对一张图片实现rnn操作，主要是通过先得到一个整体，然后进行切分，得到的最后input结果输出*_w[‘out’] + _b['out'] = 最终输出结果
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]
第二步： 初始化参数
diminput = 28
dimhidden = 128
# nclasses = 10
dimoutput = nclasses
nsteps = 28 # w参数初始化
weights = {
'hidden': tf.Variable(tf.random_normal([diminput, dimhidden])),
'out': tf.Variable(tf.random_normal([dimhidden, dimoutput]))
}
# b参数初始化
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):
# 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:
# 表示公用一份变量
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
}
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(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!")
第五步： 训练模型， 降低cost值，优化参数
# 训练次数
training_epochs = 5
# 每次训练的图片数
batch_size = 16
# 循环的展示次数
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.")