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output_he_experiments.txt
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output_he_experiments.txt
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==============
PROGRAM STARTS
==============
=======================
Loading embedding files
=======================
loading ... msrpc_POSkimprep_nolower_glove_embcap_20180611.pickle
Embeddings shape (pretrained GloVe): (20504, 300) unknown tokens: 488
loading ... msrpc_POSkimprep_nolower_POSword2vec_embcap_20180611.pickle
Embeddings shape (pretrained POS word2vec): (20504, 200) unknown tokens: 1533
loading ... msrpc_POSkimprep_nolower_paragram25_embcap_20180611.pickle
Embeddings shape (pretrained PARAGRAM): (20504, 25) unknown tokens: 1868
Final embeddings dim: 525
===================
Generating datasets
===================
Max seq length: 39
X_train: (4076, 39)
Y_train: (4076,)
X_test: (1725, 39)
Y_test: (1725,)
================
Generating model
================
Convolution activation: tanh
Dense activation: tanh
Group A: True Group B: True
Group A config
--------------
Filters: [(1, 525), (2, 525), (3, 525), (None, 525)]
Pool ops: ['max', 'min', 'mean']
Group B config
--------------
Using DepthwiseConv2D
Filters: [(1, 20), (2, 20), (3, 20)]
Pool ops: ['max', 'min']
Algorithm 1: True Algorithm 2: True
Algorithm 1
-----------
Cosine similarity: True
Euclidean distance: True
Absolute difference: False
Algorithm 2
-----------
Cosine similarity: True
Euclidean distance: True
Absolute difference: True
Other settings
--------------
MLP hidden units: 250
Regularization: 0.0001
Trainable embeddings: True
Trainable parameters: 38,841,827
Non-trainable parameters: 0
Total parameters: 38,841,827
Done!
===============
Compiling model
===============
Optimizer: adam
Learning rate: 0.001
Loss: categorical_crossentropy
Done!
==================
Training model ...
==================
Epochs: 30
Batch size: 32
Forward propagation with random values
0.664927536231884
0.7970505617977529
[[ 12 566]
[ 12 1135]]
Train on 3260 samples, validate on 816 samples
Epoch 1/30
- 21s - loss: 5.5638 - acc: 0.6709 - val_loss: 5.0991 - val_acc: 0.6924
val_acc: 0.6924
val_f1: 0.8182
val_conf_mat:
[[ 0 251]
[ 0 565]]
Epoch 00001: val_acc improved from -inf to 0.69240, saving model to ../checkpoints/he_mpcnn/cp_20180622_132544.hdf5
Epoch 2/30
- 18s - loss: 5.3847 - acc: 0.6712 - val_loss: 5.0052 - val_acc: 0.6924
val_acc: 0.6924
val_f1: 0.8182
val_conf_mat:
[[ 0 251]
[ 0 565]]
Epoch 00002: val_acc did not improve
Epoch 3/30
- 18s - loss: 5.3313 - acc: 0.6712 - val_loss: 4.9776 - val_acc: 0.6924
val_acc: 0.6924
val_f1: 0.8182
val_conf_mat:
[[ 0 251]
[ 0 565]]
Epoch 00003: val_acc did not improve
Epoch 4/30
- 19s - loss: 5.3142 - acc: 0.6712 - val_loss: 4.9677 - val_acc: 0.6924
val_acc: 0.6924
val_f1: 0.8182
val_conf_mat:
[[ 0 251]
[ 0 565]]
Epoch 00004: val_acc did not improve
Epoch 5/30
- 19s - loss: 5.3077 - acc: 0.6712 - val_loss: 4.9636 - val_acc: 0.6924
val_acc: 0.6924
val_f1: 0.8182
val_conf_mat:
[[ 0 251]
[ 0 565]]
Epoch 00005: val_acc did not improve
Epoch 6/30
- 19s - loss: 5.3048 - acc: 0.6712 - val_loss: 4.9616 - val_acc: 0.6924
val_acc: 0.6924
val_f1: 0.8182
val_conf_mat:
[[ 0 251]
[ 0 565]]
Epoch 00006: val_acc did not improve
Epoch 7/30
- 19s - loss: 5.3033 - acc: 0.6712 - val_loss: 4.9605 - val_acc: 0.6924
val_acc: 0.6924
val_f1: 0.8182
val_conf_mat:
[[ 0 251]
[ 0 565]]
Epoch 00007: val_acc did not improve
Epoch 8/30
- 19s - loss: 5.3024 - acc: 0.6712 - val_loss: 4.9598 - val_acc: 0.6924
val_acc: 0.6924
val_f1: 0.8182
val_conf_mat:
[[ 0 251]
[ 0 565]]
Epoch 00008: val_acc did not improve
Epoch 9/30
- 19s - loss: 2.5116 - acc: 0.6270 - val_loss: 0.6619 - val_acc: 0.6924
val_acc: 0.6924
val_f1: 0.8182
val_conf_mat:
[[ 0 251]
[ 0 565]]
Epoch 00009: val_acc did not improve
Epoch 10/30
- 19s - loss: 0.6591 - acc: 0.6601 - val_loss: 0.6914 - val_acc: 0.6924
val_acc: 0.6924
val_f1: 0.8182
val_conf_mat:
[[ 0 251]
[ 0 565]]
Epoch 00010: val_acc did not improve
Epoch 11/30
- 19s - loss: 0.6503 - acc: 0.6663 - val_loss: 0.6319 - val_acc: 0.6924
val_acc: 0.6924
val_f1: 0.8182
val_conf_mat:
[[ 0 251]
[ 0 565]]
Epoch 00011: val_acc did not improve
Epoch 12/30
- 19s - loss: 0.6477 - acc: 0.6712 - val_loss: 0.6267 - val_acc: 0.6924
val_acc: 0.6924
val_f1: 0.8182
val_conf_mat:
[[ 0 251]
[ 0 565]]
Epoch 00012: val_acc did not improve
Epoch 13/30
- 19s - loss: 0.6541 - acc: 0.6712 - val_loss: 0.6508 - val_acc: 0.6924
val_acc: 0.6924
val_f1: 0.8182
val_conf_mat:
[[ 0 251]
[ 0 565]]
Epoch 00013: val_acc did not improve
Epoch 14/30
- 19s - loss: 0.6622 - acc: 0.6712 - val_loss: 0.6508 - val_acc: 0.6924
val_acc: 0.6924
val_f1: 0.8182
val_conf_mat:
[[ 0 251]
[ 0 565]]
Epoch 00014: val_acc did not improve
Epoch 15/30
- 19s - loss: 0.6614 - acc: 0.6712 - val_loss: 0.6346 - val_acc: 0.6924
val_acc: 0.6924
val_f1: 0.8182
val_conf_mat:
[[ 0 251]
[ 0 565]]
Epoch 00015: val_acc did not improve
Epoch 16/30
- 19s - loss: 0.6774 - acc: 0.6601 - val_loss: 0.6537 - val_acc: 0.6924
val_acc: 0.6924
val_f1: 0.8182
val_conf_mat:
[[ 0 251]
[ 0 565]]
Epoch 00016: val_acc did not improve
Epoch 17/30
- 19s - loss: 0.6748 - acc: 0.6712 - val_loss: 0.6674 - val_acc: 0.6924
val_acc: 0.6924
val_f1: 0.8182
val_conf_mat:
[[ 0 251]
[ 0 565]]
Epoch 00017: val_acc did not improve
Epoch 18/30
- 19s - loss: 0.6735 - acc: 0.6712 - val_loss: 0.6705 - val_acc: 0.6924
val_acc: 0.6924
val_f1: 0.8182
val_conf_mat:
[[ 0 251]
[ 0 565]]
Epoch 00018: val_acc did not improve
Epoch 19/30
- 19s - loss: 0.6855 - acc: 0.6534 - val_loss: 0.6557 - val_acc: 0.6924
val_acc: 0.6924
val_f1: 0.8182
val_conf_mat:
[[ 0 251]
[ 0 565]]
Epoch 00019: val_acc did not improve
Epoch 20/30
- 19s - loss: 0.6730 - acc: 0.6712 - val_loss: 0.6443 - val_acc: 0.6924
val_acc: 0.6924
val_f1: 0.8182
val_conf_mat:
[[ 0 251]
[ 0 565]]
Epoch 00020: val_acc did not improve
Epoch 21/30
- 19s - loss: 0.6635 - acc: 0.6712 - val_loss: 0.6588 - val_acc: 0.6924
val_acc: 0.6924
val_f1: 0.8182
val_conf_mat:
[[ 0 251]
[ 0 565]]
Epoch 00021: val_acc did not improve
Epoch 22/30
- 19s - loss: 0.6704 - acc: 0.6712 - val_loss: 0.6401 - val_acc: 0.6924
val_acc: 0.6924
val_f1: 0.8182
val_conf_mat:
[[ 0 251]
[ 0 565]]
Epoch 00022: val_acc did not improve
Epoch 23/30
- 19s - loss: 0.6607 - acc: 0.6712 - val_loss: 0.6459 - val_acc: 0.6924
val_acc: 0.6924
val_f1: 0.8182
val_conf_mat:
[[ 0 251]
[ 0 565]]
Epoch 00023: val_acc did not improve
Epoch 24/30
- 19s - loss: 0.6677 - acc: 0.6620 - val_loss: 0.6417 - val_acc: 0.6924
val_acc: 0.6924
val_f1: 0.8182
val_conf_mat:
[[ 0 251]
[ 0 565]]
Epoch 00024: val_acc did not improve
Epoch 25/30
- 19s - loss: 0.6533 - acc: 0.6712 - val_loss: 0.6416 - val_acc: 0.6924
val_acc: 0.6924
val_f1: 0.8182
val_conf_mat:
[[ 0 251]
[ 0 565]]
Epoch 00025: val_acc did not improve
Epoch 26/30
- 19s - loss: 0.6463 - acc: 0.7141 - val_loss: 0.7088 - val_acc: 0.6924
val_acc: 0.6924
val_f1: 0.8182
val_conf_mat:
[[ 0 251]
[ 0 565]]
Epoch 00026: val_acc did not improve
Epoch 27/30
- 19s - loss: 0.6007 - acc: 0.8344 - val_loss: 0.6946 - val_acc: 0.7328
val_acc: 0.7328
val_f1: 0.8348
val_conf_mat:
[[ 47 204]
[ 14 551]]
Epoch 00027: val_acc improved from 0.69240 to 0.73284, saving model to ../checkpoints/he_mpcnn/cp_20180622_132544.hdf5
Epoch 28/30
- 19s - loss: 0.4961 - acc: 0.9037 - val_loss: 0.7237 - val_acc: 0.7451
val_acc: 0.7451
val_f1: 0.8123
val_conf_mat:
[[158 93]
[115 450]]
Epoch 00028: val_acc improved from 0.73284 to 0.74510, saving model to ../checkpoints/he_mpcnn/cp_20180622_132544.hdf5
Epoch 29/30
- 19s - loss: 0.4435 - acc: 0.9196 - val_loss: 0.7188 - val_acc: 0.7610
val_acc: 0.7610
val_f1: 0.8282
val_conf_mat:
[[151 100]
[ 95 470]]
Epoch 00029: val_acc improved from 0.74510 to 0.76103, saving model to ../checkpoints/he_mpcnn/cp_20180622_132544.hdf5
Epoch 30/30
- 19s - loss: 0.3979 - acc: 0.9399 - val_loss: 0.6632 - val_acc: 0.7684
val_acc: 0.7684
val_f1: 0.8394
val_conf_mat:
[[133 118]
[ 71 494]]
Epoch 00030: val_acc improved from 0.76103 to 0.76838, saving model to ../checkpoints/he_mpcnn/cp_20180622_132544.hdf5
Done!
======================
Last epoch predictions
======================
acc: 0.7275362318840579
f1: 0.8103309120258273
confusion matrix
[[ 251 327]
[ 143 1004]]
======================
Best epoch predictions
======================
acc: 0.7275362318840579
f1: 0.8103309120258273
confusion matrix
[[ 251 327]
[ 143 1004]]
DONE
==============
PROGRAM STARTS
==============
=======================
Loading embedding files
=======================
loading ... msrpc_POSkimprep_nolower_glove_embcap_20180611.pickle
Embeddings shape (pretrained GloVe): (20504, 300) unknown tokens: 488
loading ... msrpc_POSkimprep_nolower_paragram25_embcap_20180611.pickle
Embeddings shape (pretrained PARAGRAM): (20504, 25) unknown tokens: 1868
Final embeddings dim: 325
===================
Generating datasets
===================
Max seq length: 39
X_train: (4076, 39)
Y_train: (4076,)
X_test: (1725, 39)
Y_test: (1725,)
================
Generating model
================
Convolution activation: tanh
Dense activation: tanh
Group A: True Group B: True
Group A config
--------------
Filters: [(1, 325), (2, 325), (3, 325), (None, 325)]
Pool ops: ['max', 'min', 'mean']
Group B config
--------------
Using DepthwiseConv2D
Filters: [(1, 20), (2, 20), (3, 20)]
Pool ops: ['max', 'min']
Algorithm 1: True Algorithm 2: True
Algorithm 1
-----------
Cosine similarity: True
Euclidean distance: True
Absolute difference: False
Algorithm 2
-----------
Cosine similarity: True
Euclidean distance: True
Absolute difference: True
Other settings
--------------
MLP hidden units: 250
Regularization: 0.0001
Trainable embeddings: True
Trainable parameters: 22,907,227
Non-trainable parameters: 0
Total parameters: 22,907,227
Done!
===============
Compiling model
===============
Optimizer: adam
Learning rate: 0.001
Loss: categorical_crossentropy
Done!
==================
Training model ...
==================
Epochs: 30
Batch size: 32
Forward propagation with random values
0.43768115942028984
0.46349557522123896
[[336 242]
[728 419]]
Train on 3260 samples, validate on 816 samples
Epoch 1/30
- 14s - loss: 5.4831 - acc: 0.6687 - val_loss: 5.0864 - val_acc: 0.6924
val_acc: 0.6924
val_f1: 0.8182
val_conf_mat:
[[ 0 251]
[ 0 565]]
Epoch 00001: val_acc improved from -inf to 0.69240, saving model to ../checkpoints/he_mpcnn/cp_20180622_133617.hdf5
Epoch 2/30
- 11s - loss: 5.3858 - acc: 0.6712 - val_loss: 5.0131 - val_acc: 0.6924
val_acc: 0.6924
val_f1: 0.8182
val_conf_mat:
[[ 0 251]
[ 0 565]]
Epoch 00002: val_acc did not improve
Epoch 3/30
- 11s - loss: 5.3400 - acc: 0.6712 - val_loss: 4.9860 - val_acc: 0.6924
val_acc: 0.6924
val_f1: 0.8182
val_conf_mat:
[[ 0 251]
[ 0 565]]
Epoch 00003: val_acc did not improve
Epoch 4/30
- 12s - loss: 5.3217 - acc: 0.6712 - val_loss: 4.9741 - val_acc: 0.6924
val_acc: 0.6924
val_f1: 0.8182
val_conf_mat:
[[ 0 251]
[ 0 565]]
Epoch 00004: val_acc did not improve
Epoch 5/30
- 12s - loss: 5.3132 - acc: 0.6712 - val_loss: 4.9683 - val_acc: 0.6924
val_acc: 0.6924
val_f1: 0.8182
val_conf_mat:
[[ 0 251]
[ 0 565]]
Epoch 00005: val_acc did not improve
Epoch 6/30
- 12s - loss: 5.3088 - acc: 0.6712 - val_loss: 4.9651 - val_acc: 0.6924
val_acc: 0.6924
val_f1: 0.8182
val_conf_mat:
[[ 0 251]
[ 0 565]]
Epoch 00006: val_acc did not improve
Epoch 7/30
- 12s - loss: 5.3063 - acc: 0.6712 - val_loss: 4.9631 - val_acc: 0.6924
val_acc: 0.6924
val_f1: 0.8182
val_conf_mat:
[[ 0 251]
[ 0 565]]
Epoch 00007: val_acc did not improve
Epoch 8/30
- 12s - loss: 5.3048 - acc: 0.6712 - val_loss: 4.9619 - val_acc: 0.6924
val_acc: 0.6924
val_f1: 0.8182
val_conf_mat:
[[ 0 251]
[ 0 565]]
Epoch 00008: val_acc did not improve
Epoch 9/30
- 12s - loss: 5.3037 - acc: 0.6712 - val_loss: 4.9610 - val_acc: 0.6924
val_acc: 0.6924
val_f1: 0.8182
val_conf_mat:
[[ 0 251]
[ 0 565]]
Epoch 00009: val_acc did not improve
Epoch 10/30
- 12s - loss: 5.3030 - acc: 0.6712 - val_loss: 4.9604 - val_acc: 0.6924
val_acc: 0.6924
val_f1: 0.8182
val_conf_mat:
[[ 0 251]
[ 0 565]]
Epoch 00010: val_acc did not improve
Epoch 11/30
- 12s - loss: 5.3025 - acc: 0.6712 - val_loss: 4.9599 - val_acc: 0.6924
val_acc: 0.6924
val_f1: 0.8182
val_conf_mat:
[[ 0 251]
[ 0 565]]
Epoch 00011: val_acc did not improve
Epoch 12/30
- 12s - loss: 5.3021 - acc: 0.6712 - val_loss: 4.9596 - val_acc: 0.6924
val_acc: 0.6924
val_f1: 0.8182
val_conf_mat:
[[ 0 251]
[ 0 565]]
Epoch 00012: val_acc did not improve
Epoch 13/30
- 12s - loss: 5.3017 - acc: 0.6712 - val_loss: 4.9593 - val_acc: 0.6924
val_acc: 0.6924
val_f1: 0.8182
val_conf_mat:
[[ 0 251]
[ 0 565]]
Epoch 00013: val_acc did not improve
Epoch 14/30
- 12s - loss: 5.3015 - acc: 0.6712 - val_loss: 4.9591 - val_acc: 0.6924
val_acc: 0.6924
val_f1: 0.8182
val_conf_mat:
[[ 0 251]
[ 0 565]]
Epoch 00014: val_acc did not improve
Epoch 15/30
- 12s - loss: 2.3416 - acc: 0.6393 - val_loss: 0.6629 - val_acc: 0.6924
val_acc: 0.6924
val_f1: 0.8182
val_conf_mat:
[[ 0 251]
[ 0 565]]
Epoch 00015: val_acc did not improve
Epoch 16/30
- 12s - loss: 0.6707 - acc: 0.6577 - val_loss: 0.6382 - val_acc: 0.6924
val_acc: 0.6924
val_f1: 0.8182
val_conf_mat:
[[ 0 251]
[ 0 565]]
Epoch 00016: val_acc did not improve
Epoch 17/30
- 12s - loss: 0.6623 - acc: 0.6712 - val_loss: 0.6406 - val_acc: 0.6924
val_acc: 0.6924
val_f1: 0.8182
val_conf_mat:
[[ 0 251]
[ 0 565]]
Epoch 00017: val_acc did not improve
Epoch 18/30
- 11s - loss: 0.6614 - acc: 0.6721 - val_loss: 0.6632 - val_acc: 0.6924
val_acc: 0.6924
val_f1: 0.8182
val_conf_mat:
[[ 0 251]
[ 0 565]]
Epoch 00018: val_acc did not improve
Epoch 19/30
- 12s - loss: 0.6535 - acc: 0.7077 - val_loss: 0.7046 - val_acc: 0.7157
val_acc: 0.7157
val_f1: 0.8281
val_conf_mat:
[[ 25 226]
[ 6 559]]
Epoch 00019: val_acc improved from 0.69240 to 0.71569, saving model to ../checkpoints/he_mpcnn/cp_20180622_133617.hdf5
Epoch 20/30
- 11s - loss: 0.5769 - acc: 0.8233 - val_loss: 0.6754 - val_acc: 0.7316
val_acc: 0.7316
val_f1: 0.7872
val_conf_mat:
[[192 59]
[160 405]]
Epoch 00020: val_acc improved from 0.71569 to 0.73162, saving model to ../checkpoints/he_mpcnn/cp_20180622_133617.hdf5
Epoch 21/30
- 12s - loss: 0.4401 - acc: 0.9117 - val_loss: 0.6542 - val_acc: 0.7696
val_acc: 0.7696
val_f1: 0.8476
val_conf_mat:
[[105 146]
[ 42 523]]
Epoch 00021: val_acc improved from 0.73162 to 0.76961, saving model to ../checkpoints/he_mpcnn/cp_20180622_133617.hdf5
Epoch 22/30
- 11s - loss: 0.3727 - acc: 0.9457 - val_loss: 0.7453 - val_acc: 0.7610
val_acc: 0.7610
val_f1: 0.8203
val_conf_mat:
[[176 75]
[120 445]]
Epoch 00022: val_acc did not improve
Epoch 23/30
- 12s - loss: 0.2454 - acc: 0.9745 - val_loss: 0.7483 - val_acc: 0.7672
val_acc: 0.7672
val_f1: 0.8509
val_conf_mat:
[[ 84 167]
[ 23 542]]
Epoch 00023: val_acc did not improve
Epoch 24/30
- 12s - loss: 0.1819 - acc: 0.9868 - val_loss: 0.6598 - val_acc: 0.7819
val_acc: 0.7819
val_f1: 0.8430
val_conf_mat:
[[160 91]
[ 87 478]]
Epoch 00024: val_acc improved from 0.76961 to 0.78186, saving model to ../checkpoints/he_mpcnn/cp_20180622_133617.hdf5
Epoch 25/30
- 12s - loss: 0.1428 - acc: 0.9923 - val_loss: 0.7721 - val_acc: 0.7488
val_acc: 0.7488
val_f1: 0.8053
val_conf_mat:
[[187 64]
[141 424]]
Epoch 00025: val_acc did not improve
Epoch 26/30
- 12s - loss: 0.1132 - acc: 0.9942 - val_loss: 0.8472 - val_acc: 0.7549
val_acc: 0.7549
val_f1: 0.8134
val_conf_mat:
[[180 71]
[129 436]]
Epoch 00026: val_acc did not improve
Epoch 27/30
- 12s - loss: 0.0932 - acc: 0.9969 - val_loss: 0.8519 - val_acc: 0.7770
val_acc: 0.7770
val_f1: 0.8473
val_conf_mat:
[[129 122]
[ 60 505]]
Epoch 00027: val_acc did not improve
Epoch 28/30
- 12s - loss: 0.0778 - acc: 0.9982 - val_loss: 0.9653 - val_acc: 0.7868
val_acc: 0.7868
val_f1: 0.8427
val_conf_mat:
[[176 75]
[ 99 466]]
Epoch 00028: val_acc improved from 0.78186 to 0.78676, saving model to ../checkpoints/he_mpcnn/cp_20180622_133617.hdf5
Epoch 29/30
- 11s - loss: 0.0573 - acc: 0.9985 - val_loss: 0.8910 - val_acc: 0.7770
val_acc: 0.7770
val_f1: 0.8375
val_conf_mat:
[[165 86]
[ 96 469]]
Epoch 00029: val_acc did not improve
Epoch 30/30
- 12s - loss: 0.0524 - acc: 0.9982 - val_loss: 1.0507 - val_acc: 0.7243
val_acc: 0.7243
val_f1: 0.7788
val_conf_mat:
[[195 56]
[169 396]]
Epoch 00030: val_acc did not improve
Done!
======================
Last epoch predictions
======================
acc: 0.7240579710144928
f1: 0.7709335899903755
confusion matrix
[[448 130]
[346 801]]
======================
Best epoch predictions
======================
acc: 0.76
f1: 0.818739054290718
confusion matrix
[[376 202]
[212 935]]
DONE
==============
PROGRAM STARTS
==============
=======================
Loading embedding files
=======================
loading ... msrpc_POSkimprep_nolower_glove_embcap_20180611.pickle
Embeddings shape (pretrained GloVe): (20504, 300) unknown tokens: 488
loading ... msrpc_POSkimprep_nolower_POSword2vec_embcap_20180611.pickle
Embeddings shape (pretrained POS word2vec): (20504, 200) unknown tokens: 1533
Final embeddings dim: 500
===================
Generating datasets
===================
Max seq length: 39
X_train: (4076, 39)
Y_train: (4076,)
X_test: (1725, 39)
Y_test: (1725,)
================
Generating model
================
Convolution activation: tanh
Dense activation: tanh
Group A: True Group B: True
Group A config
--------------
Filters: [(1, 500), (2, 500), (3, 500), (None, 500)]
Pool ops: ['max', 'min', 'mean']
Group B config
--------------
Using DepthwiseConv2D
Filters: [(1, 20), (2, 20), (3, 20)]
Pool ops: ['max', 'min']
Algorithm 1: True Algorithm 2: True
Algorithm 1
-----------
Cosine similarity: True
Euclidean distance: True
Absolute difference: False
Algorithm 2
-----------
Cosine similarity: True
Euclidean distance: True
Absolute difference: True
Other settings
--------------
MLP hidden units: 250
Regularization: 0.0001
Trainable embeddings: True
Trainable parameters: 36,771,252
Non-trainable parameters: 0
Total parameters: 36,771,252
Done!
===============
Compiling model
===============
Optimizer: adam
Learning rate: 0.001
Loss: categorical_crossentropy
Done!
==================
Training model ...
==================
Epochs: 30
Batch size: 32
Forward propagation with random values
0.3356521739130435
0.0017421602787456446
[[ 578 0]
[1146 1]]
Train on 3260 samples, validate on 816 samples
Epoch 1/30
- 19s - loss: 5.5734 - acc: 0.6693 - val_loss: 5.1338 - val_acc: 0.6924
val_acc: 0.6924
val_f1: 0.8182
val_conf_mat:
[[ 0 251]
[ 0 565]]
Epoch 00001: val_acc improved from -inf to 0.69240, saving model to ../checkpoints/he_mpcnn/cp_20180622_134251.hdf5
Epoch 2/30
- 17s - loss: 5.4144 - acc: 0.6712 - val_loss: 5.0294 - val_acc: 0.6924
val_acc: 0.6924
val_f1: 0.8182
val_conf_mat:
[[ 0 251]
[ 0 565]]
Epoch 00002: val_acc did not improve
Epoch 3/30
- 18s - loss: 5.3511 - acc: 0.6712 - val_loss: 4.9936 - val_acc: 0.6924
val_acc: 0.6924
val_f1: 0.8182
val_conf_mat:
[[ 0 251]
[ 0 565]]
Epoch 00003: val_acc did not improve
Epoch 4/30
- 18s - loss: 5.3276 - acc: 0.6712 - val_loss: 4.9788 - val_acc: 0.6924
val_acc: 0.6924
val_f1: 0.8182
val_conf_mat:
[[ 0 251]
[ 0 565]]
Epoch 00004: val_acc did not improve
Epoch 5/30
- 18s - loss: 5.3173 - acc: 0.6712 - val_loss: 4.9718 - val_acc: 0.6924
val_acc: 0.6924
val_f1: 0.8182
val_conf_mat:
[[ 0 251]
[ 0 565]]
Epoch 00005: val_acc did not improve
Epoch 6/30
- 18s - loss: 5.3121 - acc: 0.6712 - val_loss: 4.9680 - val_acc: 0.6924
val_acc: 0.6924
val_f1: 0.8182
val_conf_mat:
[[ 0 251]
[ 0 565]]
Epoch 00006: val_acc did not improve
Epoch 7/30
- 18s - loss: 5.3090 - acc: 0.6712 - val_loss: 4.9656 - val_acc: 0.6924
val_acc: 0.6924
val_f1: 0.8182
val_conf_mat:
[[ 0 251]
[ 0 565]]
Epoch 00007: val_acc did not improve
Epoch 8/30
- 18s - loss: 5.3071 - acc: 0.6712 - val_loss: 4.9640 - val_acc: 0.6924
val_acc: 0.6924
val_f1: 0.8182
val_conf_mat:
[[ 0 251]
[ 0 565]]
Epoch 00008: val_acc did not improve
Epoch 9/30
- 17s - loss: 5.3057 - acc: 0.6712 - val_loss: 4.9628 - val_acc: 0.6924
val_acc: 0.6924
val_f1: 0.8182
val_conf_mat:
[[ 0 251]
[ 0 565]]
Epoch 00009: val_acc did not improve
Epoch 10/30
- 18s - loss: 5.3047 - acc: 0.6712 - val_loss: 4.9619 - val_acc: 0.6924
val_acc: 0.6924
val_f1: 0.8182
val_conf_mat:
[[ 0 251]
[ 0 565]]
Epoch 00010: val_acc did not improve
Epoch 11/30
- 18s - loss: 5.3039 - acc: 0.6712 - val_loss: 4.9613 - val_acc: 0.6924
val_acc: 0.6924
val_f1: 0.8182
val_conf_mat:
[[ 0 251]
[ 0 565]]
Epoch 00011: val_acc did not improve
Epoch 12/30
- 18s - loss: 5.3033 - acc: 0.6712 - val_loss: 4.9607 - val_acc: 0.6924
val_acc: 0.6924
val_f1: 0.8182
val_conf_mat:
[[ 0 251]
[ 0 565]]
Epoch 00012: val_acc did not improve
Epoch 13/30
- 17s - loss: 5.3028 - acc: 0.6712 - val_loss: 4.9603 - val_acc: 0.6924
val_acc: 0.6924
val_f1: 0.8182
val_conf_mat:
[[ 0 251]
[ 0 565]]
Epoch 00013: val_acc did not improve
Epoch 14/30
- 18s - loss: 5.3024 - acc: 0.6712 - val_loss: 4.9599 - val_acc: 0.6924
val_acc: 0.6924
val_f1: 0.8182
val_conf_mat:
[[ 0 251]
[ 0 565]]
Epoch 00014: val_acc did not improve
Epoch 15/30
- 18s - loss: 5.3020 - acc: 0.6712 - val_loss: 4.9596 - val_acc: 0.6924
val_acc: 0.6924
val_f1: 0.8182
val_conf_mat:
[[ 0 251]
[ 0 565]]
Epoch 00015: val_acc did not improve
Epoch 16/30
- 18s - loss: 5.3018 - acc: 0.6712 - val_loss: 4.9594 - val_acc: 0.6924
val_acc: 0.6924
val_f1: 0.8182
val_conf_mat:
[[ 0 251]
[ 0 565]]
Epoch 00016: val_acc did not improve
Epoch 17/30
- 18s - loss: 3.6243 - acc: 0.6282 - val_loss: 0.6462 - val_acc: 0.6924
val_acc: 0.6924
val_f1: 0.8182
val_conf_mat:
[[ 0 251]
[ 0 565]]
Epoch 00017: val_acc did not improve
Epoch 18/30
- 18s - loss: 0.6604 - acc: 0.6663 - val_loss: 0.6528 - val_acc: 0.6924
val_acc: 0.6924
val_f1: 0.8182
val_conf_mat:
[[ 0 251]