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提交 eb7e7c9b authored 作者: Ian Goodfellow's avatar Ian Goodfellow
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make test_scan use deterministic interface

上级 964ce8d8
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正在显示 包含 27 行增加 和 26 行删除
theano/scan_module/tests/test_scan.py
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......@@ -16,6 +16,7 @@ from theano.compile.pfunc import rebuild_collect_shared
from theano.gof.python25 import any
from theano.tests import unittest_tools as utt
import theano.scalar.sharedvar
from theano.gof.python25 import OrderedDict
from numpy.testing.noseclasses import KnownFailureTest
......@@ -1009,7 +1010,7 @@ class T_Scan(unittest.TestCase):
x0 = theano.tensor.constant(x0)
to_replace = outputs[0].owner.inputs[0].owner.inputs[1]
outputs = theano.clone(outputs,
replace={to_replace: x0})
replace=[(to_replace, x0)])
mode = theano.compile.mode.get_mode(None).including('inplace')
f9 = theano.function([],
outputs,
......@@ -1299,7 +1300,7 @@ class T_Scan(unittest.TestCase):
state = theano.shared(v_state, 'vstate')
def f_2():
return {state: 2 * state}
return OrderedDict([(state, 2 * state)])
n_steps = theano.tensor.iscalar('nstep')
output, updates = theano.scan(f_2,
[],
......@@ -1829,7 +1830,7 @@ class T_Scan(unittest.TestCase):
X = theano.shared(numpy.array(1))
out, updates = theano.scan(
lambda: {X: X + 1},
lambda: OrderedDict([(X, (X + 1))]),
outputs_info=[],
non_sequences=[],
sequences=[],
......@@ -1844,7 +1845,7 @@ class T_Scan(unittest.TestCase):
y = theano.shared(numpy.array(1))
out, updates = theano.scan(
lambda: {x: x + 1, y: x},
lambda: OrderedDict([(x, x + 1), (y, x)]),
outputs_info=[],
non_sequences=[],
sequences=[],
......@@ -1880,11 +1881,11 @@ class T_Scan(unittest.TestCase):
b = theano.shared(numpy.random.rand(5, 4))
def inner_func(a):
return a + 1, {b: 2 * b}
return a + 1, OrderedDict([(b, 2 * b)])
out, updates = theano.scan(
inner_func,
outputs_info=[{'initial': init_a}],
outputs_info=[OrderedDict([('initial', init_a)])],
n_steps=1)
out = out[-1]
assert out.type.ndim == a.type.ndim
......@@ -1967,7 +1968,7 @@ class T_Scan(unittest.TestCase):
f1 = z * (x + y) ** 2 + 5
f2 = theano.clone(f1,
replace={y: y2},
replace=OrderedDict([(y, y2)]),
strict=True,
copy_inputs=True)
f2_inp = theano.gof.graph.inputs([f2])
......@@ -1986,7 +1987,7 @@ class T_Scan(unittest.TestCase):
f1 = z * (x + y) ** 2 + 5
f2 = theano.clone(f1,
replace={y: y2},
replace=OrderedDict([(y, y2)]),
strict=False,
copy_inputs=True)
f2_inp = theano.gof.graph.inputs([f2])
......@@ -2005,7 +2006,7 @@ class T_Scan(unittest.TestCase):
f1 = z * (x + y) ** 2 + 5
f2 = theano.clone(f1,
replace={y: y2},
replace=[(y, y2)],
strict=True,
copy_inputs=False)
f2_inp = theano.gof.graph.inputs([f2])
......@@ -2024,7 +2025,7 @@ class T_Scan(unittest.TestCase):
f1 = z * (x + y) ** 2 + 5
f2 = theano.clone(f1,
replace={y: y2},
replace=[(y, y2)],
strict=False,
copy_inputs=False)
f2_inp = theano.gof.graph.inputs([f2])
......@@ -2204,15 +2205,15 @@ class T_Scan(unittest.TestCase):
v2 = theano.shared(numpy.ones((5, 5), dtype=theano.config.floatX))
shapef = theano.function([W],
expr,
givens={initial: v1,
inpt: v2})
givens=OrderedDict([(initial, v1),
(inpt, v2)]))
# First execution to cache n_steps
shapef(numpy.ones((5, 5), dtype=theano.config.floatX))
cost = expr.sum()
d_cost_wrt_W = tensor.grad(cost, [W])
f = theano.function([W, inpt], d_cost_wrt_W,
givens={initial: theano.shared(numpy.zeros(5))})
givens=OrderedDict([(initial, theano.shared(numpy.zeros(5)))]))
rval = numpy.asarray([[5187989] * 5] * 5, dtype=theano.config.floatX)
arg1 = numpy.ones((5, 5), dtype=theano.config.floatX)
......@@ -3166,7 +3167,7 @@ class T_Scan(unittest.TestCase):
shared_var = theano.shared(numpy.float32(1.))
def inner_fn():
return [], {shared_var: shared_var + numpy.float32(1.)}
return [], OrderedDict([(shared_var, shared_var + numpy.float32(1.))])
_, updates = theano.scan(inner_fn,
n_steps=10,
truncate_gradient=-1,
......@@ -3239,7 +3240,7 @@ class T_Scan(unittest.TestCase):
seq = tensor.matrix()
initial_value = theano.shared(numpy.zeros((4, 1),
dtype=theano.config.floatX))
outputs_info = [{'initial': initial_value, 'taps': [-4]}, None]
outputs_info = [OrderedDict([('initial', initial_value), ('taps', [-4])]), None]
results, updates = theano.scan(fn=onestep,
sequences=seq,
outputs_info=outputs_info)
......@@ -3259,13 +3260,13 @@ class T_Scan(unittest.TestCase):
seq = tensor.matrix()
initial_value = theano.shared(numpy.zeros((4, 1),
dtype=theano.config.floatX))
outputs_info = [{'initial': initial_value, 'taps': [-4]}, None]
outputs_info = [OrderedDict([('initial', initial_value), ('taps', [-4])]), None]
results, _ = theano.scan(fn=onestep,
sequences=seq,
outputs_info=outputs_info)
sharedvar = theano.shared(numpy.zeros((1, 1),
dtype=theano.config.floatX))
updates = {sharedvar: results[0][-1:]}
updates = OrderedDict([(sharedvar, results[0][-1:])])
f = theano.function([seq], results[1], updates=updates)
assert numpy.all(exp_out == f(inp))
......@@ -3354,9 +3355,9 @@ def test_speed():
theano.printing.debugprint(s_rinc)
f = theano.function([],
[],
updates={
s_i: s_i + 1,
shared_r: s_rinc},
updates=OrderedDict([
(s_i, s_i + 1),
(shared_r, s_rinc)]),
mode=theano.Mode(linker='cvm'))
f._check_for_aliased_inputs = False
t2 = time.time()
......@@ -3430,9 +3431,9 @@ def test_speed_rnn():
w)),
tolerate_inplace_aliasing=True)
f = theano.function([], [],
updates={
s_i: s_i + 1,
shared_r: s_rinc},
updates=OrderedDict([
(s_i, s_i + 1),
(shared_r, s_rinc)]),
mode=theano.Mode(linker='cvm'))
#theano.printing.debugprint(f)
f_fn = f.fn
......@@ -3495,9 +3496,9 @@ def test_speed_batchrnn():
tolerate_inplace_aliasing=True)
f = theano.function([],
[],
updates={
s_i: s_i + 1,
shared_r: s_rinc},
updates=[
(s_i, s_i + 1),
(shared_r, s_rinc)],
mode=theano.Mode(linker='cvm'))
#theano.printing.debugprint(f)
f_fn = f.fn
......
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