MNIST_Faster.py

#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Sat May  6 20:02:25 2017

@author: dhingratul
Final model , reducing the parameters by 236 times, memory footprint by 19.5
times as compared to base model
"""
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
import time
start = time.perf_counter()
mnist = input_data.read_data_sets('MNIST_data', one_hot=True)
sess = tf.InteractiveSession()
x = tf.placeholder(tf.float32, shape=[None, 784])
y_ = tf.placeholder(tf.float32, shape=[None, 10])


def weight_variable(shape):
    initial = tf.truncated_normal(shape, stddev=0.1)
    return tf.Variable(initial)


def bias_variable(shape):
    initial = tf.constant(0.1, shape=shape)
    return tf.Variable(initial)


def conv2d(x, W):
    return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='SAME')


def max_pool_2x2(x):
    return tf.nn.max_pool(x, ksize=[1, 2, 2, 1],
                          strides=[1, 2, 2, 1], padding='SAME')


def max_pool_4x4(x):
    return tf.nn.max_pool(x, ksize=[1, 4, 4, 1],
                          strides=[1, 4, 4, 1], padding='SAME')
W_conv1 = weight_variable([5, 5, 1, 2])
b_conv1 = bias_variable([2])
x_image = tf.reshape(x, [-1, 28, 28, 1])
h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1)
# Computes rectified linear: `max(features, 0)`.
h_pool1 = max_pool_4x4(h_conv1)
W_fc1 = weight_variable([7 * 7 * 2, 128])
b_fc1 = bias_variable([128])
h_pool1_flat = tf.reshape(h_pool1, [-1, 7*7*2])
h_fc1 = tf.nn.relu(tf.matmul(h_pool1_flat, W_fc1) + b_fc1)
keep_prob = tf.placeholder(tf.float32)
h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)
W_fc2 = weight_variable([128, 10])
b_fc2 = bias_variable([10])
y_conv = tf.matmul(h_fc1_drop, W_fc2) + b_fc2
cross_entropy = tf.reduce_mean(
    tf.nn.softmax_cross_entropy_with_logits(labels=y_, logits=y_conv))
train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)
correct_prediction = tf.equal(tf.argmax(y_conv, 1), tf.argmax(y_, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
sess.run(tf.initialize_all_variables())
for i in range(20000):
    batch = mnist.train.next_batch(50)
    if i % 1 == 0:
        train_accuracy = accuracy.eval(feed_dict={
                x: batch[0], y_: batch[1], keep_prob: 1.0})
        print("step %d, training accuracy %g" % (i, train_accuracy))
        train_step.run(feed_dict={x: batch[0], y_: batch[1], keep_prob: 0.5})

print("test accuracy %g" % accuracy.eval(feed_dict={
    x: mnist.test.images, y_: mnist.test.labels, keep_prob: 1.0}))
elapsed = time.perf_counter() - start
print('Elapsed %.3f seconds.' % elapsed)