BPTT （Back PropagationThough Time）公式推导
由于RNN被广泛应用于序列标注问题（SequenceLabeling），所以这里选取该
问题作为实例来解释BPTT。下图是典型的RNN结构展开之后的结构，非常常见。
（图一）
则将图一具体化为下图：
（图二）
基于LSTM 进行MNIST手写数字识别实验笔记
那么将基础结构构造成时序结构如下所示：
注意，上图显示的并不是不同Block中的不同神经元，而是同一个Block中同一个神经元
在不同时刻的状态以及不同时刻之间如何传递信息。具体Block中的细节以及公式如下图所示：
代码如下：
from __future__ import print_function
import numpy as np
import tensorflow as tf
from tensorflow.contrib import rnn
from tensorflow.contrib.learn.python.learn.datasets.mnist import read_data_sets
classmnistExp:
def__init__(self,datapath,logpath):
self.mnist = read_data_sets(datapath, one_hot=True)
self.logpath = logpath
self.learning_rate = 0.001
self.iters_num = 100000
self.batch_size = 128
self.print_step = 10
self.input_size = 28
self.timesteps = 28
self.hidden_size = 128
self.class_num = 10
self.epoch_num = 30
self.test_len = 300
defInitGblVar(self):
with tf.name_scope('Inputs'):
self.x = tf.placeholder("float", [None, self.timesteps, self.input_size], name='X')
self.istate = tf.placeholder("float", [None, 2 * self.hidden_size], name='istate')
self.y = tf.placeholder("float", [None, self.class_num], name='Y')
with tf.name_scope('Weights'):
self.weights = {
'hidden': tf.Variable(tf.random_normal([self.input_size, self.hidden_size])),
'out': tf.Variable(tf.random_normal([self.hidden_size, self.class_num]))
}
tf.summary.histogram('weights_hidden',self.weights['hidden'])
tf.summary.histogram('weights_out',self.weights['out'])
with tf.name_scope('Biases'):
self.biases = {
'hidden': tf.Variable(tf.random_normal([self.hidden_size])),
'out': tf.Variable(tf.random_normal([self.class_num]))
}
tf.summary.histogram('bias_hidden',self.biases['hidden'])
tf.summary.histogram('bias_out',self.biases['out'])
self.pred = self.LSTM(self.x, self.istate, self.weights, self.biases)
with tf.name_scope('Cost'):
self.cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=self.pred, labels=self.y))
tf.summary.scalar('Cost',self.cost)
with tf.name_scope('Train'):
self.optimizer = tf.train.AdamOptimizer(learning_rate=self.learning_rate).minimize(self.cost)
with tf.name_scope('Prediction'):
self.correct_pred = tf.equal(tf.argmax(self.pred, 1), tf.argmax(self.y, 1))
self.accuracy = tf.reduce_mean(tf.cast(self.correct_pred, tf.float32))
tf.summary.scalar('Accuracy',self.accuracy)
self.init = tf.global_variables_initializer()
defLSTM(self,_batch_x, _istate, _weights, _biases):
with tf.name_scope('lstm_block'):
_batch_x = tf.transpose(_batch_x, [1, 0, 2])
_batch_x = tf.reshape(_batch_x, [-1, self.input_size])
_batch_x = tf.matmul(_batch_x, _weights['hidden']) + _biases['hidden']
_batch_x = tf.split(_batch_x, self.timesteps, 0)
lstm_block = rnn.BasicLSTMCell(self.hidden_size, forget_bias=1.0)
outputs, states = rnn.static_rnn(lstm_block, _batch_x, dtype=tf.float32)
return tf.matmul(outputs[-1], _weights['out']) + _biases['out']
defrun(self):
self.InitGblVar()
with tf.Session() as sess:
sess.run(self.init)
merged = tf.summary.merge_all()
writer = tf.summary.FileWriter(self.logpath, sess.graph)
for i in range(self.epoch_num):
step = 1
while step * self.batch_size < self.iters_num:
batch_xs, batch_ys = self.mnist.train.next_batch(self.batch_size)
# tf.summary.image('batch_xs',batch_xs,max_outputs=10)
batch_xs = batch_xs.reshape((self.batch_size, self.timesteps, self.input_size))
sess.run(self.optimizer, feed_dict={self.x: batch_xs, self.y: batch_ys,
self.istate: np.zeros((self.batch_size, 2 * self.hidden_size))})
if step % self.print_step == 0:
acc, loss, summary = sess.run([self.accuracy, self.cost, merged], feed_dict={self.x: batch_xs, self.y: batch_ys,
self.istate: np.zeros((self.batch_size, 2 * self.hidden_size))})
step += 1
test_data = self.mnist.test.images[:self.test_len].reshape((-1, self.timesteps, self.input_size))
test_label = self.mnist.test.labels[:self.test_len]
print(" [*] Epoch " + str(i+1) + ": Optimization has finished, Testing Accuracy is " ,\
sess.run(self.accuracy, feed_dict={self.x: test_data, self.y: test_label,self.istate: \
np.zeros((self.test_len, 2 * self.hidden_size))}))
summary = sess.run(merged, feed_dict={self.x: test_data, self.y: test_label})
writer.add_summary(summary, i)
if __name__ == '__main__':
datapath = "C:\\Users\\Administrator\\Desktop\\deep_lab\\mnist_data"
logpath = "/tensorboard_log/tf_Ex" #tensorboard
obj = mnistExp(datapath,logpath)
obj.run()
实验结果：
这里稍微介绍一下tensorboard，以方便直观学习。在训练的时候，会自动在log文
件夹中生成一个类似这样的文件后，
不用等训练结束也可以执行下面的语句，来观察训练情况：
训练结果：
SCALARS:
GRAPHS:
DISTRIBUTIONS:
HISTOGRAMS:
参考：
https://www.cnblogs.com/steven-yang/p/6407445.html
http://www.cnblogs.com/wacc/p/5341670.html
https://en.wikipedia.org/wiki/Matrix_calculus
https://www.cnblogs.com/zhbzz2007/p/6339346.html
http://zhwhong.ml/2017/02/24/Backpropagation-principle/
https://zhuanlan.zhihu.com/p/26892413
http://www.sohu.com/a/195366563_465975
http://colah.github.io/posts/2015-08-Understanding-LSTMs/
http://blog.csdn.net/u010754290/article/details/47167979
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