tensorflow学习（7）MNIST数据集+RNN

图一：RNN数据变换过程

batchsize为5，每次处理5*28大小的数据，共28序列。我们要的结果是最后一次输出的结果。

图二  RNN计算内部过程

每次计算的结果_LSTM 既作为一次输出_O，又作为下一次的输入之一。所以RNN具有记忆作用。

函数：

1、tf.transpose(input, [dimension_1, dimenaion_2,..,dimension_n]):

交换输入张量的不同维度。如果输入张量是二维，作用就是转置。dimension_n是整数，如果张量是三维，就是用0,1,2来表示。这个列表里的每个数对应相应的维度。如果是[2,1,0]，就把输入张量的第三维度和第一维度交换。
2、tf.split(dimension, num_split, input)：

dimension的意思就是输入张量的哪一个维度，如果是0就表示对第0维度进行切割。num_split就是切割的数量，如果是2就表示输入张量被切成2份，每一份是一个列表。

3、tf.nn.rnn_cell.BasicLSTMCell(n_hidden, forget_bias=1.0, state_is_tuple=True):

 n_hidden表示神经元的个数，forget_bias就是LSTM们的忘记系数，如果等于1，就是不会忘记任何信息。如果等于0，就都忘记。state_is_tuple默认就是True，官方建议用True，就是表示返回的状态用一个元祖表示。

这个里面存在一个状态初始化函数，就是zero_state（batch_size，dtype）两个参数。batch_size就是输入样本批次的数目，dtype就是数据类型。

关于RNN CELL 理解：转自：https://blog.csdn.net/mydear_11000/article/details/52414342

图中的context就是一个cell结构，可以看到它接受的输入有input(t)，context(t-1)，然后输出output(t)，比如像我们这个任务中，用到多层堆叠的rnn cell的话，也就是当前层的cell的output还要作为下一层cell的输入，因此可推出每个cell的输入和输出的shape是一样。

代码如下：input_data  在第一篇学习中。

#!/usr/bin/env python
# -*- coding:utf-8 -*-
#@Time  : 2018/12/14 19:11
#@Author: little bear
#@File  : tf_RNN.py
import tensorflow as tf
import input_data
import numpy as np
import matplotlib.pyplot as plt
print ("Packages imported")

mnist = input_data.read_data_sets("data/", one_hot=True)
trainimgs, trainlabels, testimgs, testlabels \
 = mnist.train.images, mnist.train.labels, mnist.test.images, mnist.test.labels
ntrain, ntest, dim, nclasses \
= trainimgs.shape[0], testimgs.shape[0], trainimgs.shape[1], trainlabels.shape[1]
print ("MNIST loaded")
diminput  = 28
dimhidden = 128
dimoutput = nclasses
nsteps    = 28
weights = {
    'hidden': tf.Variable(tf.random_normal([diminput, dimhidden])),
    'out': tf.Variable(tf.random_normal([dimhidden, dimoutput]))
}
biases = {
    'hidden': tf.Variable(tf.random_normal([dimhidden])),
    'out': tf.Variable(tf.random_normal([dimoutput]))
}


def _RNN(_X, _W, _b, _nsteps, _name):
    # 1. Permute input from [batchsize, nsteps, diminput]  [16*28*28]
    #   => [nsteps, batchsize, diminput]


    _X = tf.transpose(_X, [1, 0, 2])
    # 2. Reshape input to [nsteps*batchsize, diminput]
    _X = tf.reshape(_X, [-1, diminput])
    # 3. Input layer => Hidden layer
    _H = tf.matmul(_X, _W['hidden']) + _b['hidden']
    # 4. Splite data to 'nsteps' chunks. An i-th chunck indicates i-th batch data
    _Hs = tf.split(_H,_nsteps,0)  #  [28,16,28]  nsteps,batchsize,diminput

    # 5. Get LSTM's final output (_LSTM_O) and state (_LSTM_S)
    #    Both _LSTM_O and _LSTM_S consist of 'batchsize' elements
    #    Only _LSTM_O will be used to predict the output.
    # with tf.variable_scope(_name,reuse=tf.AUTO_REUSE) as scope:
    #     scope.reuse_variables()
    lstm_cell =tf.nn.rnn_cell.BasicLSTMCell(num_units=dimhidden,forget_bias=1.0)
    _LSTM_O, _LSTM_S = tf.nn.dynamic_rnn(lstm_cell,_Hs, dtype=tf.float32)
        # [ batchsize,steps,input]
    # 6. Output
    _O = tf.matmul(_LSTM_O[-1], _W['out']) + _b['out']
    # Return!
    return {
        'X': _X, 'H': _H, 'Hsplit': _Hs,
        'LSTM_O': _LSTM_O, 'LSTM_S': _LSTM_S, 'O': _O
    }


print("Network ready")


lr = 0.001
x      = tf.placeholder("float", [None, nsteps, diminput])
y      = tf.placeholder("float", [None, dimoutput])
myrnn  = _RNN(x, weights, biases, nsteps, 'basic')
pred   = myrnn['O']
cost   = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=pred, labels=y))
optm   = tf.train.GradientDescentOptimizer(learning_rate=lr).minimize(cost) # Adam Optimizer
accr   = tf.reduce_mean(tf.cast(tf.equal(tf.argmax(pred,1), tf.argmax(y,1)), tf.float32))
init   = tf.global_variables_initializer()
print ("Network Ready!")

training_epochs = 5
batch_size      = 16
display_step    = 1
sess = tf.Session()
sess.run(init)
print ("Start optimization")
for epoch in range(training_epochs):
    avg_cost = 0.
    #total_batch = int(mnist.train.num_examples/batch_size)
    total_batch = 100
    # Loop over all batches
    for i in range(total_batch):
        batch_xs, batch_ys = mnist.train.next_batch(batch_size)
        batch_xs = batch_xs.reshape((batch_size, nsteps, diminput))
        # Fit training using batch data
        feeds = {x: batch_xs, y: batch_ys}
        sess.run(optm, feed_dict=feeds)
        # Compute average loss
        avg_cost += sess.run(cost, feed_dict=feeds)/total_batch
    # Display logs per epoch step
    if epoch % display_step == 0:
        print ("Epoch: %03d/%03d cost: %.9f" % (epoch, training_epochs, avg_cost))
        feeds = {x: batch_xs, y: batch_ys}
        train_acc = sess.run(accr, feed_dict=feeds)
        print (" Training accuracy: %.3f" % (train_acc))
        testimgs = testimgs.reshape((ntest, nsteps, diminput))
        feeds = {x: testimgs, y: testlabels}
        test_acc = sess.run(accr, feed_dict=feeds)
        print (" Test accuracy: %.3f" % (test_acc))
print ("Optimization Finished.")