tensorflow实现AlexNet-黄文坚版本

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我是靠谱客的博主英俊灰狼，最近开发中收集的这篇文章主要介绍tensorflow实现AlexNet-黄文坚版本，觉得挺不错的，现在分享给大家，希望可以做个参考。

概述

人比较懒，关于背景直接截图说明，以下是整个网络结构。

关于原始网络各层参数的设置，建议移步到以下作者去学习，比较详尽https://blog.csdn.net/zyqdragon/article/details/72353420

这篇主要是实现一个完整的AlexNet卷积神经网络，对其前向传播和反向传播进行速度测试。并没有加载原始数据进行训练。值得注意的是，这个代码在结构上和原始网络一样，但中间有很多细节是不一致的，比如卷积核的代大小步长等。但是它能很清楚的让你理解整个网络。不想看分解的可以先拉到文末复制所有代码运行一下，看看效果。什么都不用修改，直接运行哦。

----------------------------------------------------进入代码分解--------------------------------------

# 导入相应的系统库以及tensorflow
from datetime import datetime
import math
import time
import tensorflow as tf

# 总共测试100组batch，每组32个
batch_size = 32
num_batches = 100

# 定义一个用来显示每一卷积层或池化层的的名称以及输出尺寸
def print_activations(t):
    print(t.op.name, '', t.get_shape().as_list())

def inference(images):
    '''
    :param images: 输入图像尺寸32*224*244*3
    :return:  最后一层pool5以及parameters 
    '''
    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')
        # s = 4,"NHWC", the data is stored in the order of: [batch, height, width, channels]
        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)   # 输出conv1的名称和形状
        parameters += [kernel, biases]

        # 第一层卷积后使用局部响应归一化，提升泛化能力
        # lrn1 = tf.nn.lrn(conv1, 4, bias = 1.0, alpha = 0.001 / 9, beta = 0.75, name = 'lrn1')
        pool1 = tf.nn.max_pool(conv1, ksize = [1, 3, 3, 1], strides = [1, 2, 2, 1], padding = 'VALID', 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, 4, bias = 1.0, alpha = 0.001 / 9, beta = 0.75, name = 'lrn2')
        pool2 = tf.nn.max_pool(conv2, 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

卷积过程结束，这里使用的LRN,其他文献已经放弃使用了，因为效果不明显，但这里使用后速度减低1/3，准确率的影响也不大。可使用可不使用。

接下来是时间测试函数。

def time_tensorflow_run(session, target, info_string):
    '''
    :param session:  创建的会话
    :param target:  需要评测的运算算子
    :param info_string:  测试的名称
    :return: 
    '''
    num_steps_burn_in = 10    # 让程序预热10轮，因为前几轮的运算有大量的显存加载时间，10轮以后再统计时间
    total_duration = 0.0       # 统计总时间
    total_duration_squared = 0.0  # 以及时间均方差

    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('%s: step %d, duration = %.3f' %(datetime.now(), i - num_steps_burn_in, duration))
            total_duration += duration
            total_duration_squared += duration * duration

    mn = total_duration / num_batches    # 平均时间
    vr = total_duration_squared / num_batches - mn * mn
    sd = math.sqrt(vr)   # 时间标准差
    print('%s: %s across %d steps, %.3f +/- %.3f sec / batch' %(datetime.now(), info_string, num_batches, mn, sd))

主函数

# 主函数
def run_benchmark():
    with tf.Graph().as_default():  # 定义默认图
        # 这里不用imagenet,代替的是随机数进行训练
        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)

        # 前向传播时间计算
        time_tensorflow_run(sess, pool5, "Forward")
        # 反向传播时间计算
        objective = tf.nn.l2_loss(pool5)
        grad = tf.gradients(objective, parameters)
        time_tensorflow_run(sess, grad, "Forward-backward")

如果要运行就一句话就行了

run_benchmark()

需要注意的是，这个代码LRN被我注释掉了，如果想试试LRN的结果，在第一个卷积和第二个卷积处修改一下就好了。

我没用GPU，在CPU上的时间比黄文坚老师用GPU慢了很多，他是用了LRN前向0.26秒，我的对应是0.468秒。

结果应有三部分组成;

conv1  [32, 56, 56, 64]
conv1/pool1  [32, 27, 27, 64]
conv2  [32, 27, 27, 192]
conv2/pool2  [32, 13, 13, 192]
conv3  [32, 13, 13, 384]
conv4  [32, 13, 13, 256]
conv5  [32, 13, 13, 256]
conv5/pool5  [32, 6, 6, 256]

2018-04-24 15:27:52.440312: step 0, duration = 0.467
2018-04-24 15:27:57.211732: step 10, duration = 0.516
2018-04-24 15:28:02.045221: step 20, duration = 0.467
2018-04-24 15:28:06.703669: step 30, duration = 0.464
2018-04-24 15:28:11.335167: step 40, duration = 0.461
2018-04-24 15:28:15.966167: step 50, duration = 0.465
2018-04-24 15:28:20.588067: step 60, duration = 0.464
2018-04-24 15:28:25.376623: step 70, duration = 0.462
2018-04-24 15:28:29.995012: step 80, duration = 0.459
2018-04-24 15:28:34.616423: step 90, duration = 0.461
2018-04-24 15:28:38.799483: Forward across 100 steps, 0.468 +/- 0.018 sec / batch

2018-04-24 15:29:09.741985: step 0, duration = 2.689
2018-04-24 15:29:37.293994: step 10, duration = 2.749
2018-04-24 15:30:04.358533: step 20, duration = 2.696
2018-04-24 15:30:31.860958: step 30, duration = 2.783
2018-04-24 15:30:58.855577: step 40, duration = 2.688
2018-04-24 15:31:25.696813: step 50, duration = 2.689
2018-04-24 15:31:52.615997: step 60, duration = 2.688
2018-04-24 15:32:20.105809: step 70, duration = 2.699
2018-04-24 15:32:47.139107: step 80, duration = 2.704
2018-04-24 15:33:14.350406: step 90, duration = 2.981
2018-04-24 15:33:39.250186: Forward-backward across 100 steps, 2.722 +/- 0.066 sec / batch

最后是所有代码：

# 这部分代码主要是建立一个完整的AlexNet卷积神经网络，对其前向传播和反向传播进行速度测试。并没有加载原始数据进行训练。
# 导入相应的系统库以及tensorflow
from datetime import datetime
import math
import time
import tensorflow as tf

# 总共测试100组batch，每组32个
batch_size = 32
num_batches = 100

# 定义一个用来显示每一卷积层或池化层的的名称以及输出尺寸
def print_activations(t):
    print(t.op.name, '', t.get_shape().as_list())

def inference(images):
    '''
    :param images: 输入图像尺寸32*224*244*3
    :return:  最后一层pool5以及parameters 
    '''
    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')
        # s = 4,"NHWC", the data is stored in the order of: [batch, height, width, channels]
        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)   # 输出conv1的名称和形状
        parameters += [kernel, biases]

        # 第一层卷积后使用局部响应归一化，提升泛化能力
        # lrn1 = tf.nn.lrn(conv1, 4, bias = 1.0, alpha = 0.001 / 9, beta = 0.75, name = 'lrn1')
        pool1 = tf.nn.max_pool(conv1, ksize = [1, 3, 3, 1], strides = [1, 2, 2, 1], padding = 'VALID', 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, 4, bias = 1.0, alpha = 0.001 / 9, beta = 0.75, name = 'lrn2')
        pool2 = tf.nn.max_pool(conv2, 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

    # 卷积过程结束，这里使用的LRN,其他文献已经放弃使用了，因为效果不明显，但这里使用后速度减低1/3，准确率的影响也不大。可使用可不使用，


def time_tensorflow_run(session, target, info_string):
    '''
    :param session:  创建的会话
    :param target:  需要评测的运算算子
    :param info_string:  测试的名称
    :return: 
    '''
    num_steps_burn_in = 10    # 让程序预热10轮，因为前几轮的运算有大量的显存加载时间，10轮以后再统计时间
    total_duration = 0.0       # 统计总时间
    total_duration_squared = 0.0  # 以及时间均方差

    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('%s: step %d, duration = %.3f' %(datetime.now(), i - num_steps_burn_in, duration))
            total_duration += duration
            total_duration_squared += duration * duration

    mn = total_duration / num_batches    # 平均时间
    vr = total_duration_squared / num_batches - mn * mn
    sd = math.sqrt(vr)   # 时间标准差
    print('%s: %s across %d steps, %.3f +/- %.3f sec / batch' %(datetime.now(), info_string, num_batches, mn, sd))

# 主函数
def run_benchmark():
    with tf.Graph().as_default():  # 定义默认图
        # 这里不用imagenet,代替的是随机数进行训练
        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)

        # 前向传播时间计算
        time_tensorflow_run(sess, pool5, "Forward")
        # 反向传播时间计算
        objective = tf.nn.l2_loss(pool5)
        grad = tf.gradients(objective, parameters)
        time_tensorflow_run(sess, grad, "Forward-backward")


run_benchmark()