ResNet ARCHITECTURE Ex-12

 import tensorflow as tf

import matplotlib.pyplot as plt

from tensorflow.keras import datasets, layers, models, losses

(x_train, y_train), (x_test, y_test)=tf.keras.datasets.mnist.load_data()

x_train = tf.pad(x_train, [[0, 0], [2,2], [2,2]])/255

x_test = tf.pad(x_test, [[0, 0], [2,2], [2,2]])/255

x_train = tf.expand_dims(x_train, axis=3, name=None)

x_test = tf.expand_dims(x_test, axis=3, name=None)

x_train = tf.repeat(x_train, 3, axis=3)

x_test = tf.repeat(x_test, 3, axis=3)

x_val = x_train[-2000:,:,:,:]

y_val = y_train[-2000:]

x_train = x_train[:-2000,:,:,:]

y_train = y_train[:-2000]

base_model = tf.keras.applications.ResNet152(weights = 'imagenet',

 include_top = False,

 input_shape = (32,32,3))

for layer in base_model.layers:

 layer.trainable = False

x = layers.Flatten()(base_model.output)

x = layers.Dense(1000, activation='relu')(x)

predictions = layers.Dense(10, activation = 'softmax')(x)

head_model = Model(inputs = base_model.input, outputs = predictions)

head_model.compile(optimizer='adam', metrics=['accuracy'],

 loss=losses.sparse_categorical_crossentropy)

history = head_model.fit(x_train, y_train,

 batch_size=64,

 epochs=40,

 validation_data=(x_val, y_val))

fig, axs = plt.subplots(2, 1, figsize=(15,15))

axs[0].plot(history.history['loss'])

axs[0].plot(history.history['val_loss'])

axs[0].title.set_text('Training Loss vs Validation Loss')

axs[0].set_xlabel('Epochs')

axs[0].set_ylabel('Loss')

axs[0].legend(['Train','Val'])

axs[1].plot(history.history['accuracy'])

axs[1].plot(history.history['val_accuracy'])

axs[1].title.set_text('Training Accuracy vs Validation Accuracy')

axs[1].set_xlabel('Epochs')

axs[1].set_ylabel('Accuracy')

axs[1].legend(['Train', 'Val'])

result = head_model.evaluate(x_test, y_test)

print(“Loss = {}, Accuracy = {}”.format(result[0), result[1])