Code snippet for building model using Keras Functional API

Keras_Functional_API

#Import required libraries
import tensorflow as tf
from keras.datasets import mnist

#Creating the MNIST Dataset
(x_train, y_train), (x_test, y_test) = mnist.load_data()
x_train, x_test= x_train/255.0, x_test/255.0
x_train=x_train[...,tf.newaxis].astype('float32')
x_test=x_test[...,tf.newaxis].astype('float32')

#Building the Keras model using Functional API
inputs = tf.keras.Input(shape=(28,28,1))
x=tf.keras.layers.Conv2D(32,3, activation='relu')(inputs)
x=tf.keras.layers.Flatten()(x)
x=tf.keras.layers.Dense(128, activation='relu')(x)
output=tf.keras.layers.Dense(10, activation='softmax')(x)
model_fapi= tf.keras.Model(inputs=inputs, outputs=output, name="mnist_model")

# compile the mode;
model_fapi.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])

# Fit the model on MNIST dataset
model_fapi.fit(x_train, y_train, epochs=10)

#Evaluate the model on the test data
test_loss, test_acc=model_fapi.evaluate(x_test, y_test)