tf.cast 是转换类型
from __future__ import print_function
import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt
from tensorflow.examples.tutorials.mnist import input_data
mnist=input_data.read_data_sets('/tmp/data/',one_hot=True)
learning_rate = 0.01
training_epochs=25
batch_size=100
display_step=1
x=tf.placeholder(tf.float32,[None,784])
y=tf.placeholder(tf.float32,[None,10])
W=tf.Variable(tf.zeros([784,10]))
b=tf.Variable(tf.zeros([10]))
predict=tf.nn.softmax(tf.matmul(x,W)+b)
loss=tf.reduce_mean(-tf.reduce_sum(y*tf.log(predict),reduction_indices=1))
optimizer=tf.train.GradientDescentOptimizer(learning_rate).minimize(loss)
init=tf.global_variables_initializer()
with tf.Session() as sess:
sess.run(init)
for epoch in range(training_epochs):
avg_loss=0.
total_batch=int(mnist.train.num_examples/batch_size)
for i in range(total_batch):
batch_xs, batch_ys=mnist.train.next_batch(batch_size)
_, c =sess.run([optimizer,loss],feed_dict={x:batch_xs,y:batch_ys})
avg_loss+=c/total_batch
print('Epoch:',epoch+1,'Loss:',avg_loss)
correct_pre=tf.equal(tf.argmax(predict,1),tf.argmax(y,1))
accuracy=tf.reduce_mean(tf.cast(correct_pre,tf.float32))
print('correct_pre:',correct_pre)
print('Accuracy:',accuracy.eval({x:mnist.test.images,y:mnist.test.labels}))