# coding: utf-8
# http://blog.csdn.net/nanjifengzi/article/details/72629593
# In[1]:
# yu Mnist-14peihe

import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data

# In[2]:

# 载入数据集
mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)

# 输入图片是28*28
n_inputs = 28  # 输入一行，一行有28个数据
max_time = 28  # 一共28行
lstm_size = 500  # 隐层单元
n_classes = 10  # 10个分类
batch_size = 50  # 每批次50个样本
n_batch = mnist.train.num_examples // batch_size  # 计算一共有多少个批次
print(n_batch)
# 这里的none表示第一个维度可以是任意的长度
x = tf.placeholder(tf.float32, [None, 784])
# 正确的标签
y = tf.placeholder(tf.float32, [None, 10])

# 初始化权值
weights = tf.Variable(tf.truncated_normal([lstm_size, n_classes], stddev=0.1))
# 初始化偏置值
biases = tf.Variable(tf.constant(0.1, shape=[n_classes]))


# 定义RNN网络
def RNN(X, weights, biases):
    # inputs=[batch_size, max_time, n_inputs]
    inputs = tf.reshape(X, [-1, max_time, n_inputs])
    # 定义LSTM基本CELL
    lstm_cell = tf.contrib.rnn.core_rnn_cell.BasicLSTMCell(lstm_size)

    #       final_state[0]是cell state
    #       final_state[1]是hidden_state  最后500个隐藏单元的输出结果，
    #       output 与time.major 如果是false 返回是 batch_size=50次, maxtime=28的长度，cell.output_size:500个隐藏单元
    #                                 但是time是 0到27，则cell.output_size 则表示对应时间的500个输出结果。
    #       finale_state返回：
    #           state【】  包括 cell_state     中间的celll，
    #                           hidden_state   最后的结果输出。
    #           batch_size  50次
    #           state_size: 隐藏单元个数 500

    #    隐藏层的单元个数， （batch次数，个数，数据数），格式
    outputs, final_state = tf.nn.dynamic_rnn(lstm_cell, inputs, dtype=tf.float32)
    print(outputs[2])
    results = tf.nn.softmax(tf.matmul(final_state[1], weights) + biases)
    return results


# 计算RNN的返回结果
prediction = RNN(x, weights, biases)
# 损失函数
cross_entropy = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=prediction, labels=y))
# 使用AdamOptimizer进行优化
train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)
# 结果存放在一个布尔型列表中
correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(prediction, 1))  # argmax返回一维张量中最大的值所在的位置
# 求准确率
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))  # 把correct_prediction变为float32类型
# 初始化
init = tf.global_variables_initializer()
saver = tf.train.Saver()
with tf.Session() as sess:
    sess.run(init)
    for epoch in range(6):
        for batch in range(n_batch):
            batch_xs, batch_ys = mnist.train.next_batch(batch_size)
            sess.run(train_step, feed_dict={x: batch_xs, y: batch_ys})

        acc = sess.run(accuracy, feed_dict={x: mnist.test.images, y: mnist.test.labels})
        print ("Iter " + str(epoch) + ", Testing Accuracy= " + str(acc))
    saver.save(sess, 'net/my_net.ckpt')


# AttributeError: 'module' object has no attribute 'rnn'     #python2.7-tensorflow-V0.80
# AttributeError: 'module' object has no attribute 'core_rnn_cell'   #python3.4-tensorflow-V1.30