Make first version of update update about cudnn to have minimal RNN doc to email about it.

doc/library/sandbox/cuda/dnn.txt

@@ -148,6 +148,7 @@ get an error when cuDNN can not be used with them, use this flag:
 * :ref:`Convolution <libdoc_cuda_dnn_convolution>`
 * :ref:`Pooling <libdoc_cuda_dnn_pooling>`
 * :ref:`Batch Normalization <libdoc_cuda_dnn_bn>`
+* :ref:`RNN <libdoc_cuda_dnn_rnn>`
 * :ref:`Softmax <libdoc_cuda_dnn_softmax>`
 * :ref:`Internal Ops <libdoc_cuda_dnn_internal_ops>`
 
@@ -194,8 +195,8 @@ New back-end only! Without dropout support.
 Softmax
 =======
 
-You can manually use the op :ref:`GpuDnnSoftmax
-<theano.sandbox.cuda.dnn.GpuDnnSoftmax>` to use its extra feature.
+You can manually use the op `GpuDnnSoftmax
+<theano.sandbox.cuda.dnn.GpuDnnSoftmax>`_ to use its extra feature.
 
 .. _libdoc_cuda_dnn_internal_ops:
 
@@ -208,4 +209,4 @@ Internal Ops
              GpuDnnConv3dGradW, GpuDnnConvGradI, GpuDnnConv3dGradI,
 	     GpuDnnPoolDesc, GpuDnnPool, GpuDnnPoolGrad,
 	     GpuDnnBatchNormInference, GpuDnnBatchNorm, GpuDnnBatchNormGrad,
-	     GpuDnnSoftmax, GpuDnnSoftmaxGrad,
+	     GpuDnnSoftmax, GpuDnnSoftmaxGrad

theano/gpuarray/dnn.py

@@ -2337,25 +2337,34 @@ class GpuDnnRNNGradWeights(DnnBase):
 
 
 class RNNBlock(object):
+    """
+    An object that allow us to use CuDNN v5 RNN implementation.
+    TODO: make an example how to use. You can check that test until we have it.
+
+
+    Parameters
+    ----------
+
+    dtype : data type of computation
+    hidden_size : int
+    num_layers : int
+    rnn_mode : {'rnn_relu', 'rnn_tanh', 'lstm', 'gru'}
+        See cudnn documentation for ``cudnnRNNMode_t``.
+
+    input_mode : {'linear', 'skip'}
+        linear: input will be multiplied by a biased matrix
+        skip: No operation is performed on the input.  The size must match the hidden size.
+    direction_mode : {'unidirectional', 'bidirectional'}
+        unidirectional: The network operates recurrently from the
+                        first input to the last.
+
+        bidirectional: The network operates from first to last then from last to first and concatenates the results at each layer.
+
+    """
+
     def __init__(self, dtype, hidden_size, num_layers, rnn_mode,
                  input_mode='linear', direction_mode='unidirectional',
                  context_name=None):
-        """
-        dtype: data type of computation
-        hidden_size: int
-        num_layers: int
-        rnn_mode: {'rnn_relu', 'rnn_tanh', 'lstm', 'gru'}
-          See cudnn documentation for cudnnRNNMode_t.
-        input_mode: {'linear', 'skip'}
-          linear: input will be multiplied by a biased matrix
-          skip: No operation is performed on the input.  The size must match the hidden size.
-        direction_mode: {'unidirectional', 'bidirectional'}
-          unidirectional: The network operates recurrently from the
-                          first input to the last.
-
-          bidirectional: The network operates from first to last then from last to first and concatenates the results at each layer.
-
-        """
         # This is not supported for any value other than 0, so don't change it
         ddesc, states = _make_dropout_desc(0, 4242, context_name)
         self.ddesc = ddesc