file theano/gpuarray/tests/test_gemmcorr3d.py

theano/gpuarray/tests/test_gemmcorr3d.py

 from __future__ import absolute_import, print_function, division
 import unittest
-import numpy
+import numpy as np
 
 import theano
 from theano import config
@@ -23,8 +23,8 @@ class TestCorr3dMM(unittest.TestCase):
         inputs_shape = [inputs_shape[i] for i in (0, 4, 1, 2, 3)]
         filters_shape = [filters_shape[i] for i in (0, 4, 1, 2, 3)]
 
-        inputs_val = numpy.random.random(inputs_shape).astype(config.floatX)
-        filters_val = numpy.random.random(filters_shape).astype(config.floatX)
+        inputs_val = np.random.random(inputs_shape).astype(config.floatX)
+        filters_val = np.random.random(filters_shape).astype(config.floatX)
 
         inputs = gpuarray_shared_constructor(inputs_val)
         filters = gpuarray_shared_constructor(filters_val)
@@ -121,11 +121,11 @@ class TestCorr3dMM(unittest.TestCase):
         filters_shape = [filters_shape[i] for i in (0, 4, 1, 2, 3)]
         dCdH_shape = [dCdH_shape[i] for i in (0, 4, 1, 2, 3)]
 
-        inputs_val = numpy.random.random(inputs_shape).astype(config.floatX)
-        dCdH_val = numpy.random.random(dCdH_shape).astype(config.floatX)
+        inputs_val = np.random.random(inputs_shape).astype(config.floatX)
+        dCdH_val = np.random.random(dCdH_shape).astype(config.floatX)
         inputs = gpuarray_shared_constructor(inputs_val)
         dCdH = gpuarray_shared_constructor(dCdH_val)
-        shape = gpuarray_shared_constructor(numpy.array(filters_shape[2:]))
+        shape = gpuarray_shared_constructor(np.array(filters_shape[2:]))
 
         if (subsample == (1, 1, 1)):
             conv_ref = Corr3dMM_gradWeights(subsample=subsample)(
@@ -168,15 +168,15 @@ class TestCorr3dMM(unittest.TestCase):
         inputs_shape = [inputs_shape[i] for i in (0, 4, 1, 2, 3)]
         filters_shape = [filters_shape[i] for i in (0, 4, 1, 2, 3)]
 
-        inputs_val = numpy.random.random(inputs_shape).astype(config.floatX)
-        filters_val = numpy.random.random(filters_shape).astype(config.floatX)
+        inputs_val = np.random.random(inputs_shape).astype(config.floatX)
+        filters_val = np.random.random(filters_shape).astype(config.floatX)
         inputs = gpuarray_shared_constructor(inputs_val)
         filters = gpuarray_shared_constructor(filters_val)
 
         bottom_height = (inputs_shape[2] - 1) * subsample[0] + filters_shape[2]
         bottom_width = (inputs_shape[3] - 1) * subsample[1] + filters_shape[3]
         bottom_depth = (inputs_shape[4] - 1) * subsample[2] + filters_shape[4]
-        bottom_shape = gpuarray_shared_constructor(numpy.array([bottom_height, bottom_width, bottom_depth]))
+        bottom_shape = gpuarray_shared_constructor(np.array([bottom_height, bottom_width, bottom_depth]))
 
         if (subsample == (1, 1, 1)):
             conv_ref = Corr3dMM_gradInputs(subsample=subsample)(