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cnn_model.py
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# encoding:utf-8
import numpy as np
import random
import tensorflow as tf
from PIL import Image
class CNN(object):
def __init__(self, image_height, image_width, max_captcha, char_set, model_save_dir):
self.image_height = image_height # 图片高度
self.image_width = image_width # 图片宽度
self.char_set = char_set # 字符集
self.char_set_len = len(char_set) # 字符集大小
self.max_captcha = max_captcha # 验证码字符长度
self.model_save_dir = model_save_dir # 模型路径
with tf.name_scope('parameters'):
self.w_alpha = 0.01
self.b_alpha = 0.1
with tf.name_scope('data'):
self.X = tf.placeholder(tf.float32, [None, self.image_height * self.image_width]) # 特征向量
self.Y = tf.placeholder(tf.float32, [None, self.max_captcha * self.char_set_len]) # 标签
self.keep_prob = tf.placeholder(tf.float32) # dropout值
@staticmethod
def convert2gray(img):
"""
图片转为灰度图
"""
if len(img.shape) > 2:
r, g, b = img[:, :, 0], img[:, :, 1], img[:, :, 2]
gray = 0.2989 * r + 0.5870 * g + 0.1140 * b
return gray
else:
return img
def text2vec(self, text):
"""
转标签为oneHot编码
"""
text_len = len(text)
if text_len > self.max_captcha:
raise ValueError('验证码最长{}个字符'.format(self.max_captcha))
vector = np.zeros(self.max_captcha * self.char_set_len)
for i, ch in enumerate(text):
idx = i * self.char_set_len + self.char_set.index(ch)
vector[idx] = 1
return vector
def alexnet_model(self):
'''CNN模型，输入为self.X,输入为y_predict'''
x = tf.reshape(self.X, shape=[-1, self.image_height, self.image_width, 1])
with tf.name_scope("conv1") as scope:
kernel1 = tf.Variable(tf.truncated_normal([11, 11, 1, 96], mean=0, stddev=0.1,
dtype=tf.float32))
conv = tf.nn.conv2d(x, kernel1, [1, 4, 4, 1], padding="SAME")
biases = tf.Variable(tf.constant(0, shape=[96], dtype=tf.float32), trainable=True)
bias = tf.nn.bias_add(conv, biases)
conv1 = tf.nn.relu(bias)
lrn1 = tf.nn.lrn(conv1, 4, bias=1, alpha=1e-3 / 9, beta=0.75)
pool1 = tf.nn.max_pool(lrn1, ksize=[1, 3, 3, 1], strides=[1, 2, 2, 1], padding="VALID")
with tf.name_scope('conv2') as scope:
kernel2 = tf.Variable(tf.truncated_normal([5, 5, 96, 256], mean=0, stddev=0.1,
dtype=tf.float32))
conv = tf.nn.conv2d(pool1, kernel2, [1, 1, 1, 1], padding="SAME")
biases = tf.Variable(tf.constant(0, shape=[256], dtype=tf.float32), trainable=True)
bias = tf.nn.bias_add(conv, biases)
conv2 = tf.nn.relu(bias)
lrn2 = tf.nn.lrn(conv2, 4, bias=1, alpha=1e-3 / 9, beta=0.75)
pool2 = tf.nn.max_pool(lrn2, ksize=[1, 3, 3, 1], strides=[1, 2, 2, 1], padding="VALID")
with tf.name_scope('conv3') as scope:
kernel3 = tf.Variable(tf.truncated_normal([3, 3, 256, 384], mean=0, stddev=0.1,
dtype=tf.float32))
conv = tf.nn.conv2d(pool2, kernel3, [1, 1, 1, 1], padding="SAME")
biases = tf.Variable(tf.constant(0, shape=[384], dtype=tf.float32), trainable=True)
bias = tf.nn.bias_add(conv, biases)
conv3 = tf.nn.relu(bias)
with tf.name_scope('conv4') as scope:
kernel4 = tf.Variable(tf.truncated_normal([3, 3, 384, 384], mean=0, stddev=0.1,
dtype=tf.float32))
conv = tf.nn.conv2d(conv3, kernel4, [1, 1, 1, 1], padding="SAME")
biases = tf.Variable(tf.constant(0, shape=[384], dtype=tf.float32), trainable=True)
bias = tf.nn.bias_add(conv, biases)
conv4 = tf.nn.relu(bias)
with tf.name_scope('conv5') as scope:
kernel5 = tf.Variable(tf.truncated_normal([3, 3, 384, 256], mean=0, stddev=0.1,
dtype=tf.float32))
conv = tf.nn.conv2d(conv4, kernel5, [1, 1, 1, 1], padding="SAME")
biases = tf.Variable(tf.constant(0, shape=[256], dtype=tf.float32), trainable=True)
bias = tf.nn.bias_add(conv, biases)
conv5 = tf.nn.relu(bias)
pool5 = tf.nn.max_pool(conv5, ksize=[1, 3, 3, 1], strides=[1, 2, 2, 1], padding="VALID")
with tf.name_scope('fc1') as scope:
pool5 = tf.reshape(pool5, (-1, 6 * 6 * 256))
weight6 = tf.Variable(tf.truncated_normal([6 * 6 * 256, 4096], stddev=0.1, dtype=tf.float32))
ful_bias1 = tf.Variable(tf.constant(0.0, dtype=tf.float32, shape=[4096]), name="ful_bias1")
ful_con1 = tf.nn.relu(tf.add(tf.matmul(pool5, weight6), ful_bias1))
with tf.name_scope('fc2') as scope:
weight7 = tf.Variable(tf.truncated_normal([4096, 4096], stddev=0.1, dtype=tf.float32))
ful_bias2 = tf.Variable(tf.constant(0.0, dtype=tf.float32, shape=[4096]), name="ful_bias2")
ful_con2 = tf.nn.relu(tf.add(tf.matmul(ful_con1, weight7), ful_bias2))
with tf.name_scope('fc3') as scope:
weight8 = tf.Variable(tf.truncated_normal([4096, 1000], stddev=0.1, dtype=tf.float32),
name="weight8")
ful_bias3 = tf.Variable(tf.constant(0.0, dtype=tf.float32, shape=[1000]), name="ful_bias3")
ful_con3 = tf.nn.relu(tf.add(tf.matmul(ful_con2, weight8), ful_bias3))
with tf.name_scope('y_prediction'):
weight9 = tf.Variable(tf.truncated_normal([1000, self.char_set_len*self.max_captcha], stddev=0.1), dtype=tf.float32)
bias9 = tf.Variable(tf.constant(0.0, shape=[self.char_set_len*self.max_captcha]), dtype=tf.float32)
y_predict = tf.matmul(ful_con3, weight9) + bias9
return y_predict
def Letnet_model(self):
'''CNN模型，输入为self.X,输入为y_predict'''
x = tf.reshape(self.X, shape=[-1, self.image_height, self.image_width, 1])
# w_c1_alpha = np.sqrt(2.0/(IMAGE_HEIGHT*IMAGE_WIDTH)) #
# w_c2_alpha = np.sqrt(2.0/(3*3*32))
# w_c3_alpha = np.sqrt(2.0/(3*3*64))
# w_d1_alpha = np.sqrt(2.0/(8*32*64))
# out_alpha = np.sqrt(2.0/1024)
# 3 conv layer
w_c1 = tf.Variable(self.w_alpha * tf.random_normal([3, 3, 1, 32])) # 从正太分布输出随机值
b_c1 = tf.Variable(self.b_alpha * tf.random_normal([32]))
conv1 = tf.nn.relu(tf.nn.bias_add(tf.nn.conv2d(x, w_c1, strides=[1, 1, 1, 1], padding='SAME'), b_c1))
conv1 = tf.nn.max_pool(conv1, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')
conv1 = tf.nn.dropout(conv1, self.keep_prob)
w_c2 = tf.Variable(self.w_alpha * tf.random_normal([3, 3, 32, 64]))
b_c2 = tf.Variable(self.b_alpha * tf.random_normal([64]))
conv2 = tf.nn.relu(tf.nn.bias_add(tf.nn.conv2d(conv1, w_c2, strides=[1, 1, 1, 1], padding='SAME'), b_c2))
conv2 = tf.nn.max_pool(conv2, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')
conv2 = tf.nn.dropout(conv2, self.keep_prob)
w_c3 = tf.Variable(self.w_alpha * tf.random_normal([3, 3, 64, 64]))
b_c3 = tf.Variable(self.b_alpha * tf.random_normal([64]))
conv3 = tf.nn.relu(tf.nn.bias_add(tf.nn.conv2d(conv2, w_c3, strides=[1, 1, 1, 1], padding='SAME'), b_c3))
conv3 = tf.nn.max_pool(conv3, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')
conv3 = tf.nn.dropout(conv3, self.keep_prob)
# Fully connected layer
w_d = tf.Variable(self.w_alpha * tf.random_normal([8 * 20 * 64, 1024]))
b_d = tf.Variable(self.b_alpha * tf.random_normal([1024]))
dense = tf.reshape(conv3, [-1, w_d.get_shape().as_list()[0]])
dense = tf.nn.relu(tf.add(tf.matmul(dense, w_d), b_d))
dense = tf.nn.dropout(dense, self.keep_prob)
w_out = tf.Variable(self.w_alpha * tf.random_normal([1024, self.char_set_len * self.max_captcha]))
b_out = tf.Variable(self.b_alpha * tf.random_normal([self.char_set_len * self.max_captcha]))
y_predict = tf.add(tf.matmul(dense, w_out), b_out)
# out = tf.nn.softmax(out)
return y_predict