TensorFlow 入门记

import numpy as np
import mnist

#将整数数组转换为one hot vector的数组
def to_onehotv(a):
    t = np.zeros((a.size,10))
    for i,n in enumerate(a):
        t[i][n] = 1.0
    return t

#分批次取出并转化mnist.train_images()与mnist.train_labels()中的数据
index = 0
def next_batch(n):
    global index
    xs = np.float32(mnist.train_images()[index:index+n]).reshape((n,28**2))/255
    ys = to_onehotv(mnist.train_labels()[index:index+n])
    index += n
    return xs,ys

test_xs = np.float32(mnist.test_images().reshape((10000,28**2)))/255
test_ys = to_onehotv(mnist.test_labels())

x = tf.placeholder(tf.float32,[None,784])
w = tf.Variable(tf.zeros([784,10]))
b = tf.Variable(tf.zeros([10]))
y = tf.nn.softmax(tf.matmul(x,w)+b)

y_label = tf.placeholder(tf.float32,[None,10])
loss = -tf.reduce_sum(y_label*tf.log(y))
train_step = tf.train.GradientDescentOptimizer(0.01).minimize(loss)

correct_prediction = tf.equal(tf.argmax(y,1),tf.argmax(y_label,1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))

init = tf.global_variables_initializer()

with tf.Session() as sess:
    sess.run(init)
    for i in range(500):
        batch_xs,batch_ys = next_batch(50)
        sess.run(train_step,feed_dict={x:batch_xs,y_label:batch_ys})
        if i%50 ==0:
            print(sess.run(accuracy,feed_dict={x:test_xs,y_label:test_ys}))
    print('training finished')
    print('final accuracy is',sess.run(accuracy,feed_dict={x:test_xs,y_label:test_ys}))

y_logits = tf.matmul(x,w)+b
y = tf.nn.softmax(y_logits)

y_label = tf.placeholder(tf.float32,[None,10])
loss = tf.nn.softmax_cross_entropy_with_logits(logits=y_logits,labels=y_label)