def __init__(self, model, n_char, char_dim, n_filter, win_sizes):
pc = model.add_subcollection()
self.clookup = pc.add_lookup_parameters((n_char, char_dim))
self.Ws = [
pc.add_parameters((char_dim, size, 1, n_filter),
init=dy.GlorotInitializer(gain=0.5))
for size in win_sizes
]
self.bs = [
pc.add_parameters((n_filter), init=dy.ConstInitializer(0))
for _ in win_sizes
]
self.win_sizes = win_sizes
self.pc = pc
self.spec = (n_char, char_dim, n_filter, win_sizes)
def get_init(shape, init):
# shape is a tuple of dims
assert init in ["default", "const", "glorot", "ortho",
"gaussian"], "Unknown init method %s" % init
if len(shape) == 1: # set bias to 0
return dy.ConstInitializer(0.)
elif len(shape) == 2:
if init == "default" or init == "glorot":
return dy.GlorotInitializer()
elif init == "gaussian":
return dy.NormalInitializer(var=0.01 * 0.01)
elif init == "ortho":
assert shape[0] % shape[
1] == 0, "Bad shape %s for ortho_init" % shape
num = shape[0] // shape[1]
arr = ortho_weight(shape[1]) if num == 1 else\
np.concatenate([ortho_weight(shape[1]) for _ in range(num)])
return dy.NumpyInitializer(arr)
else:
raise NotImplementedError(
"Currently only support parameter dim <= 2.")
def build_computation_graph(self, num_words, num_chars):
"""
build graph and link to parameters
self.predictors, self.char_rnn, self.wembeds, self.cembeds =
"""
## initialize word embeddings
if self.embeds_file:
print("loading embeddings")
embeddings, emb_dim = load_embeddings_file(self.embeds_file)
assert (emb_dim == self.in_dim)
num_words = len(
set(embeddings.keys()).union(set(
self.w2i.keys()))) # initialize all with embeddings
# init model parameters and initialize them
self.wembeds = self.model.add_lookup_parameters(
(num_words, self.in_dim), init=self.initializer)
init = 0
for word in embeddings.keys():
if word not in self.w2i:
self.w2i[word] = len(self.w2i.keys()) # add new word
self.wembeds.init_row(self.w2i[word], embeddings[word])
init += 1
elif word in embeddings:
self.wembeds.init_row(self.w2i[word], embeddings[word])
init += 1
print("initialized: {}".format(init))
del embeddings # clean up
else:
self.wembeds = self.model.add_lookup_parameters(
(num_words, self.in_dim), init=self.initializer)
## initialize character embeddings
self.cembeds = None
if self.c_in_dim > 0:
self.cembeds = self.model.add_lookup_parameters(
(num_chars, self.c_in_dim), init=self.initializer)
if self.lex_dim > 0 and self.embed_lex:
# +1 for UNK property
self.lembeds = self.model.add_lookup_parameters(
(len(self.dictionary_values) + 1, self.lex_dim),
init=dynet.GlorotInitializer()) #init=self.initializer)
# make it more flexible to add number of layers as specified by parameter
layers = [] # inner layers
output_layers_dict = {} # from task_id to actual softmax predictor
for layer_num in range(0, self.h_layers):
if layer_num == 0:
if self.c_in_dim > 0:
# in_dim: size of each layer
if self.lex_dim > 0 and self.embed_lex:
lex_embed_size = self.lex_dim * len(
self.dictionary_values)
f_builder = self.builder(
1, self.in_dim + self.c_h_dim * 2 + lex_embed_size,
self.h_dim, self.model)
b_builder = self.builder(
1, self.in_dim + self.c_h_dim * 2 + lex_embed_size,
self.h_dim, self.model)
else:
f_builder = self.builder(
1, self.in_dim + self.c_h_dim * 2 + self.lex_dim,
self.h_dim, self.model)
b_builder = self.builder(
1, self.in_dim + self.c_h_dim * 2 + self.lex_dim,
self.h_dim, self.model)
else:
f_builder = self.builder(1, self.in_dim + self.lex_dim,
self.h_dim, self.model)
b_builder = self.builder(1, self.in_dim + self.lex_dim,
self.h_dim, self.model)
layers.append(BiRNNSequencePredictor(
f_builder,
b_builder)) #returns forward and backward sequence
else:
# add inner layers (if h_layers >1)
f_builder = self.builder(1, self.h_dim, self.h_dim, self.model)
b_builder = self.builder(1, self.h_dim, self.h_dim, self.model)
layers.append(BiRNNSequencePredictor(f_builder, b_builder))
# store at which layer to predict task
task2layer = {
task_id: out_layer
for task_id, out_layer in zip(self.task2tag2idx, self.pred_layer)
}
if len(task2layer) > 1:
print("task2layer", task2layer)
for task_id in task2layer:
task_num_labels = len(self.task2tag2idx[task_id])
if not self.crf:
output_layers_dict[task_id] = FFSequencePredictor(
self.task2tag2idx[task_id],
Layer(self.model,
self.h_dim * 2,
task_num_labels,
dynet.softmax,
mlp=self.mlp,
mlp_activation=self.activation_mlp))
else:
print("CRF")
output_layers_dict[task_id] = CRFSequencePredictor(
self.model,
task_num_labels,
self.task2tag2idx[task_id],
Layer(self.model,
self.h_dim * 2,
task_num_labels,
None,
mlp=self.mlp,
mlp_activation=self.activation_mlp),
viterbi_loss=self.viterbi_loss)
self.char_rnn = BiRNNSequencePredictor(
self.builder(1, self.c_in_dim, self.c_h_dim, self.model),
self.builder(1, self.c_in_dim, self.c_h_dim, self.model))
self.predictors = {}
self.predictors["inner"] = layers
self.predictors["output_layers_dict"] = output_layers_dict
self.predictors["task_expected_at"] = task2layer
import _dynet as dynet
"""
various helper mappings
"""
# DyNet adds init option to choose initializer: https://github.com/clab/dynet/blob/master/python/CHANGES.md
INITIALIZER_MAP = {
'glorot': dynet.GlorotInitializer(),
'constant': dynet.ConstInitializer(0.01),
'uniform': dynet.UniformInitializer(0.1),
'normal': dynet.NormalInitializer(mean=0, var=1)
}
TRAINER_MAP = {
"sgd": dynet.SimpleSGDTrainer,
"adam": dynet.AdamTrainer,
"adadelta": dynet.AdadeltaTrainer,
"adagrad": dynet.AdagradTrainer,
"momentum": dynet.MomentumSGDTrainer
}
ACTIVATION_MAP = {
"tanh": dynet.tanh,
"rectify": dynet.rectify
}
BUILDERS = {
"lstm": dynet.LSTMBuilder, # is dynet.VanillaLSTMBuilder (cf. https://github.com/clab/dynet/issues/474)
"lstmc": dynet.CoupledLSTMBuilder,
"gru": dynet.GRUBuilder,
"rnn": dynet.SimpleRNNBuilder
}