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author | Soumith Chintala <soumith@gmail.com> | 2015-08-25 21:15:39 +0300 |
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committer | Soumith Chintala <soumith@gmail.com> | 2015-08-25 21:15:39 +0300 |
commit | 24fd108ef05d24cf93a7649ba39191556f45ee76 (patch) | |
tree | 5eef4e7d22e606a2d593edab5be3a50ebef3d2c6 | |
parent | 609fb78769b83c9dd3c9641cb837cfb0873d6f2f (diff) | |
parent | 16e5fac2dedb31a40cd00d800babad95be655b8a (diff) |
Merge pull request #355 from sbodenstein/documentation_fix
Fix SpatialBatchNormalization documentation example.
-rwxr-xr-x | doc/convolution.md | 6 |
1 files changed, 3 insertions, 3 deletions
diff --git a/doc/convolution.md b/doc/convolution.md index 54b8da9..b143af6 100755 --- a/doc/convolution.md +++ b/doc/convolution.md @@ -2,7 +2,7 @@ # Convolutional layers # A convolution is an integral that expresses the amount of overlap of one function `g` as it is shifted over another function `f`. It therefore "blends" one function with another. The neural network package supports convolution, pooling, subsampling and other relevant facilities. These are divided base on the dimensionality of the input and output [Tensors](https://github.com/torch/torch7/blob/master/doc/tensor.md#tensor): - + * [Temporal Modules](#nn.TemporalModules) apply to sequences with a one-dimensional relationship (e.g. sequences of words, phonemes and letters. Strings of some kind). * [TemporalConvolution](#nn.TemporalConvolution) : a 1D convolution over an input sequence ; @@ -558,12 +558,12 @@ The module only accepts 4D inputs. -- with learnable parameters model = nn.SpatialBatchNormalization(m) A = torch.randn(b, m, h, w) -C = model.forward(A) -- C will be of size `b x m x h x w` +C = model:forward(A) -- C will be of size `b x m x h x w` -- without learnable parameters model = nn.SpatialBatchNormalization(m, nil, nil, false) A = torch.randn(b, m, h, w) -C = model.forward(A) -- C will be of size `b x m x h x w` +C = model:forward(A) -- C will be of size `b x m x h x w` ``` <a name="nn.VolumetricModules"></a> |