MNIST 資料 分類

分類和回歸的區別在於輸出變數的型別上。 通俗理解定量輸出是回歸，或者說是連續變數**； 定性輸出是分類，

或者說是離散變數**。如**房價這是乙個回歸任務； 把東西分成幾類, 比如貓狗豬牛，就是乙個分類任務。

1

import
tensorflow as tf
2import
numpy as np
3from tensorflow.examples.tutorials.mnist import
input_data   #首先準備資料
4 mnist =input_data.read_data_sets('
mnist_data
',one_hot=true)
#呼叫add_layer函式搭建乙個最簡單的訓練網路結構，只有輸入層和輸出層。  
5def add_layer(inputs, in_size, out_size,activation_function=none):
6     weights =tf.variable(tf.random_normal([in_size, out_size]))
7     biases = tf.variable(tf.zeros([1, out_size]) + 0.1)
8     wx_plus_b = tf.matmul(inputs, weights) +biases
9if activation_function is
none:
10         outputs =wx_plus_b
11else
:12         outputs =activation_function(wx_plus_b)
13return
outputs
14def
compute_accuracy(v_xs, v_ys):
15global
prediction
16     y_pre = sess.run(prediction, feed_dict=)
17     correct_prediction = tf.equal(tf.argmax(y_pre,1), tf.argmax(v_ys,1))
18     accuracy =tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
19     result = sess.run(accuracy, feed_dict=)
20return
result
21 #其中輸入資料是784個特徵，輸出資料是10個特徵，激勵採用softmax函式
22 xs=tf.placeholder(tf.float32,[none,784]) #
28*28
23 ys=tf.placeholder(tf.float32,[none,10])
24 prediction=add_layer(xs,784,10,activation_function=tf.nn.softmax)25#
loss
26 cross_entropy=tf.reduce_mean(-tf.reduce_sum(ys*tf.log(prediction),reduction_indices=[1]))
2728 train_step=tf.train.gradientdescentoptimizer(0.5).minimize(cross_entropy)
2930 init =tf.global_variables_initializer()
31 sess=tf.session()
32sess.run(init)
33for i in range(1000):
34     batch_xs,batch_ys=mnist.train.next_batch(100)
35     sess.run(train_step,feed_dict=)
36if i%50==0:
37print
(compute_accuracy(
38             mnist.test.images,mnist.test.labels))

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