Merge pull request #4324 from yobibyte/fix

Theano.pyproj

@@ -228,4 +228,4 @@
     <Content Include="theano\sandbox\cuda\cuda_ndarray.cuh" />
   </ItemGroup>
   <Import Project="$(MSBuildToolsPath)\Microsoft.Common.targets" />
-</Project>
\ No newline at end of file
+</Project>

theano/sandbox/cuda/tests/test_bench_loopfusion.py

@@ -19,7 +19,7 @@ import theano
 from theano.compile import shared, pfunc
 from theano import tensor
 from theano.tensor.nnet import softplus
-from theano.sandbox.softsign import softsign
+from theano.tensor.nnet.nnet import softsign
 
 _logger = logging.getLogger('theano.sandbox.cuda.tests.test_bench_loopfusion')
 

theano/sandbox/softsign.py

 from __future__ import absolute_import, print_function, division
+from theano.tensor.nnet.nnet import softsign  # noqa
+import sys
 
-import theano
-import theano.tensor
-
-
-class ScalarSoftsign(theano.scalar.UnaryScalarOp):
-    # TODO : need description for class
-    @staticmethod
-    def static_impl(x):
-        return x / (1.0 + abs(x))
-
-    def impl(self, x):
-        return ScalarSoftsign.static_impl(x)
-
-    def grad(self, inp, grads):
-        x, = inp
-        gz, = grads
-        if 'float' in x.type.dtype:
-            d = (1.0 + abs(x))
-            return [gz / (d * d)]
-        else:
-            return NotImplemented
-
-    def c_code(self, node, name, inp, out, sub):
-        x, = inp
-        z, = out
-        if node.inputs[0].type in [theano.scalar.float32,
-                                   theano.scalar.float64]:
-            return "%(z)s = %(x)s / (1.0+fabs(%(x)s));" % locals()
-        raise NotImplementedError('only floating point x is implemented')
-
-scalar_softsign = ScalarSoftsign(theano.scalar.upgrade_to_float,
-                                 name='scalar_softsign')
-softsign = theano.tensor.Elemwise(scalar_softsign, name='softsign')
+print(
+    "DEPRECATION WARNING: softsign was moved from theano.sandbox.softsign to "
+    "theano.tensor.nnet.nnet ", file=sys.stderr
+    )

theano/tensor/nnet/nnet.py

@@ -20,7 +20,7 @@ from six.moves import xrange
 import theano
 from theano import gof
 from theano import scalar
-from theano.tensor import basic as tensor, subtensor, opt
+from theano.tensor import basic as tensor, subtensor, opt, elemwise
 from theano.tensor.type import (values_eq_approx_remove_inf,
                                 values_eq_approx_remove_nan)
 from theano.tensor.opt import copy_stack_trace
@@ -2366,3 +2366,38 @@ def elu(x, alpha=1):
         Exponential Linear Units (ELUs)" <http://arxiv.org/abs/1511.07289>`.
     """
     return tensor.switch(x > 0, x, alpha * (tensor.exp(x) - 1))
+
+
+class ScalarSoftsign(theano.scalar.UnaryScalarOp):
+    """
+    Softsign activation function
+    :math:`\\varphi(\\mathbf{x}) = \\frac{1}{1+|x|}`
+
+    """
+    @staticmethod
+    def static_impl(x):
+        return x / (1.0 + abs(x))
+
+    def impl(self, x):
+        return ScalarSoftsign.static_impl(x)
+
+    def grad(self, inp, grads):
+        x, = inp
+        gz, = grads
+        if 'float' in x.type.dtype:
+            d = (1.0 + abs(x))
+            return [gz / (d * d)]
+        else:
+            return NotImplemented
+
+    def c_code(self, node, name, inp, out, sub):
+        x, = inp
+        z, = out
+        if node.inputs[0].type in [theano.scalar.float32,
+                                   theano.scalar.float64]:
+            return "%(z)s = %(x)s / (1.0+fabs(%(x)s));" % locals()
+        raise NotImplementedError('only floating point x is implemented')
+
+scalar_softsign = ScalarSoftsign(theano.scalar.upgrade_to_float,
+                                 name='scalar_softsign')
+softsign = elemwise.Elemwise(scalar_softsign, name='softsign')

theano/tensor/nnet/tests/test_nnet.py

@@ -33,6 +33,10 @@ from theano.tensor.nnet import (categorical_crossentropy,
                                 elu,
                                 binary_crossentropy)
 from theano.tensor import matrix, vector, lvector, scalar
+from theano.tensor.nnet.nnet import softsign
+from theano.tensor.tests.test_basic import (makeBroadcastTester, check_floatX,
+                                            _good_broadcast_unary_normal_float_no_complex,
+                                            upcast_int8_nfunc)
 
 
 class T_sigmoid(unittest.TestCase):
@@ -1699,3 +1703,11 @@ def test_binary_crossentropy_reshape():
         fga = theano.function([a], ga, mode=mode)
         utt.assert_allclose(fga(numpy.array([[[[30.]]]], dtype=config.floatX)),
                             numpy.zeros((1, 1, 1, 1), dtype=config.floatX))
+
+SoftsignTester = makeBroadcastTester(
+    op=softsign,
+    expected=upcast_int8_nfunc(lambda inputs: check_floatX(
+        inputs, inputs/(1.0+numpy.fabs(inputs)))),
+    good=_good_broadcast_unary_normal_float_no_complex,
+    name='SoftsignTester',
+)