im sure it doesnt generate the same target labels. Here is how it parsed the text:
Counter({’,’: 5, ‘(’: 2, ‘)’: 2, ‘\n’: 2, ‘5’: 2, ‘fill’: 1, ‘r_two_five_five’: 1, ‘g_eight_five’: 1, ‘b_eight_five’: 1, ‘ellipse’: 1, ‘x_two_eight’: 1, ‘y_two_seven’: 1})
for this training data:
https://github.com/jtoy/overfit_image_pytorch/blob/master/training_data/code.txt 19
And here is the line that parses the actual string: https://github.com/jtoy/overfit_image_pytorch/blob/master/build_vocab.py#L28 19
That given,  I will see if I can simplify the code more to show more clearly that  that area of code is not causing the problem.
The model does learn, I just trained with a bunch of different learning rates, I get the best result with learning rate 0.01. The loss was 0.1180
Here is what training loss looks like:
`Epoch [0/200], Step [0/1], Loss: 0.9265, accuracy: 0.00 Perplexity: 2.5256
Epoch [1/200], Step [0/1], Loss: 2.8060, accuracy: 0.00 Perplexity: 16.5436
Epoch [2/200], Step [0/1], Loss: 0.6228, accuracy: 0.00 Perplexity: 1.8642
Epoch [3/200], Step [0/1], Loss: 0.8439, accuracy: 0.00 Perplexity: 2.3253
Epoch [4/200], Step [0/1], Loss: 0.9583, accuracy: 0.00 Perplexity: 2.6072
Epoch [5/200], Step [0/1], Loss: 0.9609, accuracy: 0.00 Perplexity: 2.6142
Epoch [6/200], Step [0/1], Loss: 0.9588, accuracy: 0.00 Perplexity: 2.6086
Epoch [7/200], Step [0/1], Loss: 0.9474, accuracy: 0.00 Perplexity: 2.5789
Epoch [8/200], Step [0/1], Loss: 0.9370, accuracy: 0.00 Perplexity: 2.5524
Epoch [9/200], Step [0/1], Loss: 0.9292, accuracy: 0.00 Perplexity: 2.5325
Epoch [10/200], Step [0/1], Loss: 0.9257, accuracy: 0.00 Perplexity: 2.5235
Epoch [11/200], Step [0/1], Loss: 0.9181, accuracy: 0.00 Perplexity: 2.5045
Epoch [12/200], Step [0/1], Loss: 0.9106, accuracy: 0.00 Perplexity: 2.4857
Epoch [13/200], Step [0/1], Loss: 0.9039, accuracy: 0.00 Perplexity: 2.4692
Epoch [14/200], Step [0/1], Loss: 0.8969, accuracy: 0.00 Perplexity: 2.4521
Epoch [15/200], Step [0/1], Loss: 0.8897, accuracy: 0.00 Perplexity: 2.4344
Epoch [16/200], Step [0/1], Loss: 0.8847, accuracy: 0.00 Perplexity: 2.4224
Epoch [17/200], Step [0/1], Loss: 0.8815, accuracy: 0.00 Perplexity: 2.4145
Epoch [18/200], Step [0/1], Loss: 0.8753, accuracy: 0.00 Perplexity: 2.3997
Epoch [19/200], Step [0/1], Loss: 0.8690, accuracy: 0.00 Perplexity: 2.3844
Epoch [20/200], Step [0/1], Loss: 0.8642, accuracy: 0.00 Perplexity: 2.3732
…
Epoch [126/200], Step [0/1], Loss: 0.1181, accuracy: 0.00 Perplexity: 1.1253
Epoch [127/200], Step [0/1], Loss: 0.1181, accuracy: 0.00 Perplexity: 1.1253
Epoch [128/200], Step [0/1], Loss: 0.1180, accuracy: 0.00 Perplexity: 1.1253`
And the generated image is starting to look like the training data, but  there is still tons of noise: https://cl.ly/3E1d0X0w1r0b 24
Am I wrong to expect the model to learn to duplicate/overfit the exact image with just a single training image?   How can I make the model overfit this and get the same result?