merge

theano/compile/profilemode.py

@@ -87,7 +87,6 @@ class ProfileMode(Mode):
         flops_msg=''
         for a,t in op_time.items():
             if hasattr(a,'flops'):
-                print "if true!"
                 flops=True
                 flops_msg=' <MFlops/s>'
                 print '\nHACK WARNING: we print the flops for some OP, but the logic don\' always work. You need to know the internal of Theano to make it work correctly. Otherwise don\'t use!'

theano/tensor/basic.py

@@ -870,7 +870,7 @@ class TensorFromScalar(Op):
     def perform(self, node, (s, ), (out, )):
         out[0] = numpy.asarray(s)
     def grad(self, (s,), (dt,)):
-        return [ScalarFromTensor(dt)]
+        return [scalar_from_tensor(dt)]
 tensor_from_scalar = TensorFromScalar()
 
 class ScalarFromTensor(Op):

theano/tensor/tests/test_misc.py

-import sys
+import copy, sys
 import numpy, theano
 from theano import tensor
+from theano.tensor.nnet import crossentropy_softmax_argmax_1hot_with_bias
 
 def test_bug_2009_06_02_trac_387():
 
@@ -31,3 +32,40 @@ def test_bug_2009_07_17_borrowed_output():
     print z         # Should still be zero.
     assert numpy.linalg.norm(z) == 0
 
+    # The code above was supposed to fail when it was written (or, more
+    # accurately, on the next revision, i.e. when it was merged with the
+    # rest of the code, i.e. on revision cac9c9e9f08e).
+    # However, for some reason, it does not fail anymore when at this revision.
+    # Thus, a new test (below) was added that exhibits the same issue. Note
+    # that it may better be moved into the test_nnet.py test file if it turns
+    # out the bug was caused by 'crossentropy_softmax_argmax_1hot_with_bias',
+    # and was not a more general issue.
+    test_output_activation_no_bias = theano.tensor.dmatrix()
+    test_b2 = theano.tensor.dvector()
+    test_target = theano.tensor.ivector()
+    nll_softmax_argmax = (
+            crossentropy_softmax_argmax_1hot_with_bias(
+                test_output_activation_no_bias,
+                test_b2,
+                test_target))
+    output = nll_softmax_argmax[1]
+    g = theano.function([test_output_activation_no_bias, test_b2, test_target],
+            theano.Out(output, borrow=False))
+    
+    a = numpy.zeros((1, 5))
+    b = numpy.ones(5)
+    c = numpy.zeros(1, dtype=numpy.int32)
+    
+    z = g(a, b, c)
+    z_backup = copy.copy(z)
+    id_z = id(z)
+    print('Output z after first call: %s' % (z, ))
+    a[0, 0] = 1
+    id_other = id(g(a, b, c))
+    print ('Output z after second call: %s' % (z, ))
+    # Ensure that calling the function again returns a pointer towards a new
+    # array.
+    assert id_z != id_other
+    # Just to be 100% sure, ensure that z was not altered.
+    assert (z == z_backup).all()
+