add docs

theano/tensor/nnet/abstract_conv.py

@@ -673,6 +673,90 @@ def separable_conv3d(input,
                      subsample=(1, 1, 1),
                      filter_flip=True,
                      filter_dilation=(1, 1, 1)):
+    """
+    This function will build the symbolic graph for depthwise
+    convolutions which act separately on the input channels followed by
+    pointwise convolution which mixes channels.
+
+    Parameters
+    ----------
+    input: symbolic 5D tensor
+        Mini-batch of feature map stacks, of shape
+        (batch size, input channels, input depth, input rows, input columns).
+        See the optional parameter ``input_shape``.
+
+    depthwise_filters: symbolic 5D tensor
+        Set of filters used depthwise convolution layer of shape
+        (depthwise output channels, 1, filter_depth, filter rows, filter columns).
+
+    pointwise_filters: symbolic 5D tensor
+        Set of filters used pointwise convolution layer of shape
+        (output channels, depthwise output channels, 1, 1, 1).
+
+    num_channels: int
+        The number of channels of the input. Required for depthwise
+        convolutions.
+
+    input_shape: None, tuple/list of len 5 of int or Constant variable
+        The shape of the input parameter.
+        Optional, possibly used to choose an optimal implementation.
+        You can give ``None`` for any element of the list to specify that this
+        element is not known at compile time.
+
+    depthwise_filter_shape: None, tuple/list of len 5 of int or Constant variable
+        The shape of the depthwise filters parameter.
+        Optional, possibly used to choose an optimal implementation.
+        You can give ``None`` for any element of the list to specify that this
+        element is not known at compile time.
+
+    pointwise_filter_shape: None, tuple/list of len 5 of int or Constant variable
+        The shape of the pointwise filters parameter.
+        Optional, possibly used to choose an optimal implementation.
+        You can give ``None`` for any element of the list to specify that this
+        element is not known at compile time.
+
+    border_mode: str, int or tuple of three int
+        This applies only to depthwise convolutions
+        Either of the following:
+
+        ``'valid'``: apply filter wherever it completely overlaps with the
+            input. Generates output of shape: input shape - filter shape + 1
+        ``'full'``: apply filter wherever it partly overlaps with the input.
+            Generates output of shape: input shape + filter shape - 1
+        ``'half'``: pad input with a symmetric border of ``filter // 2``,
+            then perform a valid convolution. For filters with an odd
+            number of slices, rows and columns, this leads to the output
+            shape being equal to the input shape.
+        ``int``: pad input with a symmetric border of zeros of the given
+            width, then perform a valid convolution.
+        ``(int1, int2, int3)``
+            pad input with a symmetric border of ``int1``, ``int2`` and
+            ``int3`` columns, then perform a valid convolution.
+
+    subsample: tuple of len 3
+        This applies only to depthwise convolutions
+        Factor by which to subsample the output.
+        Also called strides elsewhere.
+
+    filter_flip: bool
+        If ``True``, will flip the filter x, y and z dimensions before
+        sliding them over the input. This operation is normally
+        referred to as a convolution, and this is the default. If
+        ``False``, the filters are not flipped and the operation is
+        referred to as a cross-correlation.
+
+    filter_dilation: tuple of len 3
+        Factor by which to subsample (stride) the input.
+        Also called dilation elsewhere.
+
+    Returns
+    -------
+    Symbolic 5D tensor
+        Set of feature maps generated by convolutional layer. Tensor is
+        of shape (batch size, output channels, output_depth,
+        output rows, output columns)
+
+    """
 
     input = as_tensor_variable(input)
     depthwise_filters = as_tensor_variable(depthwise_filters)