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# -*- coding=UTF-8 -*-
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import sys
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import os
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import random
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import cv2
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import math
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import time
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import numpy as np
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import tensorflow as tf
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import linecache
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import string
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import skimage
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import imageio
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# 输入数据
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import input_data
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mnist = input_data.read_data_sets("/tmp/data/", one_hot=True)
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# 定义网络超参数
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learning_rate = 0.001
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training_iters = 200000
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batch_size = 64
|
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display_step = 20
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# 定义网络参数
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n_input = 784 # 输入的维度
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n_classes = 10 # 标签的维度
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dropout = 0.8 # Dropout 的概率
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# 占位符输入
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x = tf.placeholder(tf.types.float32, [None, n_input])
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y = tf.placeholder(tf.types.float32, [None, n_classes])
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keep_prob = tf.placeholder(tf.types.float32)
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# 卷积操作
|
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def conv2d(name, l_input, w, b):
|
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return tf.nn.relu(tf.nn.bias_add( \
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tf.nn.conv2d(l_input, w, strides=[1, 1, 1, 1], padding='SAME'),b) \
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, name=name)
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# 最大下采样操作
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def max_pool(name, l_input, k):
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return tf.nn.max_pool(l_input, ksize=[1, k, k, 1], \
|
||||
strides=[1, k, k, 1], padding='SAME', name=name)
|
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# 归一化操作
|
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def norm(name, l_input, lsize=4):
|
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return tf.nn.lrn(l_input, lsize, bias=1.0, alpha=0.001 / 9.0, beta=0.75, name=name)
|
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# 定义整个网络
|
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def alex_net(_X, _weights, _biases, _dropout):
|
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_X = tf.reshape(_X, shape=[-1, 28, 28, 1]) # 向量转为矩阵
|
||||
# 卷积层
|
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conv1 = conv2d('conv1', _X, _weights['wc1'], _biases['bc1'])
|
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# 下采样层
|
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pool1 = max_pool('pool1', conv1, k=2)
|
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# 归一化层
|
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norm1 = norm('norm1', pool1, lsize=4)
|
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# Dropout
|
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norm1 = tf.nn.dropout(norm1, _dropout)
|
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|
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# 卷积
|
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conv2 = conv2d('conv2', norm1, _weights['wc2'], _biases['bc2'])
|
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# 下采样
|
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pool2 = max_pool('pool2', conv2, k=2)
|
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# 归一化
|
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norm2 = norm('norm2', pool2, lsize=4)
|
||||
# Dropout
|
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norm2 = tf.nn.dropout(norm2, _dropout)
|
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|
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# 卷积
|
||||
conv3 = conv2d('conv3', norm2, _weights['wc3'], _biases['bc3'])
|
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# 下采样
|
||||
pool3 = max_pool('pool3', conv3, k=2)
|
||||
# 归一化
|
||||
norm3 = norm('norm3', pool3, lsize=4)
|
||||
# Dropout
|
||||
norm3 = tf.nn.dropout(norm3, _dropout)
|
||||
|
||||
# 全连接层，先把特征图转为向量
|
||||
dense1 = tf.reshape(norm3, [-1, _weights['wd1'].get_shape().as_list()[0]])
|
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dense1 = tf.nn.relu(tf.matmul(dense1, _weights['wd1']) + _biases['bd1'], name='fc1')
|
||||
# 全连接层
|
||||
dense2 = tf.nn.relu(tf.matmul(dense1, _weights['wd2']) + _biases['bd2'], name='fc2')
|
||||
# Relu activation
|
||||
# 网络输出层
|
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out = tf.matmul(dense2, _weights['out']) + _biases['out']
|
||||
return out
|
||||
|
||||
# 存储所有的网络参数
|
||||
weights = {
|
||||
'wc1': tf.Variable(tf.random_normal([3, 3, 1, 64])),
|
||||
'wc2': tf.Variable(tf.random_normal([3, 3, 64, 128])),
|
||||
'wc3': tf.Variable(tf.random_normal([3, 3, 128, 256])),
|
||||
'wd1': tf.Variable(tf.random_normal([4*4*256, 1024])),
|
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'wd2': tf.Variable(tf.random_normal([1024, 1024])),
|
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'out': tf.Variable(tf.random_normal([1024, 10]))
|
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}
|
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biases = {
|
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'bc1': tf.Variable(tf.random_normal([64])),
|
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'bc2': tf.Variable(tf.random_normal([128])),
|
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'bc3': tf.Variable(tf.random_normal([256])),
|
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'bd1': tf.Variable(tf.random_normal([1024])),
|
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'bd2': tf.Variable(tf.random_normal([1024])),
|
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'out': tf.Variable(tf.random_normal([n_classes]))
|
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}
|
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# 构建模型
|
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pred = alex_net(x, weights, biases, keep_prob)
|
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# 定义损失函数和学习步骤
|
||||
cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(pred, y))
|
||||
optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost)
|
||||
# 测试网络
|
||||
correct_pred = tf.equal(tf.argmax(pred,1), tf.argmax(y,1))
|
||||
accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))
|
||||
# 初始化所有的共享变量
|
||||
init = tf.initialize_all_variables()
|
||||
# 开启一个训练
|
||||
with tf.Session() as sess:
|
||||
sess.run(init)
|
||||
step = 1
|
||||
# Keep training until reach max iterations
|
||||
while step * batch_size < training_iters:
|
||||
batch_xs, batch_ys = mnist.train.next_batch(batch_size)
|
||||
# 获取批数据
|
||||
sess.run(optimizer, feed_dict={x: batch_xs, y: batch_ys, keep_prob: dropout})
|
||||
if step % display_step == 0:
|
||||
# 计算精度
|
||||
acc = sess.run(accuracy, feed_dict={x: batch_xs, y: batch_ys, keep_prob: 1.})
|
||||
# 计算损失值
|
||||
loss = sess.run(cost, feed_dict={x: batch_xs, y: batch_ys, keep_prob: 1.})
|
||||
print "Iter " + str(step*batch_size) + ", Minibatch Loss= " + "{:.6f}".format(loss) + ", Training Accuracy= " + "{:.5f}".format(acc)
|
||||
step += 1
|
||||
print "Optimization Finished!"
|
||||
# 计算测试精度
|
||||
print "Testing Accuracy:", sess.run(accuracy, feed_dict={x: mnist.test.images[:256], y: mnist.test.labels[:256], keep_prob: 1.})
|
||||
@ -0,0 +1,24 @@
|
||||
|
||||
nexts = [0]*100
|
||||
x = '<img class="ui image bqppsearch lazy" data-original="'
|
||||
|
||||
def KMPinit():
|
||||
i,j,m = 0,-1,len(x)
|
||||
nexts[0]=-1
|
||||
while i<m:
|
||||
while j!=-1 and x[i]!=x[j]: j=nexts[j]
|
||||
nexts[i+1]=j+1
|
||||
i,j=i+1,j+1
|
||||
|
||||
def findWord(y):
|
||||
i,j,ans,n,m=0,0,[],len(y),len(x)
|
||||
while i<n:
|
||||
while j!=-1 and y[i]!=x[j]: j=nexts[j]
|
||||
i,j=i+1,j+1
|
||||
if j==m:
|
||||
to = i+1
|
||||
while y[to:to+7]!='\" title': to=to+1
|
||||
if y[to-3:to]=='jpg' and y[i]=='h' :ans.append(y[i:to])
|
||||
j=nexts[j]
|
||||
return ans
|
||||
|
||||
@ -0,0 +1,68 @@
|
||||
import tensorflow as tf
|
||||
from tensorflow.examples.tutorials.mnist import input_data
|
||||
|
||||
def weight_variable(shape):
|
||||
initial = tf.truncated_normal(shape,stddev=0.1)
|
||||
return tf.Variable(initial)
|
||||
|
||||
def bias_variable(shape):
|
||||
initial = tf.constant(0,1,shape=shape)
|
||||
return tf.Variable(initial)
|
||||
|
||||
def conv2d(x,W):
|
||||
return tf.nn.conv2d(x,W,strides=[1,1,1,1],padding='SAME')
|
||||
|
||||
def max_pool_2x2(x):
|
||||
return tf.nn.max_pool(x,ksize=[1,2,2,1],strides=[1,2,2,1],padding='SAME')
|
||||
|
||||
|
||||
# 预处理
|
||||
mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)
|
||||
xs = tf.placeholder(tf.float32,[None,784])
|
||||
ys = tf.placeholder(tf.float32,[None,10])
|
||||
keep_prob = tf.placeholder(tf.float32)
|
||||
|
||||
# 第一层卷积
|
||||
W_conv1 = weight_variable([5,5,1,32])
|
||||
b_conv1 = bias_variable([32])
|
||||
x_image = tf.reshape(xs,[-1,28,28,1])
|
||||
h_conv1 = tf.nn.relu(conv2d(x_image,W_conv1)+b_conv1)
|
||||
h_pool1 = max_pool_2x2(h_conv1)
|
||||
|
||||
# 第二层卷积
|
||||
W_conv2 = weight_variable([5,5,32,64])
|
||||
b_conv2 = bias_variable([64])
|
||||
h_conv2 = tf.nn.relu(conv2d(h_pool1,W_conv2)+b_conv2)
|
||||
h_pool2 = max_pool_2x2(h_conv2)
|
||||
|
||||
# 第一层全连接层
|
||||
W_fc1 = weight_variable([7*7*64,1024])
|
||||
b_fc1 = bias_variable([1024])
|
||||
h_pool2_flat = tf.reshape(h_pool2,[-1,7*7*64])
|
||||
h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat,W_fc1)+b_fc1)
|
||||
h_fc1_drop = tf.nn.dropout(h_fc1,keep_prob)
|
||||
|
||||
# 第二层全连接层
|
||||
W_fc2 = weight_variable([1024,10])
|
||||
b_fc2 = bias_variable([10])
|
||||
y_conv = tf.nn.softmax(tf.matmul(h_fc1_drop,W_fc2)+b_fc2)
|
||||
|
||||
# 训练模型
|
||||
cross_entropy = -tf.reduce_sum(ys*tf.log(y_conv))
|
||||
train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)
|
||||
|
||||
# 概率计算
|
||||
correct_prediction = tf.equal(tf.argmax(y_conv,1),tf.argmax(ys,1))
|
||||
accuracy = tf.reduce_mean(tf.cast(correct_prediction,tf.float32))
|
||||
|
||||
init = tf.initialize_all_variables()
|
||||
sess = tf.Session()
|
||||
sess.run(init)
|
||||
|
||||
for i in range(2001):
|
||||
batch = mnist.train.next_batch(50)
|
||||
sess.run(train_step,feed_dict={xs:batch[0],ys:batch[1],keep_prob:0.5})
|
||||
|
||||
if i%100==0:
|
||||
tests = mnist.test.next_batch(200)
|
||||
print(sess.run(accuracy,feed_dict={xs:tests[0],ys:tests[1],keep_prob:1.0}))
|
||||
@ -0,0 +1,26 @@
|
||||
import tensorflow as tf
|
||||
from tensorflow.examples.tutorials.mnist import input_data
|
||||
|
||||
mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)
|
||||
|
||||
xs = tf.placeholder(tf.float32,[None,784])
|
||||
ys = tf.placeholder(tf.float32,[None,10])
|
||||
|
||||
Weight = tf.Variable(tf.zeros([784,10]))
|
||||
biases = tf.Variable(tf.zeros([10]))
|
||||
y = tf.nn.softmax(tf.matmul(xs,Weight)+biases)
|
||||
|
||||
loss = -tf.reduce_sum(ys*tf.log(y))
|
||||
train = tf.train.GradientDescentOptimizer(0.01).minimize(loss)
|
||||
|
||||
init = tf.initialize_all_variables()
|
||||
sess = tf.Session()
|
||||
sess.run(init)
|
||||
|
||||
for step in range(10000):
|
||||
batch = mnist.train.next_batch(100)
|
||||
sess.run(train,feed_dict={xs:batch[0],ys:batch[1]})
|
||||
if step%50==0:
|
||||
correct_prediction = tf.equal(tf.arg_max(ys,1),tf.arg_max(y,1))
|
||||
accuracy = tf.reduce_mean(tf.cast(correct_prediction,tf.float32))
|
||||
print(sess.run(accuracy,feed_dict={xs:mnist.test.images,ys:mnist.test.labels}))
|
||||
@ -0,0 +1,26 @@
|
||||
import tensorflow as tf
|
||||
from tensorflow.examples.tutorials.mnist import input_data
|
||||
|
||||
mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)
|
||||
|
||||
xs = tf.placeholder(tf.float32,[None,784])
|
||||
ys = tf.placeholder(tf.float32,[None,10])
|
||||
|
||||
Weight = tf.Variable(tf.zeros([784,10]))
|
||||
biases = tf.Variable(tf.zeros([10]))
|
||||
y = tf.nn.softmax(tf.matmul(xs,Weight)+biases)
|
||||
|
||||
loss = -tf.reduce_sum(ys*tf.log(y))
|
||||
train = tf.train.GradientDescentOptimizer(0.01).minimize(loss)
|
||||
|
||||
init = tf.initialize_all_variables()
|
||||
sess = tf.Session()
|
||||
sess.run(init)
|
||||
|
||||
for step in range(10000):
|
||||
batch = mnist.train.next_batch(100)
|
||||
sess.run(train,feed_dict={xs:batch[0],ys:batch[1]})
|
||||
if step%50==0:
|
||||
correct_prediction = tf.equal(tf.arg_max(ys,1),tf.arg_max(y,1))
|
||||
accuracy = tf.reduce_mean(tf.cast(correct_prediction,tf.float32))
|
||||
print(sess.run(accuracy,feed_dict={xs:mnist.test.images,ys:mnist.test.labels}))
|
||||
@ -0,0 +1,24 @@
|
||||
import os
|
||||
from PIL import Image
|
||||
|
||||
PATH = 'D:/tensorflow/未处理的训练源/'
|
||||
PATH2 = 'D:/tensorflow/train/'
|
||||
|
||||
def resize_image(img_path,save_path):
|
||||
total = 0
|
||||
try:
|
||||
for pic in os.listdir(img_path):
|
||||
path = img_path+pic
|
||||
image = Image.open(path).convert('L')
|
||||
new_pic = image.resize((60,60),Image.ANTIALIAS)
|
||||
new_path = save_path+str(total)+'.jpg'
|
||||
new_pic.save(new_path,quality=100)
|
||||
total = total+1
|
||||
|
||||
except Exception as e:
|
||||
print(e)
|
||||
|
||||
if __name__ == '__main__':
|
||||
for page in range(1,6):
|
||||
print("During deal with %d"%(page))
|
||||
resize_image(PATH+str(page)+'/',PATH2+str(page)+'/')
|
||||
@ -0,0 +1,22 @@
|
||||
import requests
|
||||
import KMP
|
||||
|
||||
url = 'https://fabiaoqing.com/search/search/keyword/%E4%BA%8C%E6%AC%A1%E5%85%83%E5%A6%B9%E5%AD%90/type/bq/page/'
|
||||
store_path = 'D://tensorflow//萌妹子//'
|
||||
|
||||
|
||||
KMP.KMPinit()
|
||||
total = 0
|
||||
file = store_path
|
||||
for page in range(1,20):
|
||||
pages = url+str(page)+'.html'
|
||||
response = requests.get(pages)
|
||||
Word = KMP.findWord(response.text)
|
||||
num = len(Word)
|
||||
for step in range(num):
|
||||
saver = requests.get(Word[step]).content
|
||||
hander=open(store_path+str(total)+'.jpg','wb')
|
||||
hander.write(saver)
|
||||
hander.close()
|
||||
total = total+1
|
||||
print("At Page :%d %d %d"%(page,num,total))
|
||||
@ -0,0 +1,39 @@
|
||||
import os
|
||||
import time
|
||||
import random
|
||||
import shutil
|
||||
from captcha.image import ImageCaptcha
|
||||
|
||||
CHAR_SET = ['0','1','2','3','4','5','6','7','8','9']
|
||||
CHAR_SET_LEN = 10
|
||||
CAPTCHA_LEN = 4
|
||||
|
||||
|
||||
CAPTCHA_IMAGE_PATH = 'D:/tensorflow/captcha/images/'
|
||||
TEST_IMAGE_PATH = 'D:/tensorflow/captcha/test/'
|
||||
TEST_CASE = 200
|
||||
|
||||
def generate_captcha_image(PATH):
|
||||
image = ImageCaptcha()
|
||||
for i in range(10):
|
||||
for j in range(10):
|
||||
for k in range(10):
|
||||
for z in range(10):
|
||||
cap_text = CHAR_SET[i]+CHAR_SET[j]+CHAR_SET[k]+CHAR_SET[z]
|
||||
image.write(cap_text,PATH+cap_text+'.jpg')
|
||||
print("Finish Write!")
|
||||
|
||||
def generate_test_image():
|
||||
fileNameList = []
|
||||
for filePath in os.listdir(CAPTCHA_IMAGE_PATH):
|
||||
captcha_name = filePath.split('/')[-1]
|
||||
fileNameList.append(captcha_name)
|
||||
random.seed(time.time())
|
||||
random.shuffle(fileNameList)
|
||||
for i in range(TEST_CASE):
|
||||
name = fileNameList[i]
|
||||
shutil.move(CAPTCHA_IMAGE_PATH + name, TEST_IMAGE_PATH + name)
|
||||
|
||||
if __name__ == '__main__':
|
||||
generate_captcha_image(CAPTCHA_IMAGE_PATH)
|
||||
generate_test_image()
|
||||
@ -0,0 +1,57 @@
|
||||
import tensorflow as tf
|
||||
import numpy as np
|
||||
from PIL import Image
|
||||
import os
|
||||
|
||||
flower_dict = {0:'小熊猫',1:'滑稽',2:'萌妹子',3:'小坏坏',4:'小黄鸡'}
|
||||
path = 'C:/Users/yuanshao/Desktop/test/'
|
||||
|
||||
w=100
|
||||
h=100
|
||||
c=3
|
||||
|
||||
def read_one_image(path):
|
||||
image = Image.open(path).convert('RGB')
|
||||
img = image.resize((w, h), Image.ANTIALIAS)
|
||||
return np.asarray(img)
|
||||
|
||||
def preprocess():
|
||||
count = 0
|
||||
for file in os.listdir(path):
|
||||
new_name = os.path.join(path, str(count))
|
||||
os.rename(os.path.join(path, file),new_name)
|
||||
count += 1
|
||||
for file in os.listdir(path):
|
||||
ori_name = path+file
|
||||
os.rename(ori_name,ori_name+'.jpg')
|
||||
|
||||
with tf.Session() as sess:
|
||||
PATH = cate=[path+x for x in os.listdir(path)]
|
||||
data = []
|
||||
pic = []
|
||||
preprocess()
|
||||
for i in range(len(PATH)):
|
||||
picture = path+str(i)+'.jpg';
|
||||
print(picture)
|
||||
pic.append(picture)
|
||||
data.append(read_one_image(picture))
|
||||
|
||||
saver = tf.train.import_meta_graph('D:/tensorflow/saver/model.ckpt.meta')
|
||||
saver.restore(sess,tf.train.latest_checkpoint('D:/tensorflow/saver/'))
|
||||
|
||||
graph = tf.get_default_graph()
|
||||
x = graph.get_tensor_by_name("x:0")
|
||||
|
||||
logits = graph.get_tensor_by_name("logits_eval:0")
|
||||
|
||||
classification_result = sess.run(logits,feed_dict={x:data})
|
||||
|
||||
#打印出预测矩阵
|
||||
print(classification_result)
|
||||
#打印出预测矩阵每一行最大值的索引
|
||||
print(tf.argmax(classification_result,1).eval())
|
||||
output = []
|
||||
output = tf.argmax(classification_result,1).eval()
|
||||
for i in range(len(output)):
|
||||
print("第",i,"张图片预测:"+flower_dict[output[i]])
|
||||
|
||||
@ -0,0 +1,209 @@
|
||||
import tensorflow as tf
|
||||
from captcha.image import ImageCaptcha
|
||||
import numpy as np
|
||||
import matplotlib.pyplot as plt
|
||||
from PIL import Image
|
||||
import random
|
||||
|
||||
number=['0','1','2','3','4','5','6','7','8','9']
|
||||
#alphabet = ['a','b','c','d','e','f','g','h','i','j','k','l','m','n','o','p','q','r','s','t','u','v','w','x','y','z']
|
||||
#ALPHABET = ['A','B','C','D','E','F','G','H','I','J','K','L','M','N','O','P','Q','R','S','T','U','V','W','X','Y','Z']
|
||||
|
||||
def random_captcha_text(char_set=number,captcha_size=4):
|
||||
captcha_text=[]
|
||||
for i in range(captcha_size):
|
||||
c=random.choice(char_set)
|
||||
captcha_text.append(c)
|
||||
return captcha_text
|
||||
|
||||
def gen_captcha_text_image():
|
||||
image=ImageCaptcha()
|
||||
captcha_text=random_captcha_text()
|
||||
captcha_text=''.join(captcha_text)
|
||||
captcha=image.generate(captcha_text)
|
||||
captcha_image=Image.open(captcha)
|
||||
captcha_image=np.array(captcha_image)
|
||||
return captcha_text,captcha_image
|
||||
|
||||
|
||||
def convert2gray(img):
|
||||
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(text):
|
||||
text_len = len(text)
|
||||
if text_len > max_captcha:
|
||||
raise ValueError('验证码最长4个字符')
|
||||
|
||||
vector = np.zeros(max_captcha * char_set_len)
|
||||
|
||||
def char2pos(c):
|
||||
if c == '_':
|
||||
k = 62
|
||||
return k
|
||||
k = ord(c) - 48
|
||||
if k > 9:
|
||||
k = ord(c) - 55
|
||||
if k > 35:
|
||||
k = ord(c) - 61
|
||||
if k > 61:
|
||||
raise ValueError('No Map')
|
||||
return k
|
||||
|
||||
for i, c in enumerate(text):
|
||||
idx = i * char_set_len + char2pos(c)
|
||||
vector[idx] = 1
|
||||
return vector
|
||||
|
||||
|
||||
def get_next_batch(batch_size=128):
|
||||
batch_x=np.zeros([batch_size,image_height*image_width])
|
||||
batch_y=np.zeros([batch_size,max_captcha*char_set_len])
|
||||
|
||||
def wrap_gen_captcha_text_and_image():
|
||||
while True:
|
||||
text, image = gen_captcha_text_image()
|
||||
if image.shape == (60, 160, 3):
|
||||
return text, image
|
||||
|
||||
for i in range(batch_size):
|
||||
text, image = wrap_gen_captcha_text_and_image()
|
||||
image = convert2gray(image)
|
||||
|
||||
batch_x[i, :] = image.flatten() / 255
|
||||
batch_y[i, :] = text2vec(text)
|
||||
|
||||
return batch_x, batch_y
|
||||
|
||||
def cnn_structure(w_alpha=0.01, b_alpha=0.1):
|
||||
x = tf.reshape(X, shape=[-1, image_height, image_width, 1])
|
||||
|
||||
|
||||
wc1=tf.get_variable(name='wc1',shape=[3,3,1,32],dtype=tf.float32,initializer=tf.contrib.layers.xavier_initializer())
|
||||
#wc1 = tf.Variable(w_alpha * tf.random_normal([3, 3, 1, 32]))
|
||||
bc1 = tf.Variable(b_alpha * tf.random_normal([32]))
|
||||
conv1 = tf.nn.relu(tf.nn.bias_add(tf.nn.conv2d(x, wc1, strides=[1, 1, 1, 1], padding='SAME'), bc1))
|
||||
conv1 = tf.nn.max_pool(conv1, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')
|
||||
conv1 = tf.nn.dropout(conv1, keep_prob)
|
||||
|
||||
wc2=tf.get_variable(name='wc2',shape=[3,3,32,64],dtype=tf.float32,initializer=tf.contrib.layers.xavier_initializer())
|
||||
# wc2 = tf.Variable(w_alpha * tf.random_normal([3, 3, 32, 64]))
|
||||
bc2 = tf.Variable(b_alpha * tf.random_normal([64]))
|
||||
conv2 = tf.nn.relu(tf.nn.bias_add(tf.nn.conv2d(conv1, wc2, strides=[1, 1, 1, 1], padding='SAME'), bc2))
|
||||
conv2 = tf.nn.max_pool(conv2, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')
|
||||
conv2 = tf.nn.dropout(conv2, keep_prob)
|
||||
|
||||
wc3=tf.get_variable(name='wc3',shape=[3,3,64,128],dtype=tf.float32,initializer=tf.contrib.layers.xavier_initializer())
|
||||
#wc3 = tf.Variable(w_alpha * tf.random_normal([3, 3, 64, 128]))
|
||||
bc3 = tf.Variable(b_alpha * tf.random_normal([128]))
|
||||
conv3 = tf.nn.relu(tf.nn.bias_add(tf.nn.conv2d(conv2, wc3, strides=[1, 1, 1, 1], padding='SAME'), bc3))
|
||||
conv3 = tf.nn.max_pool(conv3, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')
|
||||
conv3 = tf.nn.dropout(conv3, keep_prob)
|
||||
|
||||
|
||||
wd1=tf.get_variable(name='wd1',shape=[8*20*128,1024],dtype=tf.float32,initializer=tf.contrib.layers.xavier_initializer())
|
||||
#wd1 = tf.Variable(w_alpha * tf.random_normal([7*20*128,1024]))
|
||||
bd1 = tf.Variable(b_alpha * tf.random_normal([1024]))
|
||||
dense = tf.reshape(conv3, [-1, wd1.get_shape().as_list()[0]])
|
||||
dense = tf.nn.relu(tf.add(tf.matmul(dense, wd1), bd1))
|
||||
dense = tf.nn.dropout(dense, keep_prob)
|
||||
|
||||
wout=tf.get_variable('name',shape=[1024,max_captcha * char_set_len],dtype=tf.float32,initializer=tf.contrib.layers.xavier_initializer())
|
||||
#wout = tf.Variable(w_alpha * tf.random_normal([1024, max_captcha * char_set_len]))
|
||||
bout = tf.Variable(b_alpha * tf.random_normal([max_captcha * char_set_len]))
|
||||
out = tf.add(tf.matmul(dense, wout), bout)
|
||||
return out
|
||||
|
||||
def train_cnn():
|
||||
output=cnn_structure()
|
||||
cost=tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=output,labels=Y))
|
||||
optimizer=tf.train.AdamOptimizer(learning_rate=0.001).minimize(cost)
|
||||
predict=tf.reshape(output,[-1,max_captcha,char_set_len])
|
||||
max_idx_p = tf.argmax(predict, 2)
|
||||
max_idx_l = tf.argmax(tf.reshape(Y, [-1, max_captcha, char_set_len]), 2)
|
||||
correct_pred = tf.equal(max_idx_p, max_idx_l)
|
||||
accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))
|
||||
|
||||
saver=tf.train.Saver()
|
||||
|
||||
with tf.Session() as sess:
|
||||
init = tf.global_variables_initializer()
|
||||
sess.run(init)
|
||||
step = 0
|
||||
while True:
|
||||
batch_x, batch_y = get_next_batch(100)
|
||||
_, cost_= sess.run([optimizer, cost], feed_dict={X: batch_x, Y: batch_y, keep_prob: 0.75})
|
||||
print(step, cost_)
|
||||
if step % 10 == 0:
|
||||
batch_x_test, batch_y_test = get_next_batch(100)
|
||||
acc = sess.run(accuracy, feed_dict={X: batch_x_test, Y: batch_y_test, keep_prob: 1.})
|
||||
print(step, acc)
|
||||
if acc > 0.99:
|
||||
saver.save(sess, "./model/crack_capcha.model", global_step=step)
|
||||
break
|
||||
step += 1
|
||||
|
||||
|
||||
def crack_captcha(captcha_image):
|
||||
output = cnn_structure()
|
||||
|
||||
saver = tf.train.Saver()
|
||||
with tf.Session() as sess:
|
||||
saver.restore(sess, "./model/crack_capcha.model-1200")
|
||||
|
||||
predict = tf.argmax(tf.reshape(output, [-1, max_captcha, char_set_len]), 2)
|
||||
text_list = sess.run(predict, feed_dict={X: [captcha_image], keep_prob: 1.})
|
||||
text = text_list[0].tolist()
|
||||
return text
|
||||
|
||||
if __name__=='__main__':
|
||||
train=1
|
||||
if train==0:
|
||||
text,image=gen_captcha_text_image()
|
||||
print("验证码大小：",image.shape)#(60,160,3)
|
||||
|
||||
image_height=60
|
||||
image_width=160
|
||||
max_captcha=len(text)
|
||||
print("验证码文本最长字符数",max_captcha)
|
||||
char_set=number
|
||||
char_set_len=len(char_set)
|
||||
|
||||
X = tf.placeholder(tf.float32, [None, image_height * image_width])
|
||||
Y = tf.placeholder(tf.float32, [None, max_captcha * char_set_len])
|
||||
keep_prob = tf.placeholder(tf.float32)
|
||||
train_cnn()
|
||||
|
||||
if train == 1:
|
||||
image_height = 60
|
||||
image_width = 160
|
||||
char_set = number
|
||||
char_set_len = len(char_set)
|
||||
|
||||
text, image = gen_captcha_text_image()
|
||||
|
||||
f = plt.figure()
|
||||
ax = f.add_subplot(111)
|
||||
ax.text(0.1, 0.9, text, ha='center', va='center', transform=ax.transAxes)
|
||||
plt.imshow(image)
|
||||
|
||||
# plt.show()
|
||||
|
||||
max_captcha = len(text)
|
||||
image = convert2gray(image)
|
||||
image = image.flatten() / 255
|
||||
|
||||
X = tf.placeholder(tf.float32, [None, image_height * image_width])
|
||||
Y = tf.placeholder(tf.float32, [None, max_captcha * char_set_len])
|
||||
keep_prob = tf.placeholder(tf.float32)
|
||||
|
||||
predict_text = crack_captcha(image)
|
||||
print("正确: {} 预测: {}".format(text, predict_text))
|
||||
|
||||
|
||||
plt.show()
|
||||
Binary file not shown.
Binary file not shown.
@ -0,0 +1,20 @@
|
||||
https://book.qidian.com/info/1011774352 海贼之天赋系统
|
||||
https://book.qidian.com/info/1009934766 火影之活久见
|
||||
https://book.qidian.com/info/1009352601 海贼之雷神降临
|
||||
https://book.qidian.com/info/1011987395 满级账号在异界
|
||||
https://book.qidian.com/info/1004591465 寻找走丢的舰娘
|
||||
https://book.qidian.com/info/1010837556 忍者招募大师
|
||||
https://book.qidian.com/info/1010523354 妙木山的塔姆仙人
|
||||
https://book.qidian.com/info/1004975904 海贼：厌世之歌
|
||||
https://book.qidian.com/info/1005313052 请回答火影
|
||||
https://book.qidian.com/info/1010731906 木叶之实力至上
|
||||
https://book.qidian.com/info/1011704667 漫威世界中的幽灵
|
||||
https://book.qidian.com/info/1009361813 魔法与万象卡牌系统
|
||||
https://book.qidian.com/info/1004597439 我的女仆是恶魔
|
||||
https://book.qidian.com/info/1011121140 捉妖奶爸
|
||||
https://book.qidian.com/info/1005115417 火影之最强卡卡西
|
||||
https://book.qidian.com/info/1005235019 神级剑魂系统
|
||||
https://book.qidian.com/info/1010279627 变身之萝莉主播
|
||||
https://book.qidian.com/info/1012043959 漫威之苍雷之影
|
||||
https://book.qidian.com/info/1012055792 变身之漫威天才
|
||||
https://book.qidian.com/info/1004607972 龙珠之最强神话
|
||||
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|
||||
259
|
||||
变身之漫威天才
|
||||
111
|
||||
紫妈的故事
|
||||
二次元
|
||||
|
After Width: | Height: | Size: 58 KiB |
@ -0,0 +1,5 @@
|
||||
257
|
||||
变身之萝莉主播
|
||||
111
|
||||
青棘
|
||||
二次元
|
||||
|
After Width: | Height: | Size: 39 KiB |
@ -0,0 +1,5 @@
|
||||
247
|
||||
妙木山的塔姆仙人
|
||||
111
|
||||
河流之汪
|
||||
二次元
|
||||
|
After Width: | Height: | Size: 39 KiB |
@ -0,0 +1,5 @@
|
||||
245
|
||||
寻找走丢的舰娘
|
||||
111
|
||||
海底熔岩
|
||||
二次元
|
||||
|
After Width: | Height: | Size: 74 KiB |
@ -0,0 +1,5 @@
|
||||
246
|
||||
忍者招募大师
|
||||
111
|
||||
24K纯帅鸦
|
||||
二次元
|
||||
|
After Width: | Height: | Size: 20 KiB |
@ -0,0 +1,5 @@
|
||||
253
|
||||
我的女仆是恶魔
|
||||
111
|
||||
空明音
|
||||
二次元
|
||||
|
After Width: | Height: | Size: 37 KiB |
@ -0,0 +1,5 @@
|
||||
254
|
||||
捉妖奶爸
|
||||
111
|
||||
火中物
|
||||
二次元
|
||||
|
After Width: | Height: | Size: 38 KiB |
@ -0,0 +1,5 @@
|
||||
250
|
||||
木叶之实力至上
|
||||
111
|
||||
召弓
|
||||
二次元
|
||||
|
After Width: | Height: | Size: 82 KiB |
@ -0,0 +1,5 @@
|
||||
241
|
||||
海贼之天赋系统
|
||||
111
|
||||
夜南听风
|
||||
二次元
|
||||
|
After Width: | Height: | Size: 38 KiB |
@ -0,0 +1,5 @@
|
||||
243
|
||||
海贼之雷神降临
|
||||
111
|
||||
焉得羽翼兮
|
||||
二次元
|
||||
|
After Width: | Height: | Size: 12 KiB |
@ -0,0 +1,5 @@
|
||||
248
|
||||
海贼：厌世之歌
|
||||
111
|
||||
农夫一拳
|
||||
二次元
|
||||
|
After Width: | Height: | Size: 33 KiB |
@ -0,0 +1,8 @@
|
||||
一颗恶魔果实，一身用血与汗换来的体术，一身用牵挂与疯狂换来的霸气。
|
||||
王，乃是最贪婪、最高傲、最易怒之人；王的身后，留下的不是美好，而是尸骨成山。
|
||||
心中所想，心中所念，在所不惜；一生的执念，愿抛弃一切，不折手段！
|
||||
其实，这只是一个可怜人为了执念，而变成神经病的故事。
|
||||
|
||||
世人皆可抛，唯亲人不离不弃！
|
||||
|
||||
新书:《海贼之爆炸艺术》《妖尾之金金果实》请支持，多谢！
|
||||
@ -0,0 +1,5 @@
|
||||
244
|
||||
满级账号在异界
|
||||
111
|
||||
尺间萤火
|
||||
二次元
|
||||
|
After Width: | Height: | Size: 23 KiB |
@ -0,0 +1,5 @@
|
||||
251
|
||||
漫威世界中的幽灵
|
||||
111
|
||||
职业偷懒
|
||||
二次元
|
||||
|
After Width: | Height: | Size: 48 KiB |
@ -0,0 +1,5 @@
|
||||
258
|
||||
漫威之苍雷之影
|
||||
111
|
||||
青圭大大
|
||||
二次元
|
||||
|
After Width: | Height: | Size: 54 KiB |
@ -0,0 +1,5 @@
|
||||
255
|
||||
火影之最强卡卡西
|
||||
111
|
||||
墨渊九砚
|
||||
二次元
|
||||
|
After Width: | Height: | Size: 34 KiB |
@ -0,0 +1,5 @@
|
||||
242
|
||||
火影之活久见
|
||||
111
|
||||
李四羊
|
||||
二次元
|
||||
|
After Width: | Height: | Size: 28 KiB |
@ -0,0 +1,2 @@
|
||||
说出来你不信，木叶村是我建立的，漩涡一族是我带大的。
|
||||
哦不对，现在那应该叫涡之国了。
|
||||
@ -0,0 +1,5 @@
|
||||
256
|
||||
神级剑魂系统
|
||||
111
|
||||
夜南听风
|
||||
二次元
|
||||
|
After Width: | Height: | Size: 34 KiB |
@ -0,0 +1,6 @@
|
||||
森罗万象，皆为灰烬，流刃若火！
|
||||
穿越到海贼世界，大海贼时代十一年的罗亚，得到了一个能够无限强化，抽取各种技能的剑魂系统。
|
||||
流刃若火，镜花水月，月牙天冲，千本樱……
|
||||
“我这一刀下去，你可能会被烧死。”罗亚看着眼前的赤犬，很认真的说道。
|
||||
………………………………
|
||||
新书《海贼之天赋系统》已发布，求支持！
|
||||
@ -0,0 +1,5 @@
|
||||
249
|
||||
请回答火影
|
||||
111
|
||||
蒙着面的Sama
|
||||
二次元
|
||||
|
After Width: | Height: | Size: 33 KiB |
@ -0,0 +1,5 @@
|
||||
252
|
||||
魔法与万象卡牌系统
|
||||
111
|
||||
威馆长
|
||||
二次元
|
||||
|
After Width: | Height: | Size: 66 KiB |
@ -0,0 +1,5 @@
|
||||
260
|
||||
龙珠之最强神话
|
||||
111
|
||||
枫叶缀
|
||||
二次元
|
||||
|
After Width: | Height: | Size: 14 KiB |
@ -0,0 +1,20 @@
|
||||
https://book.qidian.com/info/107580 凡人修仙传
|
||||
https://book.qidian.com/info/1735921 遮天
|
||||
https://book.qidian.com/info/118447 星辰变
|
||||
https://book.qidian.com/info/2248950 最强弃少
|
||||
https://book.qidian.com/info/2502372 莽荒纪
|
||||
https://book.qidian.com/info/1264634 仙逆
|
||||
https://book.qidian.com/info/1004995075 最强神话帝皇
|
||||
https://book.qidian.com/info/1003723851 天影
|
||||
https://book.qidian.com/info/2227457 飞天
|
||||
https://book.qidian.com/info/1010053502 反套路快穿
|
||||
https://book.qidian.com/info/3347627 妙医圣手
|
||||
https://book.qidian.com/info/2070910 求魔
|
||||
https://book.qidian.com/info/1010804088 二青
|
||||
https://book.qidian.com/info/3441641 掠天记
|
||||
https://book.qidian.com/info/3195551 造化之门
|
||||
https://book.qidian.com/info/1005317872 能穿越的修行者
|
||||
https://book.qidian.com/info/3406500 走进修仙
|
||||
https://book.qidian.com/info/3347574 从前有座灵剑山
|
||||
https://book.qidian.com/info/1005395194 仙藏
|
||||
https://book.qidian.com/info/3135391 大泼猴
|
||||
@ -0,0 +1,5 @@
|
||||
73
|
||||
二青
|
||||
111
|
||||
来不及忧伤
|
||||
仙侠
|
||||
|
After Width: | Height: | Size: 22 KiB |
@ -0,0 +1,5 @@
|
||||
78
|
||||
从前有座灵剑山
|
||||
111
|
||||
国王陛下
|
||||
仙侠
|
||||
|
After Width: | Height: | Size: 12 KiB |
@ -0,0 +1,3 @@
|
||||
灵剑派成立于九州历四二三三年，几千年来始终致力于为行业提供一流的修仙人才，如今位列万仙盟五大超品宗派之一，掌门风吟真人担任万仙盟七大常务长老，修为盖世。灵剑派坚持和平与发展的主题，门派核心价值理念是求真、求善、求种。为进一步扩充门派力量，补充新鲜血液，拟于近期召开升仙大会，诚邀各路精英前来。
|
||||
还是原来的节操
|
||||
还是一样的搞笑
|
||||
@ -0,0 +1,5 @@
|
||||
79
|
||||
仙藏
|
||||
111
|
||||
鬼雨
|
||||
仙侠
|
||||
|
After Width: | Height: | Size: 59 KiB |
@ -0,0 +1,8 @@
|
||||
虚度光阴年近不惑的秦笛穿越到了异界，从一个束发少年接受桃花洗礼开始走上修真之路，一路奇遇，炼气，筑基，金丹，元婴，步虚，合道，地仙，灵仙，天仙，祖仙，金仙，仙王，仙帝，道祖.....
|
||||
那时那日此门中，
|
||||
桃花树下初相逢。
|
||||
只见仙人种桃树，
|
||||
未闻仙人看花红。
|
||||
这是一本与众不同、悠闲安逸、富有阳光、脑洞大开的仙侠修真小说。书里没有太多的杀伐，但有一个广阔的仙侠世界，还有不少的小故事，贯穿古今，开卷有益。
|
||||
仙路漫漫，岁月悠悠，希望大家与我同行。
|
||||
书友群:47733057QQ
|
||||
@ -0,0 +1,5 @@
|
||||
66
|
||||
仙逆
|
||||
111
|
||||
耳根
|
||||
仙侠
|
||||
|
After Width: | Height: | Size: 32 KiB |
@ -0,0 +1,2 @@
|
||||
顺为凡，逆则仙，只在心中一念间……
|
||||
请看耳根作品《仙逆》
|
||||
@ -0,0 +1,5 @@
|
||||
61
|
||||
凡人修仙传
|
||||
111
|
||||
忘语
|
||||
仙侠
|
||||
|
After Width: | Height: | Size: 13 KiB |
@ -0,0 +1,7 @@
|
||||
一个普通山村小子，偶然下进入到当地江湖小门派，成了一名记名弟子。他以这样身份，如何在门派中立足,如何以平庸的资质进入到修仙者的行列，从而笑傲三界之中！
|
||||
续篇《凡人修仙之仙界篇》已经在起点中文网上传了，欢迎大家继续支持哦！
|
||||
凡人修仙，风云再起
|
||||
时空穿梭，轮回逆转
|
||||
金仙太乙，大罗道祖
|
||||
三千大道，法则至尊
|
||||
《凡人修仙传》仙界篇，一个韩立叱咤仙界的故事，一个凡人小子修仙的不灭传说。
|
||||
@ -0,0 +1,5 @@
|
||||
70
|
||||
反套路快穿
|
||||
111
|
||||
良心
|
||||
仙侠
|
||||
|
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