title: keras model.fit output - "val_accuracy improved from -inf to 0.29846" - what does it mean by -inf?
body: <p>I'm training a CNN model using keras.</p>
  
  <p>After end of each epoch, I save the weights as checkpoints if the validation accuracy has improved.</p>
  
  <pre><code>from keras.callbacks import ModelCheckpoint
  
  
  checkpoint = ModelCheckpoint(checkpoint_path, monitor='val_accuracy', mode='max', 
  save_best_only=True, verbose=1)
  
  callbacks = [checkpoint]
  
  #load checkpoints if existing
  import os
  
  num_of_epochs = 65
  epochs_done = 0
  
  if(os.path.exists(checkpoint_path)):
      model.load_weights(checkpoint_path)
      num_of_epochs = num_of_epochs - epochs_done
      print('checkpoints loaded')
  </code></pre>
  
  <p>When I restart training after stopping, this is how my first epoch output looks like.</p>
  
  <pre><code>Epoch 1/65
  425/425 [==============================] - 224s 526ms/step - loss: 2.1739 - accuracy: 0.2939 - val_loss: 2.1655 - val_accuracy: 0.2985
  
  Epoch 00001: val_accuracy improved from -inf to 0.29846, saving model to checkpoints-finetuning.hdf5
  </code></pre>
  
  <p>I noticed this happening at at the first epoch every time I restart training. Why does it happen? Does my checkpoint file get overwritten by worse accurate weights each time I restart?</p>