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提交 5b0f5ac7 authored 作者: Frederic Bastien's avatar Frederic Bastien
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make scan tests work with float32 to allow to test it with the gpu too.

上级 cc3fe3ca
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正在显示 包含 82 行增加 和 72 行删除
theano/tests/test_scan.py
浏览文件 @ 5b0f5ac7
......@@ -86,6 +86,8 @@ def scan_project_sum(*args, **kwargs):
rng.add_default_updates = False
return sum([(s * rng.uniform(size=s.shape)).sum() for s in scan_outputs])
def asarrayX(value):
return theano._asarray(value, dtype=theano.config.floatX)
class T_Scan(unittest.TestCase):
......@@ -98,8 +100,8 @@ class T_Scan(unittest.TestCase):
def f_pow2(x_tm1):
return 2*x_tm1
state = theano.tensor.dscalar()
n_steps = theano.tensor.dscalar()
state = theano.tensor.scalar()
n_steps = theano.tensor.scalar()
output, updates = theano.scan(f_pow2, [],state, [],n_steps = n_steps, truncate_gradient
= -1, go_backwards = False)
my_f = theano.function([state,n_steps], output, updates = updates)
......@@ -120,10 +122,10 @@ class T_Scan(unittest.TestCase):
def f_rnn(u_t,x_tm1,W_in, W):
return u_t*W_in+x_tm1*W
u = theano.tensor.dvector()
x0 = theano.tensor.dscalar()
W_in = theano.tensor.dscalar()
W = theano.tensor.dscalar()
u = theano.tensor.vector()
x0 = theano.tensor.scalar()
W_in = theano.tensor.scalar()
W = theano.tensor.scalar()
output, updates = theano.scan(f_rnn, u,x0,[W_in,W], n_steps = None, truncate_gradient =
-1, go_backwards = False)
......@@ -150,10 +152,10 @@ class T_Scan(unittest.TestCase):
# are vectors, weights are scalars; using shared variables
def test_one_sequence_one_output_weights_shared(self):
rng = numpy.random.RandomState(utt.fetch_seed())
u = theano.tensor.dvector()
x0 = theano.tensor.dscalar()
W_in = theano.shared(rng.uniform(), name = 'w_in')
W = theano.shared(rng.uniform(), name ='w')
u = theano.tensor.vector()
x0 = theano.tensor.scalar()
W_in = theano.shared(asarrayX(rng.uniform()), name = 'w_in')
W = theano.shared(asarrayX(rng.uniform()), name ='w')
def f_rnn_shared(u_t,x_tm1, tmp_W_in, tmp_W):
return u_t*tmp_W_in+x_tm1*tmp_W
......@@ -179,23 +181,23 @@ class T_Scan(unittest.TestCase):
# dimension instead of scalars/vectors
def test_multiple_inputs_multiple_outputs(self):
rng = numpy.random.RandomState(utt.fetch_seed())
vW_in2 = rng.uniform(size = (2,), low = -5.,high = 5.)
vW = rng.uniform(size = (2,2), low = -5.,high = 5.)
vWout = rng.uniform(size = (2,), low = -5.,high = 5.)
vW_in1 = rng.uniform(size = (2,2), low = -5.,high = 5.)
v_u1 = rng.uniform(size = (3,2), low = -5., high = 5.)
v_u2 = rng.uniform(size = (3,), low = -5.,high = 5.)
v_x0 = rng.uniform(size = (2,), low = -5.,high = 5.)
v_y0 = rng.uniform()
vW_in2 = asarrayX(rng.uniform(size = (2,), low = -5.,high = 5.))
vW = asarrayX(rng.uniform(size = (2,2), low = -5.,high = 5.))
vWout = asarrayX(rng.uniform(size = (2,), low = -5.,high = 5.))
vW_in1 = asarrayX(rng.uniform(size = (2,2), low = -5.,high = 5.))
v_u1 = asarrayX(rng.uniform(size = (3,2), low = -5., high = 5.))
v_u2 = asarrayX(rng.uniform(size = (3,), low = -5.,high = 5.))
v_x0 = asarrayX(rng.uniform(size = (2,), low = -5.,high = 5.))
v_y0 = asarrayX(rng.uniform())
W_in2 = theano.shared(vW_in2, name='win2')
W = theano.shared(vW, name='w')
W_out = theano.shared(vWout, name = 'wout')
W_in1 = theano.tensor.dmatrix('win')
u1 = theano.tensor.dmatrix('u1')
u2 = theano.tensor.dvector('u2')
x0 = theano.tensor.dvector('x0')
y0 = theano.tensor.dscalar('y0')
W_in1 = theano.tensor.matrix('win')
u1 = theano.tensor.matrix('u1')
u2 = theano.tensor.vector('u2')
x0 = theano.tensor.vector('x0')
y0 = theano.tensor.scalar('y0')
def f_rnn_cmpl(u1_t, u2_t, x_tm1, y_tm1, W_in1):
return [theano.dot(u1_t,W_in1) + u2_t* W_in2 + \
......@@ -206,8 +208,8 @@ class T_Scan(unittest.TestCase):
f4 = theano.function([u1,u2,x0,y0,W_in1], outputs, updates = updates)
# compute the values in numpy
v_x = numpy.zeros((3,2))
v_y = numpy.zeros((3,))
v_x = numpy.zeros((3,2),dtype=theano.config.floatX)
v_y = numpy.zeros((3,),dtype=theano.config.floatX)
v_x[0] = numpy.dot(v_u1[0],vW_in1) + v_u2[0]*vW_in2 + numpy.dot(v_x0,vW)
v_y[0] = numpy.dot(v_x0,vWout)
for i in xrange(1,3):
......@@ -225,13 +227,13 @@ class T_Scan(unittest.TestCase):
# taps (sequences and outputs)
def test_using_taps_input_output(self):
rng = numpy.random.RandomState(utt.fetch_seed())
vW = rng.uniform()
vW_in = rng.uniform()
vu = rng.uniform(size=(4,), low = -5., high = 5.)
vx0 = rng.uniform(size=(2,), low = -5., high = 5.)
vW = asarrayX(rng.uniform())
vW_in = asarrayX(rng.uniform())
vu = asarrayX(rng.uniform(size=(4,), low = -5., high = 5.))
vx0 = asarrayX(rng.uniform(size=(2,), low = -5., high = 5.))
u = theano.tensor.dvector()
x0 = theano.tensor.dvector()
u = theano.tensor.vector()
x0 = theano.tensor.vector()
W_in = theano.shared(vW_in, name = 'w_in')
W = theano.shared(vW, name ='w')
......@@ -265,13 +267,13 @@ class T_Scan(unittest.TestCase):
# taps (sequences and outputs) and future taps for sequences
def test_past_future_taps_shared(self):
rng = numpy.random.RandomState(utt.fetch_seed())
vW = rng.uniform()
vW_in = rng.uniform()
vu = rng.uniform(size=(6,), low = -5., high = 5.)
vx0 = rng.uniform(size=(2,), low = -5., high = 5.)
vW = asarrayX(rng.uniform())
vW_in = asarrayX(rng.uniform())
vu = asarrayX(rng.uniform(size=(6,), low = -5., high = 5.))
vx0 = asarrayX(rng.uniform(size=(2,), low = -5., high = 5.))
u = theano.tensor.dvector()
x0 = theano.tensor.dvector()
u = theano.tensor.vector()
x0 = theano.tensor.vector()
W_in = theano.shared(vW_in, name = 'w_in')
W = theano.shared(vW, name ='w')
......@@ -297,22 +299,22 @@ class T_Scan(unittest.TestCase):
# simple rnn ; compute inplace version 1
def test_inplace1(self):
rng = numpy.random.RandomState(utt.fetch_seed())
vW = numpy.random.uniform()
vW_in = numpy.random.uniform()
vu0 = rng.uniform(size=(3,), low = -5., high = 5.)
vu1 = rng.uniform(size=(3,), low = -5., high = 5.)
vu2 = rng.uniform(size=(3,), low = -5., high = 5.)
vx0 = rng.uniform()
vx1 = rng.uniform()
u0 = theano.tensor.dvector('u0')
u1 = theano.tensor.dvector('u1')
u2 = theano.tensor.dvector('u2')
vW = asarrayX(numpy.random.uniform())
vW_in = asarrayX(numpy.random.uniform())
vu0 = asarrayX(rng.uniform(size=(3,), low = -5., high = 5.))
vu1 = asarrayX(rng.uniform(size=(3,), low = -5., high = 5.))
vu2 = asarrayX(rng.uniform(size=(3,), low = -5., high = 5.))
vx0 = asarrayX(rng.uniform())
vx1 = asarrayX(rng.uniform())
u0 = theano.tensor.vector('u0')
u1 = theano.tensor.vector('u1')
u2 = theano.tensor.vector('u2')
mu0 = theano.Param( u0, mutable = False)
mu1 = theano.Param( u1, mutable = True)
mu2 = theano.Param( u2, mutable = True)
x0 = theano.tensor.dscalar('x0')
x1 = theano.tensor.dscalar('y0')
x0 = theano.tensor.scalar('x0')
x1 = theano.tensor.scalar('y0')
W_in = theano.shared(vW_in,'Win')
W = theano.shared(vW,'W')
mode = theano.compile.mode.get_mode(None).including('inplace')
......@@ -345,22 +347,22 @@ class T_Scan(unittest.TestCase):
# simple rnn ; compute inplace version 2
def test_inplace2(self):
rng = numpy.random.RandomState(utt.fetch_seed())
vW = numpy.random.uniform()
vW_in = numpy.random.uniform()
vu0 = rng.uniform(size=(3,), low = -5., high = 5.)
vu1 = rng.uniform(size=(4,), low = -5., high = 5.)
vu2 = rng.uniform(size=(5,), low = -5., high = 5.)
vx0 = rng.uniform()
vx1 = rng.uniform()
u0 = theano.tensor.dvector('u0')
u1 = theano.tensor.dvector('u1')
u2 = theano.tensor.dvector('u2')
vW = asarrayX(numpy.random.uniform())
vW_in = asarrayX(numpy.random.uniform())
vu0 = asarrayX(rng.uniform(size=(3,), low = -5., high = 5.))
vu1 = asarrayX(rng.uniform(size=(4,), low = -5., high = 5.))
vu2 = asarrayX(rng.uniform(size=(5,), low = -5., high = 5.))
vx0 = asarrayX(rng.uniform())
vx1 = asarrayX(rng.uniform())
u0 = theano.tensor.vector('u0')
u1 = theano.tensor.vector('u1')
u2 = theano.tensor.vector('u2')
mu0 = theano.Param( u0, mutable = True)
mu1 = theano.Param( u1, mutable = True)
mu2 = theano.Param( u2, mutable = True)
x0 = theano.tensor.dscalar('x0')
x1 = theano.tensor.dscalar('y0')
x0 = theano.tensor.scalar('x0')
x1 = theano.tensor.scalar('y0')
W_in = theano.shared(vW_in,'Win')
W = theano.shared(vW,'W')
mode = theano.compile.mode.get_mode(None).including('inplace')
......@@ -400,13 +402,21 @@ class T_Scan(unittest.TestCase):
def test_shared_arguments_with_updates(self):
rng = numpy.random.RandomState(utt.fetch_seed())
vW1 = asarrayX(rng.rand(20,30))
vW2 = asarrayX(rng.rand(30,20))
vu1 = asarrayX(rng.rand(3,20))
vu2 = asarrayX(rng.rand(3,30))
vy0 = asarrayX(rng.rand(3,20))
vy1 = asarrayX(rng.rand(20))
vy2 = asarrayX(rng.rand(30))
import pdb;pdb.set_trace()#TODO
vW1 = rng.rand(20,30)
vW2 = rng.rand(30,20)
vu1 = rng.rand(3,20)
vu2 = rng.rand(3,30)
vy0 = rng.rand(3,20)
vy1 = rng.rand(20)
vy2 = rng.rand(30)
vy1 = rng.rand(20)
vy2 = rng.rand(30)
W1 = theano.shared(vW1,'W1')
W2 = theano.shared(vW2,'W2')
u1 = theano.shared(vu1,'u1')
......@@ -523,11 +533,11 @@ class T_Scan(unittest.TestCase):
def test_only_shared_no_input_no_output(self):
rng = numpy.random.RandomState(utt.fetch_seed())
v_state = rng.uniform()
v_state = asarrayX(rng.uniform())
state = theano.shared(v_state)
def f_2():
return {state: 2*state}
n_steps = theano.tensor.dscalar()
n_steps = theano.tensor.scalar()
output, updates = theano.scan(f_2,[],[],[],n_steps = n_steps, truncate_gradient = -1,
go_backwards = False)
this_f = theano.function([n_steps], output, updates = updates)
......@@ -540,7 +550,7 @@ class T_Scan(unittest.TestCase):
def f_rnn(u_t):
return u_t + 3
u = theano.tensor.dvector()
u = theano.tensor.vector()
outputs, updates = theano.scan(f_rnn, u,[],[], n_steps =None , truncate_gradient = -1,
go_backwards = False)
......@@ -570,10 +580,10 @@ class T_Scan(unittest.TestCase):
def f_rnn(u_t,x_tm1,W_in, W):
return u_t*W_in+x_tm1*W
u = theano.tensor.dvector()
x0 = theano.tensor.dscalar()
W_in = theano.tensor.dscalar()
W = theano.tensor.dscalar()
u = theano.tensor.vector()
x0 = theano.tensor.scalar()
W_in = theano.tensor.scalar()
W = theano.tensor.scalar()
output, updates = theano.scan(f_rnn, u,x0,[W_in,W], n_steps = None, truncate_gradient =
-1, go_backwards = True)
......
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