# from __future__ import print_function
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data


mnist = input_data.read_data_sets('MNIST_data', one_hot=True)


def compute_accuracy(v_xs, v_ys):
    # global prediction
    y_pre = sess.run(prediction, feed_dict={xs: v_xs, keep_prob: 1})
    correct_prediction = tf.equal(tf.argmax(y_pre, 1), tf.argmax(v_ys, 1))
    accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
    result = sess.run(accuracy, feed_dict={xs: v_xs, ys: v_ys, keep_prob: 1})
    return result


def weight_variable(shape):
    '''
        # 从截断的正态分布中输出随机值，生成的值遵循具有指定平均值和标准偏差的正态分布。
        tf.truncated_normal(
            shape,                      # tensor的形状
            mean=0.0,                   # 均值
            stddev=1.0,                 # 标准差
            dtype=tf.float32,           # 输出类型
            seed=None,                  # 随机种子
            name=None                   # 操作的名称(可选)
        )
    '''
    initial = tf.truncated_normal(shape, stddev=0.1)                              # 生成符合正态分布的随机值
    return tf.Variable(initial)


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


def conv2d(x, W):
    # stride[1, x_movement, y_movement, 1]
    # stride[0]和stride[3]必须为1
    return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='SAME')


def max_pooling_2x2(x):
    return tf.nn.max_pool(x, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')


# 定义数据-start #
xs = tf.placeholder(tf.float32, [None, 784])                # 28*28
ys = tf.placeholder(tf.float32, [None, 10])
keep_prob = tf.placeholder(tf.float32)
x_image = tf.reshape(xs, [-1, 28, 28, 1])
# 定义数据-end #


# conv1 layer-start #
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_pooling_2x2(h_conv1)
# conv1 layer-end #


# conv2 layer-start #
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_pooling_2x2(h_conv2)
# conv2 layer-end #

# func1 layer-start #
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)
h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)
# func1 layer-end #


# func2 layer-start #
W_fc2 = weight_variable([1024, 10])
b_fc2 = bias_variable([10])
prediction = tf.nn.relu(tf.matmul(h_fc1_drop, W_fc2) + b_fc2)
# func2 layer-end #


# 损失函数-start #
cross_entropy = tf.reduce_mean(-tf.reduce_sum(ys*tf.log(prediction), reduction_indices=[1]))
train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)
# 损失函数-end #


sess = tf.Session()
sess.run(tf.global_variables_initializer())


for i in range(1000):
    batch_xs, batch_ys = mnist.train.next_batch(100)
    sess.run(train_step, feed_dict={xs:batch_xs, ys:batch_ys, keep_prob:0.5})
    if i%50 == 0:
        print(compute_accuracy(mnist.test.images[:1000], mnist.test.labels[:1000]))
