【VGG】简单应用
好的，以下是基于VGG的CIFAR-10图像分类代码，resize=32： ```python import keras from keras.datasets import cifar10 from keras.models import Sequential from keras.layers import Dense, Dropout, Flatten from keras.layers import Conv2D, MaxPooling2D from keras.optimizers import SGD from keras.preprocessing.image import ImageDataGenerator from keras.utils import np_utils import numpy as np # 超参数定义 batch_size = 128 num_classes = 10 epochs = 100 data_augmentation = True num_predictions = 20 # 数据加载和预处理 (x_train, y_train), (x_test, y_test) = cifar10.load_data() x_train = x_train.astype('float32') x_test = x_test.astype('float32') x_train /= 255 x_test /= 255 y_train = np_utils.to_categorical(y_train, num_classes) y_test = np_utils.to_categorical(y_test, num_classes) # VGG网络定义 model = Sequential() model.add(Conv2D(32, (3, 3), padding='same', input_shape=x_train.shape[1:], activation='relu')) model.add(Conv2D(32, (3, 3), activation='relu')) model.add(MaxPooling2D(pool_size=(2, 2))) model.add(Dropout(0.25)) model.add(Conv2D(64, (3, 3), padding='same', activation='relu')) model.add(Conv2D(64, (3, 3), activation='relu')) model.add(MaxPooling2D(pool_size=(2, 2))) model.add(Dropout(0.25)) model.add(Flatten()) model.add(Dense(512, activation='relu')) model.add(Dropout(0.5)) model.add(Dense(num_classes, activation='softmax')) # 模型编译和训练 sgd = SGD(lr=0.01, decay=1e-6, momentum=0.9, nesterov=True) model.compile(loss='categorical_crossentropy', optimizer=sgd, metrics=['accuracy']) if not data_augmentation: print('Not using data augmentation.') model.fit(x_train, y_train, batch_size=batch_size, epochs=epochs, validation_data=(x_test, y_test), shuffle=True) else: print('Using real-time data augmentation.') datagen = ImageDataGenerator( featurewise_center=False, samplewise_center=False, featurewise_std_normalization=False, samplewise_std_normalization=False, zca_whitening=False, rotation_range=0, width_shift_range=0.1, height_shift_range=0.1, horizontal_flip=True, vertical_flip=False) datagen.fit(x_train) model.fit_generator(datagen.flow(x_train, y_train, batch_size=batch_size), steps_per_epoch=x_train.shape[0] // batch_size, epochs=epochs, validation_data=(x_test, y_test), workers=4) # 模型评估 scores = model.evaluate(x_test, y_test, verbose=1) print('Test loss:', scores[0]) print('Test accuracy:', scores[1]) ``` 希望可以帮到你！
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