Merged

ChangeLog

-2010-10-22 Theano 0.3
----------------------
-
-This is the first major release of Theano since 0.1. Version 0.2 development
-started internally but it was never advertised as a release.
-
-There have been so many changes since 0.1 that we have lost track of many of
-them. Below is a *partial* list of changes since 0.1.
-
- * GPU code using NVIDIA's CUDA framework is now generated for many Ops.
- * Some interface changes since 0.1:
-     * A new "shared variable" system to allow reusing memory space between
-       Theano functions.
-         * A new memory contract has been formally written for Theano,
-           for people who want to minimize memory copies.
-     * The old module system has been deprecated.
-     * By default, inputs to a Theano function will not be silently
-       downcasted (e.g. from float64 to float32).
-     * An error is now raised when using the result of logical operation on
-       Theano variable in an 'if' (i.e. an implicit call to __nonzeros__).
-     * An error is now raised when we receive a non-aligned ndarray as
-       input to a function (this is not supported).
-     * An error is raised when the list of dimensions passed to
-       dimshuffle() contains duplicates or is otherwise not sensible.
-     * Call NumPy BLAS bindings for gemv operations in addition to the
-       already supported gemm.
-     * If gcc is unavailable at import time, Theano now falls back to a
-       Python-based emulation mode after raising a warning.
-     * An error is now raised when tensor.grad is called on a non-scalar
-       Theano variable (in the past we would implicitly do a sum on the
-       tensor to make it a scalar).
-     * Added support for "erf" and "erfc" functions.
- * The current default value of the parameter axis of theano.{max,min,
-   argmax,argmin,max_and_argmax} is deprecated. We now use the default NumPy
-   behavior of operating on the entire tensor.
- * Theano is now available from PyPI and installable through "easy_install" or
-   "pip".

HISTORY.txt

+
+.. _NEWS:
+
+=============
+Release Notes
+=============
+
+Theano 0.1
+==========
+
+*Release date: 2009-04-02*
+
+What works
+----------
+
+- building symbolic expression.
+- arranging symbolic expressions into Modules so that multiple functions
+  can work on the same data.
+- symbolic gradient descent.
+- graph optimization.
+- compilation to C for many kinds of expression.
+- a debugging mode that checks that your expression results are correct,
+  using a variety of sanity checks.
+
+What's missing?
+---------------
+
+- An algorithm library. We're missing a library of examples and standard
+  component implementations.  Some examples will find their way into
+  the Theano repo, but standard algorithms will go into the 'pylearn'
+  project (toolbox style). Now that we have a stable foundation, we
+  can reach a consensus on style for algorithms.

NEWS.txt

-doc/NEWS.txt
\ No newline at end of file

NEWS.txt

+Theano 0.3 (2010-11-23)
+-----------------------
+
+This is the first major release of Theano since 0.1. Version 0.2 development started internally but it was never advertised as a release.
+
+There have been so many changes since 0.1 that we have lost track of many of them. Below is a *partial* list of changes since 0.1.
+
+ * GPU code using NVIDIA's CUDA framework is now generated for many Ops.
+ * Some interface changes since 0.1:
+     * A new "shared variable" system to allow reusing memory space between Theano functions.
+         * A new memory contract has been formally written for Theano, for people who want to minimize memory copies.
+     * The old module system has been deprecated.
+     * By default, inputs to a Theano function will not be silently downcasted (e.g. from float64 to float32).
+     * An error is now raised when using the result of logical operation on Theano variable in an 'if' (i.e. an implicit call to __nonzeros__).
+     * An error is now raised when we receive a non-aligned ndarray as input to a function (this is not supported).
+     * An error is raised when the list of dimensions passed to dimshuffle() contains duplicates or is otherwise not sensible.
+     * Call NumPy BLAS bindings for gemv operations in addition to the already supported gemm.
+     * If gcc is unavailable at import time, Theano now falls back to a Python-based emulation mode after raising a warning.
+     * An error is now raised when tensor.grad is called on a non-scalar Theano variable (in the past we would implicitly do a sum on the tensor to make it a scalar).
+     * Added support for "erf" and "erfc" functions.
+ * The current default value of the parameter axis of theano.{max,min,argmax,argmin,max_and_argmax} is deprecated. We now use the default NumPy behavior of operating on the entire tensor.
+ * Theano is now available from PyPI and installable through "easy_install" or "pip".

setup.py

@@ -36,7 +36,8 @@ MAINTAINER          = "LISA laboratory, University of Montreal"
 MAINTAINER_EMAIL    = "theano-dev@googlegroups.com"
 DESCRIPTION         = ('Optimizing compiler for evaluating mathematical ' +
                        'expressions on CPUs and GPUs.')
-LONG_DESCRIPTION    = open("DESCRIPTION.txt").read()
+LONG_DESCRIPTION    = (open("DESCRIPTION.txt").read() + "\n\n"
+                       open("NEWS.txt").read())
 URL                 = "http://deeplearning.net/software/theano/"
 DOWNLOAD_URL        = ""
 LICENSE             = 'BSD'

theano/sandbox/cuda/tests/test_tensor_op.py

@@ -40,6 +40,18 @@ def test_shape_i():
         assert len(topo)==1
         assert isinstance(topo[0].op,T.opt.Shape_i)
 
+def test_shape():
+    x = cuda.ftensor3()
+    v = cuda.CudaNdarray(numpy.zeros((3,4,5),dtype='float32'))
+    f = theano.function([x],x.shape)
+    topo = f.maker.env.toposort()
+    assert numpy.all(f(v)==(3,4,5))
+    if theano.config.mode!='FAST_COMPILE':
+        assert len(topo)==4
+        assert isinstance(topo[0].op,T.opt.Shape_i)
+        assert isinstance(topo[1].op,T.opt.Shape_i)
+        assert isinstance(topo[2].op,T.opt.Shape_i)
+        assert isinstance(topo[3].op,T.opt.MakeVector)
 
 def test_softmax_optimizations():
     from theano.tensor.nnet.nnet import softmax, crossentropy_categorical_1hot, crossentropy_softmax_argmax_1hot_with_bias

theano/sparse/basic.py

@@ -248,6 +248,12 @@ class _sparse_py_operators:
     def __dot__(left, right): return structured_dot(left, right)
     def __rdot__(right, left): return structured_dot(left, right)
 
+    #def _as_TensorVariable(self):
+    #    return dense_from_sparse(self)
+    shape = property(lambda self: tensor.shape(self))
+    ndim = property(lambda self: self.type.ndim)
+    dtype = property(lambda self: self.type.dtype)
+
 
 class SparseVariable(gof.Variable, _sparse_py_operators):
     dtype = property(lambda self: self.type.dtype)
@@ -1380,4 +1386,3 @@ class StructuredDotGradCSR(gof.Op):
 
         """% dict(locals(), **sub)
 sdg_csr = StructuredDotGradCSR()
-

theano/tensor/basic.py

@@ -2094,8 +2094,8 @@ class Mean(elemwise.CAReduce):
         ret = elemwise.CAReduce.c_code(self, node, name, inames, onames, sub)
         #TODO: c_code perform support only axis==None
         return ret + """
-*((double *)PyArray_DATA(%s)) /= PyArray_SIZE(%s);
-"""%(onames[0],inames[0])
+  *((double *)PyArray_DATA(%s)) /= PyArray_SIZE(%s);
+  """%(onames[0],inames[0])
 
 #TODO: implement the grad. When done and tested, you can make this the default version.
 #    def grad(self, (x,), (gout,)):

theano/tensor/opt.py

@@ -3098,5 +3098,3 @@ if config.tensor.local_elemwise_fusion:
 else:
     _logger.debug("not enabling optimization fusion elemwise in fast_run")
     compile.optdb.register('elemwise_fusion', FusionOptimizer(local_elemwise_fusion), 71.00, 'fusion', 'local_elemwise_fusion')
-
-