使用基于tensorflow的vit手写数字识别代码

以下是使用基于TensorFlow的ViT（Vision Transformer）手写数字识别的代码：

import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers
from vit_keras import vit

# Load MNIST dataset
(x_train, y_train), (x_test, y_test) = keras.datasets.mnist.load_data()
input_shape = (28, 28, 1)

# Normalize pixel values between 0 and 1
x_train = x_train.astype("float32") / 255.0
x_test = x_test.astype("float32") / 255.0

# Add channel dimension to input data
x_train = x_train.reshape((-1, 28, 28, 1))
x_test = x_test.reshape((-1, 28, 28, 1))

# Define ViT model
model = vit.ViT(
    image_size=28,
    patch_size=7,
    num_classes=10,
    transformer_layers=8,
    num_heads=8,
    mlp_dim=128,
    channels=1,
    dropout=0.1,
)

# Train ViT model
model.compile(
    optimizer=keras.optimizers.Adam(),
    loss=keras.losses.SparseCategoricalCrossentropy(from_logits=True),
    metrics=[keras.metrics.SparseCategoricalAccuracy()],
)

history = model.fit(x_train, y_train, batch_size=32, epochs=10, validation_data=(x_test, y_test))

# Evaluate ViT model on test data
score = model.evaluate(x_test, y_test, verbose=0)
print("Test loss:", score[0])
print("Test accuracy:", score[1])

在上面的代码中，我们首先加载MNIST数据集并将像素值标准化为0到1之间的浮点数。我们然后将输入数据的形状从28x28调整为28x28x1，其中1是通道维度。我们使用vit_keras库中的ViT类定义了一个ViT模型，并编译了模型，使用Adam优化器和稀疏分类交叉熵损失函数进行训练。最后，我们评估了模型在测试数据上的性能，输出了损失和准确度。