LearnAlexNet.py

# -*- coding: UTF-8 -*-
# 学习AlexNet网络,并测试其训练速度

from datetime import datetime
import tensorflow as tf
import math
import time

batch_size = 32
num_batches = 100


# 展示每一层输出tensor的尺寸
def print_activations(t):
    print '{} {}'.format(t.op.name, t.get_shape().as_list())


def inference(images):
    parameters = []

    with tf.name_scope('conv1') as scope:
        kernel = tf.Variable(tf.truncated_normal([11, 11, 3, 64],
                             dtype = tf.float32,
                             stddev = 1e-1), name='weights')
        conv = tf.nn.conv2d(images, kernel, [1, 4, 4, 1], padding='SAME')
        biases = tf.Variable(tf.constant(0.0, shape=[64], dtype=tf.float32),
                            trainable=True, name='biases')
        bias = tf.nn.bias_add(conv, biases)
        conv1 = tf.nn.relu(bias, name=scope)
        print_activations(conv1)

        parameters += [kernel, biases]

    lrn1 = tf.nn.lrn(conv1, 4, bias=1.0, alpha=0.001/9, beta=0.75, name='lrn1')
    pool1 = tf.nn.max_pool(lrn1, ksize=[1, 3, 3, 1], strides=[1, 2, 2, 1],
                           padding='SAME', name='pool1')
    print_activations(pool1)

    with tf.name_scope('conv2') as scope:
        kernel = tf.Variable(tf.truncated_normal([5, 5, 64, 192],
                                                 dtype=tf.float32,
                                                 stddev=1e-1), name='weights')
        conv = tf.nn.conv2d(pool1, kernel, [1, 1, 1, 1], padding='SAME')
        biases = tf.Variable(tf.constant(0.0, shape=[192], dtype=tf.float32),
                             trainable=True, name='biases')
        bias = tf.nn.bias_add(conv, biases)
        conv2 = tf.nn.relu(bias, name=scope)

        parameters += [kernel, biases]
        print_activations(conv2)

    lrn2 = tf.nn.lrn(conv2, depth_radius=4,
                     bias=1.0, alpha=0.001/9, beta=0.75,
                     name='lrn2')
    pool2 = tf.nn.max_pool(lrn2, ksize=[1, 3, 3, 1], strides=[1, 2, 2, 1],
                           padding='VALID', name='pool2')
    print_activations(pool2)

    with tf.name_scope('conv3') as scope:
        kernel = tf.Variable(tf.truncated_normal([3, 3, 192, 384],
                                                 dtype=tf.float32,
                                                 stddev=1e-1), name='weights')
        conv = tf.nn.conv2d(pool2, kernel, [1, 1, 1, 1], padding='SAME')
        biases = tf.Variable(tf.constant(0.0, shape=[384], dtype=tf.float32),
                             trainable=True, name='biases')
        bias = tf.nn.bias_add(conv, biases)
        conv3 = tf.nn.relu(bias, name=scope)

        parameters += [kernel, biases]
        print_activations(conv3)

    with tf.name_scope('conv4') as scope:
        kernel = tf.Variable(tf.truncated_normal([3, 3, 384, 256],
                                                 dtype=tf.float32,
                                                 stddev=1e-1), name='weights')
        conv = tf.nn.conv2d(conv3, kernel, [1, 1, 1, 1], padding='SAME')
        biases = tf.Variable(tf.constant(0.0, shape=[256], dtype=tf.float32),
                             trainable=True, name='biases')
        bias = tf.nn.bias_add(conv, biases)
        conv4 = tf.nn.relu(bias, name=scope)

        parameters += [kernel, biases]
        print_activations(conv4)

    with tf.name_scope('conv5') as scope:
        kernel = tf.Variable(tf.truncated_normal([3, 3, 256, 256],
                                                 dtype=tf.float32,
                                                 stddev=1e-1), name='weights')
        conv = tf.nn.conv2d(conv4, kernel, [1, 1, 1, 1], padding='SAME')
        biases = tf.Variable(tf.constant(0.0, shape=[256], dtype=tf.float32),
                             trainable=True, name='biases')
        bias = tf.nn.bias_add(conv, biases)
        conv5 = tf.nn.relu(bias, name=scope)

        parameters += [kernel, biases]
        print_activations(conv5)

    pool5 = tf.nn.max_pool(conv5, ksize=[1, 3, 3, 1], strides=[1, 2, 2, 1],
                           padding='VALID', name='pool5')
    print_activations(pool5)

    return pool5, parameters


def time_tensorflow_run(session, target, info_string):
    num_steps_burn_in = 10
    total_duration = 0.0
    total_duration_squared = 0.0

    for i in range(num_batches + num_steps_burn_in):
        start_time = time.time()
        _ = session.run(target)
        duration = time.time() - start_time
        if i >= num_steps_burn_in:
            if not i % 10:
                print 'time: {}, step: {}, duration: {}'.format(datetime.now(),
                                                                i - num_steps_burn_in,
                                                                duration)
            total_duration += duration
            total_duration_squared += duration * duration
    mn = total_duration / num_batches
    vr = total_duration_squared / num_batches - mn * mn
    sd = math.sqrt(vr)
    print '{}: {} across {} steps, {} +/- {} sec per batch'.format(datetime.now(),
                                                                   info_string,
                                                                   num_batches,
                                                                   mn, sd)


def run_benchmark():
    with tf.Graph().as_default():
        image_size = 224
        images = tf.Variable(tf.random_normal([batch_size,
                                               image_size,
                                               image_size, 3],
                                              dtype=tf.float32,
                                              stddev=1e-1))

        pool5, parameters = inference(images)

        init = tf.global_variables_initializer()
        sess = tf.Session()
        sess.run(init)
        # or
        # with tf.Session(graph=graph) as sess:
        #   tf.global_variables_initializer().run()
        summary_writer = tf.train.SummaryWriter('/tmp/alexnet', sess.graph)

        time_tensorflow_run(sess, pool5, 'Forward')

        objective = tf.nn.l2_loss(pool5)
        grad = tf.gradients(objective, parameters)
        time_tensorflow_run(sess, grad, 'Forward-backward')

run_benchmark()