深度学习-使用tensorflow实现猫狗识别

最近一直在撸猫，为了猫主子的事情忧三愁四，皱纹多了不少，头发也掉了好几根，神态也多了几分忧郁，唯一不变的还是那份闲鱼的懒散和浪荡的心。

要说到深度学习图像分类的经典案例之一，那就是猫狗大战了。猫和狗在外观上的差别还是挺明显的，无论是体型、四肢、脸庞和毛发等等，都是能通过肉眼很容易区分的。

那么如何让机器来识别猫和狗呢？这就需要使用卷积神经网络来实现了。

网上已经有不少人写过这案例了，我也来尝试下练练手。

完整代码：https://github.com/ADlead/Dogs-Cats.git

一. 数据集的准备

猫狗照片的数据集直接从kaggle官网(https://www.kaggle.com/c/dogs-vs-cats)下载即可，下载后解压，可以看到有训练集和测试集

train文件夹内容如下：

test1文件夹内容如下：

编写代码将图片存储成batch文件

import cv2 as cv
import os
import numpy as np
import random
import pickle
import timestart_time = time.time()data_dir = './data'
batch_save_path = './batch_files'# 创建batch文件存储的文件夹
os.makedirs(batch_save_path, exist_ok=True)# 图片统一大小：100 * 100
# 训练集 20000：100个batch文件，每个文件200张图片
# 验证集 5000：一个测试文件，测试时 50张 x 100 批次# 进入图片数据的目录，读取图片信息
all_data_files = os.listdir(os.path.join(data_dir, 'train/'))# print(all_data_files)# 打算数据的顺序
random.shuffle(all_data_files)all_train_files = all_data_files[:20000]
all_test_files = all_data_files[20000:]train_data = []
train_label = []
train_filenames = []test_data = []
test_label = []
test_filenames = []# 训练集
for each in all_train_files:img = cv.imread(os.path.join(data_dir,'train/',each),1)resized_img = cv.resize(img, (100,100))img_data = np.array(resized_img)train_data.append(img_data)if 'cat' in each:train_label.append(0)elif 'dog' in each:train_label.append(1)else:raise Exception('%s is wrong train file'%(each))train_filenames.append(each)# 测试集
for each in all_test_files:img = cv.imread(os.path.join(data_dir,'train/',each), 1)resized_img = cv.resize(img, (100,100))img_data = np.array(resized_img)test_data.append(img_data)if 'cat' in each:test_label.append(0)elif 'dog' in each:test_label.append(1)else:raise Exception('%s is wrong test file'%(each))test_filenames.append(each)print(len(train_data), len(test_data))# 制作100个batch文件
start = 0
end = 200
for num in range(1, 101):batch_data = train_data[start: end]batch_label = train_label[start: end]batch_filenames = train_filenames[start: end]batch_name = 'training batch {} of 15'.format(num)all_data = {'data':batch_data,'label':batch_label,'filenames':batch_filenames,'name':batch_name}with open(os.path.join(batch_save_path, 'train_batch_{}'.format(num)), 'wb') as f:pickle.dump(all_data, f)start += 200end += 200# 制作测试文件
all_test_data = {'data':test_data,'label':test_label,'filenames':test_filenames,'name':'test batch 1 of 1'}with open(os.path.join(batch_save_path, 'test_batch'), 'wb') as f:pickle.dump(all_test_data, f)end_time = time.time()
print('制作结束, 用时{}秒'.format(end_time - start_time))

运行程序后，文件就处理好了

二. 神经网络的编写

cnn卷积神经网络的编写如下，编写卷积层、池化层和全连接层的代码

conv1_1 = tf.layers.conv2d(x, 16, (3, 3), padding='same', activation=tf.nn.relu, name='conv1_1')
conv1_2 = tf.layers.conv2d(conv1_1, 16, (3, 3), padding='same', activation=tf.nn.relu, name='conv1_2')
pool1 = tf.layers.max_pooling2d(conv1_2, (2, 2), (2, 2), name='pool1')
conv2_1 = tf.layers.conv2d(pool1, 32, (3, 3), padding='same', activation=tf.nn.relu, name='conv2_1')
conv2_2 = tf.layers.conv2d(conv2_1, 32, (3, 3), padding='same', activation=tf.nn.relu, name='conv2_2')
pool2 = tf.layers.max_pooling2d(conv2_2, (2, 2), (2, 2), name='pool2')
conv3_1 = tf.layers.conv2d(pool2, 64, (3, 3), padding='same', activation=tf.nn.relu, name='conv3_1')
conv3_2 = tf.layers.conv2d(conv3_1, 64, (3, 3), padding='same', activation=tf.nn.relu, name='conv3_2')
pool3 = tf.layers.max_pooling2d(conv3_2, (2, 2), (2, 2), name='pool3')
conv4_1 = tf.layers.conv2d(pool3, 128, (3, 3), padding='same', activation=tf.nn.relu, name='conv4_1')
conv4_2 = tf.layers.conv2d(conv4_1, 128, (3, 3), padding='same', activation=tf.nn.relu, name='conv4_2')
pool4 = tf.layers.max_pooling2d(conv4_2, (2, 2), (2, 2), name='pool4')flatten = tf.layers.flatten(pool4)
fc1 = tf.layers.dense(flatten, 512, tf.nn.relu)
fc1_dropout = tf.nn.dropout(fc1, keep_prob=keep_prob)
fc2 = tf.layers.dense(fc1, 256, tf.nn.relu)
fc2_dropout = tf.nn.dropout(fc2, keep_prob=keep_prob)
fc3 = tf.layers.dense(fc2, 2, None)

三. tensorflow计算图的搭建

然后，再搭建tensorflow的计算图，定义占位符，计算损失函数、预测值和准确率等等

self.x = tf.placeholder(tf.float32, [None, IMAGE_SIZE, IMAGE_SIZE, 3], 'input_data')
self.y = tf.placeholder(tf.int64, [None], 'output_data')
self.keep_prob = tf.placeholder(tf.float32)# 图片输入网络中
fc = self.conv_net(self.x, self.keep_prob)
self.loss = tf.losses.sparse_softmax_cross_entropy(labels=self.y, logits=fc)
self.y_ = tf.nn.softmax(fc) # 计算每一类的概率
self.predict = tf.argmax(fc, 1)
self.acc = tf.reduce_mean(tf.cast(tf.equal(self.predict, self.y), tf.float32))
self.train_op = tf.train.AdamOptimizer(LEARNING_RATE).minimize(self.loss)
self.saver = tf.train.Saver(max_to_keep=1)

最后的saver是要将训练好的模型保存到本地。

四. 模型的训练和测试

然后编写训练部分的代码，训练步骤为1万步

acc_list = []
with tf.Session() as sess:sess.run(tf.global_variables_initializer())for i in range(TRAIN_STEP):train_data, train_label, _ = self.batch_train_data.next_batch(TRAIN_SIZE)eval_ops = [self.loss, self.acc, self.train_op]eval_ops_results = sess.run(eval_ops, feed_dict={self.x:train_data,self.y:train_label,self.keep_prob:0.7})loss_val, train_acc = eval_ops_results[0:2]acc_list.append(train_acc)if (i+1) % 100 == 0:acc_mean = np.mean(acc_list)print('step:{0},loss:{1:.5},acc:{2:.5},acc_mean:{3:.5}'.format(i+1,loss_val,train_acc,acc_mean))if (i+1) % 1000 == 0:test_acc_list = []for j in range(TEST_STEP):test_data, test_label, _ = self.batch_test_data.next_batch(TRAIN_SIZE)acc_val = sess.run([self.acc],feed_dict={self.x:test_data,self.y:test_label,self.keep_prob:1.0})test_acc_list.append(acc_val)print('[Test ] step:{0}, mean_acc:{1:.5}'.format(i+1, np.mean(test_acc_list)))# 保存训练后的模型os.makedirs(SAVE_PATH, exist_ok=True)self.saver.save(sess, SAVE_PATH + 'my_model.ckpt')

训练结果如下

训练1万步后模型测试的平均准确率有0.82。

五. 识别和分类

最后，使用自己训练好的模型，把官网的测试图片(共12500张)识别后进行分类，并将分类后的图片分别写入到两个文件夹中，结果如下

分类为狗的图片如下：

分类为猫的图片如下：

可以看出，分类之后还有少数分类不对的结果。看来模型还有待提升，还可以调整网络参数、调整学习率和学习步数、使用图像增强等技术对模型识别准确率进行提高。

搞了一番，手和脚也有些累了。是时候撸撸猫继续闲鱼了。。

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