Training the model

In this section, we will train the model.

Here, we can see that model.fit helps us start the training process:

# Display training progress by printing a single dot for each completed epoch
In[32]: class PrintDot(keras.callbacks.Callback):
         def on_epoch_end(self, epoch, logs):
            if epoch % 100 == 0: print('')
            print('.', end='')

In[33]: EPOCHS = 1000

In[34]: history = model.fit(normed_train_data, train_labels,
  epochs=EPOCHS, validation_split = 0.2, verbose=0,
  callbacks=[PrintDot()])

Now, we will visualize the model's training progress:

In[35]: hist = pd.DataFrame(history.history)
In[36]: hist['epoch'] = history.epoch
In[37]: hist.tail()
In[38]: def plot_training_history(history):
          hist = pd.DataFrame(history.history)
          hist['epoch'] = history.epoch

          plt.figure()
          plt.xlabel('Epoch')
          plt.ylabel('Mean Abs Error [MPG]')
   ...