英文识别活动

import tensorflow as tf
from tensorflow.keras.layers import Conv2D, MaxPooling2D, Flatten, Dense

# 假设你已经有了一个包含英文字符图片和标签的数据集
# 数据预处理...

# 构建CNN模型
model = tf.keras.Sequential([
    Conv2D(32, (3, 3), activation='relu', input_shape=(32, 32, 1)),
    MaxPooling2D((2, 2)),
    Conv2D(64, (3, 3), activation='relu'),
    MaxPooling2D((2, 2)),
    Flatten(),
    Dense(64, activation='relu'),
    Dense(26, activation='softmax')  # 假设只识别26个英文字母
])

# 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)

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