Fitting the Model

Use model.fit to train the model. We shall pass the X_train, y_train, epochs=30, validation_data=(X_valid, y_valid) as input parameters to the method.

history = model.fit(X_train, y_train, epochs=30,
            validation_data=(X_valid, y_valid))

This might take some time, so go and have a sip of water if you like.

Let us now print the parameter history of the history.

history.params

Let us print the name of the first hidden layer:

hidden1 = model.layers[1]
print(hidden1.name)

Let us use get_weights() function to see the trained weights and biases of the hidden1.

weights, biases = hidden1.get_weights() # getting the weights and biases
print(weights.shape, weights)
print(biases)

Let us visualize the model training history. Here, history.history is a dictionary containing information about the accuracy and loss measures through the epochs.

Let us store this dictionary as a pandas dataframe and plot the values.

import pandas as pd

pd.DataFrame(history.history).plot(figsize=(8, 5))
plt.grid(True)
plt.gca().set_ylim(0, 1) # setting limits for y-axis
plt.show()