利用vgg16进行二分类-vgg16.zip

''' VGG16 网络由ImageNet数据集训练1000类得到，所有最后一层全连接层输出为1000类，为了实现对猫狗的识别我们将最后 一层输出改为2，并对最后的三层全连接层进行训练，所有在下面的VGG16_model.py文件中可以看到，只有最后三层全连 接层我们选择了trainable=True ''' import numpy as np import tensorflow as tf class vgg16: def __init__(self, imgs): self.parameters = []#类的初始化加入全局列表，将所需共享的参数加载进来 self.imgs = imgs self.convlayers() self.fc_layers() self.probs = self.fc8 #输出属于每个类别的分类 def saver(self): return tf.train.Saver() def maxpool(self, name, input_data, trainable): out = tf.nn.max_pool(input_data, [1, 2, 2, 1], [1, 2, 2, 1], padding="SAME", name=name) return out def conv(self, name, input_data, out_channel, trainable):#trainable参数变动 in_channel = input_data.get_shape()[-1] with tf.variable_scope(name): kernel = tf.get_variable("weights", [3, 3, in_channel, out_channel], dtype=tf.float32, trainable=False) biases = tf.get_variable("biases", [out_channel], dtype=tf.float32, trainable=False) 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, name=name) self.parameters += [kernel, biases]# 将卷积层定义的参数加入列表 return out def fc(self, name, input_data, out_channel, trainable=True): 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="weights", shape=[size, out_channel], dtype=tf.float32, trainable=trainable) biases = tf.get_variable(name="biases", shape=[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): # zero-mean input # conv1 self.conv1_1 = self.conv("conv1re_1", self.imgs, 64, trainable=False) self.conv1_2 = self.conv("conv1_2", self.conv1_1, 64, trainable=False) self.pool1 = self.maxpool("poolre1", self.conv1_2, trainable=False) # conv2 self.conv2_1 = self.conv("conv2_1", self.pool1, 128, trainable=False) self.conv2_2 = self.conv("convwe2_2", self.conv2_1, 128, trainable=False) self.pool2 = self.maxpool("pool2", self.conv2_2, trainable=False) # conv3 self.conv3_1 = self.conv("conv3_1", self.pool2, 256, trainable=False) self.conv3_2 = self.conv("convrwe3_2", self.conv3_1, 256, trainable=False) self.conv3_3 = self.conv("convrew3_3", self.conv3_2, 256, trainable=False) self.pool3 = self.maxpool("poolre3", self.conv3_3, trainable=False) # conv4 self.conv4_1 = self.conv("conv4_1", self.pool3, 512, trainable=False) self.conv4_2 = self.conv("convrwe4_2", self.conv4_1, 512, trainable=False) self.conv4_3 = self.conv("conv4rwe_3", self.conv4_2, 512, trainable=False) self.pool4 = self.maxpool("pool4", self.conv4_3, trainable=False) # conv5 self.conv5_1 = self.conv("conv5_1", self.pool4, 512, trainable=False) self.conv5_2 = self.conv("convrwe5_2", self.conv5_1, 512, trainable=False) self.conv5_3 = self.conv("conv5_3", self.conv5_2, 512, trainable=False) self.pool5 = self.maxpool("poorwel5", self.conv5_3, trainable=False) def fc_layers(self): self.fc6 = self.fc("fc6", self.pool5, 4096) self.fc7 = self.fc("fc7", self.fc6, 4096) self.fc8 = self.fc("fc8", self.fc7, 2)## 这是一个二分类问题所以设置参数为2 def load_weights(self, weight_file, sess):#获取权重载入VGG模型 weights = np.load(weight_file) #'vgg16_weights.npz' keys = sorted(weights.keys()) for i, k in enumerate(keys): if i not in [30, 31]: #剔除不需要载入的层 sess.run(self.parameters[i].assign(weights[k])) print("-----------all done---------------")