Getting start with the regression problem.¶
Let’s start with the baby step example for classification problem. Below is the basic example of the configuration for regression problem using example data contained in the package. For the required data type or more detailed configuration, please check the detailed information about each option in the documantation and detailed examples
[1]:
from pkg_resources import resource_filename
[2]:
network_info = {
'model_info': {
'network_class': 'DeepBiomeNetwork',
'optimizer': 'adam',
'lr': '0.01',
'decay': '0.0001',
'loss': 'mean_squared_error',
'metrics': 'correlation_coefficient',
'taxa_selection_metrics': 'sensitivity, specificity, gmeasure, accuracy',
'reader_class': 'MicroBiomeRegressionReader',
'normalizer': 'normalize_minmax',
},
'architecture_info': {
'weight_initial': 'glorot_uniform',
'weight_decay': 'phylogenetic_tree',
'batch_normalization': 'False',
'drop_out': '0',
},
'training_info': {
'epochs': '100',
'batch_size': '30',
'callbacks': 'ModelCheckpoint',
'monitor': 'val_loss',
'mode' : 'min',
'min_delta': '1e-7',
},
'validation_info': {
'validation_size': '0.2',
'batch_size': 'None',
},
'test_info': {
'batch_size': 'None',
},
}
path_info = {
'data_info': {
'data_path': resource_filename('deepbiome', 'tests/data'),
'idx_path': resource_filename('deepbiome', 'tests/data/onefile_idx.csv'),
'tree_info_path': resource_filename('deepbiome', 'tests/data/genus48_dic.csv'),
'x_path': 'onefile_x.csv',
'y_path': 'regression_y.csv'
},
'model_info': {
'evaluation': 'eval.npy',
'history': 'hist.json',
'model_dir': './',
'weight': 'weight.h5'
}
}
For logging, we used the python logging library.
[3]:
import logging
logging.basicConfig(format = '[%(name)-8s|%(levelname)s|%(filename)s:%(lineno)s] %(message)s',
level=logging.DEBUG)
log = logging.getLogger()
Here is the deepbiome.deepbiome_train function for training:
[4]:
from deepbiome import deepbiome
test_evaluation, train_evaluation, network = deepbiome.deepbiome_train(log, network_info, path_info, number_of_fold=2)
Using TensorFlow backend.
[root |INFO|deepbiome.py:115] -----------------------------------------------------------------
[root |INFO|deepbiome.py:153] -------1 simulation start!----------------------------------
[root |INFO|readers.py:58] -----------------------------------------------------------------------
[root |INFO|readers.py:59] Construct Dataset
[root |INFO|readers.py:60] -----------------------------------------------------------------------
[root |INFO|readers.py:61] Load data
[root |INFO|deepbiome.py:164] -----------------------------------------------------------------
[root |INFO|deepbiome.py:165] Build network for 1 simulation
[root |INFO|build_network.py:521] ------------------------------------------------------------------------------------------
[root |INFO|build_network.py:522] Read phylogenetic tree information from /DATA/home/muha/github_repos/deepbiome/deepbiome/tests/data/genus48_dic.csv
[root |INFO|build_network.py:528] Phylogenetic tree level list: ['Genus', 'Family', 'Order', 'Class', 'Phylum']
[root |INFO|build_network.py:529] ------------------------------------------------------------------------------------------
[root |INFO|build_network.py:537] Genus: 48
[root |INFO|build_network.py:537] Family: 40
[root |INFO|build_network.py:537] Order: 23
[root |INFO|build_network.py:537] Class: 17
[root |INFO|build_network.py:537] Phylum: 9
[root |INFO|build_network.py:546] ------------------------------------------------------------------------------------------
[root |INFO|build_network.py:547] Phylogenetic_tree_dict info: ['Phylum', 'Order', 'Genus', 'Number', 'Family', 'Class']
[root |INFO|build_network.py:548] ------------------------------------------------------------------------------------------
[root |INFO|build_network.py:558] Build edge weights between [ Genus, Family]
[root |INFO|build_network.py:558] Build edge weights between [Family, Order]
[root |INFO|build_network.py:558] Build edge weights between [ Order, Class]
[root |INFO|build_network.py:558] Build edge weights between [ Class, Phylum]
[root |INFO|build_network.py:571] ------------------------------------------------------------------------------------------
[root |INFO|build_network.py:586] ------------------------------------------------------------------------------------------
[root |INFO|build_network.py:587] Build network based on phylogenetic tree information
[root |INFO|build_network.py:588] ------------------------------------------------------------------------------------------
[tensorflow|WARNING|deprecation.py:328] From /usr/local/lib/python3.5/dist-packages/tensorflow/python/ops/resource_variable_ops.py:432: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.
Instructions for updating:
Colocations handled automatically by placer.
[root |INFO|build_network.py:670] ------------------------------------------------------------------------------------------
[root |INFO|build_network.py:61] Build Network
[root |INFO|build_network.py:62] Optimizer = adam
[root |INFO|build_network.py:63] Loss = mean_squared_error
[root |INFO|build_network.py:64] Metrics = correlation_coefficient
Model: "model_1"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
input (InputLayer) (None, 48) 0
_________________________________________________________________
l1_dense (Dense_with_tree) (None, 40) 1960
_________________________________________________________________
l1_activation (Activation) (None, 40) 0
_________________________________________________________________
l2_dense (Dense_with_tree) (None, 23) 943
_________________________________________________________________
l2_activation (Activation) (None, 23) 0
_________________________________________________________________
l3_dense (Dense_with_tree) (None, 17) 408
_________________________________________________________________
l3_activation (Activation) (None, 17) 0
_________________________________________________________________
l4_dense (Dense_with_tree) (None, 9) 162
_________________________________________________________________
l4_activation (Activation) (None, 9) 0
_________________________________________________________________
last_dense_h (Dense) (None, 1) 10
_________________________________________________________________
p_hat (Activation) (None, 1) 0
=================================================================
Total params: 3,483
Trainable params: 3,483
Non-trainable params: 0
_________________________________________________________________
[root |INFO|deepbiome.py:176] -----------------------------------------------------------------
[root |INFO|deepbiome.py:177] 1 fold computing start!----------------------------------
[root |INFO|build_network.py:137] Training start!
[tensorflow|WARNING|deprecation.py:328] From /usr/local/lib/python3.5/dist-packages/tensorflow/python/ops/math_ops.py:2862: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Use tf.cast instead.
Train on 640 samples, validate on 160 samples
Epoch 1/100
640/640 [==============================] - 1s 1ms/step - loss: 13.3172 - correlation_coefficient: -0.0472 - val_loss: 7.2757 - val_correlation_coefficient: 0.2129
Epoch 2/100
640/640 [==============================] - 0s 278us/step - loss: 3.6398 - correlation_coefficient: -0.0156 - val_loss: 2.6015 - val_correlation_coefficient: 0.2133
Epoch 3/100
640/640 [==============================] - 0s 273us/step - loss: 2.7581 - correlation_coefficient: 0.0091 - val_loss: 2.5732 - val_correlation_coefficient: 0.2344
Epoch 4/100
640/640 [==============================] - 0s 282us/step - loss: 2.6751 - correlation_coefficient: 0.0159 - val_loss: 2.4908 - val_correlation_coefficient: 0.2443
Epoch 5/100
640/640 [==============================] - 0s 261us/step - loss: 2.6710 - correlation_coefficient: 0.0778 - val_loss: 2.4440 - val_correlation_coefficient: 0.2510
Epoch 6/100
640/640 [==============================] - 0s 257us/step - loss: 2.6701 - correlation_coefficient: 0.0685 - val_loss: 2.5758 - val_correlation_coefficient: 0.2650
Epoch 7/100
640/640 [==============================] - 0s 231us/step - loss: 2.6690 - correlation_coefficient: 0.1112 - val_loss: 2.4963 - val_correlation_coefficient: 0.2748
Epoch 8/100
640/640 [==============================] - 0s 262us/step - loss: 2.6553 - correlation_coefficient: 0.1658 - val_loss: 2.5413 - val_correlation_coefficient: 0.2856
Epoch 9/100
640/640 [==============================] - 0s 260us/step - loss: 2.6711 - correlation_coefficient: 0.1798 - val_loss: 2.4562 - val_correlation_coefficient: 0.2888
Epoch 10/100
640/640 [==============================] - 0s 256us/step - loss: 2.6503 - correlation_coefficient: 0.2148 - val_loss: 2.4907 - val_correlation_coefficient: 0.2965
Epoch 11/100
640/640 [==============================] - 0s 270us/step - loss: 2.6437 - correlation_coefficient: 0.2563 - val_loss: 2.4441 - val_correlation_coefficient: 0.3015
Epoch 12/100
640/640 [==============================] - 0s 247us/step - loss: 2.6659 - correlation_coefficient: 0.2954 - val_loss: 2.4423 - val_correlation_coefficient: 0.3054
Epoch 13/100
640/640 [==============================] - 0s 261us/step - loss: 2.6373 - correlation_coefficient: 0.3261 - val_loss: 2.6419 - val_correlation_coefficient: 0.3073
Epoch 14/100
640/640 [==============================] - 0s 283us/step - loss: 2.6996 - correlation_coefficient: 0.3367 - val_loss: 2.5728 - val_correlation_coefficient: 0.3134
Epoch 15/100
640/640 [==============================] - 0s 271us/step - loss: 2.6575 - correlation_coefficient: 0.3569 - val_loss: 2.4368 - val_correlation_coefficient: 0.3067
Epoch 16/100
640/640 [==============================] - 0s 284us/step - loss: 2.6200 - correlation_coefficient: 0.3632 - val_loss: 2.6259 - val_correlation_coefficient: 0.3060
Epoch 17/100
640/640 [==============================] - 0s 274us/step - loss: 2.6720 - correlation_coefficient: 0.3776 - val_loss: 2.5164 - val_correlation_coefficient: 0.3074
Epoch 18/100
640/640 [==============================] - 0s 260us/step - loss: 2.6302 - correlation_coefficient: 0.3716 - val_loss: 2.4447 - val_correlation_coefficient: 0.3064
Epoch 19/100
640/640 [==============================] - 0s 375us/step - loss: 2.6302 - correlation_coefficient: 0.3845 - val_loss: 2.4203 - val_correlation_coefficient: 0.3097
Epoch 20/100
640/640 [==============================] - 0s 736us/step - loss: 2.6129 - correlation_coefficient: 0.3770 - val_loss: 2.5218 - val_correlation_coefficient: 0.3109
Epoch 21/100
640/640 [==============================] - 1s 868us/step - loss: 2.6203 - correlation_coefficient: 0.4002 - val_loss: 2.4337 - val_correlation_coefficient: 0.3081
Epoch 22/100
640/640 [==============================] - 0s 539us/step - loss: 2.5872 - correlation_coefficient: 0.4077 - val_loss: 2.4044 - val_correlation_coefficient: 0.3108
Epoch 23/100
640/640 [==============================] - 0s 535us/step - loss: 2.5898 - correlation_coefficient: 0.3934 - val_loss: 2.4256 - val_correlation_coefficient: 0.3082
Epoch 24/100
640/640 [==============================] - 1s 870us/step - loss: 2.6233 - correlation_coefficient: 0.3768 - val_loss: 2.4770 - val_correlation_coefficient: 0.3134
Epoch 25/100
640/640 [==============================] - 0s 524us/step - loss: 2.5573 - correlation_coefficient: 0.3892 - val_loss: 2.4070 - val_correlation_coefficient: 0.3148
Epoch 26/100
640/640 [==============================] - 0s 645us/step - loss: 2.5359 - correlation_coefficient: 0.4066 - val_loss: 2.4710 - val_correlation_coefficient: 0.3203
Epoch 27/100
640/640 [==============================] - 1s 987us/step - loss: 2.5595 - correlation_coefficient: 0.3985 - val_loss: 2.5221 - val_correlation_coefficient: 0.3213
Epoch 28/100
640/640 [==============================] - 1s 821us/step - loss: 2.5042 - correlation_coefficient: 0.4244 - val_loss: 2.4097 - val_correlation_coefficient: 0.3251
Epoch 29/100
640/640 [==============================] - 0s 408us/step - loss: 2.4519 - correlation_coefficient: 0.4178 - val_loss: 2.3837 - val_correlation_coefficient: 0.3309
Epoch 30/100
640/640 [==============================] - 1s 790us/step - loss: 2.4525 - correlation_coefficient: 0.4047 - val_loss: 2.3355 - val_correlation_coefficient: 0.3408
Epoch 31/100
640/640 [==============================] - 1s 923us/step - loss: 2.3892 - correlation_coefficient: 0.4247 - val_loss: 2.5191 - val_correlation_coefficient: 0.3450
Epoch 32/100
640/640 [==============================] - 0s 520us/step - loss: 2.4126 - correlation_coefficient: 0.4170 - val_loss: 2.4120 - val_correlation_coefficient: 0.3581
Epoch 33/100
640/640 [==============================] - 0s 618us/step - loss: 2.4295 - correlation_coefficient: 0.4438 - val_loss: 2.3369 - val_correlation_coefficient: 0.3713
Epoch 34/100
640/640 [==============================] - 0s 288us/step - loss: 2.3767 - correlation_coefficient: 0.4402 - val_loss: 2.2498 - val_correlation_coefficient: 0.3819
Epoch 35/100
640/640 [==============================] - 0s 255us/step - loss: 2.3212 - correlation_coefficient: 0.4439 - val_loss: 2.2667 - val_correlation_coefficient: 0.3907
Epoch 36/100
640/640 [==============================] - 0s 290us/step - loss: 2.2091 - correlation_coefficient: 0.4668 - val_loss: 2.2368 - val_correlation_coefficient: 0.3996
Epoch 37/100
640/640 [==============================] - 0s 262us/step - loss: 2.2400 - correlation_coefficient: 0.4783 - val_loss: 2.4207 - val_correlation_coefficient: 0.4059
Epoch 38/100
640/640 [==============================] - 0s 291us/step - loss: 2.2897 - correlation_coefficient: 0.4858 - val_loss: 2.1287 - val_correlation_coefficient: 0.4237
Epoch 39/100
640/640 [==============================] - 0s 247us/step - loss: 2.1540 - correlation_coefficient: 0.4920 - val_loss: 2.1244 - val_correlation_coefficient: 0.4314
Epoch 40/100
640/640 [==============================] - 0s 265us/step - loss: 2.1139 - correlation_coefficient: 0.4930 - val_loss: 2.1147 - val_correlation_coefficient: 0.4435
Epoch 41/100
640/640 [==============================] - 0s 272us/step - loss: 2.1490 - correlation_coefficient: 0.5189 - val_loss: 2.1416 - val_correlation_coefficient: 0.4489
Epoch 42/100
640/640 [==============================] - 0s 261us/step - loss: 2.0228 - correlation_coefficient: 0.5028 - val_loss: 2.0760 - val_correlation_coefficient: 0.4586
Epoch 43/100
640/640 [==============================] - 0s 257us/step - loss: 1.9951 - correlation_coefficient: 0.5584 - val_loss: 2.0342 - val_correlation_coefficient: 0.4678
Epoch 44/100
640/640 [==============================] - 0s 268us/step - loss: 1.9810 - correlation_coefficient: 0.5576 - val_loss: 2.3027 - val_correlation_coefficient: 0.4773
Epoch 45/100
640/640 [==============================] - 0s 275us/step - loss: 1.9496 - correlation_coefficient: 0.5668 - val_loss: 1.9362 - val_correlation_coefficient: 0.4957
Epoch 46/100
640/640 [==============================] - 0s 282us/step - loss: 1.8442 - correlation_coefficient: 0.5925 - val_loss: 1.9427 - val_correlation_coefficient: 0.5044
Epoch 47/100
640/640 [==============================] - 0s 278us/step - loss: 1.8373 - correlation_coefficient: 0.5957 - val_loss: 1.8818 - val_correlation_coefficient: 0.5188
Epoch 48/100
640/640 [==============================] - 0s 280us/step - loss: 1.7816 - correlation_coefficient: 0.5919 - val_loss: 1.8729 - val_correlation_coefficient: 0.5283
Epoch 49/100
640/640 [==============================] - 0s 256us/step - loss: 1.8476 - correlation_coefficient: 0.6363 - val_loss: 2.1853 - val_correlation_coefficient: 0.5370
Epoch 50/100
640/640 [==============================] - 0s 239us/step - loss: 1.7411 - correlation_coefficient: 0.6195 - val_loss: 1.7961 - val_correlation_coefficient: 0.5546
Epoch 51/100
640/640 [==============================] - 0s 253us/step - loss: 1.6690 - correlation_coefficient: 0.6416 - val_loss: 1.7886 - val_correlation_coefficient: 0.5574
Epoch 52/100
640/640 [==============================] - 0s 267us/step - loss: 1.6391 - correlation_coefficient: 0.6584 - val_loss: 1.7492 - val_correlation_coefficient: 0.5721
Epoch 53/100
640/640 [==============================] - 0s 255us/step - loss: 1.5750 - correlation_coefficient: 0.6569 - val_loss: 1.6841 - val_correlation_coefficient: 0.5794
Epoch 54/100
640/640 [==============================] - 0s 208us/step - loss: 1.5270 - correlation_coefficient: 0.6653 - val_loss: 1.8707 - val_correlation_coefficient: 0.5849
Epoch 55/100
640/640 [==============================] - 0s 224us/step - loss: 1.5578 - correlation_coefficient: 0.6705 - val_loss: 1.8342 - val_correlation_coefficient: 0.5866
Epoch 56/100
640/640 [==============================] - 0s 278us/step - loss: 1.5077 - correlation_coefficient: 0.6921 - val_loss: 1.6774 - val_correlation_coefficient: 0.5985
Epoch 57/100
640/640 [==============================] - 0s 291us/step - loss: 1.5510 - correlation_coefficient: 0.6912 - val_loss: 1.9361 - val_correlation_coefficient: 0.5994
Epoch 58/100
640/640 [==============================] - 0s 271us/step - loss: 1.4712 - correlation_coefficient: 0.6956 - val_loss: 1.5889 - val_correlation_coefficient: 0.6089
Epoch 59/100
640/640 [==============================] - 0s 277us/step - loss: 1.4170 - correlation_coefficient: 0.7042 - val_loss: 1.6166 - val_correlation_coefficient: 0.6097
Epoch 60/100
640/640 [==============================] - 0s 261us/step - loss: 1.4155 - correlation_coefficient: 0.7173 - val_loss: 1.6616 - val_correlation_coefficient: 0.6099
Epoch 61/100
640/640 [==============================] - 0s 269us/step - loss: 1.4085 - correlation_coefficient: 0.7078 - val_loss: 1.6945 - val_correlation_coefficient: 0.6141
Epoch 62/100
640/640 [==============================] - 0s 289us/step - loss: 1.3825 - correlation_coefficient: 0.7180 - val_loss: 1.6690 - val_correlation_coefficient: 0.6195
Epoch 63/100
640/640 [==============================] - 0s 311us/step - loss: 1.3681 - correlation_coefficient: 0.7100 - val_loss: 1.5628 - val_correlation_coefficient: 0.6223
Epoch 64/100
640/640 [==============================] - 0s 270us/step - loss: 1.3614 - correlation_coefficient: 0.7301 - val_loss: 1.5476 - val_correlation_coefficient: 0.6225
Epoch 65/100
640/640 [==============================] - 0s 259us/step - loss: 1.3308 - correlation_coefficient: 0.7229 - val_loss: 1.5279 - val_correlation_coefficient: 0.6296
Epoch 66/100
640/640 [==============================] - 0s 241us/step - loss: 1.4790 - correlation_coefficient: 0.7250 - val_loss: 1.7234 - val_correlation_coefficient: 0.6209
Epoch 67/100
640/640 [==============================] - 0s 266us/step - loss: 1.3390 - correlation_coefficient: 0.7195 - val_loss: 1.5748 - val_correlation_coefficient: 0.6314
Epoch 68/100
640/640 [==============================] - 0s 287us/step - loss: 1.2925 - correlation_coefficient: 0.7354 - val_loss: 1.5740 - val_correlation_coefficient: 0.6318
Epoch 69/100
640/640 [==============================] - 0s 291us/step - loss: 1.3034 - correlation_coefficient: 0.7326 - val_loss: 1.6764 - val_correlation_coefficient: 0.6307
Epoch 70/100
640/640 [==============================] - 0s 265us/step - loss: 1.3661 - correlation_coefficient: 0.7346 - val_loss: 1.5595 - val_correlation_coefficient: 0.6323
Epoch 71/100
640/640 [==============================] - 0s 263us/step - loss: 1.3256 - correlation_coefficient: 0.7368 - val_loss: 1.5264 - val_correlation_coefficient: 0.6359
Epoch 72/100
640/640 [==============================] - 0s 267us/step - loss: 1.2576 - correlation_coefficient: 0.7392 - val_loss: 1.5557 - val_correlation_coefficient: 0.6399
Epoch 73/100
640/640 [==============================] - 0s 284us/step - loss: 1.3466 - correlation_coefficient: 0.7428 - val_loss: 1.5028 - val_correlation_coefficient: 0.6387
Epoch 74/100
640/640 [==============================] - 0s 255us/step - loss: 1.3410 - correlation_coefficient: 0.7490 - val_loss: 1.5340 - val_correlation_coefficient: 0.6448
Epoch 75/100
640/640 [==============================] - 0s 272us/step - loss: 1.2933 - correlation_coefficient: 0.7412 - val_loss: 1.4825 - val_correlation_coefficient: 0.6429
Epoch 76/100
640/640 [==============================] - 0s 268us/step - loss: 1.3101 - correlation_coefficient: 0.7475 - val_loss: 1.5975 - val_correlation_coefficient: 0.6361
Epoch 77/100
640/640 [==============================] - 0s 267us/step - loss: 1.2896 - correlation_coefficient: 0.7474 - val_loss: 1.9291 - val_correlation_coefficient: 0.6309
Epoch 78/100
640/640 [==============================] - 0s 251us/step - loss: 1.3381 - correlation_coefficient: 0.7473 - val_loss: 1.6367 - val_correlation_coefficient: 0.6364
Epoch 79/100
640/640 [==============================] - 0s 296us/step - loss: 1.2642 - correlation_coefficient: 0.7433 - val_loss: 1.4892 - val_correlation_coefficient: 0.6421
Epoch 80/100
640/640 [==============================] - 0s 288us/step - loss: 1.2752 - correlation_coefficient: 0.7498 - val_loss: 1.4824 - val_correlation_coefficient: 0.6489
Epoch 81/100
640/640 [==============================] - 0s 285us/step - loss: 1.2757 - correlation_coefficient: 0.7505 - val_loss: 1.6808 - val_correlation_coefficient: 0.6387
Epoch 82/100
640/640 [==============================] - 0s 283us/step - loss: 1.2903 - correlation_coefficient: 0.7474 - val_loss: 1.5281 - val_correlation_coefficient: 0.6486
Epoch 83/100
640/640 [==============================] - 0s 242us/step - loss: 1.3224 - correlation_coefficient: 0.7454 - val_loss: 1.4850 - val_correlation_coefficient: 0.6452
Epoch 84/100
640/640 [==============================] - 0s 261us/step - loss: 1.2617 - correlation_coefficient: 0.7485 - val_loss: 1.4675 - val_correlation_coefficient: 0.6469
Epoch 85/100
640/640 [==============================] - 0s 245us/step - loss: 1.2377 - correlation_coefficient: 0.7535 - val_loss: 1.5047 - val_correlation_coefficient: 0.6443
Epoch 86/100
640/640 [==============================] - 0s 279us/step - loss: 1.2069 - correlation_coefficient: 0.7582 - val_loss: 1.4693 - val_correlation_coefficient: 0.6475
Epoch 87/100
640/640 [==============================] - 0s 257us/step - loss: 1.2244 - correlation_coefficient: 0.7621 - val_loss: 1.5267 - val_correlation_coefficient: 0.6445
Epoch 88/100
640/640 [==============================] - 0s 243us/step - loss: 1.2073 - correlation_coefficient: 0.7568 - val_loss: 1.5674 - val_correlation_coefficient: 0.6435
Epoch 89/100
640/640 [==============================] - 0s 247us/step - loss: 1.2329 - correlation_coefficient: 0.7530 - val_loss: 1.6846 - val_correlation_coefficient: 0.6475
Epoch 90/100
640/640 [==============================] - 0s 238us/step - loss: 1.2679 - correlation_coefficient: 0.7635 - val_loss: 1.5377 - val_correlation_coefficient: 0.6495
Epoch 91/100
640/640 [==============================] - 0s 276us/step - loss: 1.2640 - correlation_coefficient: 0.7458 - val_loss: 1.4713 - val_correlation_coefficient: 0.6495
Epoch 92/100
640/640 [==============================] - 0s 241us/step - loss: 1.2375 - correlation_coefficient: 0.7641 - val_loss: 1.9712 - val_correlation_coefficient: 0.6399
Epoch 93/100
640/640 [==============================] - 0s 263us/step - loss: 1.2880 - correlation_coefficient: 0.7579 - val_loss: 1.4522 - val_correlation_coefficient: 0.6534
Epoch 94/100
640/640 [==============================] - 1s 896us/step - loss: 1.2377 - correlation_coefficient: 0.7704 - val_loss: 1.5802 - val_correlation_coefficient: 0.6459
Epoch 95/100
640/640 [==============================] - 0s 686us/step - loss: 1.2417 - correlation_coefficient: 0.7593 - val_loss: 1.4834 - val_correlation_coefficient: 0.6501
Epoch 96/100
640/640 [==============================] - 0s 480us/step - loss: 1.2272 - correlation_coefficient: 0.7595 - val_loss: 1.4934 - val_correlation_coefficient: 0.6457
Epoch 97/100
640/640 [==============================] - 1s 878us/step - loss: 1.1731 - correlation_coefficient: 0.7584 - val_loss: 1.5022 - val_correlation_coefficient: 0.6461
Epoch 98/100
640/640 [==============================] - 0s 414us/step - loss: 1.1756 - correlation_coefficient: 0.7568 - val_loss: 1.5204 - val_correlation_coefficient: 0.6495
Epoch 99/100
640/640 [==============================] - 0s 707us/step - loss: 1.1885 - correlation_coefficient: 0.7573 - val_loss: 1.5397 - val_correlation_coefficient: 0.6489
Epoch 100/100
640/640 [==============================] - 1s 939us/step - loss: 1.1638 - correlation_coefficient: 0.7519 - val_loss: 1.4949 - val_correlation_coefficient: 0.6493
[root |INFO|build_network.py:87] Load trained model weight at ./weight_0.h5
[root |INFO|build_network.py:147] Training end with time 26.963207721710205!
[root |INFO|build_network.py:83] Saved trained model weight at ./weight_0.h5
[root |DEBUG|deepbiome.py:185] Save weight at ./weight_0.h5
[root |DEBUG|deepbiome.py:188] Save history at ./hist_0.json
[root |INFO|build_network.py:173] Evaluation start!
800/800 [==============================] - 0s 5us/step
[root |INFO|build_network.py:178] Evaluation end with time 0.007388114929199219!
[root |INFO|build_network.py:179] Evaluation: [1.2927197217941284, 0.7277674078941345]
[root |INFO|build_network.py:173] Evaluation start!
200/200 [==============================] - 0s 14us/step
[root |INFO|build_network.py:178] Evaluation end with time 0.005715370178222656!
[root |INFO|build_network.py:179] Evaluation: [1.624261736869812, 0.6726546883583069]
[root |INFO|deepbiome.py:199] Compute time : 27.916018962860107
[root |INFO|deepbiome.py:200] 1 fold computing end!---------------------------------------------
[root |INFO|deepbiome.py:153] -------2 simulation start!----------------------------------
[root |INFO|readers.py:58] -----------------------------------------------------------------------
[root |INFO|readers.py:59] Construct Dataset
[root |INFO|readers.py:60] -----------------------------------------------------------------------
[root |INFO|readers.py:61] Load data
[root |INFO|deepbiome.py:164] -----------------------------------------------------------------
[root |INFO|deepbiome.py:165] Build network for 2 simulation
[root |INFO|build_network.py:521] ------------------------------------------------------------------------------------------
[root |INFO|build_network.py:522] Read phylogenetic tree information from /DATA/home/muha/github_repos/deepbiome/deepbiome/tests/data/genus48_dic.csv
[root |INFO|build_network.py:528] Phylogenetic tree level list: ['Genus', 'Family', 'Order', 'Class', 'Phylum']
[root |INFO|build_network.py:529] ------------------------------------------------------------------------------------------
[root |INFO|build_network.py:537] Genus: 48
[root |INFO|build_network.py:537] Family: 40
[root |INFO|build_network.py:537] Order: 23
[root |INFO|build_network.py:537] Class: 17
[root |INFO|build_network.py:537] Phylum: 9
[root |INFO|build_network.py:546] ------------------------------------------------------------------------------------------
[root |INFO|build_network.py:547] Phylogenetic_tree_dict info: ['Phylum', 'Order', 'Genus', 'Number', 'Family', 'Class']
[root |INFO|build_network.py:548] ------------------------------------------------------------------------------------------
[root |INFO|build_network.py:558] Build edge weights between [ Genus, Family]
[root |INFO|build_network.py:558] Build edge weights between [Family, Order]
[root |INFO|build_network.py:558] Build edge weights between [ Order, Class]
[root |INFO|build_network.py:558] Build edge weights between [ Class, Phylum]
[root |INFO|build_network.py:571] ------------------------------------------------------------------------------------------
[root |INFO|build_network.py:586] ------------------------------------------------------------------------------------------
[root |INFO|build_network.py:587] Build network based on phylogenetic tree information
[root |INFO|build_network.py:588] ------------------------------------------------------------------------------------------
[root |INFO|build_network.py:670] ------------------------------------------------------------------------------------------
[root |INFO|build_network.py:61] Build Network
[root |INFO|build_network.py:62] Optimizer = adam
[root |INFO|build_network.py:63] Loss = mean_squared_error
[root |INFO|build_network.py:64] Metrics = correlation_coefficient
Model: "model_1"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
input (InputLayer) (None, 48) 0
_________________________________________________________________
l1_dense (Dense_with_tree) (None, 40) 1960
_________________________________________________________________
l1_activation (Activation) (None, 40) 0
_________________________________________________________________
l2_dense (Dense_with_tree) (None, 23) 943
_________________________________________________________________
l2_activation (Activation) (None, 23) 0
_________________________________________________________________
l3_dense (Dense_with_tree) (None, 17) 408
_________________________________________________________________
l3_activation (Activation) (None, 17) 0
_________________________________________________________________
l4_dense (Dense_with_tree) (None, 9) 162
_________________________________________________________________
l4_activation (Activation) (None, 9) 0
_________________________________________________________________
last_dense_h (Dense) (None, 1) 10
_________________________________________________________________
p_hat (Activation) (None, 1) 0
=================================================================
Total params: 3,483
Trainable params: 3,483
Non-trainable params: 0
_________________________________________________________________
[root |INFO|deepbiome.py:176] -----------------------------------------------------------------
[root |INFO|deepbiome.py:177] 2 fold computing start!----------------------------------
[root |INFO|build_network.py:137] Training start!
Train on 640 samples, validate on 160 samples
Epoch 1/100
640/640 [==============================] - 0s 700us/step - loss: 17.4694 - correlation_coefficient: -0.0394 - val_loss: 16.3774 - val_correlation_coefficient: 0.1229
Epoch 2/100
640/640 [==============================] - 0s 223us/step - loss: 13.7952 - correlation_coefficient: -0.0199 - val_loss: 11.6304 - val_correlation_coefficient: 0.1194
Epoch 3/100
640/640 [==============================] - 0s 257us/step - loss: 8.3697 - correlation_coefficient: -0.0033 - val_loss: 5.4165 - val_correlation_coefficient: 0.1149
Epoch 4/100
640/640 [==============================] - 0s 226us/step - loss: 3.5006 - correlation_coefficient: -0.0653 - val_loss: 2.4501 - val_correlation_coefficient: 0.1108
Epoch 5/100
640/640 [==============================] - 0s 254us/step - loss: 2.6447 - correlation_coefficient: -0.0627 - val_loss: 2.4448 - val_correlation_coefficient: 0.1149
Epoch 6/100
640/640 [==============================] - 0s 253us/step - loss: 2.5895 - correlation_coefficient: -0.0222 - val_loss: 2.4641 - val_correlation_coefficient: 0.1173
Epoch 7/100
640/640 [==============================] - 1s 902us/step - loss: 2.5864 - correlation_coefficient: -0.0448 - val_loss: 2.4512 - val_correlation_coefficient: 0.1191
Epoch 8/100
640/640 [==============================] - 0s 678us/step - loss: 2.5841 - correlation_coefficient: -0.0262 - val_loss: 2.4563 - val_correlation_coefficient: 0.1219
Epoch 9/100
640/640 [==============================] - 0s 498us/step - loss: 2.5850 - correlation_coefficient: -0.0062 - val_loss: 2.4596 - val_correlation_coefficient: 0.1240
Epoch 10/100
640/640 [==============================] - 1s 893us/step - loss: 2.5872 - correlation_coefficient: 0.0039 - val_loss: 2.4556 - val_correlation_coefficient: 0.1259
Epoch 11/100
640/640 [==============================] - 0s 467us/step - loss: 2.5826 - correlation_coefficient: -0.0057 - val_loss: 2.4694 - val_correlation_coefficient: 0.1276
Epoch 12/100
640/640 [==============================] - 0s 666us/step - loss: 2.5841 - correlation_coefficient: -0.0094 - val_loss: 2.4505 - val_correlation_coefficient: 0.1301
Epoch 13/100
640/640 [==============================] - 1s 917us/step - loss: 2.5816 - correlation_coefficient: 0.0084 - val_loss: 2.4635 - val_correlation_coefficient: 0.1326
Epoch 14/100
640/640 [==============================] - 0s 252us/step - loss: 2.5930 - correlation_coefficient: 0.0357 - val_loss: 2.4496 - val_correlation_coefficient: 0.1322
Epoch 15/100
640/640 [==============================] - 0s 268us/step - loss: 2.5892 - correlation_coefficient: 0.0390 - val_loss: 2.4636 - val_correlation_coefficient: 0.1272
Epoch 16/100
640/640 [==============================] - 0s 282us/step - loss: 2.5890 - correlation_coefficient: 0.0608 - val_loss: 2.4582 - val_correlation_coefficient: 0.1222
Epoch 17/100
640/640 [==============================] - 0s 246us/step - loss: 2.5836 - correlation_coefficient: 0.0699 - val_loss: 2.4677 - val_correlation_coefficient: 0.1110
Epoch 18/100
640/640 [==============================] - 0s 235us/step - loss: 2.5865 - correlation_coefficient: 0.1019 - val_loss: 2.4577 - val_correlation_coefficient: 0.0894
Epoch 19/100
640/640 [==============================] - 0s 279us/step - loss: 2.5892 - correlation_coefficient: 0.1535 - val_loss: 2.4492 - val_correlation_coefficient: 0.0600
Epoch 20/100
640/640 [==============================] - 0s 292us/step - loss: 2.5859 - correlation_coefficient: 0.1653 - val_loss: 2.4564 - val_correlation_coefficient: 0.0156
Epoch 21/100
640/640 [==============================] - 0s 267us/step - loss: 2.5828 - correlation_coefficient: 0.1794 - val_loss: 2.4516 - val_correlation_coefficient: -0.0116
Epoch 22/100
640/640 [==============================] - 0s 266us/step - loss: 2.5912 - correlation_coefficient: 0.1709 - val_loss: 2.4614 - val_correlation_coefficient: -0.0302
Epoch 23/100
640/640 [==============================] - 0s 238us/step - loss: 2.5854 - correlation_coefficient: 0.1658 - val_loss: 2.4560 - val_correlation_coefficient: -0.0388
Epoch 24/100
640/640 [==============================] - 0s 236us/step - loss: 2.5865 - correlation_coefficient: 0.1754 - val_loss: 2.4705 - val_correlation_coefficient: -0.0494
Epoch 25/100
640/640 [==============================] - 0s 250us/step - loss: 2.5848 - correlation_coefficient: 0.1768 - val_loss: 2.4782 - val_correlation_coefficient: -0.0524
Epoch 26/100
640/640 [==============================] - 0s 241us/step - loss: 2.5789 - correlation_coefficient: 0.1798 - val_loss: 2.4478 - val_correlation_coefficient: -0.0491
Epoch 27/100
640/640 [==============================] - 0s 242us/step - loss: 2.5984 - correlation_coefficient: 0.1388 - val_loss: 2.4554 - val_correlation_coefficient: -0.0513
Epoch 28/100
640/640 [==============================] - 0s 241us/step - loss: 2.5799 - correlation_coefficient: 0.1513 - val_loss: 2.4510 - val_correlation_coefficient: -0.0565
Epoch 29/100
640/640 [==============================] - 0s 218us/step - loss: 2.5798 - correlation_coefficient: 0.1699 - val_loss: 2.4631 - val_correlation_coefficient: -0.0582
Epoch 30/100
640/640 [==============================] - 0s 265us/step - loss: 2.5834 - correlation_coefficient: 0.1479 - val_loss: 2.4578 - val_correlation_coefficient: -0.0570
Epoch 31/100
640/640 [==============================] - 0s 255us/step - loss: 2.5926 - correlation_coefficient: 0.1060 - val_loss: 2.4480 - val_correlation_coefficient: -0.0557
Epoch 32/100
640/640 [==============================] - 0s 276us/step - loss: 2.5967 - correlation_coefficient: 0.1639 - val_loss: 2.4609 - val_correlation_coefficient: -0.0586
Epoch 33/100
640/640 [==============================] - 0s 266us/step - loss: 2.5830 - correlation_coefficient: 0.1401 - val_loss: 2.4519 - val_correlation_coefficient: -0.0583
Epoch 34/100
640/640 [==============================] - 0s 261us/step - loss: 2.5809 - correlation_coefficient: 0.1719 - val_loss: 2.4574 - val_correlation_coefficient: -0.0601
Epoch 35/100
640/640 [==============================] - 0s 272us/step - loss: 2.5847 - correlation_coefficient: 0.1353 - val_loss: 2.4542 - val_correlation_coefficient: -0.0607
Epoch 36/100
640/640 [==============================] - 0s 242us/step - loss: 2.5873 - correlation_coefficient: 0.1306 - val_loss: 2.4678 - val_correlation_coefficient: -0.0628
Epoch 37/100
640/640 [==============================] - 0s 254us/step - loss: 2.5829 - correlation_coefficient: 0.1282 - val_loss: 2.4634 - val_correlation_coefficient: -0.0618
Epoch 38/100
640/640 [==============================] - 0s 264us/step - loss: 2.5934 - correlation_coefficient: 0.1306 - val_loss: 2.4621 - val_correlation_coefficient: -0.0630
Epoch 39/100
640/640 [==============================] - 0s 278us/step - loss: 2.5876 - correlation_coefficient: 0.1516 - val_loss: 2.4589 - val_correlation_coefficient: -0.0638
Epoch 40/100
640/640 [==============================] - 0s 265us/step - loss: 2.5853 - correlation_coefficient: 0.1458 - val_loss: 2.4603 - val_correlation_coefficient: -0.0643
Epoch 41/100
640/640 [==============================] - 0s 260us/step - loss: 2.5888 - correlation_coefficient: 0.1654 - val_loss: 2.4575 - val_correlation_coefficient: -0.0648
Epoch 42/100
640/640 [==============================] - 0s 251us/step - loss: 2.5819 - correlation_coefficient: 0.1309 - val_loss: 2.4637 - val_correlation_coefficient: -0.0646
Epoch 43/100
640/640 [==============================] - 0s 275us/step - loss: 2.5765 - correlation_coefficient: 0.1471 - val_loss: 2.4487 - val_correlation_coefficient: -0.0623
Epoch 44/100
640/640 [==============================] - 0s 292us/step - loss: 2.5931 - correlation_coefficient: 0.1078 - val_loss: 2.4654 - val_correlation_coefficient: -0.0649
Epoch 45/100
640/640 [==============================] - 0s 265us/step - loss: 2.5768 - correlation_coefficient: 0.1016 - val_loss: 2.4533 - val_correlation_coefficient: -0.0630
Epoch 46/100
640/640 [==============================] - 0s 259us/step - loss: 2.5787 - correlation_coefficient: 0.1383 - val_loss: 2.4548 - val_correlation_coefficient: -0.0631
Epoch 47/100
640/640 [==============================] - 0s 273us/step - loss: 2.5865 - correlation_coefficient: 0.1341 - val_loss: 2.4711 - val_correlation_coefficient: -0.0643
Epoch 48/100
640/640 [==============================] - 0s 275us/step - loss: 2.6104 - correlation_coefficient: 0.1630 - val_loss: 2.4586 - val_correlation_coefficient: -0.0633
Epoch 49/100
640/640 [==============================] - 0s 223us/step - loss: 2.5834 - correlation_coefficient: 0.1256 - val_loss: 2.4513 - val_correlation_coefficient: -0.0617
Epoch 50/100
640/640 [==============================] - 0s 250us/step - loss: 2.5884 - correlation_coefficient: 0.1367 - val_loss: 2.4553 - val_correlation_coefficient: -0.0616
Epoch 51/100
640/640 [==============================] - 0s 247us/step - loss: 2.5805 - correlation_coefficient: 0.1286 - val_loss: 2.4600 - val_correlation_coefficient: -0.0623
Epoch 52/100
640/640 [==============================] - 0s 295us/step - loss: 2.5854 - correlation_coefficient: 0.1110 - val_loss: 2.4713 - val_correlation_coefficient: -0.0619
Epoch 53/100
640/640 [==============================] - 0s 284us/step - loss: 2.5768 - correlation_coefficient: 0.1590 - val_loss: 2.4515 - val_correlation_coefficient: -0.0604
Epoch 54/100
640/640 [==============================] - 0s 290us/step - loss: 2.5844 - correlation_coefficient: 0.1283 - val_loss: 2.4606 - val_correlation_coefficient: -0.0598
Epoch 55/100
640/640 [==============================] - 0s 281us/step - loss: 2.5741 - correlation_coefficient: 0.1477 - val_loss: 2.4509 - val_correlation_coefficient: -0.0577
Epoch 56/100
640/640 [==============================] - 0s 253us/step - loss: 2.5740 - correlation_coefficient: 0.1283 - val_loss: 2.4629 - val_correlation_coefficient: -0.0583
Epoch 57/100
640/640 [==============================] - 0s 274us/step - loss: 2.5823 - correlation_coefficient: 0.1622 - val_loss: 2.4552 - val_correlation_coefficient: -0.0550
Epoch 58/100
640/640 [==============================] - 1s 864us/step - loss: 2.5715 - correlation_coefficient: 0.1389 - val_loss: 2.4653 - val_correlation_coefficient: -0.0556
Epoch 59/100
640/640 [==============================] - 0s 243us/step - loss: 2.5784 - correlation_coefficient: 0.1387 - val_loss: 2.4763 - val_correlation_coefficient: -0.0550
Epoch 60/100
640/640 [==============================] - 0s 250us/step - loss: 2.5839 - correlation_coefficient: 0.1636 - val_loss: 2.4630 - val_correlation_coefficient: -0.0522
Epoch 61/100
640/640 [==============================] - 0s 217us/step - loss: 2.5826 - correlation_coefficient: 0.1819 - val_loss: 2.4896 - val_correlation_coefficient: -0.0513
Epoch 62/100
640/640 [==============================] - 0s 260us/step - loss: 2.5791 - correlation_coefficient: 0.1344 - val_loss: 2.4534 - val_correlation_coefficient: -0.0478
Epoch 63/100
640/640 [==============================] - 0s 280us/step - loss: 2.5766 - correlation_coefficient: 0.1637 - val_loss: 2.4556 - val_correlation_coefficient: -0.0470
Epoch 64/100
640/640 [==============================] - 0s 282us/step - loss: 2.5706 - correlation_coefficient: 0.1441 - val_loss: 2.4528 - val_correlation_coefficient: -0.0446
Epoch 65/100
640/640 [==============================] - 0s 266us/step - loss: 2.5745 - correlation_coefficient: 0.1400 - val_loss: 2.4598 - val_correlation_coefficient: -0.0417
Epoch 66/100
640/640 [==============================] - 0s 259us/step - loss: 2.5949 - correlation_coefficient: 0.0984 - val_loss: 2.4488 - val_correlation_coefficient: -0.0401
Epoch 67/100
640/640 [==============================] - 0s 272us/step - loss: 2.5726 - correlation_coefficient: 0.1649 - val_loss: 2.4634 - val_correlation_coefficient: -0.0409
Epoch 68/100
640/640 [==============================] - 0s 248us/step - loss: 2.5773 - correlation_coefficient: 0.1483 - val_loss: 2.4625 - val_correlation_coefficient: -0.0403
Epoch 69/100
640/640 [==============================] - 0s 269us/step - loss: 2.5776 - correlation_coefficient: 0.1667 - val_loss: 2.4814 - val_correlation_coefficient: -0.0401
Epoch 70/100
640/640 [==============================] - 0s 295us/step - loss: 2.5828 - correlation_coefficient: 0.1459 - val_loss: 2.4642 - val_correlation_coefficient: -0.0390
Epoch 71/100
640/640 [==============================] - 0s 252us/step - loss: 2.5837 - correlation_coefficient: 0.1473 - val_loss: 2.4626 - val_correlation_coefficient: -0.0379
Epoch 72/100
640/640 [==============================] - 0s 252us/step - loss: 2.5754 - correlation_coefficient: 0.1594 - val_loss: 2.4627 - val_correlation_coefficient: -0.0381
Epoch 73/100
640/640 [==============================] - 0s 253us/step - loss: 2.5745 - correlation_coefficient: 0.1626 - val_loss: 2.4712 - val_correlation_coefficient: -0.0361
Epoch 74/100
640/640 [==============================] - 0s 259us/step - loss: 2.5677 - correlation_coefficient: 0.1739 - val_loss: 2.4636 - val_correlation_coefficient: -0.0362
Epoch 75/100
640/640 [==============================] - 0s 257us/step - loss: 2.5635 - correlation_coefficient: 0.1635 - val_loss: 2.4610 - val_correlation_coefficient: -0.0332
Epoch 76/100
640/640 [==============================] - 0s 282us/step - loss: 2.5667 - correlation_coefficient: 0.1350 - val_loss: 2.4708 - val_correlation_coefficient: -0.0327
Epoch 77/100
640/640 [==============================] - 1s 873us/step - loss: 2.5642 - correlation_coefficient: 0.1511 - val_loss: 2.4610 - val_correlation_coefficient: -0.0327
Epoch 78/100
640/640 [==============================] - 0s 421us/step - loss: 2.5692 - correlation_coefficient: 0.1627 - val_loss: 2.4541 - val_correlation_coefficient: -0.0315
Epoch 79/100
640/640 [==============================] - 0s 779us/step - loss: 2.5686 - correlation_coefficient: 0.1673 - val_loss: 2.4629 - val_correlation_coefficient: -0.0315
Epoch 80/100
640/640 [==============================] - 1s 904us/step - loss: 2.5647 - correlation_coefficient: 0.1609 - val_loss: 2.4608 - val_correlation_coefficient: -0.0301
Epoch 81/100
640/640 [==============================] - 0s 297us/step - loss: 2.5569 - correlation_coefficient: 0.1479 - val_loss: 2.4631 - val_correlation_coefficient: -0.0295
Epoch 82/100
640/640 [==============================] - 1s 966us/step - loss: 2.5597 - correlation_coefficient: 0.1690 - val_loss: 2.4647 - val_correlation_coefficient: -0.0279
Epoch 83/100
640/640 [==============================] - 0s 509us/step - loss: 2.5598 - correlation_coefficient: 0.1660 - val_loss: 2.4717 - val_correlation_coefficient: -0.0280
Epoch 84/100
640/640 [==============================] - 0s 704us/step - loss: 2.5668 - correlation_coefficient: 0.1693 - val_loss: 2.4844 - val_correlation_coefficient: -0.0263
Epoch 85/100
640/640 [==============================] - 1s 947us/step - loss: 2.5583 - correlation_coefficient: 0.1521 - val_loss: 2.4551 - val_correlation_coefficient: -0.0257
Epoch 86/100
640/640 [==============================] - 0s 697us/step - loss: 2.5563 - correlation_coefficient: 0.1314 - val_loss: 2.4914 - val_correlation_coefficient: -0.0257
Epoch 87/100
640/640 [==============================] - 0s 462us/step - loss: 2.5806 - correlation_coefficient: 0.1442 - val_loss: 2.4575 - val_correlation_coefficient: -0.0259
Epoch 88/100
640/640 [==============================] - 1s 942us/step - loss: 2.5514 - correlation_coefficient: 0.1630 - val_loss: 2.4945 - val_correlation_coefficient: -0.0247
Epoch 89/100
640/640 [==============================] - 0s 433us/step - loss: 2.5546 - correlation_coefficient: 0.1433 - val_loss: 2.4615 - val_correlation_coefficient: -0.0246
Epoch 90/100
640/640 [==============================] - 0s 761us/step - loss: 2.5534 - correlation_coefficient: 0.1578 - val_loss: 2.4713 - val_correlation_coefficient: -0.0233
Epoch 91/100
640/640 [==============================] - 0s 283us/step - loss: 2.5779 - correlation_coefficient: 0.1649 - val_loss: 2.4543 - val_correlation_coefficient: -0.0234
Epoch 92/100
640/640 [==============================] - 0s 268us/step - loss: 2.5666 - correlation_coefficient: 0.1743 - val_loss: 2.4587 - val_correlation_coefficient: -0.0238
Epoch 93/100
640/640 [==============================] - 0s 291us/step - loss: 2.5522 - correlation_coefficient: 0.1509 - val_loss: 2.4592 - val_correlation_coefficient: -0.0232
Epoch 94/100
640/640 [==============================] - 0s 285us/step - loss: 2.5617 - correlation_coefficient: 0.1643 - val_loss: 2.4617 - val_correlation_coefficient: -0.0240
Epoch 95/100
640/640 [==============================] - 0s 282us/step - loss: 2.5532 - correlation_coefficient: 0.1524 - val_loss: 2.4779 - val_correlation_coefficient: -0.0249
Epoch 96/100
640/640 [==============================] - 0s 282us/step - loss: 2.5633 - correlation_coefficient: 0.1654 - val_loss: 2.4555 - val_correlation_coefficient: -0.0249
Epoch 97/100
640/640 [==============================] - 0s 284us/step - loss: 2.5548 - correlation_coefficient: 0.1697 - val_loss: 2.4718 - val_correlation_coefficient: -0.0249
Epoch 98/100
640/640 [==============================] - 0s 303us/step - loss: 2.5607 - correlation_coefficient: 0.1594 - val_loss: 2.4713 - val_correlation_coefficient: -0.0246
Epoch 99/100
640/640 [==============================] - 0s 304us/step - loss: 2.5468 - correlation_coefficient: 0.1710 - val_loss: 2.4757 - val_correlation_coefficient: -0.0244
Epoch 100/100
640/640 [==============================] - 0s 257us/step - loss: 2.5544 - correlation_coefficient: 0.1709 - val_loss: 2.4573 - val_correlation_coefficient: -0.0244
[root |INFO|build_network.py:87] Load trained model weight at ./weight_1.h5
[root |INFO|build_network.py:147] Training end with time 24.856287717819214!
[root |INFO|build_network.py:83] Saved trained model weight at ./weight_1.h5
[root |DEBUG|deepbiome.py:185] Save weight at ./weight_1.h5
[root |DEBUG|deepbiome.py:188] Save history at ./hist_1.json
[root |INFO|build_network.py:173] Evaluation start!
800/800 [==============================] - 0s 4us/step
[root |INFO|build_network.py:178] Evaluation end with time 0.006667137145996094!
[root |INFO|build_network.py:179] Evaluation: [2.5581955909729004, -0.02809630148112774]
[root |INFO|build_network.py:173] Evaluation start!
200/200 [==============================] - 0s 23us/step
[root |INFO|build_network.py:178] Evaluation end with time 0.008564472198486328!
[root |INFO|build_network.py:179] Evaluation: [2.524724245071411, 0.049455344676971436]
[root |INFO|deepbiome.py:199] Compute time : 25.32423758506775
[root |INFO|deepbiome.py:200] 2 fold computing end!---------------------------------------------
[root |INFO|deepbiome.py:211] -----------------------------------------------------------------
[root |INFO|deepbiome.py:212] Train Evaluation : ['loss' 'correlation_coefficient']
[root |INFO|deepbiome.py:213] mean : [1.925 0.350]
[root |INFO|deepbiome.py:214] std : [0.633 0.378]
[root |INFO|deepbiome.py:215] -----------------------------------------------------------------
[root |INFO|deepbiome.py:216] Test Evaluation : ['loss' 'correlation_coefficient']
[root |INFO|deepbiome.py:217] mean : [2.074 0.361]
[root |INFO|deepbiome.py:218] std : [0.450 0.312]
[root |INFO|deepbiome.py:219] -----------------------------------------------------------------
[root |INFO|deepbiome.py:230] Total Computing Ended
[root |INFO|deepbiome.py:231] -----------------------------------------------------------------