基于tensorflow的vit模型的cifrar100图像分类代码

以下是基于TensorFlow的ViT模型的CIFAR-100图像分类代码：

import tensorflow as tf
from tensorflow.keras.datasets import cifar100
from tensorflow.keras.layers import Input, Dense
from tensorflow.keras.models import Model
from tensorflow.keras.optimizers import Adam
from tensorflow.keras.utils import to_categorical

from official.vision.image_classification.augment import preprocess_input
from official.vision.image_classification.resnet import ResNet
from official.vision.image_classification import imagenet_preprocessing

from official.vision.image_classification import augment
from official.vision.image_classification import cifar_preprocessing
from official.vision.image_classification import classification_models

NUM_CLASSES = 100
IMAGE_SIZE = 224
BATCH_SIZE = 64
EPOCHS = 10

# Load CIFAR-100 dataset
(x_train, y_train), (x_test, y_test) = cifar100.load_data()

# Preprocess images
x_train = cifar_preprocessing.preprocess_for_train(x_train)
x_test = cifar_preprocessing.preprocess_for_eval(x_test)

# One-hot encode labels
y_train = to_categorical(y_train, NUM_CLASSES)
y_test = to_categorical(y_test, NUM_CLASSES)

# Create ViT model
input_shape = (IMAGE_SIZE, IMAGE_SIZE, 3)
inputs = Input(shape=input_shape)
model = classification_models.ClassificationModel(
    'vit_small_patch16_224', input_shape=input_shape, num_classes=NUM_CLASSES)
outputs = model(inputs)
model = Model(inputs, outputs)

# Freeze all layers except for the classifier
for layer in model.layers[:-1]:
    layer.trainable = False

# Compile model
optimizer = Adam(lr=1e-4)
model.compile(optimizer=optimizer, loss='categorical_crossentropy', metrics=['accuracy'])

# Train model
history = model.fit(x_train, y_train, batch_size=BATCH_SIZE, epochs=EPOCHS, validation_data=(x_test, y_test))

# Evaluate model
model.evaluate(x_test, y_test, batch_size=BATCH_SIZE)

此代码使用了TensorFlow官方提供的ViT模型，并对CIFAR-100数据集进行了预处理和数据增强。模型训练完成后，使用测试集对模型进行了评估。