Added doc about Conv3D on the web site.

doc/library/tensor/nnet/conv.txt

@@ -21,5 +21,11 @@
 
 TODO: Give examples for how to use these things! They are pretty complicated.
 
-.. autofunction:: theano.tensor.nnet.conv.conv2d
+- Conv implemented
+    - :func:`signal.conv2d <theano.tensor.signal.conv.conv2d>`.
+    - :func:`nnet.conv2d <theano.tensor.nnet.conv.conv2d>`.
+    - :func:`conv3D <theano.tensor.nnet.Conv3D.conv3D>`.
 
+.. autofunction:: theano.tensor.signal.conv.conv2d
+.. autofunction:: theano.tensor.nnet.conv.conv2d
+.. autofunction:: theano.tensor.nnet.Conv3D.conv3D

theano/tensor/nnet/Conv3D.py

@@ -549,6 +549,20 @@ class Conv3D(theano.Op):
 
 global conv3D
 conv3D = Conv3D()
+"""
+3D "convolution" of multiple filters on a minibatch
+(does not flip the kernel, moves kernel with a user specified stride)
+
+:param V: Visible unit, input.
+    dimensions: (batch, row, column, time, in channel)
+:param W: Weights, filter.
+    dimensions: (out channel, row, column, time ,in channel)
+:param b: bias, shape == (W.shape[0],)
+:param d: strides when moving the filter over the input(dx, dy, dt)
+
+:note: The order of dimensions do not correspond with the one in `conv2d`.
+       This is for optimization.
+"""
 
 def computeH(V,W,b,d):
     assert len(W.shape) == 5