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KerasのUpSampling2D関数とConv2DTranspose関数の違いは何ですか？

このコードでは、UpSampling2DとConv2DTransposeは同じ意味で使用されているようです。これが起こっている理由を知りたい。

# u-net model with up-convolution or up-sampling and weighted binary-crossentropy as loss func

from keras.models import Model
from keras.layers import Input, Conv2D, MaxPooling2D, UpSampling2D, concatenate, Conv2DTranspose, BatchNormalization, Dropout
from keras.optimizers import Adam
from keras.utils import plot_model
from keras import backend as K

def 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
machine-learningcomputer-visionconv-neural-networkconvolutiondeconvolution
15
Piyush Chauhan

UpSampling2Dは、画像のサイズを変更して単純に拡大するだけなので、スマートではありません。利点はその安いことです。

Conv2DTransposeは、モデルのトレーニング中にカーネルが学習される（通常のconv2d操作と同様に）コンボリューション操作です。 Conv2DTransposeを使用すると入力もアップサンプリングされますが、重要な違いは、モデルがジョブに最適なアップサンプリングを学習する必要があることです。

編集：転置畳み込みのニース視覚化へのリンク： https://towardsdatascience.com/types-of-convolutions-in-deep-learning-717013397f4d

24
Burton2000