Updated numpy as np

theano/tensor/nnet/tests/test_sigm.py

 from __future__ import absolute_import, print_function, division
 import unittest
 
-import numpy
+import numpy as np
 
 from theano.compat import imap
 import theano.tensor.inplace
@@ -26,12 +26,12 @@ class T_sigmoid(unittest.TestCase):
         utt.seed_rng()
 
     def test_elemwise(self):
-        utt.verify_grad(sigmoid, [numpy.random.rand(3, 4)])
+        utt.verify_grad(sigmoid, [np.random.rand(3, 4)])
 
 SigmoidTester = makeBroadcastTester(
     op=sigmoid,
     expected=upcast_int8_nfunc(lambda inputs: check_floatX(
-        inputs, 1 / (1 + numpy.exp(-inputs)))),
+        inputs, 1 / (1 + np.exp(-inputs)))),
     good=copymod(_good_broadcast_unary_normal_no_complex,
                  without=['uint16']),  # The reason that 'uint16' is excluted is that
                                        # theano works well but numpy overflows resulting
@@ -43,7 +43,7 @@ SigmoidTester = makeBroadcastTester(
 UltraFastSigmoidTester = makeBroadcastTester(
     op=ultra_fast_sigmoid,
     expected=upcast_int8_nfunc(lambda inputs: check_floatX(
-        inputs, 1 / (1 + numpy.exp(-inputs)))),
+        inputs, 1 / (1 + np.exp(-inputs)))),
     good=copymod(_good_broadcast_unary_normal_no_complex,
                  without=['uint16']),  # numpy fucnting overflows with uint16.
     # grad=_grad_broadcast_unary_normal,
@@ -54,7 +54,7 @@ UltraFastSigmoidTester = makeBroadcastTester(
 HardSigmoidTester = makeBroadcastTester(
     op=hard_sigmoid,
     expected=upcast_int8_nfunc(lambda inputs: check_floatX(
-        inputs, 1 / (1 + numpy.exp(-inputs)))),
+        inputs, 1 / (1 + np.exp(-inputs)))),
     good=copymod(_good_broadcast_unary_normal_no_complex,
                  without=['uint16']),  # numpy fucnting overflows with uint16.
     # grad=_grad_broadcast_unary_normal,
@@ -66,11 +66,11 @@ HardSigmoidTester = makeBroadcastTester(
 SoftplusTester = makeBroadcastTester(
     op=softplus,
     expected=upcast_int8_nfunc(lambda inputs: check_floatX(
-        inputs, numpy.log1p(numpy.exp(inputs)))),
+        inputs, np.log1p(np.exp(inputs)))),
     good=dict(copymod(_good_broadcast_unary_normal_no_complex,
                       without=['uint8', 'uint16']),  # numpy fucnting overflows with uint16.
-              uint8=[numpy.arange(0, 89, dtype='uint8')],  # the range is different in new added uint8.
-              int8=[numpy.arange(-127, 89, dtype='int8')]),
+              uint8=[np.arange(0, 89, dtype='uint8')],  # the range is different in new added uint8.
+              int8=[np.arange(-127, 89, dtype='int8')]),
     # grad=_grad_broadcast_unary_normal,
     name='SoftplusTester',
 )
@@ -81,7 +81,7 @@ class T_softplus(unittest.TestCase):
         utt.seed_rng()
 
     def test_elemwise(self):
-        utt.verify_grad(softplus, [numpy.random.rand(3, 4)])
+        utt.verify_grad(softplus, [np.random.rand(3, 4)])
 
 
 class T_sigmoid_opts(unittest.TestCase):
@@ -112,7 +112,7 @@ class T_sigmoid_opts(unittest.TestCase):
         m = self.get_mode(excluding=['local_elemwise_fusion'])
 
         x = T.vector()
-        data = numpy.random.rand(54).astype(config.floatX)
+        data = np.random.rand(54).astype(config.floatX)
 
         backup = config.warn.identify_1pexp_bug
         config.warn.identify_1pexp_bug = False
@@ -321,7 +321,7 @@ class T_sigmoid_opts(unittest.TestCase):
         if not isinstance(mode, theano.compile.DebugMode):
             f = theano.function([x, lr], ux, mode=mode)
             ux_v = f([[50]], 0.1)
-            assert not numpy.isnan(ux_v)
+            assert not np.isnan(ux_v)
 
     def test_local_ultra_fast_sigmoid(self):
         x = tensor.matrix('x')
@@ -391,7 +391,7 @@ class T_softplus_opts(unittest.TestCase):
         assert isinstance(topo[1].op.scalar_op,
                           theano.tensor.nnet.sigm.ScalarSoftplus)
         assert isinstance(topo[2].op.scalar_op, theano.scalar.Neg)
-        f(numpy.random.rand(54).astype(config.floatX))
+        f(np.random.rand(54).astype(config.floatX))
 
     def test_log1msigm_to_softplus(self):
         x = T.matrix()
@@ -404,7 +404,7 @@ class T_softplus_opts(unittest.TestCase):
                           theano.tensor.nnet.sigm.ScalarSoftplus)
         assert isinstance(topo[1].op.scalar_op, theano.scalar.Neg)
         # assert check_stack_trace(f, ops_to_check='all')
-        f(numpy.random.rand(54, 11).astype(config.floatX))
+        f(np.random.rand(54, 11).astype(config.floatX))
 
         # Same test with a flatten
         out = T.log(1 - T.flatten(sigmoid(x)))
@@ -417,7 +417,7 @@ class T_softplus_opts(unittest.TestCase):
         assert isinstance(topo[1].op.scalar_op,
                           theano.tensor.nnet.sigm.ScalarSoftplus)
         assert isinstance(topo[2].op.scalar_op, theano.scalar.Neg)
-        f(numpy.random.rand(54, 11).astype(config.floatX))
+        f(np.random.rand(54, 11).astype(config.floatX))
 
         # Same test with a reshape
         out = T.log(1 - sigmoid(x).reshape([x.size]))
@@ -428,7 +428,7 @@ class T_softplus_opts(unittest.TestCase):
         assert any(isinstance(getattr(node.op, 'scalar_op', None),
                               theano.tensor.nnet.sigm.ScalarSoftplus)
                    for node in topo)
-        f(numpy.random.rand(54, 11).astype(config.floatX))
+        f(np.random.rand(54, 11).astype(config.floatX))
 
     def test_log1pexp_to_softplus(self):
         m = theano.config.mode
@@ -446,7 +446,7 @@ class T_softplus_opts(unittest.TestCase):
         assert len(topo) == 1
         assert isinstance(topo[0].op.scalar_op,
                           theano.tensor.nnet.sigm.ScalarSoftplus)
-        f(numpy.random.rand(54).astype(config.floatX))
+        f(np.random.rand(54).astype(config.floatX))
 
 
 class T_sigmoid_utils(unittest.TestCase):