docs fixes

Author: soumith

docs/source/nn.rst

@@ -503,6 +503,12 @@ Loss functions
 .. autoclass:: MultiMarginLoss
  :members:
 
+:hidden:`TripletMarginLoss`
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: TripletMarginLoss
+ :members:
+
 
 Vision layers
 ----------------
@@ -838,6 +844,11 @@ Loss functions
 
 .. autofunction:: smooth_l1_loss
 
+:hidden:`triplet_margin_loss`
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autofunction:: triplet_margin_loss
+
 Vision functions
 ----------------
 

docs/source/sparse.rst

@@ -55,33 +55,33 @@ sparse tensors to prevent them from growing too large.
 
 .. class:: FloatTensor()
 
- .. automethod:: add
- .. automethod:: add_
- .. automethod:: clone
- .. automethod:: contiguous
- .. automethod:: dim
- .. automethod:: div
- .. automethod:: div_
- .. automethod:: get_device
- .. automethod:: hspmm
- .. automethod:: indices
- .. automethod:: is_contiguous
- .. automethod:: mm
- .. automethod:: mul
- .. automethod:: mul_
- .. automethod:: nnz
- .. automethod:: resizeAs_
- .. automethod:: size
- .. automethod:: spadd
- .. automethod:: sparse_mask
- .. automethod:: spmm
- .. automethod:: sspaddmm
- .. automethod:: sspmm
- .. automethod:: sub
- .. automethod:: sub_
- .. automethod:: t_
- .. automethod:: toDense
- .. automethod:: transpose
- .. automethod:: transpose_
- .. automethod:: values
- .. automethod:: zero_
+ .. method:: add
+ .. method:: add_
+ .. method:: clone
+ .. method:: contiguous
+ .. method:: dim
+ .. method:: div
+ .. method:: div_
+ .. method:: get_device
+ .. method:: hspmm
+ .. method:: indices
+ .. method:: is_contiguous
+ .. method:: mm
+ .. method:: mul
+ .. method:: mul_
+ .. method:: nnz
+ .. method:: resizeAs_
+ .. method:: size
+ .. method:: spadd
+ .. method:: sparse_mask
+ .. method:: spmm
+ .. method:: sspaddmm
+ .. method:: sspmm
+ .. method:: sub
+ .. method:: sub_
+ .. method:: t_
+ .. method:: toDense
+ .. method:: transpose
+ .. method:: transpose_
+ .. method:: values
+ .. method:: zero_

torch/nn/functional.py

@@ -691,7 +691,7 @@ def triplet_margin_loss(anchor, positive, negative, margin=1.0, p=2, eps=1e-6, s
  .. math::
  L(a, p, n) = \frac{1}{N} \left( \sum_{i=1}^N \max \{d(a_i, p_i) - d(a_i, n_i) + {\rm margin}, 0\} \right)
 
- where :math: `d(x_i, y_i) = \| {\bf x}_i - {\bf y}_i \|_2^2`.
+ where :math:`d(x_i, y_i) = \| {\bf x}_i - {\bf y}_i \|_2^2`.
 
  Args:
  anchor: anchor input tensor

torch/nn/modules/instancenorm.py

@@ -105,9 +105,11 @@ class InstanceNorm2d(_InstanceNorm):
  eps: a value added to the denominator for numerical stability. Default: 1e-5
  momentum: the value used for the running_mean and running_var computation. Default: 0.1
  affine: a boolean value that when set to true, gives the layer learnable affine parameters.
+
  Shape:
  - Input: :math:`(N, C, H, W)`
  - Output: :math:`(N, C, H, W)` (same shape as input)
+
  Examples:
  >>> # With Learnable Parameters
  >>> m = nn.InstanceNorm2d(100)

torch/nn/modules/loss.py

@@ -448,7 +448,7 @@ class TripletMarginLoss(Module):
  .. math::
  L(a, p, n) = \frac{1}{N} \left( \sum_{i=1}^N \max \{d(a_i, p_i) - d(a_i, n_i) + {\rm margin}, 0\} \right)
 
- where :math: `d(x_i, y_i) = \| {\bf x}_i - {\bf y}_i \|_2^2`.
+ where :math:`d(x_i, y_i) = \| {\bf x}_i - {\bf y}_i \|_2^2`.
 
  Args:
  anchor: anchor input tensor