# -*- encoding:utf-8 -*-
# quick start

import tensorflow as tf
'''
matrix1 = tf.constant([[3., 3.]])
matrix2 = tf.constant([[2.], [2.]])
product = tf.matmul(matrix1, matrix2)
sess = tf.Session()
with tf.Session() as sess:
	result = sess.run(product)
	print(result)
sess.close()
'''

'''
sess = tf.InteractiveSession()
x = tf.Variable([1.0, 2.0])
a = tf.constant([3.0, 3.0])
x.initializer.run()
sub = tf.sub(x, a)
print(sub.eval())
# sub not found
'''

'''
state = tf.Variable(0, name="counter")
one = tf.constant(1)
new_value = tf.add(state, one)
update = tf.assign(state, new_value)
init_op =  tf.initialize_all_variables()
with tf.Session() as sess:
	sess.run(init_op)
	print(sess.run(state))
	for _ in range(3):
		sess.run(update)
		print(sess.run(state))
'''

'''
input1 = tf.constant(1)
input2 = tf.constant(2)
input3 = tf.constant(2)

a = tf.add(input3, input2)
b = tf.matmul(input1, a)

with tf.Session() as sess:
	result = sess.run([a, b])
	print(result)
# ValueError: Shape must be rank 2 but is rank 0 for 'MatMul' (op: 'MatMul') with input shapes: [], [].
'''	

'''
input1 = tf.placeholder(tf.float32)
input2 = tf.placeholder(tf.float32)
output = tf.mul(input1, input2)

with tf.Session() as sess:
  print(sess.run([output], feed_dict={input1:[7.], input2:[2.]}))
# AttributeError: module 'tensorflow' has no attribute 'mul'
# 输出:
# [array([ 14.], dtype=float32)]
'''

import tensorflow.examples.tutorials.mnist.input_data as input_data
mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)

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("float", [None,10])
cross_entropy = -tf.reduce_sum(y_*tf.log(y))
train_step = tf.train.GradientDescentOptimizer(0.01).minimize(cross_entropy)
init = tf.initialize_all_variables()
sess = tf.Session()
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, "float"))
print(sess.run(accuracy, feed_dict={x: mnist.test.images, y_: mnist.test.labels}))

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_2x2(x):
  return tf.nn.max_pool(x, ksize=[1, 2, 2, 1],
                        strides=[1, 2, 2, 1], padding='SAME')
# 第一层卷积
W_conv1 = weight_variable([5, 5, 1, 32])
b_conv1 = bias_variable([32])

x_image = tf.reshape(x, [-1,28,28,1])

h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1)
h_pool1 = max_pool_2x2(h_conv1)

# 第二层卷积
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_2x2(h_conv2)

# 密集连接层

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)

# 输出层
W_fc2 = weight_variable([1024, 10])
b_fc2 = bias_variable([10])

y_conv=tf.nn.softmax(tf.matmul(h_fc1_drop, W_fc2) + b_fc2)

cross_entropy = -tf.reduce_sum(y_*tf.log(y_conv))
train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)
correct_prediction = tf.equal(tf.argmax(y_conv,1), tf.argmax(y_,1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float"))
with sess.as_default():
	sess.run(tf.initialize_all_variables())
	for i in range(20000):
	  batch = mnist.train.next_batch(50)
	  if i%100 == 0:
	    train_accuracy = accuracy.eval(feed_dict={
	        x:batch[0], y_: batch[1], keep_prob: 1.0})
	    print("step %d, training accuracy %g"%(i, train_accuracy))
	  train_step.run(feed_dict={x: batch[0], y_: batch[1], keep_prob: 0.5})

	print("test accuracy %g"%accuracy.eval(feed_dict={
	    x: mnist.test.images, y_: mnist.test.labels, keep_prob: 1.0}))