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提交 0c17002e authored 作者: Frederic's avatar Frederic
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theano/scan_module/tests/test_scan.py
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......@@ -16,7 +16,7 @@ import theano.sandbox.rng_mrg
from theano import tensor
from theano.compile.pfunc import rebuild_collect_shared
from theano.gof.python25 import any
from theano.tests import unittest_tools as utt
from theano.tests import unittest_tools as utt
import theano.scalar.sharedvar
from theano.gof.python25 import OrderedDict
from theano.compat import PY3
......@@ -46,7 +46,7 @@ mode_with_gpu = mode_with_opt.including('gpu', 'scan')
class multiple_outputs_numeric_grad:
"""WRITEME"""
type_eps = {'float64': 1e-7,
'float32': 3e-3}
'float32': 3e-3}
def __init__(self, f, pt, ndarray_mask=None, eps=None):
"""Return the gradient of f at pt.
......@@ -81,12 +81,12 @@ class multiple_outputs_numeric_grad:
if ndarray_mask[i]:
pt[i] = numpy.array(p)
_eps = multiple_outputs_numeric_grad.type_eps[str(
pt[i].dtype)]
pt[i].dtype)]
if _eps > dtype_eps:
dtype_eps = _eps
self.ndarray_mask = ndarray_mask
#'''
# '''
# Compute clean output:
f_x = f(*pt)
gx = []
......@@ -148,7 +148,7 @@ class multiple_outputs_numeric_grad:
return numpy.inf, 0
#TODO: Test this function, and if it works,
# TODO: Test this function, and if it works,
# use it with the normal verify_grad rather than the
# copy-and-pasted one above.
# Also - add a reference to this technique in the
......@@ -201,7 +201,6 @@ def grab_scan_node(output):
class T_Scan(unittest.TestCase):
#class T_Scan(object):
def setUp(self):
utt.seed_rng()
......@@ -230,7 +229,7 @@ class T_Scan(unittest.TestCase):
updates=updates,
allow_input_downcast=True)
### TESTING PICKLE-ing this function
# TESTING PICKLE-ing this function
origdir = os.getcwd()
tmpdir = None
try:
......@@ -367,7 +366,7 @@ class T_Scan(unittest.TestCase):
# This first version test the first case in the optimizer to the gpu.
def test_one_sequence_one_output_weights_gpu1(self):
from theano.sandbox import cuda
if cuda.cuda_available == False:
if not cuda.cuda_available:
raise SkipTest('Optional package cuda disabled')
def f_rnn(u_t, x_tm1, W_in, W):
......@@ -447,7 +446,7 @@ class T_Scan(unittest.TestCase):
# This second version test the second case in the optimizer to the gpu.
def test_one_sequence_one_output_weights_gpu2(self):
from theano.sandbox import cuda
if cuda.cuda_available == False:
if not cuda.cuda_available:
raise SkipTest('Optional package cuda disabled')
def f_rnn(u_t, x_tm1, W_in, W):
......@@ -511,7 +510,7 @@ class T_Scan(unittest.TestCase):
# outputs when is running on GPU
def test_gpu3_mixture_dtype_outputs(self):
from theano.sandbox import cuda
if cuda.cuda_available == False:
if not cuda.cuda_available:
raise SkipTest('Optional package cuda disabled')
def f_rnn(u_t, x_tm1, W_in, W):
......@@ -595,11 +594,11 @@ class T_Scan(unittest.TestCase):
v_out = numpy.zeros((4,))
v_out[0] = v_u[0] * W_in.get_value() + v_x0 * W.get_value()
for step in xrange(1, 4):
v_out[step] = v_u[step] * W_in.get_value() + \
v_out[step - 1] * W.get_value()
v_out[step] = (v_u[step] * W_in.get_value() +
v_out[step - 1] * W.get_value())
theano_values = f3(v_u, v_x0)
assert numpy.allclose(theano_values, v_out)
assert numpy.allclose(theano_values, v_out)
# some rnn with multiple outputs and multiple inputs; other
# dimension instead of scalars/vectors
......@@ -624,7 +623,7 @@ class T_Scan(unittest.TestCase):
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 + \
return [theano.dot(u1_t, W_in1) + u2_t * W_in2 +
theano.dot(x_tm1, W), theano.dot(x_tm1, W_out)]
outputs, updates = theano.scan(f_rnn_cmpl,
......@@ -643,12 +642,12 @@ class T_Scan(unittest.TestCase):
# compute the values in numpy
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_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):
v_x[i] = numpy.dot(v_u1[i], vW_in1) + v_u2[i] * vW_in2 + \
numpy.dot(v_x[i - 1], vW)
v_x[i] = (numpy.dot(v_u1[i], vW_in1) + v_u2[i] * vW_in2 +
numpy.dot(v_x[i - 1], vW))
v_y[i] = numpy.dot(v_x[i - 1], vWout)
(theano_x, theano_y) = f4(v_u1, v_u2, v_x0, v_y0, vW_in1)
......@@ -684,9 +683,9 @@ class T_Scan(unittest.TestCase):
y_tm1,
y_tm3,
W_in1):
return [theano.dot(u1_t, W_in1) + \
(u2_t + u2_tm1 * u2_tp1) * W_in2 + \
theano.dot(x_tm1, W),
return [theano.dot(u1_t, W_in1) +
(u2_t + u2_tm1 * u2_tp1) * W_in2 +
theano.dot(x_tm1, W),
(y_tm1 + y_tm3) * theano.dot(x_tm1, W_out),
theano.dot(u1_t, W_in1)]
......@@ -891,10 +890,10 @@ class T_Scan(unittest.TestCase):
numpy_x0[0] = vu0[0] * vW_in + vx0 * vW + vu1[0] * vu2[0]
numpy_x1[0] = vu0[0] * vW_in + vx1 * vW + vu1[0] + vu2[0]
for i in xrange(1, 3):
numpy_x0[i] = vu0[i] * vW_in + numpy_x0[i - 1] * vW + \
vu1[i] * vu2[i]
numpy_x1[i] = vu0[i] * vW_in + numpy_x1[i - 1] * vW + \
vu1[i] + vu2[i]
numpy_x0[i] = (vu0[i] * vW_in + numpy_x0[i - 1] * vW +
vu1[i] * vu2[i])
numpy_x1[i] = (vu0[i] * vW_in + numpy_x1[i - 1] * vW +
vu1[i] + vu2[i])
# note theano computes inplace, so call function after numpy
# equivalent is done
......@@ -908,8 +907,8 @@ class T_Scan(unittest.TestCase):
# Old way of doing inplace operations is deprecated .. tests don't
# make sense anymore.
##utt.assert_allclose( theano_x0 , vu2)
## utt.assert_allclose( theano_x1 , vu1)
## utt.assert_allclose(theano_x0 , vu2)
## utt.assert_allclose(theano_x1 , vu1)
# simple rnn ; compute inplace version 2
def test_inplace2(self):
......@@ -965,16 +964,16 @@ class T_Scan(unittest.TestCase):
if isinstance(x.op, theano.scan_module.scan_op.Scan)]
assert 0 in scan_node[0].op.destroy_map.keys()
assert 1 in scan_node[0].op.destroy_map.keys()
# compute output in numpy
# compute output in numpy
numpy_x0 = numpy.zeros((3,))
numpy_x1 = numpy.zeros((3,))
numpy_x0[0] = vu0[0] * vW_in + vx0 * vW + vu1[0] * vu1[1]
numpy_x1[0] = vu0[0] * vW_in + vx1 * vW + vu2[0] + vu2[1] + vu2[2]
for i in xrange(1, 3):
numpy_x0[i] = vu0[i] * vW_in + numpy_x0[i - 1] * vW + \
vu1[i] * vu1[i + 1]
numpy_x1[i] = vu0[i] * vW_in + numpy_x1[i - 1] * vW + \
vu2[i] + vu2[i + 1] + vu2[i + 2]
numpy_x0[i] = (vu0[i] * vW_in + numpy_x0[i - 1] * vW +
vu1[i] * vu1[i + 1])
numpy_x1[i] = (vu0[i] * vW_in + numpy_x1[i - 1] * vW +
vu2[i] + vu2[i + 1] + vu2[i + 2])
# note theano computes inplace, so call function after numpy
# equivalent is done
......@@ -1069,8 +1068,8 @@ class T_Scan(unittest.TestCase):
y1 = theano.shared(vy1, 'y1')
def f(u1_t, u2_t, y0_tm3, y0_tm2, y0_tm1, y1_tm1):
y0_t = theano.dot(theano.dot(u1_t, W1), W2) + 0.1 * y0_tm1 + \
0.33 * y0_tm2 + 0.17 * y0_tm3
y0_t = (theano.dot(theano.dot(u1_t, W1), W2) + 0.1 * y0_tm1 +
0.33 * y0_tm2 + 0.17 * y0_tm3)
y1_t = theano.dot(u2_t, W2) + y1_tm1
y2_t = theano.dot(u1_t, W1)
nwW1 = W1 + .1
......@@ -1106,14 +1105,13 @@ class T_Scan(unittest.TestCase):
numpy_W1 = vW1.copy()
numpy_W2 = vW2.copy()
for idx in xrange(3):
numpy_y0[idx + 3] = numpy.dot(\
numpy.dot(vu1[idx, :], numpy_W1), \
numpy_y0[idx + 3] = numpy.dot(numpy.dot(vu1[idx, :], numpy_W1),
numpy_W2) + \
0.1 * numpy_y0[idx + 2] + \
0.33 * numpy_y0[idx + 1] + \
0.17 * numpy_y0[idx]
numpy_y1[idx + 1] = numpy.dot(vu2[idx, :], numpy_W2) +\
numpy_y1[idx]
numpy_y1[idx + 1] = (numpy.dot(vu2[idx, :], numpy_W2) +
numpy_y1[idx])
numpy_y2[idx] = numpy.dot(vu1[idx, :], numpy_W1)
numpy_W1 = numpy_W1 + .1
numpy_W2 = numpy_W2 + .05
......@@ -1168,7 +1166,7 @@ class T_Scan(unittest.TestCase):
def test_simple_shared_random(self):
theano_rng = theano.tensor.shared_randomstreams.RandomStreams(
utt.fetch_seed())
utt.fetch_seed())
values, updates = theano.scan(lambda: theano_rng.uniform((2,), -1, 1),
[],
......@@ -1196,7 +1194,7 @@ class T_Scan(unittest.TestCase):
def test_cuda_gibbs_chain(self):
from theano.sandbox import cuda
if cuda.cuda_available == False:
if not cuda.cuda_available:
raise SkipTest('Optional package cuda disabled')
rng = numpy.random.RandomState(utt.fetch_seed())
......@@ -1204,7 +1202,7 @@ class T_Scan(unittest.TestCase):
dtype='float32')
vsample = theano.shared(v_vsample)
trng = theano.sandbox.rng_mrg.MRG_RandomStreams(
utt.fetch_seed())
utt.fetch_seed())
def f(vsample_tm1):
return trng.binomial(vsample_tm1.shape, n=1, p=0.3,
......@@ -1240,7 +1238,7 @@ class T_Scan(unittest.TestCase):
bvis = theano.shared(v_bvis, 'vbvis')
vsample = theano.tensor.matrix(dtype='float32')
trng = theano.tensor.shared_randomstreams.RandomStreams(
utt.fetch_seed())
utt.fetch_seed())
def f(vsample_tm1):
hmean_t = theano.tensor.nnet.sigmoid(
......
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