tensorflow数字识别

这里讲述一个数字识别图像的AI

参考网址：http://www.tensorfly.cn/tfdoc/tutorials/mnist_beginners.html

pip install minst

y = XW + b

import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data

#下载相应的文件放在本地目录
mnist = input_data.read_data_sets("D:/work_space/eclipse/Pb/MNIST_data", one_hot=True)
print("---mnist info-----")
print(mnist.train.images.shape, mnist.train.labels.shape)
print(mnist.test.images.shape, mnist.test.labels.shape)
print(mnist.validation.images.shape, mnist.validation.labels.shape)

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_ = tf.placeholder(tf.float32, [None, 10])

cross_entropy = tf.reduce_mean(-tf.reduce_sum(y_ * tf.log(y), reduction_indices=[1]))

train_step = tf.train.GradientDescentOptimizer(0.5).minimize(cross_entropy)
init = tf.global_variables_initializer()

with tf.Session() as sess:
    sess.run(init)
    for i in range(1000):
        batch_xs, batch_ys = mnist.train.next_batch(100)
        sess.run(train_step, feed_dict = {x: batch_xs, y_: batch_ys})

    correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(y_, 1))
    accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
    print(sess.run(accuracy, feed_dict={x: mnist.test.images, y_: mnist.test.labels}))

---mnist info-----

(55000, 784) (55000, 10)
(10000, 784) (10000, 10)
(5000, 784) (5000, 10)
0.9188