u-net关于图像分割训练

### U-Net 图像分割训练教程 #### 3.1 数据准备与预处理 为了成功地训练U-Net模型，数据集的选择至关重要。通常情况下，图像分割的数据集由成对的输入图片及其对应的标签组成，其中标签是对原始图像中各个像素所属类别的标注[^2]。 ```python import numpy as np from keras.preprocessing.image import ImageDataGenerator # 定义路径到图像文件夹和掩码文件夹 image_folder = 'path_to_images' mask_folder = 'path_to_masks' # 创建ImageDataGenerators来加载并增强训练数据 datagen_args = dict(rescale=1./255, validation_split=0.2) image_datagen = ImageDataGenerator(**datagen_args) mask_datagen = ImageDataGenerator(**datagen_args) seed = 1 batch_size = 8 train_image_generator = image_datagen.flow_from_directory( image_folder, class_mode=None, batch_size=batch_size, target_size=(IMG_HEIGHT, IMG_WIDTH), subset='training', seed=seed) train_mask_generator = mask_datagen.flow_from_directory( mask_folder, color_mode="grayscale", class_mode=None, batch_size=batch_size, target_size=(IMG_HEIGHT, IMG_WIDTH), subset='training', seed=seed) val_image_generator = image_datagen.flow_from_directory( image_folder, class_mode=None, batch_size=batch_size, target_size=(IMG_HEIGHT, IMG_WIDTH), subset='validation', seed=seed) val_mask_generator = mask_datagen.flow_from_directory( mask_folder, color_mode="grayscale", class_mode=None, batch_size=batch_size, target_size=(IMG_HEIGHT, IMG_WIDTH), subset='validation', seed=seed) def combine_generators(image_gen, mask_gen): while True: yield(next(image_gen), next(mask_gen)) train_generator = combine_generators(train_image_generator, train_mask_generator) valid_generator = combine_generators(val_image_generator, val_mask_generator) ``` #### 3.2 构建U-Net模型 U-Net是一种特殊的卷积神经网络(CNN)，它通过跳跃连接(skip connections)将编码器部分获取的位置信息传递给解码器部分，从而提高了最终预测的质量[^3]。 ```python from tensorflow.keras.models import Model from tensorflow.keras.layers import Input, Conv2D, MaxPooling2D, UpSampling2D, concatenate def unet_model(input_shape=(None, None, 1)): inputs = Input(shape=input_shape) conv1 = Conv2D(64, 3, activation='relu', padding='same')(inputs) conv1 = Conv2D(64, 3, activation='relu', padding='same')(conv1) pool1 = MaxPooling2D(pool_size=(2, 2))(conv1) # 更多层... up9 = concatenate([Conv2DTranspose(64, (2, 2), strides=(2, 2), padding='same')(conv9), conv1], axis=-1) conv10 = Conv2D(num_classes, 1, activation='softmax')(up9) model = Model(inputs=[inputs], outputs=[conv10]) return model ``` #### 3.3 编译与训练模型 编译阶段指定了损失函数、优化算法以及其他评估指标；而训练则涉及迭代更新权重以最小化指定的目标函数。 ```python model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy']) history = model.fit( train_generator, steps_per_epoch=train_steps, epochs=EPOCHS, verbose=1, callbacks=callbacks_list, validation_data=valid_generator, validation_steps=val_steps) ``` #### 3.4 模型评估与调优 完成初步训练之后，应该仔细检查验证集上的表现，并考虑调整超参数如学习率、批次大小等来进行进一步改进。此外还可以尝试不同的正则化技术防止过拟合现象的发生。