tf_demo3.py

import tensorflow as tf 

from tensorflow.examples.tutorials.mnist import input_data




mnist = input_data.read_data_sets("MNIST_data/",one_hot=True)

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_sum(y_*tf.log(y))
cross_entropy = tf.reduce_mean(-tf.reduce_sum(y_ * tf.log(y)))
                              
train_step = tf.train.GradientDescentOptimizer(0.5).minimize(cross_entropy)

init = tf.initialize_all_variables()
sess = tf.Session()
#tf.global_variables_initializer().run(session=sess)
with sess.as_default():
    
    sess.run(init)

    for i in range(1000):
        batch_xs,batch_ys = mnist.train.next_batch(100)
        train_step.run({x:batch_xs,y_:batch_ys})
        #train_step.run({x:batch_xs,y:batch_ys})
        #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(accuracy.eval({x:mnist.test.images,y_:mnist.test.labels}))