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