cnn算法如何实现猫狗分类，Python代码实现

下面是使用CNN实现猫狗分类的Python代码实现：

# 导入必要的库
import os
import cv2
import numpy as np
from random import shuffle
from tqdm import tqdm

TRAIN_DIR = 'train' # 训练集路径
TEST_DIR = 'test' # 测试集路径
IMG_SIZE = 50 # 图片大小
LR = 1e-3 # 学习率

MODEL_NAME = 'dogsvscats-{}-{}.model'.format(LR, '2conv-basic') # 模型名称

# 狗和猫的标签
def label_img(img):
    word_label = img.split('.')[-3]
    if word_label == 'cat': return [1,0]
    elif word_label == 'dog': return [0,1]

# 加载训练集数据
def create_train_data():
    training_data = []
    for img in tqdm(os.listdir(TRAIN_DIR)):
        label = label_img(img)
        path = os.path.join(TRAIN_DIR,img)
        img = cv2.imread(path,cv2.IMREAD_GRAYSCALE)
        img = cv2.resize(img, (IMG_SIZE,IMG_SIZE))
        training_data.append([np.array(img),np.array(label)])
    shuffle(training_data)
    np.save('train_data.npy', training_data)
    return training_data

# 加载测试集数据
def process_test_data():
    testing_data = []
    for img in tqdm(os.listdir(TEST_DIR)):
        path = os.path.join(TEST_DIR,img)
        img_num = img.split('.')[0]
        img = cv2.imread(path,cv2.IMREAD_GRAYSCALE)
        img = cv2.resize(img, (IMG_SIZE,IMG_SIZE))
        testing_data.append([np.array(img), img_num])
    np.save('test_data.npy', testing_data)
    return testing_data

# 构建CNN模型
def cnn_model():
    model = Sequential()
    model.add(Conv2D(32, (3, 3), input_shape=(IMG_SIZE, IMG_SIZE, 1)))
    model.add(Activation('relu'))
    model.add(MaxPooling2D(pool_size=(2, 2)))

    model.add(Conv2D(64, (3, 3)))
    model.add(Activation('relu'))
    model.add(MaxPooling2D(pool_size=(2, 2)))

    model.add(Flatten())
    model.add(Dense(64))
    model.add(Activation('relu'))

    model.add(Dense(2))
    model.add(Activation('softmax'))

    model.compile(loss='categorical_crossentropy',
                  optimizer=Adam(lr=LR),
                  metrics=['accuracy'])

    return model

# 训练模型
def train_model():
    train_data = np.load('train_data.npy', allow_pickle=True)
    train = train_data[:-500]
    test = train_data[-500:]

    X = np.array([i[0] for i in train]).reshape(-1,IMG_SIZE,IMG_SIZE,1)
    y = [i[1] for i in train]

    test_x = np.array([i[0] for i in test]).reshape(-1,IMG_SIZE,IMG_SIZE,1)
    test_y = [i[1] for i in test]

    model = cnn_model()

    model.fit(X, y, batch_size=32, epochs=10, validation_data=(test_x, test_y))

    model.save(MODEL_NAME)

    return model

# 预测结果
def predict(model):
    test_data = np.load('test_data.npy', allow_pickle=True)
    fig = plt.figure()

    for num,data in enumerate(test_data[:12]):
        img_num = data[1]
        img_data = data[0]

        y = fig.add_subplot(3,4,num+1)
        orig = img_data
        data = img_data.reshape(IMG_SIZE,IMG_SIZE,1)
        model_out = model.predict([data])[0]

        if np.argmax(model_out) == 1: str_label='Dog'
        else: str_label='Cat'

        y.imshow(orig,cmap='gray')
        plt.title(str_label)
        y.axes.get_xaxis().set_visible(False)
        y.axes.get_yaxis().set_visible(False)
    plt.show()

# 主函数
def main():
    if not os.path.exists('train_data.npy'):
        train_data = create_train_data()
    else:
        train_data = np.load('train_data.npy', allow_pickle=True)

    if not os.path.exists('test_data.npy'):
        test_data = process_test_data()
    else:
        test_data = np.load('test_data.npy', allow_pickle=True)

    model = train_model()
    predict(model)

main()

这个代码实现了一个简单的CNN模型，对猫和狗的图片进行分类。你可以根据自己的需要进行修改和扩展。