In tensorflow, how to enumerate training data (compared to pytorch)
In pytorch, this is how I enumerate training data.

for epoch in range(0, args.epoches):
    for i, batch in enumerate(train_data):
        model.update(batch)


train_data contains multiple batches and batches are getting enumerated and updating the model, which is very clear to me.



I think this is a basic example of how tensorflow treats the batches.

for step in range(num_steps):
    batch_data, batch_labels = generate_batch(batch_size, num_skips, skip_window)
    feed_dict = {train_dataset : batch_data, train_labels : batch_labels}
    _, l = session.run([optimizer, loss], feed_dict=feed_dict)


Maybe this is a very obvious question, but I'm not clear how enumerating training batches is handled by session.run in tensorflow. I can't find batches are getting looped through in the code. All I see is feed_dict and I assume it handles the looping. 

Can someone shed some light on this?

TensorFlow has a History object for this purpose. You get History object as a return from the model.fit() method.

A History object and its History.history attribute is a record of training loss values and metrics values at successive epochs, as well as validation loss values       and validation metrics values (if applicable).

Hope this is what you need.
