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提交 3076ff52 authored 作者: amrithasuresh's avatar amrithasuresh
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Updated numpy as np

上级 10bb500a
隐藏空白字符变更
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正在显示 包含 28 行增加 和 28 行删除
theano/tests/test_rop.py
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......@@ -18,7 +18,7 @@ from theano import function
import theano
from theano import tensor
import itertools
import numpy
import numpy as np
from theano.gof import Op, Apply
from theano.gradient import grad_undefined
from theano.tests.unittest_tools import SkipTest
......@@ -64,7 +64,7 @@ class RopLop_checker(unittest.TestCase):
# computations using scan
self.x = tensor.vector('x')
self.v = tensor.vector('v')
self.rng = numpy.random.RandomState(utt.fetch_seed())
self.rng = np.random.RandomState(utt.fetch_seed())
self.in_shape = (5 + self.rng.randint(3),)
self.mx = tensor.matrix('mx')
self.mv = tensor.matrix('mv')
......@@ -103,9 +103,9 @@ class RopLop_checker(unittest.TestCase):
If you want to test an Op with an output matrix, add a sum
after the Op you want to test.
"""
vx = numpy.asarray(self.rng.uniform(size=self.mat_in_shape),
vx = np.asarray(self.rng.uniform(size=self.mat_in_shape),
theano.config.floatX)
vv = numpy.asarray(self.rng.uniform(size=self.mat_in_shape),
vv = np.asarray(self.rng.uniform(size=self.mat_in_shape),
theano.config.floatX)
yv = tensor.Rop(y, self.mx, self.mv)
rop_f = function([self.mx, self.mv], yv, on_unused_input='ignore')
......@@ -118,11 +118,11 @@ class RopLop_checker(unittest.TestCase):
v1 = rop_f(vx, vv)
v2 = scan_f(vx, vv)
assert numpy.allclose(v1, v2), ('ROP mismatch: %s %s' % (v1, v2))
assert np.allclose(v1, v2), ('ROP mismatch: %s %s' % (v1, v2))
self.check_nondiff_rop(theano.clone(y, replace={self.mx: break_op(self.mx)}))
vv = numpy.asarray(self.rng.uniform(size=out_shape), theano.config.floatX)
vv = np.asarray(self.rng.uniform(size=out_shape), theano.config.floatX)
yv = tensor.Lop(y, self.mx, self.v)
lop_f = function([self.mx, self.v], yv)
......@@ -131,7 +131,7 @@ class RopLop_checker(unittest.TestCase):
v1 = lop_f(vx, vv)
v2 = scan_f(vx, vv)
assert numpy.allclose(v1, v2), ('LOP mismatch: %s %s' % (v1, v2))
assert np.allclose(v1, v2), ('LOP mismatch: %s %s' % (v1, v2))
def check_rop_lop(self, y, out_shape):
"""
......@@ -140,9 +140,9 @@ class RopLop_checker(unittest.TestCase):
"""
# TEST ROP
vx = numpy.asarray(self.rng.uniform(size=self.in_shape),
vx = np.asarray(self.rng.uniform(size=self.in_shape),
theano.config.floatX)
vv = numpy.asarray(self.rng.uniform(size=self.in_shape),
vv = np.asarray(self.rng.uniform(size=self.in_shape),
theano.config.floatX)
yv = tensor.Rop(y, self.x, self.v)
......@@ -156,7 +156,7 @@ class RopLop_checker(unittest.TestCase):
v1 = rop_f(vx, vv)
v2 = scan_f(vx, vv)
assert numpy.allclose(v1, v2), ('ROP mismatch: %s %s' % (v1, v2))
assert np.allclose(v1, v2), ('ROP mismatch: %s %s' % (v1, v2))
known_fail = False
try:
self.check_nondiff_rop(theano.clone(y, replace={self.x: break_op(self.x)}))
......@@ -165,9 +165,9 @@ class RopLop_checker(unittest.TestCase):
# TEST LOP
vx = numpy.asarray(self.rng.uniform(size=self.in_shape),
vx = np.asarray(self.rng.uniform(size=self.in_shape),
theano.config.floatX)
vv = numpy.asarray(self.rng.uniform(size=out_shape),
vv = np.asarray(self.rng.uniform(size=out_shape),
theano.config.floatX)
yv = tensor.Lop(y, self.x, self.v)
......@@ -181,7 +181,7 @@ class RopLop_checker(unittest.TestCase):
v1 = lop_f(vx, vv)
v2 = scan_f(vx, vv)
assert numpy.allclose(v1, v2), ('LOP mismatch: %s %s' % (v1, v2))
assert np.allclose(v1, v2), ('LOP mismatch: %s %s' % (v1, v2))
if known_fail:
raise SkipTest('Rop does not handle non-differentiable inputs '
......@@ -213,21 +213,21 @@ class test_RopLop(RopLop_checker):
self.check_rop_lop(self.x[:4], (4,))
def test_incsubtensor1(self):
tv = numpy.asarray(self.rng.uniform(size=(3,)),
tv = np.asarray(self.rng.uniform(size=(3,)),
theano.config.floatX)
t = theano.shared(tv)
out = tensor.inc_subtensor(self.x[:3], t)
self.check_rop_lop(out, self.in_shape)
def test_incsubtensor2(self):
tv = numpy.asarray(self.rng.uniform(size=(10,)),
tv = np.asarray(self.rng.uniform(size=(10,)),
theano.config.floatX)
t = theano.shared(tv)
out = tensor.inc_subtensor(t[:4], self.x[:4])
self.check_rop_lop(out, (10,))
def test_setsubtensor1(self):
tv = numpy.asarray(self.rng.uniform(size=(3,)),
tv = np.asarray(self.rng.uniform(size=(3,)),
theano.config.floatX)
t = theano.shared(tv)
out = tensor.set_subtensor(self.x[:3], t)
......@@ -238,7 +238,7 @@ class test_RopLop(RopLop_checker):
self.check_rop_lop(out, self.in_shape)
def test_setsubtensor2(self):
tv = numpy.asarray(self.rng.uniform(size=(10,)),
tv = np.asarray(self.rng.uniform(size=(10,)),
theano.config.floatX)
t = theano.shared(tv)
out = tensor.set_subtensor(t[:4], self.x[:4])
......@@ -258,7 +258,7 @@ class test_RopLop(RopLop_checker):
(1,))
def test_downsample(self):
rng = numpy.random.RandomState(utt.fetch_seed())
rng = np.random.RandomState(utt.fetch_seed())
# ws, shp
examples = (
((2,), (16,)),
......@@ -296,7 +296,7 @@ class test_RopLop(RopLop_checker):
scan_f = function([], sy, on_unused_input='ignore', mode=mode)
v1 = rop_f()
v2 = scan_f()
assert numpy.allclose(v1, v2), ("Rop mismatch: %s %s" % (v1, v2))
assert np.allclose(v1, v2), ("Rop mismatch: %s %s" % (v1, v2))
def test_conv(self):
for conv_op in [conv.conv2d, conv2d]:
......@@ -337,16 +337,16 @@ class test_RopLop(RopLop_checker):
scan_f = function([input, filters, ev_input, ev_filters], sy,
on_unused_input='ignore', mode=mode)
dtype = theano.config.floatX
image_data = numpy.random.random(image_shape).astype(dtype)
filter_data = numpy.random.random(filter_shape).astype(dtype)
ev_image_data = numpy.random.random(image_shape).astype(dtype)
ev_filter_data = numpy.random.random(filter_shape).astype(dtype)
image_data = np.random.random(image_shape).astype(dtype)
filter_data = np.random.random(filter_shape).astype(dtype)
ev_image_data = np.random.random(image_shape).astype(dtype)
ev_filter_data = np.random.random(filter_shape).astype(dtype)
v1 = rop_f(image_data, filter_data, ev_image_data, ev_filter_data)
v2 = scan_f(image_data, filter_data, ev_image_data, ev_filter_data)
assert numpy.allclose(v1, v2), ("Rop mismatch: %s %s" % (v1, v2))
assert np.allclose(v1, v2), ("Rop mismatch: %s %s" % (v1, v2))
def test_join(self):
tv = numpy.asarray(self.rng.uniform(size=(10,)),
tv = np.asarray(self.rng.uniform(size=(10,)),
theano.config.floatX)
t = theano.shared(tv)
out = tensor.join(0, self.x, t)
......@@ -354,7 +354,7 @@ class test_RopLop(RopLop_checker):
def test_dot(self):
insh = self.in_shape[0]
vW = numpy.asarray(self.rng.uniform(size=(insh, insh)),
vW = np.asarray(self.rng.uniform(size=(insh, insh)),
theano.config.floatX)
W = theano.shared(vW)
self.check_rop_lop(tensor.dot(self.x, W), self.in_shape)
......@@ -367,7 +367,7 @@ class test_RopLop(RopLop_checker):
self.in_shape)
def test_reshape(self):
new_shape = tensor.constant(numpy.asarray([
new_shape = tensor.constant(np.asarray([
self.mat_in_shape[0] * self.mat_in_shape[1]],
dtype='int64'))
......@@ -435,6 +435,6 @@ class test_RopLop(RopLop_checker):
# one differentiable path (i.e. there is no gradient wrt to one of
# the inputs).
x = tensor.arange(20.0).reshape([1, 20])
v = theano.shared(numpy.ones([20]))
v = theano.shared(np.ones([20]))
d = tensor.dot(x, v).sum()
tensor.Rop(tensor.grad(d, v), v, v)
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