training epoch 1884, recon_loss:0.606358, zinb_loss:0.959106, cluster_loss:0.335041
Clustering 1884: AMI= 0.7182, NMI= 0.7191, ARI= 0.4942, ACC= 0.5994
0.02068488130624211
Training epoch 1885, recon_loss:0.605929, zinb_loss:0.958723, cluster_loss:0.334366
Clustering 1885: AMI= 0.7202, NMI= 0.7211, ARI= 0.5004, ACC= 0.6027
0.02023698033307545
Training epoch 1886, recon_loss:0.606120, zinb_loss:0.959056, cluster_loss:0.335075
Clustering 1886: AMI= 0.7182, NMI= 0.7191, ARI= 0.4941, ACC= 0.5992
0.019056150494726982
Training epoch 1887, recon_loss:0.605718, zinb_loss:0.958692, cluster_loss:0.334526
Clustering 1887: AMI= 0.7201, NMI= 0.7210, ARI= 0.5001, ACC= 0.6026
0.018323221629545177
Training epoch 1888, recon_loss:0.605982, zinb_loss:0.959044, cluster_loss:0.335094
Clustering 1888: AMI= 0.7181, NMI= 0.7190, ARI= 0.4943, ACC= 0.5993
0.01807891200781791
Training epoch 1889, recon_loss:0.605584, zinb_loss:0.958693, cluster_loss:0.334655
Clustering 1889: AMI= 0.7200, NMI= 0.7209, ARI= 0.4999, ACC= 0.6024
0.017671729304939127
Training epoch 1890, recon_loss:0.605911, zinb_loss:0.959064, cluster_loss:0.335092
Clustering 1890: AMI= 0.7181, NMI= 0.7190, ARI= 0.4946, ACC= 0.5994
0.01738670141292398
Training epoch 1891, recon_loss:0.605514, zinb_loss:0.958721, cluster_loss:0.334746
Clustering 1891: AMI= 0.7200, NMI= 0.7209, ARI= 0.4998, ACC= 0.6024
0.017508856223787613
Training epoch 1892, recon_loss:0.605909, zinb_loss:0.959119, cluster_loss:0.335067
Clustering 1892: AMI= 0.7183, NMI= 0.7192, ARI= 0.4946, ACC= 0.5992
0.017590292764363368
Training epoch 1893, recon_loss:0.605484, zinb_loss:0.958773, cluster_loss:0.334816
Clustering 1893: AMI= 0.7200, NMI= 0.7209, ARI= 0.4999, ACC= 0.6026
0.01754957449407549
Training epoch 1894, recon_loss:0.605952, zinb_loss:0.959202, cluster_loss:0.335021
Clustering 1894: AMI= 0.7184, NMI= 0.7193, ARI= 0.4949, ACC= 0.5996
0.01779388411580276
Training epoch 1895, recon_loss:0.605491, zinb_loss:0.958857, cluster_loss:0.334869
Clustering 1895: AMI= 0.7199, NMI= 0.7208, ARI= 0.5001, ACC= 0.6030
0.018363939899833055
Training epoch 1896, recon_loss:0.606021, zinb_loss:0.959317, cluster_loss:0.334959
Clustering 1896: AMI= 0.7184, NMI= 0.7193, ARI= 0.4949, ACC= 0.5996
0.018648967791848204
Training epoch 1897, recon_loss:0.605518, zinb_loss:0.958968, cluster_loss:0.334916
Clustering 1897: AMI= 0.7200, NMI= 0.7210, ARI= 0.5001, ACC= 0.6028
0.01893399568386335
Training epoch 1898, recon_loss:0.606103, zinb_loss:0.959455, cluster_loss:0.334886
Clustering 1898: AMI= 0.7186, NMI= 0.7195, ARI= 0.4952, ACC= 0.5997
0.018771122602711836
Training epoch 1899, recon_loss:0.605548, zinb_loss:0.959103, cluster_loss:0.334958
Clustering 1899: AMI= 0.7197, NMI= 0.7206, ARI= 0.4995, ACC= 0.6026
0.019056150494726982
Training epoch 1900, recon_loss:0.606190, zinb_loss:0.959610, cluster_loss:0.334819
Clustering 1900: AMI= 0.7186, NMI= 0.7195, ARI= 0.4954, ACC= 0.5996
0.01917830530559062
Training epoch 1901, recon_loss:0.605514, zinb_loss:0.959246, cluster_loss:0.335001
Clustering 1901: AMI= 0.7196, NMI= 0.7205, ARI= 0.4993, ACC= 0.6024
0.01893399568386335
Training epoch 1902, recon_loss:0.606265, zinb_loss:0.959765, cluster_loss:0.334763
Clustering 1902: AMI= 0.7188, NMI= 0.7197, ARI= 0.4958, ACC= 0.5998
0.019015432224439105
Training epoch 1903, recon_loss:0.605517, zinb_loss:0.959376, cluster_loss:0.335040
Clustering 1903: AMI= 0.7196, NMI= 0.7206, ARI= 0.4992, ACC= 0.6023
0.0182825033592573
Training epoch 1904, recon_loss:0.606337, zinb_loss:0.959901, cluster_loss:0.334721
Clustering 1904: AMI= 0.7190, NMI= 0.7199, ARI= 0.4959, ACC= 0.5996
0.017916038926666395
Training epoch 1905, recon_loss:0.605587, zinb_loss:0.959489, cluster_loss:0.335069
Clustering 1905: AMI= 0.7195, NMI= 0.7205, ARI= 0.4990, ACC= 0.6022
0.01763101103465125
Training epoch 1906, recon_loss:0.606476, zinb_loss:0.960017, cluster_loss:0.334675
Clustering 1906: AMI= 0.7192, NMI= 0.7202, ARI= 0.4963, ACC= 0.5997
0.0169795187100452
Training epoch 1907, recon_loss:0.605800, zinb_loss:0.959575, cluster_loss:0.335068
Clustering 1907: AMI= 0.7197, NMI= 0.7206, ARI= 0.4989, ACC= 0.6021
0.015839407141984608
Training epoch 1908, recon_loss:0.606698, zinb_loss:0.960090, cluster_loss:0.334622
Clustering 1908: AMI= 0.7193, NMI= 0.7202, ARI= 0.4966, ACC= 0.5998
0.015025041736227046
Training epoch 1909, recon_loss:0.606132, zinb_loss:0.959632, cluster_loss:0.335031
Clustering 1909: AMI= 0.7197, NMI= 0.7206, ARI= 0.4989, ACC= 0.6020
0.014007084979030091
Training epoch 1910, recon_loss:0.606933, zinb_loss:0.960102, cluster_loss:0.334562
Clustering 1910: AMI= 0.7194, NMI= 0.7203, ARI= 0.4970, ACC= 0.6002
0.013681338816727066
Training epoch 1911, recon_loss:0.606328, zinb_loss:0.959634, cluster_loss:0.334972
Clustering 1911: AMI= 0.7198, NMI= 0.7207, ARI= 0.4993, ACC= 0.6022
0.013233437843560406
Training epoch 1912, recon_loss:0.606954, zinb_loss:0.960033, cluster_loss:0.334506
Clustering 1912: AMI= 0.7192, NMI= 0.7201, ARI= 0.4969, ACC= 0.6002
0.013681338816727066
Training epoch 1913, recon_loss:0.606723, zinb_loss:0.959584, cluster_loss:0.334904
Clustering 1913: AMI= 0.7198, NMI= 0.7207, ARI= 0.4992, ACC= 0.6020
0.013722057087014943
Training epoch 1914, recon_loss:0.606877, zinb_loss:0.959866, cluster_loss:0.334476
Clustering 1914: AMI= 0.7194, NMI= 0.7203, ARI= 0.4974, ACC= 0.6008
0.013722057087014943
Training epoch 1915, recon_loss:0.606532, zinb_loss:0.959473, cluster_loss:0.334898
Clustering 1915: AMI= 0.7196, NMI= 0.7206, ARI= 0.4989, ACC= 0.6017
0.013966366708742213
Training epoch 1916, recon_loss:0.606300, zinb_loss:0.959732, cluster_loss:0.334519
Clustering 1916: AMI= 0.7193, NMI= 0.7202, ARI= 0.4975, ACC= 0.6010
0.014414267681908873
Training epoch 1917, recon_loss:0.606095, zinb_loss:0.959327, cluster_loss:0.334858
Clustering 1917: AMI= 0.7197, NMI= 0.7207, ARI= 0.4987, ACC= 0.6014
0.014577140763060385
Training epoch 1918, recon_loss:0.605958, zinb_loss:0.959562, cluster_loss:0.334541
Clustering 1918: AMI= 0.7194, NMI= 0.7203, ARI= 0.4979, ACC= 0.6017
0.015513660979681583
Training epoch 1919, recon_loss:0.605863, zinb_loss:0.959186, cluster_loss:0.334778
Clustering 1919: AMI= 0.7193, NMI= 0.7202, ARI= 0.4980, ACC= 0.6006
0.01579868887169673
Training epoch 1920, recon_loss:0.605606, zinb_loss:0.959456, cluster_loss:0.334563
Clustering 1920: AMI= 0.7196, NMI= 0.7205, ARI= 0.4983, ACC= 0.6023
0.015757970601408853
Training epoch 1921, recon_loss:0.605803, zinb_loss:0.959048, cluster_loss:0.334576
Clustering 1921: AMI= 0.7191, NMI= 0.7200, ARI= 0.4974, ACC= 0.6000
0.016042998493424
Training epoch 1922, recon_loss:0.605598, zinb_loss:0.959375, cluster_loss:0.334469
Clustering 1922: AMI= 0.7196, NMI= 0.7206, ARI= 0.4985, ACC= 0.6028
0.016287308115151267
Training epoch 1923, recon_loss:0.605974, zinb_loss:0.958972, cluster_loss:0.334296
Clustering 1923: AMI= 0.7190, NMI= 0.7199, ARI= 0.4973, ACC= 0.5994
0.016816645628893685
Training epoch 1924, recon_loss:0.605757, zinb_loss:0.959337, cluster_loss:0.334303
Clustering 1924: AMI= 0.7196, NMI= 0.7206, ARI= 0.4990, ACC= 0.6034
0.0173459831426361
Training epoch 1925, recon_loss:0.606281, zinb_loss:0.958966, cluster_loss:0.334080
Clustering 1925: AMI= 0.7190, NMI= 0.7199, ARI= 0.4970, ACC= 0.5988
0.018241785088969422
Training epoch 1926, recon_loss:0.605992, zinb_loss:0.959305, cluster_loss:0.334175
Clustering 1926: AMI= 0.7198, NMI= 0.7207, ARI= 0.4995, ACC= 0.6041
0.018893277413575472
Training epoch 1927, recon_loss:0.606537, zinb_loss:0.959028, cluster_loss:0.334060
Clustering 1927: AMI= 0.7190, NMI= 0.7199, ARI= 0.4971, ACC= 0.5986
0.019504051467893645
Training epoch 1928, recon_loss:0.606091, zinb_loss:0.959269, cluster_loss:0.334158
Clustering 1928: AMI= 0.7196, NMI= 0.7206, ARI= 0.4996, ACC= 0.6042
0.019626206278757278
Training epoch 1929, recon_loss:0.606596, zinb_loss:0.959121, cluster_loss:0.334227
Clustering 1929: AMI= 0.7188, NMI= 0.7197, ARI= 0.4966, ACC= 0.5981
0.020399853414226964
Training epoch 1930, recon_loss:0.606056, zinb_loss:0.959235, cluster_loss:0.334249
Clustering 1930: AMI= 0.7198, NMI= 0.7207, ARI= 0.5002, ACC= 0.6048
0.02092919092796938
Training epoch 1931, recon_loss:0.606479, zinb_loss:0.959222, cluster_loss:0.334468
Clustering 1931: AMI= 0.7189, NMI= 0.7198, ARI= 0.4967, ACC= 0.5982
0.02113278227940877
Training epoch 1932, recon_loss:0.605939, zinb_loss:0.959223, cluster_loss:0.334383
Clustering 1932: AMI= 0.7199, NMI= 0.7208, ARI= 0.5007, ACC= 0.6053
0.021458528441711797
Training epoch 1933, recon_loss:0.606300, zinb_loss:0.959337, cluster_loss:0.334699
Clustering 1933: AMI= 0.7187, NMI= 0.7196, ARI= 0.4960, ACC= 0.5978
0.022150739036605725
Training epoch 1934, recon_loss:0.605811, zinb_loss:0.959245, cluster_loss:0.334515
Clustering 1934: AMI= 0.7200, NMI= 0.7210, ARI= 0.5009, ACC= 0.6054
0.02251720346919663
Training epoch 1935, recon_loss:0.606099, zinb_loss:0.959430, cluster_loss:0.334885
Clustering 1935: AMI= 0.7187, NMI= 0.7197, ARI= 0.4960, ACC= 0.5979
0.022110020766317847
Training epoch 1936, recon_loss:0.605703, zinb_loss:0.959283, cluster_loss:0.334632
Clustering 1936: AMI= 0.7201, NMI= 0.7210, ARI= 0.5010, ACC= 0.6055
0.02227289384746936
Training epoch 1937, recon_loss:0.605927, zinb_loss:0.959522, cluster_loss:0.335018
Clustering 1937: AMI= 0.7186, NMI= 0.7195, ARI= 0.4954, ACC= 0.5973
0.02272079482063602
Training epoch 1938, recon_loss:0.605633, zinb_loss:0.959328, cluster_loss:0.334725
Clustering 1938: AMI= 0.7201, NMI= 0.7210, ARI= 0.5011, ACC= 0.6055
0.023087259253226924
Training epoch 1939, recon_loss:0.605807, zinb_loss:0.959616, cluster_loss:0.335111
Clustering 1939: AMI= 0.7184, NMI= 0.7193, ARI= 0.4949, ACC= 0.5972
0.023779469848120852
Training epoch 1940, recon_loss:0.605624, zinb_loss:0.959383, cluster_loss:0.334795
Clustering 1940: AMI= 0.7201, NMI= 0.7211, ARI= 0.5013, ACC= 0.6057
0.02455311698359054
Training epoch 1941, recon_loss:0.605768, zinb_loss:0.959723, cluster_loss:0.335168
Clustering 1941: AMI= 0.7183, NMI= 0.7192, ARI= 0.4945, ACC= 0.5969
0.025082454497332953
Training epoch 1942, recon_loss:0.605657, zinb_loss:0.959435, cluster_loss:0.334840
Clustering 1942: AMI= 0.7201, NMI= 0.7211, ARI= 0.5013, ACC= 0.6057
0.025245327578484467
Training epoch 1943, recon_loss:0.605807, zinb_loss:0.959845, cluster_loss:0.335184
Clustering 1943: AMI= 0.7183, NMI= 0.7192, ARI= 0.4944, ACC= 0.5968
0.025978256443666272
Training epoch 1944, recon_loss:0.605750, zinb_loss:0.959489, cluster_loss:0.334862
Clustering 1944: AMI= 0.7202, NMI= 0.7211, ARI= 0.5015, ACC= 0.6058
0.026833340119711713
Training epoch 1945, recon_loss:0.605933, zinb_loss:0.959961, cluster_loss:0.335163
Clustering 1945: AMI= 0.7183, NMI= 0.7192, ARI= 0.4942, ACC= 0.5968
0.027688423795757155
Training epoch 1946, recon_loss:0.605932, zinb_loss:0.959524, cluster_loss:0.334858
Clustering 1946: AMI= 0.7201, NMI= 0.7210, ARI= 0.5016, ACC= 0.6060
0.028299197850075328
Training epoch 1947, recon_loss:0.606133, zinb_loss:0.960068, cluster_loss:0.335108
Clustering 1947: AMI= 0.7179, NMI= 0.7189, ARI= 0.4937, ACC= 0.5966
0.02935787287756016
Training epoch 1948, recon_loss:0.606176, zinb_loss:0.959525, cluster_loss:0.334831
Clustering 1948: AMI= 0.7203, NMI= 0.7212, ARI= 0.5019, ACC= 0.6059
0.029887210391302578
Training epoch 1949, recon_loss:0.606422, zinb_loss:0.960121, cluster_loss:0.335002
Clustering 1949: AMI= 0.7180, NMI= 0.7189, ARI= 0.4937, ACC= 0.5963
0.03049798444562075
Training epoch 1950, recon_loss:0.606410, zinb_loss:0.959465, cluster_loss:0.334790
Clustering 1950: AMI= 0.7203, NMI= 0.7212, ARI= 0.5019, ACC= 0.6060
0.03074229406734802
Training epoch 1951, recon_loss:0.606590, zinb_loss:0.960135, cluster_loss:0.334929
Clustering 1951: AMI= 0.7180, NMI= 0.7189, ARI= 0.4936, ACC= 0.5962
0.03131234985137831
Training epoch 1952, recon_loss:0.606514, zinb_loss:0.959360, cluster_loss:0.334773
Clustering 1952: AMI= 0.7201, NMI= 0.7210, ARI= 0.5016, ACC= 0.6056
0.0311494767702268
Training epoch 1953, recon_loss:0.606668, zinb_loss:0.960067, cluster_loss:0.334868
Clustering 1953: AMI= 0.7181, NMI= 0.7190, ARI= 0.4938, ACC= 0.5966
0.03094588541878741
Training epoch 1954, recon_loss:0.606474, zinb_loss:0.959226, cluster_loss:0.334799
Clustering 1954: AMI= 0.7200, NMI= 0.7210, ARI= 0.5015, ACC= 0.6052
0.0302129565536056
Training epoch 1955, recon_loss:0.606507, zinb_loss:0.959962, cluster_loss:0.334919
Clustering 1955: AMI= 0.7182, NMI= 0.7191, ARI= 0.4940, ACC= 0.5970
0.02935787287756016
Training epoch 1956, recon_loss:0.606317, zinb_loss:0.959109, cluster_loss:0.334871
Clustering 1956: AMI= 0.7200, NMI= 0.7210, ARI= 0.5012, ACC= 0.6050
0.02846207093122684
Training epoch 1957, recon_loss:0.606381, zinb_loss:0.959849, cluster_loss:0.334980
Clustering 1957: AMI= 0.7183, NMI= 0.7193, ARI= 0.4945, ACC= 0.5975
0.027036931471151104
Training epoch 1958, recon_loss:0.606262, zinb_loss:0.959033, cluster_loss:0.334924
Clustering 1958: AMI= 0.7200, NMI= 0.7209, ARI= 0.5012, ACC= 0.6051
0.026833340119711713
Training epoch 1959, recon_loss:0.606342, zinb_loss:0.959794, cluster_loss:0.335074
Clustering 1959: AMI= 0.7184, NMI= 0.7193, ARI= 0.4946, ACC= 0.5978
0.025978256443666272
Training epoch 1960, recon_loss:0.606278, zinb_loss:0.959013, cluster_loss:0.334951
Clustering 1960: AMI= 0.7199, NMI= 0.7208, ARI= 0.5006, ACC= 0.6046
0.024960299686469317
Training epoch 1961, recon_loss:0.606451, zinb_loss:0.959770, cluster_loss:0.335135
Clustering 1961: AMI= 0.7186, NMI= 0.7196, ARI= 0.4950, ACC= 0.5982
0.02455311698359054
Training epoch 1962, recon_loss:0.606459, zinb_loss:0.959033, cluster_loss:0.334923
Clustering 1962: AMI= 0.7198, NMI= 0.7207, ARI= 0.5003, ACC= 0.6044
0.023657315037257216
Training epoch 1963, recon_loss:0.606658, zinb_loss:0.959760, cluster_loss:0.335146
Clustering 1963: AMI= 0.7188, NMI= 0.7198, ARI= 0.4953, ACC= 0.5986
0.022598640009772384
Training epoch 1964, recon_loss:0.606666, zinb_loss:0.959080, cluster_loss:0.334813
Clustering 1964: AMI= 0.7199, NMI= 0.7208, ARI= 0.5003, ACC= 0.6044
0.022354330388045116
Training epoch 1965, recon_loss:0.606817, zinb_loss:0.959821, cluster_loss:0.335185
Clustering 1965: AMI= 0.7188, NMI= 0.7197, ARI= 0.4954, ACC= 0.5988
0.022232175577181483
Training epoch 1966, recon_loss:0.606611, zinb_loss:0.959148, cluster_loss:0.334657
Clustering 1966: AMI= 0.7198, NMI= 0.7207, ARI= 0.4999, ACC= 0.6045
0.022802231361211775
Training epoch 1967, recon_loss:0.606774, zinb_loss:0.959860, cluster_loss:0.335206
Clustering 1967: AMI= 0.7187, NMI= 0.7196, ARI= 0.4954, ACC= 0.5989
0.023127977523514802
Training epoch 1968, recon_loss:0.606447, zinb_loss:0.959205, cluster_loss:0.334507
Clustering 1968: AMI= 0.7197, NMI= 0.7206, ARI= 0.4998, ACC= 0.6043
0.023209414064090557
Training epoch 1969, recon_loss:0.606616, zinb_loss:0.959855, cluster_loss:0.335209
Clustering 1969: AMI= 0.7187, NMI= 0.7197, ARI= 0.4955, ACC= 0.5991
0.023250132334378434
Training epoch 1970, recon_loss:0.606132, zinb_loss:0.959230, cluster_loss:0.334404
Clustering 1970: AMI= 0.7197, NMI= 0.7206, ARI= 0.4998, ACC= 0.6045
0.023779469848120852
Training epoch 1971, recon_loss:0.606307, zinb_loss:0.959861, cluster_loss:0.335274
Clustering 1971: AMI= 0.7188, NMI= 0.7197, ARI= 0.4956, ACC= 0.5992
0.023494441956105706
Training epoch 1972, recon_loss:0.605632, zinb_loss:0.959246, cluster_loss:0.334363
Clustering 1972: AMI= 0.7197, NMI= 0.7207, ARI= 0.4999, ACC= 0.6046
0.024145934280711757
Training epoch 1973, recon_loss:0.606015, zinb_loss:0.959912, cluster_loss:0.335367
Clustering 1973: AMI= 0.7188, NMI= 0.7197, ARI= 0.4957, ACC= 0.5992
0.02426808909157539
Training epoch 1974, recon_loss:0.605209, zinb_loss:0.959302, cluster_loss:0.334345
Clustering 1974: AMI= 0.7198, NMI= 0.7207, ARI= 0.5001, ACC= 0.6048
0.02467527179445417
Training epoch 1975, recon_loss:0.605772, zinb_loss:0.960024, cluster_loss:0.335473
Clustering 1975: AMI= 0.7187, NMI= 0.7197, ARI= 0.4956, ACC= 0.5991
0.024797426605317807
Training epoch 1976, recon_loss:0.604984, zinb_loss:0.959394, cluster_loss:0.334374
Clustering 1976: AMI= 0.7198, NMI= 0.7207, ARI= 0.5001, ACC= 0.6048
0.02491958141618144
Training epoch 1977, recon_loss:0.605607, zinb_loss:0.960098, cluster_loss:0.335537
Clustering 1977: AMI= 0.7187, NMI= 0.7197, ARI= 0.4957, ACC= 0.5990
0.02471599006474205
Training epoch 1978, recon_loss:0.604827, zinb_loss:0.959498, cluster_loss:0.334457
Clustering 1978: AMI= 0.7198, NMI= 0.7207, ARI= 0.5002, ACC= 0.6049
0.024838144875605685
Training epoch 1979, recon_loss:0.605558, zinb_loss:0.960154, cluster_loss:0.335587
Clustering 1979: AMI= 0.7189, NMI= 0.7199, ARI= 0.4958, ACC= 0.5992
0.024756708335029926
Training epoch 1980, recon_loss:0.604826, zinb_loss:0.959612, cluster_loss:0.334499
Clustering 1980: AMI= 0.7197, NMI= 0.7206, ARI= 0.5000, ACC= 0.6049
0.025041736227045076
Training epoch 1981, recon_loss:0.605503, zinb_loss:0.960216, cluster_loss:0.335614
Clustering 1981: AMI= 0.7189, NMI= 0.7199, ARI= 0.4960, ACC= 0.5992
0.024838144875605685
Training epoch 1982, recon_loss:0.604883, zinb_loss:0.959741, cluster_loss:0.334547
Clustering 1982: AMI= 0.7196, NMI= 0.7206, ARI= 0.4995, ACC= 0.6041
0.025163891037908708
Training epoch 1983, recon_loss:0.605573, zinb_loss:0.960283, cluster_loss:0.335636
Clustering 1983: AMI= 0.7189, NMI= 0.7198, ARI= 0.4960, ACC= 0.5992
0.025489637200211735
Training epoch 1984, recon_loss:0.605068, zinb_loss:0.959876, cluster_loss:0.334556
Clustering 1984: AMI= 0.7196, NMI= 0.7205, ARI= 0.4994, ACC= 0.6038
0.02565251028136325
Training epoch 1985, recon_loss:0.605587, zinb_loss:0.960336, cluster_loss:0.335645
Clustering 1985: AMI= 0.7191, NMI= 0.7201, ARI= 0.4964, ACC= 0.5996
0.026141129524817786
Training epoch 1986, recon_loss:0.605221, zinb_loss:0.959998, cluster_loss:0.334574
Clustering 1986: AMI= 0.7195, NMI= 0.7204, ARI= 0.4993, ACC= 0.6037
0.026181847795105663
Training epoch 1987, recon_loss:0.605749, zinb_loss:0.960375, cluster_loss:0.335653
Clustering 1987: AMI= 0.7191, NMI= 0.7200, ARI= 0.4965, ACC= 0.5999
0.026344720876257176
Training epoch 1988, recon_loss:0.605552, zinb_loss:0.960100, cluster_loss:0.334548
Clustering 1988: AMI= 0.7195, NMI= 0.7205, ARI= 0.4992, ACC= 0.6033
0.026385439146545054
Training epoch 1989, recon_loss:0.605869, zinb_loss:0.960350, cluster_loss:0.335642
Clustering 1989: AMI= 0.7189, NMI= 0.7198, ARI= 0.4965, ACC= 0.6002
0.026141129524817786
Training epoch 1990, recon_loss:0.605865, zinb_loss:0.960153, cluster_loss:0.334548
Clustering 1990: AMI= 0.7194, NMI= 0.7203, ARI= 0.4987, ACC= 0.6026
0.025937538173378395
Training epoch 1991, recon_loss:0.606142, zinb_loss:0.960293, cluster_loss:0.335633
Clustering 1991: AMI= 0.7191, NMI= 0.7200, ARI= 0.4969, ACC= 0.6008
0.02601897471395415
Training epoch 1992, recon_loss:0.606289, zinb_loss:0.960128, cluster_loss:0.334522
Clustering 1992: AMI= 0.7195, NMI= 0.7204, ARI= 0.4985, ACC= 0.6021
0.02605969298424203
Training epoch 1993, recon_loss:0.606394, zinb_loss:0.960175, cluster_loss:0.335607
Clustering 1993: AMI= 0.7189, NMI= 0.7199, ARI= 0.4970, ACC= 0.6009
0.026100411254529908
Training epoch 1994, recon_loss:0.606670, zinb_loss:0.960048, cluster_loss:0.334504
Clustering 1994: AMI= 0.7192, NMI= 0.7201, ARI= 0.4981, ACC= 0.6018
0.025733946821939004
Training epoch 1995, recon_loss:0.606513, zinb_loss:0.959983, cluster_loss:0.335580
Clustering 1995: AMI= 0.7190, NMI= 0.7199, ARI= 0.4972, ACC= 0.6011
0.025448918929923858
Training epoch 1996, recon_loss:0.606802, zinb_loss:0.959894, cluster_loss:0.334518
Clustering 1996: AMI= 0.7191, NMI= 0.7200, ARI= 0.4978, ACC= 0.6013
0.025082454497332953
Training epoch 1997, recon_loss:0.606578, zinb_loss:0.959801, cluster_loss:0.335564
Clustering 1997: AMI= 0.7191, NMI= 0.7200, ARI= 0.4973, ACC= 0.6014
0.02471599006474205
Training epoch 1998, recon_loss:0.606799, zinb_loss:0.959686, cluster_loss:0.334551
Clustering 1998: AMI= 0.7190, NMI= 0.7199, ARI= 0.4976, ACC= 0.6009
0.023983061199560243
Training epoch 1999, recon_loss:0.606388, zinb_loss:0.959575, cluster_loss:0.335586
Clustering 1999: AMI= 0.7191, NMI= 0.7201, ARI= 0.4976, ACC= 0.6017
0.023494441956105706
Training epoch 2000, recon_loss:0.606603, zinb_loss:0.959469, cluster_loss:0.334630
Clustering 2000: AMI= 0.7189, NMI= 0.7199, ARI= 0.4976, ACC= 0.6008
0.022680076550348142
Final Result : AMI= 0.7189, NMI= 0.7199, ARI= 0.4976, ACC= 0.6008
[15]:
import numpy as np
np.savetxt("../results/GSE163120_pred.csv", y_pred, delimiter=",")
np.savetxt("../results/GSE163120_embedding.csv", final_latent.cpu().detach().numpy(), delimiter=",")
[16]:
from scib_metrics.benchmark import Benchmarker,BioConservation,BatchCorrection
[17]:
adata2.obs["cell_type"] = np.array(pd.read_csv('../datasets/GSE163120/GSE163120_label.csv', index_col=0)['cell_label'])
adata2.obs["batch"] = np.array(pd.read_csv('../datasets/GSE163120/GSE163120_label.csv', index_col=0)['batch_id'])
adata2.obsm["scMAGCA"] = np.array(pd.read_csv('../results/GSE163120_embedding.csv',header=None))
[18]:
bm = Benchmarker(
adata2,
label_key="cell_type",
batch_key="batch",
embedding_obsm_keys=["scMAGCA"],
bio_conservation_metrics=BioConservation(nmi_ari_cluster_labels_kmeans=True,isolated_labels=True,silhouette_label=True,clisi_knn=True),
batch_correction_metrics=BatchCorrection(silhouette_batch=True,ilisi_knn=True,kbet_per_label=True,graph_connectivity=True,pcr_comparison=False),
n_jobs=1,
)
bm.benchmark()
Computing neighbors: 100%|███████████████████████████████████████████████████████████████| 1/1 [02:16<00:00, 136.67s/it]
Embeddings: 0%| | 0/1 [00:00<?, ?it/s]
Metrics: 0%| | 0/10 [00:00<?, ?it/s]
Metrics: 0%| | 0/10 [00:00<?, ?it/s, Bio conservation: isolated_labels]WARNING:jax._src.xla_bridge:An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.
Metrics: 10%|████ | 1/10 [00:03<00:28, 3.13s/it, Bio conservation: isolated_labels]
Metrics: 10%|██▌ | 1/10 [00:03<00:28, 3.13s/it, Bio conservation: nmi_ari_cluster_labels_kmeans]
Metrics: 20%|█████▏ | 2/10 [00:08<00:35, 4.39s/it, Bio conservation: nmi_ari_cluster_labels_kmeans]
Metrics: 20%|███████▊ | 2/10 [00:08<00:35, 4.39s/it, Bio conservation: silhouette_label]
Metrics: 30%|███████████▋ | 3/10 [00:10<00:22, 3.24s/it, Bio conservation: silhouette_label]
Metrics: 30%|█████████████▊ | 3/10 [00:10<00:22, 3.24s/it, Bio conservation: clisi_knn]
Metrics: 40%|██████████████████▍ | 4/10 [00:11<00:14, 2.42s/it, Bio conservation: clisi_knn]
Metrics: 40%|███████████████▌ | 4/10 [00:11<00:14, 2.42s/it, Batch correction: silhouette_batch]
Metrics: 50%|███████████████████▌ | 5/10 [00:22<00:27, 5.50s/it, Batch correction: silhouette_batch]
Metrics: 50%|███████████████████████ | 5/10 [00:22<00:27, 5.50s/it, Batch correction: ilisi_knn]
Metrics: 60%|███████████████████████████▌ | 6/10 [00:22<00:14, 3.70s/it, Batch correction: ilisi_knn]
Metrics: 60%|████████████████████████▌ | 6/10 [00:22<00:14, 3.70s/it, Batch correction: kbet_per_label]
Metrics: 70%|████████████████████████████▋ | 7/10 [00:50<00:35, 11.72s/it, Batch correction: kbet_per_label]
Embeddings: 100%|█████████████████████████████████████████████████████████████████████████| 1/1 [00:50<00:00, 50.88s/it]
[19]:
bm.get_results(min_max_scale=False)
[19]:
| Isolated labels | KMeans NMI | KMeans ARI | Silhouette label | cLISI | Silhouette batch | iLISI | KBET | Graph connectivity | Batch correction | Bio conservation | Total | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Embedding | ||||||||||||
| scMAGCA | 0.621098 | 0.714943 | 0.476137 | 0.588032 | 0.99651 | 0.845322 | 0.449404 | 0.648513 | 0.928204 | 0.717861 | 0.679344 | 0.694751 |
| Metric Type | Bio conservation | Bio conservation | Bio conservation | Bio conservation | Bio conservation | Batch correction | Batch correction | Batch correction | Batch correction | Aggregate score | Aggregate score | Aggregate score |
[1]:
library(umap)
library(ggplot2)
library(scattermore)
[2]:
latent <- read.csv(file = "../results/GSE163120_embedding.csv", sep = ",", header = FALSE)
batch_label <- read.csv(file = "../datasets/GSE163120/GSE163120_label.csv", sep = ",")['batch_id']
[3]:
z.umap<-umap(latent)
batch_label<-as.factor(batch_label$batch_id)
[28]:
bm_table_num <- data.frame(UMAP1=z.umap$layout[,1], UMAP2=z.umap$layout[,2],Cluster = batch_label)
bm_plot1 <- ggplot(bm_table_num, aes(x = UMAP1, y = UMAP2, color = Cluster)) +
geom_point(size = 0.1) +
ggtitle('scMAGCA') +
theme_bw(base_line_size = 1,base_rect_size = 1)+
scale_color_manual(values = c("0"="#55A9CF", "1"="#C24640"))+
labs(x = "", y = "", color = "Label")+ # Set x and y axis labels
theme(
plot.title = element_text(size = 20, hjust = 0.5, face = 'bold'), # Set title to center
panel.border = element_blank(),
axis.ticks.length=unit(0, "lines"), # Remove axis ticks but keep axis labels
axis.text = element_blank(),
panel.grid.minor = element_blank(),
panel.grid.major = element_line(color = NA),
axis.title.x = element_text(size = 15, hjust = 0.5, vjust = 0.5, color = "black"),
axis.title.y = element_text(size = 15, hjust = 0.5, vjust = 0.5, color = "black"),
legend.title = element_blank(),
legend.text = element_text(size = 15),
legend.position = "none")+
guides(color = guide_legend(override.aes = list(size = 5), nrow=2))
bm_plot1
