CNN.py

"""
File Name: CNN
Author: Ryan Cho
CNN using tensorflow
"""
import tensorflow as tf
import numpy as np
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets('MNIST_data', one_hot=True)

def run_cnn():
    learning_rate = 0.0001
    epochs = 10
    batch_size = 50

    x = tf.placeholder(tf.float32, [None, 784])
    x_shaped = tf.reshape(x, [-1, 28, 28, 1])
    y = tf.placeholder(tf.float32, [None, 10])

    layer1 = create_new_conv_layer(x_shaped, 1, 32, [5, 5], [2, 2], name='layer1')
    layer2 = create_new_conv_layer(layer1, 32, 64, [5, 5], [2, 2], name='layer2')
    
    flattened = tf.reshape(layer2, [-1, 7 * 7 * 64])
    wd1 = tf.Variable(tf.truncated_normal([7 * 7 * 64, 1000], stddev=0.03), name='wd1')
    bd1 = tf.Variable(tf.truncated_normal([1000], stddev=0.01), name='bd1')
    dense_layer1 = tf.matmul(flattened, wd1) + bd1
    dense_layer1 = tf.nn.relu(dense_layer1)

    wd2 = tf.Variable(tf.truncated_normal([1000, 10], stddev=0.03), name='wd2')
    bd2 = tf.Variable(tf.truncated_normal([10], stddev=0.01), name='bd2')
    dense_layer2 = tf.matmul(dense_layer1, wd2) + bd2
    y_ = tf.nn.softmax(dense_layer2)

    cross_entropy = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=dense_layer2, labels=y))

    # add an optimiser
    optimiser = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cross_entropy)

    # define an accuracy assessment operation
    correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(y_, 1))
    accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))

    # setup the initialisation operator
    init_op = tf.global_variables_initializer()

    with tf.Session() as sess:
        # initialise the variables
        sess.run(init_op)
        total_batch = int(len(mnist.train.labels) / batch_size)
        for epoch in range(epochs):
            avg_cost = 0
            for i in range(total_batch):
                batch_x, batch_y = mnist.train.next_batch(batch_size=batch_size)
                _, c = sess.run([optimiser, cross_entropy], 
                                feed_dict={x: batch_x, y: batch_y})
                avg_cost += c / total_batch
            test_acc = sess.run(accuracy, 
                           feed_dict={x: mnist.test.images, y: mnist.test.labels})
            print("Epoch:", (epoch + 1), "cost =", "{:.3f}".format(avg_cost), "test accuracy: {:.3f}".format(test_acc))

        print("\nTraining complete!")
        print(sess.run(accuracy, feed_dict={x: mnist.test.images, y: mnist.test.labels}))

def create_new_conv_layer(input_data, num_input_channels, num_filters, filter_shape, pool_shape, name):
    conv_filt_shape = [filter_shape[0], filter_shape[1], num_input_channels, num_filters]
    weights = tf.Variable(tf.truncated_normal(conv_filt_shape, stddev=0.03), name=name+'_W')
    bias = tf.Variable(tf.truncated_normal([num_filters]), name=name+'_b')
    out_layer = tf.nn.conv2d(input_data, weights, [1, 1, 1, 1], padding='SAME')
    out_layer += bias
    out_layer = tf.nn.relu(out_layer)
    ksize = [1, pool_shape[0], pool_shape[1], 1]
    strides = [1, 2, 2, 1]
    out_layer = tf.nn.max_pool(out_layer, ksize=ksize, strides=strides, padding='SAME')

    return out_layer

if __name__ == "__main__":
    run_cnn()