'''
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets('MNIST_data/', one_hot = True)
x = tf.placeholder(tf.float32, shape = [None, 784])
y_actual = tf.placeholder(tf.float32,shape = [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_indies=1))
cross_entropy = tf.reduce_mean(-tf.reduce_sum(y_actual*tf.log(y_predict)))

train_step = tf.train.GradientDescentOptimizer(0.01).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, 'float'))

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:',sess.run(accuracy, feed_dict = {x: mnist.test.images,y_actual:mnist.test.labels}))

'''

import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets('MNIST_data/', one_hot = True)
x = tf.placeholder(tf.float32, shape = [None, 784])
y_actual = tf.placeholder(tf.float32,shape = [None, 10])
#W = tf.Variable(tf.zeros([784,10]))
#b = tf.Variable(tf.zeros([10]))

##定义函数

def weight_variable(shape):
    initial = tf.truncated_normal(shape,stddev = 0.1)
    return tf.Variable(initial)

def bias_variable(shape):
    initial = tf.constant(0.1,shape = shape)
    return tf.Variable(initial)

def conv2d(x, W):
    return tf.nn.conv2d(x, W, strides = [1,1,1,1],padding = 'SAME')

def max_pool(x):
    return tf.nn.max_pool(x, ksize = [1,2,2,1],strides= [1,2,2,1], padding = 'SAME')

x_image = tf.reshape(x, [-1,28,28,1])
W_conv1 = weight_variable([5,5,1,32])
b_conv1 = bias_variable([32])
h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1)
h_pool1 = max_pool(h_conv1)


W_conv2 = weight_variable([5,5,32,64])
b_conv2 = bias_variable([64])
h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2)
h_pool2 = max_pool(h_conv2)

W_fc1 = weight_variable([7*7*64, 1024])
b_fc1 = bias_variable([1024])
h_pool2_flat = tf.reshape(h_pool2, [-1, 7*7*64])
h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, W_fc1)+b_fc1)

keep_prob = tf.placeholder('float')
h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)
W_fc2 = weight_variable([1024,10])
b_fc2 = bias_variable([10])
y_predict = tf.nn.softmax(tf.matmul(h_fc1_drop,W_fc2) + b_fc2)

cross_entropy = -tf.reduce_sum(y_actual*tf.log(y_predict))
train_step = tf.train.GradientDescentOptimizer(1e-3).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,'float'))
sess = tf.InteractiveSession()
sess.run(tf.global_variables_initializer())
for i in range(200000):
    batch = mnist.train.next_batch(50)
    if i%100==0:
        train_acc = accuracy.eval(feed_dict={x:batch[0],y_actual:batch[1],keep_prob:1.0})
        print('step %d, training accuracy %g'%(i, train_acc))
        train_step.run(feed_dict = {x:batch[0],y_actual:batch[1],keep_prob:0.5})

test_acc = accuracy.eval(feed_dict={x: mnist.test.images, y_actual: mnist.test.labels, keep_prob: 1.0})

print("test accuracy %g"%test_acc)



    
