import tensorflow as tf
import os
import matplotlib.image as implt
import numpy as np
batch_size = 100
H = 60
W = 120
class Sample:
def __init__(self):
# 定义一个数据集,用于存放训练样本和标签
self.datasets = []
"""
获取训练样本和标签
"""
for filename in os.listdir('D:\code'): # 遍历每一个数据
# 获取训练样本,os.path.join('D:\code',filename)是将两个字符串拼接在一起形成一个新的字符串作为路径
x = implt.imread(os.path.join('D:\code', filename))
y = filename.split('.')[0] # 将文件名作为标签,对整个文件名进行分割获取真正的标签
y = self.to_onehot(y) # 将标签转化为onehot形式
self.datasets.append([x, y]) # 将样本和标签打包添加进数据集
def to_onehot(self, y):
z = np.zeros([4, 10]) # 创建一个4行10列的全零数组构造出onehot形式
# 遍历标签中的每一个字符
for i in range(4):
index = int(y[i]) # 将字符转化为整形
z[i][index] = 1 # 将对应位置1
return z # 返回onehot形式的标签
def get_batch(self, n):
xs = [] # 用于存放拿到的随机样本
ys = [] # 用于存放拿到的随机样本的标签
for i in range(n):
index = np.random.randint(0, len(self.datasets)) # 生成用于随机抽取样本的索引
xs.append(self.datasets[index][0])
ys.append(self.datasets[index][1])
return xs, ys
class EncoderNet:
def __init__(self):
self.conv1_w = tf.Variable(tf.truncated_normal(shape=[3, 3, 3, 16], stddev=0.1))
self.conv1_b = tf.Variable(tf.zeros(dtype=tf.float32, shape=[16]))
self.conv2_w = tf.Variable(tf.truncated_normal(shape=[3, 3, 16, 32], stddev=0.1))
self.conv2_b = tf.Variable(tf.zeros(dtype=tf.float32, shape=[32]))
self.w1 = tf.Variable(tf.truncated_normal(shape=[15*30*32,100]))
def forward(self, x):
self.conv1 = tf.nn.relu(tf.nn.conv2d(x, self.conv1_w, [1, 1, 1, 1], padding="SAME") + self.conv1_b)
self.pool1 = tf.nn.max_pool(self.conv1, [1, 2, 2, 1], [1, 2, 2, 1], padding="SAME")
# 30,60
self.conv2 = tf.nn.relu(tf.nn.conv2d(self.pool1, self.conv2_w, [1, 1, 1, 1], padding="SAME") + self.conv2_b)
self.pool2 = tf.nn.max_pool(self.conv2, [1, 2, 2, 1], [1, 2, 2, 1], padding="SAME")
# 15,30
self.flat = tf.reshape(self.pool2,shape=[-1,15*30*32])
self.output = tf.matmul(self.flat,self.w1)
return self.output
class DecoderNet:
def __init__(self):
self.cell = tf.contrib.rnn.BasicLSTMCell(128)
self.init_state = self.cell.zero_state(100, dtype=tf.float32)
self.w1 = tf.Variable(tf.truncated_normal(shape=[128,10]))
def forward(self,x):
y = tf.expand_dims(x, axis=1)
y = tf.tile(y, [1, 4, 1])
y, _ = tf.nn.dynamic_rnn(self.cell, y, initial_state=self.init_state, time_major=False)
y = tf.reshape(y,(-1,128))
y = tf.nn.softmax(tf.matmul(y, self.w1))
y = tf.reshape(y,(-1,4,10))
return y
class Net:
def __init__(self):
self.x = tf.placeholder(dtype=tf.float32, shape=[batch_size, H,W, 3])
self.y = tf.placeholder(dtype=tf.float32, shape=[batch_size, 4, 10])
self.encoderNet = EncoderNet()
self.decoderNet = DecoderNet()
def forward(self):
y = self.encoderNet.forward(self.x)
self.output = self.decoderNet.forward(y)
def backward(self):
self.loss = tf.reduce_mean((self.output - self.y) ** 2)
self.opt = tf.train.AdamOptimizer().minimize(self.loss)
if __name__ == '__main__':
sample = Sample()
net = Net()
net.forward()
net.backward()
init = tf.global_variables_initializer()
with tf.Session() as sess:
sess.run(init)
for i in range(120):
x, y = sample.get_batch(batch_size)
loss, _ = sess.run([net.loss, net.opt], feed_dict={net.x: x, net.y: y})
print(loss)
CNN to LSTM编解码结构识别验证码
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转载自blog.csdn.net/weixin_38241876/article/details/85465736
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