Keras:Unet网络实现多类语义分割方式

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Unet网络实现多类语义分割方式

Unet网络是一种用于图像分割的深度学习模型。在这个示例中，我们将使用Unet网络来实现多类语义分割方式。

示例1：使用Keras实现Unet网络

我们可以使用Keras来实现Unet网络。以下是一个简单的Unet网络实现示例。

from keras.models import Model
from keras.layers import Input, Conv2D, MaxPooling2D, Dropout, UpSampling2D, concatenate

def unet(input_size=(256, 256, 3), num_classes=2):
    inputs = Input(input_size)

    # 下采样
    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)

    # 上采样
    up6 = Conv2D(512, 2, activation='relu', padding='same', kernel_initializer='he_normal')(UpSampling2D(size=(2, 2))(drop5))
    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 = Conv2D(256, 2, activation='relu', padding='same', kernel_initializer='he_normal')(UpSampling2D(size=(2, 2))(conv6))
    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 = Conv2D(128, 2, activation='relu', padding='same', kernel_initializer='he_normal')(UpSampling2D(size=(2, 2))(conv7))
    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 = Conv2D(64, 2, activation='relu', padding='same', kernel_initializer='he_normal')(UpSampling2D(size=(2, 2))(conv8))
    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(num_classes, 1, activation='softmax')(conv9)

    model = Model(inputs=inputs, outputs=conv9)

    return model

在这个示例中，我们定义了一个Unet网络模型，并使用Keras的Conv2D、MaxPooling2D、Dropout、UpSampling2D和concatenate等函数来构建模型。我们使用softmax作为输出层的激活函数，并将输出层的通道数设置为类别数。

示例2：使用Unet网络进行多类语义分割

我们可以使用上面的Unet网络模型来进行多类语义分割。以下是一个简单的多类语义分割示例。

from keras.optimizers import Adam
from keras.losses import categorical_crossentropy
from keras.metrics import categorical_accuracy
from keras.preprocessing.image import ImageDataGenerator

# 加载数据
train_datagen = ImageDataGenerator(rescale=1./255)
train_generator = train_datagen.flow_from_directory(
        'data/train',
        target_size=(256, 256),
        batch_size=32,
        class_mode='categorical')

val_datagen = ImageDataGenerator(rescale=1./255)
val_generator = val_datagen.flow_from_directory(
        'data/val',
        target_size=(256, 256),
        batch_size=32,
        class_mode='categorical')

# 定义模型
model = unet(num_classes=3)
model.compile(optimizer=Adam(lr=1e-4), loss=categorical_crossentropy, metrics=[categorical_accuracy])

# 训练模型
model.fit_generator(
        train_generator,
        steps_per_epoch=2000,
        epochs=50,
        validation_data=val_generator,
        validation_steps=800)

在这个示例中，我们使用Keras的ImageDataGenerator函数来加载数据，并使用unet函数定义了一个Unet网络模型。我们使用Adam优化器和categorical_crossentropy损失函数来编译模型，并使用categorical_accuracy作为评估指标。然后，我们使用fit_generator函数来训练模型。