2

#1

from tensorflow.keras.datasets import fashion_mnist

(x_train, y_train), (x_test, y_test) = fashion_mnist.load_data()

x_train,x_test=x_train/255.0,x_test/255.0

#2

import tensorflow as tf

model = tf.keras.Sequential([

  tf.keras.layers.Flatten(input_shape=(28, 28)),

  tf.keras.layers.Dense(128, activation='relu'),

  tf.keras.layers.BatchNormalization(),

  tf.keras.layers.Dropout(0.5),

  tf.keras.layers.Dense(64, activation='relu'),

  tf.keras.layers.Dense(10, activation='softmax')

])

model.compile(optimizer='adam',

              loss='sparse_categorical_crossentropy',

              metrics=['accuracy'])

#3

lr_schedule = tf.keras.callbacks.LearningRateScheduler(

    lambda epoch: 1e-3 * 10**(epoch / 20))

history = model.fit(x_train, y_train, epochs=10,

                    validation_data=(x_test, y_test),

                    callbacks=[lr_schedule])

#4

import matplotlib.pyplot as plt

plt.plot(history.history['accuracy'],label='Training Accuracy')

plt.plot(history.history['val_accuracy'],label='Validation Accuracy')

plt.xlabel('Epochs')

plt.ylabel('Accuracy')

plt.legend()

plt.show() 

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