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提交 ccf6deb0 authored 作者: ballasn's avatar ballasn 提交者： GitHub
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Merge pull request #5452 from bscellier/import_numpy

Update "import numpy" to "import numpy as np" (theano/compile directory)
上级 8c5cad8f 3b7aa75e
隐藏空白字符变更
内嵌 并排
正在显示 包含 369 行增加 和 372 行删除
theano/compile/builders.py
浏览文件 @ ccf6deb0
......@@ -59,11 +59,11 @@ class OpFromGraph(gof.Op):
.. code-block:: python
import numpy
import numpy as np
import theano
from theano import config, function, OpFromGraph, tensor
x, y, z = tensor.scalars('xyz')
s = theano.shared(numpy.random.rand(2, 2).astype(config.floatX))
s = theano.shared(np.random.rand(2, 2).astype(config.floatX))
e = x + y * z + s
op = OpFromGraph([x, y, z], [e])
# op behaves like a normal theano op
......
theano/compile/debugmode.py
浏览文件 @ ccf6deb0
......@@ -14,7 +14,7 @@ import six.moves.copyreg as copyreg
from itertools import chain, product as itertools_product
from theano.compat import izip
import numpy
import numpy as np
import theano
from theano import gof, config
......@@ -270,15 +270,15 @@ class BadOptimization(DebugModeError):
print(" New Value: ", str(self.new_r_val), file=sio)
try:
ov = numpy.asarray(self.old_r_val)
nv = numpy.asarray(self.new_r_val)
ov = np.asarray(self.old_r_val)
nv = np.asarray(self.new_r_val)
ssio = StringIO()
abs_diff = numpy.absolute(nv - ov)
print(" Max Abs Diff: ", numpy.max(abs_diff), file=ssio)
print(" Mean Abs Diff: ", numpy.mean(abs_diff), file=ssio)
print(" Median Abs Diff: ", numpy.median(abs_diff), file=ssio)
print(" Std Abs Diff: ", numpy.std(abs_diff), file=ssio)
arg_max_val = numpy.argmax(abs_diff)
abs_diff = np.absolute(nv - ov)
print(" Max Abs Diff: ", np.max(abs_diff), file=ssio)
print(" Mean Abs Diff: ", np.mean(abs_diff), file=ssio)
print(" Median Abs Diff: ", np.median(abs_diff), file=ssio)
print(" Std Abs Diff: ", np.std(abs_diff), file=ssio)
arg_max_val = np.argmax(abs_diff)
values_at_max = (nv.flatten()[arg_max_val],
ov.flatten()[arg_max_val])
print(" Value at Max Diff: ", values_at_max, file=ssio)
......@@ -286,13 +286,13 @@ class BadOptimization(DebugModeError):
# N.B. the maximum(..., 1e-8) protects against div by 0 when
# nv == ov == 0
reldiff = (abs_diff /
numpy.maaximum(numpy.absolute(nv) + numpy.absolute(ov),
1e-8))
print(" Max Rel Diff: ", numpy.max(reldiff), file=ssio)
print(" Mean Rel Diff: ", numpy.mean(reldiff), file=ssio)
print(" Median Rel Diff: ", numpy.median(reldiff), file=ssio)
print(" Std Rel Diff: ", numpy.std(reldiff), file=ssio)
arg_max_val = numpy.argmax(reldiff)
np.maximum(np.absolute(nv) + np.absolute(ov),
1e-8))
print(" Max Rel Diff: ", np.max(reldiff), file=ssio)
print(" Mean Rel Diff: ", np.mean(reldiff), file=ssio)
print(" Median Rel Diff: ", np.median(reldiff), file=ssio)
print(" Std Rel Diff: ", np.std(reldiff), file=ssio)
arg_max_val = np.argmax(reldiff)
values_at_max = (nv.flatten()[arg_max_val],
ov.flatten()[arg_max_val])
print(" Value at Max Diff: ", values_at_max, file=ssio)
......@@ -342,8 +342,8 @@ class BadDestroyMap(DebugModeError):
print(" repr (old val):", repr(self.old_val), file=sio)
print(" repr (new val):", repr(self.new_val), file=sio)
try:
npy_old_val = numpy.asarray(self.old_val)
npy_new_val = numpy.asarray(self.new_val)
npy_old_val = np.asarray(self.old_val)
npy_new_val = np.asarray(self.new_val)
print(" value dtype (new <space> old):", npy_new_val.dtype,
npy_old_val.dtype, file=sio)
print(" value shape (new <space> old):", npy_new_val.shape,
......@@ -356,13 +356,13 @@ class BadDestroyMap(DebugModeError):
print(" value min (new-old):", delta.min(), file=sio)
print(" value max (new-old):", delta.max(), file=sio)
print(" value argmin (new-old):",
numpy.unravel_index(delta.argmin(), npy_new_val.shape),
np.unravel_index(delta.argmin(), npy_new_val.shape),
file=sio)
print(" value argmax (new-old):",
numpy.unravel_index(delta.argmax(), npy_new_val.shape),
np.unravel_index(delta.argmax(), npy_new_val.shape),
file=sio)
print(" location of first 10 mismatches:",
numpy.transpose(numpy.nonzero(delta))[:10], file=sio)
np.transpose(np.nonzero(delta))[:10], file=sio)
print("", file=sio)
except Exception as e:
print("(Numpy-hints failed with: %s)" % str(e), file=sio)
......@@ -453,7 +453,7 @@ class InvalidValueError(DebugModeError):
v_dtype = v.dtype
v_min = v.min()
v_max = v.max()
v_isfinite = numpy.all(numpy.isfinite(v))
v_isfinite = np.all(np.isfinite(v))
except Exception:
pass
client_node = self.client_node
......@@ -1025,7 +1025,7 @@ def _lessbroken_deepcopy(a):
# this exists because copy.deepcopy on numpy arrays is broken
# This logic is also in link.py
from theano.gof.type import _cdata_type
if type(a) in (numpy.ndarray, numpy.memmap):
if type(a) in (np.ndarray, np.memmap):
rval = a.copy()
elif type(a) is _cdata_type:
# This is not copyable (and should be used for constant data).
......@@ -1034,7 +1034,7 @@ def _lessbroken_deepcopy(a):
rval = copy.deepcopy(a)
assert type(rval) == type(a), (type(rval), type(a))
if isinstance(rval, numpy.ndarray):
if isinstance(rval, np.ndarray):
assert rval.dtype == a.dtype
return rval
......@@ -1241,7 +1241,7 @@ def _get_preallocated_maps(node, thunk, prealloc_modes, def_val,
# There is no risk to overwrite inputs, since r does not work
# inplace.
if isinstance(r.type, (TensorType, CudaNdarrayType)):
reuse_outputs[r][...] = numpy.asarray(
reuse_outputs[r][...] = np.asarray(
def_val).astype(r.type.dtype)
if reuse_outputs:
......@@ -1259,7 +1259,7 @@ def _get_preallocated_maps(node, thunk, prealloc_modes, def_val,
new_buf = r.type.value_zeros(r_vals[r].shape)
# CudaNdarray don't have flags field
# assert new_buf.flags["C_CONTIGUOUS"]
new_buf[...] = numpy.asarray(def_val).astype(r.type.dtype)
new_buf[...] = np.asarray(def_val).astype(r.type.dtype)
c_cont_outputs[r] = new_buf
......@@ -1273,7 +1273,7 @@ def _get_preallocated_maps(node, thunk, prealloc_modes, def_val,
f_cont_outputs = {}
for r in considered_outputs:
if isinstance(r.type, (TensorType, CudaNdarrayType)):
new_buf = numpy.zeros(
new_buf = np.zeros(
shape=r_vals[r].shape,
dtype=r_vals[r].dtype,
order='F')
......@@ -1331,7 +1331,7 @@ def _get_preallocated_maps(node, thunk, prealloc_modes, def_val,
else:
buf_shape.append(s * 2)
new_buf = r.type.value_zeros(buf_shape)
new_buf[...] = numpy.asarray(def_val).astype(r.type.dtype)
new_buf[...] = np.asarray(def_val).astype(r.type.dtype)
init_strided[r] = new_buf
# The number of combinations is exponential in the number of
......@@ -1377,7 +1377,7 @@ def _get_preallocated_maps(node, thunk, prealloc_modes, def_val,
r_buf = r_buf[tuple(strides)][tuple(shapes)]
assert r_buf.shape == r_vals[r].shape
r_buf[...] = numpy.asarray(def_val).astype(r_buf.dtype)
r_buf[...] = np.asarray(def_val).astype(r_buf.dtype)
strided[r] = r_buf
if strided:
......@@ -1405,7 +1405,7 @@ def _get_preallocated_maps(node, thunk, prealloc_modes, def_val,
for s, sd in zip(r_vals[r].shape,
r_shape_diff)]
new_buf = r.type.value_zeros(out_shape)
new_buf[...] = numpy.asarray(
new_buf[...] = np.asarray(
def_val).astype(r.type.dtype)
wrong_size[r] = new_buf
......@@ -2261,7 +2261,7 @@ class _Linker(gof.link.LocalLinker):
# HACK TO LOOK LIKE A REAL DESTRUCTIVE ACTION
# TOOK PLACE
if ((type(dr_vals[r][0]) in
(numpy.ndarray, numpy.memmap)) and
(np.ndarray, np.memmap)) and
(dr_vals[r][0].dtype ==
storage_map[r][0].dtype) and
(dr_vals[r][0].shape ==
......
theano/compile/function.py
浏览文件 @ ccf6deb0
......@@ -13,7 +13,7 @@ from six import string_types
from theano.compile.io import In
from theano.compile.function_module import orig_function
from theano.compile.pfunc import pfunc
from numpy import any
import numpy as np
import warnings
from theano import compat
......@@ -286,7 +286,7 @@ def function(inputs, outputs=None, mode=None, updates=None, givens=None,
"input.")
# compute some features of the arguments:
uses_tuple = any([isinstance(i, (list, tuple)) for i in inputs])
uses_tuple = np.any([isinstance(i, (list, tuple)) for i in inputs])
uses_updates = bool(updates)
uses_givens = bool(givens)
......
theano/compile/function_module.py
浏览文件 @ ccf6deb0
......@@ -12,7 +12,7 @@ import six.moves.cPickle as pickle
from itertools import chain
import time
import warnings
import numpy
import numpy as np
import theano
from theano import config, gof
......@@ -837,9 +837,9 @@ class Function(object):
in args_share_memory[j]],
[self.input_storage[k].storage[0] for k
in args_share_memory[j]])
if numpy.any([(var.type is i_var.type and
var.type.may_share_memory(val, i_val))
for (var, val) in group_j]):
if np.any([(var.type is i_var.type and
var.type.may_share_memory(val, i_val))
for (var, val) in group_j]):
is_aliased = True
args_share_memory[j].append(i)
......@@ -1028,9 +1028,9 @@ def _pickle_Function(f):
all_data = input_storage + inputs_data
for i, d_i in enumerate(all_data):
for j, d_j in enumerate(all_data):
if ((i < j) and isinstance(d_i, numpy.ndarray) and
isinstance(d_j, numpy.ndarray)):
if numpy.may_share_memory(d_i, d_j):
if ((i < j) and isinstance(d_i, np.ndarray) and
isinstance(d_j, np.ndarray)):
if np.may_share_memory(d_i, d_j):
if f.pickle_aliased_memory_strategy == 'warn':
_logger.warning('aliased relationship between '
'Function arguments %s, %s '
......@@ -1050,7 +1050,7 @@ def _constructor_Function(maker, input_storage, inputs_data):
assert len(f.input_storage) == len(inputs_data)
for container, x in zip(f.input_storage, inputs_data):
assert (container.data is x) or \
(isinstance(x, numpy.ndarray) and (container.data == x).all()) or \
(isinstance(x, np.ndarray) and (container.data == x).all()) or \
(container.data == x)
return f
......
theano/compile/monitormode.py
浏览文件 @ ccf6deb0
from __future__ import absolute_import, print_function, division
# Note: this code was initially copied from the 'pyutools' package by its
# original author, and re-licensed under Theano's license.
import numpy
import numpy as np
import theano
from theano.compile.mode import Mode
......@@ -93,8 +93,8 @@ class MonitorMode(Mode):
def detect_nan(i, node, fn):
for output in fn.outputs:
if (not isinstance(output[0], numpy.random.RandomState) and
numpy.isnan(output[0]).any()):
if (not isinstance(output[0], np.random.RandomState) and
np.isnan(output[0]).any()):
print('*** NaN detected ***')
theano.printing.debugprint(node)
print('Inputs : %s' % [input[0] for input in fn.inputs])
......
theano/compile/ops.py
浏览文件 @ ccf6deb0
......@@ -17,7 +17,7 @@ from six import iteritems, integer_types
from six.moves import xrange
import numpy
import numpy as np
def register_view_op_c_code(type, code, version=()):
......@@ -338,7 +338,7 @@ class Shape_i(gof.Op):
def __init__(self, i):
# As i will be used in the hash and that ndarray are not hashable,
# we need to convert it to an int as it is hashable.
if isinstance(i, numpy.ndarray):
if isinstance(i, np.ndarray):
assert i.dtype in theano.tensor.integer_dtypes
assert i == int(i)
i = int(i)
......@@ -665,11 +665,11 @@ class Rebroadcast(gof.Op):
items = sorted(axis)
self.axis = OrderedDict(items)
for axis, broad in iteritems(self.axis):
if not isinstance(axis, (numpy.integer, integer_types)):
if not isinstance(axis, (np.integer, integer_types)):
raise TypeError("Rebroadcast needs integer axes. "
"Got {}".format(axis))
if not isinstance(broad, (numpy.bool_, bool)):
if not isinstance(broad, (np.bool_, bool)):
raise TypeError("Rebroadcast needs bool for new broadcast "
"pattern. Got {}".format(broad))
......@@ -835,8 +835,8 @@ class SpecifyShape(gof.Op):
x, shape = inp
out, = out_
assert x.ndim == shape.size
assert numpy.all(x.shape == shape), ("got shape", x.shape,
"expected", shape)
assert np.all(x.shape == shape), ("got shape", x.shape,
"expected", shape)
out[0] = x
def infer_shape(self, node, shapes):
......
theano/compile/profiling.py
浏览文件 @ ccf6deb0
......@@ -27,7 +27,7 @@ import sys
import time
from collections import defaultdict
import numpy
import numpy as np
import theano
from six import iteritems
......@@ -477,7 +477,7 @@ class ProfileStats(object):
hs += ['<#apply>']
es += [' %4d ']
upto_length = numpy.sum([len(x) for x in hs]) + len(hs)
upto_length = np.sum([len(x) for x in hs]) + len(hs)
maxlen = max(self.line_width - upto_length, 0)
hs += ['<Class name>']
es += ['%s']
......@@ -559,7 +559,7 @@ class ProfileStats(object):
hs += ['<#apply>']
es += [' %4d ']
upto_length = numpy.sum([len(x) for x in hs]) + len(hs)
upto_length = np.sum([len(x) for x in hs]) + len(hs)
maxlen = max(self.line_width - upto_length, 0)
hs += ['<Op name>']
es += ['%s']
......@@ -627,7 +627,7 @@ class ProfileStats(object):
if self.variable_shape:
hs += ['<Mflops>', '<Gflops/s>']
upto_length = numpy.sum([len(x) for x in hs]) + len(hs)
upto_length = np.sum([len(x) for x in hs]) + len(hs)
maxlen = max(self.line_width - upto_length, 0)
hs += ['<Apply name>']
es += ['%s']
......@@ -929,7 +929,7 @@ class ProfileStats(object):
node_list = list(node_list)
mem_count = 0
max_mem_count = 0
mem_bound = numpy.inf
mem_bound = np.inf
# This take only the inputs/outputs dependencies.
dependencies = fgraph.profile.dependencies
done_set = set([])
......
theano/compile/sharedvalue.py
浏览文件 @ ccf6deb0
......@@ -9,7 +9,7 @@ import copy
import logging
# Third-party imports
import numpy
import numpy as np
# Theano imports
from theano.gof import Container, Variable, generic, utils
......@@ -187,7 +187,7 @@ class SharedVariable(Variable):
# implemented at all, but with a more explicit error message to help
# Theano users figure out the root of the problem more easily.
value = self.get_value(borrow=True)
if isinstance(value, numpy.ndarray):
if isinstance(value, np.ndarray):
# Array probably had an unknown dtype.
msg = ("a Numpy array with dtype: '%s'. This data type is not "
"currently recognized by Theano tensors: please cast "
......
theano/compile/tests/test_builders.py
浏览文件 @ ccf6deb0
from __future__ import absolute_import, print_function, division
import numpy
import numpy as np
from theano import config, shared
......@@ -23,14 +23,14 @@ class T_OpFromGraph(unittest_tools.InferShapeTester):
f = op(x, y, z) - op(y, z, x)
fn = function([x, y, z], f)
xv = numpy.ones((2, 2), dtype=config.floatX)
yv = numpy.ones((2, 2), dtype=config.floatX) * 3
zv = numpy.ones((2, 2), dtype=config.floatX) * 5
xv = np.ones((2, 2), dtype=config.floatX)
yv = np.ones((2, 2), dtype=config.floatX) * 3
zv = np.ones((2, 2), dtype=config.floatX) * 5
# print function, function.__module__
# print fn.maker.fgraph.toposort()
fn(xv, yv, zv)
assert numpy.all(8.0 == fn(xv, yv, zv))
assert numpy.all(8.0 == fn(xv, yv, zv))
assert np.all(8.0 == fn(xv, yv, zv))
assert np.all(8.0 == fn(xv, yv, zv))
def test_size_changes(self):
x, y, z = T.matrices('xyz')
......@@ -38,15 +38,15 @@ class T_OpFromGraph(unittest_tools.InferShapeTester):
op = OpFromGraph([x, y], [e])
f = op(x, op(y, z))
fn = function([x, y, z], f)
xv = numpy.ones((2, 3), dtype=config.floatX)
yv = numpy.ones((3, 4), dtype=config.floatX) * 3
zv = numpy.ones((4, 5), dtype=config.floatX) * 5
xv = np.ones((2, 3), dtype=config.floatX)
yv = np.ones((3, 4), dtype=config.floatX) * 3
zv = np.ones((4, 5), dtype=config.floatX) * 5
res = fn(xv, yv, zv)
assert res.shape == (2, 5)
assert numpy.all(180.0 == res)
assert np.all(180.0 == res)
res = fn(xv, yv, zv)
assert res.shape == (2, 5)
assert numpy.all(180.0 == res)
assert np.all(180.0 == res)
def test_grad(self):
x, y, z = T.matrices('xyz')
......@@ -55,10 +55,10 @@ class T_OpFromGraph(unittest_tools.InferShapeTester):
f = op(x, y, z)
f = f - T.grad(T.sum(f), y)
fn = function([x, y, z], f)
xv = numpy.ones((2, 2), dtype=config.floatX)
yv = numpy.ones((2, 2), dtype=config.floatX) * 3
zv = numpy.ones((2, 2), dtype=config.floatX) * 5
assert numpy.all(11.0 == fn(xv, yv, zv))
xv = np.ones((2, 2), dtype=config.floatX)
yv = np.ones((2, 2), dtype=config.floatX) * 3
zv = np.ones((2, 2), dtype=config.floatX) * 5
assert np.all(11.0 == fn(xv, yv, zv))
def test_grad_grad(self):
x, y, z = T.matrices('xyz')
......@@ -68,47 +68,47 @@ class T_OpFromGraph(unittest_tools.InferShapeTester):
f = f - T.grad(T.sum(f), y)
f = f - T.grad(T.sum(f), y)
fn = function([x, y, z], f)
xv = numpy.ones((2, 2), dtype=config.floatX)
yv = numpy.ones((2, 2), dtype=config.floatX) * 3
zv = numpy.ones((2, 2), dtype=config.floatX) * 5
assert numpy.allclose(6.0, fn(xv, yv, zv))
xv = np.ones((2, 2), dtype=config.floatX)
yv = np.ones((2, 2), dtype=config.floatX) * 3
zv = np.ones((2, 2), dtype=config.floatX) * 5
assert np.allclose(6.0, fn(xv, yv, zv))
def test_shared(self):
x, y, z = T.matrices('xyz')
s = shared(numpy.random.rand(2, 2).astype(config.floatX))
s = shared(np.random.rand(2, 2).astype(config.floatX))
e = x + y * z + s
op = OpFromGraph([x, y, z], [e])
# (1+3*5=array of 16) - (3+1*5=array of 8)
f = op(x, y, z) - op(y, z, x)
fn = function([x, y, z], f)
xv = numpy.ones((2, 2), dtype=config.floatX)
yv = numpy.ones((2, 2), dtype=config.floatX) * 3
zv = numpy.ones((2, 2), dtype=config.floatX) * 5
xv = np.ones((2, 2), dtype=config.floatX)
yv = np.ones((2, 2), dtype=config.floatX) * 3
zv = np.ones((2, 2), dtype=config.floatX) * 5
# print function, function.__module__
# print fn.maker.fgraph.toposort()
assert numpy.allclose(8.0, fn(xv, yv, zv))
assert numpy.allclose(8.0, fn(xv, yv, zv))
assert np.allclose(8.0, fn(xv, yv, zv))
assert np.allclose(8.0, fn(xv, yv, zv))
def test_shared_grad(self):
x, y, z = T.matrices('xyz')
s = shared(numpy.random.rand(2, 2).astype(config.floatX))
s = shared(np.random.rand(2, 2).astype(config.floatX))
e = x + y * z + s
op = OpFromGraph([x, y, z], [e])
f = op(x, y, z)
f = f - T.grad(T.sum(f), y)
fn = function([x, y, z], f)
xv = numpy.ones((2, 2), dtype=config.floatX)
yv = numpy.ones((2, 2), dtype=config.floatX) * 3
zv = numpy.ones((2, 2), dtype=config.floatX) * 5
assert numpy.allclose(11.0 + s.get_value(), fn(xv, yv, zv))
xv = np.ones((2, 2), dtype=config.floatX)
yv = np.ones((2, 2), dtype=config.floatX) * 3
zv = np.ones((2, 2), dtype=config.floatX) * 5
assert np.allclose(11.0 + s.get_value(), fn(xv, yv, zv))
# grad again the shared variable
f = op(x, y, z)
f = f - T.grad(T.sum(f), s)
fn = function([x, y, z], f)
assert numpy.allclose(15.0 + s.get_value(),
fn(xv, yv, zv))
assert np.allclose(15.0 + s.get_value(),
fn(xv, yv, zv))
def test_connection_pattern(self):
# Basic case
......@@ -163,6 +163,6 @@ class T_OpFromGraph(unittest_tools.InferShapeTester):
p = T.matrix('p')
self._compile_and_check([q, p],
op_graph(q, p),
[numpy.ones([3, 4], dtype=config.floatX),
numpy.ones([3, 4], dtype=config.floatX)],
[np.ones([3, 4], dtype=config.floatX),
np.ones([3, 4], dtype=config.floatX)],
OpFromGraph)
theano/compile/tests/test_debugmode.py
浏览文件 @ ccf6deb0
......@@ -2,7 +2,7 @@ from __future__ import absolute_import, print_function, division
from nose.plugins.skip import SkipTest
import unittest
import numpy
import numpy as np
from theano import config
from theano import gof
......@@ -316,7 +316,7 @@ def test_just_c_code():
x = theano.tensor.dvector()
f = theano.function([x], wb2(x),
mode=debugmode.DebugMode(check_py_code=False))
assert numpy.all(f([1, 2]) == [2, 4])
assert np.all(f([1, 2]) == [2, 4])
def test_baddestroymap():
......@@ -349,7 +349,7 @@ def test_baddestroymap_c():
f = theano.function([x], wb2i(x),
mode=debugmode.DebugMode(check_py_code=False))
try:
assert numpy.all(f([1, 2]) == [2, 4])
assert np.all(f([1, 2]) == [2, 4])
assert False # failed to raise error
except debugmode.BadDestroyMap:
pass
......@@ -445,8 +445,8 @@ class Test_ViewMap(unittest.TestCase):
r0, r1 = f([1, 2, 3, 4], [5, 6, 7, 8])
assert numpy.all(r0 == [1, 2, 3, 4])
assert numpy.all(r1 == [2, 3, 4])
assert np.all(r0 == [1, 2, 3, 4])
assert np.all(r1 == [2, 3, 4])
def test_aliased_outputs_ok_output(self):
# here aliased outputs is ok because they are both outputs of the
......@@ -470,8 +470,8 @@ class Test_ViewMap(unittest.TestCase):
r0, r1 = f([1, 2, 3, 4], [5, 6, 7, 8])
assert numpy.all(r0 == [2, 4, 6, 8])
assert numpy.all(r1 == [4, 6, 8])
assert np.all(r0 == [2, 4, 6, 8])
assert np.all(r1 == [4, 6, 8])
def test_aliased_outputs_ok_shadow(self):
# here the alias between outputs is ok because one of them is not used
......@@ -496,7 +496,7 @@ class Test_ViewMap(unittest.TestCase):
r0 = f([1, 2, 3, 4], [5, 6, 7, 8])
assert numpy.all(r0 == [2, 4, 6, 8])
assert np.all(r0 == [2, 4, 6, 8])
def test_aliased_outputs_bad(self):
# here the alias between outputs is not ok because destroying one
......@@ -555,31 +555,31 @@ class Test_check_isfinite(unittest.TestCase):
g = theano.function([x], theano.tensor.log(x), mode='DEBUG_MODE')
# this should work
f(numpy.log([3, 4, 5]).astype(config.floatX))
f(np.log([3, 4, 5]).astype(config.floatX))
# if TensorType.filter_checks_isfinite were true, these would raise
# ValueError
# if not, DebugMode will check internally, and raise InvalidValueError
# passing an invalid value as an input should trigger ValueError
self.assertRaises(debugmode.InvalidValueError, f,
numpy.log([3, -4, 5]).astype(config.floatX))
np.log([3, -4, 5]).astype(config.floatX))
self.assertRaises(debugmode.InvalidValueError, f,
(numpy.asarray([0, 1.0, 0]) / 0).astype(config.floatX))
(np.asarray([0, 1.0, 0]) / 0).astype(config.floatX))
self.assertRaises(debugmode.InvalidValueError, f,
(numpy.asarray([1.0, 1.0, 1.0]) / 0).astype(config.floatX))
(np.asarray([1.0, 1.0, 1.0]) / 0).astype(config.floatX))
# generating an invalid value internally should trigger
# InvalidValueError
self.assertRaises(debugmode.InvalidValueError, g,
numpy.asarray([3, -4, 5], dtype=config.floatX))
np.asarray([3, -4, 5], dtype=config.floatX))
# this should disable the exception
theano.tensor.TensorType.filter_checks_isfinite = False
theano.compile.mode.predefined_modes[
'DEBUG_MODE'].check_isfinite = False
# insert several Inf
f(numpy.asarray(numpy.asarray([1.0, 1.0, 1.0]) / 0,
dtype=config.floatX))
f(np.asarray(np.asarray([1.0, 1.0, 1.0]) / 0,
dtype=config.floatX))
def test_check_isfinite_disabled(self):
x = theano.tensor.dvector()
......@@ -587,10 +587,10 @@ class Test_check_isfinite(unittest.TestCase):
mode=debugmode.DebugMode(check_isfinite=False))
# nan should go through
f(numpy.log([3, -4, 5]))
f(np.log([3, -4, 5]))
# inf should go through
infs = numpy.asarray([1.0, 1., 1.]) / 0
infs = np.asarray([1.0, 1., 1.]) / 0
# print infs
f(infs)
return
......@@ -721,14 +721,14 @@ class VecAsRowAndCol(gof.Op):
class Test_preallocated_output(unittest.TestCase):
def setUp(self):
self.rng = numpy.random.RandomState(seed=utt.fetch_seed())
self.rng = np.random.RandomState(seed=utt.fetch_seed())
def test_f_contiguous(self):
a = theano.tensor.fmatrix('a')
b = theano.tensor.fmatrix('b')
z = BrokenCImplementationAdd()(a, b)
# In this test, we do not want z to be an output of the graph.
out = theano.tensor.dot(z, numpy.eye(7))
out = theano.tensor.dot(z, np.eye(7))
a_val = self.rng.randn(7, 7).astype('float32')
b_val = self.rng.randn(7, 7).astype('float32')
......
theano/compile/tests/test_function.py
浏览文件 @ ccf6deb0
......@@ -5,7 +5,7 @@ import shutil
import tempfile
import unittest
import numpy
import numpy as np
import theano
from theano.compile.io import In
......@@ -27,7 +27,7 @@ def test_function_dump():
fct2 = theano.function(**l)
x = [1, 2, 3]
assert numpy.allclose(fct1(x), fct2(x))
assert np.allclose(fct1(x), fct2(x))
class TestFunctionIn(unittest.TestCase):
......@@ -40,14 +40,14 @@ class TestFunctionIn(unittest.TestCase):
f = theano.function([In(a, strict=False)], out)
# works, rand generates float64 by default
f(numpy.random.rand(8))
f(np.random.rand(8))
# works, casting is allowed
f(numpy.array([1, 2, 3, 4], dtype='int32'))
f(np.array([1, 2, 3, 4], dtype='int32'))
f = theano.function([In(a, strict=True)], out)
try:
# fails, f expects float64
f(numpy.array([1, 2, 3, 4], dtype='int32'))
f(np.array([1, 2, 3, 4], dtype='int32'))
except TypeError:
pass
......@@ -70,17 +70,17 @@ class TestFunctionIn(unittest.TestCase):
# using mutable=True will let f change the value in aval
f = theano.function([In(a, mutable=True)], a_out, mode='FAST_RUN')
aval = numpy.random.rand(10)
aval = np.random.rand(10)
aval2 = aval.copy()
assert numpy.all(f(aval) == (aval2 * 2))
assert not numpy.all(aval == aval2)
assert np.all(f(aval) == (aval2 * 2))
assert not np.all(aval == aval2)
# using mutable=False should leave the input untouched
f = theano.function([In(a, mutable=False)], a_out, mode='FAST_RUN')
aval = numpy.random.rand(10)
aval = np.random.rand(10)
aval2 = aval.copy()
assert numpy.all(f(aval) == (aval2 * 2))
assert numpy.all(aval == aval2)
assert np.all(f(aval) == (aval2 * 2))
assert np.all(aval == aval2)
def test_in_update(self):
a = theano.tensor.dscalar('a')
......@@ -115,7 +115,7 @@ class TestFunctionIn(unittest.TestCase):
# changes occur at the same time and one doesn't overwrite the other.
for i in range(5):
f()
assert numpy.allclose(shared_var.get_value(), i % 2)
assert np.allclose(shared_var.get_value(), i % 2)
def test_in_allow_downcast_int(self):
a = theano.tensor.wvector('a') # int16
......@@ -128,16 +128,16 @@ class TestFunctionIn(unittest.TestCase):
# Both values are in range. Since they're not ndarrays (but lists),
# they will be converted, and their value checked.
assert numpy.all(f([3], [6], 1) == 10)
assert np.all(f([3], [6], 1) == 10)
# Values are in range, but a dtype too large has explicitly been given
# For performance reasons, no check of the data is explicitly performed
# (It might be OK to change this in the future.)
self.assertRaises(TypeError, f, [3], numpy.array([6], dtype='int16'),
self.assertRaises(TypeError, f, [3], np.array([6], dtype='int16'),
1)
# Value too big for a, silently ignored
assert numpy.all(f([2 ** 20], numpy.ones(1, dtype='int8'), 1) == 2)
assert np.all(f([2 ** 20], np.ones(1, dtype='int8'), 1) == 2)
# Value too big for b, raises TypeError
self.assertRaises(TypeError, f, [3], [312], 1)
......@@ -156,17 +156,17 @@ class TestFunctionIn(unittest.TestCase):
(a + b + c))
# If the values can be accurately represented, everything is OK
assert numpy.all(f(0, 0, 0) == 0)
assert np.all(f(0, 0, 0) == 0)
# If allow_downcast is True, idem
assert numpy.allclose(f(0.1, 0, 0), 0.1)
assert np.allclose(f(0.1, 0, 0), 0.1)
# If allow_downcast is False, nope
self.assertRaises(TypeError, f, 0, 0.1, 0)
# If allow_downcast is None, it should work iff floatX=float32
if theano.config.floatX == 'float32':
assert numpy.allclose(f(0, 0, 0.1), 0.1)
assert np.allclose(f(0, 0, 0.1), 0.1)
else:
self.assertRaises(TypeError, f, 0, 0, 0.1)
......@@ -182,10 +182,10 @@ class TestFunctionIn(unittest.TestCase):
# If the values can be accurately represented, everything is OK
z = [0]
assert numpy.all(f(z, z, z) == 0)
assert np.all(f(z, z, z) == 0)
# If allow_downcast is True, idem
assert numpy.allclose(f([0.1], z, z), 0.1)
assert np.allclose(f([0.1], z, z), 0.1)
# If allow_downcast is False, nope
self.assertRaises(TypeError, f, z, [0.1], z)
......
theano/compile/tests/test_function_module.py
浏览文件 @ ccf6deb0
from __future__ import absolute_import, print_function, division
import copy
import six.moves.cPickle as pickle
import numpy
import numpy as np
import unittest
......@@ -18,8 +18,6 @@ from theano import tensor
from theano import tensor as T
import theano
import numpy as N
def PatternOptimizer(p1, p2, ign=True):
return gof.OpKeyOptimizer(gof.PatternSub(p1, p2), ignore_newtrees=ign)
......@@ -281,7 +279,7 @@ class T_function(unittest.TestCase):
def test_swap_SharedVariable(self):
i = T.iscalar()
x_list = theano.shared(value=numpy.random.rand(10).astype(config.floatX))
x_list = theano.shared(value=np.random.rand(10).astype(config.floatX))
x = T.scalar('x')
# SharedVariable for tests, one of them has update
......@@ -343,11 +341,11 @@ class T_function(unittest.TestCase):
A special testcase for logistic_sgd.py in Deep Learning Tutorial
This test assert that SharedVariable in different function have same storage
"""
train_x = theano.shared(value=numpy.random.rand(10, 10).astype(config.floatX))
test_x = theano.shared(value=numpy.random.rand(10, 10).astype(config.floatX))
train_x = theano.shared(value=np.random.rand(10, 10).astype(config.floatX))
test_x = theano.shared(value=np.random.rand(10, 10).astype(config.floatX))
train_y = theano.shared(value=numpy.random.rand(10, 1).astype(config.floatX))
test_y = theano.shared(value=numpy.random.rand(10, 1).astype(config.floatX))
train_y = theano.shared(value=np.random.rand(10, 1).astype(config.floatX))
test_y = theano.shared(value=np.random.rand(10, 1).astype(config.floatX))
i = T.iscalar('index')
x = T.vector('x')
......@@ -500,42 +498,42 @@ class T_function(unittest.TestCase):
when borrow=True is implemented.
"""
a = T.dmatrix()
aval = numpy.random.rand(3, 3)
aval = np.random.rand(3, 3)
# when borrow=False, test that a destroy map cannot alias output to input
f = theano.function([In(a, borrow=False)], Out(a + 1, borrow=True))
assert numpy.all(f(aval) == aval + 1)
assert not numpy.may_share_memory(aval, f(aval))
assert np.all(f(aval) == aval + 1)
assert not np.may_share_memory(aval, f(aval))
# when borrow=False, test that a viewmap cannot alias output to input
f = theano.function([In(a, borrow=False)], Out(a[0, :], borrow=True))
assert numpy.all(f(aval) == aval[0, :])
assert not numpy.may_share_memory(aval, f(aval))
assert np.all(f(aval) == aval[0, :])
assert not np.may_share_memory(aval, f(aval))
def test_borrow_output(self):
a = T.dmatrix()
f = function([a], Out(a, borrow=False))
o = N.ones((3, 3))
o = np.ones((3, 3))
assert o is not f(o) # function no longer permits aliasing outputs to inputs
f = function([a], Out(a * 4, borrow=False))
o = N.ones((3, 3))
o = np.ones((3, 3))
four = f(o)
assert numpy.all(four == 4)
assert np.all(four == 4)
f(o + .1) # should not clobber the memory used to store four
assert numpy.all(four == 4)
assert np.all(four == 4)
f = function([a], Out(a * 4, borrow=True), mode=theano.Mode('c|py_nogc', 'fast_run'))
o = N.ones((3, 3))
o = np.ones((3, 3))
four = f(o)
assert numpy.all(four == 4)
assert np.all(four == 4)
f(o + .1) # should clobber the memory used to store four
if theano.config.cxx:
assert not numpy.all(four == 4)
assert not np.all(four == 4)
else:
# The Elemwise.perform method don't reuse memory
# as some numpy version don't support that correctly.
assert numpy.all(four == 4)
assert np.all(four == 4)
def test_disconnected_input(self):
a = T.scalar('a')
......@@ -767,7 +765,7 @@ class T_picklefunction(unittest.TestCase):
assert f2.container[s].storage is f1.container[s].storage
# now put in a function with non-scalar
v_value = numpy.asarray([2, 3, 4.], dtype=config.floatX)
v_value = np.asarray([2, 3, 4.], dtype=config.floatX)
f3 = function([x, In(v, value=v_value)], x + v)
list_of_things.append(f3)
......@@ -814,13 +812,13 @@ class T_picklefunction(unittest.TestCase):
assert nl[5](3) == ol[5](3)
assert nl[4].value[nl[0]] == 6
assert numpy.all(nl[6][nl[2]] == numpy.asarray([2, 3., 4]))
assert np.all(nl[6][nl[2]] == np.asarray([2, 3., 4]))
def test_broken_pickle_with_shared(self):
saves = []
def pers_save(obj):
if isinstance(obj, numpy.ndarray):
if isinstance(obj, np.ndarray):
saves.append(obj)
return len(saves) - 1
else:
......@@ -829,7 +827,7 @@ class T_picklefunction(unittest.TestCase):
def pers_load(id):
return saves[id]
b = numpy.random.rand(5, 4)
b = np.random.rand(5, 4)
x = theano.tensor.matrix()
y = theano.shared(b)
......
theano/compile/tests/test_misc.py
浏览文件 @ ccf6deb0
from __future__ import absolute_import, print_function, division
import numpy
import numpy as np
import unittest
from theano.compile.pfunc import pfunc
......@@ -20,8 +20,8 @@ class NNet(object):
self.input = input
self.target = target
self.lr = shared(lr, 'learning_rate')
self.w1 = shared(numpy.zeros((n_hidden, n_input)), 'w1')
self.w2 = shared(numpy.zeros((n_output, n_hidden)), 'w2')
self.w1 = shared(np.zeros((n_hidden, n_input)), 'w1')
self.w2 = shared(np.zeros((n_output, n_hidden)), 'w2')
# print self.lr.type
self.hidden = sigmoid(tensor.dot(self.w1, self.input))
......@@ -45,7 +45,7 @@ class NNet(object):
class TestNnet(unittest.TestCase):
def test_nnet(self):
rng = numpy.random.RandomState(1827)
rng = np.random.RandomState(1827)
data = rng.rand(10, 4)
nnet = NNet(n_input=3, n_hidden=10)
for epoch in range(3):
......@@ -60,4 +60,4 @@ class TestNnet(unittest.TestCase):
self.assertTrue(abs(mean_cost - 0.20588975452) < 1e-6)
# Just call functions to make sure they do not crash.
nnet.compute_output(input)
nnet.output_from_hidden(numpy.ones(10))
nnet.output_from_hidden(np.ones(10))
theano/compile/tests/test_monitormode.py
浏览文件 @ ccf6deb0
from __future__ import absolute_import, print_function, division
import numpy
import numpy as np
import theano
......@@ -12,7 +12,7 @@ def test_detect_nan():
def detect_nan(i, node, fn):
for output in fn.outputs:
if numpy.isnan(output[0]).any():
if np.isnan(output[0]).any():
print('*** NaN detected ***')
theano.printing.debugprint(node)
print('Inputs : %s' % [input[0] for input in fn.inputs])
......@@ -36,7 +36,7 @@ def test_optimizer():
def detect_nan(i, node, fn):
for output in fn.outputs:
if numpy.isnan(output[0]).any():
if np.isnan(output[0]).any():
print('*** NaN detected ***')
theano.printing.debugprint(node)
print('Inputs : %s' % [input[0] for input in fn.inputs])
......@@ -65,7 +65,7 @@ def test_not_inplace():
def detect_nan(i, node, fn):
for output in fn.outputs:
if numpy.isnan(output[0]).any():
if np.isnan(output[0]).any():
print('*** NaN detected ***')
theano.printing.debugprint(node)
print('Inputs : %s' % [input[0] for input in fn.inputs])
......
theano/compile/tests/test_nanguardmode.py
浏览文件 @ ccf6deb0
......@@ -6,7 +6,7 @@ from __future__ import absolute_import, print_function, division
import logging
from nose.tools import assert_raises
import numpy
import numpy as np
from theano.compile.nanguardmode import NanGuardMode
import theano
......@@ -18,20 +18,20 @@ def test_NanGuardMode():
# intentionally. A working implementation should be able to capture all
# the abnormalties.
x = T.matrix()
w = theano.shared(numpy.random.randn(5, 7).astype(theano.config.floatX))
w = theano.shared(np.random.randn(5, 7).astype(theano.config.floatX))
y = T.dot(x, w)
fun = theano.function(
[x], y,
mode=NanGuardMode(nan_is_error=True, inf_is_error=True)
)
a = numpy.random.randn(3, 5).astype(theano.config.floatX)
infa = numpy.tile(
(numpy.asarray(100.) ** 1000000).astype(theano.config.floatX), (3, 5))
nana = numpy.tile(
numpy.asarray(numpy.nan).astype(theano.config.floatX), (3, 5))
biga = numpy.tile(
numpy.asarray(1e20).astype(theano.config.floatX), (3, 5))
a = np.random.randn(3, 5).astype(theano.config.floatX)
infa = np.tile(
(np.asarray(100.) ** 1000000).astype(theano.config.floatX), (3, 5))
nana = np.tile(
np.asarray(np.nan).astype(theano.config.floatX), (3, 5))
biga = np.tile(
np.asarray(1e20).astype(theano.config.floatX), (3, 5))
fun(a) # normal values
......@@ -46,14 +46,14 @@ def test_NanGuardMode():
_logger.propagate = True
# slices
a = numpy.random.randn(3, 4, 5).astype(theano.config.floatX)
infa = numpy.tile(
(numpy.asarray(100.) ** 1000000).astype(theano.config.floatX),
a = np.random.randn(3, 4, 5).astype(theano.config.floatX)
infa = np.tile(
(np.asarray(100.) ** 1000000).astype(theano.config.floatX),
(3, 4, 5))
nana = numpy.tile(
numpy.asarray(numpy.nan).astype(theano.config.floatX), (3, 4, 5))
biga = numpy.tile(
numpy.asarray(1e20).astype(theano.config.floatX), (3, 4, 5))
nana = np.tile(
np.asarray(np.nan).astype(theano.config.floatX), (3, 4, 5))
biga = np.tile(
np.asarray(1e20).astype(theano.config.floatX), (3, 4, 5))
x = T.tensor3()
y = x[:, T.arange(2), T.arange(2)]
......
theano/compile/tests/test_ops.py
浏览文件 @ ccf6deb0
......@@ -9,7 +9,6 @@ from theano.tests import unittest_tools as utt
from theano import function
import theano
from theano.tensor import dmatrix, dvector
from numpy import allclose
from theano.compile import as_op
import pickle
......@@ -34,7 +33,7 @@ class OpDecoratorTests(utt.InferShapeTester):
r = fn([[1.5, 5], [2, 2]])
r0 = np.array([1.5, 7.5, 15., 30.])
assert allclose(r, r0), (r, r0)
assert np.allclose(r, r0), (r, r0)
def test_2arg(self):
x = dmatrix('x')
......@@ -50,7 +49,7 @@ class OpDecoratorTests(utt.InferShapeTester):
r = fn([[1.5, 5], [2, 2]], [1, 100, 2, 200])
r0 = np.array([2.5, 107.5, 17., 230.])
assert allclose(r, r0), (r, r0)
assert np.allclose(r, r0), (r, r0)
def test_infer_shape(self):
x = dmatrix('x')
......
theano/compile/tests/test_pfunc.py
浏览文件 @ ccf6deb0
......@@ -2,7 +2,7 @@ from __future__ import absolute_import, print_function, division
import unittest
from nose.plugins.skip import SkipTest
import numpy
import numpy as np
import theano
from theano.tensor import dmatrix, iscalar, lscalar, dmatrices
......@@ -50,7 +50,7 @@ class Test_pfunc(unittest.TestCase):
def test_shared(self):
# CHECK: two functions (f1 and f2) can share w
w = shared(numpy.random.rand(2, 2), 'w')
w = shared(np.random.rand(2, 2), 'w')
wval = w.get_value(borrow=False)
x = dmatrix()
......@@ -58,24 +58,24 @@ class Test_pfunc(unittest.TestCase):
out2 = w * x
f1 = pfunc([x], [out1])
f2 = pfunc([x], [out2])
xval = numpy.random.rand(2, 2)
assert numpy.all(f1(xval) == xval + wval)
assert numpy.all(f2(xval) == xval * wval)
xval = np.random.rand(2, 2)
assert np.all(f1(xval) == xval + wval)
assert np.all(f2(xval) == xval * wval)
# CHECK: updating a shared value
f3 = pfunc([x], out1, updates=[(w, (w - 1))])
# f3 changes the value of w
assert numpy.all(f3(xval) == xval + wval)
assert np.all(f3(xval) == xval + wval)
# this same value is read by f1
assert numpy.all(f1(xval) == xval + (wval - 1))
assert np.all(f1(xval) == xval + (wval - 1))
w.set_value(w.get_value(borrow=True) * 10, borrow=True)
# this same value is read by f1
assert numpy.all(f1(xval) == xval + w.get_value(borrow=True))
assert np.all(f1(xval) == xval + w.get_value(borrow=True))
def test_no_shared_as_input(self):
"""Test that shared variables cannot be used as function inputs."""
w_init = numpy.random.rand(2, 2)
w_init = np.random.rand(2, 2)
w = shared(w_init.copy(), 'w')
try:
pfunc([w], theano.tensor.sum(w * w))
......@@ -89,16 +89,16 @@ class Test_pfunc(unittest.TestCase):
# Ensure it is possible to (implicitly) use a shared variable in a
# function, as a 'state' that can be updated at will.
rng = numpy.random.RandomState(1827)
rng = np.random.RandomState(1827)
w_init = rng.rand(5)
w = shared(w_init.copy(), 'w')
reg = theano.tensor.sum(w * w)
f = pfunc([], reg)
assert f() == numpy.sum(w_init * w_init)
assert f() == np.sum(w_init * w_init)
# Change the value of w and ensure the output changes accordingly.
w.set_value(w.get_value(borrow=True) + 1.0, borrow=True)
assert f() == numpy.sum((w_init + 1) ** 2)
assert f() == np.sum((w_init + 1) ** 2)
def test_default_scalar_container(self):
# Similar in spirit to test_default_container, but updating a scalar
......@@ -117,14 +117,14 @@ class Test_pfunc(unittest.TestCase):
f = pfunc([In(a, strict=False)], [out])
# works, rand generates float64 by default
f(numpy.random.rand(8))
f(np.random.rand(8))
# works, casting is allowed
f(numpy.array([1, 2, 3, 4], dtype='int32'))
f(np.array([1, 2, 3, 4], dtype='int32'))
f = pfunc([In(a, strict=True)], [out])
try:
# fails, f expects float64
f(numpy.array([1, 2, 3, 4], dtype='int32'))
f(np.array([1, 2, 3, 4], dtype='int32'))
except TypeError:
pass
......@@ -134,20 +134,20 @@ class Test_pfunc(unittest.TestCase):
# using mutable=True will let fip change the value in aval
fip = pfunc([In(a, mutable=True)], [a_out], mode='FAST_RUN')
aval = numpy.random.rand(10)
aval = np.random.rand(10)
aval2 = aval.copy()
assert numpy.all(fip(aval) == (aval2 * 2))
assert not numpy.all(aval == aval2)
assert np.all(fip(aval) == (aval2 * 2))
assert not np.all(aval == aval2)
# using mutable=False should leave the input untouched
f = pfunc([In(a, mutable=False)], [a_out], mode='FAST_RUN')
aval = numpy.random.rand(10)
aval = np.random.rand(10)
aval2 = aval.copy()
assert numpy.all(f(aval) == (aval2 * 2))
assert numpy.all(aval == aval2)
assert np.all(f(aval) == (aval2 * 2))
assert np.all(aval == aval2)
def test_shared_mutable(self):
bval = numpy.arange(5)
bval = np.arange(5)
b = shared(bval)
b_out = b * 2
......@@ -158,26 +158,26 @@ class Test_pfunc(unittest.TestCase):
# by default, shared are not mutable unless doing an explicit update
f = pfunc([], [b_out], mode='FAST_RUN')
assert (f() == numpy.arange(5) * 2).all()
assert numpy.all(b.get_value(borrow=True) == numpy.arange(5))
assert (f() == np.arange(5) * 2).all()
assert np.all(b.get_value(borrow=True) == np.arange(5))
# using updates, b is now a mutable parameter
f = pfunc([], [b_out], updates=[(b, b_out)], mode='FAST_RUN')
assert (f() == (numpy.arange(5) * 2)).all()
assert (f() == (np.arange(5) * 2)).all()
# because of the update
assert (b.get_value(borrow=True) == (numpy.arange(5) * 2)).all()
assert (bval == (numpy.arange(5) * 2)).all() # because of mutable=True
assert (b.get_value(borrow=True) == (np.arange(5) * 2)).all()
assert (bval == (np.arange(5) * 2)).all() # because of mutable=True
# do not depend on updates being in-place though!
bval = numpy.arange(5)
bval = np.arange(5)
b.set_value(bval, borrow=True)
bval = data_of(b)
f = pfunc([], [b_out], updates=[(b, (b_out + 3))], mode='FAST_RUN')
assert (f() == (numpy.arange(5) * 2)).all()
assert (f() == (np.arange(5) * 2)).all()
# because of the update
assert (b.get_value(borrow=True) == ((numpy.arange(5) * 2) + 3)).all()
assert (b.get_value(borrow=True) == ((np.arange(5) * 2) + 3)).all()
# bval got modified to something...
assert not (bval == numpy.arange(5)).all()
assert not (bval == np.arange(5)).all()
# ... but not to b.value !
assert not (bval == b.get_value(borrow=True)).all()
......@@ -192,16 +192,16 @@ class Test_pfunc(unittest.TestCase):
# Both values are in range. Since they're not ndarrays (but lists),
# they will be converted, and their value checked.
assert numpy.all(f([3], [6], 1) == 10)
assert np.all(f([3], [6], 1) == 10)
# Values are in range, but a dtype too large has explicitly been given
# For performance reasons, no check of the data is explicitly performed
# (It might be OK to change this in the future.)
self.assertRaises(TypeError, f,
[3], numpy.array([6], dtype='int16'), 1)
[3], np.array([6], dtype='int16'), 1)
# Value too big for a, silently ignored
assert numpy.all(f([2 ** 20], numpy.ones(1, dtype='int8'), 1) == 2)
assert np.all(f([2 ** 20], np.ones(1, dtype='int8'), 1) == 2)
# Value too big for b, raises TypeError
self.assertRaises(TypeError, f, [3], [312], 1)
......@@ -220,17 +220,17 @@ class Test_pfunc(unittest.TestCase):
(a + b + c))
# If the values can be accurately represented, everything is OK
assert numpy.all(f(0, 0, 0) == 0)
assert np.all(f(0, 0, 0) == 0)
# If allow_downcast is True, idem
assert numpy.allclose(f(0.1, 0, 0), 0.1)
assert np.allclose(f(0.1, 0, 0), 0.1)
# If allow_downcast is False, nope
self.assertRaises(TypeError, f, 0, 0.1, 0)
# If allow_downcast is None, it should work iff floatX=float32
if config.floatX == 'float32':
assert numpy.allclose(f(0, 0, 0.1), 0.1)
assert np.allclose(f(0, 0, 0.1), 0.1)
else:
self.assertRaises(TypeError, f, 0, 0, 0.1)
......@@ -246,10 +246,10 @@ class Test_pfunc(unittest.TestCase):
# If the values can be accurately represented, everything is OK
z = [0]
assert numpy.all(f(z, z, z) == 0)
assert np.all(f(z, z, z) == 0)
# If allow_downcast is True, idem
assert numpy.allclose(f([0.1], z, z), 0.1)
assert np.allclose(f([0.1], z, z), 0.1)
# If allow_downcast is False, nope
self.assertRaises(TypeError, f, z, [0.1], z)
......@@ -271,22 +271,22 @@ class Test_pfunc(unittest.TestCase):
g = pfunc([a, b, c], (a + b + c), allow_input_downcast=False)
# All values are in range. Since they're not ndarrays (but lists
# or scalars), they will be converted, and their value checked.
assert numpy.all(g([3], [6], 0) == 9)
assert np.all(g([3], [6], 0) == 9)
# Values are in range, but a dtype too large has explicitly been given
# For performance reasons, no check of the data is explicitly performed
# (It might be OK to change this in the future.)
self.assertRaises(TypeError, g,
[3], numpy.array([6], dtype='int16'), 0)
[3], np.array([6], dtype='int16'), 0)
# Value too big for b, raises TypeError
self.assertRaises(TypeError, g, [3], [312], 0)
h = pfunc([a, b, c], (a + b + c)) # Default: allow_input_downcast=None
# Everything here should behave like with False
assert numpy.all(h([3], [6], 0) == 9)
assert np.all(h([3], [6], 0) == 9)
self.assertRaises(TypeError, h,
[3], numpy.array([6], dtype='int16'), 0)
[3], np.array([6], dtype='int16'), 0)
self.assertRaises(TypeError, h, [3], [312], 0)
def test_allow_downcast_floatX(self):
......@@ -298,21 +298,21 @@ class Test_pfunc(unittest.TestCase):
h = pfunc([a, b], (a + b), allow_input_downcast=None)
# If the values can be accurately represented, OK
assert numpy.all(f(0, [0]) == 0)
assert numpy.all(g(0, [0]) == 0)
assert numpy.all(h(0, [0]) == 0)
assert np.all(f(0, [0]) == 0)
assert np.all(g(0, [0]) == 0)
assert np.all(h(0, [0]) == 0)
# For the vector: OK iff allow_input_downcast is True
assert numpy.allclose(f(0, [0.1]), 0.1)
assert np.allclose(f(0, [0.1]), 0.1)
self.assertRaises(TypeError, g, 0, [0.1])
self.assertRaises(TypeError, h, 0, [0.1])
# For the scalar: OK if allow_input_downcast is True,
# or None and floatX==float32
assert numpy.allclose(f(0.1, [0]), 0.1)
assert np.allclose(f(0.1, [0]), 0.1)
self.assertRaises(TypeError, g, 0.1, [0])
if config.floatX == 'float32':
assert numpy.allclose(h(0.1, [0]), 0.1)
assert np.allclose(h(0.1, [0]), 0.1)
else:
self.assertRaises(TypeError, h, 0.1, [0])
......@@ -340,7 +340,7 @@ class Test_pfunc(unittest.TestCase):
def test_update_err_broadcast(self):
# Test that broadcastable dimensions raise error
data = numpy.random.rand(10, 10).astype('float32')
data = np.random.rand(10, 10).astype('float32')
output_var = shared(name="output", value=data)
# the update_var has type matrix, and the update expression
......@@ -350,7 +350,7 @@ class Test_pfunc(unittest.TestCase):
def test_duplicate_updates(self):
x, y = dmatrices('x', 'y')
z = shared(numpy.ones((2, 3)))
z = shared(np.ones((2, 3)))
self.assertRaises(ValueError, theano.function, [x, y], [z],
updates=[(z, (z + x + y)), (z, (z - x))])
......@@ -362,29 +362,29 @@ class Test_pfunc(unittest.TestCase):
y = tensor.ivector()
f = pfunc([y], (y * x), givens={x: 6})
assert numpy.all(f([1, 1, 1]) == [6, 6, 6])
assert np.all(f([1, 1, 1]) == [6, 6, 6])
assert x.get_value() == 0
z = tensor.ivector()
c = z * y
f = pfunc([y], (c + 7),
givens={z: theano._asarray([4, 4, 4], dtype='int32')})
assert numpy.all(f([1, 1, 1]) == [11, 11, 11])
assert np.all(f([1, 1, 1]) == [11, 11, 11])
assert x.get_value() == 0
def test_clone0(self):
x = shared(numpy.asarray([4, 4, 4]))
y = shared(numpy.asarray([4, 4, 4]))
z = shared(numpy.asarray([2, 2, 2]))
x = shared(np.asarray([4, 4, 4]))
y = shared(np.asarray([4, 4, 4]))
z = shared(np.asarray([2, 2, 2]))
up = pfunc([], [], updates={
x: (x * 5),
y: ((x * 5) + y),
z: (((x * 5) + y) ** z)})
up()
assert numpy.all(x.get_value() == 20)
assert numpy.all(y.get_value() == 24)
assert numpy.all(z.get_value() == (24 ** 2))
assert np.all(x.get_value() == 20)
assert np.all(y.get_value() == 24)
assert np.all(z.get_value() == (24 ** 2))
def test_default_updates(self):
x = shared(0)
......@@ -625,7 +625,7 @@ class Test_pfunc(unittest.TestCase):
# There was a bug in CVM, triggered when a shared variable
# was its own update expression.
a = shared(1., 'a')
b = shared(numpy.ones((2, 3)), 'b')
b = shared(np.ones((2, 3)), 'b')
# The order of the variables is not determined, so we try
# both shared variables.
......@@ -650,7 +650,7 @@ class Test_pfunc(unittest.TestCase):
# Like test_update_same, but the update expression is simplified until
# it is found to be equal to the original variable
a = shared(1., 'a')
b = shared(numpy.ones((2, 3)), 'b')
b = shared(np.ones((2, 3)), 'b')
# See comment in test_update_same about why we try both
# shared variables.
......@@ -695,13 +695,13 @@ class Test_aliasing_rules(unittest.TestCase):
def test_shared_constructor_copies(self):
# shared constructor makes copy
# (rule #2)
orig_a = numpy.zeros((2, 2))
orig_a = np.zeros((2, 2))
A = self.shared(orig_a)
assert not numpy.may_share_memory(orig_a, data_of(A))
assert not np.may_share_memory(orig_a, data_of(A))
# rule #2 reading back from theano-managed memory
assert not numpy.may_share_memory(A.get_value(borrow=False),
data_of(A))
assert not np.may_share_memory(A.get_value(borrow=False),
data_of(A))
def test_sparse_input_aliasing_affecting_inplace_operations(self):
##
......@@ -732,7 +732,7 @@ class Test_aliasing_rules(unittest.TestCase):
# Test 1. If the same variable is given twice
# Compute bogus values
m = sp.csc_matrix(numpy.asarray(
m = sp.csc_matrix(np.asarray(
[[1, 0, 0, 0, 0],
[0, 1, 0, 0, 0],
[0, 0, 1, 0, 0],
......@@ -742,7 +742,7 @@ class Test_aliasing_rules(unittest.TestCase):
# Since we used inplace operation v and m may be corrupted
# so we need to recreate them
m = sp.csc_matrix(numpy.asarray(
m = sp.csc_matrix(np.asarray(
[[1, 0, 0, 0, 0],
[0, 1, 0, 0, 0],
[0, 0, 1, 0, 0],
......@@ -751,7 +751,7 @@ class Test_aliasing_rules(unittest.TestCase):
m_copy = m.copy()
vals = f(m, m_copy)
assert numpy.allclose(vals.todense(), bogus_vals.todense())
assert np.allclose(vals.todense(), bogus_vals.todense())
def test_input_aliasing_affecting_inplace_operations(self):
......@@ -771,27 +771,27 @@ class Test_aliasing_rules(unittest.TestCase):
# Test 1. If the same variable is given twice
# Compute bogus values
v = numpy.asarray([1, 2, 3, 4, 5], dtype='float64')
m = numpy.asarray([[1, 0, 0, 0, 0],
[0, 1, 0, 0, 0],
[0, 0, 1, 0, 0],
[0, 0, 0, 1, 0],
[0, 0, 0, 0, 1]], dtype='float64')
v = np.asarray([1, 2, 3, 4, 5], dtype='float64')
m = np.asarray([[1, 0, 0, 0, 0],
[0, 1, 0, 0, 0],
[0, 0, 1, 0, 0],
[0, 0, 0, 1, 0],
[0, 0, 0, 0, 1]], dtype='float64')
bogus_vals = f(v, v, m, m)
# Since we used inplace operation v and m may be corrupted
# so we need to recreate them
v = numpy.asarray([1, 2, 3, 4, 5], dtype='float64')
m = numpy.asarray([[1, 0, 0, 0, 0],
[0, 1, 0, 0, 0],
[0, 0, 1, 0, 0],
[0, 0, 0, 1, 0],
[0, 0, 0, 0, 1]], dtype='float64')
v = np.asarray([1, 2, 3, 4, 5], dtype='float64')
m = np.asarray([[1, 0, 0, 0, 0],
[0, 1, 0, 0, 0],
[0, 0, 1, 0, 0],
[0, 0, 0, 1, 0],
[0, 0, 0, 0, 1]], dtype='float64')
m_copy = m.copy()
v_copy = v.copy()
vals = f(v, v_copy, m, m_copy)
assert numpy.allclose(vals, bogus_vals)
assert np.allclose(vals, bogus_vals)
def test_partial_input_aliasing_affecting_inplace_operations(self):
......@@ -822,102 +822,102 @@ class Test_aliasing_rules(unittest.TestCase):
theano.dot((z * 4), m3)))
# Compute bogus values
v = numpy.asarray([1, 2, 3, 4, 5], dtype='float64')
m = numpy.asarray([[1, 0],
[0, 1]], dtype='float64')
v = np.asarray([1, 2, 3, 4, 5], dtype='float64')
m = np.asarray([[1, 0],
[0, 1]], dtype='float64')
bogus_vals = f(v[:2], v[1:3], v[2:4], m, m, m)
# Since we used inplace operation v and m may be corrupted
# so we need to recreate them
v = numpy.asarray([1, 2, 3, 4, 5], dtype='float64')
m = numpy.asarray([[1, 0],
[0, 1]], dtype='float64')
v = np.asarray([1, 2, 3, 4, 5], dtype='float64')
m = np.asarray([[1, 0],
[0, 1]], dtype='float64')
m_copy1 = m.copy()
v_copy1 = v.copy()
m_copy2 = m.copy()
v_copy2 = v.copy()
vals = f(v[:2], v_copy1[1:3], v_copy2[2:4], m, m_copy1, m_copy2)
assert numpy.allclose(vals, bogus_vals)
assert np.allclose(vals, bogus_vals)
def test_potential_output_aliasing_induced_by_updates(self):
A = self.shared(numpy.zeros((2, 2)))
B = self.shared(numpy.zeros((2, 2)))
C = numpy.zeros((2, 2))
A = self.shared(np.zeros((2, 2)))
B = self.shared(np.zeros((2, 2)))
C = np.zeros((2, 2))
D = tensor.dmatrix()
DD = D + 5
f = pfunc([D], [], updates=[(A, D), (B, D)])
f(C)
assert not numpy.may_share_memory(data_of(A), data_of(B))
assert not np.may_share_memory(data_of(A), data_of(B))
f = pfunc([D], [], updates=[(A, D[:]), (B, D)])
f(C)
assert not numpy.may_share_memory(data_of(A), data_of(B))
assert not np.may_share_memory(data_of(A), data_of(B))
f = pfunc([D], [], updates=[(A, (D + 5)), (B, D[:])])
f(C)
assert not numpy.may_share_memory(data_of(A), data_of(B))
assert not np.may_share_memory(data_of(A), data_of(B))
f = pfunc([D], [], updates=[(A, (D + 5)), (B, D)])
f(C)
assert not numpy.may_share_memory(data_of(A), data_of(B))
assert not np.may_share_memory(data_of(A), data_of(B))
f = pfunc([D], DD, updates=[(A, DD[:1]), (B, DD)])
R = f(C)
assert not numpy.may_share_memory(data_of(A), data_of(B))
assert not numpy.may_share_memory(R, data_of(B))
assert not numpy.may_share_memory(R, data_of(A))
assert not np.may_share_memory(data_of(A), data_of(B))
assert not np.may_share_memory(R, data_of(B))
assert not np.may_share_memory(R, data_of(A))
f = pfunc([D], DD, updates=[(A, DD[:1]), (B, (DD[:1] * 2))])
R = f(C)
assert not numpy.may_share_memory(data_of(A), data_of(B))
assert not numpy.may_share_memory(R, data_of(B))
assert not numpy.may_share_memory(R, data_of(A))
assert not np.may_share_memory(data_of(A), data_of(B))
assert not np.may_share_memory(R, data_of(B))
assert not np.may_share_memory(R, data_of(A))
f = pfunc([D], (DD * 4),
updates=[(A, (DD[:1] * 3)), (B, (DD[:1] * 2))])
R = f(C)
assert not numpy.may_share_memory(data_of(A), data_of(B))
assert not numpy.may_share_memory(R, data_of(B))
assert not numpy.may_share_memory(R, data_of(A))
assert not np.may_share_memory(data_of(A), data_of(B))
assert not np.may_share_memory(R, data_of(B))
assert not np.may_share_memory(R, data_of(A))
f = pfunc([D], (DD * 4),
updates=[(A, (DD[:1] * 3)), (B, (DD[:1] * 3))])
R = f(C)
assert not numpy.may_share_memory(data_of(A), data_of(B))
assert not numpy.may_share_memory(R, data_of(B))
assert not numpy.may_share_memory(R, data_of(A))
assert not np.may_share_memory(data_of(A), data_of(B))
assert not np.may_share_memory(R, data_of(B))
assert not np.may_share_memory(R, data_of(A))
def test_no_aliasing_0(self):
# B is a shared variable, A is updated with B's contents
# we need A to be copied to avoid aliasing
A = self.shared(numpy.zeros((2, 2)) + .5)
B = self.shared(numpy.zeros((2, 2)) - .5)
A = self.shared(np.zeros((2, 2)) + .5)
B = self.shared(np.zeros((2, 2)) - .5)
f = pfunc([], [], updates=[(A, B)])
f()
assert not numpy.may_share_memory(data_of(A), data_of(B))
assert not np.may_share_memory(data_of(A), data_of(B))
def test_no_aliasing_1(self):
# B is a shared variable, A is updated with B's contents
# since B is being updated as well, we don't need to copy anything
# to avoid aliasing shared variables.
A = self.shared(numpy.zeros((2, 2)) + .5)
B = self.shared(numpy.zeros((2, 2)) - .5)
A = self.shared(np.zeros((2, 2)) + .5)
B = self.shared(np.zeros((2, 2)) - .5)
C = tensor.dmatrix()
f = pfunc([C], [], updates=[(A, B), (B, C)])
z = numpy.zeros((2, 2))
z = np.zeros((2, 2))
f(z)
assert not numpy.may_share_memory(data_of(A), data_of(B))
assert not np.may_share_memory(data_of(A), data_of(B))
# Theano tries to maintain its own memory space.
assert not numpy.may_share_memory(z, data_of(B))
assert numpy.all(data_of(B) == z)
assert not np.may_share_memory(z, data_of(B))
assert np.all(data_of(B) == z)
def test_no_aliasing_2(self):
# B and A take one another's values
# no copying is necessary since each one is updated.
orig_a = numpy.zeros((2, 2)) + .5
orig_b = numpy.zeros((2, 2)) - .5
orig_a = np.zeros((2, 2)) + .5
orig_b = np.zeros((2, 2)) - .5
A = self.shared(orig_a)
B = self.shared(orig_b)
......@@ -927,15 +927,15 @@ class Test_aliasing_rules(unittest.TestCase):
f = pfunc([], [], updates=[(A, B), (B, A)])
f()
# correctness
assert numpy.all(data_of(A) == -.5)
assert numpy.all(data_of(B) == +.5)
assert np.all(data_of(A) == -.5)
assert np.all(data_of(B) == +.5)
# shared vars may not be aliased
assert not numpy.may_share_memory(data_of(A), data_of(B))
assert not np.may_share_memory(data_of(A), data_of(B))
# theano should have been smart enough to not make copies
assert numpy.may_share_memory(data_of(A), data_of_b)
assert numpy.may_share_memory(data_of(B), data_of_a)
assert np.may_share_memory(data_of(A), data_of_b)
assert np.may_share_memory(data_of(B), data_of_a)
def test_no_aliasing_2b(self):
# B and A take one another's values
......@@ -943,8 +943,8 @@ class Test_aliasing_rules(unittest.TestCase):
# The twist one `test_no_aliasing_2` is that each shared var is updated
# with a view of the other one.
orig_a = numpy.zeros((2, 2)) + .5
orig_b = numpy.zeros((2, 2)) - .5
orig_a = np.zeros((2, 2)) + .5
orig_b = np.zeros((2, 2)) - .5
A = self.shared(orig_a)
B = self.shared(orig_b)
......@@ -955,30 +955,30 @@ class Test_aliasing_rules(unittest.TestCase):
# theano.printing.debugprint(f)
f()
# correctness (doesn't actually test the view...)
assert numpy.all(data_of(A) == -.5)
assert numpy.all(data_of(B) == +.5)
assert np.all(data_of(A) == -.5)
assert np.all(data_of(B) == +.5)
# shared vars may not be aliased
assert not numpy.may_share_memory(data_of(A), data_of(B))
assert not np.may_share_memory(data_of(A), data_of(B))
# theano should have been smart enough to not make copies
if theano.config.mode not in [
'DebugMode', 'DEBUG_MODE', 'FAST_COMPILE']:
# We don't ask DebugMode and FAST_COMPILE not to make copy.
# We have the right to do so.
assert numpy.all(data_of(A) < 5)
assert np.all(data_of(A) < 5)
data_of_b += 10
assert numpy.all(data_of(A) > 5)
assert np.all(data_of(A) > 5)
data_of_b -= 10
assert numpy.all(data_of(B) < 5)
assert np.all(data_of(B) < 5)
data_of_a += 10
assert numpy.all(data_of(B) > 5)
assert np.all(data_of(B) > 5)
data_of_a -= 10
# N.B. may_share_memory is what we mean, but does it work?
assert numpy.may_share_memory(data_of(A), data_of_b)
assert numpy.may_share_memory(data_of(B), data_of_a)
assert np.may_share_memory(data_of(A), data_of_b)
assert np.may_share_memory(data_of(B), data_of_a)
# N.B. This pattern could form a memory leak - each shared
# variable always points to a view, and that view gets
......@@ -995,9 +995,9 @@ class Test_rebuild_strict(unittest.TestCase):
x, y = tensor.ivectors('x', 'y')
z = x * y
f = theano.function([w, y], z, givens=[(x, w)], rebuild_strict=False)
z_val = f(numpy.ones((3, 5), dtype='int32'), numpy.arange(5, dtype='int32'))
z_val = f(np.ones((3, 5), dtype='int32'), np.arange(5, dtype='int32'))
assert z_val.ndim == 2
assert numpy.all(z_val == numpy.ones((3, 5)) * numpy.arange(5))
assert np.all(z_val == np.ones((3, 5)) * np.arange(5))
if __name__ == '__main__':
......
theano/compile/tests/test_profiling.py
浏览文件 @ ccf6deb0
......@@ -6,7 +6,7 @@ from __future__ import absolute_import, print_function, division
import unittest
import numpy
import numpy as np
import theano
from six.moves import StringIO
......@@ -45,7 +45,7 @@ class Test_profiling(unittest.TestCase):
f = theano.function(x, z, profile=p, name="test_profiling",
mode=m)
inp = [numpy.arange(1024, dtype='float32') + 1 for i in range(len(x))]
inp = [np.arange(1024, dtype='float32') + 1 for i in range(len(x))]
f(*inp)
buf = StringIO()
......
theano/compile/tests/test_shared.py
浏览文件 @ ccf6deb0
from __future__ import absolute_import, print_function, division
import numpy
import numpy as np
import unittest
import theano
......@@ -18,14 +18,14 @@ class Test_SharedVariable(unittest.TestCase):
else:
assert shared(7).type == theano.tensor.lscalar, shared(7).type
assert shared(7.0).type == theano.tensor.dscalar
assert shared(numpy.float32(7)).type == theano.tensor.fscalar
assert shared(np.float32(7)).type == theano.tensor.fscalar
# test tensor constructor
b = shared(numpy.zeros((5, 5), dtype='int32'))
b = shared(np.zeros((5, 5), dtype='int32'))
assert b.type == TensorType('int32', broadcastable=[False, False])
b = shared(numpy.random.rand(4, 5))
b = shared(np.random.rand(4, 5))
assert b.type == TensorType('float64', broadcastable=[False, False])
b = shared(numpy.random.rand(5, 1, 2))
b = shared(np.random.rand(5, 1, 2))
assert b.type == TensorType('float64', broadcastable=[False, False, False])
assert shared([]).type == generic
......@@ -52,7 +52,7 @@ class Test_SharedVariable(unittest.TestCase):
SharedVariable(
name='u',
type=Tensor(broadcastable=[False], dtype='float64'),
value=numpy.asarray([1., 2.]),
value=np.asarray([1., 2.]),
strict=False)
# here the value is castable, and we're not strict about it,
......@@ -90,14 +90,14 @@ class Test_SharedVariable(unittest.TestCase):
u = SharedVariable(
name='u',
type=Tensor(broadcastable=[False], dtype='float64'),
value=numpy.asarray([1., 2.]),
value=np.asarray([1., 2.]),
strict=False)
# check that assignments to value are cast properly
u.set_value([3, 4])
assert type(u.get_value()) is numpy.ndarray
assert type(u.get_value()) is np.ndarray
assert str(u.get_value(borrow=True).dtype) == 'float64'
assert numpy.all(u.get_value() == [3, 4])
assert np.all(u.get_value() == [3, 4])
# check that assignments of nonsense fail
try:
......@@ -115,31 +115,31 @@ class Test_SharedVariable(unittest.TestCase):
def f(var, val):
var.set_value(val)
b = shared(numpy.int64(7), strict=True)
b = shared(np.int64(7), strict=True)
assert b.type == theano.tensor.lscalar
self.assertRaises(TypeError, f, b, 8.23)
b = shared(numpy.int32(7), strict=True)
b = shared(np.int32(7), strict=True)
assert b.type == theano.tensor.iscalar
self.assertRaises(TypeError, f, b, 8.23)
b = shared(numpy.int16(7), strict=True)
b = shared(np.int16(7), strict=True)
assert b.type == theano.tensor.wscalar
self.assertRaises(TypeError, f, b, 8.23)
b = shared(numpy.int8(7), strict=True)
b = shared(np.int8(7), strict=True)
assert b.type == theano.tensor.bscalar
self.assertRaises(TypeError, f, b, 8.23)
b = shared(numpy.float64(7.234), strict=True)
b = shared(np.float64(7.234), strict=True)
assert b.type == theano.tensor.dscalar
self.assertRaises(TypeError, f, b, 8)
b = shared(numpy.float32(7.234), strict=True)
b = shared(np.float32(7.234), strict=True)
assert b.type == theano.tensor.fscalar
self.assertRaises(TypeError, f, b, 8)
b = shared(numpy.float(7.234), strict=True)
b = shared(np.float(7.234), strict=True)
assert b.type == theano.tensor.dscalar
self.assertRaises(TypeError, f, b, 8)
......@@ -147,39 +147,39 @@ class Test_SharedVariable(unittest.TestCase):
assert b.type == theano.tensor.dscalar
self.assertRaises(TypeError, f, b, 8)
b = shared(numpy.zeros((5, 5), dtype='float32'))
self.assertRaises(TypeError, f, b, numpy.random.rand(5, 5))
b = shared(np.zeros((5, 5), dtype='float32'))
self.assertRaises(TypeError, f, b, np.random.rand(5, 5))
def test_tensor_strict(self):
def f(var, val):
var.set_value(val)
b = shared(numpy.int64([7]), strict=True)
b = shared(np.int64([7]), strict=True)
assert b.type == theano.tensor.lvector
self.assertRaises(TypeError, f, b, 8.23)
b = shared(numpy.int32([7]), strict=True)
b = shared(np.int32([7]), strict=True)
assert b.type == theano.tensor.ivector
self.assertRaises(TypeError, f, b, 8.23)
b = shared(numpy.int16([7]), strict=True)
b = shared(np.int16([7]), strict=True)
assert b.type == theano.tensor.wvector
self.assertRaises(TypeError, f, b, 8.23)
b = shared(numpy.int8([7]), strict=True)
b = shared(np.int8([7]), strict=True)
assert b.type == theano.tensor.bvector
self.assertRaises(TypeError, f, b, 8.23)
b = shared(numpy.float64([7.234]), strict=True)
b = shared(np.float64([7.234]), strict=True)
assert b.type == theano.tensor.dvector
self.assertRaises(TypeError, f, b, 8)
b = shared(numpy.float32([7.234]), strict=True)
b = shared(np.float32([7.234]), strict=True)
assert b.type == theano.tensor.fvector
self.assertRaises(TypeError, f, b, 8)
# numpy.float([7.234]) don't work
# b = shared(numpy.float([7.234]), strict=True)
# np.float([7.234]) don't work
# b = shared(np.float([7.234]), strict=True)
# assert b.type == theano.tensor.dvector
# self.assertRaises(TypeError, f, b, 8)
......@@ -188,8 +188,8 @@ class Test_SharedVariable(unittest.TestCase):
# assert b.type == theano.tensor.dvector
# self.assertRaises(TypeError, f, b, 8)
b = shared(numpy.zeros((5, 5), dtype='float32'))
self.assertRaises(TypeError, f, b, numpy.random.rand(5, 5))
b = shared(np.zeros((5, 5), dtype='float32'))
self.assertRaises(TypeError, f, b, np.random.rand(5, 5))
def test_scalar_floatX(self):
......@@ -204,37 +204,37 @@ class Test_SharedVariable(unittest.TestCase):
def f(var, val):
var.set_value(val)
b = shared(numpy.int64(7), allow_downcast=True)
b = shared(np.int64(7), allow_downcast=True)
assert b.type == theano.tensor.lscalar
f(b, 8.23)
assert b.get_value() == 8
b = shared(numpy.int32(7), allow_downcast=True)
b = shared(np.int32(7), allow_downcast=True)
assert b.type == theano.tensor.iscalar
f(b, 8.23)
assert b.get_value() == 8
b = shared(numpy.int16(7), allow_downcast=True)
b = shared(np.int16(7), allow_downcast=True)
assert b.type == theano.tensor.wscalar
f(b, 8.23)
assert b.get_value() == 8
b = shared(numpy.int8(7), allow_downcast=True)
b = shared(np.int8(7), allow_downcast=True)
assert b.type == theano.tensor.bscalar
f(b, 8.23)
assert b.get_value() == 8
b = shared(numpy.float64(7.234), allow_downcast=True)
b = shared(np.float64(7.234), allow_downcast=True)
assert b.type == theano.tensor.dscalar
f(b, 8)
assert b.get_value() == 8
b = shared(numpy.float32(7.234), allow_downcast=True)
b = shared(np.float32(7.234), allow_downcast=True)
assert b.type == theano.tensor.fscalar
f(b, 8)
assert b.get_value() == 8
b = shared(numpy.float(7.234), allow_downcast=True)
b = shared(np.float(7.234), allow_downcast=True)
assert b.type == theano.tensor.dscalar
f(b, 8)
assert b.get_value() == 8
......@@ -244,45 +244,45 @@ class Test_SharedVariable(unittest.TestCase):
f(b, 8)
assert b.get_value() == 8
b = shared(numpy.zeros((5, 5), dtype='float32'))
self.assertRaises(TypeError, f, b, numpy.random.rand(5, 5))
b = shared(np.zeros((5, 5), dtype='float32'))
self.assertRaises(TypeError, f, b, np.random.rand(5, 5))
def test_tensor_floatX(self):
def f(var, val):
var.set_value(val)
b = shared(numpy.int64([7]), allow_downcast=True)
b = shared(np.int64([7]), allow_downcast=True)
assert b.type == theano.tensor.lvector
f(b, [8.23])
assert b.get_value() == 8
b = shared(numpy.int32([7]), allow_downcast=True)
b = shared(np.int32([7]), allow_downcast=True)
assert b.type == theano.tensor.ivector
f(b, [8.23])
assert b.get_value() == 8
b = shared(numpy.int16([7]), allow_downcast=True)
b = shared(np.int16([7]), allow_downcast=True)
assert b.type == theano.tensor.wvector
f(b, [8.23])
assert b.get_value() == 8
b = shared(numpy.int8([7]), allow_downcast=True)
b = shared(np.int8([7]), allow_downcast=True)
assert b.type == theano.tensor.bvector
f(b, [8.23])
assert b.get_value() == 8
b = shared(numpy.float64([7.234]), allow_downcast=True)
b = shared(np.float64([7.234]), allow_downcast=True)
assert b.type == theano.tensor.dvector
f(b, [8])
assert b.get_value() == 8
b = shared(numpy.float32([7.234]), allow_downcast=True)
b = shared(np.float32([7.234]), allow_downcast=True)
assert b.type == theano.tensor.fvector
f(b, [8])
assert b.get_value() == 8
# numpy.float([7.234]) don't work
# b = shared(numpy.float([7.234]))
# np.float([7.234]) don't work
# b = shared(np.float([7.234]))
# assert b.type == theano.tensor.dvector
# f(b,[8])
......@@ -291,15 +291,15 @@ class Test_SharedVariable(unittest.TestCase):
# assert b.type == theano.tensor.dvector
# f(b,[8])
b = shared(numpy.asarray([7.234], dtype=theano.config.floatX),
b = shared(np.asarray([7.234], dtype=theano.config.floatX),
allow_downcast=True)
assert b.dtype == theano.config.floatX
f(b, [8])
assert b.get_value() == 8
b = shared(numpy.zeros((5, 5), dtype='float32'))
self.assertRaises(TypeError, f, b, numpy.random.rand(5, 5))
b = shared(np.zeros((5, 5), dtype='float32'))
self.assertRaises(TypeError, f, b, np.random.rand(5, 5))
def test_err_symbolic_variable(self):
self.assertRaises(TypeError, shared, theano.tensor.ones((2, 3)))
shared(numpy.ones((2, 4)))
shared(np.ones((2, 4)))
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