用tensorflow跑RNN实现手写体的识别,下面上两个RNN的结构图
import tensorflow as tf
from tensorflow.contrib import rnn
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):
#第一步：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])
#第二步：reshape _X为[nsteps*batchSize,diminput]
_X = tf.reshape(_X,[-1,diminput])
#第三步：input layer -> hidden layer
_H = tf.matmul(_X,_W['hidden'])+_b['hidden']
#第四步：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)
#第五步：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
with tf.variable_scope(_name) as scope:
#scope.reuse_variables()
# forget_bias = 1.0不忘记数据
###tensorflow <1.0
# lstm_cell = tf.nn.rnn_cell.BasicLSTMCell(dimhidden,forget_bias = 1.0)
#_LSTM_O,_SLTM_S = tf.nn.rnn(lstm_cell,_Hsplit,dtype=tf.float32)
###tensorflow 1.0
lstm_cell = rnn.BasicLSTMCell(dimhidden)
_LSTM_O,_LSTM_S = rnn.static_rnn(lstm_cell,_Hsplit,dtype=tf.float32)
#第六步：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 {
'X':_X,
'H':_H,
'_Hsplit':_Hsplit,
'LSTM_O':_LSTM_O,
'LSTM_S':_LSTM_S,
'O':_O
}
print("Network Ready!")
learning_rate = 0.001
x = tf.placeholder("float",[None,nsteps,diminput])
y = tf.placeholder("float",[None,dimoutput])
myrnn = _RNN(x,weights,biases,nsteps,'basic1')
pred = myrnn['O']
cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=pred,labels=y))
optm = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost)#Adam
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))
#print(batch_xs.shape)
#print(batch_ys.shape)
#batch_ys = batch_ys.reshape((batch_size, dimoutput))
# 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.")
打印结果：
Start optimization Epoch: 000/005 cost: 1.964034666 Training accuracy: 0.438 Test accuracy: 0.410 Epoch: 001/005 cost: 1.420921708 Training accuracy: 0.562 Test accuracy: 0.475 Epoch: 002/005 cost: 1.189191381 Training accuracy: 0.750 Test accuracy: 0.482 Epoch: 003/005 cost: 1.054024348 Training accuracy: 0.875 Test accuracy: 0.597 Epoch: 004/005 cost: 0.930983863 Training accuracy: 0.562 Test accuracy: 0.606 Optimization Finished.