tensorflow1.2.0跑mnist例子

参考了博客http://blog.youkuaiyun.com/hdmjdp/article/details/64548639，有些许不同，但是我这个采用spyder跑的，有时候能跑通，有时候会报错，先记录下来，有可能是spyder本身存在问题。

lstm_cell = rnn.BasicLSTMCell(n_hidden,state_is_tuple=True)，state_is_tuple为True或者False都可以。

outputs, states = rnn.static_rnn(lstm_cell, _X, initial_state=_istate)改成

outputs, states = rnn.static_rnn(lstm_cell, _X, dtype = tf.float32)

# -*- coding: utf-8 -*-
"""
Spyder Editor

This is a temporary script file.
"""

from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)
import tensorflow as tf

from tensorflow.contrib import rnn
import numpy as np

'''
MNIST的数据是一个28*28的图像，这里RNN测试，把他看成一行行的序列（28维度（28长的sequence）*28行）
'''

# RNN学习时使用的参数
learning_rate = 0.001
training_iters = 100000
batch_size = 128
display_step = 10

# 神经网络的参数
n_input = 28  # 输入层的n
n_steps = 28  # 28长度
n_hidden = 128  # 隐含层的特征数
n_classes = 10  # 输出的数量，因为是分类问题，0~9个数字，这里一共有10个

# 构建tensorflow的输入X的placeholder
x = tf.placeholder("float", [None, n_steps, n_input])
# tensorflow里的LSTM需要两倍于n_hidden的长度的状态，一个state和一个cell
# Tensorflow LSTM cell requires 2x n_hidden length (state & cell)
istate = tf.placeholder("float", [None, 2 * n_hidden])
# 输出Y
y = tf.placeholder("float", [None, n_classes])
                     
# 随机初始化每一层的权值和偏置
weights = {
    'hidden': tf.Variable(tf.random_normal([n_input, n_hidden])),  # Hidden layer weights
    'out': tf.Variable(tf.random_normal([n_hidden, n_classes]))
}
biases = {
    'hidden': tf.Variable(tf.random_normal([n_hidden])),
    'out': tf.Variable(tf.random_normal([n_classes]))
}

#构建RNN
def RNN(_X, _istate, _weights, _biases):
     # 规整输入的数据
    _X = tf.transpose(_X, [1, 0, 2])  # permute n_steps and batch_size

    _X = tf.reshape(_X, [-1, n_input])  # (n_steps*batch_size, n_input)
    # 输入层到隐含层，第一次是直接运算
    _X = tf.matmul(_X, _weights['hidden']) + _biases['hidden']
    # 之后使用LSTM

    #lstm_cell = rnn_cell.LayerNormBasicLSTMCell(n_hidden, forget_bias=1.0)
    lstm_cell = rnn.BasicLSTMCell(n_hidden,state_is_tuple=True)
    # 28长度的sequence，所以是需要分解位28次
    _X = tf.split(_X, n_steps, 0)  # n_steps * (batch_size, n_hidden)
    #x = tf.split(x, n_steps, 0) # tf.split(value, num_or_size_splits, axis) versions > 0.12.0
    # 开始跑RNN那部分
    outputs, states = rnn.static_rnn(lstm_cell, _X, dtype = tf.float32)
    #rnn.rnn.dynamic_rnn()
    return tf.matmul(outputs[-1],_weights['out']) + biases['out']

pred = RNN(x, istate, weights, biases)               
                    
# 定义损失和优化方法，其中算是为softmax交叉熵，优化方法为Adam  
cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=pred, labels=y))  # Softmax loss  
optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost)  # Adam Optimizer  
  
# 进行模型的评估，argmax是取出取值最大的那一个的标签作为输出  
correct_pred = tf.equal(tf.argmax(pred, 1), tf.argmax(y, 1))  
accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))  
  
# 初始化  
init = tf.global_variables_initializer()

# 开始运行
with tf.Session() as sess:
    sess.run(init)
    step = 1
    # 持续迭代
    while step * batch_size < training_iters:
        # 随机抽出这一次迭代训练时用的数据
        batch_xs, batch_ys = mnist.train.next_batch(batch_size)
        # 对数据进行处理，使得其符合输入
        batch_xs = batch_xs.reshape((batch_size, n_steps, n_input))
        # 迭代
        sess.run(optimizer, feed_dict={x: batch_xs, y: batch_ys,
                                       istate: np.zeros((batch_size, 2 * n_hidden))})
        # 在特定的迭代回合进行数据的输出
        if step % display_step == 0:
            # Calculate batch accuracy
            acc = sess.run(accuracy, feed_dict={x: batch_xs, y: batch_ys,
                                                istate: np.zeros((batch_size, 2 * n_hidden))})
            # Calculate batch loss
            loss = sess.run(cost, feed_dict={x: batch_xs, y: batch_ys,
                                             istate: np.zeros((batch_size, 2 * n_hidden))})
            print ("Iter " + str(step * batch_size) + ", Minibatch Loss= " + "{:.6f}".format(loss) + \
                  ", Training Accuracy= " + "{:.5f}".format(acc))
        step += 1
    print ("Optimization Finished!")
    # 载入测试集进行测试
    test_len = 256
    test_data = mnist.test.images[:test_len].reshape((-1, n_steps, n_input))
    test_label = mnist.test.labels[:test_len]
    print ("Testing Accuracy:", sess.run(accuracy, feed_dict={x: test_data, y: test_label,
                                                             istate: np.zeros((test_len, 2 * n_hidden))}))