title: Has Tensorflow training output style changed?
body: <p>I have been working on my deep learning model for a while. Today, when I started the model training, I noticed only a fraction of my dataset is being trained and the size of data used in each epoch changes with the batch size.</p>
  
  <pre><code>print(mixture_train_shaped.shape)
  print(clear_train_shaped.shape)
  model.fit(mixture_train_shaped, clear_train_shaped,
           validation_split=0.2,
           epochs=40,
           batch_size=32, 
           shuffle=True,
           verbose=1
          )
  </code></pre>
  
  <p>When I run this code, this is what I see.</p>
  
  <pre><code>(51226, 129, 8, 4)
  (51226, 129, 1, 1)
  Epoch 1/40
  1281/1281 [===========]
  Epoch 2/40
  1281/1281 [===========]
  </code></pre>
  
  <p>In my previous training outputs, the model would use the entire set in one epoch. On the above example though, the training set has 40,980 sample and each epoch trains only 40,980/32=1281. In a way, every epoch trains a single batch.</p>
  
  <pre><code>Train on 47 samples, validate on 6 samples
  Epoch 1/5000
  47/47 [==========]
  </code></pre>
  
  <p>I haven't changed the code. Is every epoch still using the entire training set or has it changed?</p>