当我将填充设置为 1 时,ZeroPadding2D 填充两次
ZeroPadding2D pad twices when I set padding to 1
我刚刚开始学习 Tensorflow (2.1.0)、Keras (2.3.7) Python 3.7.7。
我正在尝试使用 VGG16 的编码器-解码器网络。
我需要将层从 (12, 12, ...)
上采样到 (25, 25, ...)
以使 conv7_1
具有与 conv4_3
层相同的形状。带有 'problem' 的图层是 upsp2
:
conv4_3 (Conv2D) (None, 25, 25, 512) 2359808
_________________________________________________________________
pool_4 (MaxPooling2D) (None, 12, 12, 512) 0
_________________________________________________________________
conv5_1 (Conv2D) (None, 12, 12, 512) 2359808
_________________________________________________________________
conv5_2 (Conv2D) (None, 12, 12, 512) 2359808
_________________________________________________________________
conv5_3 (Conv2D) (None, 12, 12, 512) 2359808
_________________________________________________________________
pool_5 (MaxPooling2D) (None, 6, 6, 512) 0
_________________________________________________________________
upsp1 (UpSampling2D) (None, 12, 12, 512) 0
_________________________________________________________________
conv6_1 (Conv2D) (None, 12, 12, 512) 2359808
_________________________________________________________________
conv6_2 (Conv2D) (None, 12, 12, 512) 2359808
_________________________________________________________________
conv6_3 (Conv2D) (None, 12, 12, 512) 2359808
_________________________________________________________________
upsp2 (UpSampling2D) (None, 24, 24, 512) 0
_________________________________________________________________
conv7_1 (Conv2D) (None, 24, 24, 512) 2359808
我试过这个:
#################################
# Decoder
#################################
#conv1 = Conv2DTranspose(512, (2, 2), strides = 2, name = 'conv1')(pool5)
upsp1 = UpSampling2D(size = (2,2), name = 'upsp1')(pool5)
conv6 = Conv2D(512, 3, activation = 'relu', padding = 'same', name = 'conv6_1')(upsp1)
conv6 = Conv2D(512, 3, activation = 'relu', padding = 'same', name = 'conv6_2')(conv6)
conv6 = Conv2D(512, 3, activation = 'relu', padding = 'same', name = 'conv6_3')(conv6)
zero1 = ZeroPadding2D(padding = (1,1), data_format = 'channels_last', name='zero1')(conv6)
upsp2 = UpSampling2D(size = (2,2), name = 'upsp2')(zero1)
但是我得到那个形状 (12, 12, ...)
在 zero1
层进入 (14, 14, ...)
:
conv6_3 (Conv2D) (None, 12, 12, 512) 2359808
_________________________________________________________________
zero1 (ZeroPadding2D) (None, 14, 14, 512) 0
_________________________________________________________________
upsp2 (UpSampling2D) (None, 28, 28, 512) 0
_________________________________________________________________
如何将 (12,12,512)
上采样到 (25,25,512)
?
我使用填充作为 2 个整数的 2 个元组的元组来完成它:解释为 ((top_pad, bottom_pad), (left_pad, right_pad) ).并在卷积7层的最后设置ZeroPadding2D
:
#################################
# Decoder
#################################
#conv1 = Conv2DTranspose(512, (2, 2), strides = 2, name = 'conv1')(pool5)
upsp1 = UpSampling2D(size = (2,2), name = 'upsp1')(pool5)
conv6 = Conv2D(512, 3, activation = 'relu', padding = 'same', name = 'conv6_1')(upsp1)
conv6 = Conv2D(512, 3, activation = 'relu', padding = 'same', name = 'conv6_2')(conv6)
conv6 = Conv2D(512, 3, activation = 'relu', padding = 'same', name = 'conv6_3')(conv6)
upsp2 = UpSampling2D(size = (2,2), name = 'upsp2')(conv6)
conv7 = Conv2D(512, 3, activation = 'relu', padding = 'same', name = 'conv7_1')(upsp2)
conv7 = Conv2D(512, 3, activation = 'relu', padding = 'same', name = 'conv7_2')(conv7)
conv7 = Conv2D(512, 3, activation = 'relu', padding = 'same', name = 'conv7_3')(conv7)
zero1 = ZeroPadding2D(padding = ((1, 0), (1, 0)), data_format = 'channels_last', name='zero1')(conv7)
我刚刚开始学习 Tensorflow (2.1.0)、Keras (2.3.7) Python 3.7.7。
我正在尝试使用 VGG16 的编码器-解码器网络。
我需要将层从 (12, 12, ...)
上采样到 (25, 25, ...)
以使 conv7_1
具有与 conv4_3
层相同的形状。带有 'problem' 的图层是 upsp2
:
conv4_3 (Conv2D) (None, 25, 25, 512) 2359808
_________________________________________________________________
pool_4 (MaxPooling2D) (None, 12, 12, 512) 0
_________________________________________________________________
conv5_1 (Conv2D) (None, 12, 12, 512) 2359808
_________________________________________________________________
conv5_2 (Conv2D) (None, 12, 12, 512) 2359808
_________________________________________________________________
conv5_3 (Conv2D) (None, 12, 12, 512) 2359808
_________________________________________________________________
pool_5 (MaxPooling2D) (None, 6, 6, 512) 0
_________________________________________________________________
upsp1 (UpSampling2D) (None, 12, 12, 512) 0
_________________________________________________________________
conv6_1 (Conv2D) (None, 12, 12, 512) 2359808
_________________________________________________________________
conv6_2 (Conv2D) (None, 12, 12, 512) 2359808
_________________________________________________________________
conv6_3 (Conv2D) (None, 12, 12, 512) 2359808
_________________________________________________________________
upsp2 (UpSampling2D) (None, 24, 24, 512) 0
_________________________________________________________________
conv7_1 (Conv2D) (None, 24, 24, 512) 2359808
我试过这个:
#################################
# Decoder
#################################
#conv1 = Conv2DTranspose(512, (2, 2), strides = 2, name = 'conv1')(pool5)
upsp1 = UpSampling2D(size = (2,2), name = 'upsp1')(pool5)
conv6 = Conv2D(512, 3, activation = 'relu', padding = 'same', name = 'conv6_1')(upsp1)
conv6 = Conv2D(512, 3, activation = 'relu', padding = 'same', name = 'conv6_2')(conv6)
conv6 = Conv2D(512, 3, activation = 'relu', padding = 'same', name = 'conv6_3')(conv6)
zero1 = ZeroPadding2D(padding = (1,1), data_format = 'channels_last', name='zero1')(conv6)
upsp2 = UpSampling2D(size = (2,2), name = 'upsp2')(zero1)
但是我得到那个形状 (12, 12, ...)
在 zero1
层进入 (14, 14, ...)
:
conv6_3 (Conv2D) (None, 12, 12, 512) 2359808
_________________________________________________________________
zero1 (ZeroPadding2D) (None, 14, 14, 512) 0
_________________________________________________________________
upsp2 (UpSampling2D) (None, 28, 28, 512) 0
_________________________________________________________________
如何将 (12,12,512)
上采样到 (25,25,512)
?
我使用填充作为 2 个整数的 2 个元组的元组来完成它:解释为 ((top_pad, bottom_pad), (left_pad, right_pad) ).并在卷积7层的最后设置ZeroPadding2D
:
#################################
# Decoder
#################################
#conv1 = Conv2DTranspose(512, (2, 2), strides = 2, name = 'conv1')(pool5)
upsp1 = UpSampling2D(size = (2,2), name = 'upsp1')(pool5)
conv6 = Conv2D(512, 3, activation = 'relu', padding = 'same', name = 'conv6_1')(upsp1)
conv6 = Conv2D(512, 3, activation = 'relu', padding = 'same', name = 'conv6_2')(conv6)
conv6 = Conv2D(512, 3, activation = 'relu', padding = 'same', name = 'conv6_3')(conv6)
upsp2 = UpSampling2D(size = (2,2), name = 'upsp2')(conv6)
conv7 = Conv2D(512, 3, activation = 'relu', padding = 'same', name = 'conv7_1')(upsp2)
conv7 = Conv2D(512, 3, activation = 'relu', padding = 'same', name = 'conv7_2')(conv7)
conv7 = Conv2D(512, 3, activation = 'relu', padding = 'same', name = 'conv7_3')(conv7)
zero1 = ZeroPadding2D(padding = ((1, 0), (1, 0)), data_format = 'channels_last', name='zero1')(conv7)