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pytensor
提交 c71d9d5c authored 作者: amrithasuresh's avatar amrithasuresh
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1. Updated numpy as np

2. Fixed indentation
上级 cc612fde
隐藏空白字符变更
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正在显示 包含 111 行增加 和 111 行删除
theano/tensor/tests/test_elemwise.py
浏览文件 @ c71d9d5c
......@@ -3,7 +3,7 @@ from copy import copy
import unittest
import math
import numpy
import numpy as np
from nose.plugins.skip import SkipTest
from nose.tools import raises
from six.moves import xrange
......@@ -47,13 +47,13 @@ class test_DimShuffle(unittest_tools.InferShapeTester):
x = self.type(self.dtype, ib)('x')
e = self.op(ib, shuffle)(x)
f = copy(linker).accept(FunctionGraph([x], [e])).make_function()
assert f(numpy.ones(xsh, dtype=self.dtype)).shape == zsh
assert f(np.ones(xsh, dtype=self.dtype)).shape == zsh
# test that DimShuffle.infer_shape work correctly
x = self.type(self.dtype, ib)('x')
e = self.op(ib, shuffle)(x)
f = copy(linker).accept(FunctionGraph([x],
[e.shape])).make_function()
assert all(f(numpy.ones(xsh, dtype=self.dtype))) == all(zsh)
assert all(f(np.ones(xsh, dtype=self.dtype))) == all(zsh)
# Test when we drop a axis that is not broadcastable
ib = [False, True, False]
......@@ -65,7 +65,7 @@ class test_DimShuffle(unittest_tools.InferShapeTester):
x = self.type(self.dtype, ib)('x')
e = self.op(ib, (1, 2))(x)
f = copy(linker).accept(FunctionGraph([x], [e.shape])).make_function()
self.assertRaises(TypeError, f, numpy.ones((2, 1, 4)))
self.assertRaises(TypeError, f, np.ones((2, 1, 4)))
# Test that we can't take a dimensions multiple time
xsh, shuffle, zsh = ((1, 1, 4), (0, 1, 2, 0), (1, 4))
......@@ -94,7 +94,7 @@ class test_DimShuffle(unittest_tools.InferShapeTester):
((1,), ('x', 'x'))]:
ib = [(entry == 1) for entry in xsh]
adtens = self.type(self.dtype, ib)('x')
adtens_val = numpy.ones(xsh, dtype=self.dtype)
adtens_val = np.ones(xsh, dtype=self.dtype)
self._compile_and_check([adtens],
[self.op(ib, shuffle)(adtens)],
[adtens_val], self.op,
......@@ -102,50 +102,50 @@ class test_DimShuffle(unittest_tools.InferShapeTester):
def test_too_big_rank(self):
x = self.type(self.dtype, broadcastable=())()
y = x.dimshuffle(('x',) * (numpy.MAXDIMS + 1))
y = x.dimshuffle(('x',) * (np.MAXDIMS + 1))
self.assertRaises(ValueError, y.eval, {x: 0})
class test_reduce_axes(unittest.TestCase):
def test_sum_axes(self):
axes = [None, 0, 1, [0, 1], numpy.array(1),
[numpy.array(0), numpy.array(1)]]
axes = [None, 0, 1, [0, 1], np.array(1),
[np.array(0), np.array(1)]]
for a in axes:
x = tensor.matrix()
x.sum(a)
def test_mean_axes(self):
axes = [None, 0, 1, [0, 1], numpy.array(1),
[numpy.array(0), numpy.array(1)]]
axes = [None, 0, 1, [0, 1], np.array(1),
[np.array(0), np.array(1)]]
for a in axes:
x = tensor.matrix()
x.mean(a)
def test_max_axes(self):
axes = [None, 0, 1, [0, 1], numpy.array(1),
[numpy.array(0), numpy.array(1)]]
axes = [None, 0, 1, [0, 1], np.array(1),
[np.array(0), np.array(1)]]
for a in axes:
x = tensor.matrix()
x.max(a)
def test_min_axes(self):
axes = [None, 0, 1, [0, 1], numpy.array(1),
[numpy.array(0), numpy.array(1)]]
axes = [None, 0, 1, [0, 1], np.array(1),
[np.array(0), np.array(1)]]
for a in axes:
x = tensor.matrix()
x.min(a)
def test_argmax_axes(self):
axes = [None, 0, 1, [0, 1], numpy.array(1),
[numpy.array(0), numpy.array(1)]]
axes = [None, 0, 1, [0, 1], np.array(1),
[np.array(0), np.array(1)]]
for a in axes:
x = tensor.matrix()
x.argmax(a)
def test_var_axes(self):
axes = [None, 0, 1, [0, 1], numpy.array(1),
[numpy.array(0), numpy.array(1)]]
axes = [None, 0, 1, [0, 1], np.array(1),
[np.array(0), np.array(1)]]
for a in axes:
x = tensor.matrix()
x.var(a)
......@@ -166,12 +166,12 @@ class test_Broadcast(unittest.TestCase):
linkers = [gof.PerformLinker, gof.CLinker]
def rand_val(self, shp):
return numpy.asarray(numpy.random.rand(*shp),
dtype=theano.config.floatX)
return np.asarray(np.random.rand(*shp),
dtype=theano.config.floatX)
def rand_cval(self, shp):
return numpy.asarray(numpy.random.rand(*shp),
dtype=theano.config.floatX)
return np.asarray(np.random.rand(*shp),
dtype=theano.config.floatX)
def setUp(self):
unittest_tools.seed_rng()
......@@ -331,19 +331,19 @@ class test_Broadcast(unittest.TestCase):
def reduce_bitwise_and(x, axis=-1, dtype='int8'):
identity = numpy.array((-1,), dtype=dtype)[0]
identity = np.array((-1,), dtype=dtype)[0]
shape_without_axis = tuple([s for i, s in enumerate(x.shape) if i != axis])
if 0 in shape_without_axis:
return numpy.empty(shape=shape_without_axis, dtype=x.dtype)
return np.empty(shape=shape_without_axis, dtype=x.dtype)
def custom_reduce(a):
out = identity
for i in range(a.size):
out = numpy.bitwise_and(a[i], out)
out = np.bitwise_and(a[i], out)
return out
return numpy.apply_along_axis(custom_reduce, axis, x)
return np.apply_along_axis(custom_reduce, axis, x)
class test_CAReduce(unittest_tools.InferShapeTester):
......@@ -384,20 +384,20 @@ class test_CAReduce(unittest_tools.InferShapeTester):
tosum = list(range(len(xsh)))
f = copy(linker).accept(FunctionGraph([x], [e])).make_function()
xv = numpy.asarray(numpy.random.rand(*xsh))
xv = np.asarray(np.random.rand(*xsh))
if dtype not in tensor.discrete_dtypes:
xv = numpy.asarray(xv, dtype=dtype)
xv = np.asarray(xv, dtype=dtype)
else:
xv = numpy.asarray(xv < 0.5, dtype=dtype)
xv = np.asarray(xv < 0.5, dtype=dtype)
if test_nan and xv.size > 0:
if len(xsh) > 0:
xv = xv.flatten()
xv[0] = numpy.nan
xv[0] = np.nan
xv = xv.reshape(*xsh)
else:
xv = numpy.asarray(numpy.nan, dtype=dtype)
xv = np.asarray(np.nan, dtype=dtype)
zv = xv
if pre_scalar_op is not None:
zv = Elemwise(scalar_op=pre_scalar_op)(x).eval({x: xv})
......@@ -415,48 +415,48 @@ class test_CAReduce(unittest_tools.InferShapeTester):
tosum = tuple(axis2)
if tensor_op == tensor.all:
for axis in reversed(sorted(tosum)):
zv = numpy.all(zv, axis)
zv = np.all(zv, axis)
if len(tosum) == 0:
zv = zv != 0
elif tensor_op == tensor.any:
for axis in reversed(sorted(tosum)):
zv = numpy.any(zv, axis)
zv = np.any(zv, axis)
if len(tosum) == 0:
zv = zv != 0
elif scalar_op == scalar.add:
for axis in reversed(sorted(tosum)):
zv = numpy.add.reduce(zv, axis)
zv = np.add.reduce(zv, axis)
if dtype == 'bool':
# numpy.add of a bool upcast, while CAReduce don't
# np.add of a bool upcast, while CAReduce don't
zv = zv.astype(dtype)
elif scalar_op == scalar.mul:
for axis in reversed(sorted(tosum)):
zv = numpy.multiply.reduce(zv, axis)
zv = np.multiply.reduce(zv, axis)
elif scalar_op == scalar.maximum:
try:
for axis in reversed(sorted(tosum)):
zv = numpy.maximum.reduce(zv, axis)
zv = np.maximum.reduce(zv, axis)
except ValueError:
numpy_raised = True
elif scalar_op == scalar.minimum:
try:
for axis in reversed(sorted(tosum)):
zv = numpy.minimum.reduce(zv, axis)
zv = np.minimum.reduce(zv, axis)
except ValueError:
numpy_raised = True
elif scalar_op == scalar.or_:
for axis in reversed(sorted(tosum)):
zv = numpy.bitwise_or.reduce(zv, axis)
zv = np.bitwise_or.reduce(zv, axis)
elif scalar_op == scalar.and_:
for axis in reversed(sorted(tosum)):
zv = reduce_bitwise_and(zv, axis, dtype=dtype)
elif scalar_op == scalar.xor:
# There is no identity value for the xor function
# So we can't support shape of dimensions 0.
if numpy.prod(zv.shape) == 0:
if np.prod(zv.shape) == 0:
continue
for axis in reversed(sorted(tosum)):
zv = numpy.bitwise_xor.reduce(zv, axis)
zv = np.bitwise_xor.reduce(zv, axis)
else:
raise Exception(
"Test for CAReduce with scalar_op %s not implemented" %
......@@ -482,7 +482,7 @@ class test_CAReduce(unittest_tools.InferShapeTester):
try:
f_xv = f(xv)
self.assertTrue((f_xv.shape == zv.shape), (f_xv, zv))
self.assertTrue(numpy.allclose(f_xv, zv),
self.assertTrue(np.allclose(f_xv, zv),
(f_xv, zv, xsh, tosum))
except NotImplementedError:
# GpuCAReduce don't implement all cases when size is 0
......@@ -498,7 +498,7 @@ class test_CAReduce(unittest_tools.InferShapeTester):
f = copy(linker).accept(FunctionGraph([x],
[e.shape])).make_function()
if not(scalar_op in [scalar.maximum, scalar.minimum] and
((xsh == () or numpy.prod(xsh) == 0))):
((xsh == () or np.prod(xsh) == 0))):
try:
assert all(f(xv) == zv.shape)
except NotImplementedError:
......@@ -579,7 +579,7 @@ class test_CAReduce(unittest_tools.InferShapeTester):
x = pre_scalar_op(x)
if tosum is None:
tosum = list(range(len(xsh)))
xv = numpy.asarray(numpy.random.rand(*xsh), dtype=dtype)
xv = np.asarray(np.random.rand(*xsh), dtype=dtype)
d = {}
if pre_scalar_op is not None:
xv = x.eval({x.owner.inputs[0]: xv})
......@@ -608,8 +608,8 @@ class test_Prod(unittest.TestCase):
# including zeros, as the case with zeros is important
# (and special cases: 1 zero in the row, more than 1 zero in the row)
x_val = numpy.asarray([[.1, .2, .3], [.4, .5, .6], [.7, .8, .9]],
dtype='float32')
x_val = np.asarray([[.1, .2, .3], [.4, .5, .6], [.7, .8, .9]],
dtype='float32')
# now with verify_grad
unittest_tools.verify_grad(Prod(axis=1), [x_val], mode=self.mode)
......@@ -623,8 +623,8 @@ class test_Prod(unittest.TestCase):
def test_verify_grad_with_zeros(self):
# including zeros, as the case with zeros is important
# (and special cases: 1 zero in the row, more than 1 zero in the row)
x_val = numpy.asarray([[1., 2., 3.], [0., 5., 6.], [0., 0., 9.]],
dtype='float32')
x_val = np.asarray([[1., 2., 3.], [0., 5., 6.], [0., 0., 9.]],
dtype='float32')
x = theano.tensor.dmatrix()
# sanity check
......@@ -635,7 +635,7 @@ class test_Prod(unittest.TestCase):
# p2 = Prod(axis=1)(x2)
# fn = theano.function([x, x2], [p - p2], mode=self.mode)
# print("hand computed diff for each row")
# x2_val = numpy.asarray([[1., 2., 3.003], [0.003, 5., 6], [
# x2_val = np.asarray([[1., 2., 3.003], [0.003, 5., 6], [
# 0., 0., 9.01]])
# print(fn(x_val, x2_val))
# fn2 = theano.function([x], [theano.tensor.grad(p.sum(), x)],
......@@ -643,7 +643,7 @@ class test_Prod(unittest.TestCase):
# print("real grad")
# print(fn2(x_val))
fn3 = theano.function([x], [p], mode=self.mode)
assert numpy.allclose(fn3(x_val), [6., 0., 0.])
assert np.allclose(fn3(x_val), [6., 0., 0.])
# now with verify_grad
unittest_tools.verify_grad(Prod(axis=1), [x_val], mode=self.mode)
......@@ -665,25 +665,25 @@ class test_Prod(unittest.TestCase):
@attr('slow')
def test_prod_no_zeros_in_input(self):
x = theano.tensor.dmatrix()
x_val = numpy.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]], dtype='float32')
x_val = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]], dtype='float32')
pwz = Prod(axis=1, no_zeros_in_input=True)(x)
fn = theano.function([x], pwz, mode=self.mode)
assert numpy.allclose(fn(x_val), [6, 120, 504])
assert np.allclose(fn(x_val), [6, 120, 504])
pwz = Prod(no_zeros_in_input=True)(x)
g = theano.grad(pwz, x)
gg = theano.grad(g.sum(), x)
fn = theano.function([x], g, mode=self.mode)
assert numpy.allclose(fn(x_val),
[[362880., 181440., 120960.],
[90720., 72576., 60480.],
[51840., 45360., 40320.]])
assert np.allclose(fn(x_val),
[[362880., 181440., 120960.],
[90720., 72576., 60480.],
[51840., 45360., 40320.]])
fn = theano.function([x], gg, mode=self.mode)
assert numpy.allclose(fn(x_val),
[[663696., 422568., 301872.],
[233964., 190800., 161016.],
[139248., 122652., 109584.]])
assert np.allclose(fn(x_val),
[[663696., 422568., 301872.],
[233964., 190800., 161016.],
[139248., 122652., 109584.]])
unittest_tools.verify_grad(Prod(axis=1, no_zeros_in_input=True),
[x_val],
mode=self.mode)
......@@ -697,14 +697,14 @@ class test_Prod(unittest.TestCase):
def test_prod_without_zeros(self):
x = theano.tensor.dmatrix()
x_val = numpy.array([[1, 2, 3], [0, 5, 6], [0, 0, 9]], dtype='float32')
x_val = np.array([[1, 2, 3], [0, 5, 6], [0, 0, 9]], dtype='float32')
pwz = ProdWithoutZeros(axis=1)(x)
fn = theano.function([x], pwz, mode=self.mode)
assert numpy.allclose(fn(x_val), [6, 30, 9])
assert np.allclose(fn(x_val), [6, 30, 9])
pwz_a0 = ProdWithoutZeros(axis=0)(x)
fn_a0 = theano.function([x], pwz_a0, mode=self.mode)
assert numpy.allclose(fn_a0(x_val), [1, 10, 162])
assert np.allclose(fn_a0(x_val), [1, 10, 162])
@raises(theano.gradient.NullTypeGradError)
def test_prod_without_zeros_grad(self):
......@@ -716,33 +716,33 @@ class test_Prod(unittest.TestCase):
@attr('slow')
def test_other_grad_tests(self):
x = theano.tensor.dmatrix()
x_val1 = numpy.array([[1, 2, 3], [0, 5, 6], [0, 0, 9]],
dtype='float32')
x_val2 = numpy.array([[1, 2, 0], [0, 5, 6], [7, 8, 9], [9, 10, 0]],
dtype='float32')
rng = rng = numpy.random.RandomState(43)
x_val1 = np.array([[1, 2, 3], [0, 5, 6], [0, 0, 9]],
dtype='float32')
x_val2 = np.array([[1, 2, 0], [0, 5, 6], [7, 8, 9], [9, 10, 0]],
dtype='float32')
rng = rng = np.random.RandomState(43)
p = Prod(axis=1)
grad_p = theano.tensor.grad(p(x).sum(), x)
grad_fn = theano.function([x], grad_p, mode=self.mode)
assert numpy.allclose(
assert np.allclose(
grad_fn(x_val1),
[[6., 3., 2.], [30., 0., 0.], [0., 0., 0.]])
assert numpy.allclose(
assert np.allclose(
grad_fn(x_val2),
[[0., 0., 2.], [30., 0., 0.], [72., 63., 56.], [0., 0., 90.]])
p_axis0 = Prod(axis=0)
grad_p_axis0 = theano.tensor.grad(p_axis0(x).sum(), x)
grad_fn_axis0 = theano.function([x], grad_p_axis0, mode=self.mode)
assert numpy.allclose(
assert np.allclose(
grad_fn_axis0(x_val2),
[[0., 400., 0.], [63., 160., 0.], [0., 100., 0.], [0., 80., 0.]])
tensor.verify_grad(p, [x_val1], rng=rng, mode=self.mode)
def test_mul_without_zeros_zeros(self):
a = numpy.zeros((3, 3))
a = np.zeros((3, 3))
x = theano.tensor.dmatrix()
......@@ -763,13 +763,13 @@ class test_Prod(unittest.TestCase):
class test_IsInf_IsNan(unittest.TestCase):
def setUp(self):
self.test_vals = [numpy.array(x, dtype=config.floatX) for x in [
self.test_vals = [np.array(x, dtype=config.floatX) for x in [
0,
1,
numpy.nan,
numpy.inf,
-numpy.inf,
[numpy.nan, numpy.inf, -numpy.inf, 0, 1, -1],
np.nan,
np.inf,
-np.inf,
[np.nan, np.inf, -np.inf, 0, 1, -1],
]]
self.scalar = tensor.scalar()
self.vector = tensor.vector()
......@@ -784,7 +784,7 @@ class test_IsInf_IsNan(unittest.TestCase):
theano_isfunc = theano.function([input],
getattr(tensor, isfunc)(input),
mode=self.mode)
numpy_isfunc = getattr(numpy, isfunc)
numpy_isfunc = getattr(np, isfunc)
for x in self.test_vals:
if ((x.ndim == 0 and input is not self.scalar) or
(x.ndim == 1 and input is not self.vector)):
......@@ -830,7 +830,7 @@ class T_reduce_dtype(unittest.TestCase):
topo = f.maker.fgraph.toposort()
assert [n for n in topo if isinstance(n.op, self.op)], (topo,
dtype)
data = numpy.random.rand(3, 4) * 10
data = np.random.rand(3, 4) * 10
data = data.astype(dtype)
f(data)
......@@ -859,7 +859,7 @@ class T_reduce_dtype(unittest.TestCase):
topo = f.maker.fgraph.toposort()
assert [n for n in topo if isinstance(n.op, self.op)], (topo,
dtype)
data = numpy.random.rand(3, 4) * 10
data = np.random.rand(3, 4) * 10
data = data.astype(dtype)
f(data)
......@@ -887,7 +887,7 @@ class T_reduce_dtype(unittest.TestCase):
topo = f.maker.fgraph.toposort()
assert [n for n in topo if isinstance(n.op, self.op)], \
(topo, output_dtype)
data = numpy.random.rand(3, 4) * 10
data = np.random.rand(3, 4) * 10
data = data.astype(input_dtype)
if output_dtype == 'float16' and method == 'prod':
# We will likely get something infinite,
......@@ -943,17 +943,17 @@ class T_reduce_dtype(unittest.TestCase):
def test_reduce_precision(self):
# Check that the default accumulator precision is sufficient
for method in self.methods:
x = theano.shared(numpy.asarray([1e8, 1, -1e8],
dtype='float32'))
x = theano.shared(np.asarray([1e8, 1, -1e8],
dtype='float32'))
s = getattr(x, method)()
f = theano.function([], s, mode=self.mode)
topo = f.maker.fgraph.toposort()
assert [n for n in topo if isinstance(n.op, self.op)], topo
s_val = f()
# Use extra precision in NumPy to compute the good answer.
ret = getattr(numpy.asarray([1e8, 1, -1e8], dtype='float64'),
ret = getattr(np.asarray([1e8, 1, -1e8], dtype='float64'),
method)()
assert numpy.allclose(s_val, ret), (s_val, ret)
assert np.allclose(s_val, ret), (s_val, ret)
class T_mean_dtype(unittest.TestCase):
......@@ -971,7 +971,7 @@ class T_mean_dtype(unittest.TestCase):
else:
assert m.dtype == dtype, (m, m.dtype, dtype)
f = theano.function([x], m)
data = numpy.random.rand(3, 4) * 10
data = np.random.rand(3, 4) * 10
data = data.astype(dtype)
f(data)
......@@ -1005,7 +1005,7 @@ class T_mean_dtype(unittest.TestCase):
input_dtype != sum_dtype):
continue
f = theano.function([x], mean_var)
data = numpy.random.rand(3, 4) * 10
data = np.random.rand(3, 4) * 10
data = data.astype(input_dtype)
f(data)
# Check that we can take the gradient, when implemented
......@@ -1026,11 +1026,11 @@ class T_mean_dtype(unittest.TestCase):
def test_mean_precision(self):
# Check that the default accumulator precision is sufficient
x = theano.shared(numpy.asarray([1e8, 1, -1e8], dtype='float32'))
x = theano.shared(np.asarray([1e8, 1, -1e8], dtype='float32'))
m = x.mean()
f = theano.function([], m)
m_val = f()
assert numpy.allclose(m_val, 1. / 3)
assert np.allclose(m_val, 1. / 3)
class T_prod_without_zeros_dtype(unittest.TestCase):
......@@ -1077,7 +1077,7 @@ class T_prod_without_zeros_dtype(unittest.TestCase):
if 'complex' in dtype:
continue
f = theano.function([x], p)
data = numpy.random.rand(2, 3) * 3
data = np.random.rand(2, 3) * 3
data = data.astype(dtype)
f(data)
......@@ -1100,7 +1100,7 @@ class T_prod_without_zeros_dtype(unittest.TestCase):
'complex' in input_dtype):
continue
f = theano.function([x], prod_woz_var)
data = numpy.random.rand(2, 3) * 3
data = np.random.rand(2, 3) * 3
data = data.astype(input_dtype)
f(data)
......@@ -1129,7 +1129,7 @@ class T_prod_without_zeros_dtype(unittest.TestCase):
input_dtype != acc_dtype):
continue
f = theano.function([x], prod_woz_var)
data = numpy.random.rand(2, 3) * 3
data = np.random.rand(2, 3) * 3
data = data.astype(input_dtype)
f(data)
else:
......@@ -1143,7 +1143,7 @@ class T_prod_without_zeros_dtype(unittest.TestCase):
class TestBitOpReduceGrad(unittest.TestCase):
def setUp(self):
self.rng = numpy.random.RandomState(unittest_tools.fetch_seed())
self.rng = np.random.RandomState(unittest_tools.fetch_seed())
def test_all_grad(self):
x = tensor.bmatrix('x')
......@@ -1152,11 +1152,11 @@ class TestBitOpReduceGrad(unittest.TestCase):
f = theano.function([x], gx)
x_random = self.rng.binomial(n=1, p=0.5, size=(5, 7)).astype('int8')
for x_val in (x_random,
numpy.zeros_like(x_random),
numpy.ones_like(x_random)):
np.zeros_like(x_random),
np.ones_like(x_random)):
gx_val = f(x_val)
assert gx_val.shape == x_val.shape
assert numpy.all(gx_val == 0)
assert np.all(gx_val == 0)
def test_any_grad(self):
x = tensor.bmatrix('x')
......@@ -1165,11 +1165,11 @@ class TestBitOpReduceGrad(unittest.TestCase):
f = theano.function([x], gx)
x_random = self.rng.binomial(n=1, p=0.5, size=(5, 7)).astype('int8')
for x_val in (x_random,
numpy.zeros_like(x_random),
numpy.ones_like(x_random)):
np.zeros_like(x_random),
np.ones_like(x_random)):
gx_val = f(x_val)
assert gx_val.shape == x_val.shape
assert numpy.all(gx_val == 0)
assert np.all(gx_val == 0)
class TestElemwise(unittest_tools.InferShapeTester):
......@@ -1195,8 +1195,8 @@ class TestElemwise(unittest_tools.InferShapeTester):
dtype = theano.config.floatX
t_left = TensorType(dtype, [(entry == 1) for entry in s_left])()
t_right = TensorType(dtype, [(entry == 1) for entry in s_right])()
t_left_val = numpy.zeros(s_left, dtype=dtype)
t_right_val = numpy.zeros(s_right, dtype=dtype)
t_left_val = np.zeros(s_left, dtype=dtype)
t_right_val = np.zeros(s_right, dtype=dtype)
self._compile_and_check(
[t_left, t_right],
[Elemwise(scalar.add)(t_left, t_right)],
......@@ -1210,7 +1210,7 @@ class TestElemwise(unittest_tools.InferShapeTester):
s = a + b + c + d + e + f
g = theano.function([a, b, c, d, e, f], s,
mode=theano.compile.Mode(linker='py'))
g(*[numpy.zeros(2 ** 11, config.floatX) for i in xrange(6)])
g(*[np.zeros(2 ** 11, config.floatX) for i in xrange(6)])
def test_gt_grad():
......@@ -1226,9 +1226,9 @@ def test_gt_grad():
T = theano.tensor
input_ = T.vector(dtype=floatX)
random_values = numpy.random.RandomState(1234).uniform(
random_values = np.random.RandomState(1234).uniform(
low=-1, high=1, size=(2, 2))
W_values = numpy.asarray(random_values, dtype=floatX)
W_values = np.asarray(random_values, dtype=floatX)
W = theano.shared(value=W_values, name='weights')
correct_score = T.dot(input_, W)
wrong_input = T.vector(dtype=floatX)
......@@ -1258,7 +1258,7 @@ def test_clip_grad():
# use an x value less than y, an x value between y and z, and an x value
# greater than z
unittest_tools.verify_grad(func,
[numpy.asarray([-1., 0.5, 2.]), 0., 1.])
[np.asarray([-1., 0.5, 2.]), 0., 1.])
def test_grad_useless_sum():
......@@ -1287,16 +1287,16 @@ def test_grad_useless_sum():
tensor.type.values_eq_approx_remove_nan)
try:
for test_value in test_values:
outputs.append(f(numpy.array([test_value]).astype('float32')))
outputs.append(f(np.array([test_value]).astype('float32')))
finally:
TensorType.values_eq_approx = old_values_eq_approx
assert not any([isinstance(node.op, theano.tensor.elemwise.Sum) for node in nodes])
assert numpy.allclose(outputs, [[-3.72007598e-44],
[-0.26894142],
[-0.5],
[-0.73105858],
[-1.]])
assert np.allclose(outputs, [[-3.72007598e-44],
[-0.26894142],
[-0.5],
[-0.73105858],
[-1.]])
def test_elemwise_grad_broadcast():
......
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