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Implementation of Neural Network Using TensorFlow Library for Deep Learning

import tensorflow as tf from tensorflow.keras.datasets import mnist # Load and preprocess 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 # Define the model architecture model = tf.keras.models.Sequential([ tf.keras.layers.Flatten(input_shape=(28, 28)), tf.keras.layers.Dense(128, activation='relu'), tf.keras.layers.Dropout(0.2), tf.keras.layers.Dense(10) ]) # Compile the model loss_fn = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True) model.compile(optimizer='adam', loss=loss_fn, metrics=['accuracy']) # Train the model model.fit(x_train, y_train, epochs=5) # Evaluate the model test_loss, test_acc = model.evaluate(x_test, y_test, verbose=2) print('\nTest accuracy:', test_acc)

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