Hello,
I’m working with an autoencoder and I want to visualize the output and the original image. I already did it but my problem is that everytime the images are plotted, are different images, so I cannot make a visual comparison of the improvement.
My code is next:
    # training and so on
for batch_features, _ in train_loader:
    batch_features = batch_features.view(-1, 784)
    
    optimizer.zero_grad()
    outputs = model(batch_features)
    train_loss = criterion(outputs, batch_features)
    train_loss.backward()

    optimizer.step() #update the weights (net.parameters)
    loss += train_loss.item()

loss = loss / len(train_loader)
print("epoch : {}/{}, loss = {:.6f}".format(epoch + 1, epochs, loss))

#VISUALIZATION
reconstructed = outputs.view(-1,1,28,28)
original = batch_features.view(-1,1,28,28)
img = T.ToPILImage()(reconstructed[0]) # plot the first element of the last batch
img2 = T.ToPILImage()(original[0])


plt.subplot(121)
imgplot = plt.imshow(img)
plt.subplot(122)
imgplot2 = plt.imshow(img2)
plt.suptitle("Reconstructed vs Original")
plt.show()

Every time I plot the images, the [0] image is different, why? I think I don’t exactly understand the concept of batch. Because as far as I understand, the [0] I plot should always be the [0] image of the last plot, but it is not.
How could I control the number of the batch that is inputing the NN? How could I plot everytime the same picture? For instance, the image [23] of the batch 30, or the image [540] of the whole training set?
Thank you so much!