TensorFlow 搭建 unet网络
时间: 2023-12-27 12:02:57 浏览: 40
UNet 是一种用于图像分割的半监督卷积神经网络结构,常用于医学图像分割等领域。下面是一个简单的 TensorFlow UNet 网络模型的搭建过程。
首先,我们需要导入所需的 TensorFlow 库:
```python
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers
```
然后,我们定义一个 UNet 类,继承自 keras.Model 类,并实现其 call 方法:
```python
class UNet(keras.Model):
def __init__(self):
super(UNet, self).__init__()
# 定义下采样部分
self.down1 = downsample(64, 3)
self.down2 = downsample(128, 3)
self.down3 = downsample(256, 3)
self.down4 = downsample(512, 3)
# 定义上采样部分
self.up1 = upsample(256, 3)
self.up2 = upsample(128, 3)
self.up3 = upsample(64, 3)
# 定义输出层
self.output_layer = layers.Conv2D(1, 1, activation='sigmoid', padding='same')
def call(self, inputs):
# 定义前向传播过程
x1 = self.down1(inputs)
x2 = self.down2(x1)
x3 = self.down3(x2)
x4 = self.down4(x3)
x = self.up1(x4, x3)
x = self.up2(x, x2)
x = self.up3(x, x1)
output = self.output_layer(x)
return output
```
其中,我们定义了两个辅助函数 downsample 和 upsample,用于构建下采样和上采样部分:
```python
# 下采样部分
def downsample(filters, size, apply_batchnorm=True):
initializer = tf.random_normal_initializer(0., 0.02)
result = keras.Sequential()
result.add(layers.Conv2D(filters, size, strides=2, padding='same',
kernel_initializer=initializer, use_bias=False))
if apply_batchnorm:
result.add(layers.BatchNormalization())
result.add(layers.LeakyReLU())
return result
# 上采样部分
def upsample(filters, size, apply_dropout=False):
initializer = tf.random_normal_initializer(0., 0.02)
result = keras.Sequential()
result.add(layers.Conv2DTranspose(filters, size, strides=2, padding='same',
kernel_initializer=initializer, use_bias=False))
result.add(layers.BatchNormalization())
if apply_dropout:
result.add(layers.Dropout(0.5))
result.add(layers.ReLU())
return result
```
最后,我们可以实例化 UNet 类并编译模型:
```python
model = UNet()
model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
```
现在,我们就成功搭建了一个简单的 TensorFlow UNet 网络模型。