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model.compile(loss="sparse_categorical_crossentropy",
	      optimizer="sgd"      # Stochastic gradient Descent
              metrics=["accuracy"])

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import pandas as pd
import seaborn as sns

sns.set_theme()

pd.DataFrame(history.history).plot(figsize=(8, 5))
plt.grid(True)
plt.gca().set_ylim(0, 1)

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input_ = keras.layers.Input(shape=X_train.shape[1:])
hidden1 = keras.layers.Dense(30, activation="relu")(input_)
hidden2 = keras.layer.Dense(30, activation="relu")(hidden1)
concat = keras.layers.Concatenate()([input_, hidden2])
output = keras.layers.Dense(1)(concat)
model = keras.Model(inputs=[input_], output=[output])

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input_A = keras.layers.Input(shape=[5], name="wide_input")
input_B = keras.layers.Input(shape=[6], name="deep_input")
hidden1 = keras.layers.Dense(30, activation="relu")(input_A)
hiddden2 = keras.layers.Dense(30, activation="relu")(hidden1)
concat = keras.layers.Concatenate()([input_A, hidden2])
output = keras.layer.Dense(1, name="output")(concat)
model = keras.Model(inputs=[input_A, input_B], outputs=[output])

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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

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history = model.fit({"wide_input": X_train_A, 
                     "deep_input": X_train_B},
                     y_train, 
                     epochs=20,
                     validation_data=({"wide_input": X_valid_A, 
                                      "deep_input": X_valid_B"}, y_valid))

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[...] # Same as above, up to main output layer
output = keras.layers.Dense(1, name="main_output")(concat)
aux_output = keras.layers.Dense(1, name="aux_output")(hidden2)
model = keras.Model(inputs=[input_A, input_B], 
					outputs=[output, aux_output])

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model.compile(loss=["mse", "mse"], loss_weights=[0.9, 0.1], optimizer="sgd")

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# Here we passed the same labels y_test 
# as the auxilary output is just for regularization
total_loss, main_loss, aux_loss = model.evaluate([X_test_A, X_test_B], 
                                                 [y_test, y_test])

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y_pred_main, y_pred_aux = model.predict([X_new_A, X_new_B])