Tensorflow卷积神经网络实例进阶

import cifar10import cifar10_inputimport tensorflow as tfimport numpy as npimport time# 定义batch_size, 训练轮数max_steps, 以及下载CIFAR-10数据的默认路径max_steps = 3000batch_size = 128data_dir = 'E://tmp/cifar10_data/cifar-10-batches-bin'# 定义初始化weight的函数，定义的同时，对weight加一个L2 loss，放在集'losses'中def variable_with_weight_loss(shape, stddev, w1):  var = tf.Variable(tf.truncated_normal(shape, stddev=stddev))  if w1 is not None:    weight_loss = tf.multiply(tf.nn.l2_loss(var), w1, name='weight_loss')    tf.add_to_collection('losses', weight_loss)  return var# 使用cifar10类下载数据集，并解压、展开到其默认位置#cifar10.maybe_download_and_extract()# 在使用cifar10_input类中的distorted_inputs函数产生训练需要使用的数据。需要注意的是，返回的是已经封装好的tensor，# 且对数据进行了Data Augmentation（水平翻转、随机剪切、设置随机亮度和对比度、对数据进行标准化）images_train, labels_train = cifar10_input.distorted_inputs(data_dir=data_dir, batch_size=batch_size)# 再使用cifar10_input.inputs函数生成测试数据，这里不需要进行太多处理images_test, labels_test = cifar10_input.inputs(eval_data=True,                        data_dir=data_dir,                        batch_size=batch_size)# 创建数据的placeholderimage_holder = tf.placeholder(tf.float32, [batch_size, 24, 24, 3])label_holder = tf.placeholder(tf.int32, [batch_size])# 创建第一个卷积层weight1 = variable_with_weight_loss(shape=[5, 5, 3, 64], stddev=5e-2,                  w1=0.0)kernel1 = tf.nn.conv2d(image_holder, weight1, strides=[1, 1, 1, 1], padding='SAME')bias1 = tf.Variable(tf.constant(0.0, shape=[64]))conv1 = tf.nn.relu(tf.nn.bias_add(kernel1, bias1))pool1 = tf.nn.max_pool(conv1, ksize=[1, 3, 3, 1], strides=[1, 2, 2, 1],            padding='SAME')# LRN层对ReLU会比较有用，但不适合Sigmoid这种有固定边界并且能抑制过大值的激活函数norm1 = tf.nn.lrn(pool1, 4, bias=1.0, alpha=0.001 / 9.0, beta=0.75)# 创建第二个卷积层weight2 = variable_with_weight_loss(shape=[5, 5, 64, 64], stddev=5e-2,                  w1=0.0)kernel2 = tf.nn.conv2d(norm1, weight2, strides=[1, 1, 1, 1], padding='SAME')bias2 = tf.Variable(tf.constant(0.1, shape=[64]))conv2 = tf.nn.relu(tf.nn.bias_add(kernel2, bias2))norm2 = tf.nn.lrn(conv2, 4, bias=1.0, alpha=0.001 / 9.0, beta=0.75)pool2 = tf.nn.max_pool(norm2, ksize=[1, 3, 3, 1], strides=[1, 2, 2, 1],            padding='SAME')# 使用一个全连接层reshape = tf.reshape(pool2, [batch_size, -1])dim = reshape.get_shape()[1].valueweight3 = variable_with_weight_loss(shape=[dim, 384], stddev=0.04, w1=0.004)bias3 = tf.Variable(tf.constant(0.1, shape=[384]))local3 = tf.nn.relu(tf.matmul(reshape, weight3) + bias3)# 再使用一个全连接层，隐含节点数下降了一半，只有192个，其他的超参数保持不变weight4 = variable_with_weight_loss(shape=[384, 192], stddev=0.04, w1=0.004)bias4 = tf.Variable(tf.constant(0.1, shape=[192]))local4 = tf.nn.relu(tf.matmul(local3, weight4) + bias4)# 最后一层，将softmax放在了计算loss部分weight5 = variable_with_weight_loss(shape=[192, 10], stddev=1 / 192.0, w1=0.0)bias5 = tf.Variable(tf.constant(0.0, shape=[10]))logits = tf.add(tf.matmul(local4, weight5), bias5)# 定义lossdef loss(logits, labels):  labels = tf.cast(labels, tf.int64)  cross_entropy = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=logits, labels=labels,                                  name='cross_entropy_per_example')  cross_entropy_mean = tf.reduce_mean(cross_entropy, name='cross_entropy')  tf.add_to_collection('losses', cross_entropy_mean)  return tf.add_n(tf.get_collection('losses'), name='total_loss')# 获取最终的lossloss = loss(logits, label_holder)# 优化器train_op = tf.train.AdamOptimizer(1e-3).minimize(loss)# 使用tf.nn.in_top_k函数求输出结果中top k的准确率，默认使用top 1，也就是输出分数最高的那一类的准确率top_k_op = tf.nn.in_top_k(logits, label_holder, 1)# 使用tf.InteractiveSession创建默认的session，接着初始化全部模型参数sess = tf.InteractiveSession()tf.global_variables_initializer().run()# 启动图片数据增强线程tf.train.start_queue_runners()# 正式开始训练for step in range(max_steps):  start_time = time.time()  image_batch, label_batch = sess.run([images_train, labels_train])  _, loss_value = sess.run([train_op, loss], feed_dict={image_holder: image_batch, label_holder: label_batch})  duration = time.time() - start_time  if step % 10 == 0:    example_per_sec = batch_size / duration    sec_per_batch = float(duration)    format_str = 'step %d, loss=%.2f ,%.1f examples/sec, %.3f sec/batch'    print(format_str % (step, loss_value, example_per_sec, sec_per_batch))num_examples = 10000import mathnum_iter = int(math.ceil(num_examples / batch_size))true_count = 0total_sample_count = num_iter * batch_sizestep = 0while step < num_iter:  image_batch, label_batch = sess.run([images_test, labels_test])  predictions = sess.run([top_k_op], feed_dict={image_holder: image_batch, label_holder: label_holder})  true_count += np.sum(predictions)  step += 1precision = true_count / total_sample_countprint('precision @ 1 = %.3f'%precision)