UNext keras 代码

以下是使用Keras构建一个简单的UNet模型的代码：

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

def unet(input_shape=(256, 256, 1)):
    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(1, 1, activation='sigmoid')(conv9)
    
    model = Model(inputs=inputs, outputs=outputs)
    return model

这里使用了Keras的函数式API，将UNet模型分为编码器和解码器两部分。编码器由5个卷积层和池化层组成，解码器由4个反卷积层和卷积层组成。在每个解码器级别中，使用跳跃连接将编码器中相应级别的特征图与解码器中的特征图连接起来。输出层为单个卷积层，使用sigmoid激活函数将输出值限制在0到1之间。