Skip to content

P
pytensor
提交 818985b0 authored 作者: amrithasuresh's avatar amrithasuresh
浏览文件
操作

Updated numpy as np

上级 b8d2d3ee
隐藏空白字符变更
内嵌 并排
正在显示 包含 79 行增加 和 79 行删除
theano/tests/test_ifelse.py
浏览文件 @ 818985b0
...@@ -4,7 +4,7 @@ ...@@ -4,7 +4,7 @@
from __future__ import absolute_import, print_function, division from __future__ import absolute_import, print_function, division
import unittest import unittest
import numpy import numpy as np
from nose.plugins.skip import SkipTest from nose.plugins.skip import SkipTest
from six.moves import reduce from six.moves import reduce
...@@ -42,16 +42,16 @@ class test_ifelse(unittest.TestCase, utt.TestOptimizationMixin): ...@@ -42,16 +42,16 @@ class test_ifelse(unittest.TestCase, utt.TestOptimizationMixin):
c = tensor.iscalar('c') c = tensor.iscalar('c')
f = theano.function([c, x, y], ifelse(c, x, y), mode=self.mode) f = theano.function([c, x, y], ifelse(c, x, y), mode=self.mode)
self.assertFunctionContains1(f, self.get_ifelse(1)) self.assertFunctionContains1(f, self.get_ifelse(1))
rng = numpy.random.RandomState(utt.fetch_seed()) rng = np.random.RandomState(utt.fetch_seed())
xlen = rng.randint(200) xlen = rng.randint(200)
ylen = rng.randint(200) ylen = rng.randint(200)
vx = numpy.asarray(rng.uniform(size=(xlen,)), self.dtype) vx = np.asarray(rng.uniform(size=(xlen,)), self.dtype)
vy = numpy.asarray(rng.uniform(size=(ylen,)), self.dtype) vy = np.asarray(rng.uniform(size=(ylen,)), self.dtype)
assert numpy.allclose(vx, f(1, vx, vy)) assert np.allclose(vx, f(1, vx, vy))
assert numpy.allclose(vy, f(0, vx, vy)) assert np.allclose(vy, f(0, vx, vy))
def test_not_lazy_if_inplace(self): def test_not_lazy_if_inplace(self):
# Tests that if the outputs are scalars and the graph is big, # Tests that if the outputs are scalars and the graph is big,
...@@ -71,16 +71,16 @@ class test_ifelse(unittest.TestCase, utt.TestOptimizationMixin): ...@@ -71,16 +71,16 @@ class test_ifelse(unittest.TestCase, utt.TestOptimizationMixin):
if isinstance(n.op, IfElse)] if isinstance(n.op, IfElse)]
assert len(ifnode) == 1 assert len(ifnode) == 1
assert not ifnode[0].op.as_view assert not ifnode[0].op.as_view
rng = numpy.random.RandomState(utt.fetch_seed()) rng = np.random.RandomState(utt.fetch_seed())
xlen = rng.randint(200) xlen = rng.randint(200)
ylen = rng.randint(200) ylen = rng.randint(200)
vx = numpy.asarray(rng.uniform(size=(xlen,)), self.dtype) vx = np.asarray(rng.uniform(size=(xlen,)), self.dtype)
vy = numpy.asarray(rng.uniform(size=(ylen,)), self.dtype) vy = np.asarray(rng.uniform(size=(ylen,)), self.dtype)
assert numpy.allclose(vx, f(1, vx, vy)) assert np.allclose(vx, f(1, vx, vy))
assert numpy.allclose(vy + sum(range(200)), f(0, vx, vy)) assert np.allclose(vy + sum(range(200)), f(0, vx, vy))
def test_mixed_dtype(self): def test_mixed_dtype(self):
x1 = tensor.vector('x1', dtype='int32') x1 = tensor.vector('x1', dtype='int32')
...@@ -91,23 +91,23 @@ class test_ifelse(unittest.TestCase, utt.TestOptimizationMixin): ...@@ -91,23 +91,23 @@ class test_ifelse(unittest.TestCase, utt.TestOptimizationMixin):
f = theano.function([c, x1, x2, y1, y2], f = theano.function([c, x1, x2, y1, y2],
ifelse(c, (x1, x2), (y1, y2)), mode=self.mode) ifelse(c, (x1, x2), (y1, y2)), mode=self.mode)
self.assertFunctionContains1(f, self.get_ifelse(2)) self.assertFunctionContains1(f, self.get_ifelse(2))
rng = numpy.random.RandomState(utt.fetch_seed()) rng = np.random.RandomState(utt.fetch_seed())
xlen = rng.randint(200) xlen = rng.randint(200)
ylen = rng.randint(200) ylen = rng.randint(200)
vx1 = numpy.asarray(rng.uniform(size=(xlen,)) * 3, 'int32') vx1 = np.asarray(rng.uniform(size=(xlen,)) * 3, 'int32')
vx2 = numpy.asarray(rng.uniform(size=(xlen,)), self.dtype) vx2 = np.asarray(rng.uniform(size=(xlen,)), self.dtype)
vy1 = numpy.asarray(rng.uniform(size=(ylen,)) * 3, 'int32') vy1 = np.asarray(rng.uniform(size=(ylen,)) * 3, 'int32')
vy2 = numpy.asarray(rng.uniform(size=(ylen,)), self.dtype) vy2 = np.asarray(rng.uniform(size=(ylen,)), self.dtype)
o1, o2 = f(1, vx1, vx2, vy1, vy2) o1, o2 = f(1, vx1, vx2, vy1, vy2)
assert numpy.allclose(vx1, o1) assert np.allclose(vx1, o1)
assert numpy.allclose(vx2, o2) assert np.allclose(vx2, o2)
o1, o2 = f(0, vx1, vx2, vy1, vy2) o1, o2 = f(0, vx1, vx2, vy1, vy2)
assert numpy.allclose(vy1, o1) assert np.allclose(vy1, o1)
assert numpy.allclose(vy2, o2) assert np.allclose(vy2, o2)
def test_lazy_if_on_generics(self): def test_lazy_if_on_generics(self):
x = theano.generic() x = theano.generic()
...@@ -134,24 +134,24 @@ class test_ifelse(unittest.TestCase, utt.TestOptimizationMixin): ...@@ -134,24 +134,24 @@ class test_ifelse(unittest.TestCase, utt.TestOptimizationMixin):
# There is only 2 of the 3 ifelse that are moved on the GPU. # There is only 2 of the 3 ifelse that are moved on the GPU.
# The one that stay on the CPU is for the shape. # The one that stay on the CPU is for the shape.
self.assertFunctionContains(f, self.get_ifelse(1), min=2, max=3) self.assertFunctionContains(f, self.get_ifelse(1), min=2, max=3)
rng = numpy.random.RandomState(utt.fetch_seed()) rng = np.random.RandomState(utt.fetch_seed())
xlen = rng.randint(200) xlen = rng.randint(200)
ylen = rng.randint(200) ylen = rng.randint(200)
vx = numpy.asarray(rng.uniform(size=(xlen,)), self.dtype) vx = np.asarray(rng.uniform(size=(xlen,)), self.dtype)
vy = numpy.asarray(rng.uniform(size=(ylen,)), self.dtype) vy = np.asarray(rng.uniform(size=(ylen,)), self.dtype)
gx0, gy0 = f(1, vx, vy) gx0, gy0 = f(1, vx, vy)
assert numpy.allclose(gx0.shape, vx.shape) assert np.allclose(gx0.shape, vx.shape)
assert numpy.allclose(gy0.shape, vy.shape) assert np.allclose(gy0.shape, vy.shape)
assert numpy.all(numpy.asarray(gx0) == 1.) assert np.all(np.asarray(gx0) == 1.)
assert numpy.all(numpy.asarray(gy0) == 0.) assert np.all(np.asarray(gy0) == 0.)
gx0, gy0 = f(0, vx, vy) gx0, gy0 = f(0, vx, vy)
assert numpy.allclose(gx0.shape, vx.shape) assert np.allclose(gx0.shape, vx.shape)
assert numpy.allclose(gy0.shape, vy.shape) assert np.allclose(gy0.shape, vy.shape)
assert numpy.all(numpy.asarray(gx0) == 0.) assert np.all(np.asarray(gx0) == 0.)
assert numpy.all(numpy.asarray(gy0) == 1.) assert np.all(np.asarray(gy0) == 1.)
def test_grad_cast_input(self): def test_grad_cast_input(self):
# Tests the gradient when both inputs are on the GPU. # Tests the gradient when both inputs are on the GPU.
...@@ -178,24 +178,24 @@ class test_ifelse(unittest.TestCase, utt.TestOptimizationMixin): ...@@ -178,24 +178,24 @@ class test_ifelse(unittest.TestCase, utt.TestOptimizationMixin):
if isinstance(x.op, IfElse)][0] if isinstance(x.op, IfElse)][0]
assert len(ifnode.outputs) == 2 assert len(ifnode.outputs) == 2
rng = numpy.random.RandomState(utt.fetch_seed()) rng = np.random.RandomState(utt.fetch_seed())
x1len = rng.randint(200) x1len = rng.randint(200)
x2len = rng.randint(200) x2len = rng.randint(200)
y1len = rng.randint(200) y1len = rng.randint(200)
y2len = rng.randint(200) y2len = rng.randint(200)
vx1 = numpy.asarray(rng.uniform(size=(x1len,)), self.dtype) vx1 = np.asarray(rng.uniform(size=(x1len,)), self.dtype)
vx2 = numpy.asarray(rng.uniform(size=(x2len,)), self.dtype) vx2 = np.asarray(rng.uniform(size=(x2len,)), self.dtype)
vy1 = numpy.asarray(rng.uniform(size=(y1len,)), self.dtype) vy1 = np.asarray(rng.uniform(size=(y1len,)), self.dtype)
vy2 = numpy.asarray(rng.uniform(size=(y2len,)), self.dtype) vy2 = np.asarray(rng.uniform(size=(y2len,)), self.dtype)
ovx1, ovx2 = f(1, vx1, vx2, vy1, vy2) ovx1, ovx2 = f(1, vx1, vx2, vy1, vy2)
ovy1, ovy2 = f(0, vx1, vx2, vy1, vy2) ovy1, ovy2 = f(0, vx1, vx2, vy1, vy2)
assert numpy.allclose(vx1, ovx1) assert np.allclose(vx1, ovx1)
assert numpy.allclose(vy1, ovy1) assert np.allclose(vy1, ovy1)
assert numpy.allclose(vx2, ovx2) assert np.allclose(vx2, ovx2)
assert numpy.allclose(vy2, ovy2) assert np.allclose(vy2, ovy2)
def test_multiple_out_grad(self): def test_multiple_out_grad(self):
# Tests that we can compute the gradients through lazy if # Tests that we can compute the gradients through lazy if
...@@ -209,34 +209,34 @@ class test_ifelse(unittest.TestCase, utt.TestOptimizationMixin): ...@@ -209,34 +209,34 @@ class test_ifelse(unittest.TestCase, utt.TestOptimizationMixin):
[x1, x2, y1, y2]) [x1, x2, y1, y2])
f = theano.function([c, x1, x2, y1, y2], grads) f = theano.function([c, x1, x2, y1, y2], grads)
rng = numpy.random.RandomState(utt.fetch_seed()) rng = np.random.RandomState(utt.fetch_seed())
lens = [rng.randint(200) for i in range(4)] lens = [rng.randint(200) for i in range(4)]
values = [numpy.asarray(rng.uniform(size=(l,)), theano.config.floatX) values = [np.asarray(rng.uniform(size=(l,)), theano.config.floatX)
for l in lens] for l in lens]
outs_1 = f(1, *values) outs_1 = f(1, *values)
assert all([x.shape[0] == y for x, y in zip(outs_1, lens)]) assert all([x.shape[0] == y for x, y in zip(outs_1, lens)])
assert numpy.all(outs_1[0] == 1.) assert np.all(outs_1[0] == 1.)
assert numpy.all(outs_1[1] == 1.) assert np.all(outs_1[1] == 1.)
assert numpy.all(outs_1[2] == 0.) assert np.all(outs_1[2] == 0.)
assert numpy.all(outs_1[3] == 0.) assert np.all(outs_1[3] == 0.)
outs_0 = f(0, *values) outs_0 = f(0, *values)
assert all([x.shape[0] == y for x, y in zip(outs_1, lens)]) assert all([x.shape[0] == y for x, y in zip(outs_1, lens)])
assert numpy.all(outs_0[0] == 0.) assert np.all(outs_0[0] == 0.)
assert numpy.all(outs_0[1] == 0.) assert np.all(outs_0[1] == 0.)
assert numpy.all(outs_0[2] == 1.) assert np.all(outs_0[2] == 1.)
assert numpy.all(outs_0[3] == 1.) assert np.all(outs_0[3] == 1.)
def test_multiple_out_crash(self): def test_multiple_out_crash(self):
# This test failed up to commit 2faeb62c38 # This test failed up to commit 2faeb62c38
p0 = self.shared(numpy.asarray(numpy.random.random([4, 8]), p0 = self.shared(np.asarray(np.random.random([4, 8]),
dtype=self.dtype)) dtype=self.dtype))
p1 = self.shared(numpy.asarray(numpy.random.random(8), p1 = self.shared(np.asarray(np.random.random(8),
dtype=self.dtype)) dtype=self.dtype))
p2 = self.shared(numpy.asarray(numpy.random.random([8, 3]), p2 = self.shared(np.asarray(np.random.random([8, 3]),
dtype=self.dtype)) dtype=self.dtype))
p3 = self.shared(numpy.asarray(numpy.random.random(3), p3 = self.shared(np.asarray(np.random.random(3),
dtype=self.dtype)) dtype=self.dtype))
p = [p0, p1, p2, p3] p = [p0, p1, p2, p3]
...@@ -264,15 +264,15 @@ class test_ifelse(unittest.TestCase, utt.TestOptimizationMixin): ...@@ -264,15 +264,15 @@ class test_ifelse(unittest.TestCase, utt.TestOptimizationMixin):
updates=new_updates, mode=self.mode) updates=new_updates, mode=self.mode)
self.assertFunctionContains1(f, self.get_ifelse(4)) self.assertFunctionContains1(f, self.get_ifelse(4))
i1 = numpy.asarray(numpy.random.random([19, 4, 8]), dtype=self.dtype) i1 = np.asarray(np.random.random([19, 4, 8]), dtype=self.dtype)
i2 = numpy.asarray(numpy.random.random([19, 8]), dtype=self.dtype) i2 = np.asarray(np.random.random([19, 8]), dtype=self.dtype)
i3 = numpy.asarray(numpy.random.random([19, 8, 3]), dtype=self.dtype) i3 = np.asarray(np.random.random([19, 8, 3]), dtype=self.dtype)
i4 = numpy.asarray(numpy.random.random([19, 3]), dtype=self.dtype) i4 = np.asarray(np.random.random([19, 3]), dtype=self.dtype)
f(i1, i2, i3, i4) f(i1, i2, i3, i4)
def test_dtype_mismatch(self): def test_dtype_mismatch(self):
rng = numpy.random.RandomState(utt.fetch_seed()) rng = np.random.RandomState(utt.fetch_seed())
data = rng.rand(5).astype(self.dtype) data = rng.rand(5).astype(self.dtype)
x = self.shared(data) x = self.shared(data)
y = tensor.cast(x * 10, 'int8') y = tensor.cast(x * 10, 'int8')
...@@ -282,7 +282,7 @@ class test_ifelse(unittest.TestCase, utt.TestOptimizationMixin): ...@@ -282,7 +282,7 @@ class test_ifelse(unittest.TestCase, utt.TestOptimizationMixin):
self.assertRaises(TypeError, ifelse, cond, y, x) self.assertRaises(TypeError, ifelse, cond, y, x)
def test_ndim_mismatch(self): def test_ndim_mismatch(self):
rng = numpy.random.RandomState(utt.fetch_seed()) rng = np.random.RandomState(utt.fetch_seed())
data = rng.rand(5).astype(self.dtype) data = rng.rand(5).astype(self.dtype)
x = self.shared(data) x = self.shared(data)
y = tensor.col('y', self.dtype) y = tensor.col('y', self.dtype)
...@@ -292,7 +292,7 @@ class test_ifelse(unittest.TestCase, utt.TestOptimizationMixin): ...@@ -292,7 +292,7 @@ class test_ifelse(unittest.TestCase, utt.TestOptimizationMixin):
self.assertRaises(TypeError, ifelse, cond, y, x) self.assertRaises(TypeError, ifelse, cond, y, x)
def test_broadcast_mismatch(self): def test_broadcast_mismatch(self):
rng = numpy.random.RandomState(utt.fetch_seed()) rng = np.random.RandomState(utt.fetch_seed())
data = rng.rand(5).astype(self.dtype) data = rng.rand(5).astype(self.dtype)
x = self.shared(data) x = self.shared(data)
# print x.broadcastable # print x.broadcastable
...@@ -307,7 +307,7 @@ class test_ifelse(unittest.TestCase, utt.TestOptimizationMixin): ...@@ -307,7 +307,7 @@ class test_ifelse(unittest.TestCase, utt.TestOptimizationMixin):
import theano.sparse import theano.sparse
if not theano.sparse.enable_sparse: if not theano.sparse.enable_sparse:
raise SkipTest("Optimization temporarily disabled") raise SkipTest("Optimization temporarily disabled")
rng = numpy.random.RandomState(utt.fetch_seed()) rng = np.random.RandomState(utt.fetch_seed())
data = rng.rand(2, 3).astype(self.dtype) data = rng.rand(2, 3).astype(self.dtype)
x = self.shared(data) x = self.shared(data)
y = theano.sparse.matrix('csc', dtype=self.dtype, name='y') y = theano.sparse.matrix('csc', dtype=self.dtype, name='y')
...@@ -375,7 +375,7 @@ class test_ifelse(unittest.TestCase, utt.TestOptimizationMixin): ...@@ -375,7 +375,7 @@ class test_ifelse(unittest.TestCase, utt.TestOptimizationMixin):
f = theano.function([x1, x2, y1, y2, w1, w2, c], out, f = theano.function([x1, x2, y1, y2, w1, w2, c], out,
allow_input_downcast=True) allow_input_downcast=True)
assert isinstance(f.maker.fgraph.toposort()[-1].op, IfElse) assert isinstance(f.maker.fgraph.toposort()[-1].op, IfElse)
rng = numpy.random.RandomState(utt.fetch_seed()) rng = np.random.RandomState(utt.fetch_seed())
vx1 = rng.uniform() vx1 = rng.uniform()
vx2 = rng.uniform() vx2 = rng.uniform()
vy1 = rng.uniform() vy1 = rng.uniform()
...@@ -383,9 +383,9 @@ class test_ifelse(unittest.TestCase, utt.TestOptimizationMixin): ...@@ -383,9 +383,9 @@ class test_ifelse(unittest.TestCase, utt.TestOptimizationMixin):
vw1 = rng.uniform() vw1 = rng.uniform()
vw2 = rng.uniform() vw2 = rng.uniform()
assert numpy.allclose(f(vx1, vx2, vy1, vy2, vw1, vw2, 1), assert np.allclose(f(vx1, vx2, vy1, vy2, vw1, vw2, 1),
vx1 * vy1 * vw1) vx1 * vy1 * vw1)
assert numpy.allclose(f(vx1, vx2, vy1, vy2, vw1, vw2, 0), assert np.allclose(f(vx1, vx2, vy1, vy2, vw1, vw2, 0),
vx2 * vy2 * vw2) vx2 * vy2 * vw2)
def test_pushout3(self): def test_pushout3(self):
...@@ -394,23 +394,23 @@ class test_ifelse(unittest.TestCase, utt.TestOptimizationMixin): ...@@ -394,23 +394,23 @@ class test_ifelse(unittest.TestCase, utt.TestOptimizationMixin):
y1 = tensor.scalar('x2') y1 = tensor.scalar('x2')
y2 = tensor.scalar('y2') y2 = tensor.scalar('y2')
c = tensor.iscalar('c') c = tensor.iscalar('c')
two = numpy.asarray(2, dtype=theano.config.floatX) two = np.asarray(2, dtype=theano.config.floatX)
x, y = ifelse(c, (x1, y1), (two, y2), name='f1') x, y = ifelse(c, (x1, y1), (two, y2), name='f1')
o3 = numpy.asarray(0.3, dtype=theano.config.floatX) o3 = np.asarray(0.3, dtype=theano.config.floatX)
o2 = numpy.asarray(0.2, dtype=theano.config.floatX) o2 = np.asarray(0.2, dtype=theano.config.floatX)
z = ifelse(c, o3, o2, name='f2') z = ifelse(c, o3, o2, name='f2')
out = x * z * y out = x * z * y
f = theano.function([x1, y1, y2, c], out, f = theano.function([x1, y1, y2, c], out,
allow_input_downcast=True) allow_input_downcast=True)
assert isinstance(f.maker.fgraph.toposort()[-1].op, IfElse) assert isinstance(f.maker.fgraph.toposort()[-1].op, IfElse)
rng = numpy.random.RandomState(utt.fetch_seed()) rng = np.random.RandomState(utt.fetch_seed())
vx1 = rng.uniform() vx1 = rng.uniform()
vy1 = rng.uniform() vy1 = rng.uniform()
vy2 = rng.uniform() vy2 = rng.uniform()
assert numpy.allclose(f(vx1, vy1, vy2, 1), vx1 * vy1 * 0.3) assert np.allclose(f(vx1, vy1, vy2, 1), vx1 * vy1 * 0.3)
assert numpy.allclose(f(vx1, vy1, vy2, 0), 2 * vy2 * 0.2) assert np.allclose(f(vx1, vy1, vy2, 0), 2 * vy2 * 0.2)
def test_pushout2(self): def test_pushout2(self):
raise SkipTest("Optimization temporarily disabled") raise SkipTest("Optimization temporarily disabled")
...@@ -428,7 +428,7 @@ class test_ifelse(unittest.TestCase, utt.TestOptimizationMixin): ...@@ -428,7 +428,7 @@ class test_ifelse(unittest.TestCase, utt.TestOptimizationMixin):
f = theano.function([x1, x2, y1, y2, w1, w2, c], out, f = theano.function([x1, x2, y1, y2, w1, w2, c], out,
allow_input_downcast=True) allow_input_downcast=True)
assert isinstance(f.maker.fgraph.toposort()[-1].op, IfElse) assert isinstance(f.maker.fgraph.toposort()[-1].op, IfElse)
rng = numpy.random.RandomState(utt.fetch_seed()) rng = np.random.RandomState(utt.fetch_seed())
vx1 = rng.uniform() vx1 = rng.uniform()
vx2 = rng.uniform() vx2 = rng.uniform()
vy1 = rng.uniform() vy1 = rng.uniform()
...@@ -439,14 +439,14 @@ class test_ifelse(unittest.TestCase, utt.TestOptimizationMixin): ...@@ -439,14 +439,14 @@ class test_ifelse(unittest.TestCase, utt.TestOptimizationMixin):
vw = vw1 vw = vw1
else: else:
vw = vw2 vw = vw2
assert numpy.allclose(f(vx1, vx2, vy1, vy2, vw1, vw2, 1), assert np.allclose(f(vx1, vx2, vy1, vy2, vw1, vw2, 1),
vx1 * vy1 * vw) vx1 * vy1 * vw)
if vx2 > vy2: if vx2 > vy2:
vw = vw1 vw = vw1
else: else:
vw = vw2 vw = vw2
assert numpy.allclose(f(vx1, vx2, vy1, vy2, vw1, vw2, 0), assert np.allclose(f(vx1, vx2, vy1, vy2, vw1, vw2, 0),
vx2 * vy2 * vw) vx2 * vy2 * vw)
def test_merge_ifs_true_false(self): def test_merge_ifs_true_false(self):
...@@ -467,16 +467,16 @@ class test_ifelse(unittest.TestCase, utt.TestOptimizationMixin): ...@@ -467,16 +467,16 @@ class test_ifelse(unittest.TestCase, utt.TestOptimizationMixin):
assert len([x for x in f.maker.fgraph.toposort() assert len([x for x in f.maker.fgraph.toposort()
if isinstance(x.op, IfElse)]) == 1 if isinstance(x.op, IfElse)]) == 1
rng = numpy.random.RandomState(utt.fetch_seed()) rng = np.random.RandomState(utt.fetch_seed())
vx1 = rng.uniform() vx1 = rng.uniform()
vx2 = rng.uniform() vx2 = rng.uniform()
vy1 = rng.uniform() vy1 = rng.uniform()
vy2 = rng.uniform() vy2 = rng.uniform()
vw1 = rng.uniform() vw1 = rng.uniform()
vw2 = rng.uniform() vw2 = rng.uniform()
assert numpy.allclose(f(vx1, vx2, vy1, vy2, vw1, vw2, 1), assert np.allclose(f(vx1, vx2, vy1, vy2, vw1, vw2, 1),
vx1 + vy1 + vw1) vx1 + vy1 + vw1)
assert numpy.allclose(f(vx1, vx2, vy1, vy2, vw1, vw2, 0), assert np.allclose(f(vx1, vx2, vy1, vy2, vw1, vw2, 0),
vx2 + vy2 + vw2) vx2 + vy2 + vw2)
def test_grad_test_values(self): def test_grad_test_values(self):
...@@ -494,8 +494,8 @@ class test_ifelse(unittest.TestCase, utt.TestOptimizationMixin): ...@@ -494,8 +494,8 @@ class test_ifelse(unittest.TestCase, utt.TestOptimizationMixin):
theano.config.compute_test_value = backup theano.config.compute_test_value = backup
def test_grad_int_value(self): def test_grad_int_value(self):
w = theano.shared(numpy.random.rand(10)) w = theano.shared(np.random.rand(10))
b = theano.shared(numpy.random.rand()) b = theano.shared(np.random.rand())
params = [w, b] params = [w, b]
x = tensor.vector() x = tensor.vector()
......
Markdown 格式
0% 或
您添加了 0 人 到此讨论。请谨慎行事。
请先完成此评论的编辑！
请 注册 或者 后发表评论