tensorflow_RNN

第一步： 数据载入

import tensorflow as tf
#from tensorflow.contrib import rnn
from tensorflow.examples.tutorials.mnist 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")

第二步： 初始化参数

diminput  = 28  #每个输入都是28个像素点
dimhidden = 128  #隐层128个神经元
dimoutput = nclasses  #想要分为多少类别(10)
nsteps    = 28  #有28个1x28的小步
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]))
}

第三步： 构建RNN函数

def _RNN(_X, _W, _b, _nsteps, _name):
    #1.转换输入，输入_X是还有batchSize=5的5张28*28图片，需要将输入从
    # [batchSize,nsteps,diminput]==>[nsteps,batchSize,diminput]
    _X = tf.transpose(_X, [1, 0, 2])
    #2.reshape _X为[nsteps*batchSize,diminput]
    _X = tf.reshape(_X, [-1, diminput])
    #3.input layer -> hidden layer
    _H = tf.matmul(_X, _W['hidden']) + _b['hidden']
    #4.将数据切分为‘nsteps’个切片，第i个切片为第i个batch data
    # tensoflow >0.12
    _Hsplit = tf.split(_H, _nsteps, 0)
    # tensoflow <0.12  _Hsplit = tf.split(0,_nsteps,_H)
    # 5.计算LSTM final output(_LSTM_O) 和 state(_LSTM_S)
    # _LSTM_O和_LSTM_S都有‘batchSize’个元素
    # _LSTM_O用于预测输出
    # 指定命名域
    # with tf.variable_scope(tf.get_variable_scope(),reuse=True):先False 再True
    with tf.variable_scope(_name) as scope:
        #变量共享(运行时先注释再取消注释)
        scope.reuse_variables()  
        # lstm_cell = tf.nn.rnn_cell.BasicLSTMCell(dimhidden,forget_bias = 1.0)# forget_bias = 1.0不忘记数据
        # _LSTM_O,_SLTM_S = tf.nn.rnn(lstm_cell,_Hsplit,dtype=tf.float32)
        lstm_cell = tf.contrib.rnn.BasicLSTMCell(dimhidden)
        _LSTM_O, _LSTM_S = tf.contrib.rnn.static_rnn(lstm_cell, _Hsplit, dtype=tf.float32)
    # 6.输出,需要最后一个RNN单元作为预测输出所以取_LSTM_O[-1]
    _O = tf.matmul(_LSTM_O[-1], _W['out']) + _b['out']    
    # Return! 
    return {
        'X': _X, 'H': _H, 'Hsplit': _Hsplit,
        'LSTM_O': _LSTM_O, 'LSTM_S': _LSTM_S, 'O': _O 
    }
print ("Network ready")

第四步： 构建cost函数和准确度函数

learning_rate = 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(pred,y)) 
cost   = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=y, logits=pred)) 
optm   = tf.train.GradientDescentOptimizer(learning_rate).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!")

第五步： 训练模型， 降低cost值，优化参数

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, istate: np.zeros((ntest, 2*dimhidden))}
        feeds = {x: testimgs, y: testlabels}
        test_acc = sess.run(accr, feed_dict=feeds)
        print (" Test accuracy: %.3f" % (test_acc))
print ("Optimization Finished.")