Lowering the learning rate to TF learning rate helped but 20 epochs for PyTorch and accuracy still not the best.
Metrics PyTorch:
Loss:
0 0.6992619037628174
1 1.378086805343628
2 1.0771313905715942
3 0.904154360294342
4 0.5980193614959717
5 0.7647961378097534
6 0.6887813806533813
7 0.5120381712913513
8 0.5252910852432251
9 0.5683923959732056
10 0.48663294315338135
11 0.402211457490921
12 0.3984984755516052
13 0.396999716758728
14 0.35266047716140747
15 0.301003634929657
16 0.2879875898361206
17 0.28108495473861694
18 0.2524130642414093
19 0.2120990753173828

Accuracy on training data and test data:
Accuracy: 100.0%
Accuracy: 25.0%

I am still PyTorch believer and don’t go for performance, make it second in your priority list.
Ease of use first. Calculation after convolution. What a headache!
Second performance.
TIll now PyTorch is the best because of reinforcement learning.
And paso a paso I am moving forward. Can’t wait till my first reinforcement learning project. LSTM ahead and then reinforcement learning.
Very happy. PyTorch made me feel happy!