三、训练识别模型
首先先拷贝一个nets文件夹，主要使用的是文件夹下的两个文件nets_factory.py、alexnet.py，用于导入训练使用的网络alexnet。
nets_factory.py
# Copyright 2016 The TensorFlow Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== """Contains a factory for building various models.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function import functools import tensorflow as tf from nets import alexnet from nets import cifarnet from nets import inception from nets import lenet from nets import overfeat from nets import resnet_v1 from nets import resnet_v2 from nets import vgg slim = tf.contrib.slim networks_map = {'alexnet_v2': alexnet.alexnet_v2, 'cifarnet': cifarnet.cifarnet, 'overfeat': overfeat.overfeat, 'vgg_a': vgg.vgg_a, 'vgg_16': vgg.vgg_16, 'vgg_19': vgg.vgg_19, 'inception_v1': inception.inception_v1, 'inception_v2': inception.inception_v2, 'inception_v3': inception.inception_v3, 'inception_v4': inception.inception_v4, 'inception_resnet_v2': inception.inception_resnet_v2, 'lenet': lenet.lenet, 'resnet_v1_50': resnet_v1.resnet_v1_50, 'resnet_v1_101': resnet_v1.resnet_v1_101, 'resnet_v1_152': resnet_v1.resnet_v1_152, 'resnet_v1_200': resnet_v1.resnet_v1_200, 'resnet_v2_50': resnet_v2.resnet_v2_50, 'resnet_v2_101': resnet_v2.resnet_v2_101, 'resnet_v2_152': resnet_v2.resnet_v2_152, 'resnet_v2_200': resnet_v2.resnet_v2_200, } arg_scopes_map = {'alexnet_v2': alexnet.alexnet_v2_arg_scope, 'cifarnet': cifarnet.cifarnet_arg_scope, 'overfeat': overfeat.overfeat_arg_scope, 'vgg_a': vgg.vgg_arg_scope, 'vgg_16': vgg.vgg_arg_scope, 'vgg_19': vgg.vgg_arg_scope, 'inception_v1': inception.inception_v3_arg_scope, 'inception_v2': inception.inception_v3_arg_scope, 'inception_v3': inception.inception_v3_arg_scope, 'inception_v4': inception.inception_v4_arg_scope, 'inception_resnet_v2': inception.inception_resnet_v2_arg_scope, 'lenet': lenet.lenet_arg_scope, 'resnet_v1_50': resnet_v1.resnet_arg_scope, 'resnet_v1_101': resnet_v1.resnet_arg_scope, 'resnet_v1_152': resnet_v1.resnet_arg_scope, 'resnet_v1_200': resnet_v1.resnet_arg_scope, 'resnet_v2_50': resnet_v2.resnet_arg_scope, 'resnet_v2_101': resnet_v2.resnet_arg_scope, 'resnet_v2_152': resnet_v2.resnet_arg_scope, 'resnet_v2_200': resnet_v2.resnet_arg_scope, } def get_network_fn(name, num_classes, weight_decay=0.0, is_training=False): """Returns a network_fn such as `logits, end_points = network_fn(images)`. Args: name: The name of the network. num_classes: The number of classes to use for classification. weight_decay: The l2 coefficient for the model weights. is_training: `True` if the model is being used for training and `False` otherwise. Returns: network_fn: A function that applies the model to a batch of images. It has the following signature: logits, end_points = network_fn(images) Raises: ValueError: If network `name` is not recognized. """ if name not in networks_map: raise ValueError('Name of network unknown %s' % name) func = networks_map[name] @functools.wraps(func) def network_fn(images): arg_scope = arg_scopes_map[name](weight_decay=weight_decay) with slim.arg_scope(arg_scope): return func(images, num_classes, is_training=is_training) if hasattr(func, 'default_image_size'): network_fn.default_image_size = func.default_image_size return network_fn
alexnet.py
对源码做出一定的修改，前面的卷积和池化作为共享层保持不变，主要就是修改最后的输出。net0-net3
# Copyright 2016 The TensorFlow Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== """Contains a model definition for AlexNet. This work was first described in: ImageNet Classification with Deep Convolutional Neural Networks Alex Krizhevsky, Ilya Sutskever and Geoffrey E. Hinton and later refined in: One weird trick for parallelizing convolutional neural networks Alex Krizhevsky, 2014 Here we provide the implementation proposed in "One weird trick" and not "ImageNet Classification", as per the paper, the LRN layers have been removed. Usage: with slim.arg_scope(alexnet.alexnet_v2_arg_scope()): outputs, end_points = alexnet.alexnet_v2(inputs) @@alexnet_v2 """ from __future__ import absolute_import from __future__ import division from __future__ import print_function import tensorflow as tf slim = tf.contrib.slim trunc_normal = lambda stddev: tf.truncated_normal_initializer(0.0, stddev) def alexnet_v2_arg_scope(weight_decay=0.0005): with slim.arg_scope([slim.conv2d, slim.fully_connected], activation_fn=tf.nn.relu, biases_initializer=tf.constant_initializer(0.1), weights_regularizer=slim.l2_regularizer(weight_decay)): with slim.arg_scope([slim.conv2d], padding='SAME'): with slim.arg_scope([slim.max_pool2d], padding='VALID') as arg_sc: return arg_sc def alexnet_v2(inputs, num_classes=1000, is_training=True, dropout_keep_prob=0.5, spatial_squeeze=True, scope='alexnet_v2'): """AlexNet version 2. Described in: http://arxiv.org/pdf/1404.5997v2.pdf Parameters from: github.com/akrizhevsky/cuda-convnet2/blob/master/layers/ layers-imagenet-1gpu.cfg Note: All the fully_connected layers have been transformed to conv2d layers. To use in classification mode, resize input to 224x224. To use in fully convolutional mode, set spatial_squeeze to false. The LRN layers have been removed and change the initializers from random_normal_initializer to xavier_initializer. Args: inputs: a tensor of size [batch_size, height, width, channels]. num_classes: number of predicted classes. is_training: whether or not the model is being trained. dropout_keep_prob: the probability that activations are kept in the dropout layers during training. spatial_squeeze: whether or not should squeeze the spatial dimensions of the outputs. Useful to remove unnecessary dimensions for classification. scope: Optional scope for the variables. Returns: the last op containing the log predictions and end_points dict. """ with tf.variable_scope(scope, 'alexnet_v2', [inputs]) as sc: end_points_collection = sc.name + '_end_points' # Collect outputs for conv2d, fully_connected and max_pool2d. with slim.arg_scope([slim.conv2d, slim.fully_connected, slim.max_pool2d], outputs_collections=[end_points_collection]): net = slim.conv2d(inputs, 64, [11, 11], 4, padding='VALID', scope='conv1') net = slim.max_pool2d(net, [3, 3], 2, scope='pool1') net = slim.conv2d(net, 192, [5, 5], scope='conv2') net = slim.max_pool2d(net, [3, 3], 2, scope='pool2') net = slim.conv2d(net, 384, [3, 3], scope='conv3') net = slim.conv2d(net, 384, [3, 3], scope='conv4') net = slim.conv2d(net, 256, [3, 3], scope='conv5') net = slim.max_pool2d(net, [3, 3], 2, scope='pool5') # Use conv2d instead of fully_connected layers. with slim.arg_scope([slim.conv2d], weights_initializer=trunc_normal(0.005), biases_initializer=tf.constant_initializer(0.1)): net = slim.conv2d(net, 4096, [5, 5], padding='VALID', scope='fc6') net = slim.dropout(net, dropout_keep_prob, is_training=is_training, scope='dropout6') net = slim.conv2d(net, 4096, [1, 1], scope='fc7') net = slim.dropout(net, dropout_keep_prob, is_training=is_training, scope='dropout7') net0 = slim.conv2d(net, num_classes, [1, 1], activation_fn=None, normalizer_fn=None, biases_initializer=tf.zeros_initializer(), scope='fc8_0') net1 = slim.conv2d(net, num_classes, [1, 1], activation_fn=None, normalizer_fn=None, biases_initializer=tf.zeros_initializer(), scope='fc8_1') net2 = slim.conv2d(net, num_classes, [1, 1], activation_fn=None, normalizer_fn=None, biases_initializer=tf.zeros_initializer(), scope='fc8_2') net3 = slim.conv2d(net, num_classes, [1, 1], activation_fn=None, normalizer_fn=None, biases_initializer=tf.zeros_initializer(), scope='fc8_3') # Convert end_points_collection into a end_point dict. end_points = slim.utils.convert_collection_to_dict(end_points_collection) if spatial_squeeze: net0 = tf.squeeze(net0, [1, 2], name='fc8_0/squeezed') end_points[sc.name + '/fc8_0'] = net0 net1 = tf.squeeze(net1, [1, 2], name='fc8_1/squeezed') end_points[sc.name + '/fc8_1'] = net1 net2 = tf.squeeze(net2, [1, 2], name='fc8_2/squeezed') end_points[sc.name + '/fc8_2'] = net2 net3 = tf.squeeze(net3, [1, 2], name='fc8_3/squeezed') end_points[sc.name + '/fc8_3'] = net3 return net0,net1,net2,net3,end_points alexnet_v2.default_image_size = 224
train.py
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 = "F:/PyCharm-projects/第十周/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 # In[3]: # 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(4001): # 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50次计算一次loss和准确率 if i % 50 == 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, "./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)
Iter:0 Loss:2315.252 Accuracy:0.20,0.28,0.20,0.12 Learning_rate:0.0010
Iter:50 Loss:2.312 Accuracy:0.08,0.08,0.00,0.04 Learning_rate:0.0010
......
Iter:3850 Loss:0.055 Accuracy:0.96,0.96,1.00,0.96 Learning_rate:0.0003
Iter:3900 Loss:0.041 Accuracy:1.00,0.92,1.00,1.00 Learning_rate:0.0003
Iter:3950 Loss:0.025 Accuracy:1.00,1.00,1.00,1.00 Learning_rate:0.0003
从train.tfrecord读出数据和标签，打乱，将数据送入alexnet网络得到输出值，将输出的标签转化为one_hot形式，计算loss，对loss求和得total_loss并用优化器优化。计算准确率，迭代40001次，保存模型。
四、测试模型
import os import tensorflow as tf from PIL import Image from nets import nets_factory import numpy as np import matplotlib.pyplot as plt # 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 = 1 # tfrecord文件存放路径 TFRECORD_FILE = "F:/PyCharm-projects/第十周/test.tfrecords" # placeholder x = tf.placeholder(tf.float32, [None, 224, 224]) # 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) # 没有经过预处理的灰度图 image_raw = tf.reshape(image, [224, 224]) # 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, image_raw, label0, label1, label2, label3 # In[3]: # 获取图片数据和标签 image, image_raw, label0, label1, label2, label3 = read_and_decode(TFRECORD_FILE) #使用shuffle_batch可以随机打乱 image_batch, image_raw_batch, label_batch0, label_batch1, label_batch2, label_batch3 = tf.train.shuffle_batch( [image, image_raw, 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=False) 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) # 预测值 predict0 = tf.reshape(logits0, [-1, CHAR_SET_LEN]) predict0 = tf.argmax(predict0, 1) predict1 = tf.reshape(logits1, [-1, CHAR_SET_LEN]) predict1 = tf.argmax(predict1, 1) predict2 = tf.reshape(logits2, [-1, CHAR_SET_LEN]) predict2 = tf.argmax(predict2, 1) predict3 = tf.reshape(logits3, [-1, CHAR_SET_LEN]) predict3 = tf.argmax(predict3, 1) # 初始化 sess.run(tf.global_variables_initializer()) # 载入训练好的模型 saver = tf.train.Saver() saver.restore(sess,'./captcha/models/crack_captcha.model-4000') # 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(10): # 获取一个批次的数据和标签 b_image, b_image_raw, b_label0, b_label1 ,b_label2 ,b_label3 = sess.run([image_batch, image_raw_batch, label_batch0, label_batch1, label_batch2, label_batch3]) # 显示图片 img=Image.fromarray(b_image_raw[0],'L') plt.imshow(img) plt.axis('off') plt.show() # 打印标签 print('label:',b_label0, b_label1 ,b_label2 ,b_label3) # 预测 label0,label1,label2,label3 = sess.run([predict0,predict1,predict2,predict3], feed_dict={x: b_image}) # 打印预测值 print('predict:',label0,label1,label2,label3) # 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)
label: [5] [1] [3] [7] predict: [5] [0] [3] [7]
label: [6] [3] [5] [0] predict: [6] [3] [5] [0]
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