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Exercise

Comparing activation functions II

What you coded in the previous exercise has been executed to obtain theactivation_results variable, this time 100 epochs were used instead of 20. This way you will have more epochs to further compare how the training evolves per activation function.

For every h_callback of each activation function in activation_results:

  • The h_callback.history['val_loss'] has been extracted.
  • The h_callback.history['val_acc'] has been extracted.

Both are saved into two dictionaries: val_loss_per_function and val_acc_per_function.

Pandas is also loaded as pd for you to use. Let's plot some quick validation loss and accuracy charts!

Instructions
100 XP
  • Use pd.DataFrame()to create a new DataFrame from the val_loss_per_function dictionary.
  • Call plot() on the DataFrame.
  • Create another pandas DataFrame from val_acc_per_function.
  • Once again, plot the DataFrame.
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