Tensorflow卷积神经网络实例进阶

 import cifar10 import cifar10_input import tensorflow as tf import numpy as np import 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 = 3000 batch_size = 128 data_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placeholder image_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].value weight3 = 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loss def 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loss loss = 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 = 10000 import math num_iter = int(math.ceil(num_examples / batch_size)) true_count = 0 total_sample_count = num_iter * batch_size step = 0 while step