05-adding-laters-to-a-network.py

'''
Adding layers to a network

You've seen how to experiment with wider networks. In this exercise, you'll try a deeper network (more hidden layers).

Once again, you have a baseline model called model_1 as a starting point. It has 1 hidden layer, with 50 units. You can see a summary of that model's structure printed out. You will create a similar network with 3 hidden layers (still keeping 50 units in each layer).

This will again take a moment to fit both models, so you'll need to wait a few seconds to see the results after you run your code.

INSTRUCTIONS
100XP
Specify a model called model_2 that is like model_1, but which has 3 hidden layers of 50 units instead of only 1 hidden layer.
Use input_shape to specify the input shape in the first hidden layer.
Use 'relu' activation for the 3 hidden layers and 'softmax' for the output layer, which should have 2 units.
Compile model_2 as you have done with previous models: Using 'adam' as the optimizer, 'categorical_crossentropy' for the loss, and metrics=['accuracy'].
Hit 'Submit Answer' to fit both the models and visualize which one gives better results! For both models, you should look for the best val_loss and val_acc, which won't be the last epoch for that model.
'''
# The input shape to use in the first hidden layer
input_shape = (n_cols,)

# Create the new model: model_2
model_2 = Sequential()

# Add the first, second, and third hidden layers
model_2.add(Dense(50, activation='relu', input_shape=input_shape))
model_2.add(Dense(50, activation='relu'))
model_2.add(Dense(50, activation='relu'))

# Add the output layer
model_2.add(Dense(2, activation='softmax'))

# Compile model_2
model_2.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])

# Fit model 1
model_1_training = model_1.fit(predictors, target, epochs=20, validation_split=0.4, callbacks=[early_stopping_monitor], verbose=False)

# Fit model 2
model_2_training = model_2.fit(predictors, target, epochs=20, validation_split=0.4, callbacks=[early_stopping_monitor], verbose=False)

# Create the plot
plt.plot(model_1_training.history['val_loss'], 'r', model_2_training.history['val_loss'], 'b')
plt.xlabel('Epochs')
plt.ylabel('Validation score')
plt.show()