def get_dummy_recurrent_transform(recurrent_transform_name,
n_out=5,
n_batches=2,
n_input_t=2,
n_input_dim=2):
"""
:type recurrent_transform_name: str
:rtype: RecurrentTransformBase
This function is a useful helper for testing/debugging.
"""
cls = transform_classes[recurrent_transform_name]
from NetworkRecurrentLayer import RecurrentUnitLayer
from NetworkBaseLayer import SourceLayer
if getattr(RecurrentUnitLayer, "rng", None) is None:
RecurrentUnitLayer.initialize_rng()
index = theano.shared(numpy.array([[1] * n_batches] * n_input_t,
dtype="int8"),
name="i")
x_out = theano.shared(numpy.array([[[1.0] * n_input_dim] * n_batches] *
n_input_t,
dtype="float32"),
name="x")
layer = RecurrentUnitLayer(n_out=n_out,
index=index,
sources=[],
base=[
SourceLayer(
n_out=x_out.get_value().shape[2],
x_out=x_out,
index=index)
],
attention=recurrent_transform_name)
assert isinstance(layer.recurrent_transform, cls)
return layer.recurrent_transform
def traverse(content, layer_name, target, output_index, inherit=False):
if layer_name in network.hidden:
return network.hidden[layer_name].index
if layer_name in network.output:
return network.output[layer_name].index
source = []
obj = content[layer_name].copy()
if 'inherit' in obj:
if not obj['inherit'] in templates:
traverse(content, obj['inherit'], target, output_index,
True)
template = templates[obj['inherit']].copy()
for key in template.keys():
if not key in obj.keys():
obj[key] = template[key]
del obj['inherit']
templates[layer_name] = obj.copy()
if inherit:
return output_index
cl = obj.pop('class', None)
index = output_index
if 'target' in obj:
target = obj['target']
dtype = obj.get("dtype", "int32")
network.use_target(target, dtype=dtype)
if not 'from' in obj and cl is not None:
source = [
SourceLayer(network.n_in,
network.x,
sparse=sparse_input,
name='data',
index=network.i)
]
index = network.i
elif 'from' in obj and obj['from']:
if not isinstance(obj['from'], list):
obj['from'] = [obj['from']]
for prev in obj['from']:
if prev == 'data':
source.append(
SourceLayer(network.n_in,
network.x,
sparse=sparse_input,
name='data',
index=network.i))
index = network.i
elif not prev in content.keys() and prev != "null":
sparse = obj.pop('sparse_input', False)
dtype = 'int32' if sparse else 'float32'
source.append(
SourceLayer(0,
None,
sparse=sparse,
dtype=dtype,
name='data',
network=network,
data_key=prev))
index = source[-1].index
elif prev != "null":
index = traverse(content, prev, target, index)
source.append(network.get_layer(prev))
if 'encoder' in obj:
encoder = []
if not isinstance(obj['encoder'], list):
obj['encoder'] = [obj['encoder']]
for prev in obj['encoder']:
traverse(content, prev, target, index)
encoder.append(network.get_layer(prev))
obj['encoder'] = encoder
if 'base' in obj: # TODO(doetsch) string/layer transform should be smarter
base = []
if not isinstance(obj['base'], list):
if ',' in obj['base']:
obj['base'] = obj['base'].split(',')
else:
obj['base'] = [obj['base']]
for prev in obj['base']:
if prev == 'data':
base.append(
SourceLayer(network.n_in,
network.x,
sparse=sparse_input,
name='data',
index=network.i))
else:
traverse(content, prev, target, index)
base.append(network.get_layer(prev))
obj['base'] = base
for key in ['copy_input', 'copy_output', 'aligner']:
if key in obj:
index = traverse(content, obj[key], target, index)
obj[key] = network.get_layer(obj[key])
if 'encoder' in obj and not source:
index = output_index
if 'target' in obj and obj['target'] != "null":
index = network.j[obj['target']]
obj.pop('from', None)
params = {
'sources': source,
'dropout': 0.0,
'name': layer_name,
"train_flag": train_flag,
"eval_flag": eval_flag,
'network': network
}
params.update(obj)
params["mask"] = mask # overwrite
params['index'] = index
params['y_in'] = network.y
if cl:
templates[layer_name]['class'] = cl
if cl == 'softmax' or cl == 'decoder':
if not 'target' in params:
params['target'] = target
if 'loss' in obj and obj['loss'] in ('ctc', 'hmm'):
params['index'] = network.i
elif target != "null":
params['index'] = network.j[target] #output_index
return network.make_classifier(**params)
elif cl is not None:
layer_class = get_layer_class(cl)
params.update({'name': layer_name})
if layer_class.recurrent:
network.recurrent = True
return network.add_layer(layer_class(**params)).index
import theano import numpy from NetworkBaseLayer import Container, SourceLayer from NetworkRecurrentLayer import RecurrentUnitLayer Container.initialize_rng() # TODO more sane data index = theano.shared(numpy.array([[1]]), name="i") source = SourceLayer(n_out=2, x_out=theano.shared(numpy.array([[[1.0, -2.0]]], dtype='float32'), name="x", index=index)) def test_RecurrentUnitLayer_init(): RecurrentUnitLayer(n_out=3, sources=[source], index=index) def test_RecurrentUnitLayer_init_sampling2(): RecurrentUnitLayer(n_out=3, sources=[source], index=index, sampling=2)