unet++代码keras
时间: 2023-04-09 09:01:00 浏览: 282
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。
有没有Unet++和门结构的实例
以下是Unet和门结构的Python代码实例:
Unet模型:
```
import tensorflow as tf
from tensorflow.keras import Input, Model
from tensorflow.keras.layers import Conv2D, MaxPool2D, UpSampling2D, Concatenate
def down_block(x, filters, kernel_size=(3, 3), padding="same", strides=1):
c = Conv2D(filters, kernel_size, padding=padding, strides=strides, activation="relu")(x)
c = Conv2D(filters, kernel_size, padding=padding, strides=strides, activation="relu")(c)
p = MaxPool2D((2, 2), (2, 2))(c)
return c, p
def up_block(x, skip, filters, kernel_size=(3, 3), padding="same", strides=1):
us = UpSampling2D((2, 2))(x)
concat = Concatenate()([us, skip])
c = Conv2D(filters, kernel_size, padding=padding, strides=strides, activation="relu")(concat)
c = Conv2D(filters, kernel_size, padding=padding, strides=strides, activation="relu")(c)
return c
def UNet(image_size, channels):
input = Input(shape=(image_size, image_size, channels))
c1, p1 = down_block(input, 64)
c2, p2 = down_block(p1, 128)
c3, p3 = down_block(p2, 256)
c4, p4 = down_block(p3, 512)
x = Conv2D(1024, (3, 3), padding="same", activation="relu")(p4)
x = up_block(x, c4, 512)
x = up_block(x, c3, 256)
x = up_block(x, c2, 128)
x = up_block(x, c1, 64)
output = Conv2D(1, (1, 1), padding="same", activation="sigmoid")(x)
model = Model(input, output)
return model
```
门结构模型:
```
import tensorflow as tf
from tensorflow.keras import Input, Model
from tensorflow.keras.layers import Conv2D, Activation, Concatenate, multiply, add, BatchNormalization
def conv_block(x, filters, kernel_size=(3, 3), padding="same", strides=1):
x = Conv2D(filters, kernel_size, padding=padding, strides=strides)(x)
x = BatchNormalization()(x)
x = Activation("relu")(x)
return x
def attention_block(x, g, inter_channel):
theta_x = Conv2D(inter_channel, (1, 1), padding="same", use_bias=False)(x)
phi_g = Conv2D(inter_channel, (1, 1), padding="same", use_bias=False)(g)
f = Activation("relu")(add([theta_x, phi_g]))
psi_f = Conv2D(1, (1, 1), padding="same", use_bias=False)(f)
sigm_psi_f = Activation("sigmoid")(psi_f)
return multiply([x, sigm_psi_f])
def gated_attention_model(input_shape, classes):
input = Input(shape=input_shape)
conv1 = conv_block(input, 64)
pool1 = tf.keras.layers.MaxPooling2D(pool_size=(2, 2))(conv1)
conv2 = conv_block(pool1, 128)
pool2 = tf.keras.layers.MaxPooling2D(pool_size=(2, 2))(conv2)
conv3 = conv_block(pool2, 256)
pool3 = tf.keras.layers.MaxPooling2D(pool_size=(2, 2))(conv3)
conv4 = conv_block(pool3, 512)
pool4 = tf.keras.layers.MaxPooling2D(pool_size=(2, 2))(conv4)
atten = attention_block(conv4, conv3, 64)
up5 = Conv2D(256, 2, padding='same')(UpSampling2D(size = (2,2))(atten))
merge5 = Concatenate()([conv3, up5])
conv5 = conv_block(merge5, 256)
atten1 = attention_block(conv5, conv2, 32)
up6 = Conv2D(128, 2, padding='same')(UpSampling2D(size = (2,2))(atten1))
merge6 = Concatenate()([conv2, up6])
conv6 = conv_block(merge6, 128)
atten2 = attention_block(conv6, conv1, 16)
up7 = Conv2D(64, 2, padding='same')(UpSampling2D(size = (2,2))(atten2))
merge7 = Concatenate()([conv1, up7])
conv7 = conv_block(merge7, 64)
output = Conv2D(classes, (1,1), padding='same', activation='softmax')(conv7)
model = Model(input, output)
return model
```
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