在这段代码中，UpSampling2D和Conv2DTranspose似乎可以互换使用。我想知道为什么会这样。
# u-net model with up-convolution or up-sampling and weighted binary-crossentropy as loss funcfrom keras.models import Modelfrom keras.layers import Input, Conv2D, MaxPooling2D, UpSampling2D, concatenate, Conv2DTranspose, BatchNormalization, Dropoutfrom keras.optimizers import Adamfrom keras.utils import plot_modelfrom keras import backend as Kdef unet_model(n_classes=5, im_sz=160, n_channels=8, n_filters_start=32, growth_factor=2, upconv=True, class_weights=[0.2, 0.3, 0.1, 0.1, 0.3]): droprate=0.25 n_filters = n_filters_start inputs = Input((im_sz, im_sz, n_channels)) #inputs = BatchNormalization()(inputs) conv1 = Conv2D(n_filters, (3, 3), activation='relu', padding='same')(inputs) conv1 = Conv2D(n_filters, (3, 3), activation='relu', padding='same')(conv1) pool1 = MaxPooling2D(pool_size=(2, 2))(conv1) #pool1 = Dropout(droprate)(pool1) n_filters *= growth_factor pool1 = BatchNormalization()(pool1) conv2 = Conv2D(n_filters, (3, 3), activation='relu', padding='same')(pool1) conv2 = Conv2D(n_filters, (3, 3), activation='relu', padding='same')(conv2) pool2 = MaxPooling2D(pool_size=(2, 2))(conv2) pool2 = Dropout(droprate)(pool2) n_filters *= growth_factor pool2 = BatchNormalization()(pool2) conv3 = Conv2D(n_filters, (3, 3), activation='relu', padding='same')(pool2) conv3 = Conv2D(n_filters, (3, 3), activation='relu', padding='same')(conv3) pool3 = MaxPooling2D(pool_size=(2, 2))(conv3) pool3 = Dropout(droprate)(pool3) n_filters *= growth_factor pool3 = BatchNormalization()(pool3) conv4_0 = Conv2D(n_filters, (3, 3), activation='relu', padding='same')(pool3) conv4_0 = Conv2D(n_filters, (3, 3), activation='relu', padding='same')(conv4_0) pool4_1 = MaxPooling2D(pool_size=(2, 2))(conv4_0) pool4_1 = Dropout(droprate)(pool4_1) n_filters *= growth_factor pool4_1 = BatchNormalization()(pool4_1) conv4_1 = Conv2D(n_filters, (3, 3), activation='relu', padding='same')(pool4_1) conv4_1 = Conv2D(n_filters, (3, 3), activation='relu', padding='same')(conv4_1) pool4_2 = MaxPooling2D(pool_size=(2, 2))(conv4_1) pool4_2 = Dropout(droprate)(pool4_2) n_filters *= growth_factor conv5 = Conv2D(n_filters, (3, 3), activation='relu', padding='same')(pool4_2) conv5 = Conv2D(n_filters, (3, 3), activation='relu', padding='same')(conv5) n_filters //= growth_factor if upconv: up6_1 = concatenate([Conv2DTranspose(n_filters, (2, 2), strides=(2, 2), padding='same')(conv5), conv4_1]) else: up6_1 = concatenate([UpSampling2D(size=(2, 2))(conv5), conv4_1]) up6_1 = BatchNormalization()(up6_1) conv6_1 = Conv2D(n_filters, (3, 3), activation='relu', padding='same')(up6_1) conv6_1 = Conv2D(n_filters, (3, 3), activation='relu', padding='same')(conv6_1) conv6_1 = Dropout(droprate)(conv6_1) n_filters //= growth_factor if upconv: up6_2 = concatenate([Conv2DTranspose(n_filters, (2, 2), strides=(2, 2), padding='same')(conv6_1), conv4_0]) else: up6_2 = concatenate([UpSampling2D(size=(2, 2))(conv6_1), conv4_0]) up6_2 = BatchNormalization()(up6_2) conv6_2 = Conv2D(n_filters, (3, 3), activation='relu', padding='same')(up6_2) conv6_2 = Conv2D(n_filters, (3, 3), activation='relu', padding='same')(conv6_2) conv6_2 = Dropout(droprate)(conv6_2) n_filters //= growth_factor if upconv: up7 = concatenate([Conv2DTranspose(n_filters, (2, 2), strides=(2, 2), padding='same')(conv6_2), conv3]) else: up7 = concatenate([UpSampling2D(size=(2, 2))(conv6_2), conv3]) up7 = BatchNormalization()(up7) conv7 = Conv2D(n_filters, (3, 3), activation='relu', padding='same')(up7) conv7 = Conv2D(n_filters, (3, 3), activation='relu', padding='same')(conv7) conv7 = Dropout(droprate)(conv7) n_filters //= growth_factor if upconv: up8 = concatenate([Conv2DTranspose(n_filters, (2, 2), strides=(2, 2), padding='same')(conv7), conv2]) else: up8 = concatenate([UpSampling2D(size=(2, 2))(conv7), conv2]) up8 = BatchNormalization()(up8) conv8 = Conv2D(n_filters, (3, 3), activation='relu', padding='same')(up8) conv8 = Conv2D(n_filters, (3, 3), activation='relu', padding='same')(conv8) conv8 = Dropout(droprate)(conv8) n_filters //= growth_factor if upconv: up9 = concatenate([Conv2DTranspose(n_filters, (2, 2), strides=(2, 2), padding='same')(conv8), conv1]) else: up9 = concatenate([UpSampling2D(size=(2, 2))(conv8), conv1]) conv9 = Conv2D(n_filters, (3, 3), activation='relu', padding='same')(up9) conv9 = Conv2D(n_filters, (3, 3), activation='relu', padding='same')(conv9) conv10 = Conv2D(n_classes, (1, 1), activation='sigmoid')(conv9) model = Model(inputs=inputs, outputs=conv10) def weighted_binary_crossentropy(y_true, y_pred): class_loglosses = K.mean(K.binary_crossentropy(y_true, y_pred), axis=[0, 1, 2]) return K.sum(class_loglosses * K.constant(class_weights)) model.compile(optimizer=Adam(), loss=weighted_binary_crossentropy) return model回答：
UpSampling2D 只是简单地通过最近邻或双线性上采样来放大图像，所以没有什么智能之处。它的优势在于成本低廉。
Conv2DTranspose 是一种卷积操作，其核在训练模型时被学习（就像普通的 Conv2D 操作一样）。使用 Conv2DTranspose 也会对其输入进行上采样，但关键的区别在于模型应该学习什么是最适合任务的上采样方法。
编辑：转置卷积的精彩可视化链接：https://towardsdatascience.com/types-of-convolutions-in-deep-learning-717013397f4d
