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提交 57bec52f authored 作者: Razvan Pascanu's avatar Razvan Pascanu
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mostly intendention

上级 d200d034
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正在显示 包含 62 行增加 和 21 行删除
theano/sandbox/scan_module/tests/test_scan.py
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...@@ -247,30 +247,48 @@ class TestScan(unittest.TestCase): ...@@ -247,30 +247,48 @@ class TestScan(unittest.TestCase):
allow_input_downcast=True) allow_input_downcast=True)
if n_steps is not None and abs(n_steps) == 1: if n_steps is not None and abs(n_steps) == 1:
assert len([x for x in my_f.maker.env.toposort() all_nodes = my_f.maker.env.toposort()
if isinstance(x.op, scan_module.scan_op.ScanOp)]) == 0 assert len([x for x in all_nodes
if isinstance(x.op,ScanOp)]) == 0
print >>sys.stderr, ' n_steps', n_steps
print >>sys.stderr, ' go_backwards', go_backwards
print >>sys.stderr, ' Scenario 1. Correct shape'
if n_steps is not None:
_n_steps = n_steps
else:
_n_steps = 8
# Generating data # Generating data
# Scenario 1 : Good fit shapes # Scenario 1 : Good fit shapes
inputs_values = [] input_values = []
for info in inputs_info: for info in inputs_info:
taps = [x['tap'] for x in info] taps = [x['tap'] for x in info]
offset = abs(numpy.min([x for x in taps if x < 0])) offset = 0
if len([x for x in taps if x < 0]) > 0:
offset += abs(numpy.min([x for x in taps if x < 0]))
if len([x for x in taps if x > 0]) > 0:
offset += numpy.max([x for x in taps if x > 0]) offset += numpy.max([x for x in taps if x > 0])
data = rng.uniform(size=(n_steps + offset, 4)) data = rng.uniform(size=(abs(_n_steps) + offset, 4))
inputs_values.append(data) input_values.append(data)
state_values = [] state_values = []
for info in states_info: for info in states_info:
taps = [x['tap'] for x in info] taps = [x['tap'] for x in info]
offset = abs(numpy.min(taps)) offset = abs(numpy.min(taps))
if offset > 1:
data = rng.uniform(size=(offset, 4)) data = rng.uniform(size=(offset, 4))
else:
data = rng.uniform(size=(4,))
data = numpy.arange(4)
state_values.append(data) state_values.append(data)
param_values = [rng.uniform(size=(4,)) for k in param_values = [rng.uniform(size=(4,)) for k in
xrange(n_parameters)] xrange(n_parameters)]
param_values = [numpy.arange(4) for k in
xrange(n_parameters)]
for var, val in zip(shared_vars, original_shared_values): for var, val in zip(shared_vars, original_shared_values):
var.set_value(val) var.set_value(val)
theano_outs = my_f(*(inputs_values + state_values + theano_outs = my_f(*(input_values + state_values +
param_values)) param_values))
args = ([n_steps, go_backwards] + args = ([_n_steps, go_backwards] +
input_values + input_values +
state_values + state_values +
param_values) param_values)
...@@ -279,30 +297,48 @@ class TestScan(unittest.TestCase): ...@@ -279,30 +297,48 @@ class TestScan(unittest.TestCase):
assert len(numpy_outs) == len(theano_outs) assert len(numpy_outs) == len(theano_outs)
assert len(numpy_shared) == len(shared_vars) assert len(numpy_shared) == len(shared_vars)
for th_out, num_out in zip(theano_outs, numpy_outs): for th_out, num_out in zip(theano_outs, numpy_outs):
try:
assert numpy.allclose(th_out, num_out) assert numpy.allclose(th_out, num_out)
for th_out, num_out in zip(shared_outs, numpy_shared): except:
import ipdb; ipdb.set_trace()
for th_out, num_out in zip(shared_vars, numpy_shared):
try:
assert numpy.allclose(th_out.get_value(), num_out) assert numpy.allclose(th_out.get_value(), num_out)
except:
import ipdb; ipdb.set_trace()
# Scenario 2 : Loose fit (sequences longer then required) # Scenario 2 : Loose fit (sequences longer then required)
inputs_values = [] print >>sys.stderr, ' Scenario 2. Loose shapes'
input_values = []
for pos, info in enumerate(inputs_info): for pos, info in enumerate(inputs_info):
taps = [x['tap'] for x in info] taps = [x['tap'] for x in info]
offset = abs(numpy.min([x for x in taps if x < 0])) offset = 0
if len([x for x in taps if x < 0]) > 0:
offset += abs(numpy.min([x for x in taps if x < 0]))
if len([x for x in taps if x > 0]) > 0:
offset += numpy.max([x for x in taps if x > 0]) offset += numpy.max([x for x in taps if x > 0])
data = rng.uniform(size=(n_steps + offset + pos + 1, 4)) if n_steps is not None:
inputs_values.append(data) # loose inputs make sense only when n_steps is
# defined
data = rng.uniform(size=(abs(_n_steps) + offset + pos + 1, 4))
else:
data = rng.uniform(size=(abs(_n_steps) + offset, 4))
input_values.append(data)
state_values = [] state_values = []
for pos, info in enumerate(states_info): for pos, info in enumerate(states_info):
taps = [x['tap'] for x in info] taps = [x['tap'] for x in info]
offset = abs(numpy.min(taps)) offset = abs(numpy.min(taps))
if offset > 1:
data = rng.uniform(size=(offset + pos + 1, 4)) data = rng.uniform(size=(offset + pos + 1, 4))
else:
data = rng.uniform(size=(4,))
state_values.append(data) state_values.append(data)
param_values = [rng.uniform(size=(4,)) for k in param_values = [rng.uniform(size=(4,)) for k in
xrange(n_parameters)] xrange(n_parameters)]
for var, val in zip(shared_vars, original_shared_values): for var, val in zip(shared_vars, original_shared_values):
var.set_value(val) var.set_value(val)
theano_outs = my_f(*(inputs_values + state_values + theano_outs = my_f(*(input_values + state_values +
param_values)) param_values))
args = ([n_steps, go_backwards] + args = ([_n_steps, go_backwards] +
input_values + input_values +
state_values + state_values +
param_values) param_values)
...@@ -310,18 +346,23 @@ class TestScan(unittest.TestCase): ...@@ -310,18 +346,23 @@ class TestScan(unittest.TestCase):
numpy_outs, numpy_shared = rvals numpy_outs, numpy_shared = rvals
assert len(numpy_outs) == len(theano_outs) assert len(numpy_outs) == len(theano_outs)
assert len(numpy_shared) == len(shared_vars) assert len(numpy_shared) == len(shared_vars)
for th_out, num_out in zip(theano_outs, numpy_outs): for th_out, num_out in zip(theano_outs, numpy_outs):
assert numpy.allclose(th_out, num_out) assert numpy.allclose(th_out, num_out)
for th_out, num_out in zip(shared_outs, numpy_shared): for th_out, num_out in zip(shared_vars, numpy_shared):
assert numpy.allclose(th_out.get_value(), num_out) assert numpy.allclose(th_out.get_value(), num_out)
# Scenario 3 : Less data then required # Scenario 3 : Less data then required
inputs_values = [] print >>sys.stderr, ' Scenario 2. Wrong shapes'
input_values = []
for pos, info in enumerate(inputs_info): for pos, info in enumerate(inputs_info):
taps = [x['tap'] for x in info] taps = [x['tap'] for x in info]
offset = abs(numpy.min([x for x in taps if x < 0])) offset = 0
if len([x for x in taps if x < 0]) > 0:
offset += abs(numpy.min([x for x in taps if x < 0]))
if len([x for x in taps if x > 0]) > 0:
offset += numpy.max([x for x in taps if x > 0]) offset += numpy.max([x for x in taps if x > 0])
data = rng.uniform(size=(n_steps + offset - 1, 4)) data = rng.uniform(size=(abs(_n_steps) + offset - 1, 4))
inputs_values.append(data) input_values.append(data)
state_values = [] state_values = []
for pos, info in enumerate(states_info): for pos, info in enumerate(states_info):
taps = [x['tap'] for x in info] taps = [x['tap'] for x in info]
...@@ -335,7 +376,7 @@ class TestScan(unittest.TestCase): ...@@ -335,7 +376,7 @@ class TestScan(unittest.TestCase):
self.assertRaises(Exception, my_f, self.assertRaises(Exception, my_f,
inputs + state_values + param_values) inputs + state_values + param_values)
def test000_generate_tests(self): def test001_generate_tests(self):
rng = numpy.random.RandomState(utt.fetch_seed()) rng = numpy.random.RandomState(utt.fetch_seed())
all_inputs_info = [[]] all_inputs_info = [[]]
possible_taps_use_pairs = [[dict(tap=0, use=True)], possible_taps_use_pairs = [[dict(tap=0, use=True)],
......
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