attention unet keras
时间: 2023-05-02 11:01:17 浏览: 68
b'attention unet keras' 是关于基于Keras框架的Attention U-Net神经网络的话题。这个神经网络结合了U-Net和Attention机制,用于语义分割任务,能够提高图像分割的精度和效果。
相关问题
请写出使用attention-Unet模型分割乳腺超声图像的代码
使用attention-Unet模型分割乳腺超声图像的代码如下:from tensorflow.keras.models import Model
from tensorflow.keras.layers import Input, Conv2D, MaxPooling2D, UpSampling2D, concatenate# Input Layer
inputs = Input(shape=(None, None, 1))# Attention U-Net
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)# Attention Mechanism
attention_conv = Conv2D(1, 1, activation='sigmoid')(conv4)
up1 = UpSampling2D(size=(2,2))(attention_conv)
up1 = concatenate([conv3,up1], axis=3)
conv5 = Conv2D(256, 3, activation='relu', padding='same', kernel_initializer='he_normal')(up1)
conv5 = Conv2D(256, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv5)up2 = UpSampling2D(size=(2,2))(conv5)
up2 = concatenate([conv2,up2], axis=3)
conv6 = Conv2D(128, 3, activation='relu', padding='same', kernel_initializer='he_normal')(up2)
conv6 = Conv2D(128, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv6)up3 = UpSampling2D(size=(2,2))(conv6)
up3 = concatenate([conv1,up3], axis=3)
conv7 = Conv2D(64, 3, activation='relu', padding='same', kernel_initializer='he_normal')(up3)
conv7 = Conv2D(64, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv7)# Output Layer
outputs = Conv2D(1, 1, activation='sigmoid')(conv7)# Model Compilation
model = Model(inputs=[inputs], outputs=[outputs])
model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
unet添加注意力机制代码位置
在UNet模型中添加注意力机制有多种实现方式,以下是其中一种常见的实现方法:
1. 在模型定义文件中导入相关库:
```python
from tensorflow.keras.layers import Conv2D, Input, MaxPooling2D, concatenate, UpSampling2D, Activation, multiply, add
```
2. 定义注意力机制模块:
```python
def attention_block(input_x, input_g, inter_channel):
# Input_x: lower-level feature maps
# Input_g: higher-level feature maps
# Inter_channel: number of filters in attention block
# Lower-level feature map branch (x)
x = Conv2D(inter_channel, kernel_size=(1, 1), strides=(1, 1), padding='same', use_bias=False)(input_x)
x = Activation('relu')(x)
# Higher-level feature map branch (g)
g = Conv2D(inter_channel, kernel_size=(1, 1), strides=(1, 1), padding='same', use_bias=False)(input_g)
g = Activation('relu')(g)
# Combine branches
z = add([x, g])
z = Conv2D(inter_channel, kernel_size=(1, 1), strides=(1, 1), padding='same', use_bias=False)(z)
z = Activation('relu')(z)
# Attention map branch (s)
s = Conv2D(1, kernel_size=(1, 1), strides=(1, 1), padding='same', use_bias=False)(z)
s = Activation('sigmoid')(s)
# Apply attention map to lower-level feature map
x = multiply([x, s])
return x
```
3. 在UNet模型中添加注意力机制:
```python
def unet_attention(input_size=(256, 256, 1)):
# Encoder
inputs = Input(input_size)
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)
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)
# Decoder with attention blocks
up6 = UpSampling2D(size=(2, 2))(drop5)
att6 = attention_block(conv4, up6, 512)
merge6 = concatenate([att6, drop4], 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)
att7 = attention_block(conv3, up7, 256)
merge7 = concatenate([att7, conv3], 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)
att8 = attention_block(conv2, up8, 128)
merge8 = concatenate([att8, conv2], 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)
att9 = attention_block(conv1, up9, 64)
merge9 = concatenate([att9, conv1], 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)
conv10 = Conv2D(1, 1, activation='sigmoid')(conv9)
model = Model(inputs=inputs, outputs=conv10)
return model
```
在以上代码中,attention_block函数定义了一个注意力机制模块,其中包含了三个分支:低层特征图分支(x)、高层特征图分支(g)和注意力图分支(s)。在UNet模型中,将注意力机制模块插入到Decoder的每一层中,将低层特征图作为输入x,将上一层的上采样结果作为输入g,经过注意力机制模块处理后得到加权低层特征图x,并将其与上一层特征图进行融合。最终输出的是一个二分类的分割结果。