def __init__(self,
input_dim,
output_dim,
hidden_dim,
dep=2,
loss="nl",
optimizer="ada",
we_dict=None,
map=None):
Sequential.__init__(self,
use_mask=True,
input_value_type="int32",
prediction_type="vector",
prediction_value_type='int32')
self.option[Option.LOSS] = loss
self.option[Option.OPTIMIZER] = optimizer
l1 = Embedding(input_dim, hidden_dim, we_dict=we_dict, map=map)
self.add_layer(l1)
for i in range(dep):
l2 = LSTM(hidden_dim, hidden_dim, return_sequences=True)
self.add_layer(l2)
l4 = TimeDitributed(
core_layer=Dense(hidden_dim, output_dim, activation="softmax"))
self.add_layer(l4)
self.option[Option.IS_SEQUENCE_WORK] = True
self.compile()
def __init__(self,
input_dim,
output_dim,
hidden_dim,
max_len_out=None,
loss="nl",
optimizer="ada"):
Sequential.__init__(self,
use_mask=True,
input_value_type="int32",
prediction_type="vector",
prediction_value_type='int32')
self.option[Option.MAX_LEN_OUT] = max_len_out
self.option[Option.INPUT_DIM] = input_dim
self.option[Option.OUTPUT_DIM] = output_dim
self.option[Option.IS_SEQUENCE_WORK] = True
self.len_out = tensor.iscalar('lo')
l0 = Reverse()
l1 = Embedding(input_dim, hidden_dim)
l2 = LSTM(hidden_dim, hidden_dim)
l3 = Repeat(times=self.len_out)
l4 = LSTM(hidden_dim,
hidden_dim,
return_sequences=True,
need_swap_axis=True) # need swap axis here
l5 = TimeDitributed(
core_layer=Dense(hidden_dim, output_dim, activation="softmax"))
self.add_layer([l0, l1, l2, l3, l4, l5])
self.compile(loss=loss, optimizer=optimizer)
def __init__(self, input_dim1, input_dim2, output_dim, hidden_dim1, hidden_dim2, dep=2, loss="nl", optimizer="ada", we_dict1=None, we_dict2=None, map1=None, map2=None, use_noise=True):
Sequential.__init__(self, use_mask=True, input_value_type="int32", prediction_type="vector",
prediction_value_type='int32', use_noise=use_noise)
self.extra_input = tensor.matrix('x_extra', dtype="int32")
self.extra_output = None
self.option[Option.LOSS] = loss
self.option[Option.OPTIMIZER] = optimizer
l1 = Embedding(input_dim1, hidden_dim1, we_dict=we_dict1, map=map1)
l2 = Embedding(input_dim2, hidden_dim2, we_dict=we_dict2, map=map2)
l3 = Merge(layers=[l1,l2])
self.add_layer([l1,l2,l3])
l4 = Dropout(hidden_dim1+hidden_dim2,theano_rng= RandomStreams(128))
self.add_layer(l4)
for i in range(dep):
l5 = LSTM(hidden_dim1+hidden_dim2, hidden_dim1+hidden_dim2, return_sequences=True)
self.add_layer(l5)
l6 = Dropout(hidden_dim1+hidden_dim2,theano_rng= RandomStreams(128))
self.add_layer(l6)
l7 = TimeDitributed(core_layer=Dense(hidden_dim1+hidden_dim2, output_dim, activation="softmax"))
self.add_layer([l7])
self.option[Option.IS_SEQUENCE_WORK] = True
def __init__(self,
input_dim,
output_dim,
hidden_dim,
dep=2,
loss="nl",
optimizer="ada",
we_dict=None,
map=None,
use_noise=True):
'''
:param input_dim:
:param output_dim:
:param hidden_dim:
:param dep:
:param loss:
:param optimizer:
:param we_dict: word embedding dictionary
:param map: mapping from word to index
:return:
'''
Sequential.__init__(self,
use_mask=True,
input_value_type="int32",
prediction_type="vector",
prediction_value_type='int32',
use_noise=use_noise)
self.option[Option.LOSS] = loss
self.option[Option.OPTIMIZER] = optimizer
l1 = Embedding(input_dim, hidden_dim, we_dict=we_dict,
map=map) # first layer is an embedding layer
self.add_layer(l1)
l2 = Dropout(hidden_dim, theano_rng=RandomStreams(128))
self.add_layer(l2)
for i in range(dep):
l3 = LSTM(hidden_dim, hidden_dim, return_sequences=True)
self.add_layer(l3)
l4 = Dropout(hidden_dim, theano_rng=RandomStreams(128))
self.add_layer(l4)
l5 = TimeDitributed(
core_layer=Dense(hidden_dim, output_dim, activation="softmax"))
self.add_layer(l5)
self.option[Option.IS_SEQUENCE_WORK] = True
def __init__(self,
input_dim1,
input_dim2,
output_dim1,
output_dim2,
hidden_dim1,
hidden_dim2,
semantic_label_map,
dep=2,
loss="nl",
optimizer="ada",
we_dict1=None,
we_dict2=None,
map1=None,
map2=None,
use_noise=True):
Sequential.__init__(self,
use_mask=True,
input_value_type="int32",
prediction_type="vector",
prediction_value_type='int32',
use_noise=use_noise)
self.extra_input = tensor.matrix('x_extra', dtype="int32")
self.extra_output = None
self.extra_prediction = tensor.ivector('ye')
self.extra_gold = tensor.ivector('ge')
self.option[Option.LOSS] = loss
self.option[Option.OPTIMIZER] = optimizer
l1 = Embedding(input_dim1, hidden_dim1, we_dict=we_dict1, map=map1)
l2 = Embedding(input_dim2, hidden_dim2, we_dict=we_dict2, map=map2)
self.add_layer([l1, l2])
l11 = Dropout(hidden_dim1, theano_rng=RandomStreams(128))
self.add_layer(l11)
l21 = Dropout(hidden_dim2, theano_rng=RandomStreams(128))
self.add_layer(l21)
l4 = LSTM(hidden_dim1, hidden_dim1, return_sequences=True)
l5 = LSTM(hidden_dim2, hidden_dim2, return_sequences=True)
self.add_layer([l4, l5])
l22 = Dropout(hidden_dim2, theano_rng=RandomStreams(128))
self.add_layer(l22)
l6 = TimeDitributed(
core_layer=Dense(hidden_dim2, output_dim2, activation="softmax"))
l12 = Dropout(hidden_dim1, theano_rng=RandomStreams(128))
self.add_layer(l12)
l7 = SoftmaxHybrid(hidden_dim1, hidden_dim2, output_dim1,
semantic_label_map)
l8 = TimeDitributedHybrid(core_layer=l7)
self.add_layer([l6, l8])
self.option[Option.IS_SEQUENCE_WORK] = True