Tensorflow学习系列（1）——MNIST手写识别

代码：

import tensorflow as tfimport tensorflow.examples.tutorials.mnist.input_data as input_datamnist = input_data.read_data_sets("MNIST_data/",one_hot=True)x =tf.placeholder(tf.float32,[None,784])y_actual = tf.placeholder(tf.float32,[None,10])W =tf.Variable(tf.zeros([784,10])) b =tf.Variable(tf.zeros([10]))    y_predict =tf.nn.softmax(tf.matmul(x,W)+b)cross_entropy =tf.reduce_mean(-tf.reduce_sum(y_actual*tf.log(y_predict),reduction_indices=1))train_step = tf.train.GradientDescentOptimizer(0.5).minimize(cross_entropy)correct_prediction = tf.equal(tf.argmax(y_predict,1),tf.argmax(y_actual,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(1000):        batch_xs,batch_ys =mnist.train.next_batch(100)        sess.run(train_step,feed_dict={x:batch_xs,y_actual:batch_ys})        if(i%100==0):          print(accuracy.eval({x:mnist.test.images,y_actual:mnist.test.labels}))

结果：