# coding: utf8

import matplotlib.pyplot as plt
import tensorflow as tf

import input_data

# 卷积层
############################################################
mnist = input_data.read_data_sets('MNIST_data', one_hot=True)
sess = tf.InteractiveSession()


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')


x = tf.placeholder("float", shape=[None, 784])
# y_ = tf.placeholder("float", shape=[None, 10])

sess.run(tf.global_variables_initializer())

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(tf.add(conv2d(x_image, W_conv1), b_conv1))
h_pool1 = max_pool_2x2(h_conv1)

W_conv2 = weight_variable([5, 5, 32, 64])
b_conv2 = bias_variable([64])
h_conv2 = tf.nn.relu(tf.add(conv2d(h_pool1, W_conv2), b_conv2))
h_pool2 = max_pool_2x2(h_conv2)

############################################################

# 全连接层

############################################################

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.add(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])

fc = tf.add(tf.matmul(h_fc1_drop, W_fc2), b_fc2)
############################################################

# 全连接矩阵


sess.run(tf.global_variables_initializer())
fc_out = sess.run(fc, feed_dict={x: [mnist.test.images[0]], keep_prob: 1})
plt.imshow(fc_out, cmap='gray')
plt.draw()
plt.show()
