1 importcifar10, cifar10_input2 importtensorflow as tf3 importnumpy as np4 importtime5 importmath6

7 max_steps = 3000

8 batch_size = 128

9 data_dir = '/tmp/cifar10_data/cifar-10-batches-bin'

10

11

12 defvariable_with_weight_loss(shape, stddev, w1):13 '''定义初始化weight函数,使用tf.truncated_normal截断的正态分布，但加上L2的loss，相当于做了一个L2的正则化处理'''

14 var = tf.Variable(tf.truncated_normal(shape, stddev=stddev))15 '''w1:控制L2 loss的大小，tf.nn.l2_loss函数计算weight的L2 loss'''

16 if wl is notNone:17 weight_loss = tf.multiply(tf.nn.l2_loss(var), w1, name='weight_loss')18 '''tf.add_to_collection:把weight losses统一存到一个collection，名为losses'''

19 tf.add_to_collection('losses', weight_loss)20

21 returnvar22

23

24 #使用cifar10类下载数据集并解压展开到默认位置

25 cifar10.maybe_download_and_extract()26

27 '''distored_inputs函数产生训练需要使用的数据，包括特征和其对应的label,28 返回已经封装好的tensor，每次执行都会生成一个batch_size的数量的样本'''

29 images_train, labels_train = cifar10_input.distored_inputs(data_dir=data_dir,30 batch_size=batch_size)31

32 images_test, labels_test = cifar10_input.inputs(eval_data=True,33 data_dir=data_dir,34 batch_size=batch_size)35

36 image_holder = tf.placeholder(tf.float32, [batch_size, 24, 24, 3])37 label_holder =tf.placeholder(tf.int32, [batch_size])38

39 '''第一个卷积层：使用variable_with_weight_loss函数创建卷积核的参数并进行初始化。40 第一个卷积层卷积核大小：5x5 3：颜色通道 64：卷积核数目41 weight1初始化函数的标准差为0.05，不进行正则wl(weight loss)设为0'''

42 weight1 = variable_with_weight_loss(shape=[5, 5, 3, 64], stddev=5e-2, wl=0.0)43 #tf.nn.conv2d函数对输入image_holder进行卷积操作

44 kernel1 = tf.nn.conv2d(image_holder, weight1, [1, 1, 1, 1], padding='SAME')45

46 bias1 = tf.Variable(tf.constant(0.0, shape=[64]))47

48 conv1 =tf.nn.relu(tf.nn.bias_add(kernel1, bias1))49 #最大池化层尺寸为3x3,步长为2x2

50 pool1 = tf.nn.max_pool(conv1, ksize=[1, 3, 3, 1], strides=[1, 2, 2, 1])51 #LRN层模仿生物神经系统的'侧抑制'机制

52 norm1 = tf.nn.lrn(pool1, 4, bias=1.0, alpha=0.001 / 9.0, beta=0.75)53

54 '''第二个卷积层：'''

55 weight2 = variable_with_weight_loss(shape=[5, 5, 64, 64], stddev=5e-2, wl=0.0)56 kernel2 = tf.nn.conv2d(norm1, weight2, [1, 1, 1, 1], padding='SAME')57 #bias2初始化为0.1

58 bias2 = tf.Variable(tf.constant(0.1, shape=[64]))59

60 conv2 =tf.nn.relu(tf.nn.bias_add(kernel2, bias2))61 norm2 = tf.nn.lrn(conv2, 4, bias=1.0, alpha=0.001 / 9.0, beta=0.75)62 pool2 = tf.nn.max_pool(norm2, ksize=[1, 3, 3, 1], strides=[1, 2, 2, 1], padding='SAME')63

64 #全连接层

65 reshape = tf.reshape(pool2, [batch_size, -1])66 dim = reshape.get_shape()[1].value67 weight3 = variable_with_weight_loss(shape=[dim, 384], stddev=0.04, wl=0.004)68 bias3 = tf.Variable(tf.constant(0.1, shape=[384]))69 local3 = tf.nn.relu(tf.matmul(reshape, weight3) +bias3)70

71 #全连接层，隐含层节点数下降了一半

72 weight4 = variable_with_weight_loss(shape=[384, 182], stddev=0.04, wl=0.004)73 bias4 = tf.Variable(tf.constant(0.1, shape=[192]))74 local4 = tf.nn.relu(tf.matmul(local3, weight4) +bias4)75

76 '''正态分布标准差设为上一个隐含层节点数的倒数，且不计入L2的正则'''

77 weight5 = variable_with_weight_loss(shape=[192, 10], stddev=1 / 192.0, wl=0.0)78 bias5 = tf.Variable(tf.constant(0.0, shape=[10]))79 logits =tf.add(tf.matmul(local4, weight5), bias5)80

81

82 defloss(logits, labels):83 '''计算CNN的loss84 tf.nn.sparse_softmax_cross_entropy_with_logits作用：85 把softmax计算和cross_entropy_loss计算合在一起'''

86 labels =tf.cast(labels, tf.int64)87 cross_entropy =tf.nn.sparse_softmax_cross_entropy_with_logits(88 logits=logits, labels=labels, name='cross_entropy_per_example')89 #tf.reduce_mean对cross entropy计算均值

90 cross_entropy_mean =tf.reduce_mean(cross_entropy,91 name='cross_entropy')92 #tf.add_to_collection:把cross entropy的loss添加到整体losses的collection中

93 tf.add_to_collection('losses', cross_entropy_mean)94 #tf.add_n将整体losses的collection中的全部loss求和得到最终的loss

95 return tf.add_n(tf.get_collection('losses'), name='total_loss')96

97

98 #将logits节点和label_holder传入loss计算得到最终loss

99 loss =loss(logits, label_holder)100

101 train_op = tf.trian.AdamOptimizer(1e-3).minimize(loss)102 #求输出结果中top k的准确率，默认使用top 1(输出分类最高的那一类的准确率)

103 top_k_op = tf.nn.in_top_k(logits, label_holder, 1)104

105 sess =tf.InteractiveSession()106 tf.global_variables_initializer().run()107 tf.trian.start_queue_runners()108

109 for step inrange(max_steps):110 '''training:'''

111 start_time =time.time()112 #获得一个batch的训练数据

113 image_batch, label_batch =sess.run([images_train, labels_train])114 #将batch的数据传入train_op和loss的计算

115 _, loss_value =sess.run([train_op, loss],116 feed_dict={image_holder: image_batch, label_holder: label_batch})117

118 duration = time.time() -start_time119 if step % 10 ==0:120 #每秒能训练的数量

121 examples_per_sec = batch_size /duration122 #一个batch数据所花费的时间

123 sec_per_batch =float(duration)124

125 format_str = ('step %d, loss=%.2f (%.1f examples/sec; %.3f sec/batch)')126 print(format_str %(step, loss_value, examples_per_sec, sec_per_batch))127 #样本数

128 num_examples = 10000

129 num_iter = int(math.ceil(num_examples /batch_size))130 true_count =0131 total_sample_count = num_iter *batch_size132 step =0133 while step <134 labels_test>

135 image_batch, label_batch =sess.run([images_test, labels_test])136 #计算这个batch的top 1上预测正确的样本数

137 preditcions = sess.run([top_k_op], feed_dict={image_holder: image_batch,138 label_holder: label_batch139 })140 #全部测试样本中预测正确的数量

141 true_count +=np.sum(preditcions)142 step += 1

143 #准确率

144 precision = true_count /total_sample_count145 print('precision @ 1 = %.3f' % precision)

134>