PYTHON UNET代码

以下是一个基于Python的UNet代码示例：

import tensorflow as tf
from tensorflow.keras import layers

def unet(input_size=(256, 256, 3)):
    inputs = tf.keras.Input(input_size)

    # Contracting Path
    conv1 = layers.Conv2D(64, 3, activation='relu', padding='same')(inputs)
    conv1 = layers.Conv2D(64, 3, activation='relu', padding='same')(conv1)
    pool1 = layers.MaxPooling2D(pool_size=(2, 2))(conv1)

    conv2 = layers.Conv2D(128, 3, activation='relu', padding='same')(pool1)
    conv2 = layers.Conv2D(128, 3, activation='relu', padding='same')(conv2)
    pool2 = layers.MaxPooling2D(pool_size=(2, 2))(conv2)

    conv3 = layers.Conv2D(256, 3, activation='relu', padding='same')(pool2)
    conv3 = layers.Conv2D(256, 3, activation='relu', padding='same')(conv3)
    pool3 = layers.MaxPooling2D(pool_size=(2, 2))(conv3)

    conv4 = layers.Conv2D(512, 3, activation='relu', padding='same')(pool3)
    conv4 = layers.Conv2D(512, 3, activation='relu', padding='same')(conv4)
    drop4 = layers.Dropout(0.5)(conv4)
    pool4 = layers.MaxPooling2D(pool_size=(2, 2))(drop4)

    # Bottom
    conv5 = layers.Conv2D(1024, 3, activation='relu', padding='same')(pool4)
    conv5 = layers.Conv2D(1024, 3, activation='relu', padding='same')(conv5)
    drop5 = layers.Dropout(0.5)(conv5)

    # Expansive Path
    up6 = layers.Conv2DTranspose(512, 2, strides=(2, 2), padding='same')(drop5)
    merge6 = layers.concatenate([drop4, up6], axis=3)
    conv6 = layers.Conv2D(512, 3, activation='relu', padding='same')(merge6)
    conv6 = layers.Conv2D(512, 3, activation='relu', padding='same')(conv6)

    up7 = layers.Conv2DTranspose(256, 2, strides=(2, 2), padding='same')(conv6)
    merge7 = layers.concatenate([conv3, up7], axis=3)
    conv7 = layers.Conv2D(256, 3, activation='relu', padding='same')(merge7)
    conv7 = layers.Conv2D(256, 3, activation='relu', padding='same')(conv7)

    up8 = layers.Conv2DTranspose(128, 2, strides=(2, 2), padding='same')(conv7)
    merge8 = layers.concatenate([conv2, up8], axis=3)
    conv8 = layers.Conv2D(128, 3, activation='relu', padding='same')(merge8)
    conv8 = layers.Conv2D(128, 3, activation='relu', padding='same')(conv8)

    up9 = layers.Conv2DTranspose(64, 2, strides=(2, 2), padding='same')(conv8)
    merge9 = layers.concatenate([conv1, up9], axis=3)
    conv9 = layers.Conv2D(64, 3, activation='relu', padding='same')(merge9)
    conv9 = layers.Conv2D(64, 3, activation='relu', padding='same')(conv9)

    # Output
    outputs = layers.Conv2D(1, 1, activation='sigmoid')(conv9)

    model = tf.keras.Model(inputs=inputs, outputs=outputs)
    return model

该代码实现了一个基本的 UNet 模型。在该模型中，输入是一个大小为 `(256, 256, 3)` 的图像，输出是一个大小为 `(256, 256, 1)` 的二值图像，表示原始图像中每个像素是否属于感兴趣的目标区域。模型包含一个收缩路径和一个扩张路径，中间有一个底部路径。在每个路径中，使用了一系列卷积层、池化层、dropout 层和转置卷积层进行特征提取和上采样。在模型的输出层中，使用了一个 sigmoid 激活函数将输出的值压缩到 0 到 1 之间。