Tensorflow实战之用softmax Regression识别手写数字

from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)#通过input_data.read_data_sets函数生成的类会自动将MINIST数据集划分为train,validation,test
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)

import tensorflow as tf
sess = tf.InteractiveSession()#使用这个命令会将这个session注册为磨人的session
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)
tf.global_variables_initializer().run()

for i in range(1000):
    batch_xs, batch_ys = mnist.train.next_batch(100)
    train_step.run({x:batch_xs, y_:batch_ys})
    correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(y_, 1))#返回的是bool类型
    accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))#需要先用tf.cast将之前correct_prediction输出的bool类型转换成float32，再求平均
    print(accuracy.eval({x:mnist.test.images, y_:mnist.test.labels}))#print(sess.run(accuracy, feed_dict={x: mnist.test.images, y_: mnist.test.labels}))