1 import tensorflow as tf
 2 from tensorflow.examples.tutorials.mnist import input_data
 3 
 4 ‘‘‘数据下载‘‘‘
 5 # one_hot标签：[0,0...1,0...]
 6 mnist = input_data.read_data_sets(‘Mnist_data‘,one_hot=True)
 7 
 8 ‘‘‘生成层函数‘‘‘
 9 def add_layer(input,in_size,out_size,n_layer=‘layer‘,activation_function=None):
10     layer_name = ‘layer%s‘ % n_layer
11     with tf.name_scope(layer_name):
12         with tf.name_scope(‘weights‘):
13             Weights = tf.Variable(tf.random_normal([in_size,out_size]),name=‘W‘)
14             tf.summary.histogram(layer_name+‘/weights‘,Weights)
15         with tf.name_scope(‘biases‘):
16             biases = tf.Variable(tf.zeros([1,out_size]) + 0.1)
17             tf.summary.histogram(layer_name + ‘/biases‘, biases)
18         with tf.name_scope(‘Wx_plus_b‘):
19             # [in]*[[out]*in]+[out]
20             Wx_plus_b = tf.matmul(input,Weights) + biases
21         if activation_function is None:
22             outputs = Wx_plus_b
23         else:
24             outputs = activation_function(Wx_plus_b)
25         tf.summary.histogram(layer_name + ‘/outputs‘, outputs)
26         return outputs
27 
28 ‘‘‘准确率‘‘‘
29 def compute_accuracy(v_xs,v_ys):
30     global prediction
31     y_pre = sess.run(prediction,feed_dict={xs:v_xs}) #<------------------------
32     # tf.equal()对比预测值的索引和实际label的索引是否一样，一样返回True，不一样返回False
33     correct_prediction = tf.equal(tf.argmax(y_pre,1),tf.argmax(v_ys,1))
34     # 将pred即True或False转换为1或0,并对所有的判断结果求均值
35     accuracy = tf.reduce_mean(tf.cast(correct_prediction,tf.float32))
36     result = sess.run(accuracy,feed_dict={xs:v_xs,ys:v_ys}) #<------------------------
37     return result
38 
39 
40 ‘‘‘占位符‘‘‘
41 xs = tf.placeholder(tf.float32,[None,784])
42 ys = tf.placeholder(tf.float32,[None,10])
43 
44 ‘‘‘添加层‘‘‘
45 prediction = add_layer(xs,784,10,activation_function=tf.nn.softmax)  # softmax:分类用激活函数
46 
47 ‘‘‘计算loss‘‘‘
48 # 交叉熵损失函数，参数分别为预测值pred和实际label值y，reduce_mean为求batch平均loss
49 cross_entropy = tf.reduce_mean(-tf.reduce_sum(ys*tf.log(prediction),reduction_indices=[1])) #<------------------------
50 train_step = tf.train.GradientDescentOptimizer(0.5).minimize(cross_entropy) # 梯度下降优化器
51 
52 ‘‘‘会话生成‘‘‘
53 with tf.Session() as sess:
54     sess.run(tf.global_variables_initializer())
55     for i in range(1000):
56         batch_xs,batch_ys = mnist.train.next_batch(100) # 逐个batch的去取数据
57         sess.run(train_step,feed_dict={xs:batch_xs,ys:batch_ys})
58         if i % 50 == 0:
59             print(compute_accuracy(mnist.test.images,mnist.test.labels))

1 ‘‘‘准确率‘‘‘
2 def compute_accuracy(v_xs,v_ys):
3     global prediction
4     y_pre = sess.run(prediction,feed_dict={xs:v_xs}) 
5     correct_prediction = tf.equal(tf.argmax(y_pre,1),tf.argmax(v_ys,1))
6     accuracy = tf.reduce_mean(tf.cast(correct_prediction,tf.float32))
7     result = sess.run(accuracy,feed_dict={xs:v_xs,ys:v_ys}) 
8     print(y_pre,v_ys) #<------------------------
9     return result

[[  4.28935193e-10   1.35160810e-14   1.13027321e-08 ...,   9.99981165e-01
    2.55498271e-08   8.07754896e-06]
 [  1.76165886e-05   4.70404247e-16   9.99964714e-01 ...,   1.73039527e-16
    1.82767259e-08   7.25102686e-11]
 [  2.34255353e-08   9.98970389e-01   1.14979991e-06 ...,   1.53422152e-04
    5.52863348e-04   1.47934190e-06]
 ..., 
 [  5.98009420e-11   2.45282070e-08   4.45539960e-08 ...,   6.09112252e-03
    4.90780873e-03   1.23983808e-01]
 [  5.55463521e-05   6.45160526e-02   2.27339001e-06 ...,   1.37123177e-04
    6.74951375e-01   1.96006240e-05]
 [  2.16443485e-08   7.36915445e-15   4.69437926e-11 ...,   3.93592785e-17
    3.30396951e-13   3.78093485e-15]] [[ 0.  0.  0. ...,  1.  0.  0.]
 [ 0.  0.  1. ...,  0.  0.  0.]
 [ 0.  1.  0. ...,  0.  0.  0.]
 ..., 
 [ 0.  0.  0. ...,  0.  0.  0.]
 [ 0.  0.  0. ...,  0.  0.  0.]
 [ 0.  0.  0. ...,  0.  0.  0.]]

[7 2 1 ..., 4 5 6]

1 def argmax(input, axis=None, name=None, dimension=None):

/home/hellcat/anaconda2/envs/python3_6/bin/python /home/hellcat/PycharmProjects/data_analysis/TensorFlow/classification.py
Extracting Mnist_data/train-images-idx3-ubyte.gz
Extracting Mnist_data/train-labels-idx1-ubyte.gz
Extracting Mnist_data/t10k-images-idx3-ubyte.gz
Extracting Mnist_data/t10k-labels-idx1-ubyte.gz
2017-05-19 10:46:49.230735: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn‘t compiled to use SSE4.1 instructions, but these are available on your machine and could speed up CPU computations.
2017-05-19 10:46:49.230758: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn‘t compiled to use SSE4.2 instructions, but these are available on your machine and could speed up CPU computations.
2017-05-19 10:46:49.230764: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn‘t compiled to use AVX instructions, but these are available on your machine and could speed up CPU computations.
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