Merge pull request #2912 from nouiz/doc

doc/tutorial/examples.txt

@@ -325,9 +325,10 @@ Here's a brief example.  The setup code is:
 Here, 'rv_u' represents a random stream of 2x2 matrices of draws from a uniform
 distribution.  Likewise,  'rv_n' represents a random stream of 2x2 matrices of
 draws from a normal distribution.  The distributions that are implemented are
-defined in :class:`RandomStreams` and, at a lower level, in :ref:`raw_random<libdoc_tensor_raw_random>`.
+defined in :class:`RandomStreams` and, at a lower level,
+in :ref:`raw_random<libdoc_tensor_raw_random>`. They only work on CPU.
+See `Other Implementations`_ for GPU version.
 
-  .. TODO: repair the latter reference on RandomStreams
 
 Now let's use these objects.  If we call f(), we get random uniform numbers.
 The internal state of the random number generator is automatically updated,
@@ -459,10 +460,15 @@ Other Random Distributions
 
 There are :ref:`other distributions implemented <libdoc_tensor_raw_random>`. 
 
+.. _example_other_random:
+
 Other Implementations
 ---------------------
 
-There is 2 other implementations based on :class:`CURAND <theano.sandbox.cuda.rng_curand>` and :ref:`MRG31k3p <libdoc_rng_mrg>`
+There is 2 other implementations based on :class:`CURAND
+<theano.sandbox.cuda.rng_curand>` and :ref:`MRG31k3p
+<libdoc_rng_mrg>`. The RandomStream only work on the CPU, MRG31k3p
+work on the CPU and GPU. CURAND only work on the GPU.
 
 .. _logistic_regression:
 

doc/tutorial/using_gpu.txt

@@ -744,3 +744,9 @@ efficiency over the basic solution that is asked here, the two operations would
 have to be jointly optimized explicitly in the code.)
 
 Modify and execute to support *stride* (i.e. to avoid constraining the input to be *C-contiguous*).
+
+Note
+----
+
+See :ref:`example_other_random` to know how to handle random numbers
+on the GPU.

theano/tensor/basic.py

@@ -1196,28 +1196,6 @@ def cast(x, dtype):
 ##########################
 
 
-@constructor
-def old_shape(a):
-    """
-    Return the shape tuple of a TensorType Variable.
-
-    It may be either symbolic or nonsymbolic.
-
-    If the shape of the expression is not known at graph-construction time,
-    then a symbolic lvector will be returned, corresponding to the actual
-    shape at graph-execution time.
-    """
-    va = as_tensor_variable(a)
-    # print 'HERE', va, va.type
-    if None in va.type.shape:
-        # Some shape components are unknown at this time
-        return _shape(va)
-    else:
-        # all shape components are known at compile time, so we return
-        # a tuple directly.  This tuple is like the numpy.ndarray.shape tuple.
-        return va.type.shape
-
-
 class MaxAndArgmax(Op):
     """Calculate the max and argmax over a given axis or over all axes.
     """
@@ -3306,13 +3284,15 @@ def addbroadcast(x, *axes):
             Input theano tensor.
         axis : an int or an iterable object such as list or tuple
                of int values
-            The dimension along which the tensor x should be broadcastable.
-            if the length of x along these dimensions is not 1,
-            a ValueError will be raised.
+
+               The dimension along which the tensor x should be
+               broadcastable.  if the length of x along these
+               dimensions is not 1, a ValueError will be raised.
 
     returns:
     ----------
         a theano tensor, which is broadcastable along the specified dimensions.
+
     """
     rval = Rebroadcast(*[(axis, True) for axis in axes])(x)
     return theano.tensor.opt.apply_rebroadcast_opt(rval)
@@ -3334,13 +3314,15 @@ def unbroadcast(x, *axes):
             Input theano tensor.
         axis : an int or an iterable object such as list or tuple
                of int values
-            The dimension along which the tensor x should be unbroadcastable.
-            if the length of x along these dimensions is not 1,
-            a ValueError will be raised.
+
+               The dimension along which the tensor x should be
+               unbroadcastable.  if the length of x along these
+               dimensions is not 1, a ValueError will be raised.
 
     returns:
     ----------
         a theano tensor, which is unbroadcastable along the specified dimensions.
+
     """
     rval = Rebroadcast(*[(axis, False) for axis in axes])(x)
     return theano.tensor.opt.apply_rebroadcast_opt(rval)
@@ -3363,6 +3345,7 @@ def patternbroadcast(x, broadcastable):
             Input theano tensor.
         broadcastable : an iterable object such as list or tuple
                         of bool values
+
             a set of boolean values indicating whether a dimension
             should be broadcastable or not.
             if the length of x along these dimensions is not 1,
@@ -5468,8 +5451,6 @@ class Choose(Op):
                     "We currently didn't implemented that case. "
                     "To make it work, explicitly add dimensions "
                     "of size one for dimensions that will be broadcasted")
-                assert isinstance(node.inputs[1],
-                                  theano.typed_list.TypedListVariable)
 
         bcast = [False] * out_ndim
         for idx, (b1, b2) in enumerate(

theano/tensor/extra_ops.py

@@ -456,6 +456,7 @@ def bincount(x, weights=None, minlength=None, assert_nonneg=False):
     :param assert_nonneg: A flag that inserts an assert_op to check if
         every input x is nonnegative.
         Optional.
+
     .. versionadded:: 0.6
     """
     compatible_type = ('int8', 'int16', 'int32', 'int64',
@@ -521,7 +522,7 @@ def compress(condition, x, axis=None):
     :param x: Input data, tensor variable
 
     :param condition: 1 dimensional array of non-zero and zero values
-    corresponding to indices of slices along a selected axis
+        corresponding to indices of slices along a selected axis
 
     :return: `x` with selected slices