Python深度学习之Unet 语义分割模型(Keras)

下面是关于“Python深度学习之Unet 语义分割模型(Keras)”的完整攻略。

问题描述

Unet是一种用于图像分割的深度学习模型，可以用于医学图像分割、自然图像分割等领域。那么，在Python中，如何使用Keras实现Unet模型？

解决方法

以下是使用Keras实现Unet模型的方法：

首先，导入必要的库：

python from keras.models import Model from keras.layers import Input, Conv2D, MaxPooling2D, Dropout, UpSampling2D, concatenate from keras.optimizers import Adam from keras.callbacks import ModelCheckpoint

然后，定义Unet模型：

```python
def unet(input_size=(256, 256, 1)):
inputs = Input(input_size)

   # Encoder
   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)

   # Decoder
   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)

   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(2, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv9)

   outputs = Conv2D(1, 1, activation='sigmoid')(conv9)

   model = Model(inputs=inputs, outputs=outputs)
   model.compile(optimizer=Adam(lr=1e-4), loss='binary_crossentropy', metrics=['accuracy'])

   return model

```

在上面的代码中，我们定义了一个Unet模型，包括Encoder和Decoder两部分。在Encoder部分，我们使用了四个卷积层和四个池化层；在Decoder部分，我们使用了四个上采样层和四个卷积层。最后，我们使用sigmoid激活函数和二元交叉熵损失函数进行训练。

接着，使用Unet模型进行训练和预测

在Python脚本中，我们可以使用该模型进行训练和预测。以下是两个示例：

示例1：训练Unet模型

```python
# Load data
X_train, Y_train = load_data()

# Create model
model = unet()

# Train model
model_checkpoint = ModelCheckpoint('unet.hdf5', monitor='loss', verbose=1, save_best_only=True)
model.fit(X_train, Y_train, batch_size=32, epochs=100, verbose=1, validation_split=0.2, shuffle=True, callbacks=[model_checkpoint])
```

在上面的示例中，我们使用了load_data函数加载了训练数据，并使用unet函数创建了一个Unet模型。然后，我们使用ModelCheckpoint回调函数来保存最佳模型，并使用fit方法进行训练。

示例2：使用Unet模型进行预测

```python
# Load model
model = load_model('unet.hdf5')

# Load test data
X_test, Y_test = load_data()

# Predict
Y_pred = model.predict(X_test, verbose=1)
```

在上面的示例中，我们使用了load_model函数加载了训练好的Unet模型，并使用load_data函数加载了测试数据。然后，我们使用predict方法进行预测，并将预测结果保存在Y_pred中。