unet++代码keras

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。

相关问题

unet++的python代码

以下是一个简单的 UNet 模型的 Python 代码示例：

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