import tensorflow as tf from tensorflow.keras.layers import Input, Conv2D, MaxPooling2D, Dropout, UpSampling2D, concatenate def unet(input_shape=(256, 256, 1), num_classes=2): inputs = Input(input_shape) # Contracting Path 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) # Bottom 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) # Expanding Path 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) # Output outputs = Conv2D(num_classes, 1, activation='softmax')(conv9) model = tf.keras.Model(inputs=inputs, outputs=outputs) return model错在哪
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