Merge pull request #5179 from kvmanohar22/import_numpy

benchmark/autoencoder/aa.py

 #!/usr/bin/env python
 from __future__ import absolute_import, print_function, division
-import numpy
+import numpy as np
 import sys
 import time
 
@@ -198,7 +198,7 @@ print(mod.pretty(mode=mode))
 m = mod.make(mode=mode)
 
 neg, nout, nhid, niter = [int(a) for a in sys.argv[1:]]
-rng = numpy.random.RandomState(342)
+rng = np.random.RandomState(342)
 m.w = rng.rand(nout, nhid)
 m.a = rng.randn(nhid) * 0.0
 m.b = rng.randn(nout) * 0.0

benchmark/autoencoder/aa_numpy.py

 #!/usr/bin/env python
 from __future__ import absolute_import, print_function, division
-import numpy as N
+import numpy as np
 import sys
 import time
 from six.moves import xrange
@@ -10,7 +10,7 @@ from six.moves import xrange
 neg, nout, nhid, niter = [int(a) for a in sys.argv[1:]]
 lr = 0.01
 
-rng = N.random.RandomState(342)
+rng = np.random.RandomState(342)
 
 w = rng.rand(nout, nhid)
 a = rng.randn(nhid) * 0.0
@@ -22,38 +22,38 @@ dot_time = 0.0
 t = time.time()
 for i in xrange(niter):
     tt = time.time()
-    d = N.dot(x, w)
+    d = np.dot(x, w)
     dot_time += time.time() - tt
 
-    hid = N.tanh(d + a)
+    hid = np.tanh(d + a)
 
     tt = time.time()
-    d = N.dot(hid, w.T)
+    d = np.dot(hid, w.T)
     dot_time += time.time() - tt
-    out = N.tanh(d + b)
+    out = np.tanh(d + b)
 
     g_out = out - x
-    err = 0.5 * N.sum(g_out**2)
+    err = 0.5 * np.sum(g_out**2)
 
     g_hidwt = g_out * (1.0 - out**2)
 
-    b -= lr * N.sum(g_hidwt, axis=0)
+    b -= lr * np.sum(g_hidwt, axis=0)
 
     tt = time.time()
-    g_hid = N.dot(g_hidwt, w)
+    g_hid = np.dot(g_hidwt, w)
     dot_time += time.time() - tt
 
     g_hidin = g_hid * (1.0 - hid**2)
 
     tt = time.time()
-    d = N.dot(g_hidwt.T, hid)
-    dd = N.dot(x.T, g_hidin)
+    d = np.dot(g_hidwt.T, hid)
+    dd = np.dot(x.T, g_hidin)
     dot_time += time.time() - tt
 
     gw = (d + dd)
     w -= lr * gw
 
-    a -= lr * N.sum(g_hidin, axis=0)
+    a -= lr * np.sum(g_hidin, axis=0)
 
 total_time = time.time() - t
 print('time: ',total_time, 'err: ', err)

benchmark/convolution/conv2d.py

 from __future__ import absolute_import, print_function, division
 import sys, timeit, time
-import numpy
+import numpy as np
 import theano, theano.tensor.signal.conv
 
 try:
@@ -17,16 +17,16 @@ if len(sys.argv)>6:
 
 setup="""
 import sys, timeit, time
-import numpy
+import numpy as np
 import theano, theano.tensor.signal.conv
 
 img_shape =  int(sys.argv[1]), int(sys.argv[2])
 ker_shape =  int(sys.argv[3]), int(sys.argv[4])
 dtype = sys.argv[5]
 
-img = theano.shared(numpy.ones(img_shape, dtype=dtype))
-ker = theano.shared(numpy.ones(ker_shape, dtype=dtype))
-out = theano.shared(numpy.ones((2,2,2), dtype=dtype))
+img = theano.shared(np.ones(img_shape, dtype=dtype))
+ker = theano.shared(np.ones(ker_shape, dtype=dtype))
+out = theano.shared(np.ones((2,2,2), dtype=dtype))
 """
 
 T = timeit.Timer("f()", 

benchmark/convolution/opencv.py

 from __future__ import absolute_import, print_function, division
 import sys, timeit
-import numpy
+import numpy as np
 import scikits.image.opencv
 
 try:
@@ -16,13 +16,13 @@ if len(sys.argv)>6:
     nb_call=int(sys.argv[6])
 
 T = timeit.Timer("f()","""
-import scikits.image.opencv, sys, numpy
+import scikits.image.opencv, sys, numpy as np
 img_shape =  int(sys.argv[1]), int(sys.argv[2])
 ker_shape =  int(sys.argv[3]), int(sys.argv[4])
 dtype = sys.argv[5]
 
-img = numpy.ones(img_shape, dtype=dtype)
-ker = numpy.ones(ker_shape, dtype=dtype)
+img = np.ones(img_shape, dtype=dtype)
+ker = np.ones(ker_shape, dtype=dtype)
 
 def f():
     scikits.image.opencv.cvFilter2D(img, ker)

benchmark/regression/regression.py

 from __future__ import absolute_import, print_function, division
 import theano
-import numpy as N
+import numpy as np
 from theano import tensor as T
 from theano.tensor import nnet as NN
 from six.moves import xrange
@@ -50,9 +50,9 @@ class RegressionLayer(M.Module):
         if input_size and target_size:
             # initialize w and b in a special way using input_size and target_size
             sz = (input_size, target_size)
-            rng = N.random.RandomState(seed)
+            rng = np.random.RandomState(seed)
             obj.w = rng.uniform(size = sz, low = -0.5, high = 0.5)
-            obj.b = N.zeros(target_size)
+            obj.b = np.zeros(target_size)
             obj.stepsize = 0.01
         # here we call the default_initialize method, which takes all the name: value
         # pairs in init and sets the property with that name to the provided value
@@ -93,8 +93,8 @@ def test_module_advanced_example():
 
     profmode = PrintEverythingMode(theano.gof.OpWiseCLinker(), 'fast_run')
 
-    data_x = N.random.randn(4, 10)
-    data_y = [ [int(x)] for x in (N.random.randn(4) > 0)]
+    data_x = np.random.randn(4, 10)
+    data_y = [ [int(x)] for x in (np.random.randn(4) > 0)]
 
 
     model = SpecifiedRegressionLayer(regularize = False).make(input_size = 10,