GoogLeNet ARCHITECTURE EX-11

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]

def inception(x,

 filters_1x1,

 filters_3x3_reduce,

 filters_3x3,

 filters_5x5_reduce,

 filters_5x5,

 filters_pool):

 path1 = layers.Conv2D(filters_1x1, (1, 1), padding='same', activation='relu')(x)

 path2 = layers.Conv2D(filters_3x3_reduce, (1, 1), padding='same',

activation='relu')(x)

 path2 = layers.Conv2D(filters_3x3, (1, 1), padding='same', activation='relu')(path2)

 path3 = layers.Conv2D(filters_5x5_reduce, (1, 1), padding='same',

activation='relu')(x)

 path3 = layers.Conv2D(filters_5x5, (1, 1), padding='same', activation='relu')(path3)

 path4 = layers.MaxPool2D((3, 3), strides=(1, 1), padding='same')(x)

 path4 = layers.Conv2D(filters_pool, (1, 1), padding='same',

activation='relu')(path4)

 return tf.concat([path1, path2, path3, path4], axis=3)

inp = layers.Input(shape=(32, 32, 3))

input_tensor = layers.experimental.preprocessing.Resizing(224, 224,

interpolation="bilinear", input_shape=x_train.shape[1:])(inp)

x = layers.Conv2D(64, 7, strides=2, padding='same', activation='relu')(input_tensor)

x = layers.MaxPooling2D(3, strides=2)(x)

x = layers.Conv2D(64, 1, strides=1, padding='same', activation='relu')(x)

x = layers.Conv2D(192, 3, strides=1, padding='same', activation='relu')(x)

x = layers.MaxPooling2D(3, strides=2)(x)

x = inception(x,

 filters_1x1=64,

 filters_3x3_reduce=96,

 filters_3x3=128,

 filters_5x5_reduce=16,

 filters_5x5=32,

 filters_pool=32)

x = inception(x,

 filters_1x1=128,

 filters_3x3_reduce=128,

 filters_3x3=192,

 filters_5x5_reduce=32,

 filters_5x5=96,

 filters_pool=64)

x = layers.MaxPooling2D(3, strides=2)(x)

x = inception(x,

 filters_1x1=192,

 filters_3x3_reduce=96,

 filters_3x3=208,

 filters_5x5_reduce=16,

 filters_5x5=48,

 filters_pool=64)

aux1 = layers.AveragePooling2D((5, 5), strides=3)(x)

aux1 = layers.Conv2D(128, 1, padding='same', activation='relu')(aux1)

aux1 = layers.Flatten()(aux1)

aux1 = layers.Dense(1024, activation='relu')(aux1)

aux1 = layers.Dropout(0.7)(aux1)

aux1 = layers.Dense(10, activation='softmax')(aux1)

x = inception(x,

 filters_1x1=160,

 filters_3x3_reduce=112,

 filters_3x3=224,

 filters_5x5_reduce=24,

 filters_5x5=64,

 filters_pool=64)

x = inception(x,

 filters_1x1=128,

 filters_3x3_reduce=128,

 filters_3x3=256,

 filters_5x5_reduce=24,

 filters_5x5=64,

 filters_pool=64)

x = inception(x,

 filters_1x1=112,

 filters_3x3_reduce=144,

 filters_3x3=288,

 filters_5x5_reduce=32,

 filters_5x5=64,

 filters_pool=64)

aux2 = layers.AveragePooling2D((5, 5), strides=3)(x)

aux2 = layers.Conv2D(128, 1, padding='same', activation='relu')(aux2)

aux2 = layers.Flatten()(aux2)

aux2 = layers.Dense(1024, activation='relu')(aux2)

aux2 = layers.Dropout(0.7)(aux2)

aux2 = layers.Dense(10, activation='softmax')(aux2)

x = inception(x,

 filters_1x1=256,

 filters_3x3_reduce=160,

 filters_3x3=320,

 filters_5x5_reduce=32,

 filters_5x5=128,

 filters_pool=128)

x = layers.MaxPooling2D(3, strides=2)(x)

x = inception(x,

 filters_1x1=256,

 filters_3x3_reduce=160,

 filters_3x3=320,

 filters_5x5_reduce=32,

 filters_5x5=128,

 filters_pool=128)

x = inception(x,

 filters_1x1=384,

 filters_3x3_reduce=192,

 filters_3x3=384,

 filters_5x5_reduce=48,

 filters_5x5=128,

 filters_pool=128)

x = layers.GlobalAveragePooling2D()(x)

x = layers.Dropout(0.4)(x)

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

model = Model(inputs = inp, outputs = [out, aux1, aux2])

model.compile(optimizer='adam', loss=[losses.sparse_categorical_crossentropy,

losses.sparse_categorical_crossentropy, losses.sparse_categorical_crossentropy],

loss_weights=[1, 0.3, 0.3], metrics=['accuracy'])

history = model.fit(x_train, [y_train, y_train, y_train], validation_data=(x_val, [

y_val, y_val, y_val]), batch_size=64, epochs=40)

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['dense_4_accuracy'])

axs[1].plot(history.history['val_dense_4_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'])

model.evaluate(x_test, y_test)