tensorflow猫狗识别 Python通过TensorFlow卷积神经网络实现猫狗识别

import tensorflow as tf
import os 
import numpy as np
def 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/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()

# 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

#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

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 >= 10000
learning_rate = 0.0001 # 学习率，建议刚开始的 learning_rate <= 0.0001
def 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() 
# train
run_training()