VGG16模型

import tensorflow as tf class Vgg16(): def __init__(self,imgs): self.parameters = [] self.imgs = [] self.convlayers() self.fc_layers() def conv(self,name,input_data,out_channel,trainable=False): in_channel = input_data.get_shape()[-1] with tf.variable_scope(name): kernel = tf.get_variable(name,[3,3,input_data,out_channel],dtype=tf.float32,trainable=trainable) biases = tf.get_variable(name,[out_channel],dtype=tf.float32,trainable=trainable) conv_res = tf.nn.conv2d(input_data,kernel,[1,1,1,1],padding="SAME") res = tf.nn.bias_add(conv_res,biases) out = tf.nn.relu(res) self.parameters +=[kernel,biases] return out def maxpool(self,name,input_data): out = tf.nn.max_pool(input_data,[1,2,2,1],[1,2,2,1],name = name) return out def fc(self,name,input_data,out_channel,trainable): shape = input_data.get_shape().as_list() if len(shape) == 4: size = shape[-1]*shape[-2]*shape[-3] else: size = shape[1] input_data_flat = tf.reshape(input_data,[-1,size]) with tf.variable_scope(name): weights = tf.get_variable(name,[size,out_channel],dtype=tf.float32,trainable=trainable) biases = tf.get_variable(name,[out_channel],dtype=tf.float32,trainable=trainable) res = tf.matmul(input_data_flat,weights) out = tf.nn.relu(tf.nn.bias_add(res,biases)) self.parameters +=[weights,biases] return out def convlayers(self): self.conv1_1 = self.conv("conv1_1",self.imgs,64,trainable=False) self.conv1_2 = self.conv("conv1_2",self.conv1_1,64,trainable=False) self.pool1 = self.maxpool("pool1",self.conv1_2) self.conv2_1 = self.conv("conv2_1",self.pool1,128,trainable=False) self.conv2_2 = self.conv("conv2_2",self.conv2_1,128,trainable=False) self.pool2 = self.maxpool("pool2",self.conv2_2) self.conv3_1 = self.conv("conv3_1",self.pool2,256,trainable=False) self.conv3_2 = self.conv("conv3_2",self.conv3_1,256,trainable=False) self.conv3_3 = self.conv("conv3_3",self.conv3_2,256,trainable=False) self.conv4_1 = self.conv("conv4_1", self.pool3, 512, trainable=False) self.conv4_2 = self.conv("conv4_2", self.conv4_1, 512, trainable=False) self.conv4_3 = self.conv("conv4_3", self.conv4_2, 512, trainable=False) self.pool4 = self.maxpool("pool4", self.conv4_3) self.conv5_1 = self.conv("conv5_1", self.pool4, 512, trainable=False) self.conv5_2 = self.conv("conv5_2", self.conv5_1, 512, trainable=False) self.conv5_3 = self.conv("conv5_3", self.conv5_2, 512, trainable=False) self.pool5 = self.maxpool("pool5", self.conv5_3) def fc_layers(self): self.fc6 = self.fc("f1", self.pool5, 4096, trainable=False) self.fc7 = self.fc("fc2", self.fc6, 4096, trainable=False) self.fc8 = self.fc("fc3", self.fc7, 2, trainable=True) # 2表示需要分类的类别的数量