I want to know how to use your code.
This is my code
`import tensorflow as tf
import numpy as np
import os
from PIL import Image
import random
import math
import torch
import torch.nn as nn
from torch.nn import init
class CNN(object):
def init(self, image_height, image_width, max_captcha, char_set, model_save_dir):
self.image_height = image_height
self.image_width = image_width
self.max_captcha = max_captcha
self.char_set = char_set
self.char_set_len = len(char_set)
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
tf初始化占位符
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):
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
:param text: str
:return: numpy.array
"""
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 spatial_pyramid_pool(self, previous_conv, num_sample, previous_conv_size, out_pool_size):
'''
previous_conv: a tensor vector of previous convolution layer
num_sample: an int number of image in the batch
previous_conv_size: an int vector [height, width] of the matrix features size of previous convolution layer
out_pool_size: a int vector of expected output size of max pooling layer
returns: a tensor vector with shape [1 x n] is the concentration of multi-level pooling
'''
# print(previous_conv.size())
for i in range(len(out_pool_size)):
# print(previous_conv_size)
h_wid = int(math.ceil(previous_conv_size[0] / out_pool_size[i]))
w_wid = int(math.ceil(previous_conv_size[1] / out_pool_size[i]))
h_pad = (h_wid * out_pool_size[i] - previous_conv_size[0] + 1) / 2
w_pad = (w_wid * out_pool_size[i] - previous_conv_size[1] + 1) / 2
maxpool = nn.MaxPool2d((h_wid, w_wid), stride=(h_wid, w_wid), padding=(h_pad, w_pad))
x = maxpool(previous_conv)
if (i == 0):
spp = x.view(num_sample, -1)
# print("spp size:",spp.size())
else:
# print("size:",spp.size())
spp = torch.cat((spp, x.view(num_sample, -1)), 1)
return spp
def model(self):
x = tf.reshape(self.X, shape=[-1, self.image_height, self.image_width, 1])
print(">>> input x: {}".format(x))
# Convolution layer1
wc1 = tf.get_variable(name='wc1', shape=[3, 3, 1, 32], dtype=tf.float32,
initializer=tf.contrib.layers.xavier_initializer())
bc1 = tf.Variable(self.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, self.keep_prob)
# Convolution layer 2
wc2 = tf.get_variable(name='wc2', shape=[3, 3, 32, 64], dtype=tf.float32,
initializer=tf.contrib.layers.xavier_initializer())
bc2 = tf.Variable(self.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, self.keep_prob)
# Convolution layer 3
wc3 = tf.get_variable(name='wc3', shape=[3, 3, 64, 128], dtype=tf.float32,
initializer=tf.contrib.layers.xavier_initializer())
bc3 = tf.Variable(self.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, self.keep_prob)
print(">>> convolution 3: ", conv3.shape)
next_shape = conv3.shape[1] * conv3.shape[2] * conv3.shape[3]
#I want to know how to use your code.
#
# Fully connected layer 1
wd1 = tf.get_variable(name='wd1', shape=[next_shape, 1024], dtype=tf.float32,
initializer=tf.contrib.layers.xavier_initializer())
bd1 = tf.Variable(self.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, self.keep_prob)
# Fully connected layer 2
wout = tf.get_variable('name', shape=[1024, self.max_captcha * self.char_set_len], dtype=tf.float32,
initializer=tf.contrib.layers.xavier_initializer())
bout = tf.Variable(self.b_alpha * tf.random_normal([self.max_captcha * self.char_set_len]))
with tf.name_scope('y_prediction'):
y_predict = tf.add(tf.matmul(dense, wout), bout)
return y_predict
I want to know how to use your code. This is my code `import tensorflow as tf import numpy as np import os from PIL import Image import random import math import torch import torch.nn as nn from torch.nn import init class CNN(object): def init(self, image_height, image_width, max_captcha, char_set, model_save_dir): self.image_height = image_height self.image_width = image_width self.max_captcha = max_captcha self.char_set = char_set self.char_set_len = len(char_set) 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
tf初始化占位符
`