【深度学习笔记2.3】VGG

本文主要是介绍【深度学习笔记2.3】VGG，希望对大家解决编程问题提供一定的参考价值，需要的开发者们随着小编来一起学习吧！

vgg网络结构具体参见论文，网上也已经有很多资料了，这里不再赘述，这里主要记录下我在vgg训练代码和一些心得。

vgg16_1

代码示例如下(详见文献[2]vgg16_1.py)：

import numpy as np
import cv2
import tensorflow as tf
from datetime import datetime
import matplotlib.pyplot as plt
from tensorflow.examples.tutorials.mnist import input_datadatapath = '/home/***/res/MNIST_data'
mnist_data_set = input_data.read_data_sets(datapath, validation_size=0, one_hot=True)num_classes = 10
learning_rate = 1e-4
training_epoch = 50
batch_size = 16
input_image_shape = (224, 224, 1)
conv_layer_trainable = Truedef image_shape_scale(batch_xs, input_image_shape):images = np.reshape(batch_xs, [batch_xs.shape[0], 28, 28])imlist = [][imlist.append(cv2.resize(img, input_image_shape[0:2])) for img in images]images = np.array(imlist)# cv2.imwrite('scale1.jpg', images[0]*200)# cv2.imwrite('scale2.jpg', images[1]*200)# batch_xs = np.reshape(images, [batch_xs.shape[0], 227 * 227 * input_image_channel])batch_xs = np.reshape(images, [batch_xs.shape[0], input_image_shape[0], input_image_shape[1], input_image_shape[2]])return batch_xsclass vgg16:def __init__(self, imgs):self.parameters = []self.imgs = imgsself.convlayers()self.fc_layers()self.probs = self.fc8def saver(self):return tf.train.Saver()def maxpool(self, name, input_data):out = tf.nn.max_pool(input_data, [1, 2, 2, 1], [1, 2, 2, 1], padding='SAME', name=name)return outdef conv(self, name, input_data, out_channel):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=conv_layer_trainable)biases = tf.get_variable('biases', [out_channel], dtype=tf.float32, trainable=conv_layer_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, name=name)self.parameters += [kernel, biases]return outdef fc(self, name, input_data, out_channel, is_output=False):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)biases = tf.get_variable(name="biases", shape=[out_channel], dtype=tf.float32)res = tf.matmul(input_data_flat, weights)out = tf.nn.bias_add(res, biases)if is_output is False:out = tf.nn.relu(out, name=name)self.parameters += [weights, biases]return outdef convlayers(self):# zero-mean input# conv1self.conv1_1 = self.conv("conv1_1", self.imgs, 64)self.conv1_2 = self.conv("conv1_2", self.conv1_1, 64)self.pool1 = self.maxpool("pool1", self.conv1_2)# conv2self.conv2_1 = self.conv("conv2_1", self.pool1, 128)self.conv2_2 = self.conv("conv2_2", self.conv2_1, 128)self.pool2 = self.maxpool("pool2", self.conv2_2)# conv3self.conv3_1 = self.conv("conv3_1", self.pool2, 256)self.conv3_2 = self.conv("conv3_2", self.conv3_1, 256)self.conv3_3 = self.conv("conv3_3", self.conv3_2, 256)self.pool3 = self.maxpool("pool3", self.conv3_3)# conv4self.conv4_1 = self.conv("conv4_1", self.pool3, 512)self.conv4_2 = self.conv("conv4_2", self.conv4_1, 512)self.conv4_3 = self.conv("conv4_3", self.conv4_2, 512)self.pool4 = self.maxpool("pool4", self.conv4_3)# conv5self.conv5_1 = self.conv("conv5_1", self.pool4, 512)self.conv5_2 = self.conv("conv5_2", self.conv5_1, 512)self.conv5_3 = self.conv("conv5_3", self.conv5_2, 512)self.pool5 = self.maxpool("pool5", self.conv5_3)def fc_layers(self):self.fc6 = self.fc("fc1", self.pool5, 4096)# self.fc6 = tf.nn.dropout(self.fc6, 0.5)self.fc7 = self.fc("fc2", self.fc6, 4096)# self.fc7 = tf.nn.dropout(self.fc7, 0.5)self.fc8 = self.fc("fc3", self.fc7, num_classes, is_output=True)def load_weights(self, weight_file, sess):weights = np.load(weight_file)keys = sorted(weights.keys())for i, k in enumerate(keys):sess.run(self.parameters[i].assign(weights[k]))print("-----------all done---------------")if __name__ == '__main__':X = tf.placeholder(tf.float32, [None, input_image_shape[0], input_image_shape[1], input_image_shape[2]])y = tf.placeholder(tf.float32, [None, num_classes])learning_rate_holder = tf.placeholder(tf.float32)vgg = vgg16(X)prob = vgg.probswith tf.name_scope("cross_ent"):y_output = tf.nn.softmax(prob)cross_entropy = -tf.reduce_sum(y * tf.log(y_output))loss = tf.reduce_mean(cross_entropy)# Train opwith tf.name_scope("train"):optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate_holder)train_op = optimizer.minimize(loss)# Evaluation op: Accuracy of the modelwith tf.name_scope("accuracy"):correct_pred = tf.equal(tf.argmax(y_output, 1), tf.argmax(y, 1))accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))init = tf.global_variables_initializer()# init = tf.glorot_normal_initializer()  # failed, 也称之为 Xavier normal initializer. 参考文献[A]loss_buf = []accuracy_buf = []with tf.Session() as sess:sess.run(init)# Load the pretrained weights into the non-trainable layer# model.load_initial_weights(sess)total_batch = mnist_data_set.train.num_examples // batch_sizefor step in range(training_epoch):print("{} Epoch number: {}".format(datetime.now(), step + 1))tmp_loss = []for iteration in range(total_batch):batch_xs, batch_ys = mnist_data_set.train.next_batch(batch_size)batch_xs = image_shape_scale(batch_xs, input_image_shape)if step < 10:sess.run(train_op, feed_dict={X: batch_xs, y: batch_ys, learning_rate_holder: learning_rate})elif step < 20:sess.run(train_op, feed_dict={X: batch_xs, y: batch_ys, learning_rate_holder: learning_rate/10.0})elif step < 30:sess.run(train_op, feed_dict={X: batch_xs, y: batch_ys, learning_rate_holder: learning_rate/100.0})else:sess.run(train_op, feed_dict={X: batch_xs, y: batch_ys, learning_rate_holder: learning_rate/1000.0})if iteration % 50 == 0:loss_val = sess.run(loss, feed_dict={X: batch_xs, y: batch_ys})train_accuracy = sess.run(accuracy, feed_dict={X: batch_xs, y: batch_ys})print("step {}, iteration {}, loss {}, training accuracy {}".format(step, iteration, loss_val, train_accuracy))_loss_buf = []_accuracy_buf = []test_total_batch = mnist_data_set.test.num_examples // batch_sizefor iteration in range(test_total_batch):batch_xs, batch_ys = mnist_data_set.test.next_batch(batch_size)  # GPU内存不足，只好分批测试准确率batch_xs = image_shape_scale(batch_xs, input_image_shape)loss_val = sess.run(loss, feed_dict={X: batch_xs, y: batch_ys})test_accuracy = sess.run(accuracy, feed_dict={X: batch_xs, y: batch_ys})_loss_buf.append(loss_val)_accuracy_buf.append(test_accuracy)loss_val = np.array(_loss_buf).mean()test_accuracy = np.array(_accuracy_buf).mean()print("step {}, loss {}, testing accuracy {}".format(step, loss_val, test_accuracy))loss_buf.append(loss_val)accuracy_buf.append(test_accuracy)# 画出准确率曲线
accuracy_ndarray = np.array(accuracy_buf)
accuracy_size = np.arange(len(accuracy_ndarray))
plt.plot(accuracy_size, accuracy_ndarray, 'b+', label='accuracy')loss_ndarray = np.array(loss_buf)
loss_size = np.arange(len(loss_ndarray))
plt.plot(loss_size, loss_ndarray, 'r*', label='loss')plt.show()# 保存loss和测试准确率到csv文件
with open('VGGNet16.csv', 'w') as fid:for loss, acc in zip(loss_buf, accuracy_buf):strText = str(loss) + ',' + str(acc) + '\n'fid.write(strText)
fid.close()print('end')# 参考文献
# [A]：tensorflow参数初始化, https://blog.csdn.net/m0_37167788/article/details/79073070

训练测试结果打印如下：
step 1, loss 8.525571823120117, testing accuracy 0.9576321840286255
step 2, loss 3.323066234588623, testing accuracy 0.9828726053237915
… …
step 12, loss 2.2948389053344727, testing accuracy 0.9907852411270142
step 13, loss 2.297200918197632, testing accuracy 0.9905849099159241
step 14, loss nan, testing accuracy 0.09865785390138626
… …

使用dropout优化

关于dropout的理解与总结请参考文献[5].

vgg16共有3个fc-layer，这里在前两个fc-layer后面都使用dropout，即对vgg16_1.py中的fc_layers做如下改进（此部分完整代码详见文献[2]vgg16_2.py）：

    def fc_layers(self):self.fc6 = self.fc("fc1", self.pool5, 4096)self.fc6 = tf.nn.dropout(self.fc6, 0.5)self.fc7 = self.fc("fc2", self.fc6, 4096)self.fc7 = tf.nn.dropout(self.fc7, 0.5)self.fc8 = self.fc("fc3", self.fc7, num_classes, is_output=True)

其他代码不变，训练测试结果打印如下：
step 1, loss 146.34462, testing accuracy 0.1280048
step 2, loss 7.3694496, testing accuracy 0.9635417
step 3, loss 3.3861883, testing accuracy 0.9823718
… …
step 17, loss 2.1597393, testing accuracy 0.9910857
step 18, loss 2.096352, testing accuracy 0.99138623
step 19, loss nan, testing accuracy 0.098958336
… …

使用Batch Normalization优化

在vgg16_1.py的基础上做如下改动：（此部分代码详见vgg16_3.py）

# batch_norm定义同文献[3]
def batch_norm(inputs, is_training, is_conv_out=True, decay=0.999):scale = tf.Variable(tf.ones([inputs.get_shape()[-1]]))beta = tf.Variable(tf.zeros([inputs.get_shape()[-1]]))pop_mean = tf.Variable(tf.zeros([inputs.get_shape()[-1]]), trainable=False)pop_var = tf.Variable(tf.ones([inputs.get_shape()[-1]]), trainable=False)if is_training:if is_conv_out:batch_mean, batch_var = tf.nn.moments(inputs, [0, 1, 2])else:batch_mean, batch_var = tf.nn.moments(inputs, [0])train_mean = tf.assign(pop_mean, pop_mean*decay+batch_mean*(1-decay))train_var = tf.assign(pop_var, pop_var*decay+batch_var*(1-decay))with tf.control_dependencies([train_mean, train_var]):return tf.nn.batch_normalization(inputs, batch_mean, batch_var, beta, scale, 0.001)else:return tf.nn.batch_normalization(inputs, pop_mean, pop_var, beta, scale, 0.001)class vgg16:... ...def conv(self, name, input_data, out_channel):... ...with tf.variable_scope(name):... ...res = tf.nn.bias_add(conv_res, biases)res = batch_norm(res, True)out = tf.nn.relu(res, name=name)self.parameters += [kernel, biases]return outdef fc(self, name, input_data, out_channel, is_output=False):... ...with tf.variable_scope(name):... ...out = tf.nn.bias_add(res, biases)if is_output is False:out = batch_norm(out, True, False)out = tf.nn.relu(out, name=name)self.parameters += [weights, biases]return out

其他代码不变，训练测试结果打印如下：
step 1, loss 2.2608318, testing accuracy 0.9890825
step 2, loss 1.5252224, testing accuracy 0.9931891
… …
step 49, loss 1.1548673, testing accuracy 0.99348956
step 50, loss 1.1780967, testing accuracy 0.9938902
end
可以看到，相比之前不适用BN，使用BN可以有效避免梯度爆炸。