1、VGGNet简介

VGGNet是牛津大学计算机视觉组和Google DeepMind公司的研究员一起研发的深度卷积神经网络，VGGNet探索了卷积神经网络的深度与其性能之间的关系，反复使用33的小型卷积核和22的最大池化层来构筑卷积神经网络。到目前为止，VGGNet依然经常被用来提取图像特征。
VGGNet拥有5段卷积，每一段内有2~3个卷积层，同时每段尾部会连接一个最大池层用来缩小图片尺寸。每段内的卷积核数量一样，越靠后的段的卷积核数量越多：64-128-256-512-512。

VGGNet特点：
（1）LRN层作用不大
（2）越深的网络效果越好
（3）11的卷积也是很有效的，但是没有33的卷积好，大一些的卷积核可以学习更大的空间特性。

2、代码如下

#导入库
import time
import math
import datetime
import tensorflow as tf

#函数conv_op，用来创建卷积层并把本层的参数存入参数列表
def conv_op(input_op, name, kh, kw, n_out, dh, dw, p):
    n_in = input_op.get_shape()[-1].value

    with tf.name_scope(name) as scope:
        kernel = tf.get_variable(scope+"w",
                                 shape=[kh, kw, n_in, n_out], dtype=tf.float32,
                                 initializer=tf.contrib.layers.xavier_initializer_conv2d())

        conv = tf.nn.conv2d(input_op, kernel, (1, dh, dw, 1),
                            padding='SAME')
        bias_init_val = tf.constant(0.0, shape=[n_out], dtype=tf.float32)
        biases = tf.Variable(bias_init_val, trainable=True, name='b')
        z = tf.nn.bias_add(conv, biases)
        activation = tf.nn.relu(z, name=scope)
        p +=[kernel, biases]
        return activation

#定义全连接层的创建函数fc_op
def fc_op(input_op, name, n_out, p):
    n_in = input_op.get_shape()[-1].value

    with tf.name_scope(name) as scope:
        kernel = tf.get_variable(scope+"w",
                                 shape=[n_in, n_out],dtype=tf.float32,
                                 initializer=tf.contrib.layers.xavier_initializer())
        biases = tf.Variable(tf.constant(0.1, shape=[n_out],
                                         dtype=tf.float32), name='b')
        activation = tf.nn.relu_layer(input_op, kernel, biases, name=scope)
        p += [kernel, biases]
        return activation

#定义最大池化层的创建函数mpool_op
def mpool_op(input_op, name, kh, kw, dh, dw):
    return tf.nn.max_pool(input_op,
                          ksize=[1, kh, kw, 1],
                          strides=[1, dh, dw, 1],
                          padding='SAME',
                          name=name)

#完成卷积层、池化层、最大连接层的创建
def inference_op(input_op, keep_prob):
    p = []

    conv1_1 = conv_op(input_op, name="conv1_1", kh=3, kw=3, n_out=64, dh=1,dw=1, p=p)
    conv1_2 = conv_op(conv1_1, name="conv1_2", kh=3, kw=3, n_out=64, dh=1, dw=1, p=p)
    pool1 = mpool_op(conv1_2, name="pool1",kh=2,kw=2,dw=2,dh=2)

    conv2_1 = conv_op(pool1, name="conv2_1", kh=3, kw=3, n_out=128, dh=1, dw=1, p=p)
    conv2_2 = conv_op(conv2_1, name="conv2_2", kh=3, kw=3, n_out=128, dh=1, dw=1, p=p)
    pool2 = mpool_op(conv2_2, name="pool2", kh=2, kw=2, dw=2, dh=2)

    conv3_1 = conv_op(pool2, name="conv3_1", kh=3, kw=3, n_out=256, dh=1, dw=1, p=p)
    conv3_2 = conv_op(conv3_1, name="conv3_2", kh=3, kw=3, n_out=256, dh=1, dw=1, p=p)
    conv3_3 = conv_op(conv3_2, name="conv3_3", kh=3, kw=3, n_out=256, dh=1, dw=1, p=p)
    pool3 = mpool_op(conv3_3, name="pool3", kh=2, kw=2, dw=2, dh=2)

    conv4_1 = conv_op(pool3, name="conv4_1", kh=3, kw=3, n_out=512, dh=1, dw=1, p=p)
    conv4_2 = conv_op(conv4_1, name="conv4_2", kh=3, kw=3, n_out=512, dh=1, dw=1, p=p)
    conv4_3 = conv_op(conv4_2, name="conv4_3", kh=3, kw=3, n_out=512, dh=1, dw=1, p=p)
    pool4 = mpool_op(conv4_3, name="pool4", kh=2, kw=2, dw=2, dh=2)

    conv5_1 = conv_op(pool4, name="conv5_1", kh=3, kw=3, n_out=512, dh=1, dw=1, p=p)
    conv5_2 = conv_op(conv5_1, name="conv5_2", kh=3, kw=3, n_out=512, dh=1, dw=1, p=p)
    conv5_3 = conv_op(conv5_2, name="conv5_3", kh=3, kw=3, n_out=512, dh=1, dw=1, p=p)
    pool5 = mpool_op(conv5_3, name="pool5", kh=2, kw=2, dw=2, dh=2)

    shp = pool5.get_shape()
    flattened_shape = shp[1].value * shp[2].value * shp[3].value
    resh1 = tf.reshape(pool5, [-1, flattened_shape], name="resh1")

    fc6 = fc_op(resh1,name="fc6", n_out=4096, p=p)
    fc6_drop = tf.nn.dropout(fc6, keep_prob, name="fc6_drop")

    fc7 = fc_op(fc6_drop,name="fc7",n_out=4096,p=p)
    fc7_drop = tf.nn.dropout(fc7, keep_prob, name="fc7_drop")

    fc8 = fc_op(fc7_drop, name="fc8", n_out=1000, p=p)
    softmax = tf.nn.softmax(fc8)
    predictions = tf.argmax(softmax, 1)
    return predictions, softmax, fc8, p

#评测函数
def time_tensorflow_run(session,target, feed, 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, feed_dict=feed)
        duration = time.time() - start_time
        if i >= num_steps_burn_in:
            if not i % 10:
                print('%s: step %d, duration = %.3f' %
                      (datetime.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('%s: %s across %d steps, %.3f +/- %.3f sec / batch' %
          (datetime.datetime.now(), info_string, num_batches, mn,sd))

#定义主函数
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))
        keep_prob = tf.placeholder(tf.float32)
        predictions, softmax, fc8, p = inference_op(images, keep_prob)

        init = tf.global_variables_initializer()
        sess = tf.Session()
        sess.run(init)

        time_tensorflow_run(sess, predictions, {keep_prob:1.0}, "Forward")
        objective = tf.nn.l2_loss(fc8)
        grad = tf.gradients(objective, p)
        time_tensorflow_run(sess, grad, {keep_prob:0.5}, "Forward-backward")

batch_size=32
num_batches=100
run_benchmark()

最后

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