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DeepPPI
Commit 8aad158b authored by Florian RICHOUX's avatar Florian RICHOUX
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Tons of fc xps 


Former-commit-id: 9fd438d7
parent a2e3e07e
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Showing with 1301 additions and 79 deletions
keras/models/fc2_2_2dense.py
View file @ 8aad158b
......@@ -13,20 +13,20 @@ class FC2_2_2Dense(AbstractModel):
def get_model(self):
input1 = Input(shape=(1166,20,), dtype=np.float32, name='protein1')
protein1 = layers.Flatten()(input1)
protein1 = layers.Dense(2, activation='relu')(protein1)
protein1 = layers.Dense(8, activation='relu')(protein1)
protein1 = layers.BatchNormalization()(protein1)
protein1 = layers.Dense(2, activation='relu')(protein1)
protein1 = layers.Dense(8, activation='relu')(protein1)
protein1 = layers.BatchNormalization()(protein1)
input2 = Input(shape=(1166,20,), dtype=np.float32, name='protein2')
protein2 = layers.Flatten()(input2)
protein2 = layers.Dense(2, activation='relu')(protein2)
protein2 = layers.Dense(8, activation='relu')(protein2)
protein2 = layers.BatchNormalization()(protein2)
protein2 = layers.Dense(2, activation='relu')(protein2)
protein2 = layers.Dense(8, activation='relu')(protein2)
protein2 = layers.BatchNormalization()(protein2)
head = layers.concatenate([protein1, protein2], axis=-1)
head = layers.Dense(2, activation='relu')(head)
head = layers.Dense(8, activation='relu')(head)
head = layers.BatchNormalization()(head)
head = layers.Dense(1)(head)
output = layers.Activation(tf.nn.sigmoid)(head)
......
keras/models/figs/fc2_100_2dense_summary.txt 0 → 100644
View file @ 8aad158b
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
protein1 (InputLayer) (None, 1166, 20) 0
__________________________________________________________________________________________________
protein2 (InputLayer) (None, 1166, 20) 0
__________________________________________________________________________________________________
flatten_1 (Flatten) (None, 23320) 0 protein1[0][0]
__________________________________________________________________________________________________
flatten_2 (Flatten) (None, 23320) 0 protein2[0][0]
__________________________________________________________________________________________________
dense_1 (Dense) (None, 100) 2332100 flatten_1[0][0]
__________________________________________________________________________________________________
dense_3 (Dense) (None, 100) 2332100 flatten_2[0][0]
__________________________________________________________________________________________________
batch_normalization_1 (BatchNor (None, 100) 400 dense_1[0][0]
__________________________________________________________________________________________________
batch_normalization_3 (BatchNor (None, 100) 400 dense_3[0][0]
__________________________________________________________________________________________________
dense_2 (Dense) (None, 100) 10100 batch_normalization_1[0][0]
__________________________________________________________________________________________________
dense_4 (Dense) (None, 100) 10100 batch_normalization_3[0][0]
__________________________________________________________________________________________________
batch_normalization_2 (BatchNor (None, 100) 400 dense_2[0][0]
__________________________________________________________________________________________________
batch_normalization_4 (BatchNor (None, 100) 400 dense_4[0][0]
__________________________________________________________________________________________________
concatenate_1 (Concatenate) (None, 200) 0 batch_normalization_2[0][0]
batch_normalization_4[0][0]
__________________________________________________________________________________________________
dense_5 (Dense) (None, 100) 20100 concatenate_1[0][0]
__________________________________________________________________________________________________
batch_normalization_5 (BatchNor (None, 100) 400 dense_5[0][0]
__________________________________________________________________________________________________
dense_6 (Dense) (None, 1) 101 batch_normalization_5[0][0]
__________________________________________________________________________________________________
activation_1 (Activation) (None, 1) 0 dense_6[0][0]
==================================================================================================
Total params: 4,706,601
Trainable params: 4,705,601
Non-trainable params: 1,000
__________________________________________________________________________________________________
keras/models/figs/fc2_20_2dense_summary.txt 0 → 100644
View file @ 8aad158b
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
protein1 (InputLayer) (None, 1166, 20) 0
__________________________________________________________________________________________________
protein2 (InputLayer) (None, 1166, 20) 0
__________________________________________________________________________________________________
flatten_1 (Flatten) (None, 23320) 0 protein1[0][0]
__________________________________________________________________________________________________
flatten_2 (Flatten) (None, 23320) 0 protein2[0][0]
__________________________________________________________________________________________________
dense_1 (Dense) (None, 20) 466420 flatten_1[0][0]
__________________________________________________________________________________________________
dense_3 (Dense) (None, 20) 466420 flatten_2[0][0]
__________________________________________________________________________________________________
batch_normalization_1 (BatchNor (None, 20) 80 dense_1[0][0]
__________________________________________________________________________________________________
batch_normalization_3 (BatchNor (None, 20) 80 dense_3[0][0]
__________________________________________________________________________________________________
dense_2 (Dense) (None, 20) 420 batch_normalization_1[0][0]
__________________________________________________________________________________________________
dense_4 (Dense) (None, 20) 420 batch_normalization_3[0][0]
__________________________________________________________________________________________________
batch_normalization_2 (BatchNor (None, 20) 80 dense_2[0][0]
__________________________________________________________________________________________________
batch_normalization_4 (BatchNor (None, 20) 80 dense_4[0][0]
__________________________________________________________________________________________________
concatenate_1 (Concatenate) (None, 40) 0 batch_normalization_2[0][0]
batch_normalization_4[0][0]
__________________________________________________________________________________________________
dense_5 (Dense) (None, 20) 820 concatenate_1[0][0]
__________________________________________________________________________________________________
batch_normalization_5 (BatchNor (None, 20) 80 dense_5[0][0]
__________________________________________________________________________________________________
dense_6 (Dense) (None, 1) 21 batch_normalization_5[0][0]
__________________________________________________________________________________________________
activation_1 (Activation) (None, 1) 0 dense_6[0][0]
==================================================================================================
Total params: 934,921
Trainable params: 934,721
Non-trainable params: 200
__________________________________________________________________________________________________
keras/models/figs/fc2_2_2dense_summary.txt 0 → 100644
View file @ 8aad158b
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
protein1 (InputLayer) (None, 1166, 20) 0
__________________________________________________________________________________________________
protein2 (InputLayer) (None, 1166, 20) 0
__________________________________________________________________________________________________
flatten_1 (Flatten) (None, 23320) 0 protein1[0][0]
__________________________________________________________________________________________________
flatten_2 (Flatten) (None, 23320) 0 protein2[0][0]
__________________________________________________________________________________________________
dense_1 (Dense) (None, 8) 186568 flatten_1[0][0]
__________________________________________________________________________________________________
dense_3 (Dense) (None, 8) 186568 flatten_2[0][0]
__________________________________________________________________________________________________
batch_normalization_1 (BatchNor (None, 8) 32 dense_1[0][0]
__________________________________________________________________________________________________
batch_normalization_3 (BatchNor (None, 8) 32 dense_3[0][0]
__________________________________________________________________________________________________
dense_2 (Dense) (None, 8) 72 batch_normalization_1[0][0]
__________________________________________________________________________________________________
dense_4 (Dense) (None, 8) 72 batch_normalization_3[0][0]
__________________________________________________________________________________________________
batch_normalization_2 (BatchNor (None, 8) 32 dense_2[0][0]
__________________________________________________________________________________________________
batch_normalization_4 (BatchNor (None, 8) 32 dense_4[0][0]
__________________________________________________________________________________________________
concatenate_1 (Concatenate) (None, 16) 0 batch_normalization_2[0][0]
batch_normalization_4[0][0]
__________________________________________________________________________________________________
dense_5 (Dense) (None, 8) 136 concatenate_1[0][0]
__________________________________________________________________________________________________
batch_normalization_5 (BatchNor (None, 8) 32 dense_5[0][0]
__________________________________________________________________________________________________
dense_6 (Dense) (None, 1) 9 batch_normalization_5[0][0]
__________________________________________________________________________________________________
activation_1 (Activation) (None, 1) 0 dense_6[0][0]
==================================================================================================
Total params: 373,585
Trainable params: 373,505
Non-trainable params: 80
__________________________________________________________________________________________________
keras/results/fc2_100_2dense_2019-01-05_15:30_gpu-3-1_adam_0.001_2048_2_mirror-double.txt 0 → 100644
View file @ 8aad158b
File fc2_100_2dense_2019-01-05_15:30_gpu-3-1_adam_0.001_2048_2_mirror-double.txt
fc2_100_2dense, epochs=2, batch=2048, optimizer=adam, learning rate=0.001, patience=2
Number of training samples: 91036
Loss
0: train_loss=0.3872924578609405, val_loss=0.24862531490007936
1: train_loss=0.1984376458212091, val_loss=0.21774172615131798
///////////////////////////////////////////
Accuracy
0: train_acc=0.8374159675139137, val_acc=0.9012474013808516
1: train_acc=0.9260292632298319, val_acc=0.9217175760022092
///////////////////////////////////////////
Validation metrics
Number of 0 predicted: 6003
Number of 1 predicted: 6503
Validation precision: 0.9532326878897276
Validation recall: 0.8932800246040289
Validation F1-score: 0.9222830832737953
keras/results/fc2_100_2dense_2019-01-05_15:31_gpu-3-1_adam_0.001_2048_6_mirror-double.txt 0 → 100644
View file @ 8aad158b
File fc2_100_2dense_2019-01-05_15:31_gpu-3-1_adam_0.001_2048_6_mirror-double.txt
fc2_100_2dense, epochs=6, batch=2048, optimizer=adam, learning rate=0.001, patience=6
Number of training samples: 91036
Loss
0: train_loss=0.38646583883528435, val_loss=0.24874810088773203
1: train_loss=0.19818387037146631, val_loss=0.2137485619937632
2: train_loss=0.13475485231030557, val_loss=0.19514386956165186
3: train_loss=0.08879141405691628, val_loss=0.18851766600792605
4: train_loss=0.05506245791028184, val_loss=0.18956206869777825
5: train_loss=0.0320371428593876, val_loss=0.18431813973685027
///////////////////////////////////////////
Accuracy
0: train_acc=0.8395140386826178, val_acc=0.9008475929837064
1: train_acc=0.9256338152751766, val_acc=0.9199584202729815
2: train_acc=0.9516674725047377, val_acc=0.9304333922864151
3: train_acc=0.9714838086555689, val_acc=0.9365104754104931
4: train_acc=0.9846654069240873, val_acc=0.9378698221897099
5: train_acc=0.9925963353951486, val_acc=0.9437869821341347
///////////////////////////////////////////
Validation metrics
Number of 0 predicted: 5969
Number of 1 predicted: 6537
Validation precision: 0.9786675418444372
Validation recall: 0.9123451124368976
Validation F1-score: 0.9443432824004433
keras/results/fc2_100_2dense_2019-01-05_15:33_gpu-3-1_adam_0.001_2048_50_mirror-double.txt 0 → 100644
View file @ 8aad158b
File fc2_100_2dense_2019-01-05_15:33_gpu-3-1_adam_0.001_2048_50_mirror-double.txt
fc2_100_2dense, epochs=50, batch=2048, optimizer=adam, learning rate=0.001, patience=50
Number of training samples: 91036
Loss
0: train_loss=0.39084005259719556, val_loss=0.24820902741529188
1: train_loss=0.19868336829688596, val_loss=0.21982846556235253
2: train_loss=0.13446721005543993, val_loss=0.19419807280023135
3: train_loss=0.09127538339409418, val_loss=0.19119984586868366
4: train_loss=0.057243844823147136, val_loss=0.19539317698139735
5: train_loss=0.03417560099190948, val_loss=0.19053472828030033
6: train_loss=0.019209828702688315, val_loss=0.19376062100531863
7: train_loss=0.010719036995995322, val_loss=0.20701293355843248
8: train_loss=0.006273768865402091, val_loss=0.2096985401429311
9: train_loss=0.003392524909270808, val_loss=0.21952877723082112
10: train_loss=0.0021173997586649914, val_loss=0.22810169825187096
11: train_loss=0.0015779878676677997, val_loss=0.23237065726635878
12: train_loss=0.0012973357506271587, val_loss=0.24036148083738074
13: train_loss=0.000980192218761744, val_loss=0.2449238352988713
14: train_loss=0.0008160841430854868, val_loss=0.2500674303352366
15: train_loss=0.0007044590684325056, val_loss=0.2538989233757121
16: train_loss=0.0006136990582911797, val_loss=0.25692077814894027
17: train_loss=0.0005446311450516558, val_loss=0.26114509836037064
18: train_loss=0.0004909603822186533, val_loss=0.26408119393638013
19: train_loss=0.00043652642148861887, val_loss=0.26690373849376914
20: train_loss=0.00040427474553936076, val_loss=0.2688997212589483
21: train_loss=0.0003737249798131803, val_loss=0.2716394969840517
22: train_loss=0.00033937537942469294, val_loss=0.2734848748878843
23: train_loss=0.00031484651955307534, val_loss=0.27757503865586075
24: train_loss=0.0002823829447792764, val_loss=0.27911913659922244
25: train_loss=0.0002650592014468279, val_loss=0.2811659785932147
26: train_loss=0.00024356591878407297, val_loss=0.28317420343084565
27: train_loss=0.0002288777369707194, val_loss=0.28445635900180205
28: train_loss=0.00021498552894707454, val_loss=0.2877035659398153
29: train_loss=0.00019978455341870238, val_loss=0.2894007458641837
30: train_loss=0.00018547798961860652, val_loss=0.290796727375776
31: train_loss=0.00017899703322312122, val_loss=0.29315332840313707
32: train_loss=0.00016441875571085525, val_loss=0.29486377897328153
33: train_loss=0.00015179964051989555, val_loss=0.29581815520992244
34: train_loss=0.00014454797966356397, val_loss=0.2977618077040367
35: train_loss=0.00013634766936709145, val_loss=0.2998111712137604
36: train_loss=0.00012988271101178333, val_loss=0.3010743594962693
37: train_loss=0.00012321842486698288, val_loss=0.302838659092996
38: train_loss=0.00011716578809035263, val_loss=0.30434125327747497
39: train_loss=0.00010814161058372393, val_loss=0.3050017132056383
40: train_loss=0.0001035789430454219, val_loss=0.3069154392828393
41: train_loss=9.777023387633345e-05, val_loss=0.30693599115118836
42: train_loss=9.248107107998815e-05, val_loss=0.30841674197297775
43: train_loss=8.866932920930427e-05, val_loss=0.3102477753894454
44: train_loss=8.531291696624557e-05, val_loss=0.3118808348760707
45: train_loss=7.980685583184382e-05, val_loss=0.31308416464358124
46: train_loss=7.750269896423334e-05, val_loss=0.3145050155902965
47: train_loss=7.23596472968019e-05, val_loss=0.31575389449718033
48: train_loss=6.908433984412263e-05, val_loss=0.316218740569674
49: train_loss=6.617149797810436e-05, val_loss=0.3175697931857485
///////////////////////////////////////////
Accuracy
0: train_acc=0.8329452084287137, val_acc=0.8998880540487691
1: train_acc=0.9255898765637882, val_acc=0.922757076917792
2: train_acc=0.9521288283313639, val_acc=0.9325123943654171
3: train_acc=0.9700338326516011, val_acc=0.9372301293662428
4: train_acc=0.9837426951267927, val_acc=0.9371501677478197
5: train_acc=0.992167933444272, val_acc=0.9415480569422044
6: train_acc=0.9962981679516123, val_acc=0.9424276348687773
7: train_acc=0.9983852542820586, val_acc=0.9439469059238468
8: train_acc=0.9993409200755745, val_acc=0.9436270594501541
9: train_acc=0.9998681840151149, val_acc=0.9443467132819856
10: train_acc=0.9999450766729645, val_acc=0.9444266752054381
11: train_acc=0.9999450766729645, val_acc=0.9453062530080929
12: train_acc=0.9999450766729645, val_acc=0.9441867907791162
13: train_acc=1.0, val_acc=0.9448264837265015
14: train_acc=1.0, val_acc=0.9449064453449246
15: train_acc=1.0, val_acc=0.9443467140159625
16: train_acc=1.0, val_acc=0.944586598871232
17: train_acc=1.0, val_acc=0.9445066372528088
18: train_acc=1.0, val_acc=0.9443467140159625
19: train_acc=1.0, val_acc=0.9443467137109331
20: train_acc=1.0, val_acc=0.9443467134059038
21: train_acc=1.0, val_acc=0.9438669436953648
22: train_acc=1.0, val_acc=0.9441867901690575
23: train_acc=1.0, val_acc=0.9441068285506343
24: train_acc=1.0, val_acc=0.9441068285506343
25: train_acc=1.0, val_acc=0.9441867901690575
26: train_acc=1.0, val_acc=0.9441867901690575
27: train_acc=1.0, val_acc=0.9445865982611733
28: train_acc=1.0, val_acc=0.9441867901690575
29: train_acc=1.0, val_acc=0.9442667517874807
30: train_acc=1.0, val_acc=0.9441867901690575
31: train_acc=1.0, val_acc=0.9438669436953648
32: train_acc=1.0, val_acc=0.9438669436953648
33: train_acc=1.0, val_acc=0.9438669436953648
34: train_acc=1.0, val_acc=0.9435470976506197
35: train_acc=1.0, val_acc=0.943627058535066
36: train_acc=1.0, val_acc=0.9435470976506197
37: train_acc=1.0, val_acc=0.9435470976506197
38: train_acc=1.0, val_acc=0.9433871744137734
39: train_acc=1.0, val_acc=0.9435470976506197
40: train_acc=1.0, val_acc=0.9434671352982197
41: train_acc=1.0, val_acc=0.9434671352982197
42: train_acc=1.0, val_acc=0.9433871736797965
43: train_acc=1.0, val_acc=0.9432272504429502
44: train_acc=1.0, val_acc=0.9429074047032345
45: train_acc=1.0, val_acc=0.9429074047032345
46: train_acc=1.0, val_acc=0.9428274430848114
47: train_acc=1.0, val_acc=0.9428274423508345
48: train_acc=1.0, val_acc=0.9430673272061039
49: train_acc=1.0, val_acc=0.9430673279400809
///////////////////////////////////////////
Validation metrics
Number of 0 predicted: 5950
Number of 1 predicted: 6556
Validation precision: 0.9794880210042665
Validation recall: 0.910463697376449
Validation F1-score: 0.9437154150197627
keras/results/fc2_20_2dense_2019-01-05_15:42_gpu-2-1_adam_0.001_2048_20_mirror-double.txt 0 → 100644
View file @ 8aad158b
File fc2_20_2dense_2019-01-05_15:42_gpu-2-1_adam_0.001_2048_20_mirror-double.txt
fc2_20_2dense, epochs=20, batch=2048, optimizer=adam, learning rate=0.001, patience=20
Number of training samples: 91036
Loss
0: train_loss=0.4751717909942052, val_loss=0.36167701824310394
1: train_loss=0.26656405013649603, val_loss=0.25266405521117075
2: train_loss=0.2017244575091377, val_loss=0.23424971794273763
3: train_loss=0.16585638454197277, val_loss=0.2240795402903948
4: train_loss=0.13853465034733434, val_loss=0.21331772702456703
5: train_loss=0.11767534195939436, val_loss=0.20687382655984093
6: train_loss=0.09937362206244627, val_loss=0.20277746367050364
7: train_loss=0.08366907403489147, val_loss=0.210596172710085
8: train_loss=0.07142954099429209, val_loss=0.20367909573029275
9: train_loss=0.05996454702862521, val_loss=0.21156825548683988
10: train_loss=0.0499209595021718, val_loss=0.21942374207449974
11: train_loss=0.04224595477338614, val_loss=0.21850184912531354
12: train_loss=0.03535187740918779, val_loss=0.2212953207999177
13: train_loss=0.027854681962501043, val_loss=0.23788432226206957
14: train_loss=0.02302185243350078, val_loss=0.2406154996906874
15: train_loss=0.019515616889155686, val_loss=0.24057855774607828
16: train_loss=0.01622680872781878, val_loss=0.26460734653640666
17: train_loss=0.013829232191442631, val_loss=0.2502898833055068
18: train_loss=0.011817266413113491, val_loss=0.2552086538588278
19: train_loss=0.010112505969337932, val_loss=0.27750313566548146
///////////////////////////////////////////
Accuracy
0: train_acc=0.7765609207264992, val_acc=0.8404765712648663
1: train_acc=0.9005668089130944, val_acc=0.8991683986917908
2: train_acc=0.926226987133829, val_acc=0.9087637936365465
3: train_acc=0.9407816685075222, val_acc=0.9171597637520892
4: train_acc=0.9513159627036347, val_acc=0.9214776911469397
5: train_acc=0.9605870207183951, val_acc=0.9272349275494888
6: train_acc=0.9677380379455528, val_acc=0.9291540057815858
7: train_acc=0.9739883123186258, val_acc=0.9290740441631626
8: train_acc=0.97868974901596, val_acc=0.9352310893918044
9: train_acc=0.982819983329498, val_acc=0.9329122021525036
10: train_acc=0.9862801529091614, val_acc=0.9324324327469939
11: train_acc=0.9890153348128999, val_acc=0.9368303217602676
12: train_acc=0.9913330991640363, val_acc=0.9354709738181264
13: train_acc=0.9940133572326634, val_acc=0.9333120098156718
14: train_acc=0.9951008394955698, val_acc=0.9329921633419791
15: train_acc=0.9963091522058445, val_acc=0.936190628507853
16: train_acc=0.9967924779918325, val_acc=0.9305133539048382
17: train_acc=0.9976273121515966, val_acc=0.9362705904313054
18: train_acc=0.9981106376468813, val_acc=0.9354709742470739
19: train_acc=0.9984291930405881, val_acc=0.9329921636470085
///////////////////////////////////////////
Validation metrics
Number of 0 predicted: 5826
Number of 1 predicted: 6680
Validation precision: 0.9793239251723006
Validation recall: 0.8934131736526946
Validation F1-score: 0.9343979959292312
keras/results/fc2_2_2dense_2019-01-05_15:49_gpu-1-1_adam_0.001_2048_50_mirror-double_4.txt 0 → 100644
View file @ 8aad158b
File fc2_2_2dense_2019-01-05_15:49_gpu-1-1_adam_0.001_2048_50_mirror-double.txt
fc2_2_2dense, epochs=50, batch=2048, optimizer=adam, learning rate=0.001, patience=50
Number of training samples: 91036
Loss
0: train_loss=0.5868324072545157, val_loss=0.5400454728446654
1: train_loss=0.4309438548710755, val_loss=0.48030461853987577
2: train_loss=0.36232180356995086, val_loss=0.4169283351354097
3: train_loss=0.32342582868772324, val_loss=0.3920299895054128
4: train_loss=0.29736668314470327, val_loss=0.3665638723345579
5: train_loss=0.2755200032010073, val_loss=0.37876797349152136
6: train_loss=0.25787431544702144, val_loss=0.3569301744899463
7: train_loss=0.2413620188720323, val_loss=0.3434009244875509
8: train_loss=0.2262995223476229, val_loss=0.3141578554716298
9: train_loss=0.21541131924250365, val_loss=0.32120865183068753
10: train_loss=0.2075656501239033, val_loss=0.2911624203686445
11: train_loss=0.1987251827544134, val_loss=0.298379240799728
12: train_loss=0.1924263693372962, val_loss=0.29917733652695033
13: train_loss=0.18572623092109747, val_loss=0.2895961823309781
14: train_loss=0.18101410088165557, val_loss=0.27934024445194255
15: train_loss=0.17685751623043858, val_loss=0.2754808639013994
16: train_loss=0.17138945412497522, val_loss=0.2794833493227008
17: train_loss=0.16770235678331888, val_loss=0.2785316342909432
18: train_loss=0.1657604711938289, val_loss=0.27257961836430084
19: train_loss=0.1615844426918147, val_loss=0.2741673314226316
20: train_loss=0.15936266384674105, val_loss=0.27736032493835483
21: train_loss=0.15749476990801278, val_loss=0.2780449430303499
22: train_loss=0.15461868640447712, val_loss=0.27725645385512193
23: train_loss=0.1513382743609811, val_loss=0.27558052124699534
24: train_loss=0.1469705086658927, val_loss=0.2748024411274303
25: train_loss=0.14310727268555623, val_loss=0.2728943998920771
26: train_loss=0.14118252259533992, val_loss=0.27296533424263664
27: train_loss=0.13834196010849126, val_loss=0.27264594629480804
28: train_loss=0.13667267574315498, val_loss=0.2706256294448757
29: train_loss=0.13597924177071202, val_loss=0.2764187418907409
30: train_loss=0.1344959570110575, val_loss=0.27054289224508876
31: train_loss=0.13359275488593342, val_loss=0.27949437384183706
32: train_loss=0.1326593114817319, val_loss=0.2876403712185406
33: train_loss=0.13127935183145673, val_loss=0.278278353379933
34: train_loss=0.12796957842772244, val_loss=0.2820234261673583
35: train_loss=0.1251422458643931, val_loss=0.2798856218614598
36: train_loss=0.12393653346632784, val_loss=0.2786651059148713
37: train_loss=0.12330459573824736, val_loss=0.2780863390185443
38: train_loss=0.12253441867118267, val_loss=0.2825432524440118
39: train_loss=0.121954147646206, val_loss=0.28174191819140193
40: train_loss=0.12142228508745673, val_loss=0.2890833710768157
41: train_loss=0.11949331372330732, val_loss=0.29112275923497577
42: train_loss=0.11951255439609398, val_loss=0.2896069054810997
43: train_loss=0.11814817820492222, val_loss=0.28583111281435375
44: train_loss=0.11899578665143404, val_loss=0.2917038509412211
45: train_loss=0.11775911522924369, val_loss=0.2893431853260514
46: train_loss=0.11660375264358885, val_loss=0.28850454747362286
47: train_loss=0.11487829206794797, val_loss=0.2831823964481561
48: train_loss=0.11426390091712606, val_loss=0.2938180812907833
49: train_loss=0.11412857218727641, val_loss=0.2880550358088783
///////////////////////////////////////////
Accuracy
0: train_acc=0.7033371414685271, val_acc=0.7130177514506935
1: train_acc=0.8209279846226354, val_acc=0.7461218611061259
2: train_acc=0.8561448217239692, val_acc=0.8221653608937998
3: train_acc=0.8731051454477441, val_acc=0.8344794495781002
4: train_acc=0.8835186080860544, val_acc=0.8495921957651063
5: train_acc=0.8914165821968606, val_acc=0.8353590277525095
6: train_acc=0.898644492540184, val_acc=0.844474652004913
7: train_acc=0.9057076318652973, val_acc=0.854469854612837
8: train_acc=0.9143415792131158, val_acc=0.8683831757895187
9: train_acc=0.9179884879108975, val_acc=0.8619862467446138
10: train_acc=0.9219649367620752, val_acc=0.880777227436278
11: train_acc=0.9252273828489164, val_acc=0.874220373810491
12: train_acc=0.9283580122961353, val_acc=0.8766991845916676
13: train_acc=0.931060239622246, val_acc=0.8846953462528722
14: train_acc=0.9323015072977529, val_acc=0.891971853281543
15: train_acc=0.9334548968721753, val_acc=0.8958899730132253
16: train_acc=0.9353881981304846, val_acc=0.8982088602525262
17: train_acc=0.9367393122165001, val_acc=0.8934111630232185
18: train_acc=0.9368491587055773, val_acc=0.8998880539915761
19: train_acc=0.9391559380979843, val_acc=0.8987685910286225
20: train_acc=0.9393426775801365, val_acc=0.8992483605580504
21: train_acc=0.9396392635461279, val_acc=0.9011674397052354
22: train_acc=0.9409793925084204, val_acc=0.9003678230920563
23: train_acc=0.9421657365661885, val_acc=0.8985287066023007
24: train_acc=0.9439891913616101, val_acc=0.902526787094511
25: train_acc=0.9448020560438989, val_acc=0.9023668642866122
26: train_acc=0.9455380290137784, val_acc=0.902446825600006
27: train_acc=0.9461421857080695, val_acc=0.9034063653261132
28: train_acc=0.946966035225997, val_acc=0.9064449059682981
29: train_acc=0.9469550505370193, val_acc=0.903806172684252
30: train_acc=0.9476690538382403, val_acc=0.9062849834654286
31: train_acc=0.9469660351552854, val_acc=0.904285942394791
32: train_acc=0.9471747438923023, val_acc=0.903006556928968
33: train_acc=0.9481523794539967, val_acc=0.9040460581495803
34: train_acc=0.9495584163825422, val_acc=0.9038861343026752
35: train_acc=0.9503163580782589, val_acc=0.9065248677106394
36: train_acc=0.9507886990547977, val_acc=0.907164560963054
37: train_acc=0.9508106681184791, val_acc=0.9070046377262077
38: train_acc=0.9506458984752171, val_acc=0.905645290213014
39: train_acc=0.9505250667707537, val_acc=0.9056452899079847
40: train_acc=0.9508106682625213, val_acc=0.9055653288996203
41: train_acc=0.9515686099346673, val_acc=0.9050855584551044
42: train_acc=0.950645898524977, val_acc=0.9067647525659088
43: train_acc=0.9519420886789801, val_acc=0.9064449060922163
44: train_acc=0.9513269476099852, val_acc=0.9058851753733129
45: train_acc=0.9513599014857348, val_acc=0.9046057896024605
46: train_acc=0.9519201196152987, val_acc=0.9056452900890959
47: train_acc=0.9524803374541592, val_acc=0.9084439464288769
48: train_acc=0.952029966196039, val_acc=0.9044458663656142
49: train_acc=0.9521947360566737, val_acc=0.9065248675867212
///////////////////////////////////////////
Validation metrics
Number of 0 predicted: 6311
Number of 1 predicted: 6195
Validation precision: 0.9123728257302265
Validation recall: 0.897497982243745
Validation F1-score: 0.9048742778094232
keras/results/fc2_2_2dense_2019-01-05_15:57_gpu-1-1_adam_0.001_2048_50_mirror-double_8.txt 0 → 100644
View file @ 8aad158b
File fc2_2_2dense_2019-01-05_15:57_gpu-1-1_adam_0.001_2048_50_mirror-double_8.txt
fc2_2_2dense, epochs=50, batch=2048, optimizer=adam, learning rate=0.001, patience=50
Number of training samples: 91036
Loss
0: train_loss=0.5344712591096886, val_loss=0.4224936847455708
1: train_loss=0.3352715382271038, val_loss=0.3303721829135952
2: train_loss=0.265630046778883, val_loss=0.2911959946126952
3: train_loss=0.2304129916605775, val_loss=0.2702348824121581
4: train_loss=0.2058606543521941, val_loss=0.2569241955454552
5: train_loss=0.18724231594012383, val_loss=0.24502089602002025
6: train_loss=0.17198682377672483, val_loss=0.24615953491150847
7: train_loss=0.16164105220057118, val_loss=0.23904192374264663
8: train_loss=0.15091986672675245, val_loss=0.23973764366442427
9: train_loss=0.1429571703222369, val_loss=0.23734093756651317
10: train_loss=0.1345246459530414, val_loss=0.2301928297092719
11: train_loss=0.12652427807343572, val_loss=0.24034710654769348
12: train_loss=0.12080476483355256, val_loss=0.23924446400977623
13: train_loss=0.11523661155043642, val_loss=0.24012877862300563
14: train_loss=0.11042115571806964, val_loss=0.23530416168938556
15: train_loss=0.10533447246493582, val_loss=0.23887275232518365
16: train_loss=0.0976653429856147, val_loss=0.2523558064544561
17: train_loss=0.0917442775743881, val_loss=0.24487323217557447
18: train_loss=0.08795939981374334, val_loss=0.25438885899537966
19: train_loss=0.08546736741169166, val_loss=0.25288930759799017
20: train_loss=0.08312876693951214, val_loss=0.24905624935651005
21: train_loss=0.07815345962555967, val_loss=0.25687587441357923
22: train_loss=0.07408902098693707, val_loss=0.2740216440493329
23: train_loss=0.07154272803608293, val_loss=0.2787691712303198
24: train_loss=0.07024392430358782, val_loss=0.2798366623507792
25: train_loss=0.06869826948288126, val_loss=0.281157335791038
26: train_loss=0.0667276821113585, val_loss=0.27862836226794735
27: train_loss=0.06371263384405237, val_loss=0.29192081437422795
28: train_loss=0.06238483640975968, val_loss=0.30587797373290176
29: train_loss=0.060621403661203846, val_loss=0.29185343444509426
30: train_loss=0.05874740919336828, val_loss=0.2931189947789447
31: train_loss=0.058398200451982026, val_loss=0.3075554228755506
32: train_loss=0.05715898022247464, val_loss=0.3047667787720561
33: train_loss=0.054853411908701584, val_loss=0.3077406221897949
34: train_loss=0.05346448494844835, val_loss=0.31392198477900163
35: train_loss=0.05189913797838296, val_loss=0.3210133680263253
36: train_loss=0.050160847446155746, val_loss=0.3348484638117417
37: train_loss=0.05036856989656597, val_loss=0.33308192605726744
38: train_loss=0.048813870030912095, val_loss=0.3284035862188005
39: train_loss=0.048609034830090596, val_loss=0.342963143337179
40: train_loss=0.047596907799904636, val_loss=0.3380984117293499
41: train_loss=0.0461191867458427, val_loss=0.3428330183715491
42: train_loss=0.045473129607239794, val_loss=0.35261960535188225
43: train_loss=0.04435583245394006, val_loss=0.3419255777662055
44: train_loss=0.04380223080561693, val_loss=0.36403623463991985
45: train_loss=0.04360189446335235, val_loss=0.3504986384192143
46: train_loss=0.045412008543756, val_loss=0.35818024268250226
47: train_loss=0.04324922773446406, val_loss=0.37607159407524304
48: train_loss=0.042739301859701324, val_loss=0.3817920437359379
49: train_loss=0.042162007083543035, val_loss=0.3707650050761584
///////////////////////////////////////////
Accuracy
0: train_acc=0.745298563344569, val_acc=0.8065728449962548
1: train_acc=0.8749176149727815, val_acc=0.8615864380996322
2: train_acc=0.9015664135148997, val_acc=0.8821365749494716
3: train_acc=0.9151654292155488, val_acc=0.8927714694657746
4: train_acc=0.9251504899003634, val_acc=0.8993283220525553
5: train_acc=0.9316534116511301, val_acc=0.9056452900890959
6: train_acc=0.9387934441657397, val_acc=0.9044458660605849
7: train_acc=0.9424293687297609, val_acc=0.9096433707052244
8: train_acc=0.9470648972644209, val_acc=0.9118023348315971
9: train_acc=0.9492398608919345, val_acc=0.9112426039315825
10: train_acc=0.9528867703649249, val_acc=0.917959379326262
11: train_acc=0.955709828890042, val_acc=0.9162002241499001
12: train_acc=0.9576980532318244, val_acc=0.914680953218749
13: train_acc=0.9593896917045166, val_acc=0.9191588037265276
14: train_acc=0.961443824329445, val_acc=0.9206780741715381
15: train_acc=0.9636078033884525, val_acc=0.9219574603713381
16: train_acc=0.9663759393146019, val_acc=0.9208379977134138
17: train_acc=0.9690671824598109, val_acc=0.9225971533187233
18: train_acc=0.9701107255765835, val_acc=0.9209979209502601
19: train_acc=0.9716595629616191, val_acc=0.9201183427186579
20: train_acc=0.9718243332098582, val_acc=0.9233168077606135
21: train_acc=0.9739224044728444, val_acc=0.9230769232103734
22: train_acc=0.9750757944610606, val_acc=0.9202782663844518
23: train_acc=0.9756360120327885, val_acc=0.9197185349315715
24: train_acc=0.9767125090488813, val_acc=0.9229969615919502
25: train_acc=0.9769761412124537, val_acc=0.9217175760022092
26: train_acc=0.977887868561714, val_acc=0.923636654110388
27: train_acc=0.978316270585921, val_acc=0.9215576518502747
28: train_acc=0.9789643659418406, val_acc=0.9189189184423106
29: train_acc=0.9798431390277468, val_acc=0.924276346876662
30: train_acc=0.9807768356371096, val_acc=0.9218774984478855
31: train_acc=0.9804472954077641, val_acc=0.9217975374395211
32: train_acc=0.9804912341191525, val_acc=0.9244362701135084
33: train_acc=0.9817874248781325, val_acc=0.9241963855632682
34: train_acc=0.9818862868667964, val_acc=0.9225971533187233
35: train_acc=0.9825563512628268, val_acc=0.9229969617158684
36: train_acc=0.9833033083900374, val_acc=0.9205981128581444
37: train_acc=0.9833582318375444, val_acc=0.9228370386601333
38: train_acc=0.9838854958975565, val_acc=0.9238765396996343
39: train_acc=0.9840832198513136, val_acc=0.9199584194818116
40: train_acc=0.9842919285909495, val_acc=0.9219574605524492
41: train_acc=0.985181686585825, val_acc=0.9226771148132282
42: train_acc=0.9849949470801023, val_acc=0.9197984966739128
43: train_acc=0.9856540272219004, val_acc=0.9247561174450961
44: train_acc=0.9855441805678296, val_acc=0.9207580357899613
45: train_acc=0.9860824289789748, val_acc=0.9237965779001001
46: train_acc=0.9849070695630433, val_acc=0.9225971536237527
47: train_acc=0.9860494747653809, val_acc=0.9209979205213126
48: train_acc=0.9857309198561796, val_acc=0.9197984963688834
49: train_acc=0.9855881188916136, val_acc=0.923396769684066
///////////////////////////////////////////
Validation metrics
Number of 0 predicted: 5838
Number of 1 predicted: 6668
Validation precision: 0.9684936002625534
Validation recall: 0.885122975404919
Validation F1-score: 0.9249333960194327
keras/results/fc2_2dense_2019-01-03_02:52_gpu-0-1_nadam_0.002_1024_15_mirror-double.txt deleted 100644 → 0
View file @ a2e3e07e
File fc2_2dense_2019-01-03_02:52_gpu-0-1_nadam_0.002_1024_15_mirror-double.txt
fc2_2dense, epochs=15, batch=1024, optimizer=nadam, learning rate=0.002, patience=15
Number of training samples: 91036
Loss
0: train_loss=0.36501177620178543, val_loss=0.2392944501659002
1: train_loss=0.19815394991355786, val_loss=0.2057685309169999
2: train_loss=0.12208851836214188, val_loss=0.19690281326210674
3: train_loss=0.0715234048513653, val_loss=0.2264957801496508
4: train_loss=0.03894896965696992, val_loss=0.2476133049754511
5: train_loss=0.02054658807950634, val_loss=0.2529032679103993