tensorflow学习笔记——验证码识别（三）模型训练
我们使用的模型为Alexnet，源码在tensorflowtensorflow-master，我们提取tensorflowtensorflow-master里面的net文件夹放入我们的程序路径以便使用，并修改Alexnet模型最后一层为4个分支，来进行多任务学习。（要源码可以找我，官网下载很慢）
训练模型的代码如下：
import os
import tensorflow as tf
from PIL import Image
from nets import nets_factory
import numpy as np# 不同字符数量
CHAR_SET_LEN = 10
# 图片高度
IMAGE_HEIGHT = 60
# 图片宽度
IMAGE_WIDTH = 160
# 批次
BATCH_SIZE = 25
# tfrecord文件存放路径
TFRECORD_FILE = "C:/Users/45374/tensorflow/captcha/train.tfrecords"
# placeholder
x = tf.placeholder(tf.float32, [None, 224, 224])
y0 = tf.placeholder(tf.float32, [None])
y1 = tf.placeholder(tf.float32, [None])
y2 = tf.placeholder(tf.float32, [None])
y3 = tf.placeholder(tf.float32, [None])
# 学习率
lr = tf.Variable(0.003, dtype=tf.float32)
# 从tfrecord读出数据
def read_and_decode(filename):
# 根据文件名生成一个队列
filename_queue = tf.train.string_input_producer([filename])
reader = tf.TFRecordReader()
# 返回文件名和文件
_, serialized_example = reader.read(filename_queue)
features = tf.parse_single_example(serialized_example,
features={
'image' : tf.FixedLenFeature([], tf.string),
'label0': tf.FixedLenFeature([], tf.int64),
'label1': tf.FixedLenFeature([], tf.int64),
'label2': tf.FixedLenFeature([], tf.int64),
'label3': tf.FixedLenFeature([], tf.int64),
})
# 获取图片数据
image = tf.decode_raw(features['image'], tf.uint8)
# tf.train.shuffle_batch必须确定shape
image = tf.reshape(image, [224, 224])
# 图片预处理
image = tf.cast(image, tf.float32) / 255.0
image = tf.subtract(image, 0.5)
image = tf.multiply(image, 2.0)
# 获取label
label0 = tf.cast(features['label0'], tf.int32)
label1 = tf.cast(features['label1'], tf.int32)
label2 = tf.cast(features['label2'], tf.int32)
label3 = tf.cast(features['label3'], tf.int32)
return image, label0, label1, label2, label3
# 获取图片数据和标签
image, label0, label1, label2, label3 = read_and_decode(TFRECORD_FILE)
#使用shuffle_batch可以随机打乱
image_batch, label_batch0, label_batch1, label_batch2, label_batch3 = tf.train.shuffle_batch(
[image, label0, label1, label2, label3], batch_size = BATCH_SIZE,
capacity = 50000, min_after_dequeue=10000, num_threads=1)
#定义网络结构
train_network_fn = nets_factory.get_network_fn(
'alexnet_v2',
num_classes=CHAR_SET_LEN,
weight_decay=0.0005,
is_training=True)
with tf.Session() as sess:
# inputs: a tensor of size [batch_size, height, width, channels]
X = tf.reshape(x, [BATCH_SIZE, 224, 224, 1])
# 数据输入网络得到输出值
logits0,logits1,logits2,logits3,end_points = train_network_fn(X)
# 把标签转成one_hot的形式
one_hot_labels0 = tf.one_hot(indices=tf.cast(y0, tf.int32), depth=CHAR_SET_LEN)
one_hot_labels1 = tf.one_hot(indices=tf.cast(y1, tf.int32), depth=CHAR_SET_LEN)
one_hot_labels2 = tf.one_hot(indices=tf.cast(y2, tf.int32), depth=CHAR_SET_LEN)
one_hot_labels3 = tf.one_hot(indices=tf.cast(y3, tf.int32), depth=CHAR_SET_LEN)
# 计算loss
loss0 = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=logits0,labels=one_hot_labels0))
loss1 = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=logits1,labels=one_hot_labels1))
loss2 = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=logits2,labels=one_hot_labels2))
loss3 = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=logits3,labels=one_hot_labels3))
# 计算总的loss
total_loss = (loss0+loss1+loss2+loss3)/4.0
# 优化total_loss
optimizer = tf.train.AdamOptimizer(learning_rate=lr).minimize(total_loss)
# 计算准确率
correct_prediction0 = tf.equal(tf.argmax(one_hot_labels0,1),tf.argmax(logits0,1))
accuracy0 = tf.reduce_mean(tf.cast(correct_prediction0,tf.float32))
correct_prediction1 = tf.equal(tf.argmax(one_hot_labels1,1),tf.argmax(logits1,1))
accuracy1 = tf.reduce_mean(tf.cast(correct_prediction1,tf.float32))
correct_prediction2 = tf.equal(tf.argmax(one_hot_labels2,1),tf.argmax(logits2,1))
accuracy2 = tf.reduce_mean(tf.cast(correct_prediction2,tf.float32))
correct_prediction3 = tf.equal(tf.argmax(one_hot_labels3,1),tf.argmax(logits3,1))
accuracy3 = tf.reduce_mean(tf.cast(correct_prediction3,tf.float32))
# 用于保存模型
saver = tf.train.Saver()
# 初始化
sess.run(tf.global_variables_initializer())
# 创建一个协调器，管理线程
coord = tf.train.Coordinator()
# 启动QueueRunner, 此时文件名队列已经进队
threads = tf.train.start_queue_runners(sess=sess, coord=coord)
for i in range(6001):
# 获取一个批次的数据和标签
b_image, b_label0, b_label1 ,b_label2 ,b_label3 = sess.run([image_batch, label_batch0, label_batch1, label_batch2, label_batch3])
# 优化模型
sess.run(optimizer, feed_dict={x: b_image, y0:b_label0, y1: b_label1, y2: b_label2, y3: b_label3})
# 每迭代20次计算一次loss和准确率
if i % 20 == 0:
# 每迭代2000次降低一次学习率
if i%2000 == 0:
sess.run(tf.assign(lr, lr/3))
acc0,acc1,acc2,acc3,loss_ = sess.run([accuracy0,accuracy1,accuracy2,accuracy3,total_loss],feed_dict={x: b_image,
y0: b_label0,
y1: b_label1,
y2: b_label2,
y3: b_label3})
learning_rate = sess.run(lr)
print ("Iter:%d Loss:%.3f Accuracy:%.2f,%.2f,%.2f,%.2f Learning_rate:%.4f" % (i,loss_,acc0,acc1,acc2,acc3,learning_rate))
# 保存模型
# if acc0 > 0.90 and acc1 > 0.90 and acc2 > 0.90 and acc3 > 0.90:
if i==4000:
saver.save(sess, "C:/Users/45374/tensorflow/captcha/models/crack_captcha.model", global_step=i)
break
# 通知其他线程关闭
coord.request_stop()
# 其他所有线程关闭之后，这一函数才能返回
coord.join(threads)模型最终收敛：
对应路径下生成训练好的模型文件：
