wide_and_deep_compile_fit_evaluate.py

model.compile(loss="mse", optimizer=keras.optimizers.SGD(lr=1e-3))

# Splitting the train, test and validation data 
# for different sets of featrues
X_train_A, X_train_B = X_tain[:, :5], X_train[:, 2:]
X_valid_A, X_valid_B = X_valid[:, :5], X_valid[:, 2:]
X_test_A, X_test_B = X_test[:, :5], X_test[:, 2:]
X_new_A, X_new_B = X_test_A[:3], x_test_b[:3] # suppose an unknown sample

# Providing a tuple of training and validation data
history = model.fit((X_train_A, X_train_B), y_train, 
					           epochs=20, 
                     validation_data=((X_valid_A, X_valid_B), y_valid))

# Evaluation and prediction's inputs are also splits
mse_test = model.evaluate((X_test_A, X_test_B), y_test)
y_pred = model.predict((X_new_A, X_new_B))