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pytensor
提交 578eb363 authored 作者: turian@grenat.iro.umontreal.ca's avatar turian@grenat.iro.umontreal.ca
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Renamed tinit to astensor

上级 a68aa7e2
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正在显示 包含 59 行增加 和 60 行删除
_test_elemwise.py
浏览文件 @ 578eb363
......@@ -2,7 +2,7 @@
import unittest
import numpy
from tensor import tinit, Tensor
from tensor import astensor, Tensor
import gof
from gof import modes, Env
......@@ -29,9 +29,9 @@ def inputs():
l1 = [[1.0, 2.0], [3.0, 4.0]]
l2 = [[3.0, 4.0], [1.0, 2.0]]
l3 = numpy.ones((2, 3))
x = modes.build(tinit(l1, name = 'x'))
y = modes.build(tinit(l2, name = 'y'))
z = modes.build(tinit(l3, name = 'z'))
x = modes.build(astensor(l1, name = 'x'))
y = modes.build(astensor(l2, name = 'y'))
z = modes.build(astensor(l3, name = 'z'))
return x, y, z
def env(inputs, outputs, validate = True, features = []):
......
_test_tensor.py
浏览文件 @ 578eb363
......@@ -18,7 +18,7 @@ def verify_grad(testcase, op_cls, pt, n_tests=1, rng=numpy.random, eps=0.0000001
pt = [numpy.asarray(p) for p in pt]
for test_num in xrange(n_tests):
tensor_pt = [tinit(p,name='input %i'%i) for i,p in enumerate(pt)]
tensor_pt = [astensor(p,name='input %i'%i) for i,p in enumerate(pt)]
o = op_cls(*tensor_pt)
if len(o.outputs) > 1:
raise NotImplementedError('cant (yet) autotest gradient of op with multiple outputs')
......@@ -28,7 +28,7 @@ def verify_grad(testcase, op_cls, pt, n_tests=1, rng=numpy.random, eps=0.0000001
o_fn = Function(tensor_pt, o.outputs)
o_fn_out = o_fn(*pt)
random_projection = rng.rand(*o_fn_out.shape)
t_r = tinit(random_projection)
t_r = astensor(random_projection)
#random projection of o onto t_r
cost = sum(t_r * o.outputs[0])
......@@ -36,7 +36,7 @@ def verify_grad(testcase, op_cls, pt, n_tests=1, rng=numpy.random, eps=0.0000001
num_grad = gradient.numeric_grad(cost_fn, pt)
symbolic_grad = gradient.grad(cost, tensor_pt,tinit(1.0,name='g_cost'))
symbolic_grad = gradient.grad(cost, tensor_pt,astensor(1.0,name='g_cost'))
if 0:
print '-------'
print '----------'
......@@ -74,47 +74,47 @@ class T_argmax(unittest.TestCase):
Argmax.debug = 0
def test0(self):
n = tinit(5.0)
n = astensor(5.0)
v,i = eval_outputs(argmax(n))
self.failUnless(v == 5.0)
self.failUnless(i == 0)
def test1(self):
n = tinit([1,2,3,2,-6])
n = astensor([1,2,3,2,-6])
v,i = eval_outputs(argmax(n))
self.failUnless(v == 3)
self.failUnless(i == 2)
def test2(self):
n = tinit(numpy.random.rand(2,3))
n = astensor(numpy.random.rand(2,3))
v,i = eval_outputs(argmax(n))
self.failUnless(numpy.all(i == [0,1]))
def test2b(self):
n = tinit(numpy.random.rand(2,3))
n = astensor(numpy.random.rand(2,3))
v,i = eval_outputs(argmax(n,axis=0))
self.failUnless(numpy.all(i == [0,1,1]))
def test2_invalid(self):
n = tinit(numpy.random.rand(2,3))
n = astensor(numpy.random.rand(2,3))
try:
eval_outputs(argmax(n,axis=3))
self.fail()
except ValueError, e:
return
def test2_invalid_neg(self):
n = tinit(numpy.random.rand(2,3))
n = astensor(numpy.random.rand(2,3))
try:
eval_outputs(argmax(n,axis=-3))
self.fail()
except ValueError, e:
return
def test2_valid_neg(self):
n = tinit(numpy.random.rand(2,3))
n = astensor(numpy.random.rand(2,3))
v,i = eval_outputs(argmax(n,axis=-1))
self.failUnless(v.shape == (2,))
v,i = eval_outputs(argmax(n,axis=-2))
self.failUnless(v.shape == (3,))
def test3(self):
n = tinit(numpy.random.rand(2,3,4))
n = astensor(numpy.random.rand(2,3,4))
v,i = eval_outputs(argmax(n,axis=0))
self.failUnless(v.shape == (3,4))
self.failUnless(i.shape == (3,4))
......@@ -128,7 +128,7 @@ class T_argmax(unittest.TestCase):
class T_transpose(unittest.TestCase):
def test0(self):
n = tinit(numpy.ones(()))
n = astensor(numpy.ones(()))
t = transpose(n)
self.failUnless(t.owner.__class__ is Transpose)
f = Function([n], [t])
......@@ -140,7 +140,7 @@ class T_transpose(unittest.TestCase):
self.failUnless(n.data == 56.0)
def test1(self):
n = tinit(numpy.ones(5))
n = astensor(numpy.ones(5))
t = transpose(n)
self.failUnless(t.owner.__class__ is Transpose)
f = Function([n], [t])
......@@ -151,7 +151,7 @@ class T_transpose(unittest.TestCase):
self.failUnless(n.data[0] == 56.0)
def test2(self):
n = tinit(numpy.ones((5,3)))
n = astensor(numpy.ones((5,3)))
t = transpose(n)
self.failUnless(t.owner.__class__ is Transpose)
f = Function([n], [t])
......@@ -162,7 +162,7 @@ class T_transpose(unittest.TestCase):
self.failUnless(n.data[0,0] == 56.0)
def test3(self):
n = tinit(numpy.ones((5,3,2)))
n = astensor(numpy.ones((5,3,2)))
t = transpose(n)
self.failUnless(t.owner.__class__ is Transpose)
f = Function([n], [t])
......@@ -175,14 +175,14 @@ class T_transpose(unittest.TestCase):
class T_subtensor(unittest.TestCase):
def test0_err_invalid(self):
#it is impossible to retrieve a view of a 0-d tensor
n = tinit(numpy.ones(()))
n = astensor(numpy.ones(()))
try:
t = n[0]
self.fail()
except ValueError, e:
self.failUnless(e[0] is Subtensor.e_invalid)
def test1_err_bounds(self):
n = tinit(numpy.ones(3))
n = astensor(numpy.ones(3))
t = n[7]
self.failUnless(t.owner.__class__ is Subtensor)
try:
......@@ -192,42 +192,42 @@ class T_subtensor(unittest.TestCase):
if e[0] != 'index out of bounds':
raise
def test1_ok_range_finite(self):
n = tinit(numpy.ones(3)*5)
n = astensor(numpy.ones(3)*5)
t = n[0:2]
self.failUnless(t.owner.__class__ is Subtensor)
tval = eval_outputs([t])
self.failUnless(tval.shape == (2,))
self.failUnless(tval[1] == 5.0)
def test2_ok_range_finite(self):
n = tinit(numpy.ones((3,4))*5)
n = astensor(numpy.ones((3,4))*5)
t = n[0:2,3]
self.failUnless(t.owner.__class__ is Subtensor)
tval = eval_outputs([t])
self.failUnless(tval.shape == (2,))
self.failUnless(tval[1] == 5.0)
def test1_err_invalid(self):
n = tinit(numpy.ones(1))
n = astensor(numpy.ones(1))
try:
t = n[0,0]
self.fail()
except ValueError, e:
self.failUnless(e[0] is Subtensor.e_invalid)
def test1_ok_elem(self):
n = tinit(numpy.ones(1)*5)
n = astensor(numpy.ones(1)*5)
t = n[0]
self.failUnless(t.owner.__class__ is Subtensor)
tval = eval_outputs([t])
self.failUnless(tval.shape == ())
self.failUnless(tval == 5.0)
def test1_ok_range_infinite(self):
n = tinit(numpy.ones(3)*5)
n = astensor(numpy.ones(3)*5)
t = n[1:]
self.failUnless(t.owner.__class__ is Subtensor)
tval = eval_outputs([t])
self.failUnless(tval.shape == (2,))
self.failUnless(tval[1] == 5.0)
def test1_ok_strided(self):
n = tinit(numpy.ones(5)*5)
n = astensor(numpy.ones(5)*5)
t = n[1::2]
self.failUnless(t.owner.__class__ is Subtensor)
tval = eval_outputs([t])
......@@ -239,7 +239,7 @@ class T_subtensor(unittest.TestCase):
self.failUnless(tval[1] == 5.0)
def test2_err_bounds0(self):
n = tinit(numpy.ones((2,3))*5)
n = astensor(numpy.ones((2,3))*5)
t = n[0,4]
self.failUnless(t.owner.__class__ is Subtensor)
try:
......@@ -248,7 +248,7 @@ class T_subtensor(unittest.TestCase):
except IndexError, e:
return
def test2_err_bounds1(self):
n = tinit(numpy.ones((2,3))*5)
n = astensor(numpy.ones((2,3))*5)
t = n[4:5,2]
self.failUnless(t.owner.__class__ is Subtensor)
try:
......@@ -257,14 +257,14 @@ class T_subtensor(unittest.TestCase):
if e[0] != 'index out of bounds':
raise
def test2_ok_elem(self):
n = tinit(numpy.asarray(range(6)).reshape((2,3)))
n = astensor(numpy.asarray(range(6)).reshape((2,3)))
t = n[0,2]
self.failUnless(t.owner.__class__ is Subtensor)
tval = eval_outputs([t])
self.failUnless(tval.shape == ())
self.failUnless(numpy.all(tval == 2))
def test2_ok_row(self):
n = tinit(numpy.asarray(range(6)).reshape((2,3)))
n = astensor(numpy.asarray(range(6)).reshape((2,3)))
t = n[1]
self.failIf(any(n.broadcastable))
self.failUnless(t.owner.__class__ is Subtensor)
......@@ -273,7 +273,7 @@ class T_subtensor(unittest.TestCase):
self.failUnless(numpy.all(tval == [3,4,5]))
def test2_ok_col(self):
n = tinit(numpy.ones((2,3))*5)
n = astensor(numpy.ones((2,3))*5)
t = n[:,0]
self.failUnless(t.owner.__class__ is Subtensor)
self.failIf(any(n.broadcastable))
......@@ -282,7 +282,7 @@ class T_subtensor(unittest.TestCase):
self.failUnless(numpy.all(tval == 5.0))
def test2_ok_rows_finite(self):
n = tinit(numpy.ones((4,3))*5)
n = astensor(numpy.ones((4,3))*5)
t = n[1:3,0]
self.failUnless(t.owner.__class__ is Subtensor)
tval = eval_outputs([t])
......@@ -290,7 +290,7 @@ class T_subtensor(unittest.TestCase):
self.failUnless(numpy.all(tval == 5.0))
def test2_ok_cols_infinite(self):
n = tinit(numpy.asarray(range(12)).reshape((4,3)))
n = astensor(numpy.asarray(range(12)).reshape((4,3)))
t = n[1,2:]
self.failUnless(t.owner.__class__ is Subtensor)
tval = eval_outputs([t])
......@@ -298,7 +298,7 @@ class T_subtensor(unittest.TestCase):
self.failUnless(numpy.all(tval == 5))
def test2_ok_strided(self):
n = tinit(numpy.asarray(range(20)).reshape((4,5)))
n = astensor(numpy.asarray(range(20)).reshape((4,5)))
t = n[1:4:2,1:5:2]
self.failUnless(t.owner.__class__ is Subtensor)
tval = eval_outputs([t])
......@@ -306,7 +306,7 @@ class T_subtensor(unittest.TestCase):
self.failUnless(numpy.all(tval == [[6, 8],[16, 18]]))
def test3_ok_mat(self):
n = tinit(numpy.asarray(range(24)).reshape((2,3,4)))
n = astensor(numpy.asarray(range(24)).reshape((2,3,4)))
t = n[0,0,0]
self.failUnless(t.owner.__class__ is Subtensor)
tval = eval_outputs([t])
......@@ -318,8 +318,8 @@ class T_add(unittest.TestCase):
def test_complex_all_ops(self):
for nbits in (64, 128):
a = tinit(numpy.ones(3, dtype='complex%i' % nbits)+0.5j)
b = tinit(numpy.ones(3, dtype='complex%i' % nbits)+1.5j)
a = astensor(numpy.ones(3, dtype='complex%i' % nbits)+0.5j)
b = astensor(numpy.ones(3, dtype='complex%i' % nbits)+1.5j)
tests = (("+", lambda x,y: x+y),
("-", lambda x,y: x-y),
("*", lambda x,y: x*y),
......@@ -331,12 +331,12 @@ class T_add(unittest.TestCase):
class T_abs(unittest.TestCase):
def test_impl(self):
t = tinit(1.0)
t = astensor(1.0)
check_eq(self, t, abs(t), 1.0, 1.0)
check_eq(self, t, abs(t), -1.0, 1.0)
for shape in (2,), (3,4):
t = tinit(numpy.ones(shape))
t = astensor(numpy.ones(shape))
d = numpy.random.rand(*shape)*2-1.0
check_eq(self, t, abs(t), d, abs(d))
check_eq(self, t, abs(t), -d, abs(-d))
......@@ -374,11 +374,11 @@ class T_fill(unittest.TestCase):
class T_sum(unittest.TestCase):
def test_impl(self):
t = tinit(0.0)
t = astensor(0.0)
check_eq(self, t, Sum(t).out, 1.0, 1.0)
check_eq(self, t, Sum(t).out, -1.0, -1.0)
t = tinit([0.0, 0.0])
t = astensor([0.0, 0.0])
d = numpy.asarray([-0.4, 1.2])
check_eq(self, t, Sum(t).out, d, numpy.sum(d))
check_eq(self, t, Sum(t).out, -d, -numpy.sum(d))
......@@ -388,14 +388,14 @@ class T_mul(unittest.TestCase):
numpy.random.seed([1,2,3,4])
def test_elemwise(self):
a = tinit(0.0)
b = tinit(0.0)
a = astensor(0.0)
b = astensor(0.0)
check_eq2(self, [a,b], mul_elemwise(a,b), [3.0, 4.0], 12.0)
check_eq2(self, [a,b], mul_elemwise(b,a), [-1.0,2.0], -2.0)
self.failUnless(isinstance(mul(a,b).owner, Scale))
a = tinit(numpy.ones(2))
b = tinit(numpy.ones(2))
a = astensor(numpy.ones(2))
b = astensor(numpy.ones(2))
aa = numpy.asarray([-0.5, 4.0])
bb = numpy.asarray([-0.5, 2.0])
check_eq2(self, [a,b], mul_elemwise(a,b), [aa,bb], numpy.asarray([0.25, 8.0]))
......@@ -404,24 +404,24 @@ class T_mul(unittest.TestCase):
def test_scalar(self):
r = numpy.random.rand(2,3)
a = tinit(r)
b = tinit(2.0)
a = astensor(r)
b = astensor(2.0)
check_eq2(self, [a,b], scale(a,b), [r, 2.0], r*2.0)
check_eq2(self, [a,b], scale(a,b), [r, 4.0], r*4.0)
self.failUnless(b.data == 2.0)
def test_operator(self):
a = tinit([1,1])
aa = tinit([1,1])
b = tinit(4)
a = astensor([1,1])
aa = astensor([1,1])
b = astensor(4)
self.failUnless(isinstance((a*b).owner, Scale))
self.failUnless(isinstance((b*a).owner, Scale))
self.failUnless(isinstance((a*aa).owner, MulElemwise))
self.failUnless(isinstance((aa*a).owner, MulElemwise))
def test_wrong_shapes(self):
a = tinit(numpy.ones(3))
b = tinit(numpy.ones(4))
a = astensor(numpy.ones(3))
b = astensor(numpy.ones(4))
try:
check_eq2(self, [a,b], MulElemwise(a,b).out,
[numpy.ones(3), numpy.ones(4)], 1.0)
......@@ -471,15 +471,14 @@ class _testCase_matinv(unittest.TestCase):
a = Tensor('float64', broadcastable=[False,False], name='a')
b = Tensor('float64', broadcastable=[False,False], name='b')
ab = a*b
# Here, tinit actually uses the data allocated by numpy.
# TODO: Rename tinit to tensor
diff = ab - tinit(numpy.ones((dim,dim)))
# Here, astensor actually uses the data allocated by numpy.
diff = ab - astensor(numpy.ones((dim,dim)))
# Sum of squared errors
ssdiff = sum((diff**2.0))
# May be able to abbreviate this by assuming default parameter
# TODO: Test that default works
g_b = gradient.grad(ssdiff, b, tinit(numpy.ones(1),name='g_cost'))
g_b = gradient.grad(ssdiff, b, astensor(numpy.ones(1),name='g_cost'))
#g_b = gradient.grad(ssdiff, b) # This should be the abbreviated version
# compilation to function
......
tensor.py
浏览文件 @ 578eb363
......@@ -67,7 +67,7 @@ class Tensor(BaseTensor):
def __getslice__(self, *args): return subtensor(self, slice(*args))
# alternate Tensor constructor
def tinit(data, broadcastable=None, role=None, name=None):
def astensor(data, broadcastable=None, role=None, name=None):
"""Return a Tensor containing given data"""
data = numpy.asarray(data)
if broadcastable is None:
......@@ -90,7 +90,7 @@ def _scalar_switch(normal_f, scalar_f, scalar_f_reverse = None):
if isinstance(obj, Tensor):
return obj
else:
return tinit(obj)
return astensor(obj)
x, y = as_tensor(x), as_tensor(y)
if 0 not in y.broadcastable:
return scalar_f(x, y)
......@@ -119,7 +119,7 @@ def _as_tensor(obj):
if isinstance(obj, Tensor):
return obj
else:
return tinit(obj)
return astensor(obj)
class _Op(BaseTensorOp):
"""A convenient base for the ops in this file"""
......
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