1. 数据准备

在文件夹下分别建立训练目录train，验证目录validation，测试目录test，每个目录下建立dogs和cats两个目录，在dogs和cats目录下分别放入拍摄的狗和猫的图片，图片的大小可以不一样。

2. 数据读取

# 存储数据集的目录base_dir = 'E:/python learn/dog_and_cat/data/' # 训练、验证数据集的目录train_dir = os.path.join(base_dir, 'train')validation_dir = os.path.join(base_dir, 'validation')test_dir = os.path.join(base_dir, 'test') # 猫训练图片所在目录train_cats_dir = os.path.join(train_dir, 'cats') # 狗训练图片所在目录train_dogs_dir = os.path.join(train_dir, 'dogs') # 猫验证图片所在目录validation_cats_dir = os.path.join(validation_dir, 'cats') # 狗验证数据集所在目录validation_dogs_dir = os.path.join(validation_dir, 'dogs') print('total training cat images:', len(os.listdir(train_cats_dir))) print('total training dog images:', len(os.listdir(train_dogs_dir))) print('total validation cat images:', len(os.listdir(validation_cats_dir))) print('total validation dog images:', len(os.listdir(validation_dogs_dir)))

3. 模型建立

# 搭建模型model = Sequential()model.add(Conv2D(32, (3, 3), activation='relu', input_shape=(150, 150, 3)))model.add(MaxPooling2D((2, 2)))model.add(Conv2D(64, (3, 3), activation='relu'))model.add(MaxPooling2D((2, 2)))model.add(Conv2D(128, (3, 3), activation='relu'))model.add(MaxPooling2D((2, 2)))model.add(Conv2D(128, (3, 3), activation='relu'))model.add(MaxPooling2D((2, 2)))model.add(Flatten())model.add(Dense(512, activation='relu'))model.add(Dense(1, activation='sigmoid')) print(model.summary()) model.compile(loss='binary_crossentropy', optimizer=RMSprop(lr=1e-4), metrics=['acc'])

4. 模型训练

train_datagen = ImageDataGenerator(rescale=1./255)test_datagen = ImageDataGenerator(rescale=1./255) train_generator = train_datagen.flow_from_directory( train_dir, # target directory target_size=(150, 150), # resize图片 batch_size=20, class_mode='binary') validation_generator = test_datagen.flow_from_directory( validation_dir, target_size=(150, 150), batch_size=20, class_mode='binary') for data_batch, labels_batch in train_generator: print('data batch shape:', data_batch.shape) print('labels batch shape:', labels_batch.shape) break hist = model.fit_generator( train_generator, steps_per_epoch=100, epochs=10, validation_data=validation_generator, validation_steps=50) model.save('cats_and_dogs_small_1.h5')

5. 模型评估

acc = hist.history['acc']val_acc = hist.history['val_acc']loss = hist.history['loss']val_loss = hist.history['val_loss'] epochs = range(len(acc)) plt.plot(epochs, acc, 'bo', label='Training acc')plt.plot(epochs, val_acc, 'b', label='Validation acc')plt.title('Training and validation accuracy') plt.legend()plt.figure() plt.figure()plt.plot(epochs, loss, 'bo', label='Training loss')plt.plot(epochs, val_loss, 'b', label='Validation loss')plt.legend()plt.show()

6. 预测

imagename = 'E:/python learn/dog_and_cat/data/validation/dogs/dog.2026.jpg'test_image = image.load_img(imagename, target_size = (150, 150))test_image = image.img_to_array(test_image)test_image = np.expand_dims(test_image, axis=0)result = model.predict(test_image) if result[0][0] == 1: prediction ='dog'else: prediction ='cat' print(prediction)

代码在spyder下运行正常，一般情况下，可以将文件分为两个部分，一部分为Train.py，包含深度学习模型建立、训练和模型的存储，另一部分Predict.py，包含模型的读取，评价和预测

补充知识：keras 猫狗大战自搭网络以及vgg16应用

导入模块

import osimport numpy as npimport tensorflow as tfimport randomimport seaborn as snsimport matplotlib.pyplot as pltimport kerasfrom keras.models import Sequential, Modelfrom keras.layers import Dense, Dropout, Activation, Flatten, Input,BatchNormalizationfrom keras.layers.convolutional import Conv2D, MaxPooling2Dfrom keras.optimizers import RMSprop, Adam, SGDfrom keras.preprocessing import imagefrom keras.preprocessing.image import ImageDataGeneratorfrom keras.applications.vgg16 import VGG16, preprocess_input from sklearn.model_selection import train_test_split

加载数据集

def read_and_process_image(data_dir,width=64, height=64, channels=3, preprocess=False): train_images= [data_dir + i for i in os.listdir(data_dir)] random.shuffle(train_images) def read_image(file_path, preprocess): img = image.load_img(file_path, target_size=(height, width)) x = image.img_to_array(img) x = np.expand_dims(x, axis=0) # if preprocess: # x = preprocess_input(x) return x def prep_data(images, proprocess): count = len(images) data = np.ndarray((count, height, width, channels), dtype = np.float32) for i, image_file in enumerate(images): image = read_image(image_file, preprocess) data[i] = image return data def read_labels(file_path): labels = [] for i in file_path: label = 1 if 'dog' in i else 0 labels.append(label) return labels X = prep_data(train_images, preprocess) labels = read_labels(train_images) assert X.shape[0] == len(labels) print("Train shape: {}".format(X.shape)) return X, labels

读取数据集

# 读取图片WIDTH = 150HEIGHT = 150CHANNELS = 3X, y = read_and_process_image('D:\\Python_Project\\train\\',width=WIDTH, height=HEIGHT, channels=CHANNELS)

查看数据集信息

# 统计ysns.countplot(y) # 显示图片def show_cats_and_dogs(X, idx): plt.figure(figsize=(10,5), frameon=True) img = X[idx,:,:,::-1] img = img/255 plt.imshow(img) plt.show() for idx in range(0,3): show_cats_and_dogs(X, idx) train_X = X[0:17500,:,:,:]train_y = y[0:17500]test_X = X[17500:25000,:,:,:]test_y = y[17500:25000]train_X.shapetest_X.shape

自定义神经网络层数

input_layer = Input((WIDTH, HEIGHT, CHANNELS))# 第一层z = input_layerz = Conv2D(64, (3,3))(z)z = BatchNormalization()(z)z = Activation('relu')(z)z = MaxPooling2D(pool_size = (2,2))(z) z = Conv2D(64, (3,3))(z)z = BatchNormalization()(z)z = Activation('relu')(z)z = MaxPooling2D(pool_size = (2,2))(z) z = Conv2D(128, (3,3))(z)z = BatchNormalization()(z)z = Activation('relu')(z)z = MaxPooling2D(pool_size = (2,2))(z) z = Conv2D(128, (3,3))(z)z = BatchNormalization()(z)z = Activation('relu')(z)z = MaxPooling2D(pool_size = (2,2))(z) z = Flatten()(z)z = Dense(64)(z)z = BatchNormalization()(z)z = Activation('relu')(z)z = Dropout(0.5)(z)z = Dense(1)(z)z = Activation('sigmoid')(z) model = Model(input_layer, z) model.compile( optimizer = keras.optimizers.RMSprop(), loss = keras.losses.binary_crossentropy, metrics = [keras.metrics.binary_accuracy]) model.summary()

训练模型

history = model.fit(train_X,train_y, validation_data=(test_X, test_y),epochs=10,batch_size=128,verbose=True)score = model.evaluate(test_X, test_y, verbose=0)print("Large CNN Error: %.2f%%" %(100-score[1]*100))

复用vgg16模型

def vgg16_model(input_shape= (HEIGHT,WIDTH,CHANNELS)): vgg16 = VGG16(include_top=False, weights='imagenet',input_shape=input_shape) for layer in vgg16.layers: layer.trainable = False last = vgg16.output # 后面加入自己的模型 x = Flatten()(last) x = Dense(256, activation='relu')(x) x = Dropout(0.5)(x) x = Dense(256, activation='relu')(x) x = Dropout(0.5)(x) x = Dense(1, activation='sigmoid')(x) model = Model(inputs=vgg16.input, outputs=x) return model

编译模型

model_vgg16 = vgg16_model()model_vgg16.summary()model_vgg16.compile(loss='binary_crossentropy',optimizer = Adam(0.0001), metrics = ['accuracy'])

训练模型

# 训练模型history = model_vgg16.fit(train_X,train_y, validation_data=(test_X, test_y),epochs=5,batch_size=128,verbose=True)score = model_vgg16.evaluate(test_X, test_y, verbose=0)print("Large CNN Error: %.2f%%" %(100-score[1]*100))

以上这篇keras分类之二分类实例(Cat and dog)就是小编分享给大家的全部内容了，希望能给大家一个参考，也希望大家多多支持。