fashion14.py

fashion14.py

epochs = 50
net = buildCNN(([64,3,1,2,2,''],[64,3,0,2,2,'']))
print(net)
trainer = init(net, optimizer='adam', learning_rate=1e-3)

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=15.14 Training=0.8748 Validation=0.8733 Diff=0.0015
Epoch#1 Time=14.41 Training=0.8986 Validation=0.8982 Diff=0.0004
Epoch#2 Time=15.05 Training=0.9113 Validation=0.9049 Diff=0.0064
Epoch#3 Time=16.17 Training=0.9214 Validation=0.9110 Diff=0.0103
Epoch#4 Time=15.85 Training=0.9302 Validation=0.9150 Diff=0.0152
Epoch#5 Time=14.98 Training=0.9340 Validation=0.9157 Diff=0.0183
Epoch#6 Time=16.12 Training=0.9495 Validation=0.9237 Diff=0.0258
Epoch#7 Time=14.98 Training=0.9569 Validation=0.9270 Diff=0.0299
Epoch#8 Time=15.43 Training=0.9619 Validation=0.9250 Diff=0.0369
Epoch#9 Time=14.99 Training=0.9697 Validation=0.9285 Diff=0.0412
Epoch#10 Time=14.76 Training=0.9716 Validation=0.9305 Diff=0.0411
Epoch#11 Time=14.80 Training=0.9769 Validation=0.9262 Diff=0.0507
Epoch#12 Time=15.74 Training=0.9766 Validation=0.9239 Diff=0.0527
Epoch#13 Time=15.16 Training=0.9825 Validation=0.9273 Diff=0.0552
Epoch#14 Time=14.88 Training=0.9836 Validation=0.9272 Diff=0.0564
Epoch#15 Time=14.95 Training=0.9858 Validation=0.9260 Diff=0.0598
Epoch#16 Time=16.32 Training=0.9899 Validation=0.9266 Diff=0.0633
Epoch#17 Time=15.98 Training=0.9904 Validation=0.9286 Diff=0.0618
Epoch#18 Time=15.70 Training=0.9900 Validation=0.9238 Diff=0.0662
Epoch#19 Time=15.25 Training=0.9846 Validation=0.9203 Diff=0.0643
Epoch#20 Time=15.13 Training=0.9921 Validation=0.9256 Diff=0.0665
Epoch#21 Time=15.65 Training=0.9922 Validation=0.9254 Diff=0.0668
Epoch#22 Time=15.01 Training=0.9954 Validation=0.9246 Diff=0.0708
Epoch#23 Time=14.64 Training=0.9926 Validation=0.9205 Diff=0.0721
Epoch#24 Time=15.36 Training=0.9956 Validation=0.9231 Diff=0.0725
Epoch#25 Time=15.12 Training=0.9940 Validation=0.9229 Diff=0.0710
Epoch#26 Time=14.96 Training=0.9933 Validation=0.9269 Diff=0.0664
Epoch#27 Time=15.32 Training=0.9895 Validation=0.9203 Diff=0.0692
Epoch#28 Time=15.66 Training=0.9979 Validation=0.9272 Diff=0.0706
Epoch#29 Time=15.13 Training=0.9975 Validation=0.9268 Diff=0.0707
Epoch#30 Time=15.52 Training=0.9970 Validation=0.9255 Diff=0.0715
Epoch#31 Time=15.01 Training=0.9937 Validation=0.9211 Diff=0.0726
Epoch#32 Time=16.90 Training=0.9970 Validation=0.9280 Diff=0.0690
Epoch#33 Time=15.06 Training=0.9936 Validation=0.9220 Diff=0.0716
Epoch#34 Time=15.92 Training=0.9964 Validation=0.9228 Diff=0.0736
Epoch#35 Time=15.36 Training=0.9950 Validation=0.9210 Diff=0.0740
Epoch#36 Time=16.18 Training=0.9948 Validation=0.9215 Diff=0.0733
Epoch#37 Time=15.06 Training=0.9961 Validation=0.9234 Diff=0.0727
Epoch#38 Time=15.67 Training=0.9974 Validation=0.9264 Diff=0.0710
Epoch#39 Time=15.05 Training=0.9951 Validation=0.9255 Diff=0.0696
Epoch#40 Time=15.20 Training=0.9952 Validation=0.9195 Diff=0.0757
Epoch#41 Time=14.96 Training=0.9935 Validation=0.9224 Diff=0.0711
Epoch#42 Time=14.96 Training=0.9963 Validation=0.9241 Diff=0.0722
Epoch#43 Time=14.86 Training=0.9971 Validation=0.9251 Diff=0.0720
Epoch#44 Time=16.41 Training=0.9951 Validation=0.9247 Diff=0.0704
Epoch#45 Time=15.36 Training=0.9981 Validation=0.9249 Diff=0.0732
Epoch#46 Time=14.91 Training=0.9953 Validation=0.9242 Diff=0.0711
Epoch#47 Time=14.68 Training=0.9972 Validation=0.9232 Diff=0.0740
Epoch#48 Time=14.26 Training=0.9950 Validation=0.9172 Diff=0.0778
Epoch#49 Time=14.49 Training=0.9950 Validation=0.9199 Diff=0.0751