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Configuration of masks in PixelCNN
2019 Community Moderator ElectionQuestions about CNN: weights and biasesCustom loss function which is included gradient in KerasIntuitive explanation of Convolutional layers in Deep CNNDetermining size of FC layer after Conv layer in PyTorchWhat is the purpose of a 1x1 convolutional layer?Wrangling data for CNNwhat happens to the depth channels when convolved by multiple filters in a cnn (keras, tensorflow)CNN architecture design guidelines when doing multilabel classification of 1K possible “easy” classesWhat is the motivation for row-wise convolution and folding in Kalchbrenner et al. (2014)?What are the possible values of a filter in a CNN?
$begingroup$
I have trouble understanding the masks in PixelCNN 1.
Here for A, each channel is connected to prior channels. For B, channels are also connected to themselves. My problem is in first layer when I go from 3 channels to number of filters. Let's say I have 128 filters, how is the mask then implemented? What I implemented so far with the assumption that there is same number of channels in each layer is:
H = KERNEL_SIZE[0]
W = KERNEL_SIZE[1]
N = OUT_CHANNEL
M = IN_CHANNEL
# implement context mask
mask = np.zeros((N, M, H, W))
mask[:,:,:(H//2)] = 1
mask[:,:,(H//2),range((W//2))] = 1
# implement channel mask
if mask_type == 'A':
mask[np.triu_indices(N, k=0, m=M)] = 0
elif mask_type == 'B':
mask[np.triu_indices(N, k=1, m=M)] = 0
cnn
New contributor
$endgroup$
add a comment |
$begingroup$
I have trouble understanding the masks in PixelCNN 1.
Here for A, each channel is connected to prior channels. For B, channels are also connected to themselves. My problem is in first layer when I go from 3 channels to number of filters. Let's say I have 128 filters, how is the mask then implemented? What I implemented so far with the assumption that there is same number of channels in each layer is:
H = KERNEL_SIZE[0]
W = KERNEL_SIZE[1]
N = OUT_CHANNEL
M = IN_CHANNEL
# implement context mask
mask = np.zeros((N, M, H, W))
mask[:,:,:(H//2)] = 1
mask[:,:,(H//2),range((W//2))] = 1
# implement channel mask
if mask_type == 'A':
mask[np.triu_indices(N, k=0, m=M)] = 0
elif mask_type == 'B':
mask[np.triu_indices(N, k=1, m=M)] = 0
cnn
New contributor
$endgroup$
add a comment |
$begingroup$
I have trouble understanding the masks in PixelCNN 1.
Here for A, each channel is connected to prior channels. For B, channels are also connected to themselves. My problem is in first layer when I go from 3 channels to number of filters. Let's say I have 128 filters, how is the mask then implemented? What I implemented so far with the assumption that there is same number of channels in each layer is:
H = KERNEL_SIZE[0]
W = KERNEL_SIZE[1]
N = OUT_CHANNEL
M = IN_CHANNEL
# implement context mask
mask = np.zeros((N, M, H, W))
mask[:,:,:(H//2)] = 1
mask[:,:,(H//2),range((W//2))] = 1
# implement channel mask
if mask_type == 'A':
mask[np.triu_indices(N, k=0, m=M)] = 0
elif mask_type == 'B':
mask[np.triu_indices(N, k=1, m=M)] = 0
cnn
New contributor
$endgroup$
I have trouble understanding the masks in PixelCNN 1.
Here for A, each channel is connected to prior channels. For B, channels are also connected to themselves. My problem is in first layer when I go from 3 channels to number of filters. Let's say I have 128 filters, how is the mask then implemented? What I implemented so far with the assumption that there is same number of channels in each layer is:
H = KERNEL_SIZE[0]
W = KERNEL_SIZE[1]
N = OUT_CHANNEL
M = IN_CHANNEL
# implement context mask
mask = np.zeros((N, M, H, W))
mask[:,:,:(H//2)] = 1
mask[:,:,(H//2),range((W//2))] = 1
# implement channel mask
if mask_type == 'A':
mask[np.triu_indices(N, k=0, m=M)] = 0
elif mask_type == 'B':
mask[np.triu_indices(N, k=1, m=M)] = 0
cnn
cnn
New contributor
New contributor
New contributor
asked Mar 21 at 12:43
Oğuz ŞerbetciOğuz Şerbetci
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1061
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add a comment |
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