Merge pull request #857 from nouiz/shape_tensor_basic2

theano/tensor/elemwise.py

@@ -1546,6 +1546,7 @@ class CAReduceDtype(CAReduce):
         # We need to redefine make_node so that, if self.dtype is None,
         # we can infer what dtype should be, and create a node from an Op
         # of the appropriate dtype.
+        input = as_tensor_variable(input)
         dtype = self._output_dtype(input.dtype)
         assert dtype is not None
         if dtype == self.dtype:

theano/tensor/extra_ops.py

@@ -360,6 +360,10 @@ class RepeatOp(theano.Op):
         repeats = node.inputs[1]
         out_shape = list(i0_shapes)
 
+        #uint64 shape are not supported.
+        dtype = None
+        if repeats.dtype in ['uint8', 'uint16', 'uint32']:
+            dtype = 'int64'
         if self.axis is None:
             if repeats.ndim == 0:
                 if len(i0_shapes) == 0:
@@ -370,12 +374,12 @@ class RepeatOp(theano.Op):
                         res = res * d
                     out_shape = (res * repeats, )
             else:
-                out_shape = [theano.tensor.sum(repeats)]
+                out_shape = [theano.tensor.sum(repeats, dtype=dtype)]
         else:
             if repeats.ndim == 0:
                 out_shape[self.axis] = out_shape[self.axis] * repeats
             else:
-                out_shape[self.axis] = theano.tensor.sum(repeats)
+                out_shape[self.axis] = theano.tensor.sum(repeats, dtype=dtype)
         return [out_shape]
 
     def __str__(self):

theano/tensor/nnet/nnet.py

@@ -869,7 +869,7 @@ class CrossentropySoftmax1HotWithBiasDx (gof.Op):
         # typically we should not need the gradient w.r.t. dy).
         y_idx_range = tensor.arange(y_idx.shape[0])
         g_dy = tensor.sum(
-                g_dx * tensor.AdvancedIncSubtensor((y_idx_range, y_idx))(
+                g_dx * tensor.AdvancedIncSubtensor()(
                     sm, tensor.fill(dy, -1), y_idx_range, y_idx),
                 axis=1)
         g_sm = dy.dimshuffle(0, 'x') * g_dx

theano/tensor/nnet/tests/test_nnet.py

@@ -769,6 +769,7 @@ class T_CrossentropyCategorical1Hot(utt.InferShapeTester):
                 theano.printing.debugprint(f)
                 raise
             g = theano.function([x, y], T.grad(expr, x), mode=mode)
+            if verbose:
                 print_graph(g)
             try:
                 ops = [node.op for node in g.maker.fgraph.toposort()]
@@ -829,6 +830,7 @@ class T_CrossentropyCategorical1Hot(utt.InferShapeTester):
             finally:
                 config.warn.sum_div_dimshuffle_bug = backup
 
+            if verbose:
                 print_graph(g)
             try:
                 ops = [node.op for node in g.maker.fgraph.toposort()]

theano/tensor/opt.py

@@ -971,6 +971,7 @@ class ShapeFeature(object):
                 # keep it this way. See #266 for a better long-term fix.
                 if getattr(d, 'dtype', 'int64') != 'int64':
                     assert d.dtype in theano.tensor.discrete_dtypes, d.dtype
+                    assert str(d.dtype) != 'uint64'
                     new_shape += sh[len(new_shape):i + 1]
                     new_shape[i] = theano.tensor.cast(d, 'int64')
             if new_shape: