fashion9.py

fashion9.py

net = buildCNN(([64,3,1,2,2,''],[64,3,0,2,2,'']))
print(net)
trainer = init(net)
training_accuracies, validation_accuracies = train(net, trainer, train_data, validation_data, epochs)

Sequential(
  (0): Conv2D(64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
  (1): Activation(relu)
  (2): MaxPool2D(size=(2, 2), stride=(2, 2), padding=(0, 0), ceil_mode=False)
  (3): Conv2D(64, kernel_size=(3, 3), stride=(1, 1))
  (4): Activation(relu)
  (5): MaxPool2D(size=(2, 2), stride=(2, 2), padding=(0, 0), ceil_mode=False)
  (6): Flatten
  (7): Dense(256, linear)
  (8): Activation(relu)
  (9): Dense(64, linear)
  (10): Activation(relu)
  (11): Dense(10, linear)
)
Epoch#0 Time=14.99 Training=0.8084 Validation=0.8102 Diff=-0.0018
Epoch#1 Time=14.50 Training=0.8562 Validation=0.8520 Diff=0.0042
Epoch#2 Time=15.01 Training=0.8752 Validation=0.8723 Diff=0.0029
Epoch#3 Time=15.52 Training=0.8744 Validation=0.8687 Diff=0.0057
Epoch#4 Time=14.33 Training=0.8932 Validation=0.8868 Diff=0.0064
Epoch#5 Time=14.52 Training=0.9029 Validation=0.8950 Diff=0.0079
Epoch#6 Time=14.57 Training=0.9077 Validation=0.8989 Diff=0.0088
Epoch#7 Time=15.08 Training=0.9093 Validation=0.8991 Diff=0.0102
Epoch#8 Time=15.40 Training=0.9150 Validation=0.9054 Diff=0.0096
Epoch#9 Time=14.52 Training=0.9105 Validation=0.9005 Diff=0.0100
Epoch#10 Time=14.77 Training=0.9191 Validation=0.9051 Diff=0.0140
Epoch#11 Time=15.69 Training=0.9252 Validation=0.9059 Diff=0.0193
Epoch#12 Time=14.70 Training=0.9298 Validation=0.9091 Diff=0.0207
Epoch#13 Time=14.38 Training=0.9295 Validation=0.9082 Diff=0.0213
Epoch#14 Time=14.50 Training=0.9374 Validation=0.9140 Diff=0.0234
Epoch#15 Time=14.28 Training=0.9372 Validation=0.9157 Diff=0.0215
Epoch#16 Time=15.20 Training=0.9416 Validation=0.9181 Diff=0.0235
Epoch#17 Time=14.50 Training=0.9456 Validation=0.9186 Diff=0.0270
Epoch#18 Time=14.46 Training=0.9358 Validation=0.9086 Diff=0.0272
Epoch#19 Time=14.50 Training=0.9475 Validation=0.9145 Diff=0.0330
Epoch#20 Time=13.97 Training=0.9510 Validation=0.9174 Diff=0.0336
Epoch#21 Time=14.81 Training=0.9563 Validation=0.9163 Diff=0.0400
Epoch#22 Time=16.40 Training=0.9525 Validation=0.9095 Diff=0.0430
Epoch#23 Time=15.79 Training=0.9633 Validation=0.9198 Diff=0.0435
Epoch#24 Time=14.50 Training=0.9682 Validation=0.9206 Diff=0.0476
Epoch#25 Time=14.93 Training=0.9596 Validation=0.9148 Diff=0.0448
Epoch#26 Time=15.71 Training=0.9671 Validation=0.9167 Diff=0.0504
Epoch#27 Time=14.76 Training=0.9702 Validation=0.9211 Diff=0.0491
Epoch#28 Time=14.51 Training=0.9765 Validation=0.9222 Diff=0.0543
Epoch#29 Time=14.59 Training=0.9718 Validation=0.9204 Diff=0.0514
Epoch#30 Time=14.34 Training=0.9661 Validation=0.9109 Diff=0.0552
Epoch#31 Time=14.59 Training=0.9791 Validation=0.9220 Diff=0.0571
Epoch#32 Time=15.27 Training=0.9818 Validation=0.9199 Diff=0.0619
Epoch#33 Time=14.93 Training=0.9801 Validation=0.9204 Diff=0.0597
Epoch#34 Time=14.51 Training=0.9739 Validation=0.9133 Diff=0.0606
Epoch#35 Time=15.46 Training=0.9869 Validation=0.9221 Diff=0.0647
Epoch#36 Time=14.92 Training=0.9886 Validation=0.9234 Diff=0.0652
Epoch#37 Time=14.81 Training=0.9829 Validation=0.9175 Diff=0.0654
Epoch#38 Time=14.51 Training=0.9859 Validation=0.9197 Diff=0.0662
Epoch#39 Time=14.65 Training=0.9932 Validation=0.9244 Diff=0.0688
Epoch#40 Time=14.57 Training=0.9930 Validation=0.9235 Diff=0.0695
Epoch#41 Time=14.65 Training=0.9941 Validation=0.9248 Diff=0.0693
Epoch#42 Time=15.10 Training=0.9941 Validation=0.9225 Diff=0.0716
Epoch#43 Time=14.52 Training=0.9878 Validation=0.9134 Diff=0.0744
Epoch#44 Time=14.61 Training=0.9932 Validation=0.9208 Diff=0.0725
Epoch#45 Time=14.85 Training=0.9882 Validation=0.9197 Diff=0.0685
Epoch#46 Time=14.69 Training=0.9913 Validation=0.9191 Diff=0.0722
Epoch#47 Time=14.65 Training=0.9948 Validation=0.9217 Diff=0.0731
Epoch#48 Time=15.27 Training=0.9967 Validation=0.9209 Diff=0.0757
Epoch#49 Time=15.12 Training=0.9988 Validation=0.9242 Diff=0.0746