mnist-classifier-runtime-mem-usage.py

"""
Further developed from code available here
http://docs.seldon.io/tensorflow-deep-mnist-example.html
"""

import tensorflow as tf
from tensorflow.contrib.memory_stats import MaxBytesInUse
from tensorflow.examples.tutorials.mnist import input_data
import math


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 convert_units(size_bytes):
    if size_bytes == 0:
        return "0B"
    size_name = ("B", "KB", "MB", "GB", "TB", "PB", "EB", "ZB", "YB")
    i = int(math.floor(math.log(size_bytes, 1024)))
    p = math.pow(1024, i)
    s = round(size_bytes / p, 2)
    return "%s %s" % (s, size_name[i])


if __name__ == '__main__':

    x = tf.placeholder(tf.float32, [None, 784])

    W_conv1 = weight_variable([5, 5, 1, 32])
    b_conv1 = bias_variable([32])

    x_image = tf.reshape(x, [-1, 28, 28, 1])

    h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1)
    h_pool1 = max_pool_2x2(h_conv1)

    W_conv2 = weight_variable([5, 5, 32, 64])
    b_conv2 = bias_variable([64])

    h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2)
    h_pool2 = max_pool_2x2(h_conv2)

    W_fc1 = weight_variable([7 * 7 * 64, 1024])
    b_fc1 = bias_variable([1024])

    h_pool2_flat = tf.reshape(h_pool2, [-1, 7*7*64])
    h_fc1 = tf.nn.relu(tf.matmul(h_pool2_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([1024, 10])
    b_fc2 = bias_variable([10])

    y_conv = tf.nn.softmax(tf.matmul(h_fc1_drop, W_fc2) + b_fc2)

    y_ = tf.placeholder(tf.float32, [None, 10])

    cross_entropy = tf.reduce_mean(-tf.reduce_sum(y_ * tf.log(y_conv),
                                                  reduction_indices=[1]))

    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))

    init = tf.initialize_all_variables()

    config = tf.ConfigProto()
    # dynamically grow the memory used on the GPU
    config.gpu_options.allow_growth = True

    sess = tf.Session(config=config)
    sess.run(init)
    mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)

    for i in range(2000):
        batch_xs, batch_ys = mnist.train.next_batch(50)
        if i % 100 == 0:

            train_accuracy = accuracy.eval(session=sess, feed_dict={
                                           x: batch_xs, y_: batch_ys,
                                           keep_prob: 1.0})
            print("Step %d, training accuracy %.3f" % (i, train_accuracy))
            print("Max memory usage: ",
                  convert_units(sess.run(MaxBytesInUse())))

        sess.run(train_step, feed_dict={
                 x: batch_xs, y_: batch_ys, keep_prob: 0.5})

    print("Test accuracy %g" % accuracy.eval(session=sess, feed_dict={
          x: mnist.test.images, y_: mnist.test.labels, keep_prob: 1.0}))