file theano/gpuarray/tests/test_elemwise.py

theano/gpuarray/tests/test_elemwise.py

 from __future__ import absolute_import, print_function, division
-import numpy
+import numpy as np
 import scipy.special
 
 import theano
@@ -45,8 +45,8 @@ def test_elemwise_pow():
             output = base ** exp
             f = theano.function([base, exp], output)
 
-            base_val = numpy.random.randint(0, 5, size=10).astype(dtype_base)
-            exp_val = numpy.random.randint(0, 3, size=10).astype(dtype_exp)
+            base_val = np.random.randint(0, 5, size=10).astype(dtype_base)
+            exp_val = np.random.randint(0, 3, size=10).astype(dtype_exp)
 
             # Call the function to make sure the output is valid
             out = f(base_val, exp_val)
@@ -68,7 +68,7 @@ class TestMathErrorFunctions(TestCase):
         # to have the GPU ops run on large data.
         default_array = [x / 10.0 for x in range(-50, 50)] * 1000
         for dtype in self.dtypes:
-            numpy_array = numpy.asarray(default_array, dtype=dtype)
+            numpy_array = np.asarray(default_array, dtype=dtype)
             self.default_arrays[dtype] = numpy_array
             self.expected_erfinv_outputs[dtype] = scipy.special.erfinv(numpy_array)
             self.expected_erfcinv_outputs[dtype] = scipy.special.erfcinv(numpy_array)
@@ -127,7 +127,7 @@ class test_float16():
         o = (cz - cz**2 +
              tensor.cast(x, 'int16') + tensor.cast(x, 'float32') +
              tensor.cast(w, 'float16') -
-             tensor.constant(numpy.float16(1.0)))
+             tensor.constant(np.float16(1.0)))
 
         theano.function([w, x, y], o, mode=mode_with_gpu)
 
@@ -154,9 +154,9 @@ class test_float16():
                              i8.astype('float32')],
                             mode=mode_with_gpu)
 
-        d1 = (numpy.random.rand(4) * 10).astype('float16')
-        d2 = (numpy.random.rand(5) * 10).astype('float32')
-        d3 = (numpy.random.rand(6) * 10).astype('int8')
+        d1 = (np.random.rand(4) * 10).astype('float16')
+        d2 = (np.random.rand(5) * 10).astype('float32')
+        d3 = (np.random.rand(6) * 10).astype('int8')
         res = f(d1, d2, d3)
 
         for i, out in enumerate(f.outputs):
@@ -337,9 +337,7 @@ class T_gpureduce_dtype(test_elemwise.T_reduce_dtype):
 
 
 def speed_reduce10():
-    import numpy
-    import theano
-    data = numpy.random.rand(1000, 1000).astype("float32")
+    data = np.random.rand(1000, 1000).astype("float32")
     m = theano.tensor.fmatrix()
     f = theano.function([m], [m.sum(axis=0), m.T.sum(axis=0)],
                         mode=mode_with_gpu)