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import tensorflow as tfimport os import numpy as npdef get_files(file_dir): cats = [] label_cats = [] dogs = [] label_dogs = [] for file in os.listdir(file_dir): name = file.split(sep='.') if 'cat' in name[0]: cats.append(file_dir + file) label_cats.append(0) else: if 'dog' in name[0]: dogs.append(file_dir + file) label_dogs.append(1) image_list = np.hstack((cats,dogs)) label_list = np.hstack((label_cats,label_dogs)) # print('There are %d cats\nThere are %d dogs' %(len(cats), len(dogs))) # 多个种类分别的时候需要把多个种类放在一起，打乱顺序,这里不需要 # 把标签和图片都放倒一个 temp 中 然后打乱顺序，然后取出来 temp = np.array([image_list,label_list]) temp = temp.transpose() # 打乱顺序 np.random.shuffle(temp) # 取出第一个元素作为 image 第二个元素作为 label image_list = list(temp[:,0]) label_list = list(temp[:,1]) label_list = [int(i) for i in label_list]  return image_list,label_list# 测试 get_files# imgs , label = get_files('/Users/yangyibo/GitWork/python/' target='_blank'>pythonLean/AI/猫狗识别/testImg/')# for i in imgs:# print("img:",i)# for i in label:# print('label:',i)# 测试 get_files end# image_W ,image_H 指定图片大小，batch_size 每批读取的个数 ，capacity队列中 最多容纳元素的个数def get_batch(image,label,image_W,image_H,batch_size,capacity): # 转换数据为 ts 能识别的格式 image = tf.cast(image,tf.string) label = tf.cast(label, tf.int32) # 将image 和 label 放倒队列里  input_queue = tf.train.slice_input_producer([image,label]) label = input_queue[1] # 读取图片的全部信息 image_contents = tf.read_file(input_queue[0]) # 把图片解码，channels ＝3 为彩色图片, r，g ，b 黑白图片为 1 ，也可以理解为图片的厚度 image = tf.image.decode_jpeg(image_contents,channels =3) # 将图片以图片中心进行裁剪或者扩充为 指定的image_W，image_H image = tf.image.resize_image_with_crop_or_pad(image, image_W, image_H) # 对数据进行标准化,标准化，就是减去它的均值，除以他的方差 image = tf.image.per_image_standardization(image) # 生成批次 num_threads 有多少个线程根据电脑配置设置 capacity 队列中 最多容纳图片的个数 tf.train.shuffle_batch 打乱顺序， image_batch, label_batch = tf.train.batch([image, label],batch_size = batch_size, num_threads = 64, capacity = capacity) # 重新定义下 label_batch 的形状 label_batch = tf.reshape(label_batch , [batch_size]) # 转化图片 image_batch = tf.cast(image_batch,tf.float32) return image_batch, label_batch# test get_batch# import matplotlib.pyplot as plt# BATCH_SIZE = 2# CAPACITY = 256 # IMG_W = 208# IMG_H = 208# train_dir = '/Users/yangyibo/GitWork/PythonLean/AI/猫狗识别/testImg/'# image_list, label_list = get_files(train_dir)# image_batch, label_batch = get_batch(image_list, label_list, IMG_W, IMG_H, BATCH_SIZE, CAPACITY)# with tf.Session() as sess:# i = 0# # Coordinator 和 start_queue_runners 监控 queue 的状态，不停的入队出队# coord = tf.train.Coordinator()# threads = tf.train.start_queue_runners(coord=coord)# # coord.should_stop() 返回 true 时也就是 数据读完了应该调用 coord.request_stop()# try: #  while not coord.should_stop() and i<1:#   # 测试一个步#   img, label = sess.run([image_batch, label_batch])#   for j in np.arange(BATCH_SIZE):#    print('label: %d' %label[j])#    # 因为是个4D 的数据所以第一个为 索引 其他的为冒号就行了#    plt.imshow(img[j,:,:,:])#    plt.show()#   i+=1# # 队列中没有数据# except tf.errors.OutOfRangeError:#  print('done!')# finally:#  coord.request_stop()# coord.join(threads) # sess.close()

2. 设计神经网络

利用卷积神经网路处理，网络结构为

# conv1 卷积层 1# pooling1_lrn 池化层 1# conv2 卷积层 2# pooling2_lrn 池化层 2# local3 全连接层 1# local4 全连接层 2# softmax 全连接层 3

新建神经网络文件，文件名 model.py

#coding=utf-8 import tensorflow as tf def inference(images, batch_size, n_classes):  with tf.variable_scope('conv1') as scope:   # 卷积盒的为 3*3 的卷积盒，图片厚度是3，输出是16个featuremap  weights = tf.get_variable('weights',          shape=[3, 3, 3, 16],          dtype=tf.float32,          initializer=tf.truncated_normal_initializer(stddev=0.1, dtype=tf.float32))   biases = tf.get_variable('biases',          shape=[16],          dtype=tf.float32,          initializer=tf.constant_initializer(0.1))   conv = tf.nn.conv2d(images, weights, strides=[1, 1, 1, 1], padding='SAME')   pre_activation = tf.nn.bias_add(conv, biases)   conv1 = tf.nn.relu(pre_activation, name=scope.name)  with tf.variable_scope('pooling1_lrn') as scope:    pool1 = tf.nn.max_pool(conv1, ksize=[1, 3, 3, 1], strides=[1, 2, 2, 1], padding='SAME', name='pooling1')    norm1 = tf.nn.lrn(pool1, depth_radius=4, bias=1.0, alpha=0.001 / 9.0, beta=0.75, name='norm1')  with tf.variable_scope('conv2') as scope:     weights = tf.get_variable('weights',            shape=[3, 3, 16, 16],            dtype=tf.float32,            initializer=tf.truncated_normal_initializer(stddev=0.1, dtype=tf.float32))     biases = tf.get_variable('biases',            shape=[16],            dtype=tf.float32,            initializer=tf.constant_initializer(0.1))     conv = tf.nn.conv2d(norm1, weights, strides=[1, 1, 1, 1], padding='SAME')     pre_activation = tf.nn.bias_add(conv, biases)     conv2 = tf.nn.relu(pre_activation, name='conv2')  # pool2 and norm2  with tf.variable_scope('pooling2_lrn') as scope:   norm2 = tf.nn.lrn(conv2, depth_radius=4, bias=1.0, alpha=0.001 / 9.0, beta=0.75, name='norm2')   pool2 = tf.nn.max_pool(norm2, ksize=[1, 3, 3, 1], strides=[1, 1, 1, 1], padding='SAME', name='pooling2')  with tf.variable_scope('local3') as scope:   reshape = tf.reshape(pool2, shape=[batch_size, -1])   dim = reshape.get_shape()[1].value   weights = tf.get_variable('weights',          shape=[dim, 128],          dtype=tf.float32,          initializer=tf.truncated_normal_initializer(stddev=0.005, dtype=tf.float32))   biases = tf.get_variable('biases',          shape=[128],          dtype=tf.float32,          initializer=tf.constant_initializer(0.1))  local3 = tf.nn.relu(tf.matmul(reshape, weights) + biases, name=scope.name)  # local4  with tf.variable_scope('local4') as scope:   weights = tf.get_variable('weights',          shape=[128, 128],          dtype=tf.float32,          initializer=tf.truncated_normal_initializer(stddev=0.005, dtype=tf.float32))   biases = tf.get_variable('biases',          shape=[128],          dtype=tf.float32,          initializer=tf.constant_initializer(0.1))   local4 = tf.nn.relu(tf.matmul(local3, weights) + biases, name='local4')  # softmax  with tf.variable_scope('softmax_linear') as scope:   weights = tf.get_variable('softmax_linear',          shape=[128, n_classes],          dtype=tf.float32,          initializer=tf.truncated_normal_initializer(stddev=0.005, dtype=tf.float32))   biases = tf.get_variable('biases',          shape=[n_classes],          dtype=tf.float32,          initializer=tf.constant_initializer(0.1))   softmax_linear = tf.add(tf.matmul(local4, weights), biases, name='softmax_linear')  return softmax_linear def losses(logits, labels):  with tf.variable_scope('loss') as scope:   cross_entropy = tf.nn.sparse_softmax_cross_entropy_with_logits \      (logits=logits, labels=labels, name='xentropy_per_example')   loss = tf.reduce_mean(cross_entropy, name='loss')   tf.summary.scalar(scope.name + '/loss', loss)  return loss def trainning(loss, learning_rate):  with tf.name_scope('optimizer'):   optimizer = tf.train.AdamOptimizer(learning_rate= learning_rate)   global_step = tf.Variable(0, name='global_step', trainable=False)   train_op = optimizer.minimize(loss, global_step= global_step)  return train_op def evaluation(logits, labels):  with tf.variable_scope('accuracy') as scope:   correct = tf.nn.in_top_k(logits, labels, 1)   correct = tf.cast(correct, tf.float16)   accuracy = tf.reduce_mean(correct)   tf.summary.scalar(scope.name + '/accuracy', accuracy)  return accuracy

3. 训练数据，并将训练的模型存储

import os import numpy as np import tensorflow as tf import input_data  import model N_CLASSES = 2 # 2个输出神经元，［1，0］ 或者 ［0，1］猫和狗的概率IMG_W = 208 # 重新定义图片的大小，图片如果过大则训练比较慢 IMG_H = 208 BATCH_SIZE = 32 #每批数据的大小CAPACITY = 256 MAX_STEP = 15000 # 训练的步数，应当 >= 10000learning_rate = 0.0001 # 学习率，建议刚开始的 learning_rate <= 0.0001def run_training():  # 数据集 train_dir = '/Users/yangyibo/GitWork/pythonLean/AI/猫狗识别/img/' #My dir--20170727-csq  #logs_train_dir 存放训练模型的过程的数据，在tensorboard 中查看  logs_train_dir = '/Users/yangyibo/GitWork/pythonLean/AI/猫狗识别/saveNet/'  # 获取图片和标签集 train, train_label = input_data.get_files(train_dir)  # 生成批次 train_batch, train_label_batch = input_data.get_batch(train,                train_label,                IMG_W,                IMG_H,                BATCH_SIZE,                CAPACITY) # 进入模型 train_logits = model.inference(train_batch, BATCH_SIZE, N_CLASSES)  # 获取 loss  train_loss = model.losses(train_logits, train_label_batch) # 训练  train_op = model.trainning(train_loss, learning_rate) # 获取准确率  train__acc = model.evaluation(train_logits, train_label_batch)  # 合并 summary summary_op = tf.summary.merge_all()  sess = tf.Session() # 保存summary train_writer = tf.summary.FileWriter(logs_train_dir, sess.graph)  saver = tf.train.Saver()  sess.run(tf.global_variables_initializer())  coord = tf.train.Coordinator()  threads = tf.train.start_queue_runners(sess=sess, coord=coord)  try:   for step in np.arange(MAX_STEP):    if coord.should_stop():      break    _, tra_loss, tra_acc = sess.run([train_op, train_loss, train__acc])    if step % 50 == 0:     print('Step %d, train loss = %.2f, train accuracy = %.2f%%' %(step, tra_loss, tra_acc*100.0))     summary_str = sess.run(summary_op)     train_writer.add_summary(summary_str, step)    if step % 2000 == 0 or (step + 1) == MAX_STEP:     # 每隔2000步保存一下模型，模型保存在 checkpoint_path 中    checkpoint_path = os.path.join(logs_train_dir, 'model.ckpt')     saver.save(sess, checkpoint_path, global_step=step)  except tf.errors.OutOfRangeError:   print('Done training -- epoch limit reached')  finally:   coord.request_stop() coord.join(threads)  sess.close() # trainrun_training()

关于保存的模型怎么使用将在下一篇文章中展示。

TensorFlow 卷积神经网络之使用训练好的模型识别猫狗图片

如果需要训练数据集可以评论留下联系方式。

原文完整代码地址：

https://github.com/527515025/My-TensorFlow-tutorials/tree/master/猫狗识别

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