unet++代码keras
时间: 2023-04-09 20:01:00 浏览: 146
我可以回答这个问题。UNet是一种用于图像分割的神经网络模型,它可以将输入图像分割成多个部分,每个部分表示不同的物体或区域。在Keras中,可以使用TensorFlow后端来实现UNet模型的训练和预测。
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unet++的python代码
以下是一个简单的 UNet 模型的 Python 代码示例:
```python
import tensorflow as tf
from tensorflow.keras.layers import Input, Conv2D, MaxPooling2D, Dropout, UpSampling2D, concatenate
def conv_block(inputs, filters, kernel_size, padding='same', activation='relu'):
conv = Conv2D(filters=filters, kernel_size=kernel_size, padding=padding, activation=activation)(inputs)
conv = Conv2D(filters=filters, kernel_size=kernel_size, padding=padding, activation=activation)(conv)
return conv
def unet(input_size, num_classes=1):
inputs = Input(input_size)
# 编码器
conv1 = conv_block(inputs, 64, 3)
pool1 = MaxPooling2D(pool_size=(2, 2))(conv1)
conv2 = conv_block(pool1, 128, 3)
pool2 = MaxPooling2D(pool_size=(2, 2))(conv2)
conv3 = conv_block(pool2, 256, 3)
pool3 = MaxPooling2D(pool_size=(2, 2))(conv3)
conv4 = conv_block(pool3, 512, 3)
drop4 = Dropout(0.5)(conv4)
# 解码器
up5 = UpSampling2D(size=(2, 2))(drop4)
up5 = concatenate([up5, conv3])
conv5 = conv_block(up5, 256, 3)
up6 = UpSampling2D(size=(2, 2))(conv5)
up6 = concatenate([up6, conv2])
conv6 = conv_block(up6, 128, 3)
up7 = UpSampling2D(size=(2, 2))(conv6)
up7 = concatenate([up7, conv1])
conv7 = conv_block(up7, 64, 3)
# 输出层
outputs = Conv2D(num_classes, (1, 1), activation='sigmoid')(conv7)
model = tf.keras.Model(inputs=inputs, outputs=outputs)
return model
```
在这个示例代码中,我们定义了一个名为 `unet` 的函数,它接收输入大小和类别数作为参数,并返回一个 UNet 模型。模型包括一个编码器和一个解码器,其中编码器将输入图像压缩成一个较小的特征图,而解码器将这个特征图还原为图像。
在编码器中,我们使用了 4 个卷积块,每个块包括两个卷积层和一个最大池化层。在解码器中,我们使用了 3 个上采样层和 3 个卷积块,每个块也包括两个卷积层。最后,我们使用一个 $1\times1$ 的卷积层作为输出层,并使用 sigmoid 函数将每个像素映射到 $[0, 1]$ 的范围内。
unet代码 keras
以下是一个基于Keras实现UNet的示例代码:
```python
from keras.models import Model
from keras.layers import Input, Conv2D, MaxPooling2D, Dropout, UpSampling2D, concatenate
def unet(input_size=(256,256,1)):
inputs = Input(input_size)
# Contracting Path
conv1 = Conv2D(64, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(inputs)
conv1 = Conv2D(64, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv1)
pool1 = MaxPooling2D(pool_size=(2, 2))(conv1)
conv2 = Conv2D(128, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(pool1)
conv2 = Conv2D(128, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv2)
pool2 = MaxPooling2D(pool_size=(2, 2))(conv2)
conv3 = Conv2D(256, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(pool2)
conv3 = Conv2D(256, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv3)
pool3 = MaxPooling2D(pool_size=(2, 2))(conv3)
conv4 = Conv2D(512, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(pool3)
conv4 = Conv2D(512, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv4)
drop4 = Dropout(0.5)(conv4)
pool4 = MaxPooling2D(pool_size=(2, 2))(drop4)
# Bottom
conv5 = Conv2D(1024, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(pool4)
conv5 = Conv2D(1024, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv5)
drop5 = Dropout(0.5)(conv5)
# Expansive Path
up6 = UpSampling2D(size = (2,2))(drop5)
up6 = Conv2D(512, 2, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(up6)
merge6 = concatenate([drop4,up6], axis = 3)
conv6 = Conv2D(512, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(merge6)
conv6 = Conv2D(512, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv6)
up7 = UpSampling2D(size = (2,2))(conv6)
up7 = Conv2D(256, 2, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(up7)
merge7 = concatenate([conv3,up7], axis = 3)
conv7 = Conv2D(256, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(merge7)
conv7 = Conv2D(256, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv7)
up8 = UpSampling2D(size = (2,2))(conv7)
up8 = Conv2D(128, 2, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(up8)
merge8 = concatenate([conv2,up8], axis = 3)
conv8 = Conv2D(128, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(merge8)
conv8 = Conv2D(128, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv8)
up9 = UpSampling2D(size = (2,2))(conv8)
up9 = Conv2D(64, 2, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(up9)
merge9 = concatenate([conv1,up9], axis = 3)
conv9 = Conv2D(64, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(merge9)
conv9 = Conv2D(64, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv9)
conv9 = Conv2D(2, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv9)
# Output
conv10 = Conv2D(1, 1, activation = 'sigmoid')(conv9)
model = Model(inputs = inputs, outputs = conv10)
return model
```
使用方法:
```python
model = unet()
model.compile(optimizer = 'adam', loss = 'binary_crossentropy', metrics = ['accuracy'])
model.summary()
```
这个代码实现了一个基本的UNet模型,输入大小为256*256*1,输出大小为256*256*1,使用了`sigmoid`作为激活函数。可以根据实际需求进行修改。