神经网络之VGGNet模型的实现（Python+TensorFlow）

代码实现的是VGG-16的结构。

# -*- coding:utf-8 -*-## VGG-16 Net modelimport numpy as npimport tensorflow as tfclass Vgg16:    def __init__(self, images, name):        self.name = name        self.input = images        self.output = self.vgg16(self.input)        list_vars = tf.trainable_variable()        self.vars = [var for var in list_vars]            def get_conv_weight(self, shape, name):        return tf.Variable(tf.truncated_normal(shape, stddev=0.1), name=name)    def get_bias(self, shape, name):        return tf.Variable(tf.constant(0.0, shape=shape), name=name)    def get_fc_weight(self, shape, name):        return tf.Variable(tf.truncated_normal(shape, stddev=0.1), name=name)        def conv_layer(self, x, ks, out_units, name):        with tf.variable_scope(name):            in_units = x.get_shape().as_list()[-1]            filt = self.get_conv_weight([ks,ks,in_units,out_units], name='weight')            bias = self.get_bias([out_units], name='bias')            out  = tf.nn.relu(tf.nn.bias_add(tf.nn.conv2d(x, filt, [1,1,1,1], padding='SAME'), bias))            return out    def fc_layer(self, x, out_units, name):        with tf.variable_scope(name):            in_units = np.prod(x.get_shape().as_list()[1:])            x_flat = tf.reshape(x, [-1, in_units])            weight = self.get_fc_weight([in_units,out_units], name='weight')            biases = self.get_bias([out_units], name='bias')            out    = tf.nn.bias_add(tf.matmul(x_flat, weight), biases)            return out    def avg_pool(self, x, name):        return tf.nn.avg_pool(x, ksize=[1,2,2,1], strides=[1,2,2,1], padding='SAME', name=name)    def max_pool(self, x, name):        return tf.nn.max_pool(x, ksize=[1,2,2,1], strides=[1,2,2,1], padding='SAME', name=name)    def vgg16(self, x, keep_prob):        conv1_1 = self.conv_layer(x, ks=3, out_units=64, 'conv1_1')        conv1_2 = self.conv_layer(conv1_1, ks=3, out_units=64, 'conv1_2')        pool1   = self.max_pool(conv1_2, 'pool1')        conv2_1 = self.conv_layer(pool1, ks=3, out_units=128, 'conv2_1')        conv2_2 = self.conv_layer(conv2_1, ks=3, out_units=128, 'conv2_2')        pool2   = self.max_pool(conv2_2, 'pool2')        conv3_1 = self.conv_layer(pool2, ks=3, out_units=256, 'conv3_1')        conv3_2 = self.conv_layer(conv3_1, ks=3, out_units=256, 'conv3_2')        conv3_3 = self.conv_layer(conv3_2, ks=3, out_units=256, 'conv3_3')        pool3   = self.max_pool(conv3_3, 'pool3')        conv4_1 = self.conv_layer(pool3, ks=3, out_units=512, 'conv4_1')        conv4_2 = self.conv_layer(conv4_1, ks=3, out_units=512, 'conv4_2')        conv4_3 = self.conv_layer(conv4_2, ks=3, out_units=512, 'conv4_3')        pool4   = self.max_pool(conv4_3, 'pool4')        conv5_1 = self.conv_layer(pool4, ks=3, out_units=512, 'conv5_1')        conv5_2 = self.conv_layer(conv5_1, ks=3, out_units=512, 'conv5_2')        conv5_3 = self.conv_layer(conv5_2, ks=3, out_units=512, 'conv5_3')        pool5   = self.max_pool(conv5_3, 'pool5')        fc6      = self.fc_layer(pool5, out_units=4096, 'fc6')        fc6_relu = tf.nn.relu(fc6)        fc6_drop = tf.nn.dropout(fc6_relu, keep_prob, name='fc6_drop')        fc7      = self.fc_layer(fc6_drop, 'fc7')        fc7_relu = tf.nn.relu(fc7)        fc7_drop = tf.nn.dropout(fc7_relu, keep_prob, name='fc7_drop')        fc8 = self.fc_layer(fc7_drop, 'fc8')        out = tf.nn.softmax(fc8, name='out')        return out