接着上一篇的 读取， 这篇是cnn的识别代码。 很多网上的代码其实都是可以直接跑的， 我只不过自己码了一遍 理解了一遍
# -*- coding: utf-8 -*-
import numpy as np
w_alpha=0.01
b_alpha=0.1
IMAGE_HEIGHT = 240
IMAGE_WIDTH = 320
MAX_CAPTCHA = 1
# 图片种类37
CHAR_SET_LEN = 37
dropout = 0.7
conv_dict = {
# 第一层卷积参数 3*3， 因为是彩色图片 所以第一层输入通道是3， 输出为32
"w_1": tf.Variable(w_alpha * tf.random_normal([3, 3, 3, 32]), name='w_1'),
"b_1": tf.Variable(b_alpha * tf.random_normal([32]), name='b_1'),
# 第二层卷积参数
"w_2": tf.Variable(w_alpha * tf.random_normal([3, 3, 32, 64]), name='w_2'),
"b_2": tf.Variable(b_alpha * tf.random_normal([64]), name='b_2'),
# 第三层卷积参数
"w_3": tf.Variable(w_alpha * tf.random_normal([3, 3, 64, 128]), name='w_3'),
"b_3": tf.Variable(b_alpha * tf.random_normal([128]), name='b_3'),
# 第四层卷积参数
"w_4": tf.Variable(w_alpha * tf.random_normal([3, 3, 128, 128]), name='w_4'),
"b_4": tf.Variable(b_alpha * tf.random_normal([128]), name='b_4'),
'out': tf.Variable(tf.random_normal([1024, CHAR_SET_LEN])),
'out_add': tf.Variable(tf.random_normal([CHAR_SET_LEN]))
}
# 批量标准化 - 防止 梯度弥散
# wx_plus_b tensor
# out_size 通道数
def batch_normal(wx_plus_b, out_size):
fc_mean, fc_var = tf.nn.moments(
wx_plus_b,
axes=[0, 1, 2], # 想要 normalize 的维度, [0] 代表 batch 维度
# 如果是图像数据, 可以传入 [0, 1, 2], 相当于求[batch, height, width] 的均值/方差, 注意不要加入 channel 维度
)
# out_size 和wx_plus_b 输出通道数一致
scale = tf.Variable(tf.ones([out_size]))
shift = tf.Variable(tf.zeros([out_size]))
epsilon = 0.001
wx_plus_b = tf.nn.batch_normalization(wx_plus_b, fc_mean, fc_var, shift, scale, epsilon)
return wx_plus_b
X = tf.placeholder(tf.float32, [None, IMAGE_HEIGHT , IMAGE_WIDTH,3])
Y = tf.placeholder(tf.float32, [None, MAX_CAPTCHA * CHAR_SET_LEN])
NOR = tf.placeholder(tf.float32)
keep_prob = tf.placeholder(tf.float32) # dropout
# 把单个数变成数组
def one_hot_n(x, n):
x = np.array(x)
return np.eye(n)[x]
def conv2d(conv, cd1, cd2, out_size, nor):
conv = tf.nn.bias_add(tf.nn.conv2d(conv, cd1, strides=[1, 1, 1, 1], padding='SAME'), cd2)
# 做 batch_normal
# if nor > 1:
# conv = batch_normal(conv, out_size)
conv = tf.nn.relu(conv)
conv = tf.nn.max_pool(conv, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')
# dropout 防止 过拟合
conv = tf.nn.dropout(conv, keep_prob)
return conv
# 定义CNN
def *****_captcha_cnn():
# 四层卷积池化
conv1 = conv2d(X, conv_dict['w_1'], conv_dict['b_1'], 32, NOR)
conv2 = conv2d(conv1, conv_dict['w_2'], conv_dict['b_2'], 64, NOR)
conv3 = conv2d(conv2, conv_dict['w_3'], conv_dict['b_3'], 128, NOR)
conv4 = conv2d(conv3, conv_dict['w_4'], conv_dict['b_4'], 128, NOR)
# Fully connected layer 全连接
# 240/16=15 320/16=20
w_d = tf.Variable(w_alpha * tf.random_normal([15 * 20 * 128, 1024]))
b_d = tf.Variable(b_alpha * tf.random_normal([1024]))
dense = tf.reshape(conv4, [-1, w_d.get_shape().as_list()[0]])
dense = tf.nn.relu(tf.add(tf.matmul(dense, w_d), b_d))
out = tf.add(tf.matmul(dense, conv_dict['out']), conv_dict['out_add'])
return out
# 读取tfrecrods 数据
def read_and_decode(filename):
filename_queue = tf.train.string_input_producer([filename])
reader = tf.TFRecordReader()
_, serialized_example = reader.read(filename_queue)
features = tf.parse_single_example(serialized_example,
features={
'label': tf.FixedLenFeature([], tf.int64),
'img_raw' : tf.FixedLenFeature([], tf.string),
})
img = tf.decode_raw(features['img_raw'], tf.uint8)
img = tf.reshape(img, [IMAGE_HEIGHT, IMAGE_WIDTH, 3])
# normalize
img = tf.cast(img, tf.float32) * (1. / 255) - 0.5
label = tf.cast(features['label'], tf.int32)
return img, label
# 训练
def train_*****_captcha_cnn():
output = *****_captcha_cnn()
# softmax ,sigmoid 第一个是用于单结果， 第二个用于多个结果
loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=output,labels=Y))
#loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(output, Y))
# optimizer 为了加快训练 learning_rate应该开始大，然后慢慢衰
optimizer = tf.train.AdamOptimizer(learning_rate=0.001).minimize(loss)
# Evaluate model
# 给出pred在 横向维度上的最大值的 index . prd tensor， 1 横向维度 ， 返回的是boolen
correct_pred = tf.equal(tf.argmax(output, 1), tf.argmax(Y, 1))
# 把boolean 转成 浮点数据 , 求平均值
accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))
img, label = read_and_decode("anm_pic_train.tfrecords")
img_batch, label_batch = tf.train.shuffle_batch([img, label], batch_size=30, capacity=7000,min_after_dequeue=1000)
saver = tf.train.Saver()
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
# saver.restore(sess, tf.train.latest_checkpoint('/home/root/wtf/yzm/code/'))
step = 0
# img, label = read_and_decode("anm_pic_train.tfrecords")
# img_batch, label_batch = tf.train.shuffle_batch([img, label], batch_size=64, capacity=70000,min_after_dequeue=1000)
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(coord=coord, sess=sess)
while True:
# for i in range(3000):
imgs, labs = sess.run([img_batch, label_batch])
# print (labs)
one_hot_labs = sess.run(tf.cast(one_hot_n(labs, CHAR_SET_LEN), tf.float32))
sess.run(optimizer, feed_dict={X: imgs, Y: one_hot_labs, keep_prob: dropout, NOR: 1.})
if step % 50 == 0:
acc = sess.run( accuracy, feed_dict={X: imgs, Y: one_hot_labs, keep_prob: 1., NOR: 1.})
print(step, acc)
if acc > 0.5:
saver.save(sess, "*****_capcha.model", global_step=step)
print("Complete!!")
coord.request_stop()
coord.join(threads)
sess.close()
break
step += 1
# print("Complete!!")
# coord.request_stop()
# coord.join(threads)
# sess.close()
train_*****_captcha_cnn()
1.batch_noraml 是为了防止梯度弥散的，但是 到底是不是放在**之前还不清楚，而且怎么做if 语句....
2.这个代码是用 cpu跑的， 别问我为什么用cpu，穷。 有条件的最好用gpu跑， 省心啊
跑了半天的结果：
推荐博客地址:
http://blog.topspeedsnail.com
https://morvanzhou.github.io/tutorials/machine-learning/tensorflow/5-13-BN/