keras unet多分类代码

以下是使用Keras构建一个简单的多分类UNet模型的代码：

from keras.models import Model
from keras.layers import Input, Conv2D, MaxPooling2D, Dropout, UpSampling2D, concatenate
from keras.utils import to_categorical

def unet(input_shape=(256, 256, 1), num_classes=3):
    inputs = Input(input_shape)
    
    # 编码器
    conv1 = Conv2D(64, 3, activation='relu', padding='same')(inputs)
    conv1 = Conv2D(64, 3, activation='relu', padding='same')(conv1)
    pool1 = MaxPooling2D(pool_size=(2, 2))(conv1)
    
    conv2 = Conv2D(128, 3, activation='relu', padding='same')(pool1)
    conv2 = Conv2D(128, 3, activation='relu', padding='same')(conv2)
    pool2 = MaxPooling2D(pool_size=(2, 2))(conv2)
    
    conv3 = Conv2D(256, 3, activation='relu', padding='same')(pool2)
    conv3 = Conv2D(256, 3, activation='relu', padding='same')(conv3)
    pool3 = MaxPooling2D(pool_size=(2, 2))(conv3)
    
    conv4 = Conv2D(512, 3, activation='relu', padding='same')(pool3)
    conv4 = Conv2D(512, 3, activation='relu', padding='same')(conv4)
    drop4 = Dropout(0.5)(conv4)
    pool4 = MaxPooling2D(pool_size=(2, 2))(drop4)
    
    conv5 = Conv2D(1024, 3, activation='relu', padding='same')(pool4)
    conv5 = Conv2D(1024, 3, activation='relu', padding='same')(conv5)
    drop5 = Dropout(0.5)(conv5)
    
    # 解码器
    up6 = Conv2D(512, 2, activation='relu', padding='same')(UpSampling2D(size=(2,2))(drop5))
    merge6 = concatenate([drop4, up6], axis=3)
    conv6 = Conv2D(512, 3, activation='relu', padding='same')(merge6)
    conv6 = Conv2D(512, 3, activation='relu', padding='same')(conv6)
    
    up7 = Conv2D(256, 2, activation='relu', padding='same')(UpSampling2D(size=(2,2))(conv6))
    merge7 = concatenate([conv3, up7], axis=3)
    conv7 = Conv2D(256, 3, activation='relu', padding='same')(merge7)
    conv7 = Conv2D(256, 3, activation='relu', padding='same')(conv7)
    
    up8 = Conv2D(128, 2, activation='relu', padding='same')(UpSampling2D(size=(2,2))(conv7))
    merge8 = concatenate([conv2, up8], axis=3)
    conv8 = Conv2D(128, 3, activation='relu', padding='same')(merge8)
    conv8 = Conv2D(128, 3, activation='relu', padding='same')(conv8)
    
    up9 = Conv2D(64, 2, activation='relu', padding='same')(UpSampling2D(size=(2,2))(conv8))
    merge9 = concatenate([conv1, up9], axis=3)
    conv9 = Conv2D(64, 3, activation='relu', padding='same')(merge9)
    conv9 = Conv2D(64, 3, activation='relu', padding='same')(conv9)
    
    outputs = Conv2D(num_classes, 1, activation='softmax')(conv9)
    
    model = Model(inputs=inputs, outputs=outputs)
    return model

# 读取数据，x_train为训练数据，y_train为训练数据标签
# 将训练数据标签转换为one-hot编码
y_train = to_categorical(y_train, num_classes=3)

# 编译模型
model = unet(input_shape=(256, 256, 1), num_classes=3)
model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])

# 训练模型
model.fit(x_train, y_train, batch_size=32, epochs=10, validation_split=0.2)

该代码与单分类的UNet模型类似，不同之处在于输出层使用softmax激活函数，并且num_classes参数指定为类别数。在训练数据标签y_train上，需要将标签转换为one-hot编码，使用Keras的`to_categorical()`函数实现。在编译模型时，损失函数使用categorical_crossentropy，评价指标为accuracy。