04-experimenting-with-wider-networks.py

'''
Experimenting with wider networks

Now you know everything you need to begin experimenting with different models!

A model called model_1 has been pre-loaded. You can see a summary of this model printed in the IPython Shell. This is a relatively small network, with only 10 units in each hidden layer.

In this exercise you'll create a new model called model_2 which is similar to model_1, except it has 100 units in each hidden layer.

After you create model_2, both models will be fitted, and a graph showing both models loss score at each epoch will be shown. We added the argument verbose=False in the fitting commands to print out fewer updates, since you will look at these graphically instead of as text.

Because you are fitting two models, it will take a moment to see the outputs after you hit run, so be patient.

INSTRUCTIONS
100XP
Create model_2 to replicate model_1, but use 100 nodes instead of 10 for the first two Dense layers you add with the 'relu' activation. Use 2 nodes for the Dense output layer with 'softmax' as the activation.
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! Notice the keyword argument verbose=False in model.fit(): This prints out fewer updates, since you'll be evaluating the models graphically instead of through text.
'''
# Define early_stopping_monitor
early_stopping_monitor = EarlyStopping(patience=2)

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

# Add the first and second layers
model_2.add(Dense(100, activation='relu', input_shape=input_shape))
model_2.add(Dense(100, 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=15, validation_split=0.2, callbacks=[early_stopping_monitor], verbose=False)

# Fit model_2
model_2_training = model_2.fit(predictors, target, epochs=15, validation_split=0.2, 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()