06_functionalCNN.py

#!/usr/bin/env python3
# -*- coding: utf-8 -*-

"""
Convoluted neural network (CNN) implementation using
functional API rather than using sequential api
on solving the MNIST dataset classification.

"""

# to supress tensorflow-gpu debug information
import os
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'

import numpy as np
from tensorflow.keras.layers import Dense, Dropout, Input
from tensorflow.keras.layers import Conv2D, MaxPooling2D, Flatten
from tensorflow.keras.models import Model
from tensorflow.keras.datasets import mnist
from tensorflow.keras.utils import to_categorical, plot_model

# load MNIST dataset
(x_train, y_train), (x_test, y_test) = mnist.load_data()

# from sparse label to categorical
num_labels = len(np.unique(y_train))
y_train = to_categorical(y_train)
y_test = to_categorical(y_test)

# reshape and normalize input images
image_size = x_train.shape[1]
x_train = np.reshape(x_train,[-1, image_size, image_size, 1])
x_test = np.reshape(x_test,[-1, image_size, image_size, 1])
x_train = x_train.astype('float32') / 255
x_test = x_test.astype('float32') / 255

# network parameters
input_shape = (image_size, image_size, 1)
batch_size = 128
kernel_size = 3
filters = 64
dropout = 0.3

# use functional API to build cnn layers
inputs = Input(shape=input_shape)
y = Conv2D(
    filters=filters,
    kernel_size=kernel_size,
    activation='relu'
)(inputs)
y = MaxPooling2D()(y)
y = Conv2D(
    filters=filters,
    kernel_size=kernel_size,
    activation='relu'
)(y)
y = MaxPooling2D()(y)
y = Conv2D(
    filters=filters,
    kernel_size=kernel_size,
    activation='relu'
)(y)
# image to vector before connecting to dense layer
y = Flatten()(y)
# dropout regularization
y = Dropout(dropout)(y)
outputs = Dense(
    num_labels,
    activation='softmax'
)(y)

# build the model by supplying inputs/outputs
model = Model(
    inputs=inputs,
    outputs=outputs
)
# network model in text
model.summary()
plot_model(model, to_file='functional-cnn.png', show_shapes=True)

# classifier loss, Adam optimizer, classifier accuracy
model.compile(
    loss='categorical_crossentropy',
    optimizer='adam',
    metrics=['accuracy']
)

# train the model with input images and labels
model.fit(
    x_train,
    y_train,
    validation_data=(
        x_test,
        y_test
    ),
    epochs=20,
    batch_size=batch_size
)

# model accuracy on test dataset
score = model.evaluate(
    x_test,
    y_test,
    batch_size=batch_size,
    verbose=0
)

print("\nTest accuracy: %.2f%%" % (100.0 * score[1]))