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提交 26480e1a authored 作者: amrithasuresh's avatar amrithasuresh
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1. Updated numpy as np

2. Fixed indentation
上级 e7d72660
隐藏空白字符变更
内嵌 并排
正在显示 包含 207 行增加 和 207 行删除
theano/tensor/tests/test_subtensor.py
浏览文件 @ 26480e1a
......@@ -4,7 +4,7 @@ import logging
import sys
import unittest
import numpy
import numpy as np
from nose.plugins.skip import SkipTest
from nose.tools import assert_equal
from numpy.testing import assert_array_equal
......@@ -119,7 +119,7 @@ class T_subtensor(unittest.TestCase, utt.TestOptimizationMixin):
def test0_err_invalid(self):
# it is impossible to retrieve a view of a 0-d tensor
n = self.shared(numpy.ones((), dtype=self.dtype))
n = self.shared(np.ones((), dtype=self.dtype))
try:
n[0]
except ValueError as e:
......@@ -128,7 +128,7 @@ class T_subtensor(unittest.TestCase, utt.TestOptimizationMixin):
self.fail()
def test1_err_bounds(self):
n = self.shared(numpy.ones(3, dtype=self.dtype))
n = self.shared(np.ones(3, dtype=self.dtype))
ctv_backup = config.compute_test_value
config.compute_test_value = 'off'
try:
......@@ -150,7 +150,7 @@ class T_subtensor(unittest.TestCase, utt.TestOptimizationMixin):
_logger.setLevel(oldlevel)
def test1_err_subslice(self):
n = self.shared(numpy.ones(3, dtype=self.dtype))
n = self.shared(np.ones(3, dtype=self.dtype))
try:
n[slice(0, slice(1, 2, None), None)]
except Exception:
......@@ -162,7 +162,7 @@ class T_subtensor(unittest.TestCase, utt.TestOptimizationMixin):
self.fail()
def test1_ok_range_finite(self):
n = self.shared(numpy.arange(3, dtype=self.dtype))
n = self.shared(np.arange(3, dtype=self.dtype))
t = n[0:2]
self.assertTrue(isinstance(t.owner.op, Subtensor))
tval = self.eval_output_and_check(t)
......@@ -170,24 +170,24 @@ class T_subtensor(unittest.TestCase, utt.TestOptimizationMixin):
self.assertTrue((tval == [0, 1]).all())
def test2_ok_range_finite(self):
n = self.shared(numpy.arange(12, dtype=self.dtype).reshape((3, 4)))
n = self.shared(np.arange(12, dtype=self.dtype).reshape((3, 4)))
# Also check negative index
for idx in [(slice(0, 2), 3), ((slice(0, 2), -1)), (slice(0, 2), -4)]:
t = n[idx] # l]#0:2,3]
self.assertTrue(isinstance(t.owner.op, Subtensor))
tval = self.eval_output_and_check(t)
self.assertTrue(tval.shape == (2,))
self.assertTrue(numpy.allclose(tval, n.get_value()[idx]))
self.assertTrue(np.allclose(tval, n.get_value()[idx]))
def test1_0_dims(self):
n = self.shared(numpy.ones((), dtype=self.dtype))
n = self.shared(np.ones((), dtype=self.dtype))
t = self.sub([])(n)
self.assertTrue(isinstance(t.owner.op, Subtensor))
self.eval_output_and_check(
t, mode=self.mode.excluding("local_useless_subtensor"))
def test1_err_invalid(self):
n = self.shared(numpy.ones(1, dtype=self.dtype))
n = self.shared(np.ones(1, dtype=self.dtype))
try:
n[0, 0]
except ValueError as e:
......@@ -196,7 +196,7 @@ class T_subtensor(unittest.TestCase, utt.TestOptimizationMixin):
self.fail()
def test1_ok_elem(self):
n = self.shared(numpy.ones(1, dtype=self.dtype) * 5)
n = self.shared(np.ones(1, dtype=self.dtype) * 5)
t = n[0]
self.assertTrue(isinstance(t.owner.op, Subtensor))
tval = self.eval_output_and_check(t)
......@@ -204,7 +204,7 @@ class T_subtensor(unittest.TestCase, utt.TestOptimizationMixin):
self.assertTrue(tval == 5.0)
def test1_ok_range_infinite(self):
n = self.shared(numpy.arange(3, dtype=self.dtype))
n = self.shared(np.arange(3, dtype=self.dtype))
t = n[1:]
self.assertTrue(isinstance(t.owner.op, Subtensor))
tval = self.eval_output_and_check(t)
......@@ -212,7 +212,7 @@ class T_subtensor(unittest.TestCase, utt.TestOptimizationMixin):
self.assertTrue((tval == [1.0, 2.0]).all())
def test1_ok_strided(self):
n = self.shared(numpy.arange(5, dtype=self.dtype))
n = self.shared(np.arange(5, dtype=self.dtype))
t = n[1::2]
self.assertTrue(isinstance(t.owner.op, Subtensor))
tval = self.eval_output_and_check(t)
......@@ -225,7 +225,7 @@ class T_subtensor(unittest.TestCase, utt.TestOptimizationMixin):
self.assertTrue((tval == [0.0, 2.0]).all())
def test2_err_bounds0(self):
n = self.shared(numpy.ones((2, 3), dtype=self.dtype) * 5)
n = self.shared(np.ones((2, 3), dtype=self.dtype) * 5)
ctv_backup = config.compute_test_value
config.compute_test_value = 'off'
try:
......@@ -245,7 +245,7 @@ class T_subtensor(unittest.TestCase, utt.TestOptimizationMixin):
config.compute_test_value = ctv_backup
def test2_err_bounds1(self):
n = self.shared((numpy.ones((2, 3), dtype=self.dtype) * 5))
n = self.shared((np.ones((2, 3), dtype=self.dtype) * 5))
t = n[4:5, 3]
self.assertTrue(isinstance(t.owner.op, Subtensor))
old_stderr = sys.stderr
......@@ -257,107 +257,107 @@ class T_subtensor(unittest.TestCase, utt.TestOptimizationMixin):
sys.stderr = old_stderr
def test2_ok_elem(self):
n = self.shared(numpy.arange(6, dtype=self.dtype).reshape((2, 3)))
n = self.shared(np.arange(6, dtype=self.dtype).reshape((2, 3)))
t = n[0, 2]
self.assertTrue(isinstance(t.owner.op, Subtensor))
tval = self.eval_output_and_check(t)
self.assertTrue(tval.shape == ())
self.assertTrue(numpy.all(tval == 2))
self.assertTrue(np.all(tval == 2))
def test2_ok_row(self):
n = self.shared(numpy.arange(6, dtype=self.dtype).reshape((2, 3)))
n = self.shared(np.arange(6, dtype=self.dtype).reshape((2, 3)))
t = n[1]
self.assertFalse(any(n.type.broadcastable))
self.assertTrue(isinstance(t.owner.op, Subtensor))
tval = self.eval_output_and_check(t)
self.assertTrue(tval.shape == (3,))
self.assertTrue(numpy.all(tval == [3, 4, 5]))
self.assertTrue(np.all(tval == [3, 4, 5]))
def test2_ok_col(self):
n = self.shared(numpy.arange(6, dtype=self.dtype).reshape((2, 3)))
n = self.shared(np.arange(6, dtype=self.dtype).reshape((2, 3)))
t = n[:, 0]
self.assertTrue(isinstance(t.owner.op, Subtensor))
self.assertFalse(any(n.type.broadcastable))
tval = self.eval_output_and_check(t)
self.assertTrue(tval.shape == (2,))
self.assertTrue(numpy.all(tval == [0, 3]))
self.assertTrue(np.all(tval == [0, 3]))
def test2_ok_rows_finite(self):
n = self.shared(numpy.arange(12, dtype=self.dtype).reshape((4, 3)))
n = self.shared(np.arange(12, dtype=self.dtype).reshape((4, 3)))
t = n[1:3, 0]
self.assertTrue(isinstance(t.owner.op, Subtensor))
tval = self.eval_output_and_check(t)
self.assertTrue(tval.shape == (2,))
self.assertTrue(numpy.all(tval == [3, 6]))
self.assertTrue(np.all(tval == [3, 6]))
def test2_ok_cols_infinite(self):
n = self.shared(numpy.arange(12, dtype=self.dtype).reshape((4, 3)))
n = self.shared(np.arange(12, dtype=self.dtype).reshape((4, 3)))
t = n[1, 2:]
self.assertTrue(isinstance(t.owner.op, Subtensor))
tval = self.eval_output_and_check(t)
self.assertTrue(tval.shape == (1,))
self.assertTrue(numpy.all(tval == 5))
self.assertTrue(np.all(tval == 5))
def test2_ok_strided(self):
n = self.shared(numpy.arange(20, dtype=self.dtype).reshape((4, 5)))
n = self.shared(np.arange(20, dtype=self.dtype).reshape((4, 5)))
t = n[1:4:2, 1:5:2]
self.assertTrue(isinstance(t.owner.op, Subtensor))
tval = self.eval_output_and_check(t)
self.assertTrue(tval.shape == (2, 2))
self.assertTrue(numpy.all(tval == [[6, 8], [16, 18]]))
self.assertTrue(np.all(tval == [[6, 8], [16, 18]]))
def test3_ok_mat(self):
n = self.shared(numpy.arange(24, dtype=self.dtype).reshape((2, 3, 4)))
n = self.shared(np.arange(24, dtype=self.dtype).reshape((2, 3, 4)))
t = n[0, 0, 0]
self.assertTrue(isinstance(t.owner.op, Subtensor))
tval = self.eval_output_and_check(t)
self.assertTrue(tval.shape == ())
self.assertTrue(numpy.all(tval == 0))
self.assertTrue(np.all(tval == 0))
def test_long(self):
n = self.shared(numpy.arange(12, dtype=self.dtype).reshape((4, 3)))
n = self.shared(np.arange(12, dtype=self.dtype).reshape((4, 3)))
t = n[L(1):L(4):L(2), L(1)]
self.assertTrue(isinstance(t.owner.op, Subtensor))
tval = self.eval_output_and_check(t)
self.assertTrue(tval.shape == (2,))
self.assertTrue(numpy.all(tval == [4, 10]))
self.assertTrue(np.all(tval == [4, 10]))
def test_long_too_big(self):
# Currently, we cast Python longs to int64 when used for indexing.
# This test checks that using a long that does not fit raises an error.
n = self.shared(numpy.arange(12, dtype=self.dtype).reshape((4, 3)))
n = self.shared(np.arange(12, dtype=self.dtype).reshape((4, 3)))
self.assertRaises(Exception, lambda: n[:L(2 ** 63)])
def test_list_slice(self):
x = theano.tensor.arange(100).reshape((5, 5, 4))
res = x[[slice(1, -1)] * x.ndim].eval()
x = numpy.arange(100).reshape((5, 5, 4))
numpy.allclose(res, x[[slice(1, -1)] * x.ndim])
x = np.arange(100).reshape((5, 5, 4))
np.allclose(res, x[[slice(1, -1)] * x.ndim])
def test_slice_symbol(self):
x = self.shared(numpy.random.rand(5, 4).astype(self.dtype))
y = self.shared(numpy.random.rand(1, 2, 3).astype(self.dtype))
x = self.shared(np.random.rand(5, 4).astype(self.dtype))
y = self.shared(np.random.rand(1, 2, 3).astype(self.dtype))
o = x[:y.shape[0], None, :]
f = theano.function([], o, mode=self.mode)
ret = f()
assert ret.shape == (1, 1, 4)
def test_ellipsis(self):
numpy_n = numpy.arange(24, dtype=self.dtype).reshape((2, 3, 4))
numpy_n = np.arange(24, dtype=self.dtype).reshape((2, 3, 4))
n = self.shared(numpy_n)
test_cases = [
(0, Subtensor, self.sub, numpy.index_exp[...]),
(1, Subtensor, self.sub, numpy.index_exp[..., 1]),
(1, Subtensor, self.sub, numpy.index_exp[1, ...]),
(1, Subtensor, self.sub, numpy.index_exp[..., 1, 2, 3]),
(1, Subtensor, self.sub, numpy.index_exp[1, ..., 2, 3]),
(1, Subtensor, self.sub, numpy.index_exp[1, 2, 3, ...]),
(0, Subtensor, self.sub, np.index_exp[...]),
(1, Subtensor, self.sub, np.index_exp[..., 1]),
(1, Subtensor, self.sub, np.index_exp[1, ...]),
(1, Subtensor, self.sub, np.index_exp[..., 1, 2, 3]),
(1, Subtensor, self.sub, np.index_exp[1, ..., 2, 3]),
(1, Subtensor, self.sub, np.index_exp[1, 2, 3, ...]),
(3, DimShuffle, self.dimshuffle,
numpy.index_exp[..., [0, 2, 3]]),
np.index_exp[..., [0, 2, 3]]),
(1, DimShuffle, self.dimshuffle,
numpy.index_exp[numpy.newaxis, ...]),
np.index_exp[np.newaxis, ...]),
(1, AdvancedSubtensor, self.adv_sub,
numpy.index_exp[..., numpy.newaxis, [1, 2]])]
np.index_exp[..., np.newaxis, [1, 2]])]
for length, op_type, op_type_opt, slice_ in test_cases:
numpy_tval = numpy_n[slice_]
......@@ -379,9 +379,9 @@ class T_subtensor(unittest.TestCase, utt.TestOptimizationMixin):
interaction with the Subtensor Op (which has no support of its own for
newaxis).
"""
newaxis = numpy.newaxis
newaxis = np.newaxis
n = self.shared(numpy.arange(24, dtype=self.dtype).reshape((2, 3, 4)))
n = self.shared(np.arange(24, dtype=self.dtype).reshape((2, 3, 4)))
assert n.ndim == 3
n4 = n[newaxis, :, :, :]
......@@ -405,15 +405,15 @@ class T_subtensor(unittest.TestCase, utt.TestOptimizationMixin):
vs1, vn3, vn4 = theano.function([s], [s1, n3, n4])(-2.0)
assert numpy.all(vs1 == [-2.0])
assert numpy.all(vn3 ==
numpy.arange(24)[newaxis, :, newaxis])
assert numpy.all(vn4 ==
numpy.arange(24).reshape((2, 3, 4))[:, :, :, newaxis])
assert np.all(vs1 == [-2.0])
assert np.all(vn3 ==
np.arange(24)[newaxis, :, newaxis])
assert np.all(vn4 ==
np.arange(24).reshape((2, 3, 4))[:, :, :, newaxis])
def test_grad_1d(self):
subi = 0
data = numpy.asarray(rand(2, 3), dtype=self.dtype)
data = np.asarray(rand(2, 3), dtype=self.dtype)
n = self.shared(data)
z = scal.constant(subi).astype('int32')
t = n[z:, z]
......@@ -425,15 +425,15 @@ class T_subtensor(unittest.TestCase, utt.TestOptimizationMixin):
self.ignore_topo)]
if not self.fast_compile:
assert len(topo_) == 6
assert numpy.sum([isinstance(node.op, self.inc_sub)
for node in topo_]) == 1
assert numpy.sum([isinstance(node.op, self.sub)
for node in topo_]) == 1
assert np.sum([isinstance(node.op, self.inc_sub)
for node in topo_]) == 1
assert np.sum([isinstance(node.op, self.sub)
for node in topo_]) == 1
gval = f()
good = numpy.zeros_like(data)
good[subi:, subi] = numpy.exp(data[subi:, subi])
self.assertTrue(numpy.allclose(gval, good), (gval, good))
good = np.zeros_like(data)
good[subi:, subi] = np.exp(data[subi:, subi])
self.assertTrue(np.allclose(gval, good), (gval, good))
def test_grad_2d_inc_set_subtensor(self):
for n_shape, m_shape in [
......@@ -444,11 +444,11 @@ class T_subtensor(unittest.TestCase, utt.TestOptimizationMixin):
]:
for op in [inc_subtensor, set_subtensor]:
subi = 2
data = numpy.asarray(rand(*n_shape), dtype=self.dtype)
data = np.asarray(rand(*n_shape), dtype=self.dtype)
n = self.shared(data)
z = scal.constant(subi)
m = matrix('m', dtype=self.dtype)
mv = numpy.asarray(rand(*m_shape), dtype=self.dtype)
mv = np.asarray(rand(*m_shape), dtype=self.dtype)
t = op(n[:z, :z], m)
gn, gm = theano.tensor.grad(theano.tensor.sum(t), [n, m])
......@@ -456,7 +456,7 @@ class T_subtensor(unittest.TestCase, utt.TestOptimizationMixin):
utt.verify_grad(lambda nn: op(nn[:z, :z], mv), [data])
def test_grad_0d(self):
data = numpy.asarray(rand(2, 3), dtype=self.dtype)
data = np.asarray(rand(2, 3), dtype=self.dtype)
n = self.shared(data)
t = n[1, 0]
gn = theano.tensor.grad(theano.tensor.sum(theano.tensor.exp(t)), n)
......@@ -466,15 +466,15 @@ class T_subtensor(unittest.TestCase, utt.TestOptimizationMixin):
if not isinstance(node.op, self.ignore_topo)]
if not self.fast_compile:
assert_equal(len(topo_), 6)
assert numpy.sum([isinstance(node.op, self.inc_sub)
for node in topo_]) == 1
assert numpy.sum([isinstance(node.op, self.sub)
for node in topo_]) == 1
assert np.sum([isinstance(node.op, self.inc_sub)
for node in topo_]) == 1
assert np.sum([isinstance(node.op, self.sub)
for node in topo_]) == 1
gval = f()
good = numpy.zeros_like(data)
good[1, 0] = numpy.exp(data[1, 0])
self.assertTrue(numpy.allclose(gval, good), (gval, good))
good = np.zeros_like(data)
good[1, 0] = np.exp(data[1, 0])
self.assertTrue(np.allclose(gval, good), (gval, good))
def test_ok_list(self):
for data, idx in [(rand(4), [1, 0]),
......@@ -492,7 +492,7 @@ class T_subtensor(unittest.TestCase, utt.TestOptimizationMixin):
(rand(4, 2, 3),
theano.tensor.constant([3, 3, 1, 1, 2, 2, 0, 0])),
]:
data = numpy.asarray(data, dtype=self.dtype)
data = np.asarray(data, dtype=self.dtype)
n = self.shared(data)
t = n[idx]
......@@ -505,7 +505,7 @@ class T_subtensor(unittest.TestCase, utt.TestOptimizationMixin):
else:
good = data[idx.data]
self.assertTrue(val.ndim == data.ndim)
self.assertTrue(numpy.allclose(val, good), (val, good))
self.assertTrue(np.allclose(val, good), (val, good))
# Test reuse of output memory
if type(self.adv_sub1) == tensor.AdvancedSubtensor1:
......@@ -524,7 +524,7 @@ class T_subtensor(unittest.TestCase, utt.TestOptimizationMixin):
gn = theano.grad(t.sum(), n)
g = self.function([], gn, op=self.adv_incsub1)
utt.verify_grad(lambda m: m[[1, 3]],
[numpy.random.rand(5, 5).astype(self.dtype)])
[np.random.rand(5, 5).astype(self.dtype)])
g()
utt.verify_grad(lambda m: m[idx],
[data])
......@@ -533,21 +533,21 @@ class T_subtensor(unittest.TestCase, utt.TestOptimizationMixin):
data = rand(4, 2, 3)
idx = [2, 2, 0, 0, 1, 1]
n = self.shared(data)
t = n[self.shared(numpy.asarray(idx).astype('int64'))[::2]]
t = n[self.shared(np.asarray(idx).astype('int64'))[::2]]
self.assertTrue(isinstance(t.owner.op, tensor.AdvancedSubtensor1))
val = self.eval_output_and_check(t, op_type=self.adv_sub1, length=2)
utt.assert_allclose(data[idx[::2]], val)
def test_err_invalid_list(self):
n = self.shared(numpy.asarray(5, dtype=self.dtype))
n = self.shared(np.asarray(5, dtype=self.dtype))
self.assertRaises(TypeError, n.__getitem__, [0, 0])
def test_err_invalid_2list_dtype(self):
n = self.shared(numpy.ones((3, 3), dtype=self.dtype) * 5)
n = self.shared(np.ones((3, 3), dtype=self.dtype) * 5)
self.assertRaises(TypeError, n.__getitem__, ([0., 0], [1, 1]))
def test_err_bound_list(self):
n = self.shared(numpy.ones((2, 3), dtype=self.dtype) * 5)
n = self.shared(np.ones((2, 3), dtype=self.dtype) * 5)
l = lvector()
t = n[l]
# We test again AdvancedSubtensor1 as we transfer data to the cpu.
......@@ -557,7 +557,7 @@ class T_subtensor(unittest.TestCase, utt.TestOptimizationMixin):
# the grad
g = self.function([l],
inc_subtensor(t, numpy.asarray([[1.]], self.dtype)),
inc_subtensor(t, np.asarray([[1.]], self.dtype)),
op=self.adv_incsub1)
for shp in [[0, 4], [0, -3], [-10]]:
......@@ -565,7 +565,7 @@ class T_subtensor(unittest.TestCase, utt.TestOptimizationMixin):
self.assertRaises(IndexError, g, shp)
def test_adv_sub1_broadcast(self):
v = numpy.arange(3, dtype=self.dtype).reshape((1, 3))
v = np.arange(3, dtype=self.dtype).reshape((1, 3))
n = self.shared(v * 5, broadcastable=(True, False))
idx = tensor.lvector()
t = n[idx]
......@@ -579,10 +579,10 @@ class T_subtensor(unittest.TestCase, utt.TestOptimizationMixin):
self.assertTrue(isinstance(topo_[0].op, self.adv_sub1))
f_0 = f([0])
self.assertTrue(f_0.shape == (1, 3))
self.assertTrue(numpy.allclose(f_0, v * 5))
self.assertTrue(np.allclose(f_0, v * 5))
f_00 = f([0, 0])
self.assertTrue(f_00.shape == (2, 3))
self.assertTrue(numpy.allclose(f_00, v * 5))
self.assertTrue(np.allclose(f_00, v * 5))
self.assertRaises(IndexError, f, [0, 1])
# Test the gradient
......@@ -591,30 +591,30 @@ class T_subtensor(unittest.TestCase, utt.TestOptimizationMixin):
g = self.function([idx], gn, op=self.adv_incsub1)
g_0 = g([0])
self.assertTrue(g_0.shape == (1, 3))
self.assertTrue(numpy.allclose(g_0, 1))
self.assertTrue(np.allclose(g_0, 1))
g_00 = g([0, 0])
self.assertTrue(g_00.shape == (1, 3))
self.assertTrue(numpy.allclose(g_00, 2))
self.assertTrue(np.allclose(g_00, 2))
utt.verify_grad(lambda m: m[[1, 3]],
[numpy.random.rand(5, 5).astype(self.dtype)])
[np.random.rand(5, 5).astype(self.dtype)])
def fun(x, y):
return advanced_inc_subtensor1(x, y, [1, 3])
utt.verify_grad(fun, [numpy.random.rand(5, 5).astype(self.dtype),
numpy.random.rand(2, 5).astype(self.dtype)])
utt.verify_grad(fun, [np.random.rand(5, 5).astype(self.dtype),
np.random.rand(2, 5).astype(self.dtype)])
def fun(x, y):
return advanced_set_subtensor1(x, y, [1, 3])
utt.verify_grad(fun, [numpy.random.rand(5, 5).astype(self.dtype),
numpy.random.rand(2, 5).astype(self.dtype)])
utt.verify_grad(fun, [np.random.rand(5, 5).astype(self.dtype),
np.random.rand(2, 5).astype(self.dtype)])
# test set_subtensor broadcast
self.dtype = 'float32'
x = tensor.tensor4('x', dtype=self.dtype)
indexes = theano.shared(numpy.int32([1, 2, 3, 4]))
W = self.shared(numpy.random.random(
indexes = theano.shared(np.int32([1, 2, 3, 4]))
W = self.shared(np.random.random(
(10, 10, 3, 3)).astype(self.dtype))
h = x + W
......@@ -625,11 +625,11 @@ class T_subtensor(unittest.TestCase, utt.TestOptimizationMixin):
N = 3
f = self.function([x], g, op=self.adv_incsub1, N=N)
f(numpy.random.random((10, 10, 3, 3)).astype(self.dtype))
f(np.random.random((10, 10, 3, 3)).astype(self.dtype))
def test_adv_sub1_idx_broadcast(self):
# The idx can be a broadcastable vector.
ones = numpy.ones((4, 3), dtype=self.dtype)
ones = np.ones((4, 3), dtype=self.dtype)
n = self.shared(ones * 5)
idx = tensor.TensorType(dtype='int64', broadcastable=(True,))()
assert idx.type.broadcastable == (True,)
......@@ -644,7 +644,7 @@ class T_subtensor(unittest.TestCase, utt.TestOptimizationMixin):
self.assertTrue(isinstance(topo_[0].op, self.adv_sub1))
f_0 = f([0])
self.assertTrue(f_0.shape == (1, 3))
self.assertTrue(numpy.allclose(f_0, 5))
self.assertTrue(np.allclose(f_0, 5))
# Test the gradient
c = t.sum()
......@@ -652,15 +652,15 @@ class T_subtensor(unittest.TestCase, utt.TestOptimizationMixin):
g = self.function([idx], gn, op=self.adv_incsub1)
g_0 = g([0])
self.assertTrue(g_0.shape == (4, 3))
self.assertTrue(numpy.allclose(g_0[0], 1))
self.assertTrue(numpy.allclose(g_0[1:], 0))
self.assertTrue(np.allclose(g_0[0], 1))
self.assertTrue(np.allclose(g_0[1:], 0))
@attr('slow')
def test_shape_i_const(self):
# Each axis is treated independently by shape_i/shape operators
mode_opt = self.mode.including("fast_run")
data = self.shared(numpy.array(numpy.arange(5), dtype=self.dtype))
data = self.shared(np.array(np.arange(5), dtype=self.dtype))
for start in [None] + [-8, -5, -1, 0, 1, 5, 8]:
outs = []
shapes = []
......@@ -673,7 +673,7 @@ class T_subtensor(unittest.TestCase, utt.TestOptimizationMixin):
op=self.ops, N=0)
t_shapes = f()
for t_shape, shape in zip(t_shapes, shapes):
assert numpy.all(t_shape == shape)
assert np.all(t_shape == shape)
assert tensor.Subtensor not in [x.op
for x in f.maker.fgraph.toposort()]
......@@ -682,7 +682,7 @@ class T_subtensor(unittest.TestCase, utt.TestOptimizationMixin):
mode_opt = self.mode.including("fast_run")
v_data = numpy.array(numpy.arange(5), dtype=self.dtype)
v_data = np.array(np.arange(5), dtype=self.dtype)
t_data = self.shared(v_data)
start = tensor.iscalar('b')
stop = tensor.iscalar('e')
......@@ -697,8 +697,8 @@ class T_subtensor(unittest.TestCase, utt.TestOptimizationMixin):
for start in [-8, -5, -4, -1, 0, 1, 4, 5, 8]:
for stop in [-8, -5, -4, -1, 0, 1, 4, 5, 8]:
for step in [-3, -1, 2, 5]:
assert numpy.all(f(start, stop, step) ==
v_data[start:stop:step].shape)
assert np.all(f(start, stop, step) ==
v_data[start:stop:step].shape)
def test_slice_canonical_form_0(self):
start = tensor.iscalar('b')
......@@ -713,15 +713,15 @@ class T_subtensor(unittest.TestCase, utt.TestOptimizationMixin):
tensor.as_tensor_variable(cnf[1])], N=0, op=self.ops)
length = 5
a = numpy.arange(length)
a = np.arange(length)
for start in [-8, -5, -4, -1, 0, 1, 4, 5, 8]:
for stop in [-8, -5, -4, -1, 0, 1, 4, 5, 8]:
for step in [-6, -3, -1, 2, 5]:
out = f(start, stop, step, length)
t_out = a[out[0]:out[1]:out[2]][::out[3]]
v_out = a[start:stop:step]
assert numpy.all(t_out == v_out)
assert numpy.all(t_out.shape == v_out.shape)
assert np.all(t_out == v_out)
assert np.all(t_out.shape == v_out.shape)
def test_slice_canonical_form_1(self):
stop = tensor.iscalar('e')
......@@ -735,14 +735,14 @@ class T_subtensor(unittest.TestCase, utt.TestOptimizationMixin):
tensor.as_tensor_variable(cnf[1])], N=0, op=self.ops)
length = 5
a = numpy.arange(length)
a = np.arange(length)
for stop in [-8, -5, -4, -1, 0, 1, 4, 5, 8]:
for step in [-6, -3, -1, 2, 5]:
out = f(stop, step, length)
t_out = a[out[0]:out[1]:out[2]][::out[3]]
v_out = a[:stop:step]
assert numpy.all(t_out == v_out)
assert numpy.all(t_out.shape == v_out.shape)
assert np.all(t_out == v_out)
assert np.all(t_out.shape == v_out.shape)
def test_slice_canonical_form_2(self):
start = tensor.iscalar('b')
......@@ -756,14 +756,14 @@ class T_subtensor(unittest.TestCase, utt.TestOptimizationMixin):
tensor.as_tensor_variable(cnf[1])], N=0, op=self.ops)
length = 5
a = numpy.arange(length)
a = np.arange(length)
for start in [-8, -5, -4, -1, 0, 1, 4, 5, 8]:
for step in [-6, -3, -1, 2, 5]:
out = f(start, step, length)
t_out = a[out[0]:out[1]:out[2]][::out[3]]
v_out = a[start:None:step]
assert numpy.all(t_out == v_out)
assert numpy.all(t_out.shape == v_out.shape)
assert np.all(t_out == v_out)
assert np.all(t_out.shape == v_out.shape)
def test_slice_canonical_form_3(self):
start = tensor.iscalar('b')
......@@ -777,14 +777,14 @@ class T_subtensor(unittest.TestCase, utt.TestOptimizationMixin):
tensor.as_tensor_variable(cnf[1])], N=0, op=self.ops)
length = 5
a = numpy.arange(length)
a = np.arange(length)
for start in [-8, -5, -4, -1, 0, 1, 4, 5, 8]:
for stop in [-8, -5, -4, -1, 0, 1, 4, 5, 8]:
out = f(start, stop, length)
t_out = a[out[0]:out[1]:out[2]][::out[3]]
v_out = a[start:stop:None]
assert numpy.all(t_out == v_out)
assert numpy.all(t_out.shape == v_out.shape)
assert np.all(t_out == v_out)
assert np.all(t_out.shape == v_out.shape)
def test_slice_canonical_form_4(self):
step = tensor.iscalar('s')
......@@ -797,13 +797,13 @@ class T_subtensor(unittest.TestCase, utt.TestOptimizationMixin):
tensor.as_tensor_variable(cnf[1])], N=0, op=self.ops)
length = 5
a = numpy.arange(length)
a = np.arange(length)
for step in [-6, -3, -1, 2, 5]:
out = f(step, length)
t_out = a[out[0]:out[1]:out[2]][::out[3]]
v_out = a[None:None:step]
assert numpy.all(t_out == v_out)
assert numpy.all(t_out.shape == v_out.shape)
assert np.all(t_out == v_out)
assert np.all(t_out.shape == v_out.shape)
def test_slice_canonical_form_5(self):
start = tensor.iscalar('b')
......@@ -816,13 +816,13 @@ class T_subtensor(unittest.TestCase, utt.TestOptimizationMixin):
tensor.as_tensor_variable(cnf[1])], N=0, op=self.ops)
length = 5
a = numpy.arange(length)
a = np.arange(length)
for start in [-8, -5, -4, -1, 0, 1, 4, 5, 8]:
out = f(start, length)
t_out = a[out[0]:out[1]:out[2]][::out[3]]
v_out = a[start:None:None]
assert numpy.all(t_out == v_out)
assert numpy.all(t_out.shape == v_out.shape)
assert np.all(t_out == v_out)
assert np.all(t_out.shape == v_out.shape)
def test_slice_canonical_form_6(self):
stop = tensor.iscalar('e')
......@@ -835,20 +835,20 @@ class T_subtensor(unittest.TestCase, utt.TestOptimizationMixin):
tensor.as_tensor_variable(cnf[1])], N=0, op=self.ops)
length = 5
a = numpy.arange(length)
a = np.arange(length)
for stop in [-8, -5, -4, -1, 0, 1, 4, 5, 8]:
out = f(stop, length)
t_out = a[out[0]:out[1]:out[2]][::out[3]]
v_out = a[None:stop:None]
assert numpy.all(t_out == v_out)
assert numpy.all(t_out.shape == v_out.shape)
assert np.all(t_out == v_out)
assert np.all(t_out.shape == v_out.shape)
def grad_list_(self, idxs, data):
n = self.shared(data)
for idx in idxs:
# Should stay on the cpu.
idx_ = _shared(numpy.asarray(idx))
idx_ = _shared(np.asarray(idx))
t = n[idx_]
gn = theano.tensor.grad(theano.tensor.sum(theano.tensor.exp(t)), n)
f = self.function([], [gn, gn.shape], op=self.adv_incsub1)
......@@ -861,14 +861,14 @@ class T_subtensor(unittest.TestCase, utt.TestOptimizationMixin):
for node in topo])
assert any([isinstance(node.op, self.adv_sub1) for node in topo])
gval, gshape = f()
good = numpy.zeros_like(data)
good = np.zeros_like(data)
# don't work when the same index is used many time
# good[idx] += numpy.exp(data[idx])
# good[idx] += np.exp(data[idx])
for i in idx:
good[i] += numpy.exp(data[i])
good[i] += np.exp(data[i])
self.assertTrue(gval.ndim == data.ndim)
self.assertTrue(numpy.allclose(gval, good), (gval, good))
self.assertTrue(numpy.allclose(gshape, data.shape))
self.assertTrue(np.allclose(gval, good), (gval, good))
self.assertTrue(np.allclose(gshape, data.shape))
def fct(t):
return theano.tensor.sum(t[idx_])
......@@ -916,7 +916,7 @@ class T_subtensor(unittest.TestCase, utt.TestOptimizationMixin):
@attr('slow')
def test_grad_list(self):
data = rand(4)
data = numpy.asarray(data, dtype=self.dtype)
data = np.asarray(data, dtype=self.dtype)
idxs = [[i] for i in range(data.shape[0])]
for i in range(data.shape[0]):
for j in range(0, data.shape[0], 2):
......@@ -924,11 +924,11 @@ class T_subtensor(unittest.TestCase, utt.TestOptimizationMixin):
self.grad_list_(idxs, data)
data = rand(4, 3)
data = numpy.asarray(data, dtype=self.dtype)
data = np.asarray(data, dtype=self.dtype)
self.grad_list_(idxs, data)
data = rand(4, 3, 2)
data = numpy.asarray(data, dtype=self.dtype)
data = np.asarray(data, dtype=self.dtype)
self.grad_list_(idxs, data)
def test_shape_list(self):
......@@ -938,12 +938,12 @@ class T_subtensor(unittest.TestCase, utt.TestOptimizationMixin):
(rand(4, 2, 3), [0, 3]),
(rand(4, 2, 3), [3, 3, 1, 2, 2, ]),
]:
data = numpy.asarray(data, dtype=self.dtype)
data = np.asarray(data, dtype=self.dtype)
n = self.shared(data)
t = n[idx]
f = self.function([], t.shape, op=self.ops, N=0, N_fast=1)
val = f()
self.assertTrue(numpy.allclose(val, data[idx].shape))
self.assertTrue(np.allclose(val, data[idx].shape))
def test_grad_advanced_inc_subtensor(self):
def inc_slice(*s):
......@@ -958,40 +958,40 @@ class T_subtensor(unittest.TestCase, utt.TestOptimizationMixin):
# vector
utt.verify_grad(
inc_slice(slice(2, 4, None)),
(numpy.asarray([0, 1, 2, 3, 4, 5.]), numpy.asarray([9, 9.]),))
(np.asarray([0, 1, 2, 3, 4, 5.]), np.asarray([9, 9.]),))
# matrix
utt.verify_grad(
inc_slice(slice(1, 2, None), slice(None, None, None)),
(numpy.asarray([[0, 1], [2, 3], [4, 5.]]),
numpy.asarray([[9, 9.]]),))
(np.asarray([[0, 1], [2, 3], [4, 5.]]),
np.asarray([[9, 9.]]),))
# single element
utt.verify_grad(
inc_slice(2, 1),
(numpy.asarray([[0, 1], [2, 3], [4, 5.]]), numpy.asarray(9.),))
(np.asarray([[0, 1], [2, 3], [4, 5.]]), np.asarray(9.),))
def test_inc_and_set_subtensor(self):
"""
Test increment and set with broadcast
"""
X = self.shared(numpy.ones((9, 9)).astype(self.dtype))
X = self.shared(np.ones((9, 9)).astype(self.dtype))
y = set_subtensor(X[1::, 1::], 0)
f = self.function([], [y],
op=self.inc_sub,
N=1)
out = f()
res = numpy.ones((9, 9))
res = np.ones((9, 9))
res[1::, 1::] = 0
assert numpy.allclose(out, res)
assert np.allclose(out, res)
def test_advanced1_inc_and_set(self):
"""
Test advanced increment and set.
"""
rng = numpy.random.RandomState(seed=utt.fetch_seed())
rng = np.random.RandomState(seed=utt.fetch_seed())
all_inputs_var = []
all_inputs_num = []
all_outputs_var = []
......@@ -1001,9 +1001,9 @@ class T_subtensor(unittest.TestCase, utt.TestOptimizationMixin):
for inplace in (False, True):
for data_shape in ((10,), (4, 5), (1, 2, 3), (4, 5, 6, 7)):
data_n_dims = len(data_shape)
data_size = numpy.product(data_shape)
data_size = np.product(data_shape)
# Corresponding numeric variable.
data_num_init = numpy.arange(data_size, dtype=self.dtype)
data_num_init = np.arange(data_size, dtype=self.dtype)
data_num_init = data_num_init.reshape(data_shape)
inc_shapes = [data_shape[i:]
for i in xrange(0, len(data_shape) + 1)]
......@@ -1031,7 +1031,7 @@ class T_subtensor(unittest.TestCase, utt.TestOptimizationMixin):
# Corresponding numeric variable.
# If set_instead_of_inc, we want to avoid repeating
# indices, as the order is not guaranteed.
idx_num = rng.choice(numpy.arange(data_shape[0]),
idx_num = rng.choice(np.arange(data_shape[0]),
n_to_inc,
replace=(not set_instead_of_inc))
idx_num = idx_num.astype('int64')
......@@ -1049,7 +1049,7 @@ class T_subtensor(unittest.TestCase, utt.TestOptimizationMixin):
# The param dtype is needed when inc_shape is empty.
# By default, it would return a float and rng.uniform
# with NumPy 1.10 will raise a Deprecation warning.
inc_size = numpy.product(inc_shape, dtype='int')
inc_size = np.product(inc_shape, dtype='int')
# Corresponding numeric variable.
inc_num = rng.uniform(size=inc_size).astype(self.dtype)
inc_num = inc_num.reshape(inc_shape)
......@@ -1100,7 +1100,7 @@ class T_subtensor(unittest.TestCase, utt.TestOptimizationMixin):
# Ensure calling `f` will not alter `data_num`.
data_num = data_num.copy()
f_out = f(data_num.copy(), idx_num, inc_num)
assert numpy.allclose(f_out, data_copy)
assert np.allclose(f_out, data_copy)
if not inplace:
# Sanity check: `data_num` should be intact.
assert (data_num == data_num_init).all()
......@@ -1121,12 +1121,12 @@ class T_subtensor(unittest.TestCase, utt.TestOptimizationMixin):
for params, f_out, output_num in izip(all_params, f_outs, all_outputs_num):
# NB: if this assert fails, it will probably be easier to debug if
# you enable the debug code above.
assert numpy.allclose(f_out, output_num), (params, f_out, output_num)
assert np.allclose(f_out, output_num), (params, f_out, output_num)
def test_adv_constant_arg(self):
# Test case provided (and bug detected, gh-607) by John Salvatier
m = matrix('m')
gv = numpy.array([0, 1, 3])
gv = np.array([0, 1, 3])
g = theano.tensor.constant(gv)
i = theano.tensor.lvector('i')
......@@ -1156,8 +1156,8 @@ class T_subtensor(unittest.TestCase, utt.TestOptimizationMixin):
m1_ref[:, idx] = 0
m2_ref[:, idx] += 1
assert numpy.allclose(m1_val, m1_ref), (m1_val, m1_ref)
assert numpy.allclose(m2_val, m2_ref), (m2_val, m2_ref)
assert np.allclose(m1_val, m1_ref), (m1_val, m1_ref)
assert np.allclose(m2_val, m2_ref), (m2_val, m2_ref)
def test_adv1_inc_sub_notlastdim_2didx(self):
# Test that taking 1-dimensional advanced indexing
......@@ -1181,8 +1181,8 @@ class T_subtensor(unittest.TestCase, utt.TestOptimizationMixin):
m1_ref[:, idx] = 0
m2_ref[:, idx] += 1
assert numpy.allclose(m1_val, m1_ref), (m1_val, m1_ref)
assert numpy.allclose(m2_val, m2_ref), (m2_val, m2_ref)
assert np.allclose(m1_val, m1_ref), (m1_val, m1_ref)
assert np.allclose(m2_val, m2_ref), (m2_val, m2_ref)
def test_adv1_inc_sub_notlastdim_1_2dval_broadcast(self):
# Test that taking 1-dimensional advanced indexing
......@@ -1202,8 +1202,8 @@ class T_subtensor(unittest.TestCase, utt.TestOptimizationMixin):
for i, shp_i, shp_v in zip(sym_i, shape_i, shape_val):
sub_m = m[:, i]
m1 = set_subtensor(sub_m, numpy.zeros(shp_v))
m2 = inc_subtensor(sub_m, numpy.ones(shp_v))
m1 = set_subtensor(sub_m, np.zeros(shp_v))
m2 = inc_subtensor(sub_m, np.ones(shp_v))
f = theano.function([m, i], [m1, m2])
m_val = rand(3, 5)
......@@ -1216,8 +1216,8 @@ class T_subtensor(unittest.TestCase, utt.TestOptimizationMixin):
m1_ref[:, idx] = 0
m2_ref[:, idx] += 1
assert numpy.allclose(m1_val, m1_ref), (m1_val, m1_ref)
assert numpy.allclose(m2_val, m2_ref), (m2_val, m2_ref)
assert np.allclose(m1_val, m1_ref), (m1_val, m1_ref)
assert np.allclose(m2_val, m2_ref), (m2_val, m2_ref)
finally:
config.warn.inc_set_subtensor1 = orig_warn
......@@ -1239,8 +1239,8 @@ class T_subtensor(unittest.TestCase, utt.TestOptimizationMixin):
config.warn.inc_set_subtensor1 = False
for i, shp_i, shp_v in zip(sym_i, shape_i, shape_val):
sub_m = m[:, i]
m1 = set_subtensor(sub_m, numpy.zeros(shp_v))
m2 = inc_subtensor(sub_m, numpy.ones(shp_v))
m1 = set_subtensor(sub_m, np.zeros(shp_v))
m2 = inc_subtensor(sub_m, np.ones(shp_v))
f = theano.function([m, i], [m1, m2])
m_val = rand(3, 5)
......@@ -1256,15 +1256,15 @@ class T_subtensor(unittest.TestCase, utt.TestOptimizationMixin):
m1_ref[:, idx] = 0
m2_ref[:, idx] += 1
assert numpy.allclose(m1_val, m1_ref), (m1_val, m1_ref)
assert numpy.allclose(m2_val, m2_ref), (m2_val, m2_ref)
assert np.allclose(m1_val, m1_ref), (m1_val, m1_ref)
assert np.allclose(m2_val, m2_ref), (m2_val, m2_ref)
finally:
config.warn.inc_set_subtensor1 = orig_warn
def test_take(self):
a = tensor.matrix()
f = theano.function([a], a.take(0, axis=-1), allow_input_downcast=True)
f(numpy.random.normal(0, 1, (30, 4)))
f(np.random.normal(0, 1, (30, 4)))
class TestIncSubtensor1(unittest.TestCase):
......@@ -1272,7 +1272,7 @@ class TestIncSubtensor1(unittest.TestCase):
# also tests set_subtensor
def setUp(self):
self.rng = numpy.random.RandomState(seed=utt.fetch_seed())
self.rng = np.random.RandomState(seed=utt.fetch_seed())
self.s = tensor.iscalar()
self.v = tensor.fvector()
......@@ -1298,7 +1298,7 @@ class TestIncSubtensor1(unittest.TestCase):
f = theano.function([self.v, self.adv1q], a, allow_input_downcast=True)
aval = f([.4, .9, .1], [1, 2])
assert numpy.allclose(aval, [.4, 0.9, 0.1])
assert np.allclose(aval, [.4, 0.9, 0.1])
def test_1d_inc_adv_selection(self):
a = inc_subtensor(self.v[self.adv1q], self.v[self.adv1q])
......@@ -1306,7 +1306,7 @@ class TestIncSubtensor1(unittest.TestCase):
assert a.type == self.v.type
f = theano.function([self.v, self.adv1q], a, allow_input_downcast=True)
aval = f([.4, .9, .1], [1, 2])
assert numpy.allclose(aval, [.4, 1.8, 0.2])
assert np.allclose(aval, [.4, 1.8, 0.2])
def test_1d_inc_adv_selection_w_broadcasting(self):
a = inc_subtensor(self.v[self.adv1q], 3.0)
......@@ -1314,7 +1314,7 @@ class TestIncSubtensor1(unittest.TestCase):
assert a.type == self.v.type
f = theano.function([self.v, self.adv1q], a, allow_input_downcast=True)
aval = f([.4, .9, .1], [1, 2])
assert numpy.allclose(aval, [.4, 3.9, 3.1])
assert np.allclose(aval, [.4, 3.9, 3.1])
def test_assigning_matrix_to_vector_selection(self):
self.assertRaises(TypeError,
......@@ -1327,7 +1327,7 @@ class TestIncSubtensor1(unittest.TestCase):
f = theano.function([self.m, idx], a2)
mval = self.rng.random_sample((4, 10))
idxval = numpy.array([[1, 2], [3, 2]])
idxval = np.array([[1, 2], [3, 2]])
a2val = f(mval, idxval)
utt.assert_allclose(a2val[0], mval[0])
......@@ -1427,7 +1427,7 @@ class TestAdvancedSubtensor(unittest.TestCase):
(rand(2, 4, 3),
theano.tensor.constant([3, 3, 1, 1, 2, 2, 0, 0])),
]:
data = numpy.asarray(data, dtype=self.dtype)
data = np.asarray(data, dtype=self.dtype)
n = self.shared(data)
t = n[0, idx]
......@@ -1439,7 +1439,7 @@ class TestAdvancedSubtensor(unittest.TestCase):
else:
good = data[0, idx.data]
self.assertTrue(val.ndim == data.ndim - 1)
self.assertTrue(numpy.allclose(val, good), (val, good))
self.assertTrue(np.allclose(val, good), (val, good))
def test_inc_adv_subtensor_w_matrix(self):
subt = self.v[self.ix2]
......@@ -1450,13 +1450,13 @@ class TestAdvancedSubtensor(unittest.TestCase):
mode=self.mode)
aval = f([.4, .9, .1], [[1, 2],
[1, 2]])
assert numpy.allclose(aval, [.4, .9 * 3, .1 * 3])
assert np.allclose(aval, [.4, .9 * 3, .1 * 3])
def test_adv_subtensor_w_int_and_matrix(self):
subt = self.ft4[0, :, self.ix2, :]
f = theano.function([self.ft4, self.ix2], subt, mode=self.mode)
ft4v = numpy.random.random((2, 3, 4, 5)).astype('float32')
ix2v = numpy.asarray([[0, 1], [1, 0]])
ft4v = np.random.random((2, 3, 4, 5)).astype('float32')
ix2v = np.asarray([[0, 1], [1, 0]])
aval = f(ft4v, ix2v)
rval = ft4v[0, :, ix2v, :]
utt.assert_allclose(rval, aval)
......@@ -1464,8 +1464,8 @@ class TestAdvancedSubtensor(unittest.TestCase):
def test_adv_subtensor_w_none_and_matrix(self):
subt = self.ft4[:, None, :, self.ix2, :]
f = theano.function([self.ft4, self.ix2], subt, mode=self.mode)
ft4v = numpy.random.random((2, 3, 4, 5)).astype('float32')
ix2v = numpy.asarray([[0, 1], [1, 0]])
ft4v = np.random.random((2, 3, 4, 5)).astype('float32')
ix2v = np.asarray([[0, 1], [1, 0]])
aval = f(ft4v, ix2v)
rval = ft4v[:, None, :, ix2v, :]
utt.assert_allclose(rval, aval)
......@@ -1473,8 +1473,8 @@ class TestAdvancedSubtensor(unittest.TestCase):
def test_adv_subtensor_w_slice_and_matrix(self):
subt = self.ft4[:, 0:1, self.ix2, :]
f = theano.function([self.ft4, self.ix2], subt, mode=self.mode)
ft4v = numpy.random.random((2, 3, 4, 5)).astype('float32')
ix2v = numpy.asarray([[0, 1], [1, 0]])
ft4v = np.random.random((2, 3, 4, 5)).astype('float32')
ix2v = np.asarray([[0, 1], [1, 0]])
aval = f(ft4v, ix2v)
rval = ft4v[:, 0:1, ix2v, :]
utt.assert_allclose(rval, aval)
......@@ -1482,8 +1482,8 @@ class TestAdvancedSubtensor(unittest.TestCase):
def test_adv_subtensor_w_matrix_and_int(self):
subt = self.ft4[:, :, self.ix2, 0]
f = theano.function([self.ft4, self.ix2], subt, mode=self.mode)
ft4v = numpy.random.random((2, 3, 4, 5)).astype('float32')
ix2v = numpy.asarray([[0, 1], [1, 0]])
ft4v = np.random.random((2, 3, 4, 5)).astype('float32')
ix2v = np.asarray([[0, 1], [1, 0]])
aval = f(ft4v, ix2v)
rval = ft4v[:, :, ix2v, 0]
utt.assert_allclose(rval, aval)
......@@ -1491,8 +1491,8 @@ class TestAdvancedSubtensor(unittest.TestCase):
def test_adv_subtensor_w_matrix_and_none(self):
subt = self.ft4[:, :, self.ix2, None, :]
f = theano.function([self.ft4, self.ix2], subt, mode=self.mode)
ft4v = numpy.random.random((2, 3, 4, 5)).astype('float32')
ix2v = numpy.asarray([[0, 1], [1, 0]])
ft4v = np.random.random((2, 3, 4, 5)).astype('float32')
ix2v = np.asarray([[0, 1], [1, 0]])
aval = f(ft4v, ix2v)
rval = ft4v[:, :, ix2v, None, :]
utt.assert_allclose(rval, aval)
......@@ -1513,10 +1513,10 @@ class TestAdvancedSubtensor(unittest.TestCase):
[.5, .3, .15]],
[1, 2, 1],
[0, 1, 0])
assert numpy.allclose(aval,
[[.4, .9, .1],
[5 * 3, 6, 7],
[.5, .3 * 2, .15]]), aval
assert np.allclose(aval,
[[.4, .9, .1],
[5 * 3, 6, 7],
[.5, .3 * 2, .15]]), aval
def test_inc_adv_subtensor_with_broadcasting(self):
if not config.cxx:
......@@ -1535,11 +1535,11 @@ class TestAdvancedSubtensor(unittest.TestCase):
[1, 2, 1],
[0, 1, 0],
2.1)
assert numpy.allclose(aval,
[[.4, .9, .1],
[5 + 2.1 * 2, 6, 7],
[.5, .3 + 2.1, .15]]), aval
assert numpy.allclose(gval, 3.0), gval
assert np.allclose(aval,
[[.4, .9, .1],
[5 + 2.1 * 2, 6, 7],
[.5, .3 + 2.1, .15]]), aval
assert np.allclose(gval, 3.0), gval
def test_inc_adv_subtensor1_with_broadcasting(self):
if not config.cxx:
......@@ -1557,11 +1557,11 @@ class TestAdvancedSubtensor(unittest.TestCase):
[.5, .3, .15]],
[0, 1, 0],
2.1)
assert numpy.allclose(aval,
[[.4 + 2.1 * 2, .9 + 2.1 * 2, .1 + 2.1 * 2],
[5 + 2.1, 6 + 2.1, 7 + 2.1],
[.5, .3, .15]]), aval
assert numpy.allclose(gval, 9.0), gval
assert np.allclose(aval,
[[.4 + 2.1 * 2, .9 + 2.1 * 2, .1 + 2.1 * 2],
[5 + 2.1, 6 + 2.1, 7 + 2.1],
[.5, .3, .15]]), aval
assert np.allclose(gval, 9.0), gval
def test_inc_adv_subtensor_with_index_broadcasting(self):
if not config.cxx:
......@@ -1578,14 +1578,14 @@ class TestAdvancedSubtensor(unittest.TestCase):
[0, 2, 0],
[[0, 1, 0],
[2, 2, 2]])
assert numpy.allclose(aval,
[[.4 + 2 * 2.1, .9, .1 + 2 * 2.1],
[5, 6, 7],
[.5, .3 + 2.1, .15 + 2.1]]), aval
assert np.allclose(aval,
[[.4 + 2 * 2.1, .9, .1 + 2 * 2.1],
[5, 6, 7],
[.5, .3 + 2.1, .15 + 2.1]]), aval
def test_advanced_indexing(self):
# tests advanced indexing in Theano for 2D and 3D tensors
rng = numpy.random.RandomState(utt.seed_rng())
rng = np.random.RandomState(utt.seed_rng())
a = rng.uniform(size=(3, 3))
b = theano.shared(a)
i = tensor.iscalar()
......@@ -1607,24 +1607,24 @@ class TestAdvancedSubtensor(unittest.TestCase):
# Reported in https://github.com/Theano/Theano/issues/5674
X = tensor.tensor3("X")
xx = numpy.zeros((3, 2, 2), config.floatX)
xx = np.zeros((3, 2, 2), config.floatX)
for i in range(3):
for j in range(2):
for k in range(2):
xx[i, j, k] = 100 * i + 10 * j + k
b_idx = numpy.zeros((2, 2), 'int32')
b_idx = np.zeros((2, 2), 'int32')
b_idx[0, 1] = 1
b_idx[1, 1] = 2
r_idx = numpy.arange(xx.shape[1])[:, numpy.newaxis]
c_idx = numpy.arange(xx.shape[2])[numpy.newaxis, :]
r_idx = np.arange(xx.shape[1])[:, np.newaxis]
c_idx = np.arange(xx.shape[2])[np.newaxis, :]
out = X[b_idx, r_idx, c_idx].eval({X: xx})
utt.assert_allclose(out, xx[b_idx, r_idx, c_idx])
def test_grad(self):
ones = numpy.ones((1, 3), dtype=self.dtype)
ones = np.ones((1, 3), dtype=self.dtype)
n = self.shared(ones * 5, broadcastable=(True, False))
idx = tensor.lvector()
idx2 = tensor.lvector()
......@@ -1632,17 +1632,17 @@ class TestAdvancedSubtensor(unittest.TestCase):
self.assertTrue(isinstance(t.owner.op, tensor.AdvancedSubtensor))
utt.verify_grad(lambda m: m[[1, 3], [2, 4]],
[numpy.random.rand(5, 5).astype(self.dtype)])
[np.random.rand(5, 5).astype(self.dtype)])
def fun(x, y):
return advanced_inc_subtensor(x, y, [1, 3], [2, 4])
utt.verify_grad(fun, [numpy.random.rand(5, 5).astype(self.dtype),
numpy.random.rand(2).astype(self.dtype)])
utt.verify_grad(fun, [np.random.rand(5, 5).astype(self.dtype),
np.random.rand(2).astype(self.dtype)])
def fun(x, y):
return advanced_set_subtensor(x, y, [1, 3], [2, 4])
utt.verify_grad(fun, [numpy.random.rand(5, 5).astype(self.dtype),
numpy.random.rand(2).astype(self.dtype)])
utt.verify_grad(fun, [np.random.rand(5, 5).astype(self.dtype),
np.random.rand(2).astype(self.dtype)])
class TestInferShape(utt.InferShapeTester):
......
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