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,28*28])
y_ = tf.placeholder(tf.float32,shape=[None,10])

# W = tf.Variable(tf.zeros([28*28,10]))
# b = tf.Variable(tf.zeros([10]))
#
# y = tf.matmul(x,W) + b
#
# cross_entropy = tf.reduce_mean(
#     tf.nn.softmax_cross_entropy_with_logits(labels=y_, logits=y))
#
# train_step = tf.train.GradientDescentOptimizer(0.5).minimize(cross_entropy)
#
# with tf.Session() as sess:
#     sess.run(tf.global_variables_initializer())
#     for _ in range(1000):
#       batch = mnist.train.next_batch(100)
#       sess.run([train_step],feed_dict={x: batch[0], y_: batch[1]})
#     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(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, 2, 2, 1], padding='VALID')

# def max_pool_3x3(x):
#   return tf.nn.max_pool(x, ksize=[1, 5, 5, 1],
#                         strides=[1, 2, 2, 1], padding='SAME')


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

W_conv1 = weight_variable([4, 4, 1, 128])
b_conv1 = bias_variable([128])
h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1)
W_conv1

h_conv1
# h_pool1 = max_pool_3x3(h_conv1)
# h_pool1
h_conv1
W_conv2 = weight_variable([5, 5, 128, 64])
b_conv2 = bias_variable([64])
h_conv2 = tf.nn.relu(conv2d(h_conv1, W_conv2) + b_conv2)
h_conv2
# h_pool2 = max_pool_3x3(h_conv2)
# h_pool2

W_fc1 = weight_variable([5 * 5 * 64, 512])
W_fc1
b_fc1 = bias_variable([512])
h_pool2_flat = tf.reshape(h_conv2, [-1, 5*5*64])
h_pool2_flat
h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, W_fc1) + b_fc1)
h_fc1

keep_prob = tf.placeholder(tf.float32)
h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)
h_fc1_drop
W_fc2 = weight_variable([512, 10])
b_fc2 = bias_variable([10])

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

cross_entropy = tf.reduce_mean(
    tf.nn.softmax_cross_entropy_with_logits(labels=y_, logits=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, tf.float32))

with tf.Session() as sess:
  sess.run(tf.global_variables_initializer())
  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}))