import numpy as np

import  tensorflow as tf

import matplotlib.pyplot as plt

from tensorflow.examples.tutorials.mnist import input_data

mnist = input_data.read_data_sets('data/', one_hot=True)

# 构造初始化参数， 方差为0.1

n_input = 784

n_output = 10

weights = {

    'wc1' : tf.Variable(tf.truncated_normal([3, 3, 1, 64], stddev=0.1)),

    'wc2' : tf.Variable(tf.truncated_normal([3, 3, 64, 128], stddev=0.1)),

    'wd1' : tf.Variable(tf.truncated_normal([7*7*128, 1024], stddev=0.1)),

    'wd2' : tf.Variable(tf.truncated_normal([1024, n_output], stddev=0.1))

}

biases = {

    'b1' : tf.Variable(tf.truncated_normal([64], stddev=0.1)),

    'b2' : tf.Variable(tf.truncated_normal([128], stddev=0.1)),

    'bd1' : tf.Variable(tf.truncated_normal([1024], stddev=0.1)),

    'bd2' : tf.Variable(tf.truncated_normal([n_output], stddev=0.1))

}

def conv_basic(_input, _w, _b, _keepratio):

    _input_r = tf.reshape(_input, shape=[-1, 28, 28, 1])

    #进行卷积操作

    _conv1 = tf.nn.conv2d(_input_r, _w['wc1'], strides=[1, 1, 1, 1], padding='SAME')

    # 使用激活函数

    _conv1 = tf.nn.relu(tf.nn.bias_add(_conv1, _b['bc1']))

    # 进行池化操作, padding='SAME', 表示维度不足就补齐

    _pool1 = tf.nn.max_pool(_conv1, ksize=[1, 2, 2, 1], stride=[1, 2, 2, 1], padding='SAME')

    #去除一部分数据

    _pool1_dr1 = tf.nn.dropout(_pool1, _keepratio)

    #第二次卷积操作

    _conv2 = tf.nn.conv2d(_pool1_dr1, _w['wc1'], strides=[1, 1, 1, 1], padding='SAME')

    # 使用激活函数

    _conv2 = tf.nn.relu(tf.nn.bias_add(_conv1, _b['bc1']))

    # 进行池化操作

    _pool2 = tf.nn.max_pool(_conv1, ksize=[1, 2, 2, 1], stride=[1, 2, 2, 1], padding='SAME')

    _pool_dr2 = tf.nn.dropout(_pool1, _keepratio)

    # 第一次全连接操作

    # 对_pool_dr2 根据wd1重新构造函数

    _densel = tf.reshape(_pool_dr2, [-1, _w['wd1'].get_shape().as_list()[0]])

    _fcl = tf.nn.relu(tf.add(tf.matmul(_densel, _w['wd1'], _b['bd1'])))

    _fc_dr1 = tf.nn.dropout(_fcl, _keepratio)

    # 第二次全连接

    _out = tf.add(tf.matmul(_fc_dr1, _w['wd2']), _b['bd2'])

    out = {'input_r': _input_r, 'conv1': _conv1, 'pool1': _pool1, 'pool1_dr1': _pool_dr1,

           'conv2': _conv2, 'pool2': _pool2, 'pool_dr2': _pool_dr2, 'dense1': _dense1,

           'fcl': _fcl, 'fc_dr1': _fc_dr1, 'out': _out

           }

    return out

x = tf.placeholder(tf.float32, [None, n_input])
y = tf.placeholder(tf.float32, [None, n_output])
keepratio = tf.placeholder(tf.float32)

# 构造cost函数

#获得预测结果

_pred =conv_basic(x, weights, biases, keepratio)['out']

# 输入预测结果与真实值构造cost 函数

cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(_pred, y))

# 优化函数使得cost最小

optm = tf.train.AdamOptimizer(learning_rate=0.001).minimize(cost)

# 计算准确率

_corr = tf.equal(tf.argmax(_pred, 1), tf.argmax(y, 1))

accr = tf.reduce_mean(tf.cast(_corr, tf.float32))

init = tf.global_variables_initializer()

# 进行训练

sess = tf.Session()

sess.run(init)

#迭代次数

training_epochs = 15

# 每次训练的样本数

batch_size      = 16

#循环打印的次数

display_step    = 1

for epoch in range(training_epochs):

    avg_cost = 0.

    #total_batch = int(mnist.train.num_examples/batch_size)

    total_batch = 10

    # Loop over all batches

    for i in range(total_batch):

        # 提取训练数据和标签

        batch_xs, batch_ys = mnist.train.next_batch(batch_size)

        #训练模型优化参数

        sess.run(optm, feed_dict={x: batch_xs, y: batch_ys, keepratio:0.7})

        # 加和损失值

        avg_cost += sess.run(cost, feed_dict={x: batch_xs, y: batch_ys, keepratio:1.})/total_batch

    # Display logs per epoch step

    if epoch % display_step == 0:

        print ("Epoch: %03d/%03d cost: %.9f" % (epoch, training_epochs, avg_cost))

        train_acc = sess.run(accr, feed_dict={x: batch_xs, y: batch_ys, keepratio:1.})

        print (" Training accuracy: %.3f" % (train_acc))

        #test_acc = sess.run(accr, feed_dict={x: testimg, y: testlabel, keepratio:1.})

        #print (" Test accuracy: %.3f" % (test_acc))

print ("OPTIMIZATION FINISHED")