Tutorial:RNA+ADT+ATAC (3omics)¶
In this tutorial, we will show how to cluster RNA+ADT+ATAC(3omics) data using scMAGCA. We use a processed human peripheral blood mononuclear sample dataset ‘GSE158013’ containing 7084 cells with three omics. Among them, ADT has 46 features, ATAC includes 2500 features and RNA contains 15000 features.
Loading package¶
[1]:
import numpy as np
import pandas as pd
import torch
import scanpy as sc
import random
import warnings
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_3omics 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¶
The required input files include:
x1: protein abundance matrix (data format is h5ad file) : GSE158013_adt.h5ad;
x2: Chromatin accessibility matrix (data format is h5ad file) : GSE158013_atac.h5ad;
x3: Gene expression matrix (data format is h5ad file) : GSE158013_rna.h5ad.
To ensure reproducibility of the results, please read the above data as follows:
[12]:
x1 = sc.read_h5ad('../datasets/GSE158013/GSE158013_adt.h5ad').layers['counts']
feature1 = sc.read_h5ad('../datasets/GSE158013/GSE158013_adt.h5ad').var.index
x2 = sc.read_h5ad('../datasets/GSE158013/GSE158013_atac.h5ad').layers['counts'].A
feature2 = sc.read_h5ad('../datasets/GSE158013/GSE158013_atac.h5ad').var.index
x3 = sc.read_h5ad('../datasets/GSE158013/GSE158013_rna.h5ad').layers['counts'].A
feature3 = sc.read_h5ad('../datasets/GSE158013/GSE158013_rna.h5ad').var.index
y = None
[13]:
x1,x2,x3
[13]:
(array([[ 1., 1., 15., ..., 0., 22., 44.],
[30., 3., 14., ..., 1., 20., 65.],
[ 2., 1., 17., ..., 1., 18., 37.],
...,
[ 0., 2., 12., ..., 31., 15., 30.],
[ 0., 0., 8., ..., 1., 22., 35.],
[ 0., 0., 18., ..., 0., 24., 44.]], dtype=float32),
array([[0., 0., 0., ..., 0., 0., 0.],
[0., 0., 0., ..., 0., 0., 0.],
[0., 0., 0., ..., 0., 0., 0.],
...,
[0., 0., 0., ..., 0., 0., 0.],
[0., 0., 0., ..., 0., 0., 0.],
[0., 0., 0., ..., 0., 0., 0.]], dtype=float32),
array([[ 0., 0., 0., ..., 0., 14., 28.],
[ 0., 0., 1., ..., 0., 15., 24.],
[ 0., 0., 1., ..., 0., 19., 33.],
...,
[ 0., 0., 0., ..., 0., 15., 56.],
[ 0., 0., 0., ..., 0., 19., 49.],
[ 0., 0., 5., ..., 0., 23., 25.]], dtype=float32))
We select the ATAC and RNA omics for high expression, and the number of chosen features are both set to 2000.
[14]:
importantGenes = geneSelection(x2, n=2000)
x2 = x2[:, importantGenes]
feature2 = feature2[importantGenes]
importantGenes = geneSelection(x3, n=2000)
x3 = x3[:, importantGenes]
feature3 = feature3[importantGenes]
Chosen offset: 1.11
Chosen offset: 0.17
[15]:
adata1 = sc.AnnData(x1)
adata1 = read_dataset(adata1, copy=True)
adata1 = preprocess_dataset(adata1, normalize_input=True, logtrans_input=True)
adata1.var['importantGenes'] = feature1
### Autoencoder: Successfully preprocessed 46 features and 7084 cells.
[16]:
adata1
[16]:
AnnData object with n_obs × n_vars = 7084 × 46
obs: 'DCA_split', 'size_factors'
var: 'mean', 'std', 'importantGenes'
uns: 'log1p'
[17]:
adata2 = sc.AnnData(x2)
adata2 = read_dataset(adata2, copy=True)
adata2 = preprocess_dataset(adata2, normalize_input=True, logtrans_input=True)
adata2.var['importantGenes'] = feature2
### Autoencoder: Successfully preprocessed 2000 features and 7084 cells.
[18]:
adata2
[18]:
AnnData object with n_obs × n_vars = 7084 × 2000
obs: 'DCA_split', 'size_factors'
var: 'mean', 'std', 'importantGenes'
uns: 'log1p'
[19]:
adata3 = sc.AnnData(x3)
adata3 = read_dataset(adata3, copy=True)
adata3 = preprocess_dataset(adata3, normalize_input=True, logtrans_input=True)
adata3.var['importantGenes'] = feature3
### Autoencoder: Successfully preprocessed 2000 features and 7084 cells.
[20]:
adata3
[20]:
AnnData object with n_obs × n_vars = 7084 × 2000
obs: 'DCA_split', 'size_factors'
var: 'mean', 'std', 'importantGenes'
uns: 'log1p'
Training the model¶
[21]:
model = scMultiCluster(input_dim1=adata1.n_vars,input_dim2=adata2.n_vars,input_dim3=adata3.n_vars,
alpha=0.2,beta=0.8,gama=0.01,device='cuda').to('cuda')
[22]:
model
[22]:
scMultiCluster(
(encoder): Encoder(
(stacked_gnn): ModuleList(
(0): GCNConv(4046, 1024)
(1): GCNConv(1024, 256)
(2): GCNConv(256, 64)
(3): GCNConv(64, 32)
)
(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(32, 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=32, 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=4046, bias=True)
)
(dec_mean): Sequential(
(0): Linear(in_features=32, out_features=256, bias=True)
(1): Linear(in_features=256, out_features=512, bias=True)
(2): Linear(in_features=512, out_features=4046, bias=True)
(3): MeanAct()
)
(dec_disp): Sequential(
(0): Linear(in_features=32, out_features=256, bias=True)
(1): Linear(in_features=256, out_features=512, bias=True)
(2): Linear(in_features=512, out_features=4046, bias=True)
(3): DispAct()
)
(dec_pi): Sequential(
(0): Linear(in_features=32, out_features=256, bias=True)
(1): Linear(in_features=256, out_features=512, bias=True)
(2): Linear(in_features=512, out_features=4046, bias=True)
(3): Sigmoid()
)
(zinb_loss): ZINBLoss()
)
[24]:
pretrain_latent = model.pretrain_autoencoder(
X1=adata1.X, X2=adata2.X, X3=adata3.X,
X1_raw=adata1.raw.X, X2_raw=adata2.raw.X, X3_raw=adata3.raw.X,
epochs=400, file='GSE158013')
Pretraining stage
Processing...
Processing full batch data
Done!
Pretrain epoch 1, recon_loss:1.175199, zinb_loss:0.855500, adversial_loss:1.378395
Pretrain epoch 2, recon_loss:1.108052, zinb_loss:0.790036, adversial_loss:1.353847
Pretrain epoch 3, recon_loss:1.013116, zinb_loss:0.732100, adversial_loss:1.349079
Pretrain epoch 4, recon_loss:0.967926, zinb_loss:0.685367, adversial_loss:1.347783
Pretrain epoch 5, recon_loss:0.955065, zinb_loss:0.643808, adversial_loss:1.350997
Pretrain epoch 6, recon_loss:0.948277, zinb_loss:0.607989, adversial_loss:1.351554
Pretrain epoch 7, recon_loss:0.940977, zinb_loss:0.578766, adversial_loss:1.349777
Pretrain epoch 8, recon_loss:0.933745, zinb_loss:0.558008, adversial_loss:1.347673
Pretrain epoch 9, recon_loss:0.928594, zinb_loss:0.546180, adversial_loss:1.345650
Pretrain epoch 10, recon_loss:0.924947, zinb_loss:0.540460, adversial_loss:1.343964
Pretrain epoch 11, recon_loss:0.921956, zinb_loss:0.537047, adversial_loss:1.342250
Pretrain epoch 12, recon_loss:0.919373, zinb_loss:0.533806, adversial_loss:1.340244
Pretrain epoch 13, recon_loss:0.916590, zinb_loss:0.530386, adversial_loss:1.338154
Pretrain epoch 14, recon_loss:0.913909, zinb_loss:0.527035, adversial_loss:1.335480
Pretrain epoch 15, recon_loss:0.911839, zinb_loss:0.523866, adversial_loss:1.331557
Pretrain epoch 16, recon_loss:0.910443, zinb_loss:0.521004, adversial_loss:1.326380
Pretrain epoch 17, recon_loss:0.909288, zinb_loss:0.518576, adversial_loss:1.322320
Pretrain epoch 18, recon_loss:0.907878, zinb_loss:0.516333, adversial_loss:1.319946
Pretrain epoch 19, recon_loss:0.906563, zinb_loss:0.514146, adversial_loss:1.318467
Pretrain epoch 20, recon_loss:0.905531, zinb_loss:0.512105, adversial_loss:1.317480
Pretrain epoch 21, recon_loss:0.904526, zinb_loss:0.510200, adversial_loss:1.316783
Pretrain epoch 22, recon_loss:0.903540, zinb_loss:0.508358, adversial_loss:1.316315
Pretrain epoch 23, recon_loss:0.902707, zinb_loss:0.506648, adversial_loss:1.316024
Pretrain epoch 24, recon_loss:0.901874, zinb_loss:0.505102, adversial_loss:1.315779
Pretrain epoch 25, recon_loss:0.901007, zinb_loss:0.503651, adversial_loss:1.315447
Pretrain epoch 26, recon_loss:0.900225, zinb_loss:0.502290, adversial_loss:1.314963
Pretrain epoch 27, recon_loss:0.899482, zinb_loss:0.501040, adversial_loss:1.314348
Pretrain epoch 28, recon_loss:0.898717, zinb_loss:0.499883, adversial_loss:1.313620
Pretrain epoch 29, recon_loss:0.897974, zinb_loss:0.498816, adversial_loss:1.312802
Pretrain epoch 30, recon_loss:0.897277, zinb_loss:0.497838, adversial_loss:1.311924
Pretrain epoch 31, recon_loss:0.896619, zinb_loss:0.496917, adversial_loss:1.310987
Pretrain epoch 32, recon_loss:0.895992, zinb_loss:0.496037, adversial_loss:1.309985
Pretrain epoch 33, recon_loss:0.895375, zinb_loss:0.495203, adversial_loss:1.308975
Pretrain epoch 34, recon_loss:0.894778, zinb_loss:0.494410, adversial_loss:1.308025
Pretrain epoch 35, recon_loss:0.894228, zinb_loss:0.493646, adversial_loss:1.307193
Pretrain epoch 36, recon_loss:0.893697, zinb_loss:0.492919, adversial_loss:1.306482
Pretrain epoch 37, recon_loss:0.893168, zinb_loss:0.492235, adversial_loss:1.305805
Pretrain epoch 38, recon_loss:0.892660, zinb_loss:0.491593, adversial_loss:1.305066
Pretrain epoch 39, recon_loss:0.892151, zinb_loss:0.490987, adversial_loss:1.304236
Pretrain epoch 40, recon_loss:0.891640, zinb_loss:0.490410, adversial_loss:1.303369
Pretrain epoch 41, recon_loss:0.891153, zinb_loss:0.489853, adversial_loss:1.302559
Pretrain epoch 42, recon_loss:0.890687, zinb_loss:0.489309, adversial_loss:1.301869
Pretrain epoch 43, recon_loss:0.890251, zinb_loss:0.488777, adversial_loss:1.301257
Pretrain epoch 44, recon_loss:0.889843, zinb_loss:0.488256, adversial_loss:1.300650
Pretrain epoch 45, recon_loss:0.889454, zinb_loss:0.487751, adversial_loss:1.300038
Pretrain epoch 46, recon_loss:0.889082, zinb_loss:0.487263, adversial_loss:1.299444
Pretrain epoch 47, recon_loss:0.888712, zinb_loss:0.486794, adversial_loss:1.298817
Pretrain epoch 48, recon_loss:0.888351, zinb_loss:0.486342, adversial_loss:1.298034
Pretrain epoch 49, recon_loss:0.888008, zinb_loss:0.485909, adversial_loss:1.297121
Pretrain epoch 50, recon_loss:0.887662, zinb_loss:0.485487, adversial_loss:1.296121
Pretrain epoch 51, recon_loss:0.887324, zinb_loss:0.485070, adversial_loss:1.295191
Pretrain epoch 52, recon_loss:0.886997, zinb_loss:0.484660, adversial_loss:1.294396
Pretrain epoch 53, recon_loss:0.886684, zinb_loss:0.484257, adversial_loss:1.293579
Pretrain epoch 54, recon_loss:0.886377, zinb_loss:0.483865, adversial_loss:1.292739
Pretrain epoch 55, recon_loss:0.886101, zinb_loss:0.483481, adversial_loss:1.291661
Pretrain epoch 56, recon_loss:0.885851, zinb_loss:0.483127, adversial_loss:1.291050
Pretrain epoch 57, recon_loss:0.885890, zinb_loss:0.482857, adversial_loss:1.289647
Pretrain epoch 58, recon_loss:0.886180, zinb_loss:0.482728, adversial_loss:1.289851
Pretrain epoch 59, recon_loss:0.885215, zinb_loss:0.482178, adversial_loss:1.288190
Pretrain epoch 60, recon_loss:0.885347, zinb_loss:0.481868, adversial_loss:1.286787
Pretrain epoch 61, recon_loss:0.884960, zinb_loss:0.481549, adversial_loss:1.287031
Pretrain epoch 62, recon_loss:0.884647, zinb_loss:0.481240, adversial_loss:1.286084
Pretrain epoch 63, recon_loss:0.884216, zinb_loss:0.480799, adversial_loss:1.284261
Pretrain epoch 64, recon_loss:0.884266, zinb_loss:0.480610, adversial_loss:1.283846
Pretrain epoch 65, recon_loss:0.883800, zinb_loss:0.480226, adversial_loss:1.284666
Pretrain epoch 66, recon_loss:0.883520, zinb_loss:0.479906, adversial_loss:1.283720
Pretrain epoch 67, recon_loss:0.883410, zinb_loss:0.479631, adversial_loss:1.282337
Pretrain epoch 68, recon_loss:0.883198, zinb_loss:0.479352, adversial_loss:1.282573
Pretrain epoch 69, recon_loss:0.882770, zinb_loss:0.478971, adversial_loss:1.282615
Pretrain epoch 70, recon_loss:0.882769, zinb_loss:0.478766, adversial_loss:1.281491
Pretrain epoch 71, recon_loss:0.882391, zinb_loss:0.478445, adversial_loss:1.281171
Pretrain epoch 72, recon_loss:0.882230, zinb_loss:0.478219, adversial_loss:1.281228
Pretrain epoch 73, recon_loss:0.881932, zinb_loss:0.477906, adversial_loss:1.280578
Pretrain epoch 74, recon_loss:0.881811, zinb_loss:0.477741, adversial_loss:1.280275
Pretrain epoch 75, recon_loss:0.881506, zinb_loss:0.477418, adversial_loss:1.280187
Pretrain epoch 76, recon_loss:0.881352, zinb_loss:0.477194, adversial_loss:1.279860
Pretrain epoch 77, recon_loss:0.881011, zinb_loss:0.476965, adversial_loss:1.279087
Pretrain epoch 78, recon_loss:0.881010, zinb_loss:0.476766, adversial_loss:1.279005
Pretrain epoch 79, recon_loss:0.880806, zinb_loss:0.476561, adversial_loss:1.279081
Pretrain epoch 80, recon_loss:0.880632, zinb_loss:0.476394, adversial_loss:1.277914
Pretrain epoch 81, recon_loss:0.880353, zinb_loss:0.476192, adversial_loss:1.278324
Pretrain epoch 82, recon_loss:0.880257, zinb_loss:0.476009, adversial_loss:1.277439
Pretrain epoch 83, recon_loss:0.879993, zinb_loss:0.475797, adversial_loss:1.277913
Pretrain epoch 84, recon_loss:0.879785, zinb_loss:0.475625, adversial_loss:1.276413
Pretrain epoch 85, recon_loss:0.879605, zinb_loss:0.475492, adversial_loss:1.277489
Pretrain epoch 86, recon_loss:0.879356, zinb_loss:0.475285, adversial_loss:1.276016
Pretrain epoch 87, recon_loss:0.879262, zinb_loss:0.475160, adversial_loss:1.276432
Pretrain epoch 88, recon_loss:0.879032, zinb_loss:0.474996, adversial_loss:1.275238
Pretrain epoch 89, recon_loss:0.878745, zinb_loss:0.474845, adversial_loss:1.275868
Pretrain epoch 90, recon_loss:0.878577, zinb_loss:0.474718, adversial_loss:1.274301
Pretrain epoch 91, recon_loss:0.878290, zinb_loss:0.474551, adversial_loss:1.275026
Pretrain epoch 92, recon_loss:0.878254, zinb_loss:0.474455, adversial_loss:1.273386
Pretrain epoch 93, recon_loss:0.878239, zinb_loss:0.474504, adversial_loss:1.274436
Pretrain epoch 94, recon_loss:0.878525, zinb_loss:0.474740, adversial_loss:1.272796
Pretrain epoch 95, recon_loss:0.878902, zinb_loss:0.475089, adversial_loss:1.275385
Pretrain epoch 96, recon_loss:0.877561, zinb_loss:0.474054, adversial_loss:1.272375
Pretrain epoch 97, recon_loss:0.877778, zinb_loss:0.474259, adversial_loss:1.271871
Pretrain epoch 98, recon_loss:0.877807, zinb_loss:0.474278, adversial_loss:1.273904
Pretrain epoch 99, recon_loss:0.877096, zinb_loss:0.473776, adversial_loss:1.272743
Pretrain epoch 100, recon_loss:0.877512, zinb_loss:0.474059, adversial_loss:1.270871
Pretrain epoch 101, recon_loss:0.876652, zinb_loss:0.473544, adversial_loss:1.271894
Pretrain epoch 102, recon_loss:0.876898, zinb_loss:0.473643, adversial_loss:1.271924
Pretrain epoch 103, recon_loss:0.876329, zinb_loss:0.473405, adversial_loss:1.270079
Pretrain epoch 104, recon_loss:0.876395, zinb_loss:0.473419, adversial_loss:1.269913
Pretrain epoch 105, recon_loss:0.876021, zinb_loss:0.473242, adversial_loss:1.271329
Pretrain epoch 106, recon_loss:0.876009, zinb_loss:0.473213, adversial_loss:1.269614
Pretrain epoch 107, recon_loss:0.875709, zinb_loss:0.473094, adversial_loss:1.269238
Pretrain epoch 108, recon_loss:0.875638, zinb_loss:0.473099, adversial_loss:1.269217
Pretrain epoch 109, recon_loss:0.875589, zinb_loss:0.473085, adversial_loss:1.270183
Pretrain epoch 110, recon_loss:0.875630, zinb_loss:0.472942, adversial_loss:1.268777
Pretrain epoch 111, recon_loss:0.875412, zinb_loss:0.472890, adversial_loss:1.269828
Pretrain epoch 112, recon_loss:0.875219, zinb_loss:0.472672, adversial_loss:1.269079
Pretrain epoch 113, recon_loss:0.875006, zinb_loss:0.472692, adversial_loss:1.267742
Pretrain epoch 114, recon_loss:0.874811, zinb_loss:0.472569, adversial_loss:1.267557
Pretrain epoch 115, recon_loss:0.874585, zinb_loss:0.472548, adversial_loss:1.266879
Pretrain epoch 116, recon_loss:0.874229, zinb_loss:0.472359, adversial_loss:1.267354
Pretrain epoch 117, recon_loss:0.874126, zinb_loss:0.472386, adversial_loss:1.267169
Pretrain epoch 118, recon_loss:0.874022, zinb_loss:0.472283, adversial_loss:1.265722
Pretrain epoch 119, recon_loss:0.873713, zinb_loss:0.472187, adversial_loss:1.266729
Pretrain epoch 120, recon_loss:0.873618, zinb_loss:0.472110, adversial_loss:1.266035
Pretrain epoch 121, recon_loss:0.873415, zinb_loss:0.472064, adversial_loss:1.264639
Pretrain epoch 122, recon_loss:0.873320, zinb_loss:0.472021, adversial_loss:1.265379
Pretrain epoch 123, recon_loss:0.873136, zinb_loss:0.471912, adversial_loss:1.264864
Pretrain epoch 124, recon_loss:0.872942, zinb_loss:0.471887, adversial_loss:1.264485
Pretrain epoch 125, recon_loss:0.872812, zinb_loss:0.471785, adversial_loss:1.263999
Pretrain epoch 126, recon_loss:0.872581, zinb_loss:0.471736, adversial_loss:1.263726
Pretrain epoch 127, recon_loss:0.872448, zinb_loss:0.471673, adversial_loss:1.263884
Pretrain epoch 128, recon_loss:0.872232, zinb_loss:0.471598, adversial_loss:1.263259
Pretrain epoch 129, recon_loss:0.872227, zinb_loss:0.471601, adversial_loss:1.262720
Pretrain epoch 130, recon_loss:0.872051, zinb_loss:0.471535, adversial_loss:1.262570
Pretrain epoch 131, recon_loss:0.872017, zinb_loss:0.471547, adversial_loss:1.262810
Pretrain epoch 132, recon_loss:0.872062, zinb_loss:0.471590, adversial_loss:1.261674
Pretrain epoch 133, recon_loss:0.872316, zinb_loss:0.471761, adversial_loss:1.263152
Pretrain epoch 134, recon_loss:0.872393, zinb_loss:0.471765, adversial_loss:1.261133
Pretrain epoch 135, recon_loss:0.871755, zinb_loss:0.471515, adversial_loss:1.262774
Pretrain epoch 136, recon_loss:0.871299, zinb_loss:0.471261, adversial_loss:1.261143
Pretrain epoch 137, recon_loss:0.871308, zinb_loss:0.471298, adversial_loss:1.260310
Pretrain epoch 138, recon_loss:0.871430, zinb_loss:0.471387, adversial_loss:1.261964
Pretrain epoch 139, recon_loss:0.871076, zinb_loss:0.471219, adversial_loss:1.260673
Pretrain epoch 140, recon_loss:0.870721, zinb_loss:0.471069, adversial_loss:1.259987
Pretrain epoch 141, recon_loss:0.870777, zinb_loss:0.471149, adversial_loss:1.260930
Pretrain epoch 142, recon_loss:0.870665, zinb_loss:0.471104, adversial_loss:1.259884
Pretrain epoch 143, recon_loss:0.870224, zinb_loss:0.470932, adversial_loss:1.260162
Pretrain epoch 144, recon_loss:0.870264, zinb_loss:0.470957, adversial_loss:1.259902
Pretrain epoch 145, recon_loss:0.870163, zinb_loss:0.470964, adversial_loss:1.258923
Pretrain epoch 146, recon_loss:0.870119, zinb_loss:0.470899, adversial_loss:1.260433
Pretrain epoch 147, recon_loss:0.869791, zinb_loss:0.470805, adversial_loss:1.259118
Pretrain epoch 148, recon_loss:0.869974, zinb_loss:0.470867, adversial_loss:1.258082
Pretrain epoch 149, recon_loss:0.870121, zinb_loss:0.470979, adversial_loss:1.260416
Pretrain epoch 150, recon_loss:0.870324, zinb_loss:0.471075, adversial_loss:1.258500
Pretrain epoch 151, recon_loss:0.870304, zinb_loss:0.471165, adversial_loss:1.258626
Pretrain epoch 152, recon_loss:0.869835, zinb_loss:0.470895, adversial_loss:1.258444
Pretrain epoch 153, recon_loss:0.869347, zinb_loss:0.470706, adversial_loss:1.258010
Pretrain epoch 154, recon_loss:0.869411, zinb_loss:0.470743, adversial_loss:1.258731
Pretrain epoch 155, recon_loss:0.869238, zinb_loss:0.470663, adversial_loss:1.257289
Pretrain epoch 156, recon_loss:0.868860, zinb_loss:0.470529, adversial_loss:1.257463
Pretrain epoch 157, recon_loss:0.868880, zinb_loss:0.470582, adversial_loss:1.258233
Pretrain epoch 158, recon_loss:0.868633, zinb_loss:0.470502, adversial_loss:1.256975
Pretrain epoch 159, recon_loss:0.868303, zinb_loss:0.470390, adversial_loss:1.256950
Pretrain epoch 160, recon_loss:0.868247, zinb_loss:0.470395, adversial_loss:1.257185
Pretrain epoch 161, recon_loss:0.868182, zinb_loss:0.470395, adversial_loss:1.257080
Pretrain epoch 162, recon_loss:0.867904, zinb_loss:0.470319, adversial_loss:1.257040
Pretrain epoch 163, recon_loss:0.867748, zinb_loss:0.470263, adversial_loss:1.256349
Pretrain epoch 164, recon_loss:0.867603, zinb_loss:0.470255, adversial_loss:1.256041
Pretrain epoch 165, recon_loss:0.867503, zinb_loss:0.470205, adversial_loss:1.256682
Pretrain epoch 166, recon_loss:0.867337, zinb_loss:0.470169, adversial_loss:1.255499
Pretrain epoch 167, recon_loss:0.867228, zinb_loss:0.470153, adversial_loss:1.255613
Pretrain epoch 168, recon_loss:0.867103, zinb_loss:0.470096, adversial_loss:1.255939
Pretrain epoch 169, recon_loss:0.866932, zinb_loss:0.470055, adversial_loss:1.255090
Pretrain epoch 170, recon_loss:0.866813, zinb_loss:0.470081, adversial_loss:1.255269
Pretrain epoch 171, recon_loss:0.867062, zinb_loss:0.470182, adversial_loss:1.254984
Pretrain epoch 172, recon_loss:0.867761, zinb_loss:0.470542, adversial_loss:1.256007
Pretrain epoch 173, recon_loss:0.868066, zinb_loss:0.470640, adversial_loss:1.254655
Pretrain epoch 174, recon_loss:0.867544, zinb_loss:0.470428, adversial_loss:1.255215
Pretrain epoch 175, recon_loss:0.866833, zinb_loss:0.470114, adversial_loss:1.254637
Pretrain epoch 176, recon_loss:0.867271, zinb_loss:0.470372, adversial_loss:1.254761
Pretrain epoch 177, recon_loss:0.866524, zinb_loss:0.470091, adversial_loss:1.254344
Pretrain epoch 178, recon_loss:0.866639, zinb_loss:0.470154, adversial_loss:1.254848
Pretrain epoch 179, recon_loss:0.866551, zinb_loss:0.470068, adversial_loss:1.254539
Pretrain epoch 180, recon_loss:0.865965, zinb_loss:0.469928, adversial_loss:1.253758
Pretrain epoch 181, recon_loss:0.866530, zinb_loss:0.470136, adversial_loss:1.255042
Pretrain epoch 182, recon_loss:0.865746, zinb_loss:0.469862, adversial_loss:1.253893
Pretrain epoch 183, recon_loss:0.865715, zinb_loss:0.469885, adversial_loss:1.253618
Pretrain epoch 184, recon_loss:0.865426, zinb_loss:0.469800, adversial_loss:1.254218
Pretrain epoch 185, recon_loss:0.865579, zinb_loss:0.469837, adversial_loss:1.253610
Pretrain epoch 186, recon_loss:0.865433, zinb_loss:0.469754, adversial_loss:1.254092
Pretrain epoch 187, recon_loss:0.865110, zinb_loss:0.469696, adversial_loss:1.253231
Pretrain epoch 188, recon_loss:0.865044, zinb_loss:0.469713, adversial_loss:1.253582
Pretrain epoch 189, recon_loss:0.864612, zinb_loss:0.469593, adversial_loss:1.253581
Pretrain epoch 190, recon_loss:0.864803, zinb_loss:0.469633, adversial_loss:1.252921
Pretrain epoch 191, recon_loss:0.864509, zinb_loss:0.469594, adversial_loss:1.253313
Pretrain epoch 192, recon_loss:0.864456, zinb_loss:0.469598, adversial_loss:1.253129
Pretrain epoch 193, recon_loss:0.864144, zinb_loss:0.469495, adversial_loss:1.252457
Pretrain epoch 194, recon_loss:0.863951, zinb_loss:0.469486, adversial_loss:1.252970
Pretrain epoch 195, recon_loss:0.863924, zinb_loss:0.469478, adversial_loss:1.252293
Pretrain epoch 196, recon_loss:0.864033, zinb_loss:0.469494, adversial_loss:1.252828
Pretrain epoch 197, recon_loss:0.863922, zinb_loss:0.469535, adversial_loss:1.251651
Pretrain epoch 198, recon_loss:0.863870, zinb_loss:0.469581, adversial_loss:1.253506
Pretrain epoch 199, recon_loss:0.864120, zinb_loss:0.469654, adversial_loss:1.251404
Pretrain epoch 200, recon_loss:0.863980, zinb_loss:0.469572, adversial_loss:1.253059
Pretrain epoch 201, recon_loss:0.863801, zinb_loss:0.469539, adversial_loss:1.252052
Pretrain epoch 202, recon_loss:0.863201, zinb_loss:0.469408, adversial_loss:1.251682
Pretrain epoch 203, recon_loss:0.863240, zinb_loss:0.469417, adversial_loss:1.252034
Pretrain epoch 204, recon_loss:0.863252, zinb_loss:0.469381, adversial_loss:1.251441
Pretrain epoch 205, recon_loss:0.862742, zinb_loss:0.469278, adversial_loss:1.251415
Pretrain epoch 206, recon_loss:0.862873, zinb_loss:0.469293, adversial_loss:1.251901
Pretrain epoch 207, recon_loss:0.862636, zinb_loss:0.469266, adversial_loss:1.251052
Pretrain epoch 208, recon_loss:0.862264, zinb_loss:0.469171, adversial_loss:1.251126
Pretrain epoch 209, recon_loss:0.862361, zinb_loss:0.469184, adversial_loss:1.251550
Pretrain epoch 210, recon_loss:0.861932, zinb_loss:0.469147, adversial_loss:1.250322
Pretrain epoch 211, recon_loss:0.861820, zinb_loss:0.469102, adversial_loss:1.250784
Pretrain epoch 212, recon_loss:0.861814, zinb_loss:0.469097, adversial_loss:1.250605
Pretrain epoch 213, recon_loss:0.861455, zinb_loss:0.469056, adversial_loss:1.249953
Pretrain epoch 214, recon_loss:0.861307, zinb_loss:0.469025, adversial_loss:1.250270
Pretrain epoch 215, recon_loss:0.861233, zinb_loss:0.468994, adversial_loss:1.250198
Pretrain epoch 216, recon_loss:0.861003, zinb_loss:0.468972, adversial_loss:1.249544
Pretrain epoch 217, recon_loss:0.860896, zinb_loss:0.468974, adversial_loss:1.250334
Pretrain epoch 218, recon_loss:0.860634, zinb_loss:0.468942, adversial_loss:1.249466
Pretrain epoch 219, recon_loss:0.860488, zinb_loss:0.468937, adversial_loss:1.249736
Pretrain epoch 220, recon_loss:0.860781, zinb_loss:0.469024, adversial_loss:1.249017
Pretrain epoch 221, recon_loss:0.861515, zinb_loss:0.469190, adversial_loss:1.250554
Pretrain epoch 222, recon_loss:0.862213, zinb_loss:0.469403, adversial_loss:1.248350
Pretrain epoch 223, recon_loss:0.861748, zinb_loss:0.469332, adversial_loss:1.250732
Pretrain epoch 224, recon_loss:0.860122, zinb_loss:0.468871, adversial_loss:1.249060
Pretrain epoch 225, recon_loss:0.860950, zinb_loss:0.469088, adversial_loss:1.248284
Pretrain epoch 226, recon_loss:0.860750, zinb_loss:0.469006, adversial_loss:1.249706
Pretrain epoch 227, recon_loss:0.860183, zinb_loss:0.468878, adversial_loss:1.249595
Pretrain epoch 228, recon_loss:0.860285, zinb_loss:0.468890, adversial_loss:1.248520
Pretrain epoch 229, recon_loss:0.859947, zinb_loss:0.468841, adversial_loss:1.248504
Pretrain epoch 230, recon_loss:0.859802, zinb_loss:0.468816, adversial_loss:1.249307
Pretrain epoch 231, recon_loss:0.859698, zinb_loss:0.468815, adversial_loss:1.248643
Pretrain epoch 232, recon_loss:0.859481, zinb_loss:0.468826, adversial_loss:1.248213
Pretrain epoch 233, recon_loss:0.859454, zinb_loss:0.468795, adversial_loss:1.248766
Pretrain epoch 234, recon_loss:0.859367, zinb_loss:0.468711, adversial_loss:1.248632
Pretrain epoch 235, recon_loss:0.858953, zinb_loss:0.468682, adversial_loss:1.248154
Pretrain epoch 236, recon_loss:0.858530, zinb_loss:0.468630, adversial_loss:1.248215
Pretrain epoch 237, recon_loss:0.858371, zinb_loss:0.468588, adversial_loss:1.248180
Pretrain epoch 238, recon_loss:0.858224, zinb_loss:0.468574, adversial_loss:1.247976
Pretrain epoch 239, recon_loss:0.857981, zinb_loss:0.468557, adversial_loss:1.247624
Pretrain epoch 240, recon_loss:0.857876, zinb_loss:0.468535, adversial_loss:1.247702
Pretrain epoch 241, recon_loss:0.857625, zinb_loss:0.468512, adversial_loss:1.247623
Pretrain epoch 242, recon_loss:0.857531, zinb_loss:0.468507, adversial_loss:1.247126
Pretrain epoch 243, recon_loss:0.857494, zinb_loss:0.468514, adversial_loss:1.247847
Pretrain epoch 244, recon_loss:0.857450, zinb_loss:0.468535, adversial_loss:1.246946
Pretrain epoch 245, recon_loss:0.857585, zinb_loss:0.468550, adversial_loss:1.247702
Pretrain epoch 246, recon_loss:0.857442, zinb_loss:0.468545, adversial_loss:1.246729
Pretrain epoch 247, recon_loss:0.857288, zinb_loss:0.468514, adversial_loss:1.247376
Pretrain epoch 248, recon_loss:0.857592, zinb_loss:0.468609, adversial_loss:1.246994
Pretrain epoch 249, recon_loss:0.858043, zinb_loss:0.468687, adversial_loss:1.246731
Pretrain epoch 250, recon_loss:0.858286, zinb_loss:0.468680, adversial_loss:1.247489
Pretrain epoch 251, recon_loss:0.857960, zinb_loss:0.468650, adversial_loss:1.246296
Pretrain epoch 252, recon_loss:0.857294, zinb_loss:0.468519, adversial_loss:1.246702
Pretrain epoch 253, recon_loss:0.857651, zinb_loss:0.468558, adversial_loss:1.247032
Pretrain epoch 254, recon_loss:0.856896, zinb_loss:0.468449, adversial_loss:1.246159
Pretrain epoch 255, recon_loss:0.856482, zinb_loss:0.468365, adversial_loss:1.246460
Pretrain epoch 256, recon_loss:0.856650, zinb_loss:0.468395, adversial_loss:1.246258
Pretrain epoch 257, recon_loss:0.855926, zinb_loss:0.468292, adversial_loss:1.246230
Pretrain epoch 258, recon_loss:0.855743, zinb_loss:0.468261, adversial_loss:1.246168
Pretrain epoch 259, recon_loss:0.855793, zinb_loss:0.468322, adversial_loss:1.245836
Pretrain epoch 260, recon_loss:0.855281, zinb_loss:0.468207, adversial_loss:1.245886
Pretrain epoch 261, recon_loss:0.855046, zinb_loss:0.468185, adversial_loss:1.245758
Pretrain epoch 262, recon_loss:0.855222, zinb_loss:0.468234, adversial_loss:1.245725
Pretrain epoch 263, recon_loss:0.854854, zinb_loss:0.468174, adversial_loss:1.245288
Pretrain epoch 264, recon_loss:0.854583, zinb_loss:0.468169, adversial_loss:1.245623
Pretrain epoch 265, recon_loss:0.854573, zinb_loss:0.468169, adversial_loss:1.245364
Pretrain epoch 266, recon_loss:0.854453, zinb_loss:0.468147, adversial_loss:1.245114
Pretrain epoch 267, recon_loss:0.854450, zinb_loss:0.468167, adversial_loss:1.245279
Pretrain epoch 268, recon_loss:0.854742, zinb_loss:0.468257, adversial_loss:1.245242
Pretrain epoch 269, recon_loss:0.854992, zinb_loss:0.468345, adversial_loss:1.244818
Pretrain epoch 270, recon_loss:0.854777, zinb_loss:0.468343, adversial_loss:1.245141
Pretrain epoch 271, recon_loss:0.854241, zinb_loss:0.468156, adversial_loss:1.244688
Pretrain epoch 272, recon_loss:0.853699, zinb_loss:0.468071, adversial_loss:1.245106
Pretrain epoch 273, recon_loss:0.853733, zinb_loss:0.468096, adversial_loss:1.244408
Pretrain epoch 274, recon_loss:0.853542, zinb_loss:0.468058, adversial_loss:1.244820
Pretrain epoch 275, recon_loss:0.853209, zinb_loss:0.468000, adversial_loss:1.244540
Pretrain epoch 276, recon_loss:0.853018, zinb_loss:0.467977, adversial_loss:1.244264
Pretrain epoch 277, recon_loss:0.852854, zinb_loss:0.467976, adversial_loss:1.244269
Pretrain epoch 278, recon_loss:0.852582, zinb_loss:0.467955, adversial_loss:1.244716
Pretrain epoch 279, recon_loss:0.852468, zinb_loss:0.467954, adversial_loss:1.243693
Pretrain epoch 280, recon_loss:0.852736, zinb_loss:0.467975, adversial_loss:1.244820
Pretrain epoch 281, recon_loss:0.853094, zinb_loss:0.468062, adversial_loss:1.243677
Pretrain epoch 282, recon_loss:0.853264, zinb_loss:0.468067, adversial_loss:1.244789
Pretrain epoch 283, recon_loss:0.853093, zinb_loss:0.468056, adversial_loss:1.243427
Pretrain epoch 284, recon_loss:0.852447, zinb_loss:0.467988, adversial_loss:1.244596
Pretrain epoch 285, recon_loss:0.852209, zinb_loss:0.467955, adversial_loss:1.243451
Pretrain epoch 286, recon_loss:0.852385, zinb_loss:0.467952, adversial_loss:1.243799
Pretrain epoch 287, recon_loss:0.852238, zinb_loss:0.467911, adversial_loss:1.243964
Pretrain epoch 288, recon_loss:0.851867, zinb_loss:0.467840, adversial_loss:1.243127
Pretrain epoch 289, recon_loss:0.851746, zinb_loss:0.467818, adversial_loss:1.243406
Pretrain epoch 290, recon_loss:0.851303, zinb_loss:0.467832, adversial_loss:1.243810
Pretrain epoch 291, recon_loss:0.850632, zinb_loss:0.467755, adversial_loss:1.242752
Pretrain epoch 292, recon_loss:0.850818, zinb_loss:0.467724, adversial_loss:1.243379
Pretrain epoch 293, recon_loss:0.850511, zinb_loss:0.467728, adversial_loss:1.243269
Pretrain epoch 294, recon_loss:0.850059, zinb_loss:0.467691, adversial_loss:1.242876
Pretrain epoch 295, recon_loss:0.850113, zinb_loss:0.467674, adversial_loss:1.243265
Pretrain epoch 296, recon_loss:0.849531, zinb_loss:0.467669, adversial_loss:1.242677
Pretrain epoch 297, recon_loss:0.849715, zinb_loss:0.467708, adversial_loss:1.243208
Pretrain epoch 298, recon_loss:0.849786, zinb_loss:0.467727, adversial_loss:1.242601
Pretrain epoch 299, recon_loss:0.849654, zinb_loss:0.467730, adversial_loss:1.242963
Pretrain epoch 300, recon_loss:0.850421, zinb_loss:0.467803, adversial_loss:1.242664
Pretrain epoch 301, recon_loss:0.850727, zinb_loss:0.467927, adversial_loss:1.243152
Pretrain epoch 302, recon_loss:0.850798, zinb_loss:0.467961, adversial_loss:1.242474
Pretrain epoch 303, recon_loss:0.850198, zinb_loss:0.467831, adversial_loss:1.242292
Pretrain epoch 304, recon_loss:0.849208, zinb_loss:0.467715, adversial_loss:1.243224
Pretrain epoch 305, recon_loss:0.849883, zinb_loss:0.467866, adversial_loss:1.241327
Pretrain epoch 306, recon_loss:0.849987, zinb_loss:0.467795, adversial_loss:1.242927
Pretrain epoch 307, recon_loss:0.849103, zinb_loss:0.467620, adversial_loss:1.241711
Pretrain epoch 308, recon_loss:0.849738, zinb_loss:0.467722, adversial_loss:1.241875
Pretrain epoch 309, recon_loss:0.849819, zinb_loss:0.467710, adversial_loss:1.241883
Pretrain epoch 310, recon_loss:0.849205, zinb_loss:0.467607, adversial_loss:1.242167
Pretrain epoch 311, recon_loss:0.849304, zinb_loss:0.467682, adversial_loss:1.241405
Pretrain epoch 312, recon_loss:0.849002, zinb_loss:0.467620, adversial_loss:1.241762
Pretrain epoch 313, recon_loss:0.849357, zinb_loss:0.467624, adversial_loss:1.241978
Pretrain epoch 314, recon_loss:0.847731, zinb_loss:0.467423, adversial_loss:1.241873
Pretrain epoch 315, recon_loss:0.848349, zinb_loss:0.467515, adversial_loss:1.241752
Pretrain epoch 316, recon_loss:0.848620, zinb_loss:0.467615, adversial_loss:1.242024
Pretrain epoch 317, recon_loss:0.847981, zinb_loss:0.467484, adversial_loss:1.242237
Pretrain epoch 318, recon_loss:0.847528, zinb_loss:0.467513, adversial_loss:1.241480
Pretrain epoch 319, recon_loss:0.847705, zinb_loss:0.467520, adversial_loss:1.241470
Pretrain epoch 320, recon_loss:0.847291, zinb_loss:0.467478, adversial_loss:1.241737
Pretrain epoch 321, recon_loss:0.846909, zinb_loss:0.467410, adversial_loss:1.241535
Pretrain epoch 322, recon_loss:0.847145, zinb_loss:0.467446, adversial_loss:1.241065
Pretrain epoch 323, recon_loss:0.846414, zinb_loss:0.467366, adversial_loss:1.241470
Pretrain epoch 324, recon_loss:0.846816, zinb_loss:0.467349, adversial_loss:1.241174
Pretrain epoch 325, recon_loss:0.845803, zinb_loss:0.467379, adversial_loss:1.240986
Pretrain epoch 326, recon_loss:0.846378, zinb_loss:0.467399, adversial_loss:1.241326
Pretrain epoch 327, recon_loss:0.845481, zinb_loss:0.467339, adversial_loss:1.240430
Pretrain epoch 328, recon_loss:0.845866, zinb_loss:0.467301, adversial_loss:1.241521
Pretrain epoch 329, recon_loss:0.845333, zinb_loss:0.467324, adversial_loss:1.240279
Pretrain epoch 330, recon_loss:0.845143, zinb_loss:0.467363, adversial_loss:1.241377
Pretrain epoch 331, recon_loss:0.845097, zinb_loss:0.467369, adversial_loss:1.239908
Pretrain epoch 332, recon_loss:0.845454, zinb_loss:0.467401, adversial_loss:1.241301
Pretrain epoch 333, recon_loss:0.845199, zinb_loss:0.467356, adversial_loss:1.240106
Pretrain epoch 334, recon_loss:0.844103, zinb_loss:0.467207, adversial_loss:1.240155
Pretrain epoch 335, recon_loss:0.843578, zinb_loss:0.467161, adversial_loss:1.240351
Pretrain epoch 336, recon_loss:0.843672, zinb_loss:0.467193, adversial_loss:1.239885
Pretrain epoch 337, recon_loss:0.843534, zinb_loss:0.467206, adversial_loss:1.240476
Pretrain epoch 338, recon_loss:0.842944, zinb_loss:0.467159, adversial_loss:1.239558
Pretrain epoch 339, recon_loss:0.842923, zinb_loss:0.467131, adversial_loss:1.240264
Pretrain epoch 340, recon_loss:0.842735, zinb_loss:0.467122, adversial_loss:1.239618
Pretrain epoch 341, recon_loss:0.842605, zinb_loss:0.467096, adversial_loss:1.239977
Pretrain epoch 342, recon_loss:0.842695, zinb_loss:0.467142, adversial_loss:1.239616
Pretrain epoch 343, recon_loss:0.843179, zinb_loss:0.467215, adversial_loss:1.239887
Pretrain epoch 344, recon_loss:0.843544, zinb_loss:0.467264, adversial_loss:1.239293
Pretrain epoch 345, recon_loss:0.844045, zinb_loss:0.467300, adversial_loss:1.240098
Pretrain epoch 346, recon_loss:0.843314, zinb_loss:0.467207, adversial_loss:1.239053
Pretrain epoch 347, recon_loss:0.843255, zinb_loss:0.467197, adversial_loss:1.239673
Pretrain epoch 348, recon_loss:0.845292, zinb_loss:0.467485, adversial_loss:1.239450
Pretrain epoch 349, recon_loss:0.845225, zinb_loss:0.467512, adversial_loss:1.239694
Pretrain epoch 350, recon_loss:0.843269, zinb_loss:0.467207, adversial_loss:1.238909
Pretrain epoch 351, recon_loss:0.842528, zinb_loss:0.467131, adversial_loss:1.239758
Pretrain epoch 352, recon_loss:0.842872, zinb_loss:0.467215, adversial_loss:1.238955
Pretrain epoch 353, recon_loss:0.842390, zinb_loss:0.467134, adversial_loss:1.239320
Pretrain epoch 354, recon_loss:0.841689, zinb_loss:0.467045, adversial_loss:1.239301
Pretrain epoch 355, recon_loss:0.842337, zinb_loss:0.467061, adversial_loss:1.239071
Pretrain epoch 356, recon_loss:0.840696, zinb_loss:0.466971, adversial_loss:1.239088
Pretrain epoch 357, recon_loss:0.841387, zinb_loss:0.467036, adversial_loss:1.239297
Pretrain epoch 358, recon_loss:0.840836, zinb_loss:0.466996, adversial_loss:1.238446
Pretrain epoch 359, recon_loss:0.840246, zinb_loss:0.466915, adversial_loss:1.239055
Pretrain epoch 360, recon_loss:0.840125, zinb_loss:0.466916, adversial_loss:1.238636
Pretrain epoch 361, recon_loss:0.839874, zinb_loss:0.466939, adversial_loss:1.238719
Pretrain epoch 362, recon_loss:0.839598, zinb_loss:0.466944, adversial_loss:1.238369
Pretrain epoch 363, recon_loss:0.839547, zinb_loss:0.466925, adversial_loss:1.239099
Pretrain epoch 364, recon_loss:0.840207, zinb_loss:0.466978, adversial_loss:1.237722
Pretrain epoch 365, recon_loss:0.839584, zinb_loss:0.466963, adversial_loss:1.239180
Pretrain epoch 366, recon_loss:0.839261, zinb_loss:0.466924, adversial_loss:1.237848
Pretrain epoch 367, recon_loss:0.838322, zinb_loss:0.466845, adversial_loss:1.238451
Pretrain epoch 368, recon_loss:0.838026, zinb_loss:0.466815, adversial_loss:1.237877
Pretrain epoch 369, recon_loss:0.837955, zinb_loss:0.466827, adversial_loss:1.238253
Pretrain epoch 370, recon_loss:0.838767, zinb_loss:0.466914, adversial_loss:1.238142
Pretrain epoch 371, recon_loss:0.839370, zinb_loss:0.466960, adversial_loss:1.238243
Pretrain epoch 372, recon_loss:0.839189, zinb_loss:0.467017, adversial_loss:1.238283
Pretrain epoch 373, recon_loss:0.838442, zinb_loss:0.466924, adversial_loss:1.238102
Pretrain epoch 374, recon_loss:0.839289, zinb_loss:0.466973, adversial_loss:1.237743
Pretrain epoch 375, recon_loss:0.839590, zinb_loss:0.466964, adversial_loss:1.237983
Pretrain epoch 376, recon_loss:0.837254, zinb_loss:0.466791, adversial_loss:1.238129
Pretrain epoch 377, recon_loss:0.837483, zinb_loss:0.466839, adversial_loss:1.237256
Pretrain epoch 378, recon_loss:0.837679, zinb_loss:0.466831, adversial_loss:1.237994
Pretrain epoch 379, recon_loss:0.836292, zinb_loss:0.466735, adversial_loss:1.237572
Pretrain epoch 380, recon_loss:0.836792, zinb_loss:0.466757, adversial_loss:1.237486
Pretrain epoch 381, recon_loss:0.836292, zinb_loss:0.466720, adversial_loss:1.237280
Pretrain epoch 382, recon_loss:0.835759, zinb_loss:0.466707, adversial_loss:1.237738
Pretrain epoch 383, recon_loss:0.836404, zinb_loss:0.466765, adversial_loss:1.237188
Pretrain epoch 384, recon_loss:0.836765, zinb_loss:0.466811, adversial_loss:1.237186
Pretrain epoch 385, recon_loss:0.838624, zinb_loss:0.466941, adversial_loss:1.237713
Pretrain epoch 386, recon_loss:0.840880, zinb_loss:0.467184, adversial_loss:1.238024
Pretrain epoch 387, recon_loss:0.836855, zinb_loss:0.466834, adversial_loss:1.236656
Pretrain epoch 388, recon_loss:0.835517, zinb_loss:0.466672, adversial_loss:1.236978
Pretrain epoch 389, recon_loss:0.836959, zinb_loss:0.466825, adversial_loss:1.237627
Pretrain epoch 390, recon_loss:0.835768, zinb_loss:0.466703, adversial_loss:1.237139
Pretrain epoch 391, recon_loss:0.834794, zinb_loss:0.466677, adversial_loss:1.236547
Pretrain epoch 392, recon_loss:0.834709, zinb_loss:0.466679, adversial_loss:1.237424
Pretrain epoch 393, recon_loss:0.834526, zinb_loss:0.466649, adversial_loss:1.237094
Pretrain epoch 394, recon_loss:0.833730, zinb_loss:0.466616, adversial_loss:1.236221
Pretrain epoch 395, recon_loss:0.833320, zinb_loss:0.466582, adversial_loss:1.237042
Pretrain epoch 396, recon_loss:0.833140, zinb_loss:0.466565, adversial_loss:1.236657
Pretrain epoch 397, recon_loss:0.832359, zinb_loss:0.466515, adversial_loss:1.236342
Pretrain epoch 398, recon_loss:0.832398, zinb_loss:0.466531, adversial_loss:1.236686
Pretrain epoch 399, recon_loss:0.831918, zinb_loss:0.466505, adversial_loss:1.236133
Pretrain epoch 400, recon_loss:0.832006, zinb_loss:0.466512, adversial_loss:1.236845
[25]:
y_pred, final_latent = model.fit(y=y, n_clusters=-1, num_epochs=2000, file='GSE158013',pretrain_latent=pretrain_latent)
Clustering stage
Estimated n_clusters is: 7
Initializing cluster centers with kmeans.
Initializing k-means: ASW= 0.4344, DB= 0.9470, CH= 4032.3028
Training epoch 1, recon_loss:0.832919, zinb_loss:0.466575, cluster_loss:0.207182
Clustering 1: ASW= 0.4344, DB= 0.9470, CH= 4032.3028
Training epoch 2, recon_loss:0.887874, zinb_loss:0.473593, cluster_loss:0.212438
Clustering 2: ASW= 0.5285, DB= 0.7033, CH= 7507.6300
Training epoch 3, recon_loss:0.913830, zinb_loss:0.476516, cluster_loss:0.226155
Clustering 3: ASW= 0.5506, DB= 0.6721, CH= 7760.1150
Training epoch 4, recon_loss:0.897848, zinb_loss:0.476857, cluster_loss:0.211880
Clustering 4: ASW= 0.5716, DB= 0.6261, CH= 9449.4753
Training epoch 5, recon_loss:0.895170, zinb_loss:0.474653, cluster_loss:0.212168
Clustering 5: ASW= 0.6029, DB= 0.5841, CH= 9793.6500
Training epoch 6, recon_loss:0.895097, zinb_loss:0.475862, cluster_loss:0.204507
Clustering 6: ASW= 0.6064, DB= 0.5652, CH= 11096.5971
Training epoch 7, recon_loss:0.896039, zinb_loss:0.475649, cluster_loss:0.204745
Clustering 7: ASW= 0.6305, DB= 0.5359, CH= 11486.3060
Training epoch 8, recon_loss:0.895833, zinb_loss:0.476619, cluster_loss:0.201410
Clustering 8: ASW= 0.6321, DB= 0.5252, CH= 12620.9400
Training epoch 9, recon_loss:0.897844, zinb_loss:0.476831, cluster_loss:0.199865
Clustering 9: ASW= 0.6488, DB= 0.5042, CH= 12946.5050
Training epoch 10, recon_loss:0.897870, zinb_loss:0.477306, cluster_loss:0.198004
Clustering 10: ASW= 0.6493, DB= 0.4980, CH= 13838.1399
Training epoch 11, recon_loss:0.898041, zinb_loss:0.477441, cluster_loss:0.195625
Clustering 11: ASW= 0.6619, DB= 0.4832, CH= 14086.1586
Training epoch 12, recon_loss:0.900377, zinb_loss:0.477832, cluster_loss:0.195087
Clustering 12: ASW= 0.6617, DB= 0.4760, CH= 14750.1328
Training epoch 13, recon_loss:0.898935, zinb_loss:0.477983, cluster_loss:0.193765
Clustering 13: ASW= 0.6716, DB= 0.4669, CH= 15034.0546
Training epoch 14, recon_loss:0.901775, zinb_loss:0.478386, cluster_loss:0.193867
Clustering 14: ASW= 0.6725, DB= 0.4592, CH= 15470.8353
Training epoch 15, recon_loss:0.899275, zinb_loss:0.478428, cluster_loss:0.192754
Clustering 15: ASW= 0.6819, DB= 0.4501, CH= 15918.6746
Training epoch 16, recon_loss:0.899870, zinb_loss:0.478486, cluster_loss:0.190681
Clustering 16: ASW= 0.6825, DB= 0.4447, CH= 16188.6884
Training epoch 17, recon_loss:0.897410, zinb_loss:0.478531, cluster_loss:0.189571
Clustering 17: ASW= 0.6899, DB= 0.4371, CH= 16554.1309
Training epoch 18, recon_loss:0.898436, zinb_loss:0.478700, cluster_loss:0.188050
Clustering 18: ASW= 0.6894, DB= 0.4340, CH= 16799.0709
Training epoch 19, recon_loss:0.896228, zinb_loss:0.478602, cluster_loss:0.186756
Clustering 19: ASW= 0.6961, DB= 0.4269, CH= 17137.5097
Training epoch 20, recon_loss:0.896804, zinb_loss:0.478696, cluster_loss:0.185572
Clustering 20: ASW= 0.6961, DB= 0.4243, CH= 17385.2659
Training epoch 21, recon_loss:0.895796, zinb_loss:0.478736, cluster_loss:0.185207
Clustering 21: ASW= 0.7015, DB= 0.4181, CH= 17618.5395
Training epoch 22, recon_loss:0.897048, zinb_loss:0.478910, cluster_loss:0.184882
Clustering 22: ASW= 0.7006, DB= 0.4170, CH= 17867.8379
Training epoch 23, recon_loss:0.895957, zinb_loss:0.478829, cluster_loss:0.183780
Clustering 23: ASW= 0.7064, DB= 0.4096, CH= 18118.7101
Training epoch 24, recon_loss:0.896396, zinb_loss:0.478856, cluster_loss:0.183362
Clustering 24: ASW= 0.7054, DB= 0.4103, CH= 18352.4814
Training epoch 25, recon_loss:0.895911, zinb_loss:0.478955, cluster_loss:0.182951
Clustering 25: ASW= 0.7110, DB= 0.4031, CH= 18525.4366
Training epoch 26, recon_loss:0.896150, zinb_loss:0.478998, cluster_loss:0.182563
Clustering 26: ASW= 0.7090, DB= 0.4046, CH= 18797.3669
Training epoch 27, recon_loss:0.895114, zinb_loss:0.479047, cluster_loss:0.181674
Clustering 27: ASW= 0.7153, DB= 0.3970, CH= 18957.1880
Training epoch 28, recon_loss:0.895192, zinb_loss:0.479065, cluster_loss:0.181096
Clustering 28: ASW= 0.7126, DB= 0.3987, CH= 19162.9736
Training epoch 29, recon_loss:0.894739, zinb_loss:0.479080, cluster_loss:0.180419
Clustering 29: ASW= 0.7188, DB= 0.3913, CH= 19395.1208
Training epoch 30, recon_loss:0.894749, zinb_loss:0.479093, cluster_loss:0.180082
Clustering 30: ASW= 0.7168, DB= 0.3926, CH= 19617.5343
Training epoch 31, recon_loss:0.893745, zinb_loss:0.479171, cluster_loss:0.179076
Clustering 31: ASW= 0.7220, DB= 0.3863, CH= 19752.8483
Training epoch 32, recon_loss:0.893349, zinb_loss:0.479098, cluster_loss:0.178515
Clustering 32: ASW= 0.7202, DB= 0.3871, CH= 19961.7235
Training epoch 33, recon_loss:0.894343, zinb_loss:0.479299, cluster_loss:0.178610
Clustering 33: ASW= 0.7251, DB= 0.3816, CH= 20120.6732
Training epoch 34, recon_loss:0.893603, zinb_loss:0.479254, cluster_loss:0.178313
Clustering 34: ASW= 0.7230, DB= 0.3831, CH= 20385.6509
Training epoch 35, recon_loss:0.893213, zinb_loss:0.479364, cluster_loss:0.177528
Clustering 35: ASW= 0.7281, DB= 0.3767, CH= 20473.3641
Training epoch 36, recon_loss:0.892874, zinb_loss:0.479267, cluster_loss:0.177341
Clustering 36: ASW= 0.7259, DB= 0.3789, CH= 20706.4483
Training epoch 37, recon_loss:0.894445, zinb_loss:0.479532, cluster_loss:0.177554
Clustering 37: ASW= 0.7307, DB= 0.3729, CH= 20812.5273
Training epoch 38, recon_loss:0.893082, zinb_loss:0.479409, cluster_loss:0.177175
Clustering 38: ASW= 0.7283, DB= 0.3752, CH= 21108.6846
Training epoch 39, recon_loss:0.892983, zinb_loss:0.479543, cluster_loss:0.176366
Clustering 39: ASW= 0.7333, DB= 0.3687, CH= 21152.8818
Training epoch 40, recon_loss:0.892380, zinb_loss:0.479396, cluster_loss:0.176257
Clustering 40: ASW= 0.7308, DB= 0.3716, CH= 21372.6040
Training epoch 41, recon_loss:0.894487, zinb_loss:0.479722, cluster_loss:0.176668
Clustering 41: ASW= 0.7354, DB= 0.3649, CH= 21465.4217
Training epoch 42, recon_loss:0.893596, zinb_loss:0.479575, cluster_loss:0.176681
Clustering 42: ASW= 0.7330, DB= 0.3679, CH= 21758.6817
Training epoch 43, recon_loss:0.895516, zinb_loss:0.479868, cluster_loss:0.176862
Clustering 43: ASW= 0.7372, DB= 0.3616, CH= 21762.7217
Training epoch 44, recon_loss:0.895423, zinb_loss:0.479748, cluster_loss:0.177448
Clustering 44: ASW= 0.7353, DB= 0.3643, CH= 22065.1812
Training epoch 45, recon_loss:0.898613, zinb_loss:0.480126, cluster_loss:0.178379
Clustering 45: ASW= 0.7382, DB= 0.3593, CH= 22015.9119
Training epoch 46, recon_loss:0.896574, zinb_loss:0.479918, cluster_loss:0.177674
Clustering 46: ASW= 0.7373, DB= 0.3608, CH= 22419.9690
Training epoch 47, recon_loss:0.897652, zinb_loss:0.480130, cluster_loss:0.177398
Clustering 47: ASW= 0.7405, DB= 0.3556, CH= 22393.7405
Training epoch 48, recon_loss:0.895305, zinb_loss:0.479887, cluster_loss:0.176258
Clustering 48: ASW= 0.7395, DB= 0.3583, CH= 22665.8365
Training epoch 49, recon_loss:0.896349, zinb_loss:0.480105, cluster_loss:0.176355
Clustering 49: ASW= 0.7421, DB= 0.3532, CH= 22678.0671
Training epoch 50, recon_loss:0.894359, zinb_loss:0.479916, cluster_loss:0.175221
Clustering 50: ASW= 0.7416, DB= 0.3546, CH= 22956.7053
Training epoch 51, recon_loss:0.894614, zinb_loss:0.480043, cluster_loss:0.174864
Clustering 51: ASW= 0.7442, DB= 0.3503, CH= 22974.5795
Training epoch 52, recon_loss:0.893814, zinb_loss:0.479914, cluster_loss:0.174496
Clustering 52: ASW= 0.7435, DB= 0.3520, CH= 23185.0945
Training epoch 53, recon_loss:0.894629, zinb_loss:0.480092, cluster_loss:0.174665
Clustering 53: ASW= 0.7458, DB= 0.3484, CH= 23221.1699
Training epoch 54, recon_loss:0.893462, zinb_loss:0.479974, cluster_loss:0.173979
Clustering 54: ASW= 0.7451, DB= 0.3492, CH= 23451.2198
Training epoch 55, recon_loss:0.893438, zinb_loss:0.480076, cluster_loss:0.173588
Clustering 55: ASW= 0.7477, DB= 0.3453, CH= 23502.7829
Training epoch 56, recon_loss:0.893491, zinb_loss:0.479992, cluster_loss:0.173689
Clustering 56: ASW= 0.7466, DB= 0.3474, CH= 23635.6027
Training epoch 57, recon_loss:0.894415, zinb_loss:0.480190, cluster_loss:0.174059
Clustering 57: ASW= 0.7491, DB= 0.3432, CH= 23741.7676
Training epoch 58, recon_loss:0.893781, zinb_loss:0.480108, cluster_loss:0.173735
Clustering 58: ASW= 0.7478, DB= 0.3456, CH= 23877.9914
Training epoch 59, recon_loss:0.893560, zinb_loss:0.480226, cluster_loss:0.173335
Clustering 59: ASW= 0.7508, DB= 0.3405, CH= 24025.6308
Training epoch 60, recon_loss:0.894262, zinb_loss:0.480159, cluster_loss:0.173799
Clustering 60: ASW= 0.7490, DB= 0.3448, CH= 23987.2433
Training epoch 61, recon_loss:0.895237, zinb_loss:0.480413, cluster_loss:0.174413
Clustering 61: ASW= 0.7522, DB= 0.3381, CH= 24290.5140
Training epoch 62, recon_loss:0.895414, zinb_loss:0.480332, cluster_loss:0.174617
Clustering 62: ASW= 0.7497, DB= 0.3440, CH= 24145.7464
Training epoch 63, recon_loss:0.895203, zinb_loss:0.480538, cluster_loss:0.174288
Clustering 63: ASW= 0.7535, DB= 0.3354, CH= 24563.4381
Training epoch 64, recon_loss:0.895713, zinb_loss:0.480397, cluster_loss:0.174410
Clustering 64: ASW= 0.7511, DB= 0.3422, CH= 24279.4558
Training epoch 65, recon_loss:0.896016, zinb_loss:0.480663, cluster_loss:0.174676
Clustering 65: ASW= 0.7551, DB= 0.3326, CH= 24814.4452
Training epoch 66, recon_loss:0.895584, zinb_loss:0.480459, cluster_loss:0.174344
Clustering 66: ASW= 0.7522, DB= 0.3407, CH= 24514.2996
Training epoch 67, recon_loss:0.894853, zinb_loss:0.480617, cluster_loss:0.173478
Clustering 67: ASW= 0.7558, DB= 0.3307, CH= 25015.9516
Training epoch 68, recon_loss:0.894613, zinb_loss:0.480430, cluster_loss:0.173252
Clustering 68: ASW= 0.7541, DB= 0.3373, CH= 24698.5128
Training epoch 69, recon_loss:0.894791, zinb_loss:0.480619, cluster_loss:0.172987
Clustering 69: ASW= 0.7566, DB= 0.3293, CH= 25229.5517
Training epoch 70, recon_loss:0.894065, zinb_loss:0.480442, cluster_loss:0.172799
Clustering 70: ASW= 0.7555, DB= 0.3349, CH= 24934.7299
Training epoch 71, recon_loss:0.893844, zinb_loss:0.480544, cluster_loss:0.171928
Clustering 71: ASW= 0.7572, DB= 0.3281, CH= 25422.1790
Training epoch 72, recon_loss:0.893937, zinb_loss:0.480462, cluster_loss:0.172407
Clustering 72: ASW= 0.7572, DB= 0.3336, CH= 25115.0504
Training epoch 73, recon_loss:0.894792, zinb_loss:0.480613, cluster_loss:0.172183
Clustering 73: ASW= 0.7577, DB= 0.3276, CH= 25614.3808
Training epoch 74, recon_loss:0.894381, zinb_loss:0.480524, cluster_loss:0.172488
Clustering 74: ASW= 0.7582, DB= 0.3315, CH= 25297.2656
Training epoch 75, recon_loss:0.894270, zinb_loss:0.480571, cluster_loss:0.171556
Clustering 75: ASW= 0.7583, DB= 0.3266, CH= 25785.3651
Training epoch 76, recon_loss:0.894531, zinb_loss:0.480569, cluster_loss:0.172188
Clustering 76: ASW= 0.7595, DB= 0.3297, CH= 25445.4490
Training epoch 77, recon_loss:0.894970, zinb_loss:0.480648, cluster_loss:0.171734
Clustering 77: ASW= 0.7590, DB= 0.3257, CH= 25950.6494
Training epoch 78, recon_loss:0.894357, zinb_loss:0.480590, cluster_loss:0.171848
Clustering 78: ASW= 0.7602, DB= 0.3292, CH= 25668.2558
Training epoch 79, recon_loss:0.893997, zinb_loss:0.480600, cluster_loss:0.170892
Clustering 79: ASW= 0.7598, DB= 0.3246, CH= 26135.3464
Training epoch 80, recon_loss:0.894154, zinb_loss:0.480609, cluster_loss:0.171461
Clustering 80: ASW= 0.7613, DB= 0.3280, CH= 25832.8497
Training epoch 81, recon_loss:0.894252, zinb_loss:0.480653, cluster_loss:0.170831
Clustering 81: ASW= 0.7606, DB= 0.3237, CH= 26292.4655
Training epoch 82, recon_loss:0.893606, zinb_loss:0.480614, cluster_loss:0.170895
Clustering 82: ASW= 0.7622, DB= 0.3274, CH= 26032.5352
Training epoch 83, recon_loss:0.893237, zinb_loss:0.480604, cluster_loss:0.170013
Clustering 83: ASW= 0.7614, DB= 0.3225, CH= 26471.8783
Training epoch 84, recon_loss:0.893415, zinb_loss:0.480637, cluster_loss:0.170561
Clustering 84: ASW= 0.7632, DB= 0.3259, CH= 26164.3040
Training epoch 85, recon_loss:0.893549, zinb_loss:0.480664, cluster_loss:0.170062
Clustering 85: ASW= 0.7621, DB= 0.3218, CH= 26642.4422
Training epoch 86, recon_loss:0.893335, zinb_loss:0.480667, cluster_loss:0.170320
Clustering 86: ASW= 0.7639, DB= 0.3250, CH= 26334.8406
Training epoch 87, recon_loss:0.893091, zinb_loss:0.480656, cluster_loss:0.169645
Clustering 87: ASW= 0.7628, DB= 0.3205, CH= 26811.5244
Training epoch 88, recon_loss:0.893380, zinb_loss:0.480707, cluster_loss:0.170172
Clustering 88: ASW= 0.7648, DB= 0.3239, CH= 26464.6821
Training epoch 89, recon_loss:0.893470, zinb_loss:0.480717, cluster_loss:0.169729
Clustering 89: ASW= 0.7634, DB= 0.3195, CH= 26977.5673
Training epoch 90, recon_loss:0.893284, zinb_loss:0.480737, cluster_loss:0.169959
Clustering 90: ASW= 0.7655, DB= 0.3246, CH= 26628.6192
Training epoch 91, recon_loss:0.892930, zinb_loss:0.480700, cluster_loss:0.169277
Clustering 91: ASW= 0.7641, DB= 0.3196, CH= 27158.2050
Training epoch 92, recon_loss:0.893235, zinb_loss:0.480768, cluster_loss:0.169756
Clustering 92: ASW= 0.7663, DB= 0.3231, CH= 26748.3523
Training epoch 93, recon_loss:0.893196, zinb_loss:0.480756, cluster_loss:0.169295
Clustering 93: ASW= 0.7649, DB= 0.3185, CH= 27317.8769
Training epoch 94, recon_loss:0.893107, zinb_loss:0.480791, cluster_loss:0.169534
Clustering 94: ASW= 0.7668, DB= 0.3235, CH= 26905.5525
Training epoch 95, recon_loss:0.892720, zinb_loss:0.480749, cluster_loss:0.168880
Clustering 95: ASW= 0.7655, DB= 0.3174, CH= 27470.2044
Training epoch 96, recon_loss:0.893005, zinb_loss:0.480816, cluster_loss:0.169311
Clustering 96: ASW= 0.7674, DB= 0.3224, CH= 27033.3678
Training epoch 97, recon_loss:0.892904, zinb_loss:0.480799, cluster_loss:0.168855
Clustering 97: ASW= 0.7663, DB= 0.3162, CH= 27622.9765
Training epoch 98, recon_loss:0.892905, zinb_loss:0.480838, cluster_loss:0.169137
Clustering 98: ASW= 0.7679, DB= 0.3224, CH= 27186.8839
Training epoch 99, recon_loss:0.892539, zinb_loss:0.480796, cluster_loss:0.168516
Clustering 99: ASW= 0.7670, DB= 0.3149, CH= 27758.0330
Training epoch 100, recon_loss:0.892821, zinb_loss:0.480861, cluster_loss:0.168957
Clustering 100: ASW= 0.7685, DB= 0.3219, CH= 27314.3810
Training epoch 101, recon_loss:0.892670, zinb_loss:0.480839, cluster_loss:0.168478
Clustering 101: ASW= 0.7677, DB= 0.3142, CH= 27906.7729
Training epoch 102, recon_loss:0.892753, zinb_loss:0.480881, cluster_loss:0.168821
Clustering 102: ASW= 0.7691, DB= 0.3215, CH= 27458.2040
Training epoch 103, recon_loss:0.892437, zinb_loss:0.480845, cluster_loss:0.168226
Clustering 103: ASW= 0.7683, DB= 0.3132, CH= 28036.2029
Training epoch 104, recon_loss:0.892694, zinb_loss:0.480901, cluster_loss:0.168690
Clustering 104: ASW= 0.7696, DB= 0.3210, CH= 27581.7720
Training epoch 105, recon_loss:0.892518, zinb_loss:0.480880, cluster_loss:0.168172
Clustering 105: ASW= 0.7690, DB= 0.3135, CH= 28181.2068
Training epoch 106, recon_loss:0.892671, zinb_loss:0.480921, cluster_loss:0.168612
Clustering 106: ASW= 0.7701, DB= 0.3205, CH= 27712.1551
Training epoch 107, recon_loss:0.892425, zinb_loss:0.480894, cluster_loss:0.168017
Clustering 107: ASW= 0.7696, DB= 0.3125, CH= 28305.6802
Training epoch 108, recon_loss:0.892636, zinb_loss:0.480939, cluster_loss:0.168535
Clustering 108: ASW= 0.7705, DB= 0.3204, CH= 27830.1395
Training epoch 109, recon_loss:0.892486, zinb_loss:0.480923, cluster_loss:0.167963
Clustering 109: ASW= 0.7703, DB= 0.3125, CH= 28448.6944
Training epoch 110, recon_loss:0.892660, zinb_loss:0.480957, cluster_loss:0.168522
Clustering 110: ASW= 0.7710, DB= 0.3199, CH= 27948.0113
Training epoch 111, recon_loss:0.892543, zinb_loss:0.480947, cluster_loss:0.167915
Clustering 111: ASW= 0.7709, DB= 0.3130, CH= 28588.7524
Training epoch 112, recon_loss:0.892682, zinb_loss:0.480971, cluster_loss:0.168520
Clustering 112: ASW= 0.7714, DB= 0.3193, CH= 28067.2763
Training epoch 113, recon_loss:0.892673, zinb_loss:0.480978, cluster_loss:0.167925
Clustering 113: ASW= 0.7715, DB= 0.3127, CH= 28716.8068
Training epoch 114, recon_loss:0.892753, zinb_loss:0.480985, cluster_loss:0.168567
Clustering 114: ASW= 0.7717, DB= 0.3188, CH= 28186.0703
Training epoch 115, recon_loss:0.892864, zinb_loss:0.481015, cluster_loss:0.167990
Clustering 115: ASW= 0.7722, DB= 0.3140, CH= 28849.9874
Training epoch 116, recon_loss:0.892796, zinb_loss:0.480995, cluster_loss:0.168603
Clustering 116: ASW= 0.7721, DB= 0.3185, CH= 28310.0429
Training epoch 117, recon_loss:0.893018, zinb_loss:0.481057, cluster_loss:0.168060
Clustering 117: ASW= 0.7728, DB= 0.3136, CH= 28937.7455
Training epoch 118, recon_loss:0.892859, zinb_loss:0.481007, cluster_loss:0.168634
Clustering 118: ASW= 0.7724, DB= 0.3188, CH= 28440.2501
Training epoch 119, recon_loss:0.893094, zinb_loss:0.481098, cluster_loss:0.168114
Clustering 119: ASW= 0.7732, DB= 0.3130, CH= 29007.3892
Training epoch 120, recon_loss:0.893008, zinb_loss:0.481034, cluster_loss:0.168699
Clustering 120: ASW= 0.7728, DB= 0.3180, CH= 28579.3903
Training epoch 121, recon_loss:0.893333, zinb_loss:0.481158, cluster_loss:0.168299
Clustering 121: ASW= 0.7737, DB= 0.3126, CH= 29056.7675
Training epoch 122, recon_loss:0.893283, zinb_loss:0.481080, cluster_loss:0.168843
Clustering 122: ASW= 0.7730, DB= 0.3183, CH= 28701.7644
Training epoch 123, recon_loss:0.893618, zinb_loss:0.481235, cluster_loss:0.168535
Clustering 123: ASW= 0.7742, DB= 0.3124, CH= 29095.0453
Training epoch 124, recon_loss:0.893566, zinb_loss:0.481138, cluster_loss:0.168963
Clustering 124: ASW= 0.7733, DB= 0.3172, CH= 28845.0855
Training epoch 125, recon_loss:0.893796, zinb_loss:0.481306, cluster_loss:0.168674
Clustering 125: ASW= 0.7745, DB= 0.3125, CH= 29120.5798
Training epoch 126, recon_loss:0.893715, zinb_loss:0.481190, cluster_loss:0.168961
Clustering 126: ASW= 0.7735, DB= 0.3170, CH= 28977.4228
Training epoch 127, recon_loss:0.893830, zinb_loss:0.481344, cluster_loss:0.168646
Clustering 127: ASW= 0.7749, DB= 0.3118, CH= 29144.7787
Training epoch 128, recon_loss:0.893660, zinb_loss:0.481217, cluster_loss:0.168805
Clustering 128: ASW= 0.7738, DB= 0.3158, CH= 29109.5086
Training epoch 129, recon_loss:0.893656, zinb_loss:0.481345, cluster_loss:0.168426
Clustering 129: ASW= 0.7753, DB= 0.3111, CH= 29189.1133
Training epoch 130, recon_loss:0.893436, zinb_loss:0.481223, cluster_loss:0.168510
Clustering 130: ASW= 0.7741, DB= 0.3155, CH= 29237.6815
Training epoch 131, recon_loss:0.893374, zinb_loss:0.481324, cluster_loss:0.168114
Clustering 131: ASW= 0.7756, DB= 0.3109, CH= 29249.7209
Training epoch 132, recon_loss:0.893151, zinb_loss:0.481219, cluster_loss:0.168170
Clustering 132: ASW= 0.7745, DB= 0.3153, CH= 29372.4262
Training epoch 133, recon_loss:0.893067, zinb_loss:0.481295, cluster_loss:0.167802
Clustering 133: ASW= 0.7760, DB= 0.3109, CH= 29331.4902
Training epoch 134, recon_loss:0.892884, zinb_loss:0.481212, cluster_loss:0.167856
Clustering 134: ASW= 0.7750, DB= 0.3150, CH= 29512.5408
Training epoch 135, recon_loss:0.892767, zinb_loss:0.481264, cluster_loss:0.167518
Clustering 135: ASW= 0.7764, DB= 0.3104, CH= 29416.8779
Training epoch 136, recon_loss:0.892682, zinb_loss:0.481211, cluster_loss:0.167598
Clustering 136: ASW= 0.7755, DB= 0.3142, CH= 29636.2899
Training epoch 137, recon_loss:0.892538, zinb_loss:0.481241, cluster_loss:0.167294
Clustering 137: ASW= 0.7767, DB= 0.3099, CH= 29509.3410
Training epoch 138, recon_loss:0.892568, zinb_loss:0.481216, cluster_loss:0.167417
Clustering 138: ASW= 0.7760, DB= 0.3135, CH= 29753.1783
Training epoch 139, recon_loss:0.892380, zinb_loss:0.481224, cluster_loss:0.167137
Clustering 139: ASW= 0.7771, DB= 0.3093, CH= 29606.1557
Training epoch 140, recon_loss:0.892529, zinb_loss:0.481230, cluster_loss:0.167294
Clustering 140: ASW= 0.7765, DB= 0.3128, CH= 29861.2074
Training epoch 141, recon_loss:0.892259, zinb_loss:0.481211, cluster_loss:0.167028
Clustering 141: ASW= 0.7773, DB= 0.3092, CH= 29706.6672
Training epoch 142, recon_loss:0.892519, zinb_loss:0.481248, cluster_loss:0.167201
Clustering 142: ASW= 0.7769, DB= 0.3122, CH= 29963.8996
Training epoch 143, recon_loss:0.892149, zinb_loss:0.481201, cluster_loss:0.166951
Clustering 143: ASW= 0.7776, DB= 0.3085, CH= 29804.3779
Training epoch 144, recon_loss:0.892493, zinb_loss:0.481268, cluster_loss:0.167125
Clustering 144: ASW= 0.7774, DB= 0.3115, CH= 30059.6885
Training epoch 145, recon_loss:0.892039, zinb_loss:0.481193, cluster_loss:0.166897
Clustering 145: ASW= 0.7780, DB= 0.3081, CH= 29909.3386
Training epoch 146, recon_loss:0.892453, zinb_loss:0.481287, cluster_loss:0.167060
Clustering 146: ASW= 0.7778, DB= 0.3114, CH= 30172.0641
Training epoch 147, recon_loss:0.891934, zinb_loss:0.481186, cluster_loss:0.166863
Clustering 147: ASW= 0.7782, DB= 0.3080, CH= 30005.9945
Training epoch 148, recon_loss:0.892418, zinb_loss:0.481306, cluster_loss:0.167005
Clustering 148: ASW= 0.7782, DB= 0.3106, CH= 30268.2671
Training epoch 149, recon_loss:0.891854, zinb_loss:0.481181, cluster_loss:0.166843
Clustering 149: ASW= 0.7785, DB= 0.3076, CH= 30099.1895
Training epoch 150, recon_loss:0.892392, zinb_loss:0.481323, cluster_loss:0.166944
Clustering 150: ASW= 0.7786, DB= 0.3099, CH= 30367.9863
Training epoch 151, recon_loss:0.891791, zinb_loss:0.481178, cluster_loss:0.166833
Clustering 151: ASW= 0.7788, DB= 0.3073, CH= 30191.3522
Training epoch 152, recon_loss:0.892369, zinb_loss:0.481342, cluster_loss:0.166879
Clustering 152: ASW= 0.7790, DB= 0.3089, CH= 30468.7984
Training epoch 153, recon_loss:0.891734, zinb_loss:0.481180, cluster_loss:0.166826
Clustering 153: ASW= 0.7792, DB= 0.3071, CH= 30284.4784
Training epoch 154, recon_loss:0.892348, zinb_loss:0.481361, cluster_loss:0.166815
Clustering 154: ASW= 0.7795, DB= 0.3082, CH= 30564.8087
Training epoch 155, recon_loss:0.891679, zinb_loss:0.481184, cluster_loss:0.166828
Clustering 155: ASW= 0.7794, DB= 0.3066, CH= 30360.4960
Training epoch 156, recon_loss:0.892337, zinb_loss:0.481382, cluster_loss:0.166765
Clustering 156: ASW= 0.7799, DB= 0.3073, CH= 30659.5433
Training epoch 157, recon_loss:0.891642, zinb_loss:0.481192, cluster_loss:0.166844
Clustering 157: ASW= 0.7796, DB= 0.3062, CH= 30431.2781
Training epoch 158, recon_loss:0.892344, zinb_loss:0.481405, cluster_loss:0.166731
Clustering 158: ASW= 0.7803, DB= 0.3063, CH= 30749.4587
Training epoch 159, recon_loss:0.891606, zinb_loss:0.481205, cluster_loss:0.166864
Clustering 159: ASW= 0.7798, DB= 0.3057, CH= 30497.7766
Training epoch 160, recon_loss:0.892360, zinb_loss:0.481432, cluster_loss:0.166709
Clustering 160: ASW= 0.7807, DB= 0.3054, CH= 30848.7077
Training epoch 161, recon_loss:0.891572, zinb_loss:0.481222, cluster_loss:0.166883
Clustering 161: ASW= 0.7800, DB= 0.3053, CH= 30567.1032
Training epoch 162, recon_loss:0.892365, zinb_loss:0.481460, cluster_loss:0.166684
Clustering 162: ASW= 0.7811, DB= 0.3052, CH= 30943.4569
Training epoch 163, recon_loss:0.891516, zinb_loss:0.481241, cluster_loss:0.166880
Clustering 163: ASW= 0.7804, DB= 0.3053, CH= 30642.1487
Training epoch 164, recon_loss:0.892321, zinb_loss:0.481482, cluster_loss:0.166638
Clustering 164: ASW= 0.7815, DB= 0.3044, CH= 31020.7238
Training epoch 165, recon_loss:0.891425, zinb_loss:0.481258, cluster_loss:0.166842
Clustering 165: ASW= 0.7806, DB= 0.3047, CH= 30717.2671
Training epoch 166, recon_loss:0.892219, zinb_loss:0.481498, cluster_loss:0.166568
Clustering 166: ASW= 0.7818, DB= 0.3038, CH= 31093.2231
Training epoch 167, recon_loss:0.891317, zinb_loss:0.481275, cluster_loss:0.166781
Clustering 167: ASW= 0.7809, DB= 0.3040, CH= 30805.5826
Training epoch 168, recon_loss:0.892087, zinb_loss:0.481511, cluster_loss:0.166485
Clustering 168: ASW= 0.7821, DB= 0.3036, CH= 31154.9556
Training epoch 169, recon_loss:0.891229, zinb_loss:0.481292, cluster_loss:0.166717
Clustering 169: ASW= 0.7813, DB= 0.3034, CH= 30904.6501
Training epoch 170, recon_loss:0.891958, zinb_loss:0.481521, cluster_loss:0.166410
Clustering 170: ASW= 0.7823, DB= 0.3031, CH= 31201.0036
Training epoch 171, recon_loss:0.891185, zinb_loss:0.481310, cluster_loss:0.166662
Clustering 171: ASW= 0.7817, DB= 0.3024, CH= 31013.0621
Training epoch 172, recon_loss:0.891860, zinb_loss:0.481530, cluster_loss:0.166355
Clustering 172: ASW= 0.7825, DB= 0.3030, CH= 31231.2577
Training epoch 173, recon_loss:0.891216, zinb_loss:0.481330, cluster_loss:0.166627
Clustering 173: ASW= 0.7821, DB= 0.3020, CH= 31144.6293
Training epoch 174, recon_loss:0.891849, zinb_loss:0.481544, cluster_loss:0.166341
Clustering 174: ASW= 0.7827, DB= 0.3030, CH= 31236.5920
Training epoch 175, recon_loss:0.891401, zinb_loss:0.481360, cluster_loss:0.166635
Clustering 175: ASW= 0.7825, DB= 0.3015, CH= 31287.2036
Training epoch 176, recon_loss:0.892020, zinb_loss:0.481572, cluster_loss:0.166405
Clustering 176: ASW= 0.7828, DB= 0.3030, CH= 31204.2851
Training epoch 177, recon_loss:0.891816, zinb_loss:0.481408, cluster_loss:0.166715
Clustering 177: ASW= 0.7828, DB= 0.3011, CH= 31470.0230
Training epoch 178, recon_loss:0.892422, zinb_loss:0.481615, cluster_loss:0.166583
Clustering 178: ASW= 0.7830, DB= 0.3022, CH= 31138.7196
Training epoch 179, recon_loss:0.892407, zinb_loss:0.481467, cluster_loss:0.166890
Clustering 179: ASW= 0.7829, DB= 0.3009, CH= 31637.0119
Training epoch 180, recon_loss:0.892806, zinb_loss:0.481647, cluster_loss:0.166786
Clustering 180: ASW= 0.7832, DB= 0.3016, CH= 31093.5441
Training epoch 181, recon_loss:0.892767, zinb_loss:0.481504, cluster_loss:0.167002
Clustering 181: ASW= 0.7829, DB= 0.3011, CH= 31748.2067
Training epoch 182, recon_loss:0.892773, zinb_loss:0.481636, cluster_loss:0.166807
Clustering 182: ASW= 0.7835, DB= 0.3010, CH= 31095.2856
Training epoch 183, recon_loss:0.892576, zinb_loss:0.481487, cluster_loss:0.166869
Clustering 183: ASW= 0.7829, DB= 0.3013, CH= 31834.0013
Training epoch 184, recon_loss:0.892434, zinb_loss:0.481593, cluster_loss:0.166661
Clustering 184: ASW= 0.7837, DB= 0.3004, CH= 31143.0711
Training epoch 185, recon_loss:0.892168, zinb_loss:0.481446, cluster_loss:0.166601
Clustering 185: ASW= 0.7831, DB= 0.3009, CH= 31911.5906
Training epoch 186, recon_loss:0.892026, zinb_loss:0.481541, cluster_loss:0.166459
Clustering 186: ASW= 0.7839, DB= 0.3001, CH= 31228.5488
Training epoch 187, recon_loss:0.891800, zinb_loss:0.481405, cluster_loss:0.166351
Clustering 187: ASW= 0.7833, DB= 0.3007, CH= 31960.0517
Training epoch 188, recon_loss:0.891740, zinb_loss:0.481501, cluster_loss:0.166305
Clustering 188: ASW= 0.7843, DB= 0.2996, CH= 31344.3444
Training epoch 189, recon_loss:0.891615, zinb_loss:0.481378, cluster_loss:0.166203
Clustering 189: ASW= 0.7836, DB= 0.3003, CH= 32000.9455
Training epoch 190, recon_loss:0.891636, zinb_loss:0.481472, cluster_loss:0.166242
Clustering 190: ASW= 0.7846, DB= 0.2990, CH= 31467.0890
Training epoch 191, recon_loss:0.891608, zinb_loss:0.481362, cluster_loss:0.166176
Clustering 191: ASW= 0.7839, DB= 0.3000, CH= 32042.3774
Training epoch 192, recon_loss:0.891695, zinb_loss:0.481455, cluster_loss:0.166267
Clustering 192: ASW= 0.7848, DB= 0.2984, CH= 31592.5033
Training epoch 193, recon_loss:0.891723, zinb_loss:0.481355, cluster_loss:0.166230
Clustering 193: ASW= 0.7843, DB= 0.2997, CH= 32069.7896
Training epoch 194, recon_loss:0.891793, zinb_loss:0.481445, cluster_loss:0.166308
Clustering 194: ASW= 0.7852, DB= 0.2974, CH= 31713.2497
Training epoch 195, recon_loss:0.891775, zinb_loss:0.481350, cluster_loss:0.166258
Clustering 195: ASW= 0.7846, DB= 0.2993, CH= 32100.5806
Training epoch 196, recon_loss:0.891795, zinb_loss:0.481433, cluster_loss:0.166295
Clustering 196: ASW= 0.7855, DB= 0.2969, CH= 31822.0128
Training epoch 197, recon_loss:0.891681, zinb_loss:0.481340, cluster_loss:0.166203
Clustering 197: ASW= 0.7849, DB= 0.2990, CH= 32137.4152
Training epoch 198, recon_loss:0.891666, zinb_loss:0.481418, cluster_loss:0.166207
Clustering 198: ASW= 0.7858, DB= 0.2965, CH= 31923.5227
Training epoch 199, recon_loss:0.891461, zinb_loss:0.481327, cluster_loss:0.166074
Clustering 199: ASW= 0.7851, DB= 0.2991, CH= 32186.4243
Training epoch 200, recon_loss:0.891445, zinb_loss:0.481399, cluster_loss:0.166063
Clustering 200: ASW= 0.7861, DB= 0.2959, CH= 32011.4007
Training epoch 201, recon_loss:0.891208, zinb_loss:0.481313, cluster_loss:0.165925
Clustering 201: ASW= 0.7854, DB= 0.2990, CH= 32244.5177
Training epoch 202, recon_loss:0.891207, zinb_loss:0.481378, cluster_loss:0.165904
Clustering 202: ASW= 0.7863, DB= 0.2953, CH= 32092.7632
Training epoch 203, recon_loss:0.890973, zinb_loss:0.481299, cluster_loss:0.165783
Clustering 203: ASW= 0.7857, DB= 0.2986, CH= 32297.9588
Training epoch 204, recon_loss:0.891023, zinb_loss:0.481360, cluster_loss:0.165771
Clustering 204: ASW= 0.7866, DB= 0.2953, CH= 32176.4718
Training epoch 205, recon_loss:0.890796, zinb_loss:0.481287, cluster_loss:0.165677
Clustering 205: ASW= 0.7859, DB= 0.2987, CH= 32355.3645
Training epoch 206, recon_loss:0.890900, zinb_loss:0.481345, cluster_loss:0.165670
Clustering 206: ASW= 0.7868, DB= 0.2947, CH= 32253.7288
Training epoch 207, recon_loss:0.890686, zinb_loss:0.481278, cluster_loss:0.165605
Clustering 207: ASW= 0.7861, DB= 0.2983, CH= 32404.9415
Training epoch 208, recon_loss:0.890849, zinb_loss:0.481333, cluster_loss:0.165600
Clustering 208: ASW= 0.7871, DB= 0.2941, CH= 32333.0215
Training epoch 209, recon_loss:0.890644, zinb_loss:0.481271, cluster_loss:0.165560
Clustering 209: ASW= 0.7863, DB= 0.2980, CH= 32451.2028
Training epoch 210, recon_loss:0.890870, zinb_loss:0.481324, cluster_loss:0.165553
Clustering 210: ASW= 0.7874, DB= 0.2934, CH= 32410.0156
Training epoch 211, recon_loss:0.890661, zinb_loss:0.481266, cluster_loss:0.165544
Clustering 211: ASW= 0.7865, DB= 0.2977, CH= 32486.9799
Training epoch 212, recon_loss:0.890959, zinb_loss:0.481318, cluster_loss:0.165528
Clustering 212: ASW= 0.7877, DB= 0.2930, CH= 32501.3319
Training epoch 213, recon_loss:0.890735, zinb_loss:0.481264, cluster_loss:0.165546
Clustering 213: ASW= 0.7867, DB= 0.2973, CH= 32511.8702
Training epoch 214, recon_loss:0.891122, zinb_loss:0.481318, cluster_loss:0.165518
Clustering 214: ASW= 0.7880, DB= 0.2924, CH= 32595.8948
Training epoch 215, recon_loss:0.890866, zinb_loss:0.481268, cluster_loss:0.165568
Clustering 215: ASW= 0.7868, DB= 0.2969, CH= 32532.7367
Training epoch 216, recon_loss:0.891363, zinb_loss:0.481325, cluster_loss:0.165533
Clustering 216: ASW= 0.7884, DB= 0.2921, CH= 32704.1919
Training epoch 217, recon_loss:0.891046, zinb_loss:0.481282, cluster_loss:0.165616
Clustering 217: ASW= 0.7869, DB= 0.2968, CH= 32548.1372
Training epoch 218, recon_loss:0.891627, zinb_loss:0.481342, cluster_loss:0.165563
Clustering 218: ASW= 0.7887, DB= 0.2920, CH= 32819.8869
Training epoch 219, recon_loss:0.891232, zinb_loss:0.481303, cluster_loss:0.165679
Clustering 219: ASW= 0.7871, DB= 0.2966, CH= 32557.7512
Training epoch 220, recon_loss:0.891837, zinb_loss:0.481365, cluster_loss:0.165594
Clustering 220: ASW= 0.7890, DB= 0.2913, CH= 32931.6840
Training epoch 221, recon_loss:0.891333, zinb_loss:0.481331, cluster_loss:0.165730
Clustering 221: ASW= 0.7873, DB= 0.2962, CH= 32563.4161
Training epoch 222, recon_loss:0.891922, zinb_loss:0.481391, cluster_loss:0.165610
Clustering 222: ASW= 0.7893, DB= 0.2907, CH= 33056.6239
Training epoch 223, recon_loss:0.891337, zinb_loss:0.481360, cluster_loss:0.165764
Clustering 223: ASW= 0.7876, DB= 0.2958, CH= 32560.0274
Training epoch 224, recon_loss:0.891891, zinb_loss:0.481415, cluster_loss:0.165618
Clustering 224: ASW= 0.7896, DB= 0.2900, CH= 33173.4896
Training epoch 225, recon_loss:0.891309, zinb_loss:0.481391, cluster_loss:0.165799
Clustering 225: ASW= 0.7878, DB= 0.2954, CH= 32569.7543
Training epoch 226, recon_loss:0.891835, zinb_loss:0.481437, cluster_loss:0.165643
Clustering 226: ASW= 0.7898, DB= 0.2899, CH= 33296.6829
Training epoch 227, recon_loss:0.891257, zinb_loss:0.481424, cluster_loss:0.165826
Clustering 227: ASW= 0.7880, DB= 0.2948, CH= 32564.2125
Training epoch 228, recon_loss:0.891754, zinb_loss:0.481454, cluster_loss:0.165666
Clustering 228: ASW= 0.7900, DB= 0.2900, CH= 33412.5602
Training epoch 229, recon_loss:0.891173, zinb_loss:0.481451, cluster_loss:0.165830
Clustering 229: ASW= 0.7882, DB= 0.2944, CH= 32550.6102
Training epoch 230, recon_loss:0.891623, zinb_loss:0.481461, cluster_loss:0.165662
Clustering 230: ASW= 0.7901, DB= 0.2898, CH= 33513.2533
Training epoch 231, recon_loss:0.891093, zinb_loss:0.481469, cluster_loss:0.165818
Clustering 231: ASW= 0.7884, DB= 0.2936, CH= 32540.3557
Training epoch 232, recon_loss:0.891446, zinb_loss:0.481454, cluster_loss:0.165633
Clustering 232: ASW= 0.7901, DB= 0.2900, CH= 33592.0757
Training epoch 233, recon_loss:0.891031, zinb_loss:0.481478, cluster_loss:0.165777
Clustering 233: ASW= 0.7887, DB= 0.2929, CH= 32556.6437
Training epoch 234, recon_loss:0.891242, zinb_loss:0.481438, cluster_loss:0.165584
Clustering 234: ASW= 0.7900, DB= 0.2902, CH= 33645.4607
Training epoch 235, recon_loss:0.890982, zinb_loss:0.481478, cluster_loss:0.165704
Clustering 235: ASW= 0.7890, DB= 0.2923, CH= 32609.4532
Training epoch 236, recon_loss:0.891014, zinb_loss:0.481413, cluster_loss:0.165513
Clustering 236: ASW= 0.7901, DB= 0.2903, CH= 33666.6997
Training epoch 237, recon_loss:0.890904, zinb_loss:0.481468, cluster_loss:0.165587
Clustering 237: ASW= 0.7892, DB= 0.2913, CH= 32701.4470
Training epoch 238, recon_loss:0.890806, zinb_loss:0.481386, cluster_loss:0.165450
Clustering 238: ASW= 0.7902, DB= 0.2901, CH= 33665.3216
Training epoch 239, recon_loss:0.890821, zinb_loss:0.481458, cluster_loss:0.165475
Clustering 239: ASW= 0.7894, DB= 0.2906, CH= 32829.6369
Training epoch 240, recon_loss:0.890672, zinb_loss:0.481367, cluster_loss:0.165434
Clustering 240: ASW= 0.7903, DB= 0.2899, CH= 33650.5155
Training epoch 241, recon_loss:0.890788, zinb_loss:0.481450, cluster_loss:0.165428
Clustering 241: ASW= 0.7897, DB= 0.2901, CH= 32973.4013
Training epoch 242, recon_loss:0.890619, zinb_loss:0.481359, cluster_loss:0.165490
Clustering 242: ASW= 0.7905, DB= 0.2899, CH= 33634.3285
Training epoch 243, recon_loss:0.890779, zinb_loss:0.481447, cluster_loss:0.165458
Clustering 243: ASW= 0.7899, DB= 0.2896, CH= 33111.1364
Training epoch 244, recon_loss:0.890655, zinb_loss:0.481360, cluster_loss:0.165625
Clustering 244: ASW= 0.7907, DB= 0.2903, CH= 33611.4834
Training epoch 245, recon_loss:0.890785, zinb_loss:0.481443, cluster_loss:0.165555
Clustering 245: ASW= 0.7900, DB= 0.2892, CH= 33243.1679
Training epoch 246, recon_loss:0.890765, zinb_loss:0.481366, cluster_loss:0.165830
Clustering 246: ASW= 0.7909, DB= 0.2903, CH= 33589.8376
Training epoch 247, recon_loss:0.890807, zinb_loss:0.481427, cluster_loss:0.165667
Clustering 247: ASW= 0.7901, DB= 0.2890, CH= 33375.8103
Training epoch 248, recon_loss:0.890908, zinb_loss:0.481372, cluster_loss:0.166050
Clustering 248: ASW= 0.7911, DB= 0.2903, CH= 33585.9665
Training epoch 249, recon_loss:0.890748, zinb_loss:0.481396, cluster_loss:0.165705
Clustering 249: ASW= 0.7902, DB= 0.2886, CH= 33487.0685
Training epoch 250, recon_loss:0.890888, zinb_loss:0.481370, cluster_loss:0.166114
Clustering 250: ASW= 0.7913, DB= 0.2904, CH= 33575.5664
Training epoch 251, recon_loss:0.890536, zinb_loss:0.481358, cluster_loss:0.165617
Clustering 251: ASW= 0.7903, DB= 0.2879, CH= 33596.8745
Training epoch 252, recon_loss:0.890665, zinb_loss:0.481363, cluster_loss:0.165985
Clustering 252: ASW= 0.7915, DB= 0.2901, CH= 33569.4899
Training epoch 253, recon_loss:0.890270, zinb_loss:0.481330, cluster_loss:0.165497
Clustering 253: ASW= 0.7905, DB= 0.2873, CH= 33699.6549
Training epoch 254, recon_loss:0.890447, zinb_loss:0.481358, cluster_loss:0.165852
Clustering 254: ASW= 0.7917, DB= 0.2908, CH= 33588.1915
Training epoch 255, recon_loss:0.890013, zinb_loss:0.481309, cluster_loss:0.165368
Clustering 255: ASW= 0.7908, DB= 0.2868, CH= 33787.6780
Training epoch 256, recon_loss:0.890238, zinb_loss:0.481353, cluster_loss:0.165717
Clustering 256: ASW= 0.7919, DB= 0.2907, CH= 33614.9089
Training epoch 257, recon_loss:0.889794, zinb_loss:0.481295, cluster_loss:0.165258
Clustering 257: ASW= 0.7910, DB= 0.2863, CH= 33859.2352
Training epoch 258, recon_loss:0.890077, zinb_loss:0.481349, cluster_loss:0.165619
Clustering 258: ASW= 0.7921, DB= 0.2904, CH= 33657.5837
Training epoch 259, recon_loss:0.889656, zinb_loss:0.481289, cluster_loss:0.165188
Clustering 259: ASW= 0.7913, DB= 0.2858, CH= 33918.7850
Training epoch 260, recon_loss:0.889974, zinb_loss:0.481346, cluster_loss:0.165554
Clustering 260: ASW= 0.7922, DB= 0.2900, CH= 33716.2731
Training epoch 261, recon_loss:0.889581, zinb_loss:0.481289, cluster_loss:0.165153
Clustering 261: ASW= 0.7915, DB= 0.2855, CH= 33962.2576
Training epoch 262, recon_loss:0.889914, zinb_loss:0.481339, cluster_loss:0.165529
Clustering 262: ASW= 0.7923, DB= 0.2899, CH= 33775.6273
Training epoch 263, recon_loss:0.889596, zinb_loss:0.481297, cluster_loss:0.165163
Clustering 263: ASW= 0.7918, DB= 0.2850, CH= 33996.5302
Training epoch 264, recon_loss:0.889940, zinb_loss:0.481334, cluster_loss:0.165565
Clustering 264: ASW= 0.7924, DB= 0.2900, CH= 33832.3937
Training epoch 265, recon_loss:0.889795, zinb_loss:0.481319, cluster_loss:0.165241
Clustering 265: ASW= 0.7921, DB= 0.2854, CH= 34035.4840
Training epoch 266, recon_loss:0.890173, zinb_loss:0.481340, cluster_loss:0.165683
Clustering 266: ASW= 0.7924, DB= 0.2897, CH= 33904.7260
Training epoch 267, recon_loss:0.890376, zinb_loss:0.481372, cluster_loss:0.165434
Clustering 267: ASW= 0.7924, DB= 0.2849, CH= 34036.4644
Training epoch 268, recon_loss:0.890738, zinb_loss:0.481366, cluster_loss:0.165910
Clustering 268: ASW= 0.7924, DB= 0.2889, CH= 33977.2369
Training epoch 269, recon_loss:0.891233, zinb_loss:0.481458, cluster_loss:0.165702
Clustering 269: ASW= 0.7928, DB= 0.2851, CH= 34015.8647
Training epoch 270, recon_loss:0.891342, zinb_loss:0.481408, cluster_loss:0.166113
Clustering 270: ASW= 0.7923, DB= 0.2887, CH= 34057.5459
Training epoch 271, recon_loss:0.891808, zinb_loss:0.481544, cluster_loss:0.165870
Clustering 271: ASW= 0.7931, DB= 0.2847, CH= 33968.2105
Training epoch 272, recon_loss:0.891560, zinb_loss:0.481446, cluster_loss:0.166115
Clustering 272: ASW= 0.7924, DB= 0.2881, CH= 34147.0999
Training epoch 273, recon_loss:0.891827, zinb_loss:0.481599, cluster_loss:0.165820
Clustering 273: ASW= 0.7933, DB= 0.2845, CH= 33937.9436
Training epoch 274, recon_loss:0.891390, zinb_loss:0.481466, cluster_loss:0.165945
Clustering 274: ASW= 0.7926, DB= 0.2878, CH= 34237.7798
Training epoch 275, recon_loss:0.891503, zinb_loss:0.481614, cluster_loss:0.165636
Clustering 275: ASW= 0.7935, DB= 0.2844, CH= 33931.0509
Training epoch 276, recon_loss:0.891008, zinb_loss:0.481465, cluster_loss:0.165706
Clustering 276: ASW= 0.7928, DB= 0.2873, CH= 34341.6208
Training epoch 277, recon_loss:0.891056, zinb_loss:0.481594, cluster_loss:0.165420
Clustering 277: ASW= 0.7936, DB= 0.2846, CH= 33923.4752
Training epoch 278, recon_loss:0.890603, zinb_loss:0.481445, cluster_loss:0.165484
Clustering 278: ASW= 0.7930, DB= 0.2866, CH= 34448.5020
Training epoch 279, recon_loss:0.890661, zinb_loss:0.481558, cluster_loss:0.165241
Clustering 279: ASW= 0.7937, DB= 0.2847, CH= 33945.9715
Training epoch 280, recon_loss:0.890259, zinb_loss:0.481415, cluster_loss:0.165284
Clustering 280: ASW= 0.7932, DB= 0.2863, CH= 34533.5507
Training epoch 281, recon_loss:0.890328, zinb_loss:0.481515, cluster_loss:0.165091
Clustering 281: ASW= 0.7938, DB= 0.2845, CH= 33969.3260
Training epoch 282, recon_loss:0.889994, zinb_loss:0.481383, cluster_loss:0.165115
Clustering 282: ASW= 0.7934, DB= 0.2859, CH= 34613.7850
Training epoch 283, recon_loss:0.890048, zinb_loss:0.481471, cluster_loss:0.164960
Clustering 283: ASW= 0.7940, DB= 0.2842, CH= 34000.0794
Training epoch 284, recon_loss:0.889762, zinb_loss:0.481350, cluster_loss:0.164955
Clustering 284: ASW= 0.7936, DB= 0.2856, CH= 34680.3544
Training epoch 285, recon_loss:0.889800, zinb_loss:0.481429, cluster_loss:0.164839
Clustering 285: ASW= 0.7941, DB= 0.2842, CH= 34053.5827
Training epoch 286, recon_loss:0.889565, zinb_loss:0.481320, cluster_loss:0.164814
Clustering 286: ASW= 0.7938, DB= 0.2851, CH= 34741.1015
Training epoch 287, recon_loss:0.889626, zinb_loss:0.481394, cluster_loss:0.164739
Clustering 287: ASW= 0.7944, DB= 0.2842, CH= 34115.6181
Training epoch 288, recon_loss:0.889436, zinb_loss:0.481294, cluster_loss:0.164692
Clustering 288: ASW= 0.7941, DB= 0.2845, CH= 34796.5930
Training epoch 289, recon_loss:0.889491, zinb_loss:0.481363, cluster_loss:0.164657
Clustering 289: ASW= 0.7946, DB= 0.2842, CH= 34189.3677
Training epoch 290, recon_loss:0.889360, zinb_loss:0.481275, cluster_loss:0.164605
Clustering 290: ASW= 0.7943, DB= 0.2842, CH= 34846.4341
Training epoch 291, recon_loss:0.889426, zinb_loss:0.481338, cluster_loss:0.164604
Clustering 291: ASW= 0.7947, DB= 0.2838, CH= 34255.0651
Training epoch 292, recon_loss:0.889341, zinb_loss:0.481260, cluster_loss:0.164539
Clustering 292: ASW= 0.7944, DB= 0.2839, CH= 34887.0441
Training epoch 293, recon_loss:0.889395, zinb_loss:0.481317, cluster_loss:0.164572
Clustering 293: ASW= 0.7950, DB= 0.2831, CH= 34328.9244
Training epoch 294, recon_loss:0.889353, zinb_loss:0.481248, cluster_loss:0.164501
Clustering 294: ASW= 0.7946, DB= 0.2837, CH= 34925.3238
Training epoch 295, recon_loss:0.889397, zinb_loss:0.481300, cluster_loss:0.164556
Clustering 295: ASW= 0.7951, DB= 0.2828, CH= 34400.8529
Training epoch 296, recon_loss:0.889398, zinb_loss:0.481240, cluster_loss:0.164475
Clustering 296: ASW= 0.7947, DB= 0.2834, CH= 34957.6112
Training epoch 297, recon_loss:0.889404, zinb_loss:0.481282, cluster_loss:0.164548
Clustering 297: ASW= 0.7953, DB= 0.2824, CH= 34476.6676
Training epoch 298, recon_loss:0.889428, zinb_loss:0.481231, cluster_loss:0.164453
Clustering 298: ASW= 0.7949, DB= 0.2833, CH= 34982.3217
Training epoch 299, recon_loss:0.889406, zinb_loss:0.481265, cluster_loss:0.164539
Clustering 299: ASW= 0.7955, DB= 0.2819, CH= 34556.2117
Training epoch 300, recon_loss:0.889446, zinb_loss:0.481223, cluster_loss:0.164427
Clustering 300: ASW= 0.7950, DB= 0.2839, CH= 35004.3850
Training epoch 301, recon_loss:0.889394, zinb_loss:0.481249, cluster_loss:0.164535
Clustering 301: ASW= 0.7958, DB= 0.2816, CH= 34645.2435
Training epoch 302, recon_loss:0.889428, zinb_loss:0.481215, cluster_loss:0.164395
Clustering 302: ASW= 0.7950, DB= 0.2841, CH= 35019.4613
Training epoch 303, recon_loss:0.889360, zinb_loss:0.481230, cluster_loss:0.164528
Clustering 303: ASW= 0.7961, DB= 0.2811, CH= 34734.8829
Training epoch 304, recon_loss:0.889396, zinb_loss:0.481208, cluster_loss:0.164358
Clustering 304: ASW= 0.7951, DB= 0.2841, CH= 35021.0447
Training epoch 305, recon_loss:0.889335, zinb_loss:0.481215, cluster_loss:0.164535
Clustering 305: ASW= 0.7964, DB= 0.2805, CH= 34829.0822
Training epoch 306, recon_loss:0.889347, zinb_loss:0.481204, cluster_loss:0.164324
Clustering 306: ASW= 0.7951, DB= 0.2841, CH= 35017.5958
Training epoch 307, recon_loss:0.889339, zinb_loss:0.481204, cluster_loss:0.164554
Clustering 307: ASW= 0.7967, DB= 0.2798, CH= 34925.4743
Training epoch 308, recon_loss:0.889319, zinb_loss:0.481205, cluster_loss:0.164306
Clustering 308: ASW= 0.7951, DB= 0.2842, CH= 35008.2854
Training epoch 309, recon_loss:0.889379, zinb_loss:0.481202, cluster_loss:0.164588
Clustering 309: ASW= 0.7970, DB= 0.2796, CH= 35014.5594
Training epoch 310, recon_loss:0.889296, zinb_loss:0.481208, cluster_loss:0.164310
Clustering 310: ASW= 0.7951, DB= 0.2843, CH= 35005.0613
Training epoch 311, recon_loss:0.889504, zinb_loss:0.481212, cluster_loss:0.164628
Clustering 311: ASW= 0.7973, DB= 0.2791, CH= 35110.5454
Training epoch 312, recon_loss:0.889366, zinb_loss:0.481218, cluster_loss:0.164350
Clustering 312: ASW= 0.7950, DB= 0.2843, CH= 34986.5680
Training epoch 313, recon_loss:0.889752, zinb_loss:0.481237, cluster_loss:0.164685
Clustering 313: ASW= 0.7976, DB= 0.2785, CH= 35214.4314
Training epoch 314, recon_loss:0.889537, zinb_loss:0.481239, cluster_loss:0.164411
Clustering 314: ASW= 0.7950, DB= 0.2846, CH= 34951.9537
Training epoch 315, recon_loss:0.890141, zinb_loss:0.481274, cluster_loss:0.164738
Clustering 315: ASW= 0.7979, DB= 0.2782, CH= 35309.1185
Training epoch 316, recon_loss:0.889791, zinb_loss:0.481265, cluster_loss:0.164474
Clustering 316: ASW= 0.7951, DB= 0.2843, CH= 34917.8505
Training epoch 317, recon_loss:0.890451, zinb_loss:0.481304, cluster_loss:0.164752
Clustering 317: ASW= 0.7982, DB= 0.2782, CH= 35428.4536
Training epoch 318, recon_loss:0.889882, zinb_loss:0.481281, cluster_loss:0.164491
Clustering 318: ASW= 0.7952, DB= 0.2834, CH= 34892.0572
Training epoch 319, recon_loss:0.890507, zinb_loss:0.481317, cluster_loss:0.164695
Clustering 319: ASW= 0.7983, DB= 0.2781, CH= 35536.3149
Training epoch 320, recon_loss:0.889777, zinb_loss:0.481282, cluster_loss:0.164448
Clustering 320: ASW= 0.7955, DB= 0.2828, CH= 34890.5981
Training epoch 321, recon_loss:0.890326, zinb_loss:0.481309, cluster_loss:0.164600
Clustering 321: ASW= 0.7984, DB= 0.2782, CH= 35629.1329
Training epoch 322, recon_loss:0.889551, zinb_loss:0.481274, cluster_loss:0.164376
Clustering 322: ASW= 0.7958, DB= 0.2821, CH= 34905.3541
Training epoch 323, recon_loss:0.890025, zinb_loss:0.481291, cluster_loss:0.164501
Clustering 323: ASW= 0.7985, DB= 0.2782, CH= 35699.8194
Training epoch 324, recon_loss:0.889337, zinb_loss:0.481265, cluster_loss:0.164311
Clustering 324: ASW= 0.7961, DB= 0.2817, CH= 34943.7529
Training epoch 325, recon_loss:0.889752, zinb_loss:0.481273, cluster_loss:0.164445
Clustering 325: ASW= 0.7985, DB= 0.2786, CH= 35757.1625
Training epoch 326, recon_loss:0.889181, zinb_loss:0.481258, cluster_loss:0.164273
Clustering 326: ASW= 0.7963, DB= 0.2814, CH= 34978.4944
Training epoch 327, recon_loss:0.889525, zinb_loss:0.481256, cluster_loss:0.164423
Clustering 327: ASW= 0.7984, DB= 0.2787, CH= 35803.0877
Training epoch 328, recon_loss:0.889123, zinb_loss:0.481258, cluster_loss:0.164269
Clustering 328: ASW= 0.7965, DB= 0.2807, CH= 35032.0391
Training epoch 329, recon_loss:0.889402, zinb_loss:0.481244, cluster_loss:0.164471
Clustering 329: ASW= 0.7984, DB= 0.2791, CH= 35828.8888
Training epoch 330, recon_loss:0.889151, zinb_loss:0.481265, cluster_loss:0.164307
Clustering 330: ASW= 0.7966, DB= 0.2798, CH= 35086.6794
Training epoch 331, recon_loss:0.889356, zinb_loss:0.481239, cluster_loss:0.164570
Clustering 331: ASW= 0.7984, DB= 0.2793, CH= 35837.8995
Training epoch 332, recon_loss:0.889251, zinb_loss:0.481282, cluster_loss:0.164383
Clustering 332: ASW= 0.7968, DB= 0.2792, CH= 35159.1892
Training epoch 333, recon_loss:0.889329, zinb_loss:0.481237, cluster_loss:0.164689
Clustering 333: ASW= 0.7984, DB= 0.2795, CH= 35834.1748
Training epoch 334, recon_loss:0.889321, zinb_loss:0.481300, cluster_loss:0.164452
Clustering 334: ASW= 0.7969, DB= 0.2789, CH= 35229.0798
Training epoch 335, recon_loss:0.889243, zinb_loss:0.481232, cluster_loss:0.164768
Clustering 335: ASW= 0.7984, DB= 0.2796, CH= 35827.3957
Training epoch 336, recon_loss:0.889279, zinb_loss:0.481309, cluster_loss:0.164484
Clustering 336: ASW= 0.7972, DB= 0.2783, CH= 35311.5011
Training epoch 337, recon_loss:0.889048, zinb_loss:0.481218, cluster_loss:0.164777
Clustering 337: ASW= 0.7984, DB= 0.2796, CH= 35813.8799
Training epoch 338, recon_loss:0.889140, zinb_loss:0.481305, cluster_loss:0.164452
Clustering 338: ASW= 0.7975, DB= 0.2777, CH= 35401.7175
Training epoch 339, recon_loss:0.888808, zinb_loss:0.481197, cluster_loss:0.164731
Clustering 339: ASW= 0.7984, DB= 0.2796, CH= 35802.7218
Training epoch 340, recon_loss:0.888974, zinb_loss:0.481295, cluster_loss:0.164391
Clustering 340: ASW= 0.7979, DB= 0.2769, CH= 35502.4453
Training epoch 341, recon_loss:0.888576, zinb_loss:0.481171, cluster_loss:0.164661
Clustering 341: ASW= 0.7985, DB= 0.2796, CH= 35795.4400
Training epoch 342, recon_loss:0.888856, zinb_loss:0.481282, cluster_loss:0.164328
Clustering 342: ASW= 0.7983, DB= 0.2764, CH= 35606.4713
Training epoch 343, recon_loss:0.888408, zinb_loss:0.481142, cluster_loss:0.164594
Clustering 343: ASW= 0.7985, DB= 0.2796, CH= 35788.0310
Training epoch 344, recon_loss:0.888809, zinb_loss:0.481271, cluster_loss:0.164276
Clustering 344: ASW= 0.7987, DB= 0.2758, CH= 35695.3809
Training epoch 345, recon_loss:0.888321, zinb_loss:0.481114, cluster_loss:0.164535
Clustering 345: ASW= 0.7985, DB= 0.2796, CH= 35783.2135
Training epoch 346, recon_loss:0.888857, zinb_loss:0.481262, cluster_loss:0.164243
Clustering 346: ASW= 0.7990, DB= 0.2753, CH= 35787.0243
Training epoch 347, recon_loss:0.888334, zinb_loss:0.481089, cluster_loss:0.164499
Clustering 347: ASW= 0.7985, DB= 0.2794, CH= 35787.2041
Training epoch 348, recon_loss:0.888982, zinb_loss:0.481256, cluster_loss:0.164223
Clustering 348: ASW= 0.7994, DB= 0.2749, CH= 35874.1495
Training epoch 349, recon_loss:0.888403, zinb_loss:0.481069, cluster_loss:0.164467
Clustering 349: ASW= 0.7984, DB= 0.2792, CH= 35785.2223
Training epoch 350, recon_loss:0.889114, zinb_loss:0.481249, cluster_loss:0.164210
Clustering 350: ASW= 0.7997, DB= 0.2746, CH= 35952.9088
Training epoch 351, recon_loss:0.888429, zinb_loss:0.481050, cluster_loss:0.164415
Clustering 351: ASW= 0.7985, DB= 0.2788, CH= 35810.0731
Training epoch 352, recon_loss:0.889147, zinb_loss:0.481240, cluster_loss:0.164160
Clustering 352: ASW= 0.8000, DB= 0.2742, CH= 36023.2587
Training epoch 353, recon_loss:0.888380, zinb_loss:0.481033, cluster_loss:0.164335
Clustering 353: ASW= 0.7985, DB= 0.2788, CH= 35846.6527
Training epoch 354, recon_loss:0.889094, zinb_loss:0.481227, cluster_loss:0.164102
Clustering 354: ASW= 0.8002, DB= 0.2738, CH= 36082.9316
Training epoch 355, recon_loss:0.888308, zinb_loss:0.481019, cluster_loss:0.164256
Clustering 355: ASW= 0.7986, DB= 0.2786, CH= 35883.1942
Training epoch 356, recon_loss:0.889027, zinb_loss:0.481215, cluster_loss:0.164056
Clustering 356: ASW= 0.8005, DB= 0.2737, CH= 36147.9577
Training epoch 357, recon_loss:0.888243, zinb_loss:0.481007, cluster_loss:0.164185
Clustering 357: ASW= 0.7987, DB= 0.2785, CH= 35926.4697
Training epoch 358, recon_loss:0.888963, zinb_loss:0.481204, cluster_loss:0.164028
Clustering 358: ASW= 0.8006, DB= 0.2734, CH= 36198.9902
Training epoch 359, recon_loss:0.888218, zinb_loss:0.480998, cluster_loss:0.164130
Clustering 359: ASW= 0.7988, DB= 0.2782, CH= 35969.8988
Training epoch 360, recon_loss:0.888947, zinb_loss:0.481194, cluster_loss:0.164024
Clustering 360: ASW= 0.8008, DB= 0.2729, CH= 36255.8438
Training epoch 361, recon_loss:0.888277, zinb_loss:0.480994, cluster_loss:0.164108
Clustering 361: ASW= 0.7989, DB= 0.2779, CH= 36006.7356
Training epoch 362, recon_loss:0.889012, zinb_loss:0.481188, cluster_loss:0.164055
Clustering 362: ASW= 0.8010, DB= 0.2729, CH= 36325.8163
Training epoch 363, recon_loss:0.888457, zinb_loss:0.481004, cluster_loss:0.164116
Clustering 363: ASW= 0.7990, DB= 0.2780, CH= 36037.5862
Training epoch 364, recon_loss:0.889173, zinb_loss:0.481190, cluster_loss:0.164134
Clustering 364: ASW= 0.8011, DB= 0.2730, CH= 36402.5490
Training epoch 365, recon_loss:0.888776, zinb_loss:0.481034, cluster_loss:0.164153
Clustering 365: ASW= 0.7991, DB= 0.2775, CH= 36044.3017
Training epoch 366, recon_loss:0.889397, zinb_loss:0.481205, cluster_loss:0.164242
Clustering 366: ASW= 0.8012, DB= 0.2733, CH= 36490.3290
Training epoch 367, recon_loss:0.889159, zinb_loss:0.481084, cluster_loss:0.164213
Clustering 367: ASW= 0.7992, DB= 0.2772, CH= 36020.9367
Training epoch 368, recon_loss:0.889589, zinb_loss:0.481230, cluster_loss:0.164354
Clustering 368: ASW= 0.8013, DB= 0.2730, CH= 36591.7856
Training epoch 369, recon_loss:0.889511, zinb_loss:0.481145, cluster_loss:0.164318
Clustering 369: ASW= 0.7992, DB= 0.2769, CH= 35967.4657
Training epoch 370, recon_loss:0.889672, zinb_loss:0.481250, cluster_loss:0.164458
Clustering 370: ASW= 0.8013, DB= 0.2734, CH= 36696.2782
Training epoch 371, recon_loss:0.889717, zinb_loss:0.481192, cluster_loss:0.164455
Clustering 371: ASW= 0.7994, DB= 0.2764, CH= 35888.3103
Training epoch 372, recon_loss:0.889561, zinb_loss:0.481251, cluster_loss:0.164524
Clustering 372: ASW= 0.8012, DB= 0.2733, CH= 36781.1342
Training epoch 373, recon_loss:0.889705, zinb_loss:0.481210, cluster_loss:0.164553
Clustering 373: ASW= 0.7997, DB= 0.2758, CH= 35829.8205
Training epoch 374, recon_loss:0.889222, zinb_loss:0.481222, cluster_loss:0.164472
Clustering 374: ASW= 0.8010, DB= 0.2736, CH= 36832.2167
Training epoch 375, recon_loss:0.889430, zinb_loss:0.481199, cluster_loss:0.164489
Clustering 375: ASW= 0.7999, DB= 0.2757, CH= 35821.0156
Training epoch 376, recon_loss:0.888787, zinb_loss:0.481177, cluster_loss:0.164320
Clustering 376: ASW= 0.8010, DB= 0.2738, CH= 36868.7189
Training epoch 377, recon_loss:0.889091, zinb_loss:0.481171, cluster_loss:0.164329
Clustering 377: ASW= 0.8002, DB= 0.2752, CH= 35869.5556
Training epoch 378, recon_loss:0.888455, zinb_loss:0.481135, cluster_loss:0.164145
Clustering 378: ASW= 0.8010, DB= 0.2738, CH= 36907.2006
Training epoch 379, recon_loss:0.888836, zinb_loss:0.481143, cluster_loss:0.164162
Clustering 379: ASW= 0.8006, DB= 0.2746, CH= 35949.4165
Training epoch 380, recon_loss:0.888244, zinb_loss:0.481101, cluster_loss:0.163977
Clustering 380: ASW= 0.8011, DB= 0.2738, CH= 36948.1858
Training epoch 381, recon_loss:0.888634, zinb_loss:0.481114, cluster_loss:0.164004
Clustering 381: ASW= 0.8008, DB= 0.2742, CH= 36033.6238
Training epoch 382, recon_loss:0.888081, zinb_loss:0.481073, cluster_loss:0.163820
Clustering 382: ASW= 0.8011, DB= 0.2737, CH= 36985.5634
Training epoch 383, recon_loss:0.888462, zinb_loss:0.481089, cluster_loss:0.163857
Clustering 383: ASW= 0.8011, DB= 0.2741, CH= 36125.3992
Training epoch 384, recon_loss:0.887944, zinb_loss:0.481048, cluster_loss:0.163666
Clustering 384: ASW= 0.8013, DB= 0.2736, CH= 37018.4820
Training epoch 385, recon_loss:0.888290, zinb_loss:0.481062, cluster_loss:0.163722
Clustering 385: ASW= 0.8013, DB= 0.2738, CH= 36212.3440
Training epoch 386, recon_loss:0.887854, zinb_loss:0.481028, cluster_loss:0.163540
Clustering 386: ASW= 0.8014, DB= 0.2733, CH= 37060.9739
Training epoch 387, recon_loss:0.888190, zinb_loss:0.481040, cluster_loss:0.163629
Clustering 387: ASW= 0.8015, DB= 0.2736, CH= 36287.2802
Training epoch 388, recon_loss:0.887859, zinb_loss:0.481015, cluster_loss:0.163459
Clustering 388: ASW= 0.8015, DB= 0.2733, CH= 37117.7609
Training epoch 389, recon_loss:0.888212, zinb_loss:0.481024, cluster_loss:0.163593
Clustering 389: ASW= 0.8017, DB= 0.2735, CH= 36343.3951
Training epoch 390, recon_loss:0.888022, zinb_loss:0.481013, cluster_loss:0.163451
Clustering 390: ASW= 0.8017, DB= 0.2729, CH= 37180.0164
Training epoch 391, recon_loss:0.888405, zinb_loss:0.481016, cluster_loss:0.163638
Clustering 391: ASW= 0.8017, DB= 0.2735, CH= 36374.6015
Training epoch 392, recon_loss:0.888343, zinb_loss:0.481020, cluster_loss:0.163538
Clustering 392: ASW= 0.8019, DB= 0.2725, CH= 37249.8259
Training epoch 393, recon_loss:0.888710, zinb_loss:0.481013, cluster_loss:0.163762
Clustering 393: ASW= 0.8017, DB= 0.2738, CH= 36381.0892
Training epoch 394, recon_loss:0.888753, zinb_loss:0.481037, cluster_loss:0.163721
Clustering 394: ASW= 0.8022, DB= 0.2719, CH= 37321.4734
Training epoch 395, recon_loss:0.889021, zinb_loss:0.481013, cluster_loss:0.163935
Clustering 395: ASW= 0.8015, DB= 0.2738, CH= 36373.5043
Training epoch 396, recon_loss:0.889123, zinb_loss:0.481054, cluster_loss:0.163933
Clustering 396: ASW= 0.8025, DB= 0.2715, CH= 37366.6558
Training epoch 397, recon_loss:0.889158, zinb_loss:0.481002, cluster_loss:0.164063
Clustering 397: ASW= 0.8013, DB= 0.2738, CH= 36370.8788
Training epoch 398, recon_loss:0.889302, zinb_loss:0.481058, cluster_loss:0.164068
Clustering 398: ASW= 0.8028, DB= 0.2712, CH= 37372.6577
Training epoch 399, recon_loss:0.888978, zinb_loss:0.480974, cluster_loss:0.164053
Clustering 399: ASW= 0.8011, DB= 0.2740, CH= 36423.2070
Training epoch 400, recon_loss:0.889208, zinb_loss:0.481042, cluster_loss:0.164079
Clustering 400: ASW= 0.8032, DB= 0.2707, CH= 37331.0075
Training epoch 401, recon_loss:0.888645, zinb_loss:0.480942, cluster_loss:0.163961
Clustering 401: ASW= 0.8009, DB= 0.2741, CH= 36501.6993
Training epoch 402, recon_loss:0.888991, zinb_loss:0.481022, cluster_loss:0.164024
Clustering 402: ASW= 0.8035, DB= 0.2707, CH= 37296.4142
Training epoch 403, recon_loss:0.888293, zinb_loss:0.480922, cluster_loss:0.163842
Clustering 403: ASW= 0.8009, DB= 0.2741, CH= 36601.7510
Training epoch 404, recon_loss:0.888737, zinb_loss:0.481004, cluster_loss:0.163938
Clustering 404: ASW= 0.8037, DB= 0.2706, CH= 37271.8778
Training epoch 405, recon_loss:0.887983, zinb_loss:0.480910, cluster_loss:0.163720
Clustering 405: ASW= 0.8010, DB= 0.2738, CH= 36697.7447
Training epoch 406, recon_loss:0.888476, zinb_loss:0.480986, cluster_loss:0.163826
Clustering 406: ASW= 0.8038, DB= 0.2705, CH= 37274.0784
Training epoch 407, recon_loss:0.887719, zinb_loss:0.480903, cluster_loss:0.163593
Clustering 407: ASW= 0.8012, DB= 0.2736, CH= 36785.0310
Training epoch 408, recon_loss:0.888238, zinb_loss:0.480971, cluster_loss:0.163708
Clustering 408: ASW= 0.8040, DB= 0.2700, CH= 37301.8732
Training epoch 409, recon_loss:0.887524, zinb_loss:0.480896, cluster_loss:0.163474
Clustering 409: ASW= 0.8013, DB= 0.2732, CH= 36860.8921
Training epoch 410, recon_loss:0.888051, zinb_loss:0.480956, cluster_loss:0.163605
Clustering 410: ASW= 0.8041, DB= 0.2698, CH= 37337.0903
Training epoch 411, recon_loss:0.887397, zinb_loss:0.480891, cluster_loss:0.163372
Clustering 411: ASW= 0.8015, DB= 0.2731, CH= 36930.3683
Training epoch 412, recon_loss:0.887917, zinb_loss:0.480942, cluster_loss:0.163520
Clustering 412: ASW= 0.8042, DB= 0.2696, CH= 37377.6610
Training epoch 413, recon_loss:0.887311, zinb_loss:0.480886, cluster_loss:0.163288
Clustering 413: ASW= 0.8017, DB= 0.2730, CH= 36996.1161
Training epoch 414, recon_loss:0.887807, zinb_loss:0.480930, cluster_loss:0.163453
Clustering 414: ASW= 0.8044, DB= 0.2694, CH= 37418.9066
Training epoch 415, recon_loss:0.887259, zinb_loss:0.480883, cluster_loss:0.163220
Clustering 415: ASW= 0.8019, DB= 0.2727, CH= 37061.5015
Training epoch 416, recon_loss:0.887722, zinb_loss:0.480917, cluster_loss:0.163415
Clustering 416: ASW= 0.8045, DB= 0.2692, CH= 37453.7369
Training epoch 417, recon_loss:0.887230, zinb_loss:0.480882, cluster_loss:0.163172
Clustering 417: ASW= 0.8020, DB= 0.2725, CH= 37125.6886
Training epoch 418, recon_loss:0.887662, zinb_loss:0.480905, cluster_loss:0.163402
Clustering 418: ASW= 0.8046, DB= 0.2690, CH= 37485.9543
Training epoch 419, recon_loss:0.887236, zinb_loss:0.480885, cluster_loss:0.163146
Clustering 419: ASW= 0.8022, DB= 0.2724, CH= 37189.3257
Training epoch 420, recon_loss:0.887626, zinb_loss:0.480893, cluster_loss:0.163424
Clustering 420: ASW= 0.8048, DB= 0.2688, CH= 37510.2313
Training epoch 421, recon_loss:0.887288, zinb_loss:0.480893, cluster_loss:0.163154
Clustering 421: ASW= 0.8023, DB= 0.2723, CH= 37252.4561
Training epoch 422, recon_loss:0.887624, zinb_loss:0.480885, cluster_loss:0.163487
Clustering 422: ASW= 0.8049, DB= 0.2686, CH= 37527.7016
Training epoch 423, recon_loss:0.887378, zinb_loss:0.480909, cluster_loss:0.163202
Clustering 423: ASW= 0.8025, DB= 0.2721, CH= 37322.2583
Training epoch 424, recon_loss:0.887631, zinb_loss:0.480880, cluster_loss:0.163592
Clustering 424: ASW= 0.8050, DB= 0.2685, CH= 37535.6939
Training epoch 425, recon_loss:0.887492, zinb_loss:0.480932, cluster_loss:0.163287
Clustering 425: ASW= 0.8026, DB= 0.2719, CH= 37396.5673
Training epoch 426, recon_loss:0.887614, zinb_loss:0.480875, cluster_loss:0.163711
Clustering 426: ASW= 0.8050, DB= 0.2684, CH= 37529.3049
Training epoch 427, recon_loss:0.887556, zinb_loss:0.480955, cluster_loss:0.163371
Clustering 427: ASW= 0.8028, DB= 0.2716, CH= 37472.4582
Training epoch 428, recon_loss:0.887514, zinb_loss:0.480868, cluster_loss:0.163790
Clustering 428: ASW= 0.8050, DB= 0.2683, CH= 37518.1707
Training epoch 429, recon_loss:0.887569, zinb_loss:0.480973, cluster_loss:0.163424
Clustering 429: ASW= 0.8031, DB= 0.2711, CH= 37549.8290
Training epoch 430, recon_loss:0.887374, zinb_loss:0.480851, cluster_loss:0.163819
Clustering 430: ASW= 0.8048, DB= 0.2682, CH= 37501.7017
Training epoch 431, recon_loss:0.887576, zinb_loss:0.480981, cluster_loss:0.163437
Clustering 431: ASW= 0.8034, DB= 0.2706, CH= 37627.6240
Training epoch 432, recon_loss:0.887307, zinb_loss:0.480831, cluster_loss:0.163807
Clustering 432: ASW= 0.8047, DB= 0.2683, CH= 37486.3702
Training epoch 433, recon_loss:0.887752, zinb_loss:0.480992, cluster_loss:0.163458
Clustering 433: ASW= 0.8037, DB= 0.2705, CH= 37701.2743
Training epoch 434, recon_loss:0.887465, zinb_loss:0.480818, cluster_loss:0.163818
Clustering 434: ASW= 0.8044, DB= 0.2685, CH= 37469.6473
Training epoch 435, recon_loss:0.888156, zinb_loss:0.481005, cluster_loss:0.163490
Clustering 435: ASW= 0.8042, DB= 0.2701, CH= 37771.1933
Training epoch 436, recon_loss:0.887768, zinb_loss:0.480811, cluster_loss:0.163808
Clustering 436: ASW= 0.8042, DB= 0.2682, CH= 37454.6337
Training epoch 437, recon_loss:0.888635, zinb_loss:0.481021, cluster_loss:0.163516
Clustering 437: ASW= 0.8046, DB= 0.2696, CH= 37823.3655
Training epoch 438, recon_loss:0.888067, zinb_loss:0.480818, cluster_loss:0.163794
Clustering 438: ASW= 0.8039, DB= 0.2682, CH= 37467.9434
Training epoch 439, recon_loss:0.889070, zinb_loss:0.481046, cluster_loss:0.163588
Clustering 439: ASW= 0.8050, DB= 0.2697, CH= 37839.1272
Training epoch 440, recon_loss:0.888316, zinb_loss:0.480844, cluster_loss:0.163859
Clustering 440: ASW= 0.8036, DB= 0.2676, CH= 37502.8480
Training epoch 441, recon_loss:0.889574, zinb_loss:0.481094, cluster_loss:0.163853
Clustering 441: ASW= 0.8054, DB= 0.2699, CH= 37804.4902
Training epoch 442, recon_loss:0.888667, zinb_loss:0.480900, cluster_loss:0.164150
Clustering 442: ASW= 0.8033, DB= 0.2670, CH= 37535.9547
Training epoch 443, recon_loss:0.890039, zinb_loss:0.481151, cluster_loss:0.164231
Clustering 443: ASW= 0.8055, DB= 0.2703, CH= 37742.4376
Training epoch 444, recon_loss:0.888660, zinb_loss:0.480920, cluster_loss:0.164353
Clustering 444: ASW= 0.8031, DB= 0.2669, CH= 37519.2888
Training epoch 445, recon_loss:0.889749, zinb_loss:0.481118, cluster_loss:0.164222
Clustering 445: ASW= 0.8052, DB= 0.2705, CH= 37739.9830
Training epoch 446, recon_loss:0.888032, zinb_loss:0.480854, cluster_loss:0.164155
Clustering 446: ASW= 0.8033, DB= 0.2669, CH= 37519.1856
Training epoch 447, recon_loss:0.889060, zinb_loss:0.481017, cluster_loss:0.164011
Clustering 447: ASW= 0.8049, DB= 0.2707, CH= 37781.2890
Training epoch 448, recon_loss:0.887600, zinb_loss:0.480793, cluster_loss:0.163916
Clustering 448: ASW= 0.8036, DB= 0.2670, CH= 37500.6651
Training epoch 449, recon_loss:0.888536, zinb_loss:0.480923, cluster_loss:0.163821
Clustering 449: ASW= 0.8049, DB= 0.2701, CH= 37845.0322
Training epoch 450, recon_loss:0.887388, zinb_loss:0.480757, cluster_loss:0.163744
Clustering 450: ASW= 0.8037, DB= 0.2672, CH= 37462.3765
Training epoch 451, recon_loss:0.888210, zinb_loss:0.480859, cluster_loss:0.163698
Clustering 451: ASW= 0.8049, DB= 0.2695, CH= 37944.9075
Training epoch 452, recon_loss:0.887331, zinb_loss:0.480752, cluster_loss:0.163622
Clustering 452: ASW= 0.8037, DB= 0.2674, CH= 37416.3065
Training epoch 453, recon_loss:0.887999, zinb_loss:0.480820, cluster_loss:0.163616
Clustering 453: ASW= 0.8050, DB= 0.2691, CH= 38054.3954
Training epoch 454, recon_loss:0.887298, zinb_loss:0.480764, cluster_loss:0.163505
Clustering 454: ASW= 0.8038, DB= 0.2676, CH= 37391.4553
Training epoch 455, recon_loss:0.887745, zinb_loss:0.480793, cluster_loss:0.163519
Clustering 455: ASW= 0.8052, DB= 0.2687, CH= 38157.6084
Training epoch 456, recon_loss:0.887244, zinb_loss:0.480783, cluster_loss:0.163378
Clustering 456: ASW= 0.8039, DB= 0.2678, CH= 37400.1524
Training epoch 457, recon_loss:0.887546, zinb_loss:0.480779, cluster_loss:0.163421
Clustering 457: ASW= 0.8053, DB= 0.2684, CH= 38238.5914
Training epoch 458, recon_loss:0.887200, zinb_loss:0.480804, cluster_loss:0.163271
Clustering 458: ASW= 0.8041, DB= 0.2679, CH= 37440.3486
Training epoch 459, recon_loss:0.887350, zinb_loss:0.480769, cluster_loss:0.163347
Clustering 459: ASW= 0.8054, DB= 0.2681, CH= 38300.8958
Training epoch 460, recon_loss:0.887210, zinb_loss:0.480825, cluster_loss:0.163189
Clustering 460: ASW= 0.8044, DB= 0.2679, CH= 37509.4297
Training epoch 461, recon_loss:0.887265, zinb_loss:0.480764, cluster_loss:0.163319
Clustering 461: ASW= 0.8054, DB= 0.2680, CH= 38340.6502
Training epoch 462, recon_loss:0.887283, zinb_loss:0.480850, cluster_loss:0.163151
Clustering 462: ASW= 0.8047, DB= 0.2676, CH= 37586.6045
Training epoch 463, recon_loss:0.887260, zinb_loss:0.480763, cluster_loss:0.163345
Clustering 463: ASW= 0.8054, DB= 0.2678, CH= 38360.5012
Training epoch 464, recon_loss:0.887556, zinb_loss:0.480884, cluster_loss:0.163184
Clustering 464: ASW= 0.8051, DB= 0.2673, CH= 37680.3256
Training epoch 465, recon_loss:0.887557, zinb_loss:0.480776, cluster_loss:0.163463
Clustering 465: ASW= 0.8054, DB= 0.2676, CH= 38349.6061
Training epoch 466, recon_loss:0.888095, zinb_loss:0.480929, cluster_loss:0.163337
Clustering 466: ASW= 0.8054, DB= 0.2669, CH= 37783.4612
Training epoch 467, recon_loss:0.887996, zinb_loss:0.480788, cluster_loss:0.163627
Clustering 467: ASW= 0.8052, DB= 0.2676, CH= 38321.5946
Training epoch 468, recon_loss:0.888438, zinb_loss:0.480948, cluster_loss:0.163463
Clustering 468: ASW= 0.8057, DB= 0.2664, CH= 37872.1469
Training epoch 469, recon_loss:0.888003, zinb_loss:0.480766, cluster_loss:0.163618
Clustering 469: ASW= 0.8052, DB= 0.2676, CH= 38294.5390
Training epoch 470, recon_loss:0.888168, zinb_loss:0.480915, cluster_loss:0.163394
Clustering 470: ASW= 0.8060, DB= 0.2660, CH= 37967.9295
Training epoch 471, recon_loss:0.887571, zinb_loss:0.480718, cluster_loss:0.163425
Clustering 471: ASW= 0.8052, DB= 0.2680, CH= 38286.8330
Training epoch 472, recon_loss:0.887628, zinb_loss:0.480857, cluster_loss:0.163216
Clustering 472: ASW= 0.8063, DB= 0.2657, CH= 38064.6629
Training epoch 473, recon_loss:0.887053, zinb_loss:0.480672, cluster_loss:0.163205
Clustering 473: ASW= 0.8053, DB= 0.2679, CH= 38290.7630
Training epoch 474, recon_loss:0.887154, zinb_loss:0.480805, cluster_loss:0.163052
Clustering 474: ASW= 0.8066, DB= 0.2654, CH= 38153.9915
Training epoch 475, recon_loss:0.886672, zinb_loss:0.480638, cluster_loss:0.163055
Clustering 475: ASW= 0.8054, DB= 0.2682, CH= 38306.3581
Training epoch 476, recon_loss:0.886848, zinb_loss:0.480769, cluster_loss:0.162952
Clustering 476: ASW= 0.8067, DB= 0.2651, CH= 38234.4542
Training epoch 477, recon_loss:0.886445, zinb_loss:0.480613, cluster_loss:0.162980
Clustering 477: ASW= 0.8054, DB= 0.2682, CH= 38314.9601
Training epoch 478, recon_loss:0.886699, zinb_loss:0.480746, cluster_loss:0.162913
Clustering 478: ASW= 0.8069, DB= 0.2648, CH= 38319.4844
Training epoch 479, recon_loss:0.886348, zinb_loss:0.480595, cluster_loss:0.162969
Clustering 479: ASW= 0.8055, DB= 0.2683, CH= 38312.9540
Training epoch 480, recon_loss:0.886680, zinb_loss:0.480734, cluster_loss:0.162929
Clustering 480: ASW= 0.8071, DB= 0.2645, CH= 38403.7924
Training epoch 481, recon_loss:0.886370, zinb_loss:0.480583, cluster_loss:0.163019
Clustering 481: ASW= 0.8055, DB= 0.2685, CH= 38290.9186
Training epoch 482, recon_loss:0.886776, zinb_loss:0.480728, cluster_loss:0.162995
Clustering 482: ASW= 0.8073, DB= 0.2641, CH= 38495.4376
Training epoch 483, recon_loss:0.886498, zinb_loss:0.480577, cluster_loss:0.163123
Clustering 483: ASW= 0.8055, DB= 0.2688, CH= 38240.9363
Training epoch 484, recon_loss:0.886945, zinb_loss:0.480729, cluster_loss:0.163105
Clustering 484: ASW= 0.8075, DB= 0.2636, CH= 38601.9123
Training epoch 485, recon_loss:0.886656, zinb_loss:0.480578, cluster_loss:0.163269
Clustering 485: ASW= 0.8054, DB= 0.2692, CH= 38161.8657
Training epoch 486, recon_loss:0.887127, zinb_loss:0.480739, cluster_loss:0.163244
Clustering 486: ASW= 0.8077, DB= 0.2627, CH= 38724.8171
Training epoch 487, recon_loss:0.886791, zinb_loss:0.480590, cluster_loss:0.163428
Clustering 487: ASW= 0.8053, DB= 0.2696, CH= 38061.5059
Training epoch 488, recon_loss:0.887269, zinb_loss:0.480763, cluster_loss:0.163365
Clustering 488: ASW= 0.8080, DB= 0.2623, CH= 38860.8198
Training epoch 489, recon_loss:0.886908, zinb_loss:0.480618, cluster_loss:0.163542
Clustering 489: ASW= 0.8053, DB= 0.2700, CH= 37967.1935
Training epoch 490, recon_loss:0.887460, zinb_loss:0.480803, cluster_loss:0.163455
Clustering 490: ASW= 0.8081, DB= 0.2618, CH= 38991.9888
Training epoch 491, recon_loss:0.887087, zinb_loss:0.480660, cluster_loss:0.163582
Clustering 491: ASW= 0.8054, DB= 0.2700, CH= 37907.3923
Training epoch 492, recon_loss:0.887731, zinb_loss:0.480842, cluster_loss:0.163464
Clustering 492: ASW= 0.8084, DB= 0.2616, CH= 39100.3204
Training epoch 493, recon_loss:0.887218, zinb_loss:0.480691, cluster_loss:0.163470
Clustering 493: ASW= 0.8055, DB= 0.2696, CH= 37889.6464
Training epoch 494, recon_loss:0.887848, zinb_loss:0.480841, cluster_loss:0.163310
Clustering 494: ASW= 0.8085, DB= 0.2614, CH= 39165.2683
Training epoch 495, recon_loss:0.887164, zinb_loss:0.480686, cluster_loss:0.163249
Clustering 495: ASW= 0.8057, DB= 0.2688, CH= 37935.2989
Training epoch 496, recon_loss:0.887801, zinb_loss:0.480810, cluster_loss:0.163106
Clustering 496: ASW= 0.8086, DB= 0.2614, CH= 39201.6270
Training epoch 497, recon_loss:0.887136, zinb_loss:0.480671, cluster_loss:0.163083
Clustering 497: ASW= 0.8058, DB= 0.2682, CH= 37995.0341
Training epoch 498, recon_loss:0.887773, zinb_loss:0.480780, cluster_loss:0.162995
Clustering 498: ASW= 0.8086, DB= 0.2616, CH= 39211.9503
Training epoch 499, recon_loss:0.887159, zinb_loss:0.480658, cluster_loss:0.163015
Clustering 499: ASW= 0.8059, DB= 0.2677, CH= 38054.7063
Training epoch 500, recon_loss:0.887706, zinb_loss:0.480752, cluster_loss:0.162960
Clustering 500: ASW= 0.8086, DB= 0.2618, CH= 39208.0264
Training epoch 501, recon_loss:0.887140, zinb_loss:0.480648, cluster_loss:0.162986
Clustering 501: ASW= 0.8060, DB= 0.2673, CH= 38112.9132
Training epoch 502, recon_loss:0.887522, zinb_loss:0.480720, cluster_loss:0.162920
Clustering 502: ASW= 0.8086, DB= 0.2624, CH= 39224.6874
Training epoch 503, recon_loss:0.886976, zinb_loss:0.480630, cluster_loss:0.162935
Clustering 503: ASW= 0.8061, DB= 0.2667, CH= 38155.0517
Training epoch 504, recon_loss:0.887232, zinb_loss:0.480683, cluster_loss:0.162853
Clustering 504: ASW= 0.8086, DB= 0.2627, CH= 39261.2201
Training epoch 505, recon_loss:0.886731, zinb_loss:0.480612, cluster_loss:0.162851
Clustering 505: ASW= 0.8062, DB= 0.2663, CH= 38202.2748
Training epoch 506, recon_loss:0.886909, zinb_loss:0.480648, cluster_loss:0.162772
Clustering 506: ASW= 0.8086, DB= 0.2628, CH= 39305.9181
Training epoch 507, recon_loss:0.886488, zinb_loss:0.480591, cluster_loss:0.162768
Clustering 507: ASW= 0.8063, DB= 0.2660, CH= 38241.5796
Training epoch 508, recon_loss:0.886634, zinb_loss:0.480618, cluster_loss:0.162707
Clustering 508: ASW= 0.8087, DB= 0.2630, CH= 39351.2363
Training epoch 509, recon_loss:0.886303, zinb_loss:0.480575, cluster_loss:0.162701
Clustering 509: ASW= 0.8063, DB= 0.2661, CH= 38273.1081
Training epoch 510, recon_loss:0.886440, zinb_loss:0.480599, cluster_loss:0.162659
Clustering 510: ASW= 0.8087, DB= 0.2630, CH= 39389.1863
Training epoch 511, recon_loss:0.886258, zinb_loss:0.480572, cluster_loss:0.162691
Clustering 511: ASW= 0.8065, DB= 0.2658, CH= 38304.6281
Training epoch 512, recon_loss:0.886395, zinb_loss:0.480595, cluster_loss:0.162661
Clustering 512: ASW= 0.8088, DB= 0.2630, CH= 39433.2527
Training epoch 513, recon_loss:0.886346, zinb_loss:0.480580, cluster_loss:0.162736
Clustering 513: ASW= 0.8066, DB= 0.2656, CH= 38340.4673
Training epoch 514, recon_loss:0.886463, zinb_loss:0.480602, cluster_loss:0.162698
Clustering 514: ASW= 0.8088, DB= 0.2631, CH= 39479.4873
Training epoch 515, recon_loss:0.886468, zinb_loss:0.480593, cluster_loss:0.162789
Clustering 515: ASW= 0.8067, DB= 0.2653, CH= 38378.0547
Training epoch 516, recon_loss:0.886491, zinb_loss:0.480610, cluster_loss:0.162716
Clustering 516: ASW= 0.8089, DB= 0.2628, CH= 39516.1509
Training epoch 517, recon_loss:0.886475, zinb_loss:0.480596, cluster_loss:0.162801
Clustering 517: ASW= 0.8069, DB= 0.2651, CH= 38416.7605
Training epoch 518, recon_loss:0.886413, zinb_loss:0.480613, cluster_loss:0.162693
Clustering 518: ASW= 0.8090, DB= 0.2627, CH= 39554.6603
Training epoch 519, recon_loss:0.886354, zinb_loss:0.480592, cluster_loss:0.162752
Clustering 519: ASW= 0.8070, DB= 0.2648, CH= 38464.0529
Training epoch 520, recon_loss:0.886274, zinb_loss:0.480611, cluster_loss:0.162644
Clustering 520: ASW= 0.8090, DB= 0.2629, CH= 39582.2927
Training epoch 521, recon_loss:0.886195, zinb_loss:0.480584, cluster_loss:0.162692
Clustering 521: ASW= 0.8071, DB= 0.2645, CH= 38511.8491
Training epoch 522, recon_loss:0.886175, zinb_loss:0.480609, cluster_loss:0.162610
Clustering 522: ASW= 0.8091, DB= 0.2630, CH= 39604.7958
Training epoch 523, recon_loss:0.886093, zinb_loss:0.480578, cluster_loss:0.162661
Clustering 523: ASW= 0.8072, DB= 0.2641, CH= 38563.2819
Training epoch 524, recon_loss:0.886182, zinb_loss:0.480609, cluster_loss:0.162630
Clustering 524: ASW= 0.8091, DB= 0.2631, CH= 39607.6261
Training epoch 525, recon_loss:0.886115, zinb_loss:0.480575, cluster_loss:0.162691
Clustering 525: ASW= 0.8073, DB= 0.2636, CH= 38614.8834
Training epoch 526, recon_loss:0.886326, zinb_loss:0.480613, cluster_loss:0.162730
Clustering 526: ASW= 0.8090, DB= 0.2635, CH= 39591.6293
Training epoch 527, recon_loss:0.886280, zinb_loss:0.480570, cluster_loss:0.162789
Clustering 527: ASW= 0.8073, DB= 0.2632, CH= 38676.9047
Training epoch 528, recon_loss:0.886575, zinb_loss:0.480616, cluster_loss:0.162897
Clustering 528: ASW= 0.8089, DB= 0.2641, CH= 39537.2997
Training epoch 529, recon_loss:0.886540, zinb_loss:0.480560, cluster_loss:0.162959
Clustering 529: ASW= 0.8073, DB= 0.2627, CH= 38748.7622
Training epoch 530, recon_loss:0.886877, zinb_loss:0.480615, cluster_loss:0.163112
Clustering 530: ASW= 0.8088, DB= 0.2647, CH= 39440.9989
Training epoch 531, recon_loss:0.886820, zinb_loss:0.480543, cluster_loss:0.163143
Clustering 531: ASW= 0.8072, DB= 0.2619, CH= 38830.2054
Training epoch 532, recon_loss:0.887078, zinb_loss:0.480602, cluster_loss:0.163265
Clustering 532: ASW= 0.8086, DB= 0.2653, CH= 39313.3982
Training epoch 533, recon_loss:0.886834, zinb_loss:0.480515, cluster_loss:0.163210
Clustering 533: ASW= 0.8073, DB= 0.2614, CH= 38937.4144
Training epoch 534, recon_loss:0.886969, zinb_loss:0.480572, cluster_loss:0.163231
Clustering 534: ASW= 0.8085, DB= 0.2657, CH= 39198.6706
Training epoch 535, recon_loss:0.886579, zinb_loss:0.480477, cluster_loss:0.163092
Clustering 535: ASW= 0.8076, DB= 0.2612, CH= 39044.0949
Training epoch 536, recon_loss:0.886749, zinb_loss:0.480537, cluster_loss:0.163068
Clustering 536: ASW= 0.8084, DB= 0.2661, CH= 39111.6179
Training epoch 537, recon_loss:0.886248, zinb_loss:0.480448, cluster_loss:0.162908
Clustering 537: ASW= 0.8080, DB= 0.2610, CH= 39159.7555
Training epoch 538, recon_loss:0.886450, zinb_loss:0.480507, cluster_loss:0.162854
Clustering 538: ASW= 0.8084, DB= 0.2661, CH= 39067.1900
Training epoch 539, recon_loss:0.885983, zinb_loss:0.480430, cluster_loss:0.162739
Clustering 539: ASW= 0.8084, DB= 0.2602, CH= 39263.5409
Training epoch 540, recon_loss:0.886266, zinb_loss:0.480492, cluster_loss:0.162690
Clustering 540: ASW= 0.8084, DB= 0.2662, CH= 39038.5089
Training epoch 541, recon_loss:0.885818, zinb_loss:0.480432, cluster_loss:0.162627
Clustering 541: ASW= 0.8088, DB= 0.2598, CH= 39384.1668
Training epoch 542, recon_loss:0.886132, zinb_loss:0.480490, cluster_loss:0.162583
Clustering 542: ASW= 0.8084, DB= 0.2662, CH= 39029.4582
Training epoch 543, recon_loss:0.885792, zinb_loss:0.480445, cluster_loss:0.162582
Clustering 543: ASW= 0.8092, DB= 0.2595, CH= 39490.3538
Training epoch 544, recon_loss:0.886127, zinb_loss:0.480499, cluster_loss:0.162541
Clustering 544: ASW= 0.8083, DB= 0.2663, CH= 39011.7598
Training epoch 545, recon_loss:0.885851, zinb_loss:0.480472, cluster_loss:0.162579
Clustering 545: ASW= 0.8096, DB= 0.2591, CH= 39600.6926
Training epoch 546, recon_loss:0.886132, zinb_loss:0.480512, cluster_loss:0.162517
Clustering 546: ASW= 0.8083, DB= 0.2665, CH= 39002.6718
Training epoch 547, recon_loss:0.885942, zinb_loss:0.480495, cluster_loss:0.162592
Clustering 547: ASW= 0.8100, DB= 0.2589, CH= 39679.8708
Training epoch 548, recon_loss:0.886155, zinb_loss:0.480521, cluster_loss:0.162506
Clustering 548: ASW= 0.8082, DB= 0.2663, CH= 39001.4343
Training epoch 549, recon_loss:0.886038, zinb_loss:0.480518, cluster_loss:0.162611
Clustering 549: ASW= 0.8104, DB= 0.2586, CH= 39773.6669
Training epoch 550, recon_loss:0.886136, zinb_loss:0.480527, cluster_loss:0.162474
Clustering 550: ASW= 0.8082, DB= 0.2660, CH= 39040.7109
Training epoch 551, recon_loss:0.886081, zinb_loss:0.480524, cluster_loss:0.162623
Clustering 551: ASW= 0.8107, DB= 0.2583, CH= 39826.6856
Training epoch 552, recon_loss:0.886069, zinb_loss:0.480515, cluster_loss:0.162444
Clustering 552: ASW= 0.8081, DB= 0.2659, CH= 39099.3149
Training epoch 553, recon_loss:0.886159, zinb_loss:0.480526, cluster_loss:0.162655
Clustering 553: ASW= 0.8110, DB= 0.2580, CH= 39876.4750
Training epoch 554, recon_loss:0.886075, zinb_loss:0.480501, cluster_loss:0.162420
Clustering 554: ASW= 0.8080, DB= 0.2656, CH= 39181.1713
Training epoch 555, recon_loss:0.886279, zinb_loss:0.480517, cluster_loss:0.162720
Clustering 555: ASW= 0.8114, DB= 0.2578, CH= 39879.5065
Training epoch 556, recon_loss:0.886072, zinb_loss:0.480469, cluster_loss:0.162416
Clustering 556: ASW= 0.8078, DB= 0.2655, CH= 39254.0694
Training epoch 557, recon_loss:0.886454, zinb_loss:0.480523, cluster_loss:0.162790
Clustering 557: ASW= 0.8117, DB= 0.2575, CH= 39880.9049
Training epoch 558, recon_loss:0.886126, zinb_loss:0.480438, cluster_loss:0.162397
Clustering 558: ASW= 0.8077, DB= 0.2653, CH= 39332.2258
Training epoch 559, recon_loss:0.886627, zinb_loss:0.480518, cluster_loss:0.162845
Clustering 559: ASW= 0.8120, DB= 0.2575, CH= 39836.8422
Training epoch 560, recon_loss:0.886185, zinb_loss:0.480402, cluster_loss:0.162417
Clustering 560: ASW= 0.8076, DB= 0.2653, CH= 39400.0624
Training epoch 561, recon_loss:0.886905, zinb_loss:0.480534, cluster_loss:0.162889
Clustering 561: ASW= 0.8121, DB= 0.2576, CH= 39828.1629
Training epoch 562, recon_loss:0.886310, zinb_loss:0.480388, cluster_loss:0.162424
Clustering 562: ASW= 0.8077, DB= 0.2649, CH= 39473.0340
Training epoch 563, recon_loss:0.887053, zinb_loss:0.480536, cluster_loss:0.162834
Clustering 563: ASW= 0.8121, DB= 0.2580, CH= 39825.8305
Training epoch 564, recon_loss:0.886234, zinb_loss:0.480380, cluster_loss:0.162385
Clustering 564: ASW= 0.8080, DB= 0.2644, CH= 39549.4522
Training epoch 565, recon_loss:0.886873, zinb_loss:0.480519, cluster_loss:0.162681
Clustering 565: ASW= 0.8119, DB= 0.2583, CH= 39846.3956
Training epoch 566, recon_loss:0.885937, zinb_loss:0.480366, cluster_loss:0.162313
Clustering 566: ASW= 0.8083, DB= 0.2637, CH= 39618.4176
Training epoch 567, recon_loss:0.886503, zinb_loss:0.480491, cluster_loss:0.162532
Clustering 567: ASW= 0.8117, DB= 0.2586, CH= 39874.0506
Training epoch 568, recon_loss:0.885598, zinb_loss:0.480354, cluster_loss:0.162254
Clustering 568: ASW= 0.8087, DB= 0.2633, CH= 39678.2314
Training epoch 569, recon_loss:0.886167, zinb_loss:0.480461, cluster_loss:0.162439
Clustering 569: ASW= 0.8116, DB= 0.2588, CH= 39893.2430
Training epoch 570, recon_loss:0.885390, zinb_loss:0.480349, cluster_loss:0.162245
Clustering 570: ASW= 0.8090, DB= 0.2629, CH= 39727.1029
Training epoch 571, recon_loss:0.885987, zinb_loss:0.480437, cluster_loss:0.162413
Clustering 571: ASW= 0.8114, DB= 0.2587, CH= 39925.8728
Training epoch 572, recon_loss:0.885311, zinb_loss:0.480350, cluster_loss:0.162275
Clustering 572: ASW= 0.8092, DB= 0.2626, CH= 39767.7985
Training epoch 573, recon_loss:0.885923, zinb_loss:0.480415, cluster_loss:0.162427
Clustering 573: ASW= 0.8112, DB= 0.2587, CH= 39954.8189
Training epoch 574, recon_loss:0.885323, zinb_loss:0.480352, cluster_loss:0.162332
Clustering 574: ASW= 0.8094, DB= 0.2625, CH= 39791.6173
Training epoch 575, recon_loss:0.885930, zinb_loss:0.480393, cluster_loss:0.162455
Clustering 575: ASW= 0.8111, DB= 0.2586, CH= 39999.3721
Training epoch 576, recon_loss:0.885413, zinb_loss:0.480353, cluster_loss:0.162395
Clustering 576: ASW= 0.8097, DB= 0.2627, CH= 39790.4234
Training epoch 577, recon_loss:0.885999, zinb_loss:0.480369, cluster_loss:0.162490
Clustering 577: ASW= 0.8108, DB= 0.2586, CH= 40023.3906
Training epoch 578, recon_loss:0.885658, zinb_loss:0.480369, cluster_loss:0.162485
Clustering 578: ASW= 0.8100, DB= 0.2628, CH= 39795.0033
Training epoch 579, recon_loss:0.886190, zinb_loss:0.480361, cluster_loss:0.162539
Clustering 579: ASW= 0.8105, DB= 0.2587, CH= 40044.9357
Training epoch 580, recon_loss:0.885958, zinb_loss:0.480385, cluster_loss:0.162593
Clustering 580: ASW= 0.8103, DB= 0.2627, CH= 39774.0808
Training epoch 581, recon_loss:0.886347, zinb_loss:0.480360, cluster_loss:0.162589
Clustering 581: ASW= 0.8101, DB= 0.2585, CH= 40032.6315
Training epoch 582, recon_loss:0.886315, zinb_loss:0.480427, cluster_loss:0.162739
Clustering 582: ASW= 0.8106, DB= 0.2629, CH= 39769.4109
Training epoch 583, recon_loss:0.886475, zinb_loss:0.480386, cluster_loss:0.162645
Clustering 583: ASW= 0.8096, DB= 0.2582, CH= 40028.5304
Training epoch 584, recon_loss:0.886546, zinb_loss:0.480464, cluster_loss:0.162994
Clustering 584: ASW= 0.8109, DB= 0.2630, CH= 39702.7830
Training epoch 585, recon_loss:0.886517, zinb_loss:0.480427, cluster_loss:0.162775
Clustering 585: ASW= 0.8092, DB= 0.2581, CH= 40024.8730
Training epoch 586, recon_loss:0.886650, zinb_loss:0.480504, cluster_loss:0.163341
Clustering 586: ASW= 0.8110, DB= 0.2631, CH= 39632.8129
Training epoch 587, recon_loss:0.886502, zinb_loss:0.480477, cluster_loss:0.162958
Clustering 587: ASW= 0.8090, DB= 0.2581, CH= 40016.4190
Training epoch 588, recon_loss:0.886442, zinb_loss:0.480499, cluster_loss:0.163596
Clustering 588: ASW= 0.8108, DB= 0.2631, CH= 39567.8452
Training epoch 589, recon_loss:0.886276, zinb_loss:0.480495, cluster_loss:0.163077
Clustering 589: ASW= 0.8090, DB= 0.2580, CH= 40011.7263
Training epoch 590, recon_loss:0.886063, zinb_loss:0.480457, cluster_loss:0.163688
Clustering 590: ASW= 0.8106, DB= 0.2631, CH= 39574.6982
Training epoch 591, recon_loss:0.885976, zinb_loss:0.480490, cluster_loss:0.163132
Clustering 591: ASW= 0.8093, DB= 0.2578, CH= 39992.8491
Training epoch 592, recon_loss:0.885732, zinb_loss:0.480393, cluster_loss:0.163698
Clustering 592: ASW= 0.8104, DB= 0.2631, CH= 39606.9068
Training epoch 593, recon_loss:0.885755, zinb_loss:0.480472, cluster_loss:0.163161
Clustering 593: ASW= 0.8097, DB= 0.2578, CH= 39964.8155
Training epoch 594, recon_loss:0.885595, zinb_loss:0.480329, cluster_loss:0.163640
Clustering 594: ASW= 0.8101, DB= 0.2629, CH= 39646.6730
Training epoch 595, recon_loss:0.885679, zinb_loss:0.480452, cluster_loss:0.163160
Clustering 595: ASW= 0.8101, DB= 0.2579, CH= 39936.7055
Training epoch 596, recon_loss:0.885594, zinb_loss:0.480272, cluster_loss:0.163512
Clustering 596: ASW= 0.8100, DB= 0.2628, CH= 39702.3775
Training epoch 597, recon_loss:0.885656, zinb_loss:0.480427, cluster_loss:0.163086
Clustering 597: ASW= 0.8105, DB= 0.2581, CH= 39923.7005
Training epoch 598, recon_loss:0.885599, zinb_loss:0.480228, cluster_loss:0.163297
Clustering 598: ASW= 0.8099, DB= 0.2626, CH= 39772.4999
Training epoch 599, recon_loss:0.885563, zinb_loss:0.480394, cluster_loss:0.162907
Clustering 599: ASW= 0.8109, DB= 0.2582, CH= 39953.1126
Training epoch 600, recon_loss:0.885486, zinb_loss:0.480193, cluster_loss:0.163004
Clustering 600: ASW= 0.8099, DB= 0.2625, CH= 39842.8243
Training epoch 601, recon_loss:0.885388, zinb_loss:0.480362, cluster_loss:0.162649
Clustering 601: ASW= 0.8113, DB= 0.2582, CH= 40017.6635
Training epoch 602, recon_loss:0.885308, zinb_loss:0.480169, cluster_loss:0.162684
Clustering 602: ASW= 0.8100, DB= 0.2622, CH= 39919.3261
Training epoch 603, recon_loss:0.885207, zinb_loss:0.480337, cluster_loss:0.162398
Clustering 603: ASW= 0.8116, DB= 0.2582, CH= 40104.0560
Training epoch 604, recon_loss:0.885122, zinb_loss:0.480154, cluster_loss:0.162407
Clustering 604: ASW= 0.8101, DB= 0.2617, CH= 40003.9246
Training epoch 605, recon_loss:0.885082, zinb_loss:0.480321, cluster_loss:0.162195
Clustering 605: ASW= 0.8118, DB= 0.2580, CH= 40179.1729
Training epoch 606, recon_loss:0.885003, zinb_loss:0.480150, cluster_loss:0.162192
Clustering 606: ASW= 0.8102, DB= 0.2616, CH= 40072.5719
Training epoch 607, recon_loss:0.885011, zinb_loss:0.480308, cluster_loss:0.162066
Clustering 607: ASW= 0.8122, DB= 0.2576, CH= 40250.7387
Training epoch 608, recon_loss:0.884930, zinb_loss:0.480151, cluster_loss:0.162037
Clustering 608: ASW= 0.8102, DB= 0.2615, CH= 40137.7942
Training epoch 609, recon_loss:0.884978, zinb_loss:0.480301, cluster_loss:0.161991
Clustering 609: ASW= 0.8124, DB= 0.2576, CH= 40312.8701
Training epoch 610, recon_loss:0.884868, zinb_loss:0.480152, cluster_loss:0.161920
Clustering 610: ASW= 0.8103, DB= 0.2614, CH= 40203.0812
Training epoch 611, recon_loss:0.884924, zinb_loss:0.480289, cluster_loss:0.161936
Clustering 611: ASW= 0.8126, DB= 0.2573, CH= 40360.1570
Training epoch 612, recon_loss:0.884794, zinb_loss:0.480150, cluster_loss:0.161831
Clustering 612: ASW= 0.8103, DB= 0.2614, CH= 40243.1525
Training epoch 613, recon_loss:0.884890, zinb_loss:0.480281, cluster_loss:0.161899
Clustering 613: ASW= 0.8128, DB= 0.2570, CH= 40406.1705
Training epoch 614, recon_loss:0.884764, zinb_loss:0.480149, cluster_loss:0.161771
Clustering 614: ASW= 0.8103, DB= 0.2614, CH= 40269.4793
Training epoch 615, recon_loss:0.884946, zinb_loss:0.480276, cluster_loss:0.161896
Clustering 615: ASW= 0.8130, DB= 0.2567, CH= 40463.9131
Training epoch 616, recon_loss:0.884862, zinb_loss:0.480154, cluster_loss:0.161769
Clustering 616: ASW= 0.8103, DB= 0.2607, CH= 40285.0590
Training epoch 617, recon_loss:0.885170, zinb_loss:0.480280, cluster_loss:0.161959
Clustering 617: ASW= 0.8131, DB= 0.2568, CH= 40525.1923
Training epoch 618, recon_loss:0.885077, zinb_loss:0.480162, cluster_loss:0.161839
Clustering 618: ASW= 0.8103, DB= 0.2606, CH= 40291.6470
Training epoch 619, recon_loss:0.885496, zinb_loss:0.480288, cluster_loss:0.162091
Clustering 619: ASW= 0.8132, DB= 0.2565, CH= 40595.9169
Training epoch 620, recon_loss:0.885291, zinb_loss:0.480168, cluster_loss:0.161952
Clustering 620: ASW= 0.8104, DB= 0.2605, CH= 40297.3823
Training epoch 621, recon_loss:0.885696, zinb_loss:0.480286, cluster_loss:0.162195
Clustering 621: ASW= 0.8132, DB= 0.2564, CH= 40654.9034
Training epoch 622, recon_loss:0.885215, zinb_loss:0.480157, cluster_loss:0.161996
Clustering 622: ASW= 0.8105, DB= 0.2608, CH= 40320.6040
Training epoch 623, recon_loss:0.885550, zinb_loss:0.480262, cluster_loss:0.162165
Clustering 623: ASW= 0.8131, DB= 0.2563, CH= 40685.7392
Training epoch 624, recon_loss:0.884862, zinb_loss:0.480129, cluster_loss:0.161957
Clustering 624: ASW= 0.8107, DB= 0.2594, CH= 40321.5305
Training epoch 625, recon_loss:0.885240, zinb_loss:0.480238, cluster_loss:0.162048
Clustering 625: ASW= 0.8131, DB= 0.2563, CH= 40753.7106
Training epoch 626, recon_loss:0.884513, zinb_loss:0.480108, cluster_loss:0.161883
Clustering 626: ASW= 0.8110, DB= 0.2591, CH= 40351.9890
Training epoch 627, recon_loss:0.884913, zinb_loss:0.480207, cluster_loss:0.161941
Clustering 627: ASW= 0.8130, DB= 0.2562, CH= 40802.1985
Training epoch 628, recon_loss:0.884254, zinb_loss:0.480090, cluster_loss:0.161870
Clustering 628: ASW= 0.8112, DB= 0.2590, CH= 40352.3772
Training epoch 629, recon_loss:0.884756, zinb_loss:0.480195, cluster_loss:0.161911
Clustering 629: ASW= 0.8129, DB= 0.2563, CH= 40886.1677
Training epoch 630, recon_loss:0.884196, zinb_loss:0.480081, cluster_loss:0.161936
Clustering 630: ASW= 0.8113, DB= 0.2591, CH= 40313.4506
Training epoch 631, recon_loss:0.884797, zinb_loss:0.480192, cluster_loss:0.161991
Clustering 631: ASW= 0.8128, DB= 0.2562, CH= 40977.6882
Training epoch 632, recon_loss:0.884509, zinb_loss:0.480094, cluster_loss:0.162189
Clustering 632: ASW= 0.8114, DB= 0.2593, CH= 40173.1794
Training epoch 633, recon_loss:0.885427, zinb_loss:0.480239, cluster_loss:0.162342
Clustering 633: ASW= 0.8127, DB= 0.2563, CH= 41094.9614
Training epoch 634, recon_loss:0.885633, zinb_loss:0.480188, cluster_loss:0.162790
Clustering 634: ASW= 0.8113, DB= 0.2599, CH= 39911.4973
Training epoch 635, recon_loss:0.887015, zinb_loss:0.480405, cluster_loss:0.163070
Clustering 635: ASW= 0.8124, DB= 0.2561, CH= 41185.7935
Training epoch 636, recon_loss:0.886758, zinb_loss:0.480349, cluster_loss:0.163260
Clustering 636: ASW= 0.8111, DB= 0.2597, CH= 39700.3384
Training epoch 637, recon_loss:0.887367, zinb_loss:0.480491, cluster_loss:0.163163
Clustering 637: ASW= 0.8127, DB= 0.2556, CH= 41307.1560
Training epoch 638, recon_loss:0.886372, zinb_loss:0.480376, cluster_loss:0.163086
Clustering 638: ASW= 0.8111, DB= 0.2592, CH= 39653.4286
Training epoch 639, recon_loss:0.886416, zinb_loss:0.480423, cluster_loss:0.162728
Clustering 639: ASW= 0.8131, DB= 0.2559, CH= 41413.0118
Training epoch 640, recon_loss:0.885691, zinb_loss:0.480344, cluster_loss:0.162778
Clustering 640: ASW= 0.8111, DB= 0.2589, CH= 39692.2786
Training epoch 641, recon_loss:0.885690, zinb_loss:0.480374, cluster_loss:0.162415
Clustering 641: ASW= 0.8133, DB= 0.2557, CH= 41467.9075
Training epoch 642, recon_loss:0.885126, zinb_loss:0.480303, cluster_loss:0.162498
Clustering 642: ASW= 0.8111, DB= 0.2597, CH= 39785.9226
Training epoch 643, recon_loss:0.885160, zinb_loss:0.480339, cluster_loss:0.162184
Clustering 643: ASW= 0.8134, DB= 0.2556, CH= 41515.7465
Training epoch 644, recon_loss:0.884692, zinb_loss:0.480262, cluster_loss:0.162280
Clustering 644: ASW= 0.8112, DB= 0.2595, CH= 39875.8721
Training epoch 645, recon_loss:0.884803, zinb_loss:0.480311, cluster_loss:0.162020
Clustering 645: ASW= 0.8136, DB= 0.2555, CH= 41556.2366
Training epoch 646, recon_loss:0.884434, zinb_loss:0.480225, cluster_loss:0.162124
Clustering 646: ASW= 0.8113, DB= 0.2594, CH= 39959.4670
Training epoch 647, recon_loss:0.884674, zinb_loss:0.480298, cluster_loss:0.161925
Clustering 647: ASW= 0.8137, DB= 0.2554, CH= 41590.2891
Training epoch 648, recon_loss:0.884423, zinb_loss:0.480200, cluster_loss:0.162045
Clustering 648: ASW= 0.8113, DB= 0.2592, CH= 40028.4024
Training epoch 649, recon_loss:0.884815, zinb_loss:0.480302, cluster_loss:0.161909
Clustering 649: ASW= 0.8139, DB= 0.2543, CH= 41598.7952
Training epoch 650, recon_loss:0.884701, zinb_loss:0.480185, cluster_loss:0.162049
Clustering 650: ASW= 0.8113, DB= 0.2591, CH= 40086.6641
Training epoch 651, recon_loss:0.885281, zinb_loss:0.480323, cluster_loss:0.161987
Clustering 651: ASW= 0.8140, DB= 0.2542, CH= 41617.0335
Training epoch 652, recon_loss:0.885220, zinb_loss:0.480180, cluster_loss:0.162125
Clustering 652: ASW= 0.8113, DB= 0.2591, CH= 40140.0635
Training epoch 653, recon_loss:0.885902, zinb_loss:0.480348, cluster_loss:0.162139
Clustering 653: ASW= 0.8142, DB= 0.2542, CH= 41609.0301
Training epoch 654, recon_loss:0.885703, zinb_loss:0.480177, cluster_loss:0.162212
Clustering 654: ASW= 0.8112, DB= 0.2588, CH= 40206.8011
Training epoch 655, recon_loss:0.886306, zinb_loss:0.480350, cluster_loss:0.162271
Clustering 655: ASW= 0.8143, DB= 0.2543, CH= 41580.1058
Training epoch 656, recon_loss:0.885762, zinb_loss:0.480158, cluster_loss:0.162200
Clustering 656: ASW= 0.8113, DB= 0.2574, CH= 40288.3060
Training epoch 657, recon_loss:0.886114, zinb_loss:0.480308, cluster_loss:0.162256
Clustering 657: ASW= 0.8143, DB= 0.2550, CH= 41528.9607
Training epoch 658, recon_loss:0.885386, zinb_loss:0.480123, cluster_loss:0.162082
Clustering 658: ASW= 0.8114, DB= 0.2568, CH= 40393.4522
Training epoch 659, recon_loss:0.885615, zinb_loss:0.480249, cluster_loss:0.162159
Clustering 659: ASW= 0.8143, DB= 0.2554, CH= 41466.8259
Training epoch 660, recon_loss:0.884878, zinb_loss:0.480086, cluster_loss:0.161955
Clustering 660: ASW= 0.8116, DB= 0.2562, CH= 40507.8840
Training epoch 661, recon_loss:0.885092, zinb_loss:0.480195, cluster_loss:0.162084
Clustering 661: ASW= 0.8141, DB= 0.2559, CH= 41383.8563
Training epoch 662, recon_loss:0.884483, zinb_loss:0.480062, cluster_loss:0.161925
Clustering 662: ASW= 0.8117, DB= 0.2557, CH= 40619.2631
Training epoch 663, recon_loss:0.884785, zinb_loss:0.480159, cluster_loss:0.162127
Clustering 663: ASW= 0.8139, DB= 0.2566, CH= 41263.2111
Training epoch 664, recon_loss:0.884290, zinb_loss:0.480061, cluster_loss:0.162028
Clustering 664: ASW= 0.8118, DB= 0.2551, CH= 40719.6197
Training epoch 665, recon_loss:0.884690, zinb_loss:0.480141, cluster_loss:0.162280
Clustering 665: ASW= 0.8136, DB= 0.2574, CH= 41101.7030
Training epoch 666, recon_loss:0.884208, zinb_loss:0.480077, cluster_loss:0.162192
Clustering 666: ASW= 0.8121, DB= 0.2546, CH= 40831.1666
Training epoch 667, recon_loss:0.884646, zinb_loss:0.480117, cluster_loss:0.162421
Clustering 667: ASW= 0.8133, DB= 0.2577, CH= 40936.6567
Training epoch 668, recon_loss:0.884149, zinb_loss:0.480091, cluster_loss:0.162271
Clustering 668: ASW= 0.8124, DB= 0.2540, CH= 40946.3979
Training epoch 669, recon_loss:0.884526, zinb_loss:0.480074, cluster_loss:0.162467
Clustering 669: ASW= 0.8130, DB= 0.2583, CH= 40795.2248
Training epoch 670, recon_loss:0.884043, zinb_loss:0.480089, cluster_loss:0.162236
Clustering 670: ASW= 0.8128, DB= 0.2537, CH= 41078.4011
Training epoch 671, recon_loss:0.884260, zinb_loss:0.480025, cluster_loss:0.162459
Clustering 671: ASW= 0.8128, DB= 0.2585, CH= 40693.0578
Training epoch 672, recon_loss:0.883914, zinb_loss:0.480087, cluster_loss:0.162166
Clustering 672: ASW= 0.8133, DB= 0.2533, CH= 41202.6225
Training epoch 673, recon_loss:0.883993, zinb_loss:0.479980, cluster_loss:0.162458
Clustering 673: ASW= 0.8127, DB= 0.2588, CH= 40614.8118
Training epoch 674, recon_loss:0.883844, zinb_loss:0.480092, cluster_loss:0.162130
Clustering 674: ASW= 0.8135, DB= 0.2529, CH= 41327.6920
Training epoch 675, recon_loss:0.883796, zinb_loss:0.479947, cluster_loss:0.162508
Clustering 675: ASW= 0.8127, DB= 0.2591, CH= 40550.7705
Training epoch 676, recon_loss:0.883879, zinb_loss:0.480109, cluster_loss:0.162130
Clustering 676: ASW= 0.8137, DB= 0.2525, CH= 41438.5071
Training epoch 677, recon_loss:0.883703, zinb_loss:0.479929, cluster_loss:0.162580
Clustering 677: ASW= 0.8126, DB= 0.2593, CH= 40494.0332
Training epoch 678, recon_loss:0.884007, zinb_loss:0.480136, cluster_loss:0.162154
Clustering 678: ASW= 0.8138, DB= 0.2522, CH= 41535.1124
Training epoch 679, recon_loss:0.883686, zinb_loss:0.479926, cluster_loss:0.162622
Clustering 679: ASW= 0.8126, DB= 0.2594, CH= 40458.7466
Training epoch 680, recon_loss:0.884164, zinb_loss:0.480164, cluster_loss:0.162162
Clustering 680: ASW= 0.8139, DB= 0.2518, CH= 41604.4546
Training epoch 681, recon_loss:0.883711, zinb_loss:0.479937, cluster_loss:0.162584
Clustering 681: ASW= 0.8127, DB= 0.2593, CH= 40465.2326
Training epoch 682, recon_loss:0.884244, zinb_loss:0.480179, cluster_loss:0.162091
Clustering 682: ASW= 0.8140, DB= 0.2515, CH= 41645.1902
Training epoch 683, recon_loss:0.883726, zinb_loss:0.479959, cluster_loss:0.162464
Clustering 683: ASW= 0.8129, DB= 0.2589, CH= 40519.2764
Training epoch 684, recon_loss:0.884276, zinb_loss:0.480183, cluster_loss:0.161978
Clustering 684: ASW= 0.8141, DB= 0.2513, CH= 41669.9263
Training epoch 685, recon_loss:0.883774, zinb_loss:0.479986, cluster_loss:0.162328
Clustering 685: ASW= 0.8130, DB= 0.2587, CH= 40590.8577
Training epoch 686, recon_loss:0.884324, zinb_loss:0.480180, cluster_loss:0.161881
Clustering 686: ASW= 0.8141, DB= 0.2507, CH= 41684.4933
Training epoch 687, recon_loss:0.883895, zinb_loss:0.480016, cluster_loss:0.162241
Clustering 687: ASW= 0.8132, DB= 0.2585, CH= 40673.1172
Training epoch 688, recon_loss:0.884441, zinb_loss:0.480170, cluster_loss:0.161844
Clustering 688: ASW= 0.8139, DB= 0.2506, CH= 41671.7160
Training epoch 689, recon_loss:0.884120, zinb_loss:0.480050, cluster_loss:0.162238
Clustering 689: ASW= 0.8135, DB= 0.2582, CH= 40751.4084
Training epoch 690, recon_loss:0.884617, zinb_loss:0.480155, cluster_loss:0.161887
Clustering 690: ASW= 0.8137, DB= 0.2502, CH= 41646.8601
Training epoch 691, recon_loss:0.884446, zinb_loss:0.480085, cluster_loss:0.162313
Clustering 691: ASW= 0.8137, DB= 0.2584, CH= 40821.0246
Training epoch 692, recon_loss:0.884781, zinb_loss:0.480125, cluster_loss:0.161983
Clustering 692: ASW= 0.8133, DB= 0.2504, CH= 41588.3800
Training epoch 693, recon_loss:0.884755, zinb_loss:0.480110, cluster_loss:0.162430
Clustering 693: ASW= 0.8140, DB= 0.2584, CH= 40875.8324
Training epoch 694, recon_loss:0.884823, zinb_loss:0.480075, cluster_loss:0.162062
Clustering 694: ASW= 0.8131, DB= 0.2508, CH= 41536.8223
Training epoch 695, recon_loss:0.884836, zinb_loss:0.480110, cluster_loss:0.162457
Clustering 695: ASW= 0.8141, DB= 0.2585, CH= 40929.8067
Training epoch 696, recon_loss:0.884604, zinb_loss:0.480008, cluster_loss:0.162040
Clustering 696: ASW= 0.8131, DB= 0.2512, CH= 41504.7813
Training epoch 697, recon_loss:0.884606, zinb_loss:0.480083, cluster_loss:0.162327
Clustering 697: ASW= 0.8143, DB= 0.2582, CH= 41003.2953
Training epoch 698, recon_loss:0.884242, zinb_loss:0.479943, cluster_loss:0.161932
Clustering 698: ASW= 0.8131, DB= 0.2504, CH= 41471.8160
Training epoch 900, recon_loss:0.882355, zinb_loss:0.479461, cluster_loss:0.161423
Clustering 900: ASW= 0.8188, DB= 0.2425, CH= 43597.3875
Training epoch 901, recon_loss:0.882180, zinb_loss:0.479366, cluster_loss:0.161362
Clustering 901: ASW= 0.8182, DB= 0.2474, CH= 43790.5240
Training epoch 902, recon_loss:0.882247, zinb_loss:0.479424, cluster_loss:0.161290
Clustering 902: ASW= 0.8190, DB= 0.2422, CH= 43771.0397
Training epoch 903, recon_loss:0.882044, zinb_loss:0.479312, cluster_loss:0.161259
Clustering 903: ASW= 0.8183, DB= 0.2473, CH= 43744.5329
Training epoch 904, recon_loss:0.882270, zinb_loss:0.479399, cluster_loss:0.161190
Clustering 904: ASW= 0.8192, DB= 0.2420, CH= 43951.6577
Training epoch 905, recon_loss:0.882057, zinb_loss:0.479281, cluster_loss:0.161197
Clustering 905: ASW= 0.8184, DB= 0.2472, CH= 43720.2498
Training epoch 906, recon_loss:0.882458, zinb_loss:0.479387, cluster_loss:0.161155
Clustering 906: ASW= 0.8193, DB= 0.2428, CH= 44112.1691
Training epoch 907, recon_loss:0.882277, zinb_loss:0.479271, cluster_loss:0.161187
Clustering 907: ASW= 0.8186, DB= 0.2470, CH= 43700.3907
Training epoch 908, recon_loss:0.882765, zinb_loss:0.479387, cluster_loss:0.161182
Clustering 908: ASW= 0.8194, DB= 0.2429, CH= 44250.9346
Training epoch 909, recon_loss:0.882565, zinb_loss:0.479279, cluster_loss:0.161215
Clustering 909: ASW= 0.8187, DB= 0.2468, CH= 43676.3820
Training epoch 910, recon_loss:0.883069, zinb_loss:0.479398, cluster_loss:0.161212
Clustering 910: ASW= 0.8194, DB= 0.2431, CH= 44344.6931
Training epoch 911, recon_loss:0.882860, zinb_loss:0.479299, cluster_loss:0.161228
Clustering 911: ASW= 0.8188, DB= 0.2466, CH= 43664.7818
Training epoch 912, recon_loss:0.883321, zinb_loss:0.479415, cluster_loss:0.161221
Clustering 912: ASW= 0.8194, DB= 0.2424, CH= 44409.2906
Training epoch 913, recon_loss:0.883115, zinb_loss:0.479330, cluster_loss:0.161226
Clustering 913: ASW= 0.8189, DB= 0.2457, CH= 43700.5773
Training epoch 914, recon_loss:0.883488, zinb_loss:0.479434, cluster_loss:0.161205
Clustering 914: ASW= 0.8194, DB= 0.2432, CH= 44455.6877
Training epoch 915, recon_loss:0.883298, zinb_loss:0.479366, cluster_loss:0.161217
Clustering 915: ASW= 0.8190, DB= 0.2452, CH= 43728.3713
Training epoch 916, recon_loss:0.883579, zinb_loss:0.479457, cluster_loss:0.161198
Clustering 916: ASW= 0.8193, DB= 0.2437, CH= 44453.2845
Training epoch 917, recon_loss:0.883349, zinb_loss:0.479397, cluster_loss:0.161241
Clustering 917: ASW= 0.8191, DB= 0.2450, CH= 43778.6389
Training epoch 918, recon_loss:0.883576, zinb_loss:0.479479, cluster_loss:0.161193
Clustering 918: ASW= 0.8192, DB= 0.2440, CH= 44398.7678
Training epoch 919, recon_loss:0.883243, zinb_loss:0.479415, cluster_loss:0.161281
Clustering 919: ASW= 0.8191, DB= 0.2444, CH= 43836.6423
Training epoch 920, recon_loss:0.883458, zinb_loss:0.479498, cluster_loss:0.161154
Clustering 920: ASW= 0.8192, DB= 0.2442, CH= 44350.8879
Training epoch 921, recon_loss:0.882958, zinb_loss:0.479413, cluster_loss:0.161268
Clustering 921: ASW= 0.8192, DB= 0.2439, CH= 43916.8048
Training epoch 922, recon_loss:0.883244, zinb_loss:0.479523, cluster_loss:0.161033
Clustering 922: ASW= 0.8191, DB= 0.2442, CH= 44340.8930
Training epoch 923, recon_loss:0.882428, zinb_loss:0.479344, cluster_loss:0.161174
Clustering 923: ASW= 0.8192, DB= 0.2436, CH= 43955.1540
Training epoch 924, recon_loss:0.882695, zinb_loss:0.479472, cluster_loss:0.160904
Clustering 924: ASW= 0.8190, DB= 0.2446, CH= 44343.4315
Training epoch 925, recon_loss:0.882216, zinb_loss:0.479354, cluster_loss:0.161101
Clustering 925: ASW= 0.8196, DB= 0.2421, CH= 44087.2715
Training epoch 926, recon_loss:0.882542, zinb_loss:0.479469, cluster_loss:0.160787
Clustering 926: ASW= 0.8191, DB= 0.2447, CH= 44364.0545
Training epoch 927, recon_loss:0.882115, zinb_loss:0.479347, cluster_loss:0.160996
Clustering 927: ASW= 0.8198, DB= 0.2416, CH= 44231.8847
Training epoch 928, recon_loss:0.882318, zinb_loss:0.479433, cluster_loss:0.160668
Clustering 928: ASW= 0.8192, DB= 0.2447, CH= 44376.9193
Training epoch 929, recon_loss:0.881924, zinb_loss:0.479328, cluster_loss:0.160844
Clustering 929: ASW= 0.8200, DB= 0.2412, CH= 44411.8732
Training epoch 930, recon_loss:0.881935, zinb_loss:0.479384, cluster_loss:0.160519
Clustering 930: ASW= 0.8194, DB= 0.2445, CH= 44401.5556
Training epoch 931, recon_loss:0.881512, zinb_loss:0.479259, cluster_loss:0.160724
Clustering 931: ASW= 0.8200, DB= 0.2411, CH= 44532.7182
Training epoch 932, recon_loss:0.881639, zinb_loss:0.479323, cluster_loss:0.160497
Clustering 932: ASW= 0.8195, DB= 0.2446, CH= 44399.3685
Training epoch 933, recon_loss:0.881587, zinb_loss:0.479277, cluster_loss:0.160733
Clustering 933: ASW= 0.8203, DB= 0.2406, CH= 44711.8253
Training epoch 934, recon_loss:0.881563, zinb_loss:0.479301, cluster_loss:0.160518
Clustering 934: ASW= 0.8197, DB= 0.2448, CH= 44392.0027
Training epoch 935, recon_loss:0.881476, zinb_loss:0.479265, cluster_loss:0.160754
Clustering 935: ASW= 0.8202, DB= 0.2402, CH= 44848.3396
Training epoch 936, recon_loss:0.881450, zinb_loss:0.479274, cluster_loss:0.160620
Clustering 936: ASW= 0.8198, DB= 0.2452, CH= 44331.9653
Training epoch 937, recon_loss:0.881422, zinb_loss:0.479266, cluster_loss:0.160842
Clustering 937: ASW= 0.8202, DB= 0.2396, CH= 44996.2191
Training epoch 938, recon_loss:0.881477, zinb_loss:0.479262, cluster_loss:0.160801
Clustering 938: ASW= 0.8199, DB= 0.2457, CH= 44233.9570
Training epoch 939, recon_loss:0.881464, zinb_loss:0.479265, cluster_loss:0.160992
Clustering 939: ASW= 0.8201, DB= 0.2394, CH= 45090.3165
Training epoch 940, recon_loss:0.881676, zinb_loss:0.479271, cluster_loss:0.160972
Clustering 940: ASW= 0.8201, DB= 0.2460, CH= 44156.7755
Training epoch 941, recon_loss:0.881662, zinb_loss:0.479271, cluster_loss:0.161149
Clustering 941: ASW= 0.8197, DB= 0.2394, CH= 45128.4600
Training epoch 942, recon_loss:0.882028, zinb_loss:0.479292, cluster_loss:0.161224
Clustering 942: ASW= 0.8203, DB= 0.2465, CH= 44074.4559
Training epoch 943, recon_loss:0.882009, zinb_loss:0.479281, cluster_loss:0.161318
Clustering 943: ASW= 0.8192, DB= 0.2396, CH= 45091.4130
Training epoch 944, recon_loss:0.882397, zinb_loss:0.479320, cluster_loss:0.161399
Clustering 944: ASW= 0.8204, DB= 0.2466, CH= 44054.6106
Training epoch 945, recon_loss:0.882191, zinb_loss:0.479265, cluster_loss:0.161308
Clustering 945: ASW= 0.8190, DB= 0.2399, CH= 45016.5419
Training epoch 946, recon_loss:0.882371, zinb_loss:0.479313, cluster_loss:0.161291
Clustering 946: ASW= 0.8206, DB= 0.2463, CH= 44140.0365
Training epoch 947, recon_loss:0.881974, zinb_loss:0.479215, cluster_loss:0.161084
Clustering 947: ASW= 0.8190, DB= 0.2400, CH= 44960.3335
Training epoch 948, recon_loss:0.882060, zinb_loss:0.479287, cluster_loss:0.161066
Clustering 948: ASW= 0.8207, DB= 0.2459, CH= 44291.3123
Training epoch 949, recon_loss:0.881612, zinb_loss:0.479163, cluster_loss:0.160880
Clustering 949: ASW= 0.8191, DB= 0.2402, CH= 44924.3614
Training epoch 950, recon_loss:0.881745, zinb_loss:0.479259, cluster_loss:0.160894
Clustering 950: ASW= 0.8208, DB= 0.2455, CH= 44439.4805
Training epoch 951, recon_loss:0.881335, zinb_loss:0.479121, cluster_loss:0.160786
Clustering 951: ASW= 0.8191, DB= 0.2407, CH= 44901.2880
Training epoch 952, recon_loss:0.881580, zinb_loss:0.479240, cluster_loss:0.160829
Clustering 952: ASW= 0.8209, DB= 0.2448, CH= 44569.8462
Training epoch 953, recon_loss:0.881256, zinb_loss:0.479090, cluster_loss:0.160816
Clustering 953: ASW= 0.8191, DB= 0.2409, CH= 44867.0944
Training epoch 954, recon_loss:0.881550, zinb_loss:0.479231, cluster_loss:0.160860
Clustering 954: ASW= 0.8211, DB= 0.2446, CH= 44658.9690
Training epoch 955, recon_loss:0.881452, zinb_loss:0.479080, cluster_loss:0.160997
Clustering 955: ASW= 0.8190, DB= 0.2413, CH= 44775.8357
Training epoch 956, recon_loss:0.881847, zinb_loss:0.479239, cluster_loss:0.161118
Clustering 956: ASW= 0.8211, DB= 0.2445, CH= 44652.8229
Training epoch 957, recon_loss:0.882240, zinb_loss:0.479116, cluster_loss:0.161552
Clustering 957: ASW= 0.8184, DB= 0.2422, CH= 44449.8946
Training epoch 958, recon_loss:0.883100, zinb_loss:0.479406, cluster_loss:0.161816
Clustering 958: ASW= 0.8211, DB= 0.2444, CH= 44560.3603
Training epoch 959, recon_loss:0.883819, zinb_loss:0.479329, cluster_loss:0.162451
Clustering 959: ASW= 0.8176, DB= 0.2433, CH= 43917.5632
Training epoch 960, recon_loss:0.884027, zinb_loss:0.479558, cluster_loss:0.162521
Clustering 960: ASW= 0.8206, DB= 0.2442, CH= 44387.1846
Training epoch 961, recon_loss:0.883190, zinb_loss:0.479297, cluster_loss:0.162481
Clustering 961: ASW= 0.8177, DB= 0.2435, CH= 43639.6782
Training epoch 962, recon_loss:0.883008, zinb_loss:0.479520, cluster_loss:0.162029
Clustering 962: ASW= 0.8207, DB= 0.2429, CH= 44623.7948
Training epoch 963, recon_loss:0.881880, zinb_loss:0.479210, cluster_loss:0.161677
Clustering 963: ASW= 0.8185, DB= 0.2431, CH= 43792.8122
Training epoch 964, recon_loss:0.881466, zinb_loss:0.479371, cluster_loss:0.161049
Clustering 964: ASW= 0.8211, DB= 0.2419, CH= 44953.9023
Training epoch 965, recon_loss:0.880860, zinb_loss:0.479129, cluster_loss:0.161099
Clustering 965: ASW= 0.8190, DB= 0.2430, CH= 43960.0003
Training epoch 966, recon_loss:0.880765, zinb_loss:0.479302, cluster_loss:0.160647
Clustering 966: ASW= 0.8211, DB= 0.2415, CH= 45136.8896
Training epoch 967, recon_loss:0.880460, zinb_loss:0.479108, cluster_loss:0.160877
Clustering 967: ASW= 0.8193, DB= 0.2430, CH= 44082.4272
Training epoch 968, recon_loss:0.880538, zinb_loss:0.479284, cluster_loss:0.160518
Clustering 968: ASW= 0.8211, DB= 0.2412, CH= 45263.1010
Training epoch 969, recon_loss:0.880378, zinb_loss:0.479112, cluster_loss:0.160850
Clustering 969: ASW= 0.8195, DB= 0.2430, CH= 44157.6636
Training epoch 970, recon_loss:0.880584, zinb_loss:0.479294, cluster_loss:0.160529
Clustering 970: ASW= 0.8210, DB= 0.2409, CH= 45355.2155
Training epoch 971, recon_loss:0.880517, zinb_loss:0.479135, cluster_loss:0.160933
Clustering 971: ASW= 0.8197, DB= 0.2430, CH= 44191.2874
Training epoch 972, recon_loss:0.880830, zinb_loss:0.479319, cluster_loss:0.160625
Clustering 972: ASW= 0.8210, DB= 0.2402, CH= 45414.3692
Training epoch 973, recon_loss:0.880842, zinb_loss:0.479174, cluster_loss:0.161078
Clustering 973: ASW= 0.8198, DB= 0.2433, CH= 44198.4642
Training epoch 974, recon_loss:0.881257, zinb_loss:0.479357, cluster_loss:0.160763
Clustering 974: ASW= 0.8210, DB= 0.2399, CH= 45441.1686
Training epoch 975, recon_loss:0.881260, zinb_loss:0.479224, cluster_loss:0.161211
Clustering 975: ASW= 0.8198, DB= 0.2435, CH= 44216.7948
Training epoch 976, recon_loss:0.881692, zinb_loss:0.479388, cluster_loss:0.160874
Clustering 976: ASW= 0.8210, DB= 0.2394, CH= 45453.2077
Training epoch 977, recon_loss:0.881544, zinb_loss:0.479260, cluster_loss:0.161256
Clustering 977: ASW= 0.8199, DB= 0.2438, CH= 44254.9954
Training epoch 978, recon_loss:0.881871, zinb_loss:0.479390, cluster_loss:0.160883
Clustering 978: ASW= 0.8210, DB= 0.2392, CH= 45455.7965
Training epoch 979, recon_loss:0.881507, zinb_loss:0.479265, cluster_loss:0.161166
Clustering 979: ASW= 0.8199, DB= 0.2438, CH= 44337.2448
Training epoch 980, recon_loss:0.881741, zinb_loss:0.479367, cluster_loss:0.160783
Clustering 980: ASW= 0.8211, DB= 0.2391, CH= 45460.6853
Training epoch 981, recon_loss:0.881257, zinb_loss:0.479253, cluster_loss:0.160994
Clustering 981: ASW= 0.8200, DB= 0.2437, CH= 44449.2126
Training epoch 982, recon_loss:0.881457, zinb_loss:0.479331, cluster_loss:0.160639
Clustering 982: ASW= 0.8212, DB= 0.2387, CH= 45474.1905
Training epoch 983, recon_loss:0.880988, zinb_loss:0.479236, cluster_loss:0.160825
Clustering 983: ASW= 0.8201, DB= 0.2437, CH= 44562.1178
Training epoch 984, recon_loss:0.881213, zinb_loss:0.479303, cluster_loss:0.160521
Clustering 984: ASW= 0.8213, DB= 0.2386, CH= 45483.4119
Training epoch 985, recon_loss:0.880798, zinb_loss:0.479222, cluster_loss:0.160698
Clustering 985: ASW= 0.8201, DB= 0.2437, CH= 44667.6741
Training epoch 986, recon_loss:0.881087, zinb_loss:0.479288, cluster_loss:0.160449
Clustering 986: ASW= 0.8214, DB= 0.2385, CH= 45490.4611
Training epoch 987, recon_loss:0.880743, zinb_loss:0.479214, cluster_loss:0.160627
Clustering 987: ASW= 0.8200, DB= 0.2437, CH= 44752.4645
Training epoch 988, recon_loss:0.881114, zinb_loss:0.479289, cluster_loss:0.160431
Clustering 988: ASW= 0.8215, DB= 0.2383, CH= 45483.8785
Training epoch 989, recon_loss:0.880826, zinb_loss:0.479215, cluster_loss:0.160598
Clustering 989: ASW= 0.8199, DB= 0.2439, CH= 44828.1920
Training epoch 990, recon_loss:0.881306, zinb_loss:0.479303, cluster_loss:0.160467
Clustering 990: ASW= 0.8217, DB= 0.2381, CH= 45461.5483
Training epoch 991, recon_loss:0.881053, zinb_loss:0.479220, cluster_loss:0.160605
Clustering 991: ASW= 0.8198, DB= 0.2441, CH= 44894.3339
Training epoch 992, recon_loss:0.881649, zinb_loss:0.479327, cluster_loss:0.160548
Clustering 992: ASW= 0.8220, DB= 0.2381, CH= 45415.7009
Training epoch 993, recon_loss:0.881382, zinb_loss:0.479222, cluster_loss:0.160638
Clustering 993: ASW= 0.8196, DB= 0.2443, CH= 44962.3426
Training epoch 994, recon_loss:0.882052, zinb_loss:0.479351, cluster_loss:0.160660
Clustering 994: ASW= 0.8222, DB= 0.2379, CH= 45362.6136
Training epoch 995, recon_loss:0.881717, zinb_loss:0.479215, cluster_loss:0.160681
Clustering 995: ASW= 0.8195, DB= 0.2446, CH= 45041.9946
Training epoch 996, recon_loss:0.882360, zinb_loss:0.479358, cluster_loss:0.160788
Clustering 996: ASW= 0.8225, DB= 0.2378, CH= 45321.1435
Training epoch 997, recon_loss:0.881966, zinb_loss:0.479198, cluster_loss:0.160706
Clustering 997: ASW= 0.8195, DB= 0.2446, CH= 45168.8987
Training epoch 998, recon_loss:0.882417, zinb_loss:0.479333, cluster_loss:0.160899
Clustering 998: ASW= 0.8227, DB= 0.2377, CH= 45251.8730
Training epoch 999, recon_loss:0.882127, zinb_loss:0.479180, cluster_loss:0.160736
Clustering 999: ASW= 0.8196, DB= 0.2441, CH= 45324.5408
Training epoch 1000, recon_loss:0.882388, zinb_loss:0.479288, cluster_loss:0.161058
Clustering 1000: ASW= 0.8227, DB= 0.2379, CH= 45153.4518
Training epoch 1001, recon_loss:0.882292, zinb_loss:0.479164, cluster_loss:0.160835
Clustering 1001: ASW= 0.8196, DB= 0.2443, CH= 45463.8211
Training epoch 1002, recon_loss:0.882380, zinb_loss:0.479244, cluster_loss:0.161279
Clustering 1002: ASW= 0.8224, DB= 0.2379, CH= 44999.3072
Training epoch 1003, recon_loss:0.882369, zinb_loss:0.479151, cluster_loss:0.160935
Clustering 1003: ASW= 0.8197, DB= 0.2440, CH= 45555.2682
Training epoch 1004, recon_loss:0.882220, zinb_loss:0.479194, cluster_loss:0.161386
Clustering 1004: ASW= 0.8222, DB= 0.2383, CH= 44908.4971
Training epoch 1005, recon_loss:0.882184, zinb_loss:0.479133, cluster_loss:0.160927
Clustering 1005: ASW= 0.8198, DB= 0.2436, CH= 45621.2431
Training epoch 1006, recon_loss:0.881832, zinb_loss:0.479139, cluster_loss:0.161293
Clustering 1006: ASW= 0.8219, DB= 0.2384, CH= 44890.5101
Training epoch 1007, recon_loss:0.881755, zinb_loss:0.479117, cluster_loss:0.160805
Clustering 1007: ASW= 0.8201, DB= 0.2432, CH= 45716.5670
Training epoch 1008, recon_loss:0.881449, zinb_loss:0.479100, cluster_loss:0.161139
Clustering 1008: ASW= 0.8217, DB= 0.2383, CH= 44932.7183
Training epoch 1009, recon_loss:0.881432, zinb_loss:0.479125, cluster_loss:0.160718
Clustering 1009: ASW= 0.8205, DB= 0.2428, CH= 45819.3562
Training epoch 1010, recon_loss:0.881397, zinb_loss:0.479107, cluster_loss:0.161088
Clustering 1010: ASW= 0.8215, DB= 0.2391, CH= 45001.2266
Training epoch 1011, recon_loss:0.881496, zinb_loss:0.479187, cluster_loss:0.160802
Clustering 1011: ASW= 0.8208, DB= 0.2425, CH= 45892.4972
Training epoch 1012, recon_loss:0.881723, zinb_loss:0.479163, cluster_loss:0.161233
Clustering 1012: ASW= 0.8211, DB= 0.2394, CH= 45016.6265
Training epoch 1013, recon_loss:0.881717, zinb_loss:0.479283, cluster_loss:0.161038
Clustering 1013: ASW= 0.8212, DB= 0.2423, CH= 45887.2383
Training epoch 1014, recon_loss:0.881966, zinb_loss:0.479181, cluster_loss:0.161472
Clustering 1014: ASW= 0.8206, DB= 0.2399, CH= 45000.2504
Training epoch 1015, recon_loss:0.881667, zinb_loss:0.479314, cluster_loss:0.161255
Clustering 1015: ASW= 0.8216, DB= 0.2424, CH= 45833.2268
Training epoch 1016, recon_loss:0.881729, zinb_loss:0.479181, cluster_loss:0.161477
Clustering 1016: ASW= 0.8204, DB= 0.2403, CH= 45027.5029
Training epoch 1017, recon_loss:0.881072, zinb_loss:0.479260, cluster_loss:0.161190
Clustering 1017: ASW= 0.8220, DB= 0.2422, CH= 45798.7842
Training epoch 1018, recon_loss:0.881176, zinb_loss:0.479105, cluster_loss:0.161294
Clustering 1018: ASW= 0.8203, DB= 0.2404, CH= 45031.5577
Training epoch 1019, recon_loss:0.880718, zinb_loss:0.479210, cluster_loss:0.161012
Clustering 1019: ASW= 0.8222, DB= 0.2420, CH= 45834.4028
Training epoch 1020, recon_loss:0.880840, zinb_loss:0.479040, cluster_loss:0.161092
Clustering 1020: ASW= 0.8203, DB= 0.2405, CH= 45104.0572
Training epoch 1021, recon_loss:0.880578, zinb_loss:0.479156, cluster_loss:0.160862
Clustering 1021: ASW= 0.8224, DB= 0.2419, CH= 45863.1150
Training epoch 1022, recon_loss:0.880751, zinb_loss:0.478991, cluster_loss:0.160983
Clustering 1022: ASW= 0.8203, DB= 0.2405, CH= 45185.1676
Training epoch 1023, recon_loss:0.880683, zinb_loss:0.479128, cluster_loss:0.160780
Clustering 1023: ASW= 0.8225, DB= 0.2418, CH= 45896.7745
Training epoch 1024, recon_loss:0.880896, zinb_loss:0.478965, cluster_loss:0.160956
Clustering 1024: ASW= 0.8204, DB= 0.2404, CH= 45257.6919
Training epoch 1025, recon_loss:0.880903, zinb_loss:0.479114, cluster_loss:0.160731
Clustering 1025: ASW= 0.8226, DB= 0.2416, CH= 45921.3175
Training epoch 1026, recon_loss:0.881051, zinb_loss:0.478948, cluster_loss:0.160927
Clustering 1026: ASW= 0.8204, DB= 0.2405, CH= 45321.1565
Training epoch 1027, recon_loss:0.881050, zinb_loss:0.479104, cluster_loss:0.160671
Clustering 1027: ASW= 0.8226, DB= 0.2414, CH= 45941.9553
Training epoch 1028, recon_loss:0.881042, zinb_loss:0.478935, cluster_loss:0.160861
Clustering 1028: ASW= 0.8204, DB= 0.2405, CH= 45390.7695
Training epoch 1029, recon_loss:0.881064, zinb_loss:0.479092, cluster_loss:0.160595
Clustering 1029: ASW= 0.8227, DB= 0.2412, CH= 45950.8497
Training epoch 1030, recon_loss:0.881018, zinb_loss:0.478936, cluster_loss:0.160784
Clustering 1030: ASW= 0.8205, DB= 0.2403, CH= 45468.5291
Training epoch 1031, recon_loss:0.881103, zinb_loss:0.479084, cluster_loss:0.160537
Clustering 1031: ASW= 0.8227, DB= 0.2411, CH= 45950.3279
Training epoch 1032, recon_loss:0.881122, zinb_loss:0.478957, cluster_loss:0.160736
Clustering 1032: ASW= 0.8205, DB= 0.2402, CH= 45556.9521
Training epoch 1033, recon_loss:0.881283, zinb_loss:0.479088, cluster_loss:0.160518
Clustering 1033: ASW= 0.8226, DB= 0.2406, CH= 45958.6949
Training epoch 1034, recon_loss:0.881355, zinb_loss:0.478998, cluster_loss:0.160719
Clustering 1034: ASW= 0.8206, DB= 0.2402, CH= 45655.1760
Training epoch 1035, recon_loss:0.881562, zinb_loss:0.479102, cluster_loss:0.160536
Clustering 1035: ASW= 0.8226, DB= 0.2405, CH= 45943.8217
Training epoch 1036, recon_loss:0.881752, zinb_loss:0.479063, cluster_loss:0.160751
Clustering 1036: ASW= 0.8207, DB= 0.2402, CH= 45749.8755
Training epoch 1037, recon_loss:0.881981, zinb_loss:0.479135, cluster_loss:0.160603
Clustering 1037: ASW= 0.8225, DB= 0.2407, CH= 45927.8960
Training epoch 1038, recon_loss:0.882186, zinb_loss:0.479142, cluster_loss:0.160838
Clustering 1038: ASW= 0.8208, DB= 0.2402, CH= 45836.7056
Training epoch 1039, recon_loss:0.882489, zinb_loss:0.479175, cluster_loss:0.160801
Clustering 1039: ASW= 0.8223, DB= 0.2405, CH= 45873.2429
Training epoch 1040, recon_loss:0.882700, zinb_loss:0.479233, cluster_loss:0.161070
Clustering 1040: ASW= 0.8208, DB= 0.2400, CH= 45858.4984
Training epoch 1041, recon_loss:0.883072, zinb_loss:0.479211, cluster_loss:0.161177
Clustering 1041: ASW= 0.8219, DB= 0.2408, CH= 45759.9860
Training epoch 1042, recon_loss:0.882965, zinb_loss:0.479299, cluster_loss:0.161265
Clustering 1042: ASW= 0.8209, DB= 0.2402, CH= 45755.1790
Training epoch 1043, recon_loss:0.882870, zinb_loss:0.479152, cluster_loss:0.161286
Clustering 1043: ASW= 0.8215, DB= 0.2410, CH= 45694.4043
Training epoch 1044, recon_loss:0.882374, zinb_loss:0.479251, cluster_loss:0.161109
Clustering 1044: ASW= 0.8210, DB= 0.2402, CH= 45624.2213
Training epoch 1045, recon_loss:0.882152, zinb_loss:0.479038, cluster_loss:0.161173
Clustering 1045: ASW= 0.8213, DB= 0.2410, CH= 45709.8297
Training epoch 1046, recon_loss:0.881748, zinb_loss:0.479172, cluster_loss:0.160938
Clustering 1046: ASW= 0.8212, DB= 0.2399, CH= 45496.1729
Training epoch 1047, recon_loss:0.881773, zinb_loss:0.478954, cluster_loss:0.161217
Clustering 1047: ASW= 0.8210, DB= 0.2413, CH= 45676.3243
Training epoch 1048, recon_loss:0.881653, zinb_loss:0.479133, cluster_loss:0.161020
Clustering 1048: ASW= 0.8212, DB= 0.2398, CH= 45274.3667
Training epoch 1049, recon_loss:0.881664, zinb_loss:0.478913, cluster_loss:0.161327
Clustering 1049: ASW= 0.8207, DB= 0.2414, CH= 45541.1838
Training epoch 1050, recon_loss:0.881520, zinb_loss:0.479118, cluster_loss:0.161075
Clustering 1050: ASW= 0.8215, DB= 0.2393, CH= 45240.2814
Training epoch 1051, recon_loss:0.881263, zinb_loss:0.478888, cluster_loss:0.161205
Clustering 1051: ASW= 0.8206, DB= 0.2415, CH= 45418.4169
Training epoch 1052, recon_loss:0.880976, zinb_loss:0.479086, cluster_loss:0.160855
Clustering 1052: ASW= 0.8219, DB= 0.2388, CH= 45506.6536
Training epoch 1053, recon_loss:0.880805, zinb_loss:0.478861, cluster_loss:0.160995
Clustering 1053: ASW= 0.8207, DB= 0.2415, CH= 45410.2090
Training epoch 1054, recon_loss:0.880702, zinb_loss:0.479079, cluster_loss:0.160640
Clustering 1054: ASW= 0.8222, DB= 0.2383, CH= 45804.8595
Training epoch 1055, recon_loss:0.880612, zinb_loss:0.478858, cluster_loss:0.160896
Clustering 1055: ASW= 0.8209, DB= 0.2416, CH= 45414.3267
Training epoch 1056, recon_loss:0.880707, zinb_loss:0.479089, cluster_loss:0.160560
Clustering 1056: ASW= 0.8224, DB= 0.2379, CH= 46022.3662
Training epoch 1057, recon_loss:0.880697, zinb_loss:0.478852, cluster_loss:0.160962
Clustering 1057: ASW= 0.8209, DB= 0.2419, CH= 45380.2067
Training epoch 1058, recon_loss:0.880948, zinb_loss:0.479108, cluster_loss:0.160640
Clustering 1058: ASW= 0.8224, DB= 0.2372, CH= 46195.8401
Training epoch 1059, recon_loss:0.880991, zinb_loss:0.478874, cluster_loss:0.161142
Clustering 1059: ASW= 0.8210, DB= 0.2422, CH= 45328.6961
Training epoch 1060, recon_loss:0.881205, zinb_loss:0.479124, cluster_loss:0.160764
Clustering 1060: ASW= 0.8224, DB= 0.2371, CH= 46296.7610
Training epoch 1061, recon_loss:0.881182, zinb_loss:0.478904, cluster_loss:0.161227
Clustering 1061: ASW= 0.8211, DB= 0.2427, CH= 45318.5010
Training epoch 1062, recon_loss:0.881266, zinb_loss:0.479132, cluster_loss:0.160802
Clustering 1062: ASW= 0.8224, DB= 0.2369, CH= 46369.1260
Training epoch 1063, recon_loss:0.881213, zinb_loss:0.478944, cluster_loss:0.161244
Clustering 1063: ASW= 0.8213, DB= 0.2427, CH= 45349.8002
Training epoch 1064, recon_loss:0.881169, zinb_loss:0.479137, cluster_loss:0.160783
Clustering 1064: ASW= 0.8223, DB= 0.2365, CH= 46387.5934
Training epoch 1065, recon_loss:0.881135, zinb_loss:0.478985, cluster_loss:0.161192
Clustering 1065: ASW= 0.8216, DB= 0.2432, CH= 45410.5624
Training epoch 1066, recon_loss:0.880953, zinb_loss:0.479130, cluster_loss:0.160716
Clustering 1066: ASW= 0.8222, DB= 0.2366, CH= 46375.0637
Training epoch 1067, recon_loss:0.880973, zinb_loss:0.479024, cluster_loss:0.161106
Clustering 1067: ASW= 0.8218, DB= 0.2431, CH= 45491.0470
Training epoch 1068, recon_loss:0.880710, zinb_loss:0.479115, cluster_loss:0.160644
Clustering 1068: ASW= 0.8220, DB= 0.2367, CH= 46344.2368
Training epoch 1069, recon_loss:0.880848, zinb_loss:0.479052, cluster_loss:0.161024
Clustering 1069: ASW= 0.8220, DB= 0.2429, CH= 45570.9231
Training epoch 1070, recon_loss:0.880534, zinb_loss:0.479098, cluster_loss:0.160592
Clustering 1070: ASW= 0.8218, DB= 0.2368, CH= 46301.4685
Training epoch 1071, recon_loss:0.880782, zinb_loss:0.479070, cluster_loss:0.160962
Clustering 1071: ASW= 0.8221, DB= 0.2432, CH= 45646.1445
Training epoch 1072, recon_loss:0.880455, zinb_loss:0.479080, cluster_loss:0.160558
Clustering 1072: ASW= 0.8218, DB= 0.2369, CH= 46278.1315
Training epoch 1073, recon_loss:0.880793, zinb_loss:0.479073, cluster_loss:0.160908
Clustering 1073: ASW= 0.8221, DB= 0.2432, CH= 45728.1732
Training epoch 1074, recon_loss:0.880453, zinb_loss:0.479054, cluster_loss:0.160557
Clustering 1074: ASW= 0.8218, DB= 0.2371, CH= 46266.1820
Training epoch 1075, recon_loss:0.880845, zinb_loss:0.479062, cluster_loss:0.160866
Clustering 1075: ASW= 0.8222, DB= 0.2432, CH= 45818.4270
Training epoch 1076, recon_loss:0.880464, zinb_loss:0.479026, cluster_loss:0.160559
Clustering 1076: ASW= 0.8218, DB= 0.2370, CH= 46267.2191
Training epoch 1077, recon_loss:0.880854, zinb_loss:0.479038, cluster_loss:0.160807
Clustering 1077: ASW= 0.8222, DB= 0.2431, CH= 45906.7686
Training epoch 1078, recon_loss:0.880452, zinb_loss:0.478997, cluster_loss:0.160532
Clustering 1078: ASW= 0.8219, DB= 0.2370, CH= 46314.4883
Training epoch 1079, recon_loss:0.880815, zinb_loss:0.479012, cluster_loss:0.160729
Clustering 1079: ASW= 0.8223, DB= 0.2429, CH= 45993.8108
Training epoch 1080, recon_loss:0.880428, zinb_loss:0.478971, cluster_loss:0.160493
Clustering 1080: ASW= 0.8221, DB= 0.2372, CH= 46375.6777
Training epoch 1081, recon_loss:0.880788, zinb_loss:0.478989, cluster_loss:0.160644
Clustering 1081: ASW= 0.8224, DB= 0.2428, CH= 46094.2661
Training epoch 1082, recon_loss:0.880377, zinb_loss:0.478938, cluster_loss:0.160456
Clustering 1082: ASW= 0.8222, DB= 0.2370, CH= 46432.8270
Training epoch 1083, recon_loss:0.880752, zinb_loss:0.478965, cluster_loss:0.160578
Clustering 1083: ASW= 0.8224, DB= 0.2424, CH= 46197.3424
Training epoch 1084, recon_loss:0.880409, zinb_loss:0.478917, cluster_loss:0.160435
Clustering 1084: ASW= 0.8223, DB= 0.2371, CH= 46505.2358
Training epoch 1085, recon_loss:0.880799, zinb_loss:0.478952, cluster_loss:0.160536
Clustering 1085: ASW= 0.8226, DB= 0.2421, CH= 46274.7420
Training epoch 1086, recon_loss:0.880466, zinb_loss:0.478889, cluster_loss:0.160460
Clustering 1086: ASW= 0.8223, DB= 0.2371, CH= 46602.9807
Training epoch 1087, recon_loss:0.880949, zinb_loss:0.478943, cluster_loss:0.160586
Clustering 1087: ASW= 0.8227, DB= 0.2417, CH= 46306.1056
Training epoch 1088, recon_loss:0.880796, zinb_loss:0.478883, cluster_loss:0.160607
Clustering 1088: ASW= 0.8222, DB= 0.2373, CH= 46724.3168
Training epoch 1089, recon_loss:0.881343, zinb_loss:0.478948, cluster_loss:0.160787
Clustering 1089: ASW= 0.8228, DB= 0.2417, CH= 46264.0621
Training epoch 1090, recon_loss:0.881358, zinb_loss:0.478891, cluster_loss:0.160954
Clustering 1090: ASW= 0.8220, DB= 0.2377, CH= 46773.9311
Training epoch 1091, recon_loss:0.881800, zinb_loss:0.478958, cluster_loss:0.161159
Clustering 1091: ASW= 0.8226, DB= 0.2412, CH= 46060.8972
Training epoch 1092, recon_loss:0.881976, zinb_loss:0.478911, cluster_loss:0.161453
Clustering 1092: ASW= 0.8214, DB= 0.2385, CH= 46612.9808
Training epoch 1093, recon_loss:0.882204, zinb_loss:0.478981, cluster_loss:0.161666
Clustering 1093: ASW= 0.8220, DB= 0.2412, CH= 45602.9900
Training epoch 1094, recon_loss:0.882452, zinb_loss:0.478932, cluster_loss:0.161865
Clustering 1094: ASW= 0.8207, DB= 0.2392, CH= 46299.0919
Training epoch 1095, recon_loss:0.882387, zinb_loss:0.479014, cluster_loss:0.161942
Clustering 1095: ASW= 0.8214, DB= 0.2414, CH= 45191.8830
Training epoch 1096, recon_loss:0.882205, zinb_loss:0.478920, cluster_loss:0.161651
Clustering 1096: ASW= 0.8209, DB= 0.2394, CH= 46271.6592
Training epoch 1097, recon_loss:0.881550, zinb_loss:0.478983, cluster_loss:0.161424
Clustering 1097: ASW= 0.8220, DB= 0.2406, CH= 45423.1098
Training epoch 1098, recon_loss:0.881541, zinb_loss:0.478867, cluster_loss:0.161088
Clustering 1098: ASW= 0.8213, DB= 0.2392, CH= 46450.5239
Training epoch 1099, recon_loss:0.880833, zinb_loss:0.478940, cluster_loss:0.160854
Clustering 1099: ASW= 0.8226, DB= 0.2401, CH= 45773.8050
Training epoch 1100, recon_loss:0.881142, zinb_loss:0.478843, cluster_loss:0.160694
Clustering 1100: ASW= 0.8217, DB= 0.2389, CH= 46651.5469
Training epoch 1101, recon_loss:0.880578, zinb_loss:0.478911, cluster_loss:0.160588
Clustering 1101: ASW= 0.8230, DB= 0.2396, CH= 45969.2636
Training epoch 1102, recon_loss:0.881129, zinb_loss:0.478848, cluster_loss:0.160522
Clustering 1102: ASW= 0.8219, DB= 0.2388, CH= 46829.3420
Training epoch 1103, recon_loss:0.880564, zinb_loss:0.478892, cluster_loss:0.160508
Clustering 1103: ASW= 0.8231, DB= 0.2396, CH= 46059.8844
Training epoch 1104, recon_loss:0.881198, zinb_loss:0.478855, cluster_loss:0.160442
Clustering 1104: ASW= 0.8220, DB= 0.2388, CH= 46974.1687
Training epoch 1105, recon_loss:0.880539, zinb_loss:0.478871, cluster_loss:0.160496
Clustering 1105: ASW= 0.8233, DB= 0.2395, CH= 46110.9143
Training epoch 1106, recon_loss:0.881183, zinb_loss:0.478858, cluster_loss:0.160383
Clustering 1106: ASW= 0.8221, DB= 0.2391, CH= 47104.5119
Training epoch 1107, recon_loss:0.880419, zinb_loss:0.478833, cluster_loss:0.160511
Clustering 1107: ASW= 0.8234, DB= 0.2395, CH= 46134.7523
Training epoch 1108, recon_loss:0.881106, zinb_loss:0.478850, cluster_loss:0.160347
Clustering 1108: ASW= 0.8221, DB= 0.2389, CH= 47226.4736
Training epoch 1109, recon_loss:0.880337, zinb_loss:0.478804, cluster_loss:0.160561
Clustering 1109: ASW= 0.8236, DB= 0.2396, CH= 46150.8646
Training epoch 1110, recon_loss:0.881082, zinb_loss:0.478861, cluster_loss:0.160342
Clustering 1110: ASW= 0.8222, DB= 0.2386, CH= 47348.6323
Training epoch 1111, recon_loss:0.880327, zinb_loss:0.478772, cluster_loss:0.160702
Clustering 1111: ASW= 0.8236, DB= 0.2400, CH= 46139.7076
Training epoch 1112, recon_loss:0.881194, zinb_loss:0.478897, cluster_loss:0.160479
Clustering 1112: ASW= 0.8223, DB= 0.2383, CH= 47463.0066
Training epoch 1113, recon_loss:0.880590, zinb_loss:0.478819, cluster_loss:0.160966
Clustering 1113: ASW= 0.8236, DB= 0.2407, CH= 46102.8253
Training epoch 1114, recon_loss:0.881641, zinb_loss:0.479037, cluster_loss:0.160724
Clustering 1114: ASW= 0.8225, DB= 0.2373, CH= 47563.0910
Training epoch 1115, recon_loss:0.880805, zinb_loss:0.478867, cluster_loss:0.161225
Clustering 1115: ASW= 0.8232, DB= 0.2414, CH= 45985.7549
Training epoch 1116, recon_loss:0.881896, zinb_loss:0.479125, cluster_loss:0.160883
Clustering 1116: ASW= 0.8225, DB= 0.2371, CH= 47564.3720
Training epoch 1117, recon_loss:0.880981, zinb_loss:0.478973, cluster_loss:0.161227
Clustering 1117: ASW= 0.8231, DB= 0.2413, CH= 45942.5680
Training epoch 1118, recon_loss:0.881894, zinb_loss:0.479175, cluster_loss:0.160743
Clustering 1118: ASW= 0.8225, DB= 0.2372, CH= 47562.3066
Training epoch 1119, recon_loss:0.881115, zinb_loss:0.479032, cluster_loss:0.161150
Clustering 1119: ASW= 0.8230, DB= 0.2411, CH= 45999.0382
Training epoch 1120, recon_loss:0.881855, zinb_loss:0.479182, cluster_loss:0.160660
Clustering 1120: ASW= 0.8224, DB= 0.2372, CH= 47482.3123
Training epoch 1121, recon_loss:0.881164, zinb_loss:0.479079, cluster_loss:0.161052
Clustering 1121: ASW= 0.8229, DB= 0.2408, CH= 46077.3019
Training epoch 1122, recon_loss:0.881727, zinb_loss:0.479176, cluster_loss:0.160619
Clustering 1122: ASW= 0.8223, DB= 0.2373, CH= 47385.2958
Training epoch 1123, recon_loss:0.881082, zinb_loss:0.479097, cluster_loss:0.160994
Clustering 1123: ASW= 0.8228, DB= 0.2400, CH= 46147.0346
Training epoch 1124, recon_loss:0.881464, zinb_loss:0.479136, cluster_loss:0.160661
Clustering 1124: ASW= 0.8223, DB= 0.2374, CH= 47265.9943
Training epoch 1125, recon_loss:0.880966, zinb_loss:0.479074, cluster_loss:0.160983
Clustering 1125: ASW= 0.8228, DB= 0.2396, CH= 46214.8066
Training epoch 1126, recon_loss:0.881230, zinb_loss:0.479086, cluster_loss:0.160721
Clustering 1126: ASW= 0.8225, DB= 0.2372, CH= 47247.6272
Training epoch 1127, recon_loss:0.880970, zinb_loss:0.479024, cluster_loss:0.161035
Clustering 1127: ASW= 0.8228, DB= 0.2394, CH= 46298.7570
Training epoch 1128, recon_loss:0.881045, zinb_loss:0.479016, cluster_loss:0.160830
Clustering 1128: ASW= 0.8229, DB= 0.2369, CH= 47248.8160
Training epoch 1129, recon_loss:0.881069, zinb_loss:0.478939, cluster_loss:0.161103
Clustering 1129: ASW= 0.8227, DB= 0.2393, CH= 46349.9719
Training epoch 1130, recon_loss:0.880887, zinb_loss:0.478957, cluster_loss:0.160883
Clustering 1130: ASW= 0.8234, DB= 0.2366, CH= 47270.4956
Training epoch 1131, recon_loss:0.880975, zinb_loss:0.478852, cluster_loss:0.161116
Clustering 1131: ASW= 0.8225, DB= 0.2391, CH= 46365.4473
Training epoch 1132, recon_loss:0.880650, zinb_loss:0.478918, cluster_loss:0.160865
Clustering 1132: ASW= 0.8238, DB= 0.2366, CH= 47238.2403
Training epoch 1133, recon_loss:0.880777, zinb_loss:0.478791, cluster_loss:0.161077
Clustering 1133: ASW= 0.8223, DB= 0.2393, CH= 46346.0835
Training epoch 1134, recon_loss:0.880505, zinb_loss:0.478925, cluster_loss:0.160799
Clustering 1134: ASW= 0.8242, DB= 0.2366, CH= 47243.3657
Training epoch 1135, recon_loss:0.880628, zinb_loss:0.478769, cluster_loss:0.161006
Clustering 1135: ASW= 0.8221, DB= 0.2395, CH= 46321.0900
Training epoch 1136, recon_loss:0.880620, zinb_loss:0.478969, cluster_loss:0.160714
Clustering 1136: ASW= 0.8246, DB= 0.2366, CH= 47317.7749
Training epoch 1137, recon_loss:0.880726, zinb_loss:0.478777, cluster_loss:0.160936
Clustering 1137: ASW= 0.8219, DB= 0.2396, CH= 46299.7274
Training epoch 1138, recon_loss:0.881071, zinb_loss:0.479025, cluster_loss:0.160677
Clustering 1138: ASW= 0.8249, DB= 0.2369, CH= 47410.5603
Training epoch 1139, recon_loss:0.881128, zinb_loss:0.478800, cluster_loss:0.160939
Clustering 1139: ASW= 0.8217, DB= 0.2396, CH= 46309.5493
Training epoch 1140, recon_loss:0.881789, zinb_loss:0.479102, cluster_loss:0.160741
Clustering 1140: ASW= 0.8251, DB= 0.2368, CH= 47539.8168
Training epoch 1141, recon_loss:0.881471, zinb_loss:0.478834, cluster_loss:0.160930
Clustering 1141: ASW= 0.8216, DB= 0.2394, CH= 46353.9708
Training epoch 1142, recon_loss:0.881866, zinb_loss:0.479087, cluster_loss:0.160784
Clustering 1142: ASW= 0.8252, DB= 0.2373, CH= 47604.8378
Training epoch 1143, recon_loss:0.881412, zinb_loss:0.478861, cluster_loss:0.160794
Clustering 1143: ASW= 0.8216, DB= 0.2391, CH= 46503.5583
Training epoch 1144, recon_loss:0.881632, zinb_loss:0.479056, cluster_loss:0.160720
Clustering 1144: ASW= 0.8253, DB= 0.2376, CH= 47663.7736
Training epoch 1145, recon_loss:0.881164, zinb_loss:0.478872, cluster_loss:0.160641
Clustering 1145: ASW= 0.8218, DB= 0.2388, CH= 46660.5166
Training epoch 1146, recon_loss:0.881296, zinb_loss:0.479016, cluster_loss:0.160647
Clustering 1146: ASW= 0.8252, DB= 0.2373, CH= 47722.6410
Training epoch 1147, recon_loss:0.880789, zinb_loss:0.478866, cluster_loss:0.160522
Clustering 1147: ASW= 0.8219, DB= 0.2385, CH= 46786.7283
Training epoch 1148, recon_loss:0.880947, zinb_loss:0.478970, cluster_loss:0.160620
Clustering 1148: ASW= 0.8251, DB= 0.2375, CH= 47754.9351
Training epoch 1149, recon_loss:0.880501, zinb_loss:0.478857, cluster_loss:0.160484
Clustering 1149: ASW= 0.8219, DB= 0.2382, CH= 46866.5401
Training epoch 1150, recon_loss:0.880830, zinb_loss:0.478947, cluster_loss:0.160687
Clustering 1150: ASW= 0.8250, DB= 0.2377, CH= 47737.2190
Training epoch 1151, recon_loss:0.880469, zinb_loss:0.478872, cluster_loss:0.160530
Clustering 1151: ASW= 0.8219, DB= 0.2380, CH= 46942.8415
Training epoch 1152, recon_loss:0.880882, zinb_loss:0.478929, cluster_loss:0.160794
Clustering 1152: ASW= 0.8248, DB= 0.2381, CH= 47635.9562
Training epoch 1153, recon_loss:0.880568, zinb_loss:0.478895, cluster_loss:0.160618
Clustering 1153: ASW= 0.8219, DB= 0.2378, CH= 46972.0114
Training epoch 1154, recon_loss:0.880941, zinb_loss:0.478923, cluster_loss:0.160902
Clustering 1154: ASW= 0.8245, DB= 0.2384, CH= 47458.7140
Training epoch 1155, recon_loss:0.880614, zinb_loss:0.478921, cluster_loss:0.160625
Clustering 1155: ASW= 0.8220, DB= 0.2375, CH= 47038.2162
Training epoch 1156, recon_loss:0.880781, zinb_loss:0.478874, cluster_loss:0.160918
Clustering 1156: ASW= 0.8242, DB= 0.2387, CH= 47290.4096
Training epoch 1157, recon_loss:0.880462, zinb_loss:0.478916, cluster_loss:0.160558
Clustering 1157: ASW= 0.8221, DB= 0.2371, CH= 47122.1185
Training epoch 1158, recon_loss:0.880546, zinb_loss:0.478811, cluster_loss:0.160855
Clustering 1158: ASW= 0.8240, DB= 0.2389, CH= 47201.8624
Training epoch 1159, recon_loss:0.880275, zinb_loss:0.478893, cluster_loss:0.160502
Clustering 1159: ASW= 0.8224, DB= 0.2369, CH= 47240.4991
Training epoch 1160, recon_loss:0.880369, zinb_loss:0.478758, cluster_loss:0.160804
Clustering 1160: ASW= 0.8238, DB= 0.2392, CH= 47175.1451
Training epoch 1161, recon_loss:0.880256, zinb_loss:0.478876, cluster_loss:0.160510
Clustering 1161: ASW= 0.8228, DB= 0.2369, CH= 47343.2728
Training epoch 1162, recon_loss:0.880417, zinb_loss:0.478722, cluster_loss:0.160816
Clustering 1162: ASW= 0.8235, DB= 0.2394, CH= 47173.2044
Training epoch 1163, recon_loss:0.880406, zinb_loss:0.478870, cluster_loss:0.160590
Clustering 1163: ASW= 0.8232, DB= 0.2365, CH= 47434.1629
Training epoch 1164, recon_loss:0.880612, zinb_loss:0.478705, cluster_loss:0.160891
Clustering 1164: ASW= 0.8233, DB= 0.2396, CH= 47199.6866
Training epoch 1165, recon_loss:0.880650, zinb_loss:0.478872, cluster_loss:0.160714
Clustering 1165: ASW= 0.8235, DB= 0.2364, CH= 47484.1552
Training epoch 1166, recon_loss:0.880899, zinb_loss:0.478704, cluster_loss:0.161000
Clustering 1166: ASW= 0.8231, DB= 0.2397, CH= 47235.9800
Training epoch 1167, recon_loss:0.880797, zinb_loss:0.478882, cluster_loss:0.160826
Clustering 1167: ASW= 0.8239, DB= 0.2362, CH= 47477.9972
Training epoch 1168, recon_loss:0.881003, zinb_loss:0.478690, cluster_loss:0.161060
Clustering 1168: ASW= 0.8230, DB= 0.2398, CH= 47318.7697
Training epoch 1169, recon_loss:0.880657, zinb_loss:0.478868, cluster_loss:0.160861
Clustering 1169: ASW= 0.8242, DB= 0.2361, CH= 47357.0149
Training epoch 1170, recon_loss:0.880802, zinb_loss:0.478646, cluster_loss:0.161002
Clustering 1170: ASW= 0.8229, DB= 0.2399, CH= 47380.1191
Training epoch 1171, recon_loss:0.880236, zinb_loss:0.478824, cluster_loss:0.160783
Clustering 1171: ASW= 0.8244, DB= 0.2359, CH= 47132.4651
Training epoch 1172, recon_loss:0.880260, zinb_loss:0.478566, cluster_loss:0.160805
Clustering 1172: ASW= 0.8229, DB= 0.2397, CH= 47393.7160
Training epoch 1173, recon_loss:0.879753, zinb_loss:0.478769, cluster_loss:0.160617
Clustering 1173: ASW= 0.8245, DB= 0.2357, CH= 46970.9149
Training epoch 1174, recon_loss:0.879761, zinb_loss:0.478512, cluster_loss:0.160611
Clustering 1174: ASW= 0.8229, DB= 0.2397, CH= 47365.2253
Training epoch 1175, recon_loss:0.879553, zinb_loss:0.478763, cluster_loss:0.160509
Clustering 1175: ASW= 0.8245, DB= 0.2354, CH= 46921.7427
Training epoch 1176, recon_loss:0.879514, zinb_loss:0.478520, cluster_loss:0.160523
Clustering 1176: ASW= 0.8229, DB= 0.2398, CH= 47288.0951
Training epoch 1177, recon_loss:0.879613, zinb_loss:0.478814, cluster_loss:0.160460
Clustering 1177: ASW= 0.8246, DB= 0.2350, CH= 47006.5664
Training epoch 1178, recon_loss:0.879530, zinb_loss:0.478577, cluster_loss:0.160561
Clustering 1178: ASW= 0.8228, DB= 0.2399, CH= 47162.4388
Training epoch 1179, recon_loss:0.879772, zinb_loss:0.478873, cluster_loss:0.160423
Clustering 1179: ASW= 0.8246, DB= 0.2346, CH= 47126.4608
Training epoch 1180, recon_loss:0.879570, zinb_loss:0.478624, cluster_loss:0.160608
Clustering 1180: ASW= 0.8228, DB= 0.2401, CH= 47035.5807
Training epoch 1181, recon_loss:0.879893, zinb_loss:0.478895, cluster_loss:0.160397
Clustering 1181: ASW= 0.8244, DB= 0.2344, CH= 47216.7435
Training epoch 1182, recon_loss:0.879595, zinb_loss:0.478641, cluster_loss:0.160623
Clustering 1182: ASW= 0.8226, DB= 0.2402, CH= 46890.2421
Training epoch 1183, recon_loss:0.880052, zinb_loss:0.478900, cluster_loss:0.160411
Clustering 1183: ASW= 0.8241, DB= 0.2341, CH= 47252.5038
Training epoch 1184, recon_loss:0.879707, zinb_loss:0.478648, cluster_loss:0.160618
Clustering 1184: ASW= 0.8226, DB= 0.2402, CH= 46812.2518
Training epoch 1185, recon_loss:0.880253, zinb_loss:0.478895, cluster_loss:0.160400
Clustering 1185: ASW= 0.8241, DB= 0.2342, CH= 47353.9102
Training epoch 1186, recon_loss:0.879906, zinb_loss:0.478649, cluster_loss:0.160583
Clustering 1186: ASW= 0.8227, DB= 0.2399, CH= 46839.8885
Training epoch 1187, recon_loss:0.880494, zinb_loss:0.478887, cluster_loss:0.160381
Clustering 1187: ASW= 0.8242, DB= 0.2345, CH= 47494.9396
Training epoch 1188, recon_loss:0.880236, zinb_loss:0.478661, cluster_loss:0.160581
Clustering 1188: ASW= 0.8230, DB= 0.2396, CH= 46927.9208
Training epoch 1189, recon_loss:0.880783, zinb_loss:0.478883, cluster_loss:0.160403
Clustering 1189: ASW= 0.8243, DB= 0.2348, CH= 47564.9870
Training epoch 1190, recon_loss:0.880479, zinb_loss:0.478687, cluster_loss:0.160569
Clustering 1190: ASW= 0.8232, DB= 0.2390, CH= 47048.3288
Training epoch 1191, recon_loss:0.880660, zinb_loss:0.478851, cluster_loss:0.160320
Clustering 1191: ASW= 0.8243, DB= 0.2353, CH= 47644.1058
Training epoch 1192, recon_loss:0.880936, zinb_loss:0.478774, cluster_loss:0.160568
Clustering 1192: ASW= 0.8236, DB= 0.2382, CH= 47249.7316
Training epoch 1193, recon_loss:0.880518, zinb_loss:0.478763, cluster_loss:0.160240
Clustering 1193: ASW= 0.8238, DB= 0.2365, CH= 47576.5463
Training epoch 1194, recon_loss:0.880743, zinb_loss:0.478768, cluster_loss:0.160650
Clustering 1194: ASW= 0.8234, DB= 0.2378, CH= 47299.7292
Training epoch 1195, recon_loss:0.881405, zinb_loss:0.478817, cluster_loss:0.160813
Clustering 1195: ASW= 0.8238, DB= 0.2374, CH= 47294.8501
Training epoch 1196, recon_loss:0.881738, zinb_loss:0.478822, cluster_loss:0.160951
Clustering 1196: ASW= 0.8236, DB= 0.2370, CH= 47542.2205
Training epoch 1197, recon_loss:0.881616, zinb_loss:0.478788, cluster_loss:0.161188
Clustering 1197: ASW= 0.8237, DB= 0.2383, CH= 46989.9168
Training epoch 1198, recon_loss:0.881667, zinb_loss:0.478793, cluster_loss:0.160991
Clustering 1198: ASW= 0.8242, DB= 0.2360, CH= 47743.2987
Training epoch 1199, recon_loss:0.880867, zinb_loss:0.478711, cluster_loss:0.161056
Clustering 1199: ASW= 0.8238, DB= 0.2385, CH= 46948.0838
Training epoch 1200, recon_loss:0.881164, zinb_loss:0.478742, cluster_loss:0.160834
Clustering 1200: ASW= 0.8244, DB= 0.2352, CH= 47927.3534
Training epoch 1201, recon_loss:0.880280, zinb_loss:0.478636, cluster_loss:0.160847
Clustering 1201: ASW= 0.8239, DB= 0.2386, CH= 46957.1404
Training epoch 1202, recon_loss:0.880766, zinb_loss:0.478716, cluster_loss:0.160705
Clustering 1202: ASW= 0.8245, DB= 0.2344, CH= 48029.9994
Training epoch 1203, recon_loss:0.879973, zinb_loss:0.478597, cluster_loss:0.160693
Clustering 1203: ASW= 0.8240, DB= 0.2386, CH= 47003.8757
Training epoch 1204, recon_loss:0.880479, zinb_loss:0.478695, cluster_loss:0.160622
Clustering 1204: ASW= 0.8243, DB= 0.2344, CH= 48087.9316
Training epoch 1205, recon_loss:0.879823, zinb_loss:0.478581, cluster_loss:0.160624
Clustering 1205: ASW= 0.8240, DB= 0.2386, CH= 47036.8224
Training epoch 1206, recon_loss:0.880288, zinb_loss:0.478691, cluster_loss:0.160581
Clustering 1206: ASW= 0.8242, DB= 0.2345, CH= 48121.0510
Training epoch 1207, recon_loss:0.879755, zinb_loss:0.478593, cluster_loss:0.160577
Clustering 1207: ASW= 0.8239, DB= 0.2391, CH= 47077.6242
Training epoch 1208, recon_loss:0.880146, zinb_loss:0.478695, cluster_loss:0.160528
Clustering 1208: ASW= 0.8241, DB= 0.2346, CH= 48165.9418
Training epoch 1209, recon_loss:0.879748, zinb_loss:0.478619, cluster_loss:0.160529
Clustering 1209: ASW= 0.8238, DB= 0.2391, CH= 47136.3378
Training epoch 1210, recon_loss:0.880074, zinb_loss:0.478715, cluster_loss:0.160459
Clustering 1210: ASW= 0.8242, DB= 0.2349, CH= 48215.2331
Training epoch 1211, recon_loss:0.879865, zinb_loss:0.478662, cluster_loss:0.160466
Clustering 1211: ASW= 0.8238, DB= 0.2389, CH= 47247.9810
Training epoch 1212, recon_loss:0.880145, zinb_loss:0.478753, cluster_loss:0.160393
Clustering 1212: ASW= 0.8243, DB= 0.2349, CH= 48279.0440
Training epoch 1213, recon_loss:0.880215, zinb_loss:0.478726, cluster_loss:0.160430
Clustering 1213: ASW= 0.8238, DB= 0.2387, CH= 47396.7441
Training epoch 1214, recon_loss:0.880469, zinb_loss:0.478820, cluster_loss:0.160356
Clustering 1214: ASW= 0.8245, DB= 0.2352, CH= 48306.6814
Training epoch 1215, recon_loss:0.880823, zinb_loss:0.478813, cluster_loss:0.160440
Clustering 1215: ASW= 0.8238, DB= 0.2387, CH= 47555.1722
Training epoch 1216, recon_loss:0.880968, zinb_loss:0.478905, cluster_loss:0.160382
Clustering 1216: ASW= 0.8247, DB= 0.2356, CH= 48258.3163
Training epoch 1217, recon_loss:0.881429, zinb_loss:0.478884, cluster_loss:0.160500
Clustering 1217: ASW= 0.8238, DB= 0.2386, CH= 47724.5579
Training epoch 1218, recon_loss:0.881189, zinb_loss:0.478936, cluster_loss:0.160436
Clustering 1218: ASW= 0.8248, DB= 0.2359, CH= 48066.6917
Training epoch 1219, recon_loss:0.881579, zinb_loss:0.478883, cluster_loss:0.160561
Clustering 1219: ASW= 0.8239, DB= 0.2378, CH= 47823.3365
Training epoch 1220, recon_loss:0.880971, zinb_loss:0.478890, cluster_loss:0.160432
Clustering 1220: ASW= 0.8249, DB= 0.2361, CH= 47857.8296
Training epoch 1221, recon_loss:0.881254, zinb_loss:0.478812, cluster_loss:0.160515
Clustering 1221: ASW= 0.8239, DB= 0.2370, CH= 47866.0513
Training epoch 1222, recon_loss:0.880626, zinb_loss:0.478828, cluster_loss:0.160320
Clustering 1222: ASW= 0.8252, DB= 0.2361, CH= 47865.8064
Training epoch 1223, recon_loss:0.880821, zinb_loss:0.478733, cluster_loss:0.160388
Clustering 1223: ASW= 0.8239, DB= 0.2369, CH= 47888.2204
Training epoch 1224, recon_loss:0.880434, zinb_loss:0.478772, cluster_loss:0.160219
Clustering 1224: ASW= 0.8255, DB= 0.2361, CH= 47993.8028
Training epoch 1225, recon_loss:0.880610, zinb_loss:0.478675, cluster_loss:0.160348
Clustering 1225: ASW= 0.8238, DB= 0.2370, CH= 47883.3981
Training epoch 1226, recon_loss:0.880463, zinb_loss:0.478744, cluster_loss:0.160216
Clustering 1226: ASW= 0.8258, DB= 0.2361, CH= 48145.7068
Training epoch 1227, recon_loss:0.880510, zinb_loss:0.478627, cluster_loss:0.160413
Clustering 1227: ASW= 0.8235, DB= 0.2372, CH= 47821.1602
Training epoch 1228, recon_loss:0.880513, zinb_loss:0.478732, cluster_loss:0.160295
Clustering 1228: ASW= 0.8259, DB= 0.2372, CH= 48270.5452
Training epoch 1229, recon_loss:0.880366, zinb_loss:0.478586, cluster_loss:0.160565
Clustering 1229: ASW= 0.8233, DB= 0.2374, CH= 47676.5778
Training epoch 1230, recon_loss:0.880372, zinb_loss:0.478718, cluster_loss:0.160416
Clustering 1230: ASW= 0.8259, DB= 0.2372, CH= 48352.5257
Training epoch 1231, recon_loss:0.880095, zinb_loss:0.478556, cluster_loss:0.160706
Clustering 1231: ASW= 0.8231, DB= 0.2376, CH= 47493.0450
Training epoch 1232, recon_loss:0.880113, zinb_loss:0.478702, cluster_loss:0.160562
Clustering 1232: ASW= 0.8257, DB= 0.2372, CH= 48336.8705
Training epoch 1233, recon_loss:0.879785, zinb_loss:0.478525, cluster_loss:0.160872
Clustering 1233: ASW= 0.8229, DB= 0.2376, CH= 47274.9189
Training epoch 1234, recon_loss:0.879905, zinb_loss:0.478690, cluster_loss:0.160741
Clustering 1234: ASW= 0.8254, DB= 0.2373, CH= 48266.2666
Training epoch 1235, recon_loss:0.879563, zinb_loss:0.478495, cluster_loss:0.160986
Clustering 1235: ASW= 0.8229, DB= 0.2374, CH= 47152.7408
Training epoch 1236, recon_loss:0.879774, zinb_loss:0.478675, cluster_loss:0.160806
Clustering 1236: ASW= 0.8253, DB= 0.2372, CH= 48293.2738
Training epoch 1237, recon_loss:0.879399, zinb_loss:0.478470, cluster_loss:0.160950
Clustering 1237: ASW= 0.8232, DB= 0.2373, CH= 47200.2243
Training epoch 1238, recon_loss:0.879614, zinb_loss:0.478661, cluster_loss:0.160667
Clustering 1238: ASW= 0.8254, DB= 0.2370, CH= 48447.3265
Training epoch 1239, recon_loss:0.879311, zinb_loss:0.478463, cluster_loss:0.160799
Clustering 1239: ASW= 0.8236, DB= 0.2373, CH= 47343.0332
Training epoch 1240, recon_loss:0.879547, zinb_loss:0.478659, cluster_loss:0.160498
Clustering 1240: ASW= 0.8256, DB= 0.2366, CH= 48549.8096
Training epoch 1241, recon_loss:0.879396, zinb_loss:0.478473, cluster_loss:0.160670
Clustering 1241: ASW= 0.8238, DB= 0.2372, CH= 47484.5219
Training epoch 1242, recon_loss:0.879652, zinb_loss:0.478675, cluster_loss:0.160424
Clustering 1242: ASW= 0.8257, DB= 0.2356, CH= 48576.7856
Training epoch 1243, recon_loss:0.879611, zinb_loss:0.478492, cluster_loss:0.160625
Clustering 1243: ASW= 0.8239, DB= 0.2374, CH= 47582.7841
Training epoch 1244, recon_loss:0.879841, zinb_loss:0.478697, cluster_loss:0.160416
Clustering 1244: ASW= 0.8257, DB= 0.2351, CH= 48514.4036
Training epoch 1245, recon_loss:0.879761, zinb_loss:0.478500, cluster_loss:0.160609
Clustering 1245: ASW= 0.8239, DB= 0.2369, CH= 47624.8942
Training epoch 1246, recon_loss:0.879907, zinb_loss:0.478698, cluster_loss:0.160394
Clustering 1246: ASW= 0.8256, DB= 0.2348, CH= 48361.4774
Training epoch 1247, recon_loss:0.879702, zinb_loss:0.478481, cluster_loss:0.160557
Clustering 1247: ASW= 0.8239, DB= 0.2371, CH= 47677.5488
Training epoch 1248, recon_loss:0.879778, zinb_loss:0.478669, cluster_loss:0.160343
Clustering 1248: ASW= 0.8254, DB= 0.2344, CH= 48144.8753
Training epoch 1249, recon_loss:0.879470, zinb_loss:0.478452, cluster_loss:0.160479
Clustering 1249: ASW= 0.8240, DB= 0.2372, CH= 47745.4799
Training epoch 1250, recon_loss:0.879507, zinb_loss:0.478629, cluster_loss:0.160275
Clustering 1250: ASW= 0.8252, DB= 0.2343, CH= 47981.4335
Training epoch 1251, recon_loss:0.879156, zinb_loss:0.478426, cluster_loss:0.160384
Clustering 1251: ASW= 0.8242, DB= 0.2372, CH= 47837.3217
Training epoch 1252, recon_loss:0.879140, zinb_loss:0.478593, cluster_loss:0.160172
Clustering 1252: ASW= 0.8251, DB= 0.2336, CH= 47940.9815
Training epoch 1253, recon_loss:0.878897, zinb_loss:0.478414, cluster_loss:0.160312
Clustering 1253: ASW= 0.8244, DB= 0.2371, CH= 47939.3841
Training epoch 1254, recon_loss:0.878903, zinb_loss:0.478572, cluster_loss:0.160109
Clustering 1254: ASW= 0.8250, DB= 0.2335, CH= 47928.0837
Training epoch 1255, recon_loss:0.878813, zinb_loss:0.478428, cluster_loss:0.160298
Clustering 1255: ASW= 0.8245, DB= 0.2372, CH= 48027.6940
Training epoch 1256, recon_loss:0.878892, zinb_loss:0.478579, cluster_loss:0.160100
Clustering 1256: ASW= 0.8249, DB= 0.2334, CH= 47879.3458
Training epoch 1257, recon_loss:0.878948, zinb_loss:0.478462, cluster_loss:0.160323
Clustering 1257: ASW= 0.8246, DB= 0.2382, CH= 48094.9462
Training epoch 1258, recon_loss:0.879058, zinb_loss:0.478589, cluster_loss:0.160140
Clustering 1258: ASW= 0.8247, DB= 0.2334, CH= 47852.5900
Training epoch 1259, recon_loss:0.879292, zinb_loss:0.478518, cluster_loss:0.160410
Clustering 1259: ASW= 0.8247, DB= 0.2377, CH= 48141.7664
Training epoch 1260, recon_loss:0.879384, zinb_loss:0.478626, cluster_loss:0.160202
Clustering 1260: ASW= 0.8246, DB= 0.2335, CH= 47805.7758
Training epoch 1261, recon_loss:0.879637, zinb_loss:0.478581, cluster_loss:0.160459
Clustering 1261: ASW= 0.8248, DB= 0.2390, CH= 48237.8276
Training epoch 1262, recon_loss:0.879597, zinb_loss:0.478638, cluster_loss:0.160238
Clustering 1262: ASW= 0.8244, DB= 0.2338, CH= 47807.2051
Training epoch 1263, recon_loss:0.879882, zinb_loss:0.478624, cluster_loss:0.160506
Clustering 1263: ASW= 0.8248, DB= 0.2389, CH= 48285.4290
Training epoch 1264, recon_loss:0.879837, zinb_loss:0.478672, cluster_loss:0.160247
Clustering 1264: ASW= 0.8244, DB= 0.2336, CH= 47889.0698
Training epoch 1265, recon_loss:0.879963, zinb_loss:0.478660, cluster_loss:0.160482
Clustering 1265: ASW= 0.8250, DB= 0.2387, CH= 48371.8369
Training epoch 1266, recon_loss:0.879870, zinb_loss:0.478697, cluster_loss:0.160236
Clustering 1266: ASW= 0.8244, DB= 0.2335, CH= 48070.4233
Training epoch 1267, recon_loss:0.879963, zinb_loss:0.478708, cluster_loss:0.160527
Clustering 1267: ASW= 0.8253, DB= 0.2384, CH= 48410.9999
Training epoch 1268, recon_loss:0.879910, zinb_loss:0.478734, cluster_loss:0.160311
Clustering 1268: ASW= 0.8243, DB= 0.2340, CH= 48312.5576
Training epoch 1269, recon_loss:0.879883, zinb_loss:0.478755, cluster_loss:0.160664
Clustering 1269: ASW= 0.8255, DB= 0.2382, CH= 48375.1510
Training epoch 1270, recon_loss:0.879811, zinb_loss:0.478742, cluster_loss:0.160372
Clustering 1270: ASW= 0.8242, DB= 0.2341, CH= 48480.0817
Training epoch 1271, recon_loss:0.879489, zinb_loss:0.478740, cluster_loss:0.160680
Clustering 1271: ASW= 0.8258, DB= 0.2382, CH= 48341.9828
Training epoch 1272, recon_loss:0.879466, zinb_loss:0.478702, cluster_loss:0.160290
Clustering 1272: ASW= 0.8242, DB= 0.2345, CH= 48536.7004
Training epoch 1273, recon_loss:0.879000, zinb_loss:0.478689, cluster_loss:0.160521
Clustering 1273: ASW= 0.8261, DB= 0.2380, CH= 48394.4114
Training epoch 1274, recon_loss:0.879138, zinb_loss:0.478613, cluster_loss:0.160170
Clustering 1274: ASW= 0.8242, DB= 0.2348, CH= 48577.2974
Training epoch 1275, recon_loss:0.878869, zinb_loss:0.478614, cluster_loss:0.160374
Clustering 1275: ASW= 0.8263, DB= 0.2379, CH= 48461.0304
Training epoch 1276, recon_loss:0.879258, zinb_loss:0.478564, cluster_loss:0.160130
Clustering 1276: ASW= 0.8242, DB= 0.2348, CH= 48615.9688
Training epoch 1277, recon_loss:0.879190, zinb_loss:0.478595, cluster_loss:0.160324
Clustering 1277: ASW= 0.8264, DB= 0.2378, CH= 48514.4083
Training epoch 1278, recon_loss:0.879767, zinb_loss:0.478526, cluster_loss:0.160181
Clustering 1278: ASW= 0.8242, DB= 0.2352, CH= 48676.3907
Training epoch 1279, recon_loss:0.879676, zinb_loss:0.478553, cluster_loss:0.160363
Clustering 1279: ASW= 0.8265, DB= 0.2375, CH= 48489.9912
Training epoch 1280, recon_loss:0.880212, zinb_loss:0.478489, cluster_loss:0.160260
Clustering 1280: ASW= 0.8242, DB= 0.2352, CH= 48723.3606
Training epoch 1281, recon_loss:0.879900, zinb_loss:0.478530, cluster_loss:0.160361
Clustering 1281: ASW= 0.8264, DB= 0.2375, CH= 48420.4681
Training epoch 1282, recon_loss:0.880335, zinb_loss:0.478451, cluster_loss:0.160264
Clustering 1282: ASW= 0.8242, DB= 0.2355, CH= 48818.3173
Training epoch 1283, recon_loss:0.879859, zinb_loss:0.478473, cluster_loss:0.160379
Clustering 1283: ASW= 0.8263, DB= 0.2377, CH= 48255.1107
Training epoch 1284, recon_loss:0.880193, zinb_loss:0.478408, cluster_loss:0.160245
Clustering 1284: ASW= 0.8241, DB= 0.2355, CH= 48921.7115
Training epoch 1285, recon_loss:0.879698, zinb_loss:0.478456, cluster_loss:0.160394
Clustering 1285: ASW= 0.8261, DB= 0.2377, CH= 48028.6916
Training epoch 1286, recon_loss:0.880081, zinb_loss:0.478416, cluster_loss:0.160215
Clustering 1286: ASW= 0.8242, DB= 0.2354, CH= 49078.3951
Training epoch 1287, recon_loss:0.879602, zinb_loss:0.478438, cluster_loss:0.160531
Clustering 1287: ASW= 0.8258, DB= 0.2378, CH= 47694.7980
Training epoch 1288, recon_loss:0.880106, zinb_loss:0.478433, cluster_loss:0.160281
Clustering 1288: ASW= 0.8241, DB= 0.2351, CH= 49181.0274
Training epoch 1289, recon_loss:0.879740, zinb_loss:0.478480, cluster_loss:0.160733
Clustering 1289: ASW= 0.8255, DB= 0.2378, CH= 47290.8068
Training epoch 1290, recon_loss:0.880379, zinb_loss:0.478512, cluster_loss:0.160380
Clustering 1290: ASW= 0.8241, DB= 0.2350, CH= 49294.5640
Training epoch 1291, recon_loss:0.879833, zinb_loss:0.478492, cluster_loss:0.160849
Clustering 1291: ASW= 0.8250, DB= 0.2379, CH= 46997.9553
Training epoch 1292, recon_loss:0.880310, zinb_loss:0.478501, cluster_loss:0.160516
Clustering 1292: ASW= 0.8238, DB= 0.2350, CH= 49213.1278
Training epoch 1293, recon_loss:0.880008, zinb_loss:0.478530, cluster_loss:0.161094
Clustering 1293: ASW= 0.8249, DB= 0.2376, CH= 46837.6785
Training epoch 1294, recon_loss:0.880221, zinb_loss:0.478514, cluster_loss:0.160448
Clustering 1294: ASW= 0.8239, DB= 0.2351, CH= 49130.9385
Training epoch 1295, recon_loss:0.879370, zinb_loss:0.478488, cluster_loss:0.160665
Clustering 1295: ASW= 0.8253, DB= 0.2366, CH= 47148.7321
Training epoch 1296, recon_loss:0.879472, zinb_loss:0.478478, cluster_loss:0.160016
Clustering 1296: ASW= 0.8245, DB= 0.2349, CH= 49298.9176
Training epoch 1297, recon_loss:0.878830, zinb_loss:0.478453, cluster_loss:0.160241
Clustering 1297: ASW= 0.8258, DB= 0.2356, CH= 47536.8491
Training epoch 1298, recon_loss:0.879112, zinb_loss:0.478473, cluster_loss:0.159750
Clustering 1298: ASW= 0.8249, DB= 0.2349, CH= 49464.1305
Training epoch 1299, recon_loss:0.878894, zinb_loss:0.478455, cluster_loss:0.160110
Clustering 1299: ASW= 0.8260, DB= 0.2352, CH= 47709.2776
Training epoch 1300, recon_loss:0.879413, zinb_loss:0.478507, cluster_loss:0.159723
Clustering 1300: ASW= 0.8249, DB= 0.2349, CH= 49542.1449
Training epoch 1301, recon_loss:0.879475, zinb_loss:0.478492, cluster_loss:0.160152
Clustering 1301: ASW= 0.8262, DB= 0.2351, CH= 47737.7234
Training epoch 1302, recon_loss:0.880061, zinb_loss:0.478554, cluster_loss:0.159824
Clustering 1302: ASW= 0.8248, DB= 0.2352, CH= 49504.3802
Training epoch 1303, recon_loss:0.880289, zinb_loss:0.478569, cluster_loss:0.160234
Clustering 1303: ASW= 0.8263, DB= 0.2347, CH= 47743.9265
Training epoch 1304, recon_loss:0.880594, zinb_loss:0.478592, cluster_loss:0.159900
Clustering 1304: ASW= 0.8247, DB= 0.2356, CH= 49381.1080
Training epoch 1305, recon_loss:0.880640, zinb_loss:0.478608, cluster_loss:0.160225
Clustering 1305: ASW= 0.8265, DB= 0.2343, CH= 47862.9574
Training epoch 1306, recon_loss:0.880634, zinb_loss:0.478588, cluster_loss:0.159897
Clustering 1306: ASW= 0.8245, DB= 0.2362, CH= 49226.3354
Training epoch 1307, recon_loss:0.880615, zinb_loss:0.478637, cluster_loss:0.160116
Clustering 1307: ASW= 0.8267, DB= 0.2337, CH= 48152.7405
Training epoch 1308, recon_loss:0.880470, zinb_loss:0.478564, cluster_loss:0.159861
Clustering 1308: ASW= 0.8245, DB= 0.2366, CH= 49065.1903
Training epoch 1309, recon_loss:0.880338, zinb_loss:0.478632, cluster_loss:0.160041
Clustering 1309: ASW= 0.8267, DB= 0.2333, CH= 48430.3579
Training epoch 1310, recon_loss:0.880298, zinb_loss:0.478550, cluster_loss:0.159855
Clustering 1310: ASW= 0.8244, DB= 0.2369, CH= 48881.6845
Training epoch 1311, recon_loss:0.880088, zinb_loss:0.478631, cluster_loss:0.160040
Clustering 1311: ASW= 0.8267, DB= 0.2331, CH= 48630.3848
Training epoch 1312, recon_loss:0.880253, zinb_loss:0.478567, cluster_loss:0.159915
Clustering 1312: ASW= 0.8242, DB= 0.2373, CH= 48650.2262
Training epoch 1313, recon_loss:0.880009, zinb_loss:0.478665, cluster_loss:0.160081
Clustering 1313: ASW= 0.8267, DB= 0.2329, CH= 48813.6304
Training epoch 1314, recon_loss:0.880280, zinb_loss:0.478597, cluster_loss:0.160016
Clustering 1314: ASW= 0.8239, DB= 0.2375, CH= 48375.6091
Training epoch 1315, recon_loss:0.879941, zinb_loss:0.478693, cluster_loss:0.160076
Clustering 1315: ASW= 0.8267, DB= 0.2328, CH= 49043.1863
Training epoch 1316, recon_loss:0.880089, zinb_loss:0.478583, cluster_loss:0.160069
Clustering 1316: ASW= 0.8238, DB= 0.2377, CH= 48160.2578
Training epoch 1317, recon_loss:0.879719, zinb_loss:0.478671, cluster_loss:0.160013
Clustering 1317: ASW= 0.8267, DB= 0.2329, CH= 49278.1978
Training epoch 1318, recon_loss:0.879735, zinb_loss:0.478534, cluster_loss:0.160070
Clustering 1318: ASW= 0.8239, DB= 0.2375, CH= 48066.0604
Training epoch 1319, recon_loss:0.879565, zinb_loss:0.478641, cluster_loss:0.159949
Clustering 1319: ASW= 0.8268, DB= 0.2330, CH= 49536.0034
Training epoch 1320, recon_loss:0.879565, zinb_loss:0.478476, cluster_loss:0.160201
Clustering 1320: ASW= 0.8240, DB= 0.2374, CH= 47964.5023
Training epoch 1321, recon_loss:0.879530, zinb_loss:0.478606, cluster_loss:0.159984
Clustering 1321: ASW= 0.8268, DB= 0.2333, CH= 49676.5984
Training epoch 1322, recon_loss:0.879524, zinb_loss:0.478435, cluster_loss:0.160437
Clustering 1322: ASW= 0.8242, DB= 0.2372, CH= 47897.5307
Training epoch 1323, recon_loss:0.879610, zinb_loss:0.478607, cluster_loss:0.160130
Clustering 1323: ASW= 0.8267, DB= 0.2336, CH= 49675.9337
Training epoch 1324, recon_loss:0.879640, zinb_loss:0.478433, cluster_loss:0.160859
Clustering 1324: ASW= 0.8243, DB= 0.2368, CH= 47810.5018
Training epoch 1325, recon_loss:0.879798, zinb_loss:0.478647, cluster_loss:0.160423
Clustering 1325: ASW= 0.8263, DB= 0.2339, CH= 49397.9426
Training epoch 1326, recon_loss:0.879644, zinb_loss:0.478460, cluster_loss:0.161232
Clustering 1326: ASW= 0.8244, DB= 0.2366, CH= 47709.8639
Training epoch 1327, recon_loss:0.879704, zinb_loss:0.478650, cluster_loss:0.160546
Clustering 1327: ASW= 0.8260, DB= 0.2342, CH= 49070.8441
Training epoch 1328, recon_loss:0.879190, zinb_loss:0.478448, cluster_loss:0.161162
Clustering 1328: ASW= 0.8246, DB= 0.2364, CH= 47691.7492
Training epoch 1329, recon_loss:0.879279, zinb_loss:0.478594, cluster_loss:0.160393
Clustering 1329: ASW= 0.8257, DB= 0.2342, CH= 48944.3655
Training epoch 1330, recon_loss:0.878726, zinb_loss:0.478407, cluster_loss:0.160936
Clustering 1330: ASW= 0.8247, DB= 0.2364, CH= 47714.2843
Training epoch 1331, recon_loss:0.879047, zinb_loss:0.478540, cluster_loss:0.160342
Clustering 1331: ASW= 0.8253, DB= 0.2345, CH= 48754.2777
Training epoch 1332, recon_loss:0.878467, zinb_loss:0.478368, cluster_loss:0.160724
Clustering 1332: ASW= 0.8247, DB= 0.2360, CH= 47827.5104
Training epoch 1333, recon_loss:0.878849, zinb_loss:0.478491, cluster_loss:0.160251
Clustering 1333: ASW= 0.8254, DB= 0.2344, CH= 48713.8323
Training epoch 1334, recon_loss:0.878277, zinb_loss:0.478329, cluster_loss:0.160481
Clustering 1334: ASW= 0.8249, DB= 0.2357, CH= 48064.0369
Training epoch 1335, recon_loss:0.878638, zinb_loss:0.478450, cluster_loss:0.160069
Clustering 1335: ASW= 0.8259, DB= 0.2341, CH= 48904.2449
Training epoch 1336, recon_loss:0.878273, zinb_loss:0.478299, cluster_loss:0.160311
Clustering 1336: ASW= 0.8251, DB= 0.2355, CH= 48290.7903
Training epoch 1337, recon_loss:0.878744, zinb_loss:0.478439, cluster_loss:0.160032
Clustering 1337: ASW= 0.8263, DB= 0.2338, CH= 49014.6202
Training epoch 1338, recon_loss:0.878500, zinb_loss:0.478282, cluster_loss:0.160254
Clustering 1338: ASW= 0.8251, DB= 0.2356, CH= 48438.3886
Training epoch 1339, recon_loss:0.879078, zinb_loss:0.478435, cluster_loss:0.160106
Clustering 1339: ASW= 0.8266, DB= 0.2335, CH= 49064.5915
Training epoch 1340, recon_loss:0.878870, zinb_loss:0.478270, cluster_loss:0.160284
Clustering 1340: ASW= 0.8250, DB= 0.2359, CH= 48531.1286
Training epoch 1341, recon_loss:0.879527, zinb_loss:0.478447, cluster_loss:0.160245
Clustering 1341: ASW= 0.8269, DB= 0.2331, CH= 49100.0617
Training epoch 1342, recon_loss:0.879129, zinb_loss:0.478253, cluster_loss:0.160326
Clustering 1342: ASW= 0.8249, DB= 0.2362, CH= 48590.1922
Training epoch 1343, recon_loss:0.879729, zinb_loss:0.478447, cluster_loss:0.160356
Clustering 1343: ASW= 0.8272, DB= 0.2328, CH= 49181.1123
Training epoch 1344, recon_loss:0.879153, zinb_loss:0.478232, cluster_loss:0.160347
Clustering 1344: ASW= 0.8249, DB= 0.2365, CH= 48632.7797
Training epoch 1345, recon_loss:0.879749, zinb_loss:0.478458, cluster_loss:0.160436
Clustering 1345: ASW= 0.8275, DB= 0.2328, CH= 49314.6047
Training epoch 1346, recon_loss:0.879172, zinb_loss:0.478226, cluster_loss:0.160405
Clustering 1346: ASW= 0.8249, DB= 0.2368, CH= 48669.8038
Training epoch 1347, recon_loss:0.879778, zinb_loss:0.478462, cluster_loss:0.160585
Clustering 1347: ASW= 0.8277, DB= 0.2326, CH= 49471.2466
Training epoch 1348, recon_loss:0.879214, zinb_loss:0.478247, cluster_loss:0.160396
Clustering 1348: ASW= 0.8251, DB= 0.2367, CH= 48730.7164
Training epoch 1349, recon_loss:0.879940, zinb_loss:0.478496, cluster_loss:0.160673
Clustering 1349: ASW= 0.8278, DB= 0.2325, CH= 49667.3710
Training epoch 1350, recon_loss:0.879461, zinb_loss:0.478299, cluster_loss:0.160428
Clustering 1350: ASW= 0.8253, DB= 0.2366, CH= 48668.5083
Training epoch 1351, recon_loss:0.880413, zinb_loss:0.478533, cluster_loss:0.160800
Clustering 1351: ASW= 0.8276, DB= 0.2326, CH= 49732.3206
Training epoch 1352, recon_loss:0.880064, zinb_loss:0.478399, cluster_loss:0.160535
Clustering 1352: ASW= 0.8255, DB= 0.2363, CH= 48245.2217
Training epoch 1353, recon_loss:0.880529, zinb_loss:0.478547, cluster_loss:0.160689
Clustering 1353: ASW= 0.8271, DB= 0.2329, CH= 49573.1098
Training epoch 1354, recon_loss:0.879941, zinb_loss:0.478448, cluster_loss:0.160369
Clustering 1354: ASW= 0.8257, DB= 0.2358, CH= 48179.6642
Training epoch 1355, recon_loss:0.879958, zinb_loss:0.478517, cluster_loss:0.160334
Clustering 1355: ASW= 0.8268, DB= 0.2326, CH= 49457.3448
Training epoch 1356, recon_loss:0.879381, zinb_loss:0.478427, cluster_loss:0.160018
Clustering 1356: ASW= 0.8261, DB= 0.2354, CH= 48521.4011
Training epoch 1357, recon_loss:0.879506, zinb_loss:0.478481, cluster_loss:0.160111
Clustering 1357: ASW= 0.8266, DB= 0.2328, CH= 49417.1599
Training epoch 1358, recon_loss:0.879186, zinb_loss:0.478415, cluster_loss:0.159889
Clustering 1358: ASW= 0.8263, DB= 0.2352, CH= 48731.9214
Training epoch 1359, recon_loss:0.879414, zinb_loss:0.478483, cluster_loss:0.160058
Clustering 1359: ASW= 0.8264, DB= 0.2328, CH= 49408.5509
Training epoch 1360, recon_loss:0.879228, zinb_loss:0.478419, cluster_loss:0.159920
Clustering 1360: ASW= 0.8263, DB= 0.2353, CH= 48810.0713
Training epoch 1361, recon_loss:0.879496, zinb_loss:0.478511, cluster_loss:0.160112
Clustering 1361: ASW= 0.8263, DB= 0.2328, CH= 49376.2528
Training epoch 1362, recon_loss:0.879385, zinb_loss:0.478427, cluster_loss:0.160013
Clustering 1362: ASW= 0.8263, DB= 0.2354, CH= 48823.2246
Training epoch 1363, recon_loss:0.879613, zinb_loss:0.478552, cluster_loss:0.160161
Clustering 1363: ASW= 0.8261, DB= 0.2328, CH= 49360.8025
Training epoch 1364, recon_loss:0.879409, zinb_loss:0.478420, cluster_loss:0.160086
Clustering 1364: ASW= 0.8261, DB= 0.2359, CH= 48812.2836
Training epoch 1365, recon_loss:0.879516, zinb_loss:0.478560, cluster_loss:0.160172
Clustering 1365: ASW= 0.8260, DB= 0.2329, CH= 49310.4545
Training epoch 1366, recon_loss:0.879430, zinb_loss:0.478388, cluster_loss:0.160105
Clustering 1366: ASW= 0.8261, DB= 0.2361, CH= 48823.2152
Training epoch 1367, recon_loss:0.879460, zinb_loss:0.478570, cluster_loss:0.160115
Clustering 1367: ASW= 0.8259, DB= 0.2322, CH= 49304.0934
Training epoch 1368, recon_loss:0.879271, zinb_loss:0.478360, cluster_loss:0.160106
Clustering 1368: ASW= 0.8260, DB= 0.2362, CH= 48873.3312
Training epoch 1369, recon_loss:0.879260, zinb_loss:0.478541, cluster_loss:0.160025
Clustering 1369: ASW= 0.8259, DB= 0.2324, CH= 49283.8807
Training epoch 1370, recon_loss:0.879190, zinb_loss:0.478295, cluster_loss:0.160037
Clustering 1370: ASW= 0.8260, DB= 0.2360, CH= 48996.3251
Training epoch 1371, recon_loss:0.879034, zinb_loss:0.478500, cluster_loss:0.159926
Clustering 1371: ASW= 0.8260, DB= 0.2321, CH= 49276.6994
Training epoch 1372, recon_loss:0.879105, zinb_loss:0.478254, cluster_loss:0.160052
Clustering 1372: ASW= 0.8260, DB= 0.2361, CH= 49144.7793
Training epoch 1373, recon_loss:0.878988, zinb_loss:0.478468, cluster_loss:0.159920
Clustering 1373: ASW= 0.8261, DB= 0.2320, CH= 49113.6961
Training epoch 1374, recon_loss:0.879461, zinb_loss:0.478217, cluster_loss:0.160292
Clustering 1374: ASW= 0.8259, DB= 0.2354, CH= 49160.5871
Training epoch 1375, recon_loss:0.879431, zinb_loss:0.478473, cluster_loss:0.160256
Clustering 1375: ASW= 0.8259, DB= 0.2322, CH= 48442.9244
Training epoch 1376, recon_loss:0.879593, zinb_loss:0.478185, cluster_loss:0.160524
Clustering 1376: ASW= 0.8257, DB= 0.2355, CH= 48960.0937
Training epoch 1377, recon_loss:0.879098, zinb_loss:0.478443, cluster_loss:0.160250
Clustering 1377: ASW= 0.8260, DB= 0.2320, CH= 48320.0098
Training epoch 1378, recon_loss:0.878762, zinb_loss:0.478139, cluster_loss:0.160269
Clustering 1378: ASW= 0.8260, DB= 0.2360, CH= 48855.4890
Training epoch 1379, recon_loss:0.878167, zinb_loss:0.478392, cluster_loss:0.159819
Clustering 1379: ASW= 0.8266, DB= 0.2316, CH= 49003.8801
Training epoch 1380, recon_loss:0.878295, zinb_loss:0.478132, cluster_loss:0.160182
Clustering 1380: ASW= 0.8262, DB= 0.2356, CH= 48889.5837
Training epoch 1381, recon_loss:0.878154, zinb_loss:0.478437, cluster_loss:0.159804
Clustering 1381: ASW= 0.8267, DB= 0.2315, CH= 49266.2588
Training epoch 1382, recon_loss:0.878443, zinb_loss:0.478180, cluster_loss:0.160333
Clustering 1382: ASW= 0.8262, DB= 0.2363, CH= 48801.0122
Training epoch 1383, recon_loss:0.878408, zinb_loss:0.478481, cluster_loss:0.159974
Clustering 1383: ASW= 0.8265, DB= 0.2313, CH= 49328.3835
Training epoch 1384, recon_loss:0.878633, zinb_loss:0.478239, cluster_loss:0.160488
Clustering 1384: ASW= 0.8263, DB= 0.2363, CH= 48682.1929
Training epoch 1385, recon_loss:0.878495, zinb_loss:0.478528, cluster_loss:0.160028
Clustering 1385: ASW= 0.8265, DB= 0.2313, CH= 49425.5806
Training epoch 1386, recon_loss:0.878486, zinb_loss:0.478285, cluster_loss:0.160564
Clustering 1386: ASW= 0.8262, DB= 0.2364, CH= 48579.8373
Training epoch 1387, recon_loss:0.878340, zinb_loss:0.478529, cluster_loss:0.160046
Clustering 1387: ASW= 0.8264, DB= 0.2313, CH= 49504.7142
Training epoch 1388, recon_loss:0.878387, zinb_loss:0.478343, cluster_loss:0.160583
Clustering 1388: ASW= 0.8263, DB= 0.2364, CH= 48557.9574
Training epoch 1389, recon_loss:0.878330, zinb_loss:0.478548, cluster_loss:0.160035
Clustering 1389: ASW= 0.8263, DB= 0.2313, CH= 49559.5827
Training epoch 1390, recon_loss:0.878469, zinb_loss:0.478402, cluster_loss:0.160568
Clustering 1390: ASW= 0.8264, DB= 0.2364, CH= 48567.9000
Training epoch 1391, recon_loss:0.878496, zinb_loss:0.478553, cluster_loss:0.160023
Clustering 1391: ASW= 0.8262, DB= 0.2314, CH= 49619.8954
Training epoch 1392, recon_loss:0.878727, zinb_loss:0.478474, cluster_loss:0.160549
Clustering 1392: ASW= 0.8266, DB= 0.2362, CH= 48635.5599
Training epoch 1393, recon_loss:0.878866, zinb_loss:0.478576, cluster_loss:0.160057
Clustering 1393: ASW= 0.8260, DB= 0.2317, CH= 49649.8605
Training epoch 1394, recon_loss:0.879141, zinb_loss:0.478529, cluster_loss:0.160508
Clustering 1394: ASW= 0.8268, DB= 0.2362, CH= 48709.2305
Training epoch 1395, recon_loss:0.879199, zinb_loss:0.478583, cluster_loss:0.160049
Clustering 1395: ASW= 0.8259, DB= 0.2318, CH= 49653.7306
Training epoch 1396, recon_loss:0.879235, zinb_loss:0.478530, cluster_loss:0.160437
Clustering 1396: ASW= 0.8269, DB= 0.2362, CH= 48801.9570
Training epoch 1397, recon_loss:0.879298, zinb_loss:0.478552, cluster_loss:0.160043
Clustering 1397: ASW= 0.8257, DB= 0.2324, CH= 49644.0004
Training epoch 1398, recon_loss:0.879235, zinb_loss:0.478504, cluster_loss:0.160365
Clustering 1398: ASW= 0.8271, DB= 0.2361, CH= 48889.3616
Training epoch 1399, recon_loss:0.879270, zinb_loss:0.478504, cluster_loss:0.160005
Clustering 1399: ASW= 0.8255, DB= 0.2327, CH= 49617.1000
Training epoch 1400, recon_loss:0.879099, zinb_loss:0.478454, cluster_loss:0.160322
Clustering 1400: ASW= 0.8271, DB= 0.2365, CH= 48953.6823
Training epoch 1401, recon_loss:0.879271, zinb_loss:0.478449, cluster_loss:0.159979
Clustering 1401: ASW= 0.8253, DB= 0.2331, CH= 49581.3545
Training epoch 1402, recon_loss:0.879016, zinb_loss:0.478406, cluster_loss:0.160299
Clustering 1402: ASW= 0.8272, DB= 0.2364, CH= 49016.0737
Training epoch 1403, recon_loss:0.879368, zinb_loss:0.478390, cluster_loss:0.159975
Clustering 1403: ASW= 0.8252, DB= 0.2335, CH= 49503.9330
Training epoch 1404, recon_loss:0.879008, zinb_loss:0.478368, cluster_loss:0.160270
Clustering 1404: ASW= 0.8271, DB= 0.2358, CH= 49092.6427
Training epoch 1405, recon_loss:0.879414, zinb_loss:0.478322, cluster_loss:0.159968
Clustering 1405: ASW= 0.8251, DB= 0.2337, CH= 49405.5792
Training epoch 1406, recon_loss:0.878976, zinb_loss:0.478300, cluster_loss:0.160275
Clustering 1406: ASW= 0.8271, DB= 0.2355, CH= 49176.5666
Training epoch 1407, recon_loss:0.879625, zinb_loss:0.478261, cluster_loss:0.160046
Clustering 1407: ASW= 0.8250, DB= 0.2339, CH= 49263.2897
Training epoch 1408, recon_loss:0.879005, zinb_loss:0.478249, cluster_loss:0.160144
Clustering 1408: ASW= 0.8268, DB= 0.2354, CH= 49230.0677
Training epoch 1409, recon_loss:0.879598, zinb_loss:0.478222, cluster_loss:0.160010
Clustering 1409: ASW= 0.8252, DB= 0.2337, CH= 49141.6521
Training epoch 1410, recon_loss:0.879391, zinb_loss:0.478232, cluster_loss:0.160333
Clustering 1410: ASW= 0.8267, DB= 0.2343, CH= 49356.2905
Training epoch 1411, recon_loss:0.880342, zinb_loss:0.478249, cluster_loss:0.160377
Clustering 1411: ASW= 0.8252, DB= 0.2341, CH= 48773.5493
Training epoch 1412, recon_loss:0.879754, zinb_loss:0.478221, cluster_loss:0.160491
Clustering 1412: ASW= 0.8264, DB= 0.2339, CH= 49407.7485
Training epoch 1413, recon_loss:0.880094, zinb_loss:0.478229, cluster_loss:0.160407
Clustering 1413: ASW= 0.8253, DB= 0.2340, CH= 48544.4518
Training epoch 1414, recon_loss:0.879223, zinb_loss:0.478180, cluster_loss:0.160292
Clustering 1414: ASW= 0.8265, DB= 0.2334, CH= 49535.2176
Training epoch 1415, recon_loss:0.879193, zinb_loss:0.478180, cluster_loss:0.160062
Clustering 1415: ASW= 0.8255, DB= 0.2338, CH= 48595.2318
Training epoch 1416, recon_loss:0.878476, zinb_loss:0.478154, cluster_loss:0.159922
Clustering 1416: ASW= 0.8268, DB= 0.2339, CH= 49758.4755
Training epoch 1417, recon_loss:0.878659, zinb_loss:0.478194, cluster_loss:0.160004
Clustering 1417: ASW= 0.8256, DB= 0.2338, CH= 48604.0300
Training epoch 1418, recon_loss:0.878618, zinb_loss:0.478199, cluster_loss:0.159831
Clustering 1418: ASW= 0.8267, DB= 0.2342, CH= 49833.8481
Training epoch 1419, recon_loss:0.878810, zinb_loss:0.478260, cluster_loss:0.160003
Clustering 1419: ASW= 0.8258, DB= 0.2332, CH= 48479.1999
Training epoch 1420, recon_loss:0.879276, zinb_loss:0.478320, cluster_loss:0.160197
Clustering 1420: ASW= 0.8262, DB= 0.2335, CH= 49756.7992
Training epoch 1421, recon_loss:0.879472, zinb_loss:0.478359, cluster_loss:0.160400
Clustering 1421: ASW= 0.8258, DB= 0.2333, CH= 48126.9525
Training epoch 1422, recon_loss:0.879592, zinb_loss:0.478369, cluster_loss:0.160221
Clustering 1422: ASW= 0.8261, DB= 0.2343, CH= 49763.4282
Training epoch 1423, recon_loss:0.879351, zinb_loss:0.478342, cluster_loss:0.160323
Clustering 1423: ASW= 0.8261, DB= 0.2333, CH= 48225.4605
Training epoch 1424, recon_loss:0.879236, zinb_loss:0.478331, cluster_loss:0.160009
Clustering 1424: ASW= 0.8263, DB= 0.2338, CH= 49880.7209
Training epoch 1425, recon_loss:0.878823, zinb_loss:0.478270, cluster_loss:0.160080
Clustering 1425: ASW= 0.8266, DB= 0.2334, CH= 48542.4932
Training epoch 1426, recon_loss:0.878880, zinb_loss:0.478274, cluster_loss:0.159863
Clustering 1426: ASW= 0.8264, DB= 0.2334, CH= 49991.5615
Training epoch 1427, recon_loss:0.878456, zinb_loss:0.478207, cluster_loss:0.159985
Clustering 1427: ASW= 0.8269, DB= 0.2336, CH= 48763.5771
Training epoch 1428, recon_loss:0.878795, zinb_loss:0.478230, cluster_loss:0.159927
Clustering 1428: ASW= 0.8264, DB= 0.2333, CH= 50023.8749
Training epoch 1429, recon_loss:0.878424, zinb_loss:0.478184, cluster_loss:0.160188
Clustering 1429: ASW= 0.8271, DB= 0.2339, CH= 48710.4379
Training epoch 1430, recon_loss:0.879029, zinb_loss:0.478203, cluster_loss:0.160323
Clustering 1430: ASW= 0.8261, DB= 0.2333, CH= 49904.2890
Training epoch 1431, recon_loss:0.878809, zinb_loss:0.478223, cluster_loss:0.160718
Clustering 1431: ASW= 0.8271, DB= 0.2350, CH= 48462.2378
Training epoch 1432, recon_loss:0.879596, zinb_loss:0.478221, cluster_loss:0.160901
Clustering 1432: ASW= 0.8256, DB= 0.2336, CH= 49649.8342
Training epoch 1433, recon_loss:0.878979, zinb_loss:0.478191, cluster_loss:0.160896
Clustering 1433: ASW= 0.8268, DB= 0.2355, CH= 48334.4961
Training epoch 1434, recon_loss:0.879000, zinb_loss:0.478058, cluster_loss:0.160843
Clustering 1434: ASW= 0.8251, DB= 0.2342, CH= 49206.3067
Training epoch 1435, recon_loss:0.878706, zinb_loss:0.478205, cluster_loss:0.160665
Clustering 1435: ASW= 0.8276, DB= 0.2350, CH= 48862.8095
Training epoch 1436, recon_loss:0.878595, zinb_loss:0.478003, cluster_loss:0.160543
Clustering 1436: ASW= 0.8251, DB= 0.2340, CH= 49153.3508
Training epoch 1437, recon_loss:0.878399, zinb_loss:0.478203, cluster_loss:0.160393
Clustering 1437: ASW= 0.8280, DB= 0.2349, CH= 49205.4554
Training epoch 1438, recon_loss:0.878189, zinb_loss:0.477983, cluster_loss:0.160269
Clustering 1438: ASW= 0.8252, DB= 0.2340, CH= 49197.9238
Training epoch 1439, recon_loss:0.878107, zinb_loss:0.478210, cluster_loss:0.160187
Clustering 1439: ASW= 0.8283, DB= 0.2344, CH= 49461.8766
Training epoch 1440, recon_loss:0.877882, zinb_loss:0.477986, cluster_loss:0.160080
Clustering 1440: ASW= 0.8253, DB= 0.2340, CH= 49268.3457
Training epoch 1441, recon_loss:0.877890, zinb_loss:0.478223, cluster_loss:0.160053
Clustering 1441: ASW= 0.8285, DB= 0.2340, CH= 49659.8781
Training epoch 1442, recon_loss:0.877685, zinb_loss:0.478004, cluster_loss:0.159961
Clustering 1442: ASW= 0.8255, DB= 0.2340, CH= 49326.4254
Training epoch 1443, recon_loss:0.877766, zinb_loss:0.478248, cluster_loss:0.159980
Clustering 1443: ASW= 0.8287, DB= 0.2336, CH= 49809.5325
Training epoch 1444, recon_loss:0.877577, zinb_loss:0.478035, cluster_loss:0.159896
Clustering 1444: ASW= 0.8255, DB= 0.2341, CH= 49358.8570
Training epoch 1445, recon_loss:0.877727, zinb_loss:0.478281, cluster_loss:0.159933
Clustering 1445: ASW= 0.8289, DB= 0.2330, CH= 49952.8808
Training epoch 1446, recon_loss:0.877547, zinb_loss:0.478069, cluster_loss:0.159870
Clustering 1446: ASW= 0.8256, DB= 0.2343, CH= 49349.3892
Training epoch 1447, recon_loss:0.877749, zinb_loss:0.478316, cluster_loss:0.159890
Clustering 1447: ASW= 0.8291, DB= 0.2320, CH= 50087.1429
Training epoch 1448, recon_loss:0.877553, zinb_loss:0.478096, cluster_loss:0.159870
Clustering 1448: ASW= 0.8257, DB= 0.2335, CH= 49287.0000
Training epoch 1449, recon_loss:0.877800, zinb_loss:0.478346, cluster_loss:0.159836
Clustering 1449: ASW= 0.8291, DB= 0.2315, CH= 50213.0226
Training epoch 1450, recon_loss:0.877583, zinb_loss:0.478102, cluster_loss:0.159908
Clustering 1450: ASW= 0.8257, DB= 0.2337, CH= 49178.1300
Training epoch 1451, recon_loss:0.877888, zinb_loss:0.478364, cluster_loss:0.159791
Clustering 1451: ASW= 0.8290, DB= 0.2313, CH= 50308.6141
Training epoch 1452, recon_loss:0.877714, zinb_loss:0.478093, cluster_loss:0.160014
Clustering 1452: ASW= 0.8257, DB= 0.2339, CH= 49007.0901
Training epoch 1453, recon_loss:0.878140, zinb_loss:0.478383, cluster_loss:0.159814
Clustering 1453: ASW= 0.8287, DB= 0.2309, CH= 50365.1428
Training epoch 1454, recon_loss:0.878014, zinb_loss:0.478081, cluster_loss:0.160226
Clustering 1454: ASW= 0.8257, DB= 0.2341, CH= 48773.0752
Training epoch 1455, recon_loss:0.878586, zinb_loss:0.478409, cluster_loss:0.159897
Clustering 1455: ASW= 0.8283, DB= 0.2302, CH= 50391.3314
Training epoch 1456, recon_loss:0.878406, zinb_loss:0.478080, cluster_loss:0.160452
Clustering 1456: ASW= 0.8258, DB= 0.2343, CH= 48576.0119
Training epoch 1457, recon_loss:0.879009, zinb_loss:0.478428, cluster_loss:0.159943
Clustering 1457: ASW= 0.8280, DB= 0.2303, CH= 50386.4043
Training epoch 1458, recon_loss:0.878601, zinb_loss:0.478076, cluster_loss:0.160493
Clustering 1458: ASW= 0.8260, DB= 0.2343, CH= 48563.5865
Training epoch 1459, recon_loss:0.879120, zinb_loss:0.478418, cluster_loss:0.159854
Clustering 1459: ASW= 0.8278, DB= 0.2303, CH= 50403.4616
Training epoch 1460, recon_loss:0.878637, zinb_loss:0.478069, cluster_loss:0.160342
Clustering 1460: ASW= 0.8263, DB= 0.2342, CH= 48742.0308
Training epoch 1461, recon_loss:0.879071, zinb_loss:0.478375, cluster_loss:0.159719
Clustering 1461: ASW= 0.8278, DB= 0.2302, CH= 50428.0064
Training epoch 1462, recon_loss:0.878779, zinb_loss:0.478077, cluster_loss:0.160188
Clustering 1462: ASW= 0.8266, DB= 0.2341, CH= 48997.6167
Training epoch 1463, recon_loss:0.879079, zinb_loss:0.478337, cluster_loss:0.159630
Clustering 1463: ASW= 0.8278, DB= 0.2303, CH= 50387.3895
Training epoch 1464, recon_loss:0.878946, zinb_loss:0.478091, cluster_loss:0.160052
Clustering 1464: ASW= 0.8268, DB= 0.2340, CH= 49250.8842
Training epoch 1465, recon_loss:0.879108, zinb_loss:0.478312, cluster_loss:0.159601
Clustering 1465: ASW= 0.8278, DB= 0.2304, CH= 50293.8498
Training epoch 1466, recon_loss:0.879067, zinb_loss:0.478109, cluster_loss:0.159943
Clustering 1466: ASW= 0.8268, DB= 0.2338, CH= 49458.5914
Training epoch 1467, recon_loss:0.879104, zinb_loss:0.478292, cluster_loss:0.159607
Clustering 1467: ASW= 0.8279, DB= 0.2304, CH= 50153.9941
Training epoch 1468, recon_loss:0.879046, zinb_loss:0.478129, cluster_loss:0.159839
Clustering 1468: ASW= 0.8268, DB= 0.2336, CH= 49642.2874
Training epoch 1469, recon_loss:0.879045, zinb_loss:0.478271, cluster_loss:0.159628
Clustering 1469: ASW= 0.8280, DB= 0.2304, CH= 50013.2687
Training epoch 1470, recon_loss:0.878988, zinb_loss:0.478151, cluster_loss:0.159765
Clustering 1470: ASW= 0.8268, DB= 0.2333, CH= 49798.9579
Training epoch 1471, recon_loss:0.878998, zinb_loss:0.478259, cluster_loss:0.159675
Clustering 1471: ASW= 0.8281, DB= 0.2308, CH= 49888.7624
Training epoch 1472, recon_loss:0.878927, zinb_loss:0.478169, cluster_loss:0.159708
Clustering 1472: ASW= 0.8268, DB= 0.2333, CH= 49950.1548
Training epoch 1473, recon_loss:0.878927, zinb_loss:0.478248, cluster_loss:0.159730
Clustering 1473: ASW= 0.8282, DB= 0.2312, CH= 49766.9132
Training epoch 1474, recon_loss:0.878838, zinb_loss:0.478182, cluster_loss:0.159687
Clustering 1474: ASW= 0.8267, DB= 0.2331, CH= 50076.4734
Training epoch 1475, recon_loss:0.878828, zinb_loss:0.478234, cluster_loss:0.159790
Clustering 1475: ASW= 0.8285, DB= 0.2312, CH= 49702.2344
Training epoch 1476, recon_loss:0.878679, zinb_loss:0.478180, cluster_loss:0.159678
Clustering 1476: ASW= 0.8267, DB= 0.2329, CH= 50177.4595
Training epoch 1477, recon_loss:0.878658, zinb_loss:0.478215, cluster_loss:0.159833
Clustering 1477: ASW= 0.8286, DB= 0.2312, CH= 49659.8808
Training epoch 1478, recon_loss:0.878428, zinb_loss:0.478167, cluster_loss:0.159648
Clustering 1478: ASW= 0.8267, DB= 0.2328, CH= 50244.5868
Training epoch 1479, recon_loss:0.878444, zinb_loss:0.478196, cluster_loss:0.159856
Clustering 1479: ASW= 0.8287, DB= 0.2313, CH= 49636.5014
Training epoch 1480, recon_loss:0.878197, zinb_loss:0.478158, cluster_loss:0.159612
Clustering 1480: ASW= 0.8266, DB= 0.2336, CH= 50322.6034
Training epoch 1481, recon_loss:0.878240, zinb_loss:0.478181, cluster_loss:0.159868
Clustering 1481: ASW= 0.8288, DB= 0.2314, CH= 49614.2485
Training epoch 1482, recon_loss:0.878010, zinb_loss:0.478149, cluster_loss:0.159583
Clustering 1482: ASW= 0.8267, DB= 0.2335, CH= 50377.9094
Training epoch 1483, recon_loss:0.878064, zinb_loss:0.478169, cluster_loss:0.159870
Clustering 1483: ASW= 0.8288, DB= 0.2315, CH= 49585.6770
Training epoch 1484, recon_loss:0.877846, zinb_loss:0.478143, cluster_loss:0.159545
Clustering 1484: ASW= 0.8267, DB= 0.2334, CH= 50426.8492
Training epoch 1485, recon_loss:0.877896, zinb_loss:0.478151, cluster_loss:0.159869
Clustering 1485: ASW= 0.8288, DB= 0.2315, CH= 49518.0535
Training epoch 1486, recon_loss:0.877762, zinb_loss:0.478131, cluster_loss:0.159545
Clustering 1486: ASW= 0.8267, DB= 0.2333, CH= 50430.5670
Training epoch 1487, recon_loss:0.877982, zinb_loss:0.478177, cluster_loss:0.159853
Clustering 1487: ASW= 0.8288, DB= 0.2315, CH= 49545.9425
Training epoch 1488, recon_loss:0.877996, zinb_loss:0.478172, cluster_loss:0.159536
Clustering 1488: ASW= 0.8267, DB= 0.2331, CH= 50517.6398
Training epoch 1489, recon_loss:0.878028, zinb_loss:0.478169, cluster_loss:0.159843
Clustering 1489: ASW= 0.8287, DB= 0.2317, CH= 49448.2034
Training epoch 1490, recon_loss:0.878010, zinb_loss:0.478167, cluster_loss:0.159558
Clustering 1490: ASW= 0.8267, DB= 0.2330, CH= 50493.6791
Training epoch 1491, recon_loss:0.878312, zinb_loss:0.478214, cluster_loss:0.159841
Clustering 1491: ASW= 0.8289, DB= 0.2316, CH= 49539.4429
Training epoch 1492, recon_loss:0.878331, zinb_loss:0.478218, cluster_loss:0.159550
Clustering 1492: ASW= 0.8268, DB= 0.2327, CH= 50663.2418
Training epoch 1493, recon_loss:0.878567, zinb_loss:0.478246, cluster_loss:0.159875
Clustering 1493: ASW= 0.8289, DB= 0.2318, CH= 49482.4189
Training epoch 1494, recon_loss:0.878417, zinb_loss:0.478236, cluster_loss:0.159548
Clustering 1494: ASW= 0.8269, DB= 0.2326, CH= 50807.6424
Training epoch 1495, recon_loss:0.878352, zinb_loss:0.478218, cluster_loss:0.159842
Clustering 1495: ASW= 0.8288, DB= 0.2319, CH= 49363.7355
Training epoch 1496, recon_loss:0.878903, zinb_loss:0.478291, cluster_loss:0.159716
Clustering 1496: ASW= 0.8270, DB= 0.2326, CH= 50958.1860
Training epoch 1497, recon_loss:0.878495, zinb_loss:0.478095, cluster_loss:0.159950
Clustering 1497: ASW= 0.8273, DB= 0.2335, CH= 48615.3355
Training epoch 1498, recon_loss:0.877826, zinb_loss:0.478045, cluster_loss:0.159509
Clustering 1498: ASW= 0.8269, DB= 0.2330, CH= 50692.0775
Training epoch 1499, recon_loss:0.877505, zinb_loss:0.478026, cluster_loss:0.159781
Clustering 1499: ASW= 0.8284, DB= 0.2318, CH= 49208.7321
Training epoch 1500, recon_loss:0.877490, zinb_loss:0.478004, cluster_loss:0.159540
Clustering 1500: ASW= 0.8270, DB= 0.2327, CH= 50814.2169
Training epoch 1501, recon_loss:0.877782, zinb_loss:0.478029, cluster_loss:0.160063
Clustering 1501: ASW= 0.8282, DB= 0.2326, CH= 49157.1059
Training epoch 1502, recon_loss:0.878534, zinb_loss:0.478043, cluster_loss:0.159971
Clustering 1502: ASW= 0.8269, DB= 0.2322, CH= 50864.7005
Training epoch 1503, recon_loss:0.879617, zinb_loss:0.478107, cluster_loss:0.161025
Clustering 1503: ASW= 0.8275, DB= 0.2339, CH= 48658.2832
Training epoch 1504, recon_loss:0.879628, zinb_loss:0.478139, cluster_loss:0.160194
Clustering 1504: ASW= 0.8270, DB= 0.2318, CH= 50807.7259
Training epoch 1505, recon_loss:0.879276, zinb_loss:0.478066, cluster_loss:0.160909
Clustering 1505: ASW= 0.8273, DB= 0.2343, CH= 48753.3473
Training epoch 1506, recon_loss:0.879329, zinb_loss:0.478220, cluster_loss:0.160038
Clustering 1506: ASW= 0.8275, DB= 0.2310, CH= 50782.9887
Training epoch 1507, recon_loss:0.878368, zinb_loss:0.478014, cluster_loss:0.160452
Clustering 1507: ASW= 0.8273, DB= 0.2340, CH= 49045.5035
Training epoch 1508, recon_loss:0.878738, zinb_loss:0.478246, cluster_loss:0.159831
Clustering 1508: ASW= 0.8277, DB= 0.2308, CH= 50762.2465
Training epoch 1509, recon_loss:0.877767, zinb_loss:0.477997, cluster_loss:0.160158
Clustering 1509: ASW= 0.8274, DB= 0.2337, CH= 49271.8591
Training epoch 1510, recon_loss:0.878345, zinb_loss:0.478236, cluster_loss:0.159675
Clustering 1510: ASW= 0.8277, DB= 0.2309, CH= 50784.6093
Training epoch 1511, recon_loss:0.877410, zinb_loss:0.478002, cluster_loss:0.159978
Clustering 1511: ASW= 0.8276, DB= 0.2334, CH= 49445.5747
Training epoch 1512, recon_loss:0.878175, zinb_loss:0.478229, cluster_loss:0.159586
Clustering 1512: ASW= 0.8276, DB= 0.2311, CH= 50803.1748
Training epoch 1513, recon_loss:0.877328, zinb_loss:0.478032, cluster_loss:0.159888
Clustering 1513: ASW= 0.8277, DB= 0.2334, CH= 49554.8172
Training epoch 1514, recon_loss:0.878349, zinb_loss:0.478249, cluster_loss:0.159606
Clustering 1514: ASW= 0.8275, DB= 0.2316, CH= 50820.1487
Training epoch 1515, recon_loss:0.877564, zinb_loss:0.478092, cluster_loss:0.159895
Clustering 1515: ASW= 0.8277, DB= 0.2331, CH= 49564.3885
Training epoch 1516, recon_loss:0.878928, zinb_loss:0.478285, cluster_loss:0.159741
Clustering 1516: ASW= 0.8274, DB= 0.2320, CH= 50811.7476
Training epoch 1517, recon_loss:0.878158, zinb_loss:0.478169, cluster_loss:0.159998
Clustering 1517: ASW= 0.8276, DB= 0.2325, CH= 49453.1994
Training epoch 1518, recon_loss:0.879658, zinb_loss:0.478302, cluster_loss:0.159999
Clustering 1518: ASW= 0.8273, DB= 0.2330, CH= 50672.0875
Training epoch 1519, recon_loss:0.878785, zinb_loss:0.478229, cluster_loss:0.160128
Clustering 1519: ASW= 0.8274, DB= 0.2321, CH= 49268.9023
Training epoch 1520, recon_loss:0.879906, zinb_loss:0.478234, cluster_loss:0.160100
Clustering 1520: ASW= 0.8273, DB= 0.2337, CH= 50493.0867
Training epoch 1521, recon_loss:0.878715, zinb_loss:0.478203, cluster_loss:0.160028
Clustering 1521: ASW= 0.8274, DB= 0.2314, CH= 49428.8126
Training epoch 1522, recon_loss:0.879454, zinb_loss:0.478109, cluster_loss:0.160044
Clustering 1522: ASW= 0.8275, DB= 0.2343, CH= 50348.8099
Training epoch 1523, recon_loss:0.878354, zinb_loss:0.478135, cluster_loss:0.159841
Clustering 1523: ASW= 0.8276, DB= 0.2307, CH= 49755.6100
Training epoch 1524, recon_loss:0.878906, zinb_loss:0.477981, cluster_loss:0.160029
Clustering 1524: ASW= 0.8277, DB= 0.2338, CH= 50217.7074
Training epoch 1525, recon_loss:0.878072, zinb_loss:0.478075, cluster_loss:0.159770
Clustering 1525: ASW= 0.8275, DB= 0.2304, CH= 49919.2569
Training epoch 1526, recon_loss:0.878461, zinb_loss:0.477901, cluster_loss:0.160069
Clustering 1526: ASW= 0.8277, DB= 0.2339, CH= 50094.1566
Training epoch 1527, recon_loss:0.877772, zinb_loss:0.478032, cluster_loss:0.159754
Clustering 1527: ASW= 0.8273, DB= 0.2303, CH= 49970.3646
Training epoch 1528, recon_loss:0.878000, zinb_loss:0.477862, cluster_loss:0.160108
Clustering 1528: ASW= 0.8277, DB= 0.2341, CH= 49971.6131
Training epoch 1529, recon_loss:0.877397, zinb_loss:0.478003, cluster_loss:0.159736
Clustering 1529: ASW= 0.8270, DB= 0.2301, CH= 49927.0956
Training epoch 1530, recon_loss:0.877550, zinb_loss:0.477856, cluster_loss:0.160083
Clustering 1530: ASW= 0.8277, DB= 0.2341, CH= 49903.7873
Training epoch 1531, recon_loss:0.877067, zinb_loss:0.477992, cluster_loss:0.159695
Clustering 1531: ASW= 0.8268, DB= 0.2301, CH= 49880.5621
Training epoch 1532, recon_loss:0.877246, zinb_loss:0.477889, cluster_loss:0.160015
Clustering 1532: ASW= 0.8278, DB= 0.2336, CH= 49902.3157
Training epoch 1533, recon_loss:0.876943, zinb_loss:0.478009, cluster_loss:0.159654
Clustering 1533: ASW= 0.8266, DB= 0.2303, CH= 49835.5708
Training epoch 1534, recon_loss:0.877202, zinb_loss:0.477951, cluster_loss:0.159968
Clustering 1534: ASW= 0.8279, DB= 0.2333, CH= 49904.4194
Training epoch 1535, recon_loss:0.877083, zinb_loss:0.478051, cluster_loss:0.159640
Clustering 1535: ASW= 0.8264, DB= 0.2306, CH= 49800.6706
Training epoch 1536, recon_loss:0.877373, zinb_loss:0.478017, cluster_loss:0.159957
Clustering 1536: ASW= 0.8280, DB= 0.2329, CH= 49899.0798
Training epoch 1537, recon_loss:0.877423, zinb_loss:0.478110, cluster_loss:0.159654
Clustering 1537: ASW= 0.8264, DB= 0.2308, CH= 49820.6146
Training epoch 1538, recon_loss:0.877742, zinb_loss:0.478082, cluster_loss:0.159992
Clustering 1538: ASW= 0.8281, DB= 0.2325, CH= 49902.0199
Training epoch 1539, recon_loss:0.877938, zinb_loss:0.478183, cluster_loss:0.159714
Clustering 1539: ASW= 0.8266, DB= 0.2310, CH= 49895.9533
Training epoch 1540, recon_loss:0.878266, zinb_loss:0.478145, cluster_loss:0.160071
Clustering 1540: ASW= 0.8281, DB= 0.2321, CH= 49918.4028
Training epoch 1541, recon_loss:0.878422, zinb_loss:0.478239, cluster_loss:0.159784
Clustering 1541: ASW= 0.8269, DB= 0.2313, CH= 50005.6608
Training epoch 1542, recon_loss:0.878608, zinb_loss:0.478183, cluster_loss:0.160103
Clustering 1542: ASW= 0.8282, DB= 0.2317, CH= 49978.9611
Training epoch 1543, recon_loss:0.878451, zinb_loss:0.478242, cluster_loss:0.159770
Clustering 1543: ASW= 0.8273, DB= 0.2313, CH= 50139.6246
Training epoch 1544, recon_loss:0.878443, zinb_loss:0.478162, cluster_loss:0.160037
Clustering 1544: ASW= 0.8282, DB= 0.2312, CH= 50083.2355
Training epoch 1545, recon_loss:0.878039, zinb_loss:0.478191, cluster_loss:0.159666
Clustering 1545: ASW= 0.8277, DB= 0.2311, CH= 50238.1215
Training epoch 1546, recon_loss:0.877951, zinb_loss:0.478109, cluster_loss:0.159905
Clustering 1546: ASW= 0.8282, DB= 0.2308, CH= 50217.1591
Training epoch 1547, recon_loss:0.877464, zinb_loss:0.478127, cluster_loss:0.159558
Clustering 1547: ASW= 0.8280, DB= 0.2311, CH= 50268.6479
Training epoch 1548, recon_loss:0.877465, zinb_loss:0.478068, cluster_loss:0.159817
Clustering 1548: ASW= 0.8282, DB= 0.2306, CH= 50331.8253
Training epoch 1549, recon_loss:0.877027, zinb_loss:0.478080, cluster_loss:0.159548
Clustering 1549: ASW= 0.8281, DB= 0.2311, CH= 50151.5677
Training epoch 1550, recon_loss:0.877188, zinb_loss:0.478055, cluster_loss:0.159866
Clustering 1550: ASW= 0.8282, DB= 0.2305, CH= 50398.4480
Training epoch 1551, recon_loss:0.876904, zinb_loss:0.478059, cluster_loss:0.159716
Clustering 1551: ASW= 0.8281, DB= 0.2312, CH= 49867.5930
Training epoch 1552, recon_loss:0.877226, zinb_loss:0.478060, cluster_loss:0.159987
Clustering 1552: ASW= 0.8282, DB= 0.2304, CH= 50412.0697
Training epoch 1553, recon_loss:0.877127, zinb_loss:0.478060, cluster_loss:0.159876
Clustering 1553: ASW= 0.8283, DB= 0.2314, CH= 49783.1421
Training epoch 1554, recon_loss:0.877601, zinb_loss:0.478070, cluster_loss:0.160047
Clustering 1554: ASW= 0.8282, DB= 0.2310, CH= 50454.9857
Training epoch 1555, recon_loss:0.877668, zinb_loss:0.478072, cluster_loss:0.159868
Clustering 1555: ASW= 0.8286, DB= 0.2314, CH= 49994.5745
Training epoch 1556, recon_loss:0.878335, zinb_loss:0.478101, cluster_loss:0.160095
Clustering 1556: ASW= 0.8282, DB= 0.2308, CH= 50521.0084
Training epoch 1557, recon_loss:0.878272, zinb_loss:0.478087, cluster_loss:0.159847
Clustering 1557: ASW= 0.8287, DB= 0.2313, CH= 50115.0659
Training epoch 1558, recon_loss:0.878799, zinb_loss:0.478120, cluster_loss:0.160200
Clustering 1558: ASW= 0.8280, DB= 0.2309, CH= 50510.4888
Training epoch 1559, recon_loss:0.878340, zinb_loss:0.478048, cluster_loss:0.159801
Clustering 1559: ASW= 0.8287, DB= 0.2317, CH= 50146.1807
Training epoch 1560, recon_loss:0.878735, zinb_loss:0.478125, cluster_loss:0.160092
Clustering 1560: ASW= 0.8281, DB= 0.2308, CH= 50621.7903
Training epoch 1561, recon_loss:0.878677, zinb_loss:0.478042, cluster_loss:0.159929
Clustering 1561: ASW= 0.8284, DB= 0.2318, CH= 50010.5682
Training epoch 1562, recon_loss:0.879110, zinb_loss:0.478158, cluster_loss:0.160204
Clustering 1562: ASW= 0.8282, DB= 0.2304, CH= 50581.7795
Training epoch 1563, recon_loss:0.878672, zinb_loss:0.478010, cluster_loss:0.160023
Clustering 1563: ASW= 0.8281, DB= 0.2322, CH= 49996.5902
Training epoch 1564, recon_loss:0.878948, zinb_loss:0.478168, cluster_loss:0.160142
Clustering 1564: ASW= 0.8282, DB= 0.2309, CH= 50599.3270
Training epoch 1565, recon_loss:0.878268, zinb_loss:0.477990, cluster_loss:0.159933
Clustering 1565: ASW= 0.8281, DB= 0.2321, CH= 50005.0752
Training epoch 1566, recon_loss:0.878403, zinb_loss:0.478146, cluster_loss:0.159893
Clustering 1566: ASW= 0.8282, DB= 0.2311, CH= 50669.1170
Training epoch 1567, recon_loss:0.877786, zinb_loss:0.477983, cluster_loss:0.159733
Clustering 1567: ASW= 0.8282, DB= 0.2319, CH= 50057.1448
Training epoch 1568, recon_loss:0.877958, zinb_loss:0.478131, cluster_loss:0.159658
Clustering 1568: ASW= 0.8281, DB= 0.2314, CH= 50716.0549
Training epoch 1569, recon_loss:0.877542, zinb_loss:0.477989, cluster_loss:0.159569
Clustering 1569: ASW= 0.8284, DB= 0.2316, CH= 50101.5619
Training epoch 1570, recon_loss:0.877814, zinb_loss:0.478143, cluster_loss:0.159527
Clustering 1570: ASW= 0.8281, DB= 0.2318, CH= 50722.9115
Training epoch 1571, recon_loss:0.877543, zinb_loss:0.478029, cluster_loss:0.159507
Clustering 1571: ASW= 0.8284, DB= 0.2313, CH= 50123.7745
Training epoch 1572, recon_loss:0.877842, zinb_loss:0.478167, cluster_loss:0.159495
Clustering 1572: ASW= 0.8280, DB= 0.2321, CH= 50656.4964
Training epoch 1573, recon_loss:0.877636, zinb_loss:0.478075, cluster_loss:0.159489
Clustering 1573: ASW= 0.8284, DB= 0.2310, CH= 50170.6679
Training epoch 1574, recon_loss:0.877907, zinb_loss:0.478183, cluster_loss:0.159514
Clustering 1574: ASW= 0.8278, DB= 0.2324, CH= 50547.1179
Training epoch 1575, recon_loss:0.877737, zinb_loss:0.478107, cluster_loss:0.159489
Clustering 1575: ASW= 0.8284, DB= 0.2311, CH= 50281.8847
Training epoch 1576, recon_loss:0.877978, zinb_loss:0.478179, cluster_loss:0.159599
Clustering 1576: ASW= 0.8277, DB= 0.2325, CH= 50400.3773
Training epoch 1577, recon_loss:0.877890, zinb_loss:0.478125, cluster_loss:0.159527
Clustering 1577: ASW= 0.8283, DB= 0.2310, CH= 50483.8350
Training epoch 1578, recon_loss:0.878158, zinb_loss:0.478151, cluster_loss:0.159807
Clustering 1578: ASW= 0.8275, DB= 0.2318, CH= 50143.4998
Training epoch 1579, recon_loss:0.878250, zinb_loss:0.478139, cluster_loss:0.159662
Clustering 1579: ASW= 0.8282, DB= 0.2307, CH= 50710.7086
Training epoch 1580, recon_loss:0.878441, zinb_loss:0.478093, cluster_loss:0.160125
Clustering 1580: ASW= 0.8272, DB= 0.2324, CH= 49847.2248
Training epoch 1581, recon_loss:0.878799, zinb_loss:0.478141, cluster_loss:0.159908
Clustering 1581: ASW= 0.8282, DB= 0.2306, CH= 50951.2099
Training epoch 1582, recon_loss:0.878512, zinb_loss:0.477997, cluster_loss:0.160415
Clustering 1582: ASW= 0.8268, DB= 0.2329, CH= 49531.7902
Training epoch 1583, recon_loss:0.879033, zinb_loss:0.478122, cluster_loss:0.160087
Clustering 1583: ASW= 0.8283, DB= 0.2300, CH= 51219.1887
Training epoch 1584, recon_loss:0.878202, zinb_loss:0.477888, cluster_loss:0.160542
Clustering 1584: ASW= 0.8266, DB= 0.2331, CH= 49296.8829
Training epoch 1585, recon_loss:0.878760, zinb_loss:0.478078, cluster_loss:0.160107
Clustering 1585: ASW= 0.8284, DB= 0.2301, CH= 51388.4884
Training epoch 1586, recon_loss:0.877799, zinb_loss:0.477796, cluster_loss:0.160517
Clustering 1586: ASW= 0.8267, DB= 0.2329, CH= 49252.0005
Training epoch 1587, recon_loss:0.878409, zinb_loss:0.478032, cluster_loss:0.160045
Clustering 1587: ASW= 0.8288, DB= 0.2297, CH= 51541.1517
Training epoch 1588, recon_loss:0.877451, zinb_loss:0.477732, cluster_loss:0.160403
Clustering 1588: ASW= 0.8269, DB= 0.2323, CH= 49347.7949
Training epoch 1589, recon_loss:0.878092, zinb_loss:0.477996, cluster_loss:0.159931
Clustering 1589: ASW= 0.8293, DB= 0.2295, CH= 51718.4797
Training epoch 1590, recon_loss:0.877178, zinb_loss:0.477697, cluster_loss:0.160250
Clustering 1590: ASW= 0.8270, DB= 0.2319, CH= 49462.5436
Training epoch 1591, recon_loss:0.877913, zinb_loss:0.477984, cluster_loss:0.159861
Clustering 1591: ASW= 0.8297, DB= 0.2299, CH= 51822.6275
Training epoch 1592, recon_loss:0.877066, zinb_loss:0.477692, cluster_loss:0.160129
Clustering 1592: ASW= 0.8270, DB= 0.2326, CH= 49508.2050
Training epoch 1593, recon_loss:0.877952, zinb_loss:0.478001, cluster_loss:0.159934
Clustering 1593: ASW= 0.8298, DB= 0.2303, CH= 51792.2197
Training epoch 1594, recon_loss:0.877246, zinb_loss:0.477726, cluster_loss:0.160156
Clustering 1594: ASW= 0.8267, DB= 0.2328, CH= 49254.8898
Training epoch 1595, recon_loss:0.878191, zinb_loss:0.478042, cluster_loss:0.160158
Clustering 1595: ASW= 0.8296, DB= 0.2315, CH= 51499.1382
Training epoch 1596, recon_loss:0.877468, zinb_loss:0.477796, cluster_loss:0.160227
Clustering 1596: ASW= 0.8265, DB= 0.2327, CH= 48908.3699
Training epoch 1597, recon_loss:0.878045, zinb_loss:0.478054, cluster_loss:0.160135
Clustering 1597: ASW= 0.8296, DB= 0.2317, CH= 51306.2050
Training epoch 1598, recon_loss:0.877125, zinb_loss:0.477842, cluster_loss:0.159909
Clustering 1598: ASW= 0.8270, DB= 0.2317, CH= 49341.8084
Training epoch 1599, recon_loss:0.877630, zinb_loss:0.478040, cluster_loss:0.159873
Clustering 1599: ASW= 0.8298, DB= 0.2318, CH= 51346.7727
Training epoch 1600, recon_loss:0.876823, zinb_loss:0.477876, cluster_loss:0.159606
Clustering 1600: ASW= 0.8276, DB= 0.2311, CH= 49923.3256
Training epoch 1601, recon_loss:0.877409, zinb_loss:0.478035, cluster_loss:0.159713
Clustering 1601: ASW= 0.8299, DB= 0.2320, CH= 51356.1466
Training epoch 1602, recon_loss:0.876772, zinb_loss:0.477905, cluster_loss:0.159463
Clustering 1602: ASW= 0.8280, DB= 0.2305, CH= 50292.5030
Training epoch 1603, recon_loss:0.877364, zinb_loss:0.478020, cluster_loss:0.159657
Clustering 1603: ASW= 0.8298, DB= 0.2315, CH= 51296.9414
Training epoch 1604, recon_loss:0.876857, zinb_loss:0.477930, cluster_loss:0.159409
Clustering 1604: ASW= 0.8283, DB= 0.2299, CH= 50535.0473
Training epoch 1605, recon_loss:0.877396, zinb_loss:0.477991, cluster_loss:0.159673
Clustering 1605: ASW= 0.8296, DB= 0.2319, CH= 51167.4591
Training epoch 1606, recon_loss:0.876982, zinb_loss:0.477934, cluster_loss:0.159411
Clustering 1606: ASW= 0.8286, DB= 0.2295, CH= 50782.8978
Training epoch 1607, recon_loss:0.877446, zinb_loss:0.477938, cluster_loss:0.159731
Clustering 1607: ASW= 0.8294, DB= 0.2323, CH= 51003.1188
Training epoch 1608, recon_loss:0.877056, zinb_loss:0.477926, cluster_loss:0.159422
Clustering 1608: ASW= 0.8288, DB= 0.2290, CH= 50999.6867
Training epoch 1609, recon_loss:0.877366, zinb_loss:0.477863, cluster_loss:0.159773
Clustering 1609: ASW= 0.8292, DB= 0.2326, CH= 50819.9348
Training epoch 1610, recon_loss:0.877078, zinb_loss:0.477915, cluster_loss:0.159435
Clustering 1610: ASW= 0.8289, DB= 0.2285, CH= 51188.6817
Training epoch 1611, recon_loss:0.877266, zinb_loss:0.477786, cluster_loss:0.159821
Clustering 1611: ASW= 0.8290, DB= 0.2333, CH= 50615.4946
Training epoch 1612, recon_loss:0.877148, zinb_loss:0.477916, cluster_loss:0.159481
Clustering 1612: ASW= 0.8289, DB= 0.2283, CH= 51354.4634
Training epoch 1613, recon_loss:0.877231, zinb_loss:0.477727, cluster_loss:0.159912
Clustering 1613: ASW= 0.8286, DB= 0.2339, CH= 50384.0385
Training epoch 1614, recon_loss:0.877293, zinb_loss:0.477930, cluster_loss:0.159572
Clustering 1614: ASW= 0.8291, DB= 0.2278, CH= 51443.0312
Training epoch 1615, recon_loss:0.877254, zinb_loss:0.477685, cluster_loss:0.159990
Clustering 1615: ASW= 0.8283, DB= 0.2344, CH= 50168.5606
Training epoch 1616, recon_loss:0.877392, zinb_loss:0.477945, cluster_loss:0.159615
Clustering 1616: ASW= 0.8292, DB= 0.2276, CH= 51474.7583
Training epoch 1617, recon_loss:0.877193, zinb_loss:0.477657, cluster_loss:0.160005
Clustering 1617: ASW= 0.8280, DB= 0.2338, CH= 50068.6813
Training epoch 1618, recon_loss:0.877404, zinb_loss:0.477956, cluster_loss:0.159594
Clustering 1618: ASW= 0.8294, DB= 0.2274, CH= 51459.9642
Training epoch 1619, recon_loss:0.877169, zinb_loss:0.477643, cluster_loss:0.159963
Clustering 1619: ASW= 0.8278, DB= 0.2339, CH= 50037.5068
Training epoch 1620, recon_loss:0.877493, zinb_loss:0.477978, cluster_loss:0.159544
Clustering 1620: ASW= 0.8297, DB= 0.2273, CH= 51398.5790
Training epoch 1621, recon_loss:0.877198, zinb_loss:0.477644, cluster_loss:0.159889
Clustering 1621: ASW= 0.8278, DB= 0.2335, CH= 50139.9435
Training epoch 1622, recon_loss:0.877541, zinb_loss:0.478004, cluster_loss:0.159516
Clustering 1622: ASW= 0.8300, DB= 0.2272, CH= 51331.8690
Training epoch 1623, recon_loss:0.877244, zinb_loss:0.477650, cluster_loss:0.159823
Clustering 1623: ASW= 0.8279, DB= 0.2329, CH= 50305.6318
Training epoch 1624, recon_loss:0.877641, zinb_loss:0.478028, cluster_loss:0.159525
Clustering 1624: ASW= 0.8304, DB= 0.2274, CH= 51297.3608
Training epoch 1625, recon_loss:0.877539, zinb_loss:0.477688, cluster_loss:0.159800
Clustering 1625: ASW= 0.8280, DB= 0.2323, CH= 50511.3133
Training epoch 1626, recon_loss:0.877807, zinb_loss:0.478034, cluster_loss:0.159598
Clustering 1626: ASW= 0.8306, DB= 0.2276, CH= 51272.1398
Training epoch 1627, recon_loss:0.877576, zinb_loss:0.477693, cluster_loss:0.159724
Clustering 1627: ASW= 0.8281, DB= 0.2320, CH= 50650.4784
Training epoch 1628, recon_loss:0.877644, zinb_loss:0.477985, cluster_loss:0.159535
Clustering 1628: ASW= 0.8307, DB= 0.2278, CH= 51290.7186
Training epoch 1629, recon_loss:0.877535, zinb_loss:0.477718, cluster_loss:0.159607
Clustering 1629: ASW= 0.8283, DB= 0.2315, CH= 50823.8955
Training epoch 1630, recon_loss:0.877543, zinb_loss:0.477950, cluster_loss:0.159552
Clustering 1630: ASW= 0.8307, DB= 0.2280, CH= 51328.5773
Training epoch 1631, recon_loss:0.877480, zinb_loss:0.477707, cluster_loss:0.159549
Clustering 1631: ASW= 0.8282, DB= 0.2313, CH= 50865.8296
Training epoch 1632, recon_loss:0.877457, zinb_loss:0.477905, cluster_loss:0.159527
Clustering 1632: ASW= 0.8307, DB= 0.2283, CH= 51342.9361
Training epoch 1633, recon_loss:0.877410, zinb_loss:0.477717, cluster_loss:0.159516
Clustering 1633: ASW= 0.8281, DB= 0.2313, CH= 50885.7367
Training epoch 1634, recon_loss:0.877666, zinb_loss:0.477922, cluster_loss:0.159487
Clustering 1634: ASW= 0.8308, DB= 0.2284, CH= 51354.6917
Training epoch 1635, recon_loss:0.877700, zinb_loss:0.477782, cluster_loss:0.159509
Clustering 1635: ASW= 0.8282, DB= 0.2311, CH= 50933.8599
Training epoch 1636, recon_loss:0.877893, zinb_loss:0.477951, cluster_loss:0.159574
Clustering 1636: ASW= 0.8306, DB= 0.2287, CH= 51304.5532
Training epoch 1637, recon_loss:0.877837, zinb_loss:0.477821, cluster_loss:0.159572
Clustering 1637: ASW= 0.8281, DB= 0.2311, CH= 50855.1971
Training epoch 1638, recon_loss:0.877901, zinb_loss:0.477961, cluster_loss:0.159630
Clustering 1638: ASW= 0.8303, DB= 0.2290, CH= 51154.4906
Training epoch 1639, recon_loss:0.877666, zinb_loss:0.477857, cluster_loss:0.159610
Clustering 1639: ASW= 0.8280, DB= 0.2311, CH= 50710.4597
Training epoch 1640, recon_loss:0.877679, zinb_loss:0.477970, cluster_loss:0.159666
Clustering 1640: ASW= 0.8298, DB= 0.2294, CH= 50895.1757
Training epoch 1641, recon_loss:0.877416, zinb_loss:0.477882, cluster_loss:0.159665
Clustering 1641: ASW= 0.8278, DB= 0.2306, CH= 50487.1036
Training epoch 1642, recon_loss:0.877399, zinb_loss:0.477980, cluster_loss:0.159683
Clustering 1642: ASW= 0.8295, DB= 0.2298, CH= 50724.1770
Training epoch 1643, recon_loss:0.877058, zinb_loss:0.477890, cluster_loss:0.159681
Clustering 1643: ASW= 0.8277, DB= 0.2315, CH= 50338.3651
Training epoch 1644, recon_loss:0.877066, zinb_loss:0.477983, cluster_loss:0.159614
Clustering 1644: ASW= 0.8294, DB= 0.2298, CH= 50837.1388
Training epoch 1645, recon_loss:0.876711, zinb_loss:0.477878, cluster_loss:0.159644
Clustering 1645: ASW= 0.8278, DB= 0.2313, CH= 50349.9151
Training epoch 1646, recon_loss:0.876843, zinb_loss:0.477980, cluster_loss:0.159558
Clustering 1646: ASW= 0.8293, DB= 0.2299, CH= 51033.6439
Training epoch 1647, recon_loss:0.876549, zinb_loss:0.477864, cluster_loss:0.159634
Clustering 1647: ASW= 0.8278, DB= 0.2312, CH= 50415.9550
Training epoch 1648, recon_loss:0.876905, zinb_loss:0.477983, cluster_loss:0.159595
Clustering 1648: ASW= 0.8293, DB= 0.2302, CH= 51178.6519
Training epoch 1649, recon_loss:0.876707, zinb_loss:0.477857, cluster_loss:0.159685
Clustering 1649: ASW= 0.8279, DB= 0.2312, CH= 50470.2603
Training epoch 1650, recon_loss:0.877293, zinb_loss:0.477987, cluster_loss:0.159699
Clustering 1650: ASW= 0.8292, DB= 0.2304, CH= 51266.8578
Training epoch 1651, recon_loss:0.877233, zinb_loss:0.477858, cluster_loss:0.159779
Clustering 1651: ASW= 0.8281, DB= 0.2311, CH= 50555.6732
Training epoch 1652, recon_loss:0.877957, zinb_loss:0.477987, cluster_loss:0.159845
Clustering 1652: ASW= 0.8291, DB= 0.2306, CH= 51318.6221
Training epoch 1653, recon_loss:0.877847, zinb_loss:0.477862, cluster_loss:0.159861
Clustering 1653: ASW= 0.8284, DB= 0.2308, CH= 50672.0427
Training epoch 1654, recon_loss:0.878461, zinb_loss:0.477961, cluster_loss:0.159894
Clustering 1654: ASW= 0.8289, DB= 0.2309, CH= 51371.1317
Training epoch 1655, recon_loss:0.877994, zinb_loss:0.477846, cluster_loss:0.159793
Clustering 1655: ASW= 0.8289, DB= 0.2303, CH= 50855.4811
Training epoch 1656, recon_loss:0.878230, zinb_loss:0.477900, cluster_loss:0.159761
Clustering 1656: ASW= 0.8289, DB= 0.2310, CH= 51436.6578
Training epoch 1657, recon_loss:0.877568, zinb_loss:0.477802, cluster_loss:0.159610
Clustering 1657: ASW= 0.8294, DB= 0.2298, CH= 51054.8430
Training epoch 1658, recon_loss:0.877740, zinb_loss:0.477818, cluster_loss:0.159527
Clustering 1658: ASW= 0.8289, DB= 0.2309, CH= 51532.8054
Training epoch 1659, recon_loss:0.877140, zinb_loss:0.477752, cluster_loss:0.159427
Clustering 1659: ASW= 0.8297, DB= 0.2299, CH= 51230.6404
Training epoch 1660, recon_loss:0.877369, zinb_loss:0.477790, cluster_loss:0.159349
Clustering 1660: ASW= 0.8290, DB= 0.2308, CH= 51633.7817
Training epoch 1661, recon_loss:0.876798, zinb_loss:0.477693, cluster_loss:0.159306
Clustering 1661: ASW= 0.8298, DB= 0.2303, CH= 51258.5690
Training epoch 1662, recon_loss:0.877125, zinb_loss:0.477728, cluster_loss:0.159255
Clustering 1662: ASW= 0.8290, DB= 0.2308, CH= 51647.1997
Training epoch 1663, recon_loss:0.876918, zinb_loss:0.477673, cluster_loss:0.159331
Clustering 1663: ASW= 0.8299, DB= 0.2303, CH= 51250.3842
Training epoch 1664, recon_loss:0.877389, zinb_loss:0.477783, cluster_loss:0.159243
Clustering 1664: ASW= 0.8290, DB= 0.2304, CH= 51570.3992
Training epoch 1665, recon_loss:0.877109, zinb_loss:0.477674, cluster_loss:0.159404
Clustering 1665: ASW= 0.8297, DB= 0.2296, CH= 51056.4048
Training epoch 1666, recon_loss:0.877670, zinb_loss:0.477864, cluster_loss:0.159348
Clustering 1666: ASW= 0.8288, DB= 0.2305, CH= 51238.7315
Training epoch 1667, recon_loss:0.877215, zinb_loss:0.477682, cluster_loss:0.159513
Clustering 1667: ASW= 0.8293, DB= 0.2299, CH= 50752.3111
Training epoch 1668, recon_loss:0.877704, zinb_loss:0.477910, cluster_loss:0.159377
Clustering 1668: ASW= 0.8287, DB= 0.2304, CH= 51023.5320
Training epoch 1669, recon_loss:0.877226, zinb_loss:0.477703, cluster_loss:0.159561
Clustering 1669: ASW= 0.8292, DB= 0.2299, CH= 50700.3340
Training epoch 1670, recon_loss:0.877552, zinb_loss:0.477907, cluster_loss:0.159368
Clustering 1670: ASW= 0.8287, DB= 0.2302, CH= 51070.5436
Training epoch 1671, recon_loss:0.877539, zinb_loss:0.477757, cluster_loss:0.159855
Clustering 1671: ASW= 0.8291, DB= 0.2296, CH= 50600.9842
Training epoch 1672, recon_loss:0.877856, zinb_loss:0.477963, cluster_loss:0.159749
Clustering 1672: ASW= 0.8283, DB= 0.2304, CH= 50674.9039
Training epoch 1673, recon_loss:0.877808, zinb_loss:0.477807, cluster_loss:0.160232
Clustering 1673: ASW= 0.8289, DB= 0.2296, CH= 50386.2347
Training epoch 1674, recon_loss:0.877738, zinb_loss:0.477942, cluster_loss:0.159979
Clustering 1674: ASW= 0.8283, DB= 0.2305, CH= 50509.0958
Training epoch 1675, recon_loss:0.877361, zinb_loss:0.477773, cluster_loss:0.160295
Clustering 1675: ASW= 0.8288, DB= 0.2294, CH= 50308.4148
Training epoch 1676, recon_loss:0.877076, zinb_loss:0.477860, cluster_loss:0.159812
Clustering 1676: ASW= 0.8288, DB= 0.2302, CH= 50772.8787
Training epoch 1677, recon_loss:0.876765, zinb_loss:0.477708, cluster_loss:0.160143
Clustering 1677: ASW= 0.8288, DB= 0.2292, CH= 50396.9109
Training epoch 1678, recon_loss:0.876594, zinb_loss:0.477805, cluster_loss:0.159622
Clustering 1678: ASW= 0.8292, DB= 0.2299, CH= 50995.0601
Training epoch 1679, recon_loss:0.876407, zinb_loss:0.477668, cluster_loss:0.159972
Clustering 1679: ASW= 0.8289, DB= 0.2291, CH= 50519.8012
Training epoch 1680, recon_loss:0.876296, zinb_loss:0.477768, cluster_loss:0.159490
Clustering 1680: ASW= 0.8295, DB= 0.2297, CH= 51169.0962
Training epoch 1681, recon_loss:0.876313, zinb_loss:0.477661, cluster_loss:0.159859
Clustering 1681: ASW= 0.8290, DB= 0.2291, CH= 50650.2154
Training epoch 1682, recon_loss:0.876286, zinb_loss:0.477767, cluster_loss:0.159432
Clustering 1682: ASW= 0.8297, DB= 0.2296, CH= 51232.5066
Training epoch 1683, recon_loss:0.876367, zinb_loss:0.477665, cluster_loss:0.159792
Clustering 1683: ASW= 0.8289, DB= 0.2290, CH= 50749.0863
Training epoch 1684, recon_loss:0.876362, zinb_loss:0.477764, cluster_loss:0.159387
Clustering 1684: ASW= 0.8299, DB= 0.2295, CH= 51306.6920
Training epoch 1685, recon_loss:0.876868, zinb_loss:0.477736, cluster_loss:0.159805
Clustering 1685: ASW= 0.8289, DB= 0.2289, CH= 50922.9171
Training epoch 1686, recon_loss:0.876633, zinb_loss:0.477763, cluster_loss:0.159392
Clustering 1686: ASW= 0.8292, DB= 0.2301, CH= 50819.2126
Training epoch 1687, recon_loss:0.876255, zinb_loss:0.477661, cluster_loss:0.159657
Clustering 1687: ASW= 0.8287, DB= 0.2294, CH= 50693.4674
Training epoch 1688, recon_loss:0.875863, zinb_loss:0.477697, cluster_loss:0.159186
Clustering 1688: ASW= 0.8297, DB= 0.2294, CH= 51301.7403
Training epoch 1689, recon_loss:0.875858, zinb_loss:0.477619, cluster_loss:0.159484
Clustering 1689: ASW= 0.8288, DB= 0.2291, CH= 50908.8277
Training epoch 1690, recon_loss:0.875808, zinb_loss:0.477691, cluster_loss:0.159136
Clustering 1690: ASW= 0.8300, DB= 0.2290, CH= 51352.1767
Training epoch 1691, recon_loss:0.875805, zinb_loss:0.477612, cluster_loss:0.159465
Clustering 1691: ASW= 0.8288, DB= 0.2291, CH= 51041.1752
Training epoch 1692, recon_loss:0.875971, zinb_loss:0.477686, cluster_loss:0.159268
Clustering 1692: ASW= 0.8302, DB= 0.2292, CH= 51353.4209
Training epoch 1693, recon_loss:0.876136, zinb_loss:0.477643, cluster_loss:0.159559
Clustering 1693: ASW= 0.8287, DB= 0.2291, CH= 51230.4572
Training epoch 1694, recon_loss:0.876389, zinb_loss:0.477693, cluster_loss:0.159512
Clustering 1694: ASW= 0.8303, DB= 0.2295, CH= 51133.1960
Training epoch 1695, recon_loss:0.876791, zinb_loss:0.477705, cluster_loss:0.159708
Clustering 1695: ASW= 0.8285, DB= 0.2289, CH= 51451.4540
Training epoch 1696, recon_loss:0.877197, zinb_loss:0.477755, cluster_loss:0.159934
Clustering 1696: ASW= 0.8305, DB= 0.2301, CH= 50632.6800
Training epoch 1697, recon_loss:0.877674, zinb_loss:0.477827, cluster_loss:0.159812
Clustering 1697: ASW= 0.8283, DB= 0.2295, CH= 51731.1759
Training epoch 1698, recon_loss:0.877603, zinb_loss:0.477807, cluster_loss:0.160334
Clustering 1698: ASW= 0.8305, DB= 0.2309, CH= 50230.1199
Training epoch 1699, recon_loss:0.877663, zinb_loss:0.477894, cluster_loss:0.159718
Clustering 1699: ASW= 0.8284, DB= 0.2293, CH= 52052.0598
Training epoch 1700, recon_loss:0.876688, zinb_loss:0.477735, cluster_loss:0.160140
Clustering 1700: ASW= 0.8306, DB= 0.2306, CH= 50117.7339
Training epoch 1701, recon_loss:0.876875, zinb_loss:0.477866, cluster_loss:0.159471
Clustering 1701: ASW= 0.8285, DB= 0.2293, CH= 52278.7013
Training epoch 1702, recon_loss:0.876008, zinb_loss:0.477651, cluster_loss:0.160144
Clustering 1702: ASW= 0.8305, DB= 0.2308, CH= 49965.8538
Training epoch 1703, recon_loss:0.876442, zinb_loss:0.477833, cluster_loss:0.159424
Clustering 1703: ASW= 0.8285, DB= 0.2292, CH= 52341.8976
Training epoch 1704, recon_loss:0.875951, zinb_loss:0.477669, cluster_loss:0.160287
Clustering 1704: ASW= 0.8303, DB= 0.2312, CH= 49762.4369
Training epoch 1705, recon_loss:0.876437, zinb_loss:0.477859, cluster_loss:0.159423
Clustering 1705: ASW= 0.8286, DB= 0.2292, CH= 52347.8740
Training epoch 1706, recon_loss:0.876086, zinb_loss:0.477692, cluster_loss:0.160381
Clustering 1706: ASW= 0.8300, DB= 0.2314, CH= 49664.3302
Training epoch 1707, recon_loss:0.876489, zinb_loss:0.477863, cluster_loss:0.159409
Clustering 1707: ASW= 0.8287, DB= 0.2290, CH= 52315.5334
Training epoch 1708, recon_loss:0.876108, zinb_loss:0.477725, cluster_loss:0.160310
Clustering 1708: ASW= 0.8299, DB= 0.2316, CH= 49812.1474
Training epoch 1709, recon_loss:0.876357, zinb_loss:0.477879, cluster_loss:0.159328
Clustering 1709: ASW= 0.8289, DB= 0.2288, CH= 52365.3662
Training epoch 1710, recon_loss:0.875953, zinb_loss:0.477717, cluster_loss:0.160162
Clustering 1710: ASW= 0.8296, DB= 0.2307, CH= 49937.5547
Training epoch 1711, recon_loss:0.876192, zinb_loss:0.477896, cluster_loss:0.159268
Clustering 1711: ASW= 0.8290, DB= 0.2287, CH= 52353.2797
Training epoch 1712, recon_loss:0.875764, zinb_loss:0.477733, cluster_loss:0.160055
Clustering 1712: ASW= 0.8293, DB= 0.2308, CH= 50031.9548
Training epoch 1713, recon_loss:0.876032, zinb_loss:0.477930, cluster_loss:0.159284
Clustering 1713: ASW= 0.8290, DB= 0.2288, CH= 52236.6393
Training epoch 1714, recon_loss:0.875659, zinb_loss:0.477757, cluster_loss:0.160011
Clustering 1714: ASW= 0.8290, DB= 0.2308, CH= 50026.9554
Training epoch 1715, recon_loss:0.875980, zinb_loss:0.477964, cluster_loss:0.159357
Clustering 1715: ASW= 0.8288, DB= 0.2290, CH= 52006.2141
Training epoch 1716, recon_loss:0.875579, zinb_loss:0.477779, cluster_loss:0.159969
Clustering 1716: ASW= 0.8286, DB= 0.2308, CH= 50011.7407
Training epoch 1717, recon_loss:0.875968, zinb_loss:0.477982, cluster_loss:0.159417
Clustering 1717: ASW= 0.8286, DB= 0.2292, CH= 51791.0225
Training epoch 1718, recon_loss:0.875541, zinb_loss:0.477785, cluster_loss:0.159874
Clustering 1718: ASW= 0.8284, DB= 0.2304, CH= 50058.5261
Training epoch 1719, recon_loss:0.876022, zinb_loss:0.477980, cluster_loss:0.159409
Clustering 1719: ASW= 0.8288, DB= 0.2294, CH= 51722.1090
Training epoch 1720, recon_loss:0.875599, zinb_loss:0.477780, cluster_loss:0.159772
Clustering 1720: ASW= 0.8284, DB= 0.2302, CH= 50148.2601
Training epoch 1721, recon_loss:0.876229, zinb_loss:0.477973, cluster_loss:0.159421
Clustering 1721: ASW= 0.8290, DB= 0.2295, CH= 51753.7259
Training epoch 1722, recon_loss:0.875843, zinb_loss:0.477762, cluster_loss:0.159748
Clustering 1722: ASW= 0.8283, DB= 0.2300, CH= 50169.9044
Training epoch 1723, recon_loss:0.876729, zinb_loss:0.477968, cluster_loss:0.159534
Clustering 1723: ASW= 0.8292, DB= 0.2296, CH= 51791.3014
Training epoch 1724, recon_loss:0.876322, zinb_loss:0.477738, cluster_loss:0.159820
Clustering 1724: ASW= 0.8282, DB= 0.2299, CH= 50052.7341
Training epoch 1725, recon_loss:0.877453, zinb_loss:0.477962, cluster_loss:0.159741
Clustering 1725: ASW= 0.8293, DB= 0.2299, CH= 51806.9469
Training epoch 1726, recon_loss:0.876738, zinb_loss:0.477719, cluster_loss:0.159893
Clustering 1726: ASW= 0.8281, DB= 0.2308, CH= 49951.4678
Training epoch 1727, recon_loss:0.877635, zinb_loss:0.477890, cluster_loss:0.159704
Clustering 1727: ASW= 0.8294, DB= 0.2301, CH= 51879.9646
Training epoch 1728, recon_loss:0.876536, zinb_loss:0.477693, cluster_loss:0.159688
Clustering 1728: ASW= 0.8285, DB= 0.2302, CH= 50163.2641
Training epoch 1729, recon_loss:0.877253, zinb_loss:0.477788, cluster_loss:0.159443
Clustering 1729: ASW= 0.8296, DB= 0.2299, CH= 51994.0726
Training epoch 1730, recon_loss:0.876266, zinb_loss:0.477659, cluster_loss:0.159481
Clustering 1730: ASW= 0.8290, DB= 0.2296, CH= 50488.3490
Training epoch 1731, recon_loss:0.877024, zinb_loss:0.477720, cluster_loss:0.159336
Clustering 1731: ASW= 0.8296, DB= 0.2303, CH= 52013.5280
Training epoch 1732, recon_loss:0.876317, zinb_loss:0.477670, cluster_loss:0.159440
Clustering 1732: ASW= 0.8293, DB= 0.2291, CH= 50683.3323
Training epoch 1733, recon_loss:0.877245, zinb_loss:0.477691, cluster_loss:0.159398
Clustering 1733: ASW= 0.8296, DB= 0.2297, CH= 51998.9344
Training epoch 1734, recon_loss:0.876735, zinb_loss:0.477708, cluster_loss:0.159537
Clustering 1734: ASW= 0.8296, DB= 0.2287, CH= 50804.6107
Training epoch 1735, recon_loss:0.877657, zinb_loss:0.477697, cluster_loss:0.159574
Clustering 1735: ASW= 0.8295, DB= 0.2300, CH= 51943.5473
Training epoch 1736, recon_loss:0.877238, zinb_loss:0.477758, cluster_loss:0.159667
Clustering 1736: ASW= 0.8297, DB= 0.2290, CH= 50835.4491
Training epoch 1737, recon_loss:0.877930, zinb_loss:0.477701, cluster_loss:0.159685
Clustering 1737: ASW= 0.8295, DB= 0.2301, CH= 51846.6259
Training epoch 1738, recon_loss:0.877414, zinb_loss:0.477790, cluster_loss:0.159718
Clustering 1738: ASW= 0.8297, DB= 0.2292, CH= 50830.6386
Training epoch 1739, recon_loss:0.877863, zinb_loss:0.477710, cluster_loss:0.159707
Clustering 1739: ASW= 0.8295, DB= 0.2299, CH= 51765.0749
Training epoch 1740, recon_loss:0.877340, zinb_loss:0.477818, cluster_loss:0.159687
Clustering 1740: ASW= 0.8296, DB= 0.2293, CH= 50822.8784
Training epoch 1741, recon_loss:0.877559, zinb_loss:0.477721, cluster_loss:0.159646
Clustering 1741: ASW= 0.8296, DB= 0.2294, CH= 51741.0188
Training epoch 1742, recon_loss:0.877148, zinb_loss:0.477837, cluster_loss:0.159582
Clustering 1742: ASW= 0.8294, DB= 0.2291, CH= 50903.2819
Training epoch 1743, recon_loss:0.877165, zinb_loss:0.477742, cluster_loss:0.159536
Clustering 1743: ASW= 0.8298, DB= 0.2288, CH= 51769.2949
Training epoch 1744, recon_loss:0.876941, zinb_loss:0.477858, cluster_loss:0.159451
Clustering 1744: ASW= 0.8292, DB= 0.2293, CH= 50987.1153
Training epoch 1745, recon_loss:0.876910, zinb_loss:0.477801, cluster_loss:0.159448
Clustering 1745: ASW= 0.8301, DB= 0.2284, CH= 51840.8659
Training epoch 1746, recon_loss:0.876879, zinb_loss:0.477904, cluster_loss:0.159369
Clustering 1746: ASW= 0.8289, DB= 0.2296, CH= 50985.4225
Training epoch 1747, recon_loss:0.876852, zinb_loss:0.477903, cluster_loss:0.159413
Clustering 1747: ASW= 0.8305, DB= 0.2280, CH= 51934.0871
Training epoch 1748, recon_loss:0.876939, zinb_loss:0.477960, cluster_loss:0.159350
Clustering 1748: ASW= 0.8285, DB= 0.2300, CH= 50930.8957
Training epoch 1749, recon_loss:0.876924, zinb_loss:0.477987, cluster_loss:0.159394
Clustering 1749: ASW= 0.8308, DB= 0.2276, CH= 52041.4364
Training epoch 1750, recon_loss:0.876970, zinb_loss:0.477984, cluster_loss:0.159339
Clustering 1750: ASW= 0.8283, DB= 0.2302, CH= 50887.7188
Training epoch 1751, recon_loss:0.877049, zinb_loss:0.478052, cluster_loss:0.159440
Clustering 1751: ASW= 0.8310, DB= 0.2284, CH= 52114.8565
Training epoch 1752, recon_loss:0.876999, zinb_loss:0.477945, cluster_loss:0.159342
Clustering 1752: ASW= 0.8281, DB= 0.2306, CH= 50855.0705
Training epoch 1753, recon_loss:0.877102, zinb_loss:0.478024, cluster_loss:0.159423
Clustering 1753: ASW= 0.8313, DB= 0.2284, CH= 52156.9419
Training epoch 1754, recon_loss:0.876862, zinb_loss:0.477855, cluster_loss:0.159337
Clustering 1754: ASW= 0.8280, DB= 0.2308, CH= 50883.2783
Training epoch 1755, recon_loss:0.877085, zinb_loss:0.477976, cluster_loss:0.159409
Clustering 1755: ASW= 0.8314, DB= 0.2289, CH= 52098.3841
Training epoch 1756, recon_loss:0.876779, zinb_loss:0.477759, cluster_loss:0.159317
Clustering 1756: ASW= 0.8282, DB= 0.2298, CH= 50920.0503
Training epoch 1757, recon_loss:0.877089, zinb_loss:0.477887, cluster_loss:0.159409
Clustering 1757: ASW= 0.8313, DB= 0.2290, CH= 52037.6751
Training epoch 1758, recon_loss:0.876562, zinb_loss:0.477627, cluster_loss:0.159301
Clustering 1758: ASW= 0.8284, DB= 0.2293, CH= 50987.1157
Training epoch 1759, recon_loss:0.876735, zinb_loss:0.477797, cluster_loss:0.159289
Clustering 1759: ASW= 0.8315, DB= 0.2291, CH= 52135.0981
Training epoch 1760, recon_loss:0.876133, zinb_loss:0.477527, cluster_loss:0.159230
Clustering 1760: ASW= 0.8286, DB= 0.2291, CH= 51113.0741
Training epoch 1761, recon_loss:0.876351, zinb_loss:0.477718, cluster_loss:0.159201
Clustering 1761: ASW= 0.8315, DB= 0.2288, CH= 52209.4205
Training epoch 1762, recon_loss:0.875761, zinb_loss:0.477435, cluster_loss:0.159214
Clustering 1762: ASW= 0.8287, DB= 0.2292, CH= 51206.7485
Training epoch 1763, recon_loss:0.876010, zinb_loss:0.477683, cluster_loss:0.159145
Clustering 1763: ASW= 0.8317, DB= 0.2286, CH= 52294.6927
Training epoch 1764, recon_loss:0.875655, zinb_loss:0.477402, cluster_loss:0.159277
Clustering 1764: ASW= 0.8288, DB= 0.2292, CH= 51286.7244
Training epoch 1765, recon_loss:0.875993, zinb_loss:0.477684, cluster_loss:0.159197
Clustering 1765: ASW= 0.8318, DB= 0.2283, CH= 52232.6743
Training epoch 1766, recon_loss:0.875731, zinb_loss:0.477393, cluster_loss:0.159409
Clustering 1766: ASW= 0.8288, DB= 0.2294, CH= 51279.6010
Training epoch 1767, recon_loss:0.876053, zinb_loss:0.477699, cluster_loss:0.159283
Clustering 1767: ASW= 0.8317, DB= 0.2279, CH= 52063.0327
Training epoch 1768, recon_loss:0.875735, zinb_loss:0.477405, cluster_loss:0.159474
Clustering 1768: ASW= 0.8289, DB= 0.2296, CH= 51223.5305
Training epoch 1769, recon_loss:0.875943, zinb_loss:0.477698, cluster_loss:0.159268
Clustering 1769: ASW= 0.8317, DB= 0.2277, CH= 52016.2938
Training epoch 1770, recon_loss:0.875608, zinb_loss:0.477412, cluster_loss:0.159456
Clustering 1770: ASW= 0.8289, DB= 0.2298, CH= 51177.1952
Training epoch 1771, recon_loss:0.875844, zinb_loss:0.477704, cluster_loss:0.159235
Clustering 1771: ASW= 0.8316, DB= 0.2273, CH= 52077.0636
Training epoch 1772, recon_loss:0.875603, zinb_loss:0.477435, cluster_loss:0.159447
Clustering 1772: ASW= 0.8289, DB= 0.2300, CH= 51148.8279
Training epoch 1773, recon_loss:0.875934, zinb_loss:0.477731, cluster_loss:0.159261
Clustering 1773: ASW= 0.8316, DB= 0.2270, CH= 52142.4367
Training epoch 1774, recon_loss:0.875763, zinb_loss:0.477465, cluster_loss:0.159510
Clustering 1774: ASW= 0.8288, DB= 0.2303, CH= 51081.9311
Training epoch 1775, recon_loss:0.876136, zinb_loss:0.477755, cluster_loss:0.159366
Clustering 1775: ASW= 0.8313, DB= 0.2269, CH= 52106.4245
Training epoch 1776, recon_loss:0.875927, zinb_loss:0.477498, cluster_loss:0.159581
Clustering 1776: ASW= 0.8287, DB= 0.2306, CH= 51003.5295
Training epoch 1777, recon_loss:0.876264, zinb_loss:0.477765, cluster_loss:0.159471
Clustering 1777: ASW= 0.8310, DB= 0.2270, CH= 51991.0833
Training epoch 1778, recon_loss:0.875943, zinb_loss:0.477516, cluster_loss:0.159598
Clustering 1778: ASW= 0.8285, DB= 0.2310, CH= 50923.0701
Training epoch 1779, recon_loss:0.876206, zinb_loss:0.477740, cluster_loss:0.159478
Clustering 1779: ASW= 0.8307, DB= 0.2271, CH= 51858.6854
Training epoch 1780, recon_loss:0.875782, zinb_loss:0.477520, cluster_loss:0.159521
Clustering 1780: ASW= 0.8285, DB= 0.2311, CH= 50891.6446
Training epoch 1781, recon_loss:0.875983, zinb_loss:0.477690, cluster_loss:0.159355
Clustering 1781: ASW= 0.8304, DB= 0.2269, CH= 51783.5805
Training epoch 1782, recon_loss:0.875604, zinb_loss:0.477511, cluster_loss:0.159432
Clustering 1782: ASW= 0.8284, DB= 0.2311, CH= 50852.3771
Training epoch 1783, recon_loss:0.875875, zinb_loss:0.477645, cluster_loss:0.159240
Clustering 1783: ASW= 0.8301, DB= 0.2271, CH= 51673.1143
Training epoch 1784, recon_loss:0.875658, zinb_loss:0.477514, cluster_loss:0.159363
Clustering 1784: ASW= 0.8285, DB= 0.2310, CH= 50851.5525
Training epoch 1785, recon_loss:0.876045, zinb_loss:0.477632, cluster_loss:0.159156
Clustering 1785: ASW= 0.8298, DB= 0.2272, CH= 51604.3400
Training epoch 1786, recon_loss:0.875883, zinb_loss:0.477527, cluster_loss:0.159403
Clustering 1786: ASW= 0.8286, DB= 0.2312, CH= 50811.8615
Training epoch 1787, recon_loss:0.876391, zinb_loss:0.477645, cluster_loss:0.159173
Clustering 1787: ASW= 0.8295, DB= 0.2274, CH= 51514.8544
Training epoch 1788, recon_loss:0.876314, zinb_loss:0.477550, cluster_loss:0.159499
Clustering 1788: ASW= 0.8288, DB= 0.2312, CH= 50819.9250
Training epoch 1789, recon_loss:0.876875, zinb_loss:0.477675, cluster_loss:0.159238
Clustering 1789: ASW= 0.8294, DB= 0.2274, CH= 51525.3989
Training epoch 1790, recon_loss:0.876768, zinb_loss:0.477554, cluster_loss:0.159704
Clustering 1790: ASW= 0.8291, DB= 0.2315, CH= 50834.2374
Training epoch 1791, recon_loss:0.877173, zinb_loss:0.477670, cluster_loss:0.159345
Clustering 1791: ASW= 0.8293, DB= 0.2276, CH= 51540.5851
Training epoch 1792, recon_loss:0.877003, zinb_loss:0.477530, cluster_loss:0.159871
Clustering 1792: ASW= 0.8295, DB= 0.2313, CH= 50968.8452
Training epoch 1793, recon_loss:0.877136, zinb_loss:0.477627, cluster_loss:0.159386
Clustering 1793: ASW= 0.8293, DB= 0.2274, CH= 51603.1275
Training epoch 1794, recon_loss:0.876837, zinb_loss:0.477469, cluster_loss:0.159950
Clustering 1794: ASW= 0.8298, DB= 0.2311, CH= 51117.2000
Training epoch 1795, recon_loss:0.876811, zinb_loss:0.477568, cluster_loss:0.159363
Clustering 1795: ASW= 0.8295, DB= 0.2273, CH= 51674.9456
Training epoch 1796, recon_loss:0.876459, zinb_loss:0.477405, cluster_loss:0.159909
Clustering 1796: ASW= 0.8301, DB= 0.2308, CH= 51288.3428
Training epoch 1797, recon_loss:0.876480, zinb_loss:0.477533, cluster_loss:0.159301
Clustering 1797: ASW= 0.8298, DB= 0.2269, CH= 51750.5992
Training epoch 1798, recon_loss:0.876107, zinb_loss:0.477366, cluster_loss:0.159807
Clustering 1798: ASW= 0.8302, DB= 0.2305, CH= 51467.5051
Training epoch 1799, recon_loss:0.876164, zinb_loss:0.477529, cluster_loss:0.159233
Clustering 1799: ASW= 0.8301, DB= 0.2268, CH= 51777.5805
Training epoch 1800, recon_loss:0.875854, zinb_loss:0.477356, cluster_loss:0.159685
Clustering 1800: ASW= 0.8302, DB= 0.2304, CH= 51625.6433
Training epoch 1801, recon_loss:0.876012, zinb_loss:0.477531, cluster_loss:0.159215
Clustering 1801: ASW= 0.8303, DB= 0.2267, CH= 51796.4285
Training epoch 1802, recon_loss:0.875785, zinb_loss:0.477363, cluster_loss:0.159603
Clustering 1802: ASW= 0.8302, DB= 0.2303, CH= 51791.1119
Training epoch 1803, recon_loss:0.875920, zinb_loss:0.477541, cluster_loss:0.159209
Clustering 1803: ASW= 0.8304, DB= 0.2264, CH= 51747.8417
Training epoch 1804, recon_loss:0.875705, zinb_loss:0.477360, cluster_loss:0.159485
Clustering 1804: ASW= 0.8302, DB= 0.2299, CH= 51895.4260
Training epoch 1805, recon_loss:0.875775, zinb_loss:0.477534, cluster_loss:0.159138
Clustering 1805: ASW= 0.8305, DB= 0.2265, CH= 51813.5051
Training epoch 1806, recon_loss:0.875665, zinb_loss:0.477353, cluster_loss:0.159367
Clustering 1806: ASW= 0.8303, DB= 0.2296, CH= 51982.8971
Training epoch 1807, recon_loss:0.875753, zinb_loss:0.477543, cluster_loss:0.159064
Clustering 1807: ASW= 0.8305, DB= 0.2264, CH= 51845.0710
Training epoch 1808, recon_loss:0.875786, zinb_loss:0.477359, cluster_loss:0.159294
Clustering 1808: ASW= 0.8303, DB= 0.2295, CH= 51993.8040
Training epoch 1809, recon_loss:0.875902, zinb_loss:0.477562, cluster_loss:0.159016
Clustering 1809: ASW= 0.8304, DB= 0.2266, CH= 51918.4022
Training epoch 1810, recon_loss:0.876014, zinb_loss:0.477377, cluster_loss:0.159280
Clustering 1810: ASW= 0.8304, DB= 0.2294, CH= 51977.2546
Training epoch 1811, recon_loss:0.876029, zinb_loss:0.477584, cluster_loss:0.159010
Clustering 1811: ASW= 0.8302, DB= 0.2269, CH= 51898.8278
Training epoch 1812, recon_loss:0.876109, zinb_loss:0.477393, cluster_loss:0.159286
Clustering 1812: ASW= 0.8304, DB= 0.2292, CH= 51924.8998
Training epoch 1813, recon_loss:0.876029, zinb_loss:0.477587, cluster_loss:0.159028
Clustering 1813: ASW= 0.8299, DB= 0.2273, CH= 51882.2357
Training epoch 1814, recon_loss:0.876128, zinb_loss:0.477409, cluster_loss:0.159329
Clustering 1814: ASW= 0.8304, DB= 0.2291, CH= 51874.0135
Training epoch 1815, recon_loss:0.875991, zinb_loss:0.477580, cluster_loss:0.159092
Clustering 1815: ASW= 0.8296, DB= 0.2279, CH= 51744.1882
Training epoch 1816, recon_loss:0.876128, zinb_loss:0.477424, cluster_loss:0.159397
Clustering 1816: ASW= 0.8304, DB= 0.2289, CH= 51839.0012
Training epoch 1817, recon_loss:0.875975, zinb_loss:0.477563, cluster_loss:0.159200
Clustering 1817: ASW= 0.8292, DB= 0.2286, CH= 51569.0394
Training epoch 1818, recon_loss:0.876131, zinb_loss:0.477438, cluster_loss:0.159458
Clustering 1818: ASW= 0.8305, DB= 0.2287, CH= 51852.4378
Training epoch 1819, recon_loss:0.875913, zinb_loss:0.477546, cluster_loss:0.159281
Clustering 1819: ASW= 0.8290, DB= 0.2291, CH= 51471.4452
Training epoch 1820, recon_loss:0.876063, zinb_loss:0.477444, cluster_loss:0.159445
Clustering 1820: ASW= 0.8307, DB= 0.2284, CH= 51908.6948
Training epoch 1821, recon_loss:0.875760, zinb_loss:0.477530, cluster_loss:0.159250
Clustering 1821: ASW= 0.8292, DB= 0.2292, CH= 51561.5530
Training epoch 1822, recon_loss:0.875933, zinb_loss:0.477449, cluster_loss:0.159340
Clustering 1822: ASW= 0.8309, DB= 0.2279, CH= 52018.6153
Training epoch 1823, recon_loss:0.875563, zinb_loss:0.477527, cluster_loss:0.159136
Clustering 1823: ASW= 0.8296, DB= 0.2290, CH= 51766.2309
Training epoch 1824, recon_loss:0.875783, zinb_loss:0.477455, cluster_loss:0.159225
Clustering 1824: ASW= 0.8311, DB= 0.2274, CH= 52133.9223
Training epoch 1825, recon_loss:0.875542, zinb_loss:0.477527, cluster_loss:0.159103
Clustering 1825: ASW= 0.8298, DB= 0.2288, CH= 51939.0132
Training epoch 1826, recon_loss:0.875839, zinb_loss:0.477490, cluster_loss:0.159132
Clustering 1826: ASW= 0.8312, DB= 0.2273, CH= 52176.6258
Training epoch 1827, recon_loss:0.875399, zinb_loss:0.477549, cluster_loss:0.158963
Clustering 1827: ASW= 0.8300, DB= 0.2285, CH= 52054.1961
Training epoch 1828, recon_loss:0.875946, zinb_loss:0.477526, cluster_loss:0.159149
Clustering 1828: ASW= 0.8311, DB= 0.2273, CH= 52201.5509
Training epoch 1829, recon_loss:0.875699, zinb_loss:0.477590, cluster_loss:0.159012
Clustering 1829: ASW= 0.8301, DB= 0.2284, CH= 52076.2177
Training epoch 1830, recon_loss:0.876175, zinb_loss:0.477583, cluster_loss:0.159184
Clustering 1830: ASW= 0.8313, DB= 0.2272, CH= 52259.7496
Training epoch 1831, recon_loss:0.875823, zinb_loss:0.477617, cluster_loss:0.159049
Clustering 1831: ASW= 0.8300, DB= 0.2285, CH= 52030.2084
Training epoch 1832, recon_loss:0.876342, zinb_loss:0.477666, cluster_loss:0.159235
Clustering 1832: ASW= 0.8315, DB= 0.2270, CH= 52336.8533
Training epoch 1833, recon_loss:0.875771, zinb_loss:0.477634, cluster_loss:0.159079
Clustering 1833: ASW= 0.8299, DB= 0.2287, CH= 51943.2785
Training epoch 1834, recon_loss:0.876349, zinb_loss:0.477731, cluster_loss:0.159301
Clustering 1834: ASW= 0.8317, DB= 0.2271, CH= 52415.8082
Training epoch 1835, recon_loss:0.875648, zinb_loss:0.477642, cluster_loss:0.159104
Clustering 1835: ASW= 0.8297, DB= 0.2288, CH= 51812.1946
Training epoch 1836, recon_loss:0.876342, zinb_loss:0.477786, cluster_loss:0.159369
Clustering 1836: ASW= 0.8319, DB= 0.2268, CH= 52575.0725
Training epoch 1837, recon_loss:0.875574, zinb_loss:0.477633, cluster_loss:0.159170
Clustering 1837: ASW= 0.8296, DB= 0.2290, CH= 51575.7228
Training epoch 1838, recon_loss:0.876504, zinb_loss:0.477822, cluster_loss:0.159517
Clustering 1838: ASW= 0.8320, DB= 0.2264, CH= 52742.3615
Training epoch 1839, recon_loss:0.875793, zinb_loss:0.477602, cluster_loss:0.159348
Clustering 1839: ASW= 0.8295, DB= 0.2293, CH= 51220.7542
Training epoch 1840, recon_loss:0.877018, zinb_loss:0.477836, cluster_loss:0.159769
Clustering 1840: ASW= 0.8321, DB= 0.2257, CH= 52893.6760
Training epoch 1841, recon_loss:0.876303, zinb_loss:0.477531, cluster_loss:0.159698
Clustering 1841: ASW= 0.8292, DB= 0.2298, CH= 50673.8809
Training epoch 1842, recon_loss:0.877476, zinb_loss:0.477780, cluster_loss:0.159935
Clustering 1842: ASW= 0.8319, DB= 0.2253, CH= 52846.7049
Training epoch 1843, recon_loss:0.876379, zinb_loss:0.477440, cluster_loss:0.159737
Clustering 1843: ASW= 0.8293, DB= 0.2301, CH= 50583.8590
Training epoch 1844, recon_loss:0.877542, zinb_loss:0.477726, cluster_loss:0.159855
Clustering 1844: ASW= 0.8316, DB= 0.2260, CH= 52813.3930
Training epoch 1845, recon_loss:0.876418, zinb_loss:0.477397, cluster_loss:0.159701
Clustering 1845: ASW= 0.8295, DB= 0.2305, CH= 50657.0951
Training epoch 1846, recon_loss:0.877299, zinb_loss:0.477665, cluster_loss:0.159771
Clustering 1846: ASW= 0.8312, DB= 0.2265, CH= 52720.4733
Training epoch 1847, recon_loss:0.876485, zinb_loss:0.477371, cluster_loss:0.159681
Clustering 1847: ASW= 0.8297, DB= 0.2306, CH= 50687.1059
Training epoch 1848, recon_loss:0.877083, zinb_loss:0.477632, cluster_loss:0.159649
Clustering 1848: ASW= 0.8310, DB= 0.2263, CH= 52652.0314
Training epoch 1849, recon_loss:0.876039, zinb_loss:0.477389, cluster_loss:0.159627
Clustering 1849: ASW= 0.8299, DB= 0.2305, CH= 50858.8325
Training epoch 1850, recon_loss:0.876613, zinb_loss:0.477631, cluster_loss:0.159572
Clustering 1850: ASW= 0.8308, DB= 0.2265, CH= 52585.8394
Training epoch 1851, recon_loss:0.876155, zinb_loss:0.477374, cluster_loss:0.159791
Clustering 1851: ASW= 0.8299, DB= 0.2309, CH= 50729.9787
Training epoch 1852, recon_loss:0.876344, zinb_loss:0.477600, cluster_loss:0.159513
Clustering 1852: ASW= 0.8308, DB= 0.2264, CH= 52454.3756
Training epoch 1853, recon_loss:0.875695, zinb_loss:0.477364, cluster_loss:0.159476
Clustering 1853: ASW= 0.8302, DB= 0.2301, CH= 51116.5029
Training epoch 1854, recon_loss:0.875864, zinb_loss:0.477542, cluster_loss:0.159338
Clustering 1854: ASW= 0.8308, DB= 0.2265, CH= 52374.6763
Training epoch 1855, recon_loss:0.875396, zinb_loss:0.477392, cluster_loss:0.159299
Clustering 1855: ASW= 0.8304, DB= 0.2296, CH= 51488.8013
Training epoch 1856, recon_loss:0.875614, zinb_loss:0.477525, cluster_loss:0.159307
Clustering 1856: ASW= 0.8309, DB= 0.2268, CH= 52269.0138
Training epoch 1857, recon_loss:0.875573, zinb_loss:0.477435, cluster_loss:0.159251
Clustering 1857: ASW= 0.8306, DB= 0.2293, CH= 51754.1472
Training epoch 1858, recon_loss:0.875747, zinb_loss:0.477515, cluster_loss:0.159426
Clustering 1858: ASW= 0.8309, DB= 0.2262, CH= 52100.6374
Training epoch 1859, recon_loss:0.875826, zinb_loss:0.477498, cluster_loss:0.159310
Clustering 1859: ASW= 0.8306, DB= 0.2289, CH= 51993.2963
Training epoch 1860, recon_loss:0.875865, zinb_loss:0.477497, cluster_loss:0.159591
Clustering 1860: ASW= 0.8307, DB= 0.2267, CH= 51885.7237
Training epoch 1861, recon_loss:0.875955, zinb_loss:0.477554, cluster_loss:0.159338
Clustering 1861: ASW= 0.8307, DB= 0.2285, CH= 52215.6208
Training epoch 1862, recon_loss:0.875868, zinb_loss:0.477466, cluster_loss:0.159714
Clustering 1862: ASW= 0.8303, DB= 0.2275, CH= 51628.4621
Training epoch 1863, recon_loss:0.875958, zinb_loss:0.477603, cluster_loss:0.159345
Clustering 1863: ASW= 0.8307, DB= 0.2281, CH= 52385.8816
Training epoch 1864, recon_loss:0.875822, zinb_loss:0.477436, cluster_loss:0.159797
Clustering 1864: ASW= 0.8299, DB= 0.2283, CH= 51373.8385
Training epoch 1865, recon_loss:0.875989, zinb_loss:0.477640, cluster_loss:0.159359
Clustering 1865: ASW= 0.8308, DB= 0.2278, CH= 52523.2298
Training epoch 1866, recon_loss:0.875818, zinb_loss:0.477418, cluster_loss:0.159883
Clustering 1866: ASW= 0.8295, DB= 0.2291, CH= 51140.0850
Training epoch 1867, recon_loss:0.876014, zinb_loss:0.477667, cluster_loss:0.159359
Clustering 1867: ASW= 0.8309, DB= 0.2271, CH= 52652.7043
Training epoch 1868, recon_loss:0.875622, zinb_loss:0.477398, cluster_loss:0.159879
Clustering 1868: ASW= 0.8293, DB= 0.2294, CH= 51022.7348
Training epoch 1869, recon_loss:0.875717, zinb_loss:0.477653, cluster_loss:0.159272
Clustering 1869: ASW= 0.8312, DB= 0.2266, CH= 52777.4804
Training epoch 1870, recon_loss:0.875188, zinb_loss:0.477363, cluster_loss:0.159747
Clustering 1870: ASW= 0.8293, DB= 0.2292, CH= 51081.7113
Training epoch 1871, recon_loss:0.875338, zinb_loss:0.477618, cluster_loss:0.159153
Clustering 1871: ASW= 0.8316, DB= 0.2262, CH= 52861.9791
Training epoch 1872, recon_loss:0.874902, zinb_loss:0.477326, cluster_loss:0.159627
Clustering 1872: ASW= 0.8294, DB= 0.2289, CH= 51208.9758
Training epoch 1873, recon_loss:0.875157, zinb_loss:0.477581, cluster_loss:0.159075
Clustering 1873: ASW= 0.8319, DB= 0.2260, CH= 52895.3320
Training epoch 1874, recon_loss:0.874908, zinb_loss:0.477292, cluster_loss:0.159534
Clustering 1874: ASW= 0.8295, DB= 0.2288, CH= 51340.3731
Training epoch 1875, recon_loss:0.875299, zinb_loss:0.477545, cluster_loss:0.159039
Clustering 1875: ASW= 0.8322, DB= 0.2258, CH= 52890.9445
Training epoch 1876, recon_loss:0.875068, zinb_loss:0.477263, cluster_loss:0.159450
Clustering 1876: ASW= 0.8297, DB= 0.2285, CH= 51509.5675
Training epoch 1877, recon_loss:0.875490, zinb_loss:0.477492, cluster_loss:0.159036
Clustering 1877: ASW= 0.8324, DB= 0.2258, CH= 52894.0183
Training epoch 1878, recon_loss:0.875184, zinb_loss:0.477229, cluster_loss:0.159401
Clustering 1878: ASW= 0.8297, DB= 0.2286, CH= 51630.3858
Training epoch 1879, recon_loss:0.875584, zinb_loss:0.477449, cluster_loss:0.159000
Clustering 1879: ASW= 0.8325, DB= 0.2262, CH= 52870.5049
Training epoch 1880, recon_loss:0.875271, zinb_loss:0.477239, cluster_loss:0.159301
Clustering 1880: ASW= 0.8300, DB= 0.2281, CH= 51846.9191
Training epoch 1881, recon_loss:0.875698, zinb_loss:0.477421, cluster_loss:0.159070
Clustering 1881: ASW= 0.8325, DB= 0.2266, CH= 52871.6929
Training epoch 1882, recon_loss:0.875525, zinb_loss:0.477267, cluster_loss:0.159329
Clustering 1882: ASW= 0.8301, DB= 0.2278, CH= 51965.0718
Training epoch 1883, recon_loss:0.875746, zinb_loss:0.477398, cluster_loss:0.159168
Clustering 1883: ASW= 0.8325, DB= 0.2270, CH= 52893.8806
Training epoch 1884, recon_loss:0.875736, zinb_loss:0.477329, cluster_loss:0.159347
Clustering 1884: ASW= 0.8302, DB= 0.2275, CH= 52032.1523
Training epoch 1885, recon_loss:0.875734, zinb_loss:0.477379, cluster_loss:0.159293
Clustering 1885: ASW= 0.8324, DB= 0.2277, CH= 52814.6364
Training epoch 1886, recon_loss:0.876275, zinb_loss:0.477459, cluster_loss:0.159595
Clustering 1886: ASW= 0.8298, DB= 0.2286, CH= 51861.2960
Training epoch 1887, recon_loss:0.875730, zinb_loss:0.477391, cluster_loss:0.159381
Clustering 1887: ASW= 0.8320, DB= 0.2274, CH= 52476.1623
Training epoch 1888, recon_loss:0.876265, zinb_loss:0.477558, cluster_loss:0.159592
Clustering 1888: ASW= 0.8295, DB= 0.2281, CH= 51556.9450
Training epoch 1889, recon_loss:0.875837, zinb_loss:0.477390, cluster_loss:0.159619
Clustering 1889: ASW= 0.8314, DB= 0.2280, CH= 52291.4257
Training epoch 1890, recon_loss:0.876633, zinb_loss:0.477627, cluster_loss:0.159754
Clustering 1890: ASW= 0.8299, DB= 0.2273, CH= 51550.2304
Training epoch 1891, recon_loss:0.875709, zinb_loss:0.477356, cluster_loss:0.159635
Clustering 1891: ASW= 0.8311, DB= 0.2287, CH= 52314.6311
Training epoch 1892, recon_loss:0.876332, zinb_loss:0.477573, cluster_loss:0.159536
Clustering 1892: ASW= 0.8304, DB= 0.2265, CH= 51825.6698
Training epoch 1893, recon_loss:0.875370, zinb_loss:0.477317, cluster_loss:0.159488
Clustering 1893: ASW= 0.8310, DB= 0.2287, CH= 52404.4956
Training epoch 1894, recon_loss:0.875766, zinb_loss:0.477489, cluster_loss:0.159253
Clustering 1894: ASW= 0.8307, DB= 0.2260, CH= 52071.3786
Training epoch 1895, recon_loss:0.875255, zinb_loss:0.477282, cluster_loss:0.159331
Clustering 1895: ASW= 0.8310, DB= 0.2286, CH= 52496.9222
Training epoch 1896, recon_loss:0.875489, zinb_loss:0.477435, cluster_loss:0.159077
Clustering 1896: ASW= 0.8309, DB= 0.2259, CH= 52127.3590
Training epoch 1897, recon_loss:0.875363, zinb_loss:0.477278, cluster_loss:0.159250
Clustering 1897: ASW= 0.8309, DB= 0.2285, CH= 52553.9522
Training epoch 1898, recon_loss:0.875615, zinb_loss:0.477399, cluster_loss:0.158988
Clustering 1898: ASW= 0.8307, DB= 0.2261, CH= 51986.8285
Training epoch 1899, recon_loss:0.875624, zinb_loss:0.477285, cluster_loss:0.159216
Clustering 1899: ASW= 0.8307, DB= 0.2286, CH= 52531.4253
Training epoch 1900, recon_loss:0.875656, zinb_loss:0.477357, cluster_loss:0.158958
Clustering 1900: ASW= 0.8304, DB= 0.2264, CH= 51711.1394
Training epoch 1901, recon_loss:0.875682, zinb_loss:0.477289, cluster_loss:0.159167
Clustering 1901: ASW= 0.8307, DB= 0.2285, CH= 52495.3004
Training epoch 1902, recon_loss:0.875396, zinb_loss:0.477298, cluster_loss:0.158924
Clustering 1902: ASW= 0.8300, DB= 0.2268, CH= 51373.7462
Training epoch 1903, recon_loss:0.875425, zinb_loss:0.477283, cluster_loss:0.159117
Clustering 1903: ASW= 0.8307, DB= 0.2283, CH= 52434.2546
Training epoch 1904, recon_loss:0.875018, zinb_loss:0.477233, cluster_loss:0.158937
Clustering 1904: ASW= 0.8297, DB= 0.2272, CH= 51014.5359
Training epoch 1905, recon_loss:0.875158, zinb_loss:0.477284, cluster_loss:0.159133
Clustering 1905: ASW= 0.8308, DB= 0.2284, CH= 52341.1083
Training epoch 1906, recon_loss:0.874743, zinb_loss:0.477192, cluster_loss:0.158981
Clustering 1906: ASW= 0.8296, DB= 0.2273, CH= 50856.9391
Training epoch 1907, recon_loss:0.875063, zinb_loss:0.477292, cluster_loss:0.159151
Clustering 1907: ASW= 0.8311, DB= 0.2280, CH= 52427.4033
Training epoch 1908, recon_loss:0.874663, zinb_loss:0.477192, cluster_loss:0.158942
Clustering 1908: ASW= 0.8300, DB= 0.2270, CH= 51098.7879
Training epoch 1909, recon_loss:0.875100, zinb_loss:0.477302, cluster_loss:0.159086
Clustering 1909: ASW= 0.8316, DB= 0.2275, CH= 52712.0974
Training epoch 1910, recon_loss:0.874853, zinb_loss:0.477228, cluster_loss:0.158866
Clustering 1910: ASW= 0.8304, DB= 0.2266, CH= 51492.7172
Training epoch 1911, recon_loss:0.875435, zinb_loss:0.477339, cluster_loss:0.159070
Clustering 1911: ASW= 0.8318, DB= 0.2274, CH= 52944.9036
Training epoch 1912, recon_loss:0.875309, zinb_loss:0.477281, cluster_loss:0.158912
Clustering 1912: ASW= 0.8308, DB= 0.2265, CH= 51740.8222
Training epoch 1913, recon_loss:0.875902, zinb_loss:0.477396, cluster_loss:0.159116
Clustering 1913: ASW= 0.8317, DB= 0.2275, CH= 53067.7751
Training epoch 1914, recon_loss:0.875700, zinb_loss:0.477336, cluster_loss:0.159014
Clustering 1914: ASW= 0.8312, DB= 0.2262, CH= 51873.6715
Training epoch 1915, recon_loss:0.876147, zinb_loss:0.477446, cluster_loss:0.159135
Clustering 1915: ASW= 0.8315, DB= 0.2276, CH= 53125.4228
Training epoch 1916, recon_loss:0.875698, zinb_loss:0.477366, cluster_loss:0.159077
Clustering 1916: ASW= 0.8315, DB= 0.2261, CH= 52007.5539
Training epoch 1917, recon_loss:0.876060, zinb_loss:0.477467, cluster_loss:0.159085
Clustering 1917: ASW= 0.8313, DB= 0.2277, CH= 53188.5128
Training epoch 1918, recon_loss:0.875458, zinb_loss:0.477374, cluster_loss:0.159108
Clustering 1918: ASW= 0.8319, DB= 0.2260, CH= 52105.9142
Training epoch 1919, recon_loss:0.875815, zinb_loss:0.477465, cluster_loss:0.158997
Clustering 1919: ASW= 0.8311, DB= 0.2278, CH= 53287.2314
Training epoch 1920, recon_loss:0.875159, zinb_loss:0.477367, cluster_loss:0.159131
Clustering 1920: ASW= 0.8323, DB= 0.2260, CH= 52162.0977
Training epoch 1921, recon_loss:0.875532, zinb_loss:0.477447, cluster_loss:0.158896
Clustering 1921: ASW= 0.8310, DB= 0.2277, CH= 53426.6943
Training epoch 1922, recon_loss:0.874905, zinb_loss:0.477351, cluster_loss:0.159145
Clustering 1922: ASW= 0.8326, DB= 0.2256, CH= 52173.6516
Training epoch 1923, recon_loss:0.875207, zinb_loss:0.477411, cluster_loss:0.158797
Clustering 1923: ASW= 0.8309, DB= 0.2274, CH= 53548.3985
Training epoch 1924, recon_loss:0.874664, zinb_loss:0.477324, cluster_loss:0.159126
Clustering 1924: ASW= 0.8327, DB= 0.2259, CH= 52175.4376
Training epoch 1925, recon_loss:0.874945, zinb_loss:0.477383, cluster_loss:0.158713
Clustering 1925: ASW= 0.8309, DB= 0.2271, CH= 53653.8240
Training epoch 1926, recon_loss:0.874516, zinb_loss:0.477304, cluster_loss:0.159121
Clustering 1926: ASW= 0.8328, DB= 0.2262, CH= 52123.3513
Training epoch 1927, recon_loss:0.874835, zinb_loss:0.477373, cluster_loss:0.158696
Clustering 1927: ASW= 0.8309, DB= 0.2268, CH= 53719.9885
Training epoch 1928, recon_loss:0.874535, zinb_loss:0.477290, cluster_loss:0.159181
Clustering 1928: ASW= 0.8327, DB= 0.2266, CH= 51979.1377
Training epoch 1929, recon_loss:0.874953, zinb_loss:0.477387, cluster_loss:0.158768
Clustering 1929: ASW= 0.8307, DB= 0.2273, CH= 53744.4561
Training epoch 1930, recon_loss:0.874774, zinb_loss:0.477265, cluster_loss:0.159377
Clustering 1930: ASW= 0.8324, DB= 0.2273, CH= 51645.9515
Training epoch 1931, recon_loss:0.875382, zinb_loss:0.477414, cluster_loss:0.159022
Clustering 1931: ASW= 0.8304, DB= 0.2272, CH= 53596.8944
Training epoch 1932, recon_loss:0.875340, zinb_loss:0.477236, cluster_loss:0.159775
Clustering 1932: ASW= 0.8318, DB= 0.2277, CH= 51067.4036
Training epoch 1933, recon_loss:0.875920, zinb_loss:0.477431, cluster_loss:0.159341
Clustering 1933: ASW= 0.8300, DB= 0.2271, CH= 53235.6202
Training epoch 1934, recon_loss:0.875646, zinb_loss:0.477175, cluster_loss:0.159998
Clustering 1934: ASW= 0.8310, DB= 0.2287, CH= 50639.7725
Training epoch 1935, recon_loss:0.876065, zinb_loss:0.477396, cluster_loss:0.159375
Clustering 1935: ASW= 0.8300, DB= 0.2269, CH= 53046.7002
Training epoch 1936, recon_loss:0.875591, zinb_loss:0.477164, cluster_loss:0.159847
Clustering 1936: ASW= 0.8308, DB= 0.2289, CH= 50824.2501
Training epoch 1937, recon_loss:0.875764, zinb_loss:0.477402, cluster_loss:0.159205
Clustering 1937: ASW= 0.8305, DB= 0.2267, CH= 53172.6411
Training epoch 1938, recon_loss:0.875399, zinb_loss:0.477178, cluster_loss:0.159694
Clustering 1938: ASW= 0.8306, DB= 0.2289, CH= 50980.3109
Training epoch 1939, recon_loss:0.875555, zinb_loss:0.477440, cluster_loss:0.159078
Clustering 1939: ASW= 0.8309, DB= 0.2263, CH= 53270.6012
Training epoch 1940, recon_loss:0.875006, zinb_loss:0.477249, cluster_loss:0.159508
Clustering 1940: ASW= 0.8307, DB= 0.2286, CH= 51297.2888
Training epoch 1941, recon_loss:0.875102, zinb_loss:0.477525, cluster_loss:0.158994
Clustering 1941: ASW= 0.8311, DB= 0.2263, CH= 53317.0673
Training epoch 1942, recon_loss:0.874720, zinb_loss:0.477324, cluster_loss:0.159442
Clustering 1942: ASW= 0.8305, DB= 0.2284, CH= 51373.2742
Training epoch 1943, recon_loss:0.875028, zinb_loss:0.477634, cluster_loss:0.159006
Clustering 1943: ASW= 0.8313, DB= 0.2263, CH= 53274.7643
Training epoch 1944, recon_loss:0.874682, zinb_loss:0.477437, cluster_loss:0.159455
Clustering 1944: ASW= 0.8303, DB= 0.2283, CH= 51447.4665
Training epoch 1945, recon_loss:0.875129, zinb_loss:0.477752, cluster_loss:0.159085
Clustering 1945: ASW= 0.8313, DB= 0.2266, CH= 53158.9509
Training epoch 1946, recon_loss:0.874828, zinb_loss:0.477565, cluster_loss:0.159529
Clustering 1946: ASW= 0.8301, DB= 0.2282, CH= 51450.9211
Training epoch 1947, recon_loss:0.875522, zinb_loss:0.477879, cluster_loss:0.159203
Clustering 1947: ASW= 0.8311, DB= 0.2269, CH= 53039.0264
Training epoch 1948, recon_loss:0.875246, zinb_loss:0.477701, cluster_loss:0.159602
Clustering 1948: ASW= 0.8301, DB= 0.2280, CH= 51469.5466
Training epoch 1949, recon_loss:0.876153, zinb_loss:0.477973, cluster_loss:0.159303
Clustering 1949: ASW= 0.8310, DB= 0.2274, CH= 52957.0116
Training epoch 1950, recon_loss:0.875807, zinb_loss:0.477806, cluster_loss:0.159628
Clustering 1950: ASW= 0.8302, DB= 0.2274, CH= 51539.0202
Training epoch 1951, recon_loss:0.876698, zinb_loss:0.478020, cluster_loss:0.159360
Clustering 1951: ASW= 0.8309, DB= 0.2279, CH= 52919.0658
Training epoch 1952, recon_loss:0.876154, zinb_loss:0.477868, cluster_loss:0.159558
Clustering 1952: ASW= 0.8305, DB= 0.2269, CH= 51713.9611
Training epoch 1953, recon_loss:0.876792, zinb_loss:0.477972, cluster_loss:0.159362
Clustering 1953: ASW= 0.8309, DB= 0.2282, CH= 52920.9512
Training epoch 1954, recon_loss:0.876085, zinb_loss:0.477834, cluster_loss:0.159427
Clustering 1954: ASW= 0.8309, DB= 0.2273, CH= 51868.0820
Training epoch 1955, recon_loss:0.876453, zinb_loss:0.477851, cluster_loss:0.159310
Clustering 1955: ASW= 0.8309, DB= 0.2273, CH= 52881.5070
Training epoch 1956, recon_loss:0.875810, zinb_loss:0.477746, cluster_loss:0.159268
Clustering 1956: ASW= 0.8313, DB= 0.2267, CH= 52060.4979
Training epoch 1957, recon_loss:0.876020, zinb_loss:0.477673, cluster_loss:0.159219
Clustering 1957: ASW= 0.8310, DB= 0.2272, CH= 52842.4263
Training epoch 1958, recon_loss:0.875565, zinb_loss:0.477629, cluster_loss:0.159075
Clustering 1958: ASW= 0.8318, DB= 0.2265, CH= 52411.8357
Training epoch 1959, recon_loss:0.875738, zinb_loss:0.477493, cluster_loss:0.159212
Clustering 1959: ASW= 0.8310, DB= 0.2271, CH= 52747.9891
Training epoch 1960, recon_loss:0.875698, zinb_loss:0.477533, cluster_loss:0.159060
Clustering 1960: ASW= 0.8322, DB= 0.2264, CH= 52708.0458
Training epoch 1961, recon_loss:0.875974, zinb_loss:0.477354, cluster_loss:0.159440
Clustering 1961: ASW= 0.8309, DB= 0.2273, CH= 52557.0110
Training epoch 1962, recon_loss:0.876159, zinb_loss:0.477467, cluster_loss:0.159249
Clustering 1962: ASW= 0.8324, DB= 0.2262, CH= 52816.9195
Training epoch 1963, recon_loss:0.876267, zinb_loss:0.477241, cluster_loss:0.159725
Clustering 1963: ASW= 0.8307, DB= 0.2275, CH= 52390.9229
Training epoch 1964, recon_loss:0.876278, zinb_loss:0.477377, cluster_loss:0.159449
Clustering 1964: ASW= 0.8324, DB= 0.2260, CH= 52733.2854
Training epoch 1965, recon_loss:0.875980, zinb_loss:0.477134, cluster_loss:0.159827
Clustering 1965: ASW= 0.8306, DB= 0.2275, CH= 52330.1871
Training epoch 1966, recon_loss:0.875874, zinb_loss:0.477293, cluster_loss:0.159471
Clustering 1966: ASW= 0.8325, DB= 0.2261, CH= 52600.6936
Training epoch 1967, recon_loss:0.875344, zinb_loss:0.477078, cluster_loss:0.159664
Clustering 1967: ASW= 0.8307, DB= 0.2273, CH= 52410.4387
Training epoch 1968, recon_loss:0.875206, zinb_loss:0.477237, cluster_loss:0.159308
Clustering 1968: ASW= 0.8325, DB= 0.2262, CH= 52535.3439
Training epoch 1969, recon_loss:0.874771, zinb_loss:0.477079, cluster_loss:0.159390
Clustering 1969: ASW= 0.8309, DB= 0.2268, CH= 52581.9604
Training epoch 1970, recon_loss:0.874628, zinb_loss:0.477200, cluster_loss:0.159146
Clustering 1970: ASW= 0.8324, DB= 0.2264, CH= 52434.9507
Training epoch 1971, recon_loss:0.874507, zinb_loss:0.477113, cluster_loss:0.159214
Clustering 1971: ASW= 0.8310, DB= 0.2264, CH= 52783.4862
Training epoch 1972, recon_loss:0.874394, zinb_loss:0.477197, cluster_loss:0.159062
Clustering 1972: ASW= 0.8322, DB= 0.2264, CH= 52233.7939
Training epoch 1973, recon_loss:0.874357, zinb_loss:0.477138, cluster_loss:0.159100
Clustering 1973: ASW= 0.8312, DB= 0.2260, CH= 52939.1144
Training epoch 1974, recon_loss:0.874353, zinb_loss:0.477189, cluster_loss:0.159116
Clustering 1974: ASW= 0.8320, DB= 0.2271, CH= 52093.3605
Training epoch 1975, recon_loss:0.874578, zinb_loss:0.477204, cluster_loss:0.159131
Clustering 1975: ASW= 0.8314, DB= 0.2253, CH= 53161.7542
Training epoch 1976, recon_loss:0.874487, zinb_loss:0.477186, cluster_loss:0.159227
Clustering 1976: ASW= 0.8316, DB= 0.2281, CH= 51844.4933
Training epoch 1977, recon_loss:0.874733, zinb_loss:0.477242, cluster_loss:0.159140
Clustering 1977: ASW= 0.8316, DB= 0.2249, CH= 53347.3212
Training epoch 1978, recon_loss:0.874403, zinb_loss:0.477152, cluster_loss:0.159271
Clustering 1978: ASW= 0.8313, DB= 0.2286, CH= 51642.1734
Training epoch 1979, recon_loss:0.874761, zinb_loss:0.477248, cluster_loss:0.159114
Clustering 1979: ASW= 0.8317, DB= 0.2247, CH= 53436.6326
Training epoch 1980, recon_loss:0.874344, zinb_loss:0.477139, cluster_loss:0.159231
Clustering 1980: ASW= 0.8312, DB= 0.2285, CH= 51608.2537
Training epoch 1981, recon_loss:0.874825, zinb_loss:0.477271, cluster_loss:0.159021
Clustering 1981: ASW= 0.8320, DB= 0.2243, CH= 53601.9121
Training epoch 1982, recon_loss:0.874183, zinb_loss:0.477128, cluster_loss:0.159166
Clustering 1982: ASW= 0.8310, DB= 0.2286, CH= 51665.0395
Training epoch 1983, recon_loss:0.874967, zinb_loss:0.477324, cluster_loss:0.159027
Clustering 1983: ASW= 0.8321, DB= 0.2241, CH= 53670.0096
Training epoch 1984, recon_loss:0.874272, zinb_loss:0.477129, cluster_loss:0.159148
Clustering 1984: ASW= 0.8308, DB= 0.2287, CH= 51680.2780
Training epoch 1985, recon_loss:0.875192, zinb_loss:0.477377, cluster_loss:0.159017
Clustering 1985: ASW= 0.8322, DB= 0.2239, CH= 53711.6483
Training epoch 1986, recon_loss:0.874380, zinb_loss:0.477164, cluster_loss:0.159079
Clustering 1986: ASW= 0.8304, DB= 0.2286, CH= 51542.2352
Training epoch 1987, recon_loss:0.875447, zinb_loss:0.477446, cluster_loss:0.159008
Clustering 1987: ASW= 0.8325, DB= 0.2239, CH= 53667.0528
Training epoch 1988, recon_loss:0.874540, zinb_loss:0.477207, cluster_loss:0.159091
Clustering 1988: ASW= 0.8301, DB= 0.2284, CH= 51435.7323
Training epoch 1989, recon_loss:0.875742, zinb_loss:0.477511, cluster_loss:0.159071
Clustering 1989: ASW= 0.8326, DB= 0.2242, CH= 53537.2950
Training epoch 1990, recon_loss:0.874707, zinb_loss:0.477244, cluster_loss:0.159123
Clustering 1990: ASW= 0.8298, DB= 0.2287, CH= 51368.5212
Training epoch 1991, recon_loss:0.875864, zinb_loss:0.477552, cluster_loss:0.159106
Clustering 1991: ASW= 0.8327, DB= 0.2257, CH= 53419.3997
Training epoch 1992, recon_loss:0.874780, zinb_loss:0.477268, cluster_loss:0.159097
Clustering 1992: ASW= 0.8297, DB= 0.2286, CH= 51480.3135
Training epoch 1993, recon_loss:0.875905, zinb_loss:0.477568, cluster_loss:0.159106
Clustering 1993: ASW= 0.8330, DB= 0.2258, CH= 53414.1313
Training epoch 1994, recon_loss:0.874796, zinb_loss:0.477282, cluster_loss:0.159056
Clustering 1994: ASW= 0.8298, DB= 0.2284, CH= 51716.1455
Training epoch 1995, recon_loss:0.875886, zinb_loss:0.477580, cluster_loss:0.159086
Clustering 1995: ASW= 0.8334, DB= 0.2258, CH= 53507.2131
Training epoch 1996, recon_loss:0.874846, zinb_loss:0.477294, cluster_loss:0.159039
Clustering 1996: ASW= 0.8299, DB= 0.2273, CH= 51914.7519
Training epoch 1997, recon_loss:0.875814, zinb_loss:0.477592, cluster_loss:0.159064
Clustering 1997: ASW= 0.8336, DB= 0.2259, CH= 53607.0182
Training epoch 1998, recon_loss:0.874871, zinb_loss:0.477305, cluster_loss:0.159039
Clustering 1998: ASW= 0.8300, DB= 0.2271, CH= 52114.2896
Training epoch 1999, recon_loss:0.875595, zinb_loss:0.477597, cluster_loss:0.159037
Clustering 1999: ASW= 0.8337, DB= 0.2260, CH= 53657.4944
Training epoch 2000, recon_loss:0.874697, zinb_loss:0.477298, cluster_loss:0.159016
Clustering 2000: ASW= 0.8302, DB= 0.2268, CH= 52273.0578
Final Result : ASW= 0.8302, DB= 0.2268, CH= 52273.0578
[26]:
import numpy as np
np.savetxt("../results/GSE158013_pred.csv", y_pred, delimiter=",")
np.savetxt("../results/GSE158013_embedding.csv", final_latent.cpu().detach().numpy(), delimiter=",")
[9]:
library(anndata)
library(reticulate)
library(dplyr)
library(tidyr)
library(Seurat)
library(ggplot2)
library(reticulate)
[20]:
set.seed(0)
use_python("/home/zhouzeming/anaconda3/bin")
ad <- import("anndata")
rna <- ad$read_h5ad('../datasets/GSE158013/GSE158013_rna.h5ad')
[22]:
mycolors <- c('B cell'='#E5D2DD', 'Naive CD4 T cell'='#53A85F','T follicular helper cell'='#F1BB72',
'CD8 T cell'='#F3B1A0','Natural killer cell'='#D6E7A3', 'CD8+ MAIT'='#57C3F3', 'Neutrophil lineage'='#E95C59')
scRNA <- CreateSeuratObject(counts = t(rna$X),project ="scRNA",min.cells = 3)
label <- read.csv("../results/GSE158013_pred.csv",header = F)
scRNA@meta.data$clusters <- label
Idents(scRNA) <- as.factor(label$V1)
scRNA <- NormalizeData(scRNA, normalization.method = "LogNormalize")
scRNA <- FindVariableFeatures(scRNA, selection.method = "vst", nfeatures = 2000)
scRNA <- ScaleData(scRNA, features = rownames(scRNA))
variable.genes <- head(VariableFeatures(scRNA), 2000)
# 数据聚类降维
mds<- read.csv('../results/GSE158013_embedding.csv',row.names=1)
mds <- as.matrix(mds)
colnames(mds) <- paste0("MDS_", 1:32)
scRNA[["mds"]] <- CreateDimReducObject(embeddings = mds, key = "MDS_", assay = DefaultAssay(scRNA))
scRNA <- RunUMAP(scRNA,dims = 1:32,label=T,reduction = "mds")
# 寻找标记基因
scRNA.markers <- FindAllMarkers(scRNA,
ident.col = "clusters",
only.pos = T,
min.pct = 0.25,
logfc.threshold = 0.25,
features = variable.genes)
top.markers <- scRNA.markers %>% group_by(cluster) %>% top_n(n = 10, wt = avg_log2FC)
# 细胞注释
new.cluster.ids <- c("B cell", "Naive CD4 T cell", "T follicular helper cell", "CD8 T cell", "Natural killer cell", "CD8+ MAIT", "Neutrophil lineage")
names(new.cluster.ids) <- levels(scRNA)
scRNA <- RenameIdents(scRNA, new.cluster.ids)
Joint_umap <-DimPlot(scRNA, reduction = "umap", label = TRUE, cols = mycolors)
Joint_umap
Warning message:
“Data is of class matrix. Coercing to dgCMatrix.”
Normalizing layer: counts
Finding variable features for layer counts
Centering and scaling data matrix
Warning message:
“The following arguments are not used: label”
10:51:01 UMAP embedding parameters a = 0.9922 b = 1.112
10:51:01 Read 7084 rows and found 32 numeric columns
10:51:01 Using Annoy for neighbor search, n_neighbors = 30
10:51:01 Building Annoy index with metric = cosine, n_trees = 50
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10:51:01 Writing NN index file to temp file /tmp/RtmpIKgN4h/file2ee48a17268e3e
10:51:01 Searching Annoy index using 1 thread, search_k = 3000
10:51:04 Annoy recall = 100%
10:51:04 Commencing smooth kNN distance calibration using 1 thread
with target n_neighbors = 30
10:51:04 Initializing from normalized Laplacian + noise (using RSpectra)
10:51:05 Commencing optimization for 500 epochs, with 274822 positive edges
10:51:05 Using rng type: pcg
10:51:12 Optimization finished
Calculating cluster 0
Calculating cluster 1
Calculating cluster 2
Calculating cluster 3
Calculating cluster 4
Calculating cluster 5
Calculating cluster 6
