竞赛 深度学习猫狗分类 - python opencv cnn

0 前言

🔥 优质竞赛项目系列，今天要分享的是

🚩 **基于深度学习猫狗分类 **

该项目较为新颖，适合作为竞赛课题方向，学长非常推荐！

🥇学长这里给一个题目综合评分(每项满分5分)

• 难度系数：3分
• 工作量：3分
• 创新点：3分

🧿 更多资料, 项目分享：

https://gitee.com/dancheng-senior/postgraduate

1 课题背景

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。
本项目的主要目标是开发一个可以识别猫狗图像的系统。分析输入图像，然后预测输出。实现的模型可以根据需要扩展到网站或任何移动设备。我们的主要目标是让模型学习猫和狗的各种独特特征。一旦模型的训练完成，它将能够区分猫和狗的图像。

2 使用CNN进行猫狗分类

卷积神经网络 (CNN)
是一种算法，将图像作为输入，然后为图像的所有方面分配权重和偏差，从而区分彼此。神经网络可以通过使用成批的图像进行训练，每个图像都有一个标签来识别图像的真实性质（这里是猫或狗）。一个批次可以包含十分之几到数百个图像。

对于每张图像，将网络预测与相应的现有标签进行比较，并评估整个批次的网络预测与真实值之间的距离。然后，修改网络参数以最小化距离，从而增加网络的预测能力。类似地，每个批次的训练过程都是类似的。

3 数据集处理

猫狗照片的数据集直接从kaggle官网下载即可，下载后解压，这是我下载的数据：

相关代码

​

import os,shutiloriginal_data_dir = "G:/Data/Kaggle/dogcat/train"base_dir = "G:/Data/Kaggle/dogcat/smallData"if os.path.isdir(base_dir) == False:os.mkdir(base_dir)# 创建三个文件夹用来存放不同的数据:train,validation,testtrain_dir = os.path.join(base_dir,'train')if os.path.isdir(train_dir) == False:os.mkdir(train_dir)validation_dir = os.path.join(base_dir,'validation')if os.path.isdir(validation_dir) == False:os.mkdir(validation_dir)test_dir = os.path.join(base_dir,'test')if os.path.isdir(test_dir) == False:os.mkdir(test_dir)# 在文件中:train,validation,test分别创建cats,dogs文件夹用来存放对应的数据train_cats_dir = os.path.join(train_dir,'cats')if os.path.isdir(train_cats_dir) == False:os.mkdir(train_cats_dir)train_dogs_dir = os.path.join(train_dir,'dogs')if os.path.isdir(train_dogs_dir) == False:os.mkdir(train_dogs_dir)validation_cats_dir = os.path.join(validation_dir,'cats')if os.path.isdir(validation_cats_dir) == False:os.mkdir(validation_cats_dir)validation_dogs_dir = os.path.join(validation_dir,'dogs')if os.path.isdir(validation_dogs_dir) == False:os.mkdir(validation_dogs_dir)test_cats_dir = os.path.join(test_dir,'cats')if os.path.isdir(test_cats_dir) == False:os.mkdir(test_cats_dir)test_dogs_dir = os.path.join(test_dir,'dogs')if os.path.isdir(test_dogs_dir) == False:os.mkdir(test_dogs_dir)#将原始数据拷贝到对应的文件夹中 catfnames = ['cat.{}.jpg'.format(i) for i in range(1000)]for fname in fnames:src = os.path.join(original_data_dir,fname)dst = os.path.join(train_cats_dir,fname)shutil.copyfile(src,dst)fnames = ['cat.{}.jpg'.format(i) for i in range(1000,1500)]for fname in fnames:src = os.path.join(original_data_dir,fname)dst = os.path.join(validation_cats_dir,fname)shutil.copyfile(src,dst)fnames = ['cat.{}.jpg'.format(i) for i in range(1500,2000)]for fname in fnames:src = os.path.join(original_data_dir,fname)dst = os.path.join(test_cats_dir,fname)shutil.copyfile(src,dst)#将原始数据拷贝到对应的文件夹中 dog
fnames = ['dog.{}.jpg'.format(i) for i in range(1000)]
for fname in fnames:src = os.path.join(original_data_dir,fname)dst = os.path.join(train_dogs_dir,fname)shutil.copyfile(src,dst)fnames = ['dog.{}.jpg'.format(i) for i in range(1000,1500)]
for fname in fnames:src = os.path.join(original_data_dir,fname)dst = os.path.join(validation_dogs_dir,fname)shutil.copyfile(src,dst)fnames = ['dog.{}.jpg'.format(i) for i in range(1500,2000)]
for fname in fnames:src = os.path.join(original_data_dir,fname)dst = os.path.join(test_dogs_dir,fname)shutil.copyfile(src,dst)
print('train cat images:', len(os.listdir(train_cats_dir)))
print('train dog images:', len(os.listdir(train_dogs_dir)))
print('validation cat images:', len(os.listdir(validation_cats_dir)))
print('validation dog images:', len(os.listdir(validation_dogs_dir)))
print('test cat images:', len(os.listdir(test_cats_dir)))
print('test dog images:', len(os.listdir(test_dogs_dir)))
train cat images: 1000
train dog images: 1000
validation cat images: 500
validation dog images: 500
test cat images: 500
test dog images: 500

4 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)

5 Tensorflow计算图的构建

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。

6 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。

7 预测效果

选取三张图片测试


可见，模型准确率还是较高的。

8 最后

🧿 更多资料, 项目分享：

https://gitee.com/dancheng-senior/postgraduate