已有UNET模型unet()，用训练集原始图像"data\train\trainvol""，标签"data\train\trainseg"，验证集原始图像"data\val\valvol"，标签"data\val\valseg"训练此网络的代码

下面是用 Keras 训练 UNet 模型的示例代码，包括数据加载、模型构建、训练和保存模型等步骤。请将数据路径和模型参数等根据实际情况进行调整。

import os
import numpy as np
import tensorflow as tf
from tensorflow.keras.layers import Input, Conv2D, MaxPooling2D, Dropout, UpSampling2D, Concatenate
from tensorflow.keras.models import Model
from tensorflow.keras.optimizers import Adam

# 设置数据路径和超参数
train_vol_dir = 'data/train/trainvol'
train_seg_dir = 'data/train/trainseg'
val_vol_dir = 'data/val/valvol'
val_seg_dir = 'data/val/valseg'
batch_size = 4
epochs = 20
learning_rate = 1e-4

# 加载数据
def load_data(image_dir, label_dir):
    images = []
    labels = []
    for filename in os.listdir(image_dir):
        image_path = os.path.join(image_dir, filename)
        label_path = os.path.join(label_dir, filename)
        image = np.load(image_path)
        label = np.load(label_path)
        images.append(image)
        labels.append(label)
    images = np.array(images)
    labels = np.array(labels)
    return images, labels

train_images, train_labels = load_data(train_vol_dir, train_seg_dir)
val_images, val_labels = load_data(val_vol_dir, val_seg_dir)

# 构建模型
inputs = Input(shape=train_images.shape[1:])
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)
conv2 = Conv2D(128, 3, activation='relu', padding='same')(pool1)
conv2 = Conv2D(128, 3, activation='relu', padding='same')(conv2)
pool2 = MaxPooling2D(pool_size=(2, 2))(conv2)
conv3 = Conv2D(256, 3, activation='relu', padding='same')(pool2)
conv3 = Conv2D(256, 3, activation='relu', padding='same')(conv3)
pool3 = MaxPooling2D(pool_size=(2, 2))(conv3)
conv4 = Conv2D(512, 3, activation='relu', padding='same')(pool3)
conv4 = Conv2D(512, 3, activation='relu', padding='same')(conv4)
drop4 = Dropout(0.5)(conv4)
pool4 = MaxPooling2D(pool_size=(2, 2))(drop4)
conv5 = Conv2D(1024, 3, activation='relu', padding='same')(pool4)
conv5 = Conv2D(1024, 3, activation='relu', padding='same')(conv5)
drop5 = Dropout(0.5)(conv5)
up6 = Conv2D(512, 2, activation='relu', padding='same')(UpSampling2D(size=(2, 2))(drop5))
merge6 = Concatenate()([drop4, up6])
conv6 = Conv2D(512, 3, activation='relu', padding='same')(merge6)
conv6 = Conv2D(512, 3, activation='relu', padding='same')(conv6)
up7 = Conv2D(256, 2, activation='relu', padding='same')(UpSampling2D(size=(2, 2))(conv6))
merge7 = Concatenate()([conv3, up7])
conv7 = Conv2D(256, 3, activation='relu', padding='same')(merge7)
conv7 = Conv2D(256, 3, activation='relu', padding='same')(conv7)
up8 = Conv2D(128, 2, activation='relu', padding='same')(UpSampling2D(size=(2, 2))(conv7))
merge8 = Concatenate()([conv2, up8])
conv8 = Conv2D(128, 3, activation='relu', padding='same')(merge8)
conv8 = Conv2D(128, 3, activation='relu', padding='same')(conv8)
up9 = Conv2D(64, 2, activation='relu', padding='same')(UpSampling2D(size=(2, 2))(conv8))
merge9 = Concatenate()([conv1, up9])
conv9 = Conv2D(64, 3, activation='relu', padding='same')(merge9)
conv9 = Conv2D(64, 3, activation='relu', padding='same')(conv9)
conv10 = Conv2D(1, 1, activation='sigmoid')(conv9)
model = Model(inputs=inputs, outputs=conv10)

# 定义损失函数和优化器
loss_fn = tf.keras.losses.BinaryCrossentropy()
optimizer = Adam(learning_rate=learning_rate)

# 编译模型
model.compile(optimizer=optimizer, loss=loss_fn, metrics=['accuracy'])

# 训练模型
model.fit(train_images, train_labels, batch_size=batch_size, epochs=epochs, validation_data=(val_images, val_labels))

# 保存模型
model.save('unet.h5')