VGG16在cifar100上如何训练及相关代码

VGG16在CIFAR100上的训练非常有效，可以使用Keras来实现。下面是一段可用于训练VGG16模型的代码：from keras.applications.vgg16 import VGG16from keras.utils import to_categoricalfrom keras.datasets import cifar100from keras.preprocessing.image import ImageDataGeneratorfrom keras import optimizersfrom keras.layers import Densefrom keras.models import Sequential# Load the CIFAR100 dataset (split into train and test sets)(x_train, y_train), (x_test, y_test) = cifar100.load_data()# Convert class vectors to binary class matricesy_train = to_categorical(y_train, 100)y_test = to_categorical(y_test, 100)# Create the modelmodel = VGG16(include_top=True, weights=None, input_tensor=None, input_shape=(32, 32, 3), pooling=None, classes=100)# Compile the modelmodel.compile(loss='categorical_crossentropy', optimizer=optimizers.SGD(lr=0.001, momentum=0.9), metrics=['accuracy'])# Train the modelmodel.fit(x_train, y_train, batch_size=32, epochs=10, validation_data=(x_test, y_test))