UpSampling2D和Conv2DTranspose在keras中有什么区别？

Question

UpSampling2D和Conv2DTranspose在keras中有什么区别？

machine-learningcomputer-visionconv-neural-networkconvolutiondeconvolution

50

在这段代码中，UpSampling2D和Conv2DTranspose似乎被互换使用了。我想知道为什么会发生这种情况。

# 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

- Piyush Chauhan

1个回答

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可以查看英文原文，
原文链接

- Burton2000 · Accepted Answer

UpSampling2D是通过最近邻或双线性上采样简单地扩大图像，所以并没有什么特别的技巧。它的优点是廉价。

Conv2DTranspose是一个卷积操作，其核是在训练模型时学习得到的（就像普通的conv2d操作一样）。使用Conv2DTranspose也会对输入进行上采样，但关键的区别是模型应该学习什么是最适合当前任务的上采样方法。

编辑：漂亮的转置卷积可视化链接：https://towardsdatascience.com/types-of-convolutions-in-deep-learning-717013397f4d