甲骨文识别

import tensorflow as tf
from tensorflow.keras import layers

# 假设我们有一个预处理好的甲骨文图像数据集
# 数据集格式：(num_samples, height, width, channels)

# 构建卷积神经网络模型
model = tf.keras.Sequential([
    layers.Conv2D(32, (3, 3), activation='relu', input_shape=(height, width, channels)),
    layers.MaxPooling2D((2, 2)),
    layers.Conv2D(64, (3, 3), activation='relu'),
    layers.MaxPooling2D((2, 2)),
    layers.Conv2D(128, (3, 3), activation='relu'),
    layers.Flatten(),
    layers.Dense(256, activation='relu'),
    layers.Dense(num_classes, activation='softmax')  # num_classes为甲骨文字的总数
])

# 编译模型
model.compile(optimizer='adam',
              loss='sparse_categorical_crossentropy',
              metrics=['accuracy'])

# 训练模型
model.fit(train_images, train_labels, epochs=10, validation_data=(val_images, val_labels))

# 评估模型
test_loss, test_acc = model.evaluate(test_images, test_labels)
print(f'Test accuracy: {test_acc}')