Merge pull request #2425 from nouiz/dnn

doc/library/sandbox/cuda/dnn.txt

@@ -40,6 +40,14 @@ To get an error if Theano can not use cuDNN, use this Theano flag:
     currently does not allow all options supported by cuDNN ops. So it is
     possible that you will need to call them manually.
 
+.. note::
+
+    The documentation of CUDNN R1 and R2 tells that, for the following
+    2 operations, the reproducibility is not guaranteed:
+    `cudnnConvolutionBackwardFilter` and `cudnnConvolutionBackwardData`.
+    Those correspond to the gradient wrt the weights and the gradient wrt the
+    input of the convolution. They are also used sometimes in the forward 
+    pass, when they give a speed up.
 
 Functions
 =========

doc/library/typed_list.txt

@@ -8,6 +8,12 @@
 
     This is not in the released version 0.6.0, but will be in the next release (0.7 or 0.6.1).
 
+.. note::
+
+    This works, but is not well integrated with the rest of Theano. If
+    speed is important, it is probably better to pad to a dense
+    tensor.
+
 This is a type that represents a list in Theano. All elements must have
 the same Theano type. Here is an example::
 

doc/tutorial/printing_drawing.txt

@@ -14,6 +14,16 @@ compilation.  These two functions print expression graphs in different ways:
 Theano also provides :func:`pydotprint` that creates a *png* image of the function.
 You can read about them in :ref:`libdoc_printing`.
 
+.. note::
+
+
+    When printing Theano functions, they can sometimes be hard to
+    read.  To help with this, you can disable some Theano optimizations
+    by using the Theano flag:
+    ``optimizer_excluding=fusion:inplace``. Do not use this during
+    real job execution, as this will make the graph slower and use more
+    memory.
+
 Consider again the logistic regression but notice the additional printing instuctions. 
 The following output depicts the pre- and post- compilation graphs.
 

theano/sandbox/cuda/dnn.py

@@ -23,6 +23,10 @@ from theano.sandbox.cuda.nvcc_compiler import NVCC_compiler
 
 def dnn_available():
     if dnn_available.avail is None:
+        if not theano.sandbox.cuda.cuda_available:
+            dnn_available.msg = "CUDA not available"
+            dnn_available.avail = False
+            return False
         dev = theano.sandbox.cuda.active_device_number()
         if theano.sandbox.cuda.device_properties(dev)['major'] < 3:
             dnn_available.msg = "Device not supported by cuDNN"