写一个基于残差U-net的医学图像分割代码
时间: 2023-02-13 16:00:49 浏览: 50
您好,这是一个基于残差 U-Net 的医学图像分割代码的示例:
```python
import tensorflow as tf
# 定义残差 U-Net 模型
class ResidualUNet(tf.keras.Model):
def __init__(self, num_classes=2):
super(ResidualUNet, self).__init__()
# 定义下采样部分
self.down_1 = tf.keras.layers.Conv2D(64, (3, 3), padding='same')
self.down_2 = tf.keras.layers.Conv2D(128, (3, 3), padding='same')
self.down_3 = tf.keras.layers.Conv2D(256, (3, 3), padding='same')
self.down_4 = tf.keras.layers.Conv2D(512, (3, 3), padding='same')
self.down_5 = tf.keras.layers.Conv2D(1024, (3, 3), padding='same')
# 定义残差块
self.residual_1 = ResidualBlock(1024)
self.residual_2 = ResidualBlock(1024)
self.residual_3 = ResidualBlock(1024)
self.residual_4 = ResidualBlock(1024)
self.residual_5 = ResidualBlock(1024)
# 定义上采样部分
self.up_1 = UpSampling2D((2, 2))
self.up_2 = UpSampling2D((2, 2))
self.up_3 = UpSampling2D((2, 2))
self.up_4 = UpSampling2D((2, 2))
self.conv_1 = tf.keras.layers.Conv2D(512, (3, 3), padding='same')
self.conv_2 = tf.keras.layers.Conv2D(256, (3, 3), padding='same')
self.conv_3 = tf.keras.layers.Conv2D(128, (3, 3), padding='same')
self.conv_4 = tf.keras.layers.Conv2D(64, (3, 3), padding='same')
self.conv_5 = tf.keras.layers.Conv2D(num_classes, (1, 1), activation='softmax')
def call(self, inputs):
# 下采样
down_1 = self.down_1(inputs)
pool_1 = MaxPooling2D((2, 2))(down_1)
down_2 = self.down_2(pool_1)
pool_2 = MaxPooling2D((2, 2))(down_2)
down_3 = self
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