def _build_network(self, orig_width, hidden_with):
with Model.define_operators({">>": chain}):
# very simple encoder-decoder model
self.encoder = Affine(hidden_with, orig_width)
self.model = self.encoder >> zero_init(
Affine(orig_width, hidden_with, drop_factor=0.0))
self.sgd = create_default_optimizer(self.model.ops)
def build_model(nr_class, width, depth, conv_depth, **kwargs):
with Model.define_operators({"|": concatenate, ">>": chain, "**": clone}):
embed = (HashEmbed(width, 5000, column=1)
| StaticVectors("spacy_pretrained_vectors", width, column=5)
| HashEmbed(width // 2, 750, column=2)
| HashEmbed(width // 2, 750, column=3)
| HashEmbed(width // 2, 750, column=4)) >> LN(Maxout(width))
sent2vec = (flatten_add_lengths >> with_getitem(
0,
embed >> Residual(ExtractWindow(nW=1) >> LN(Maxout(width)))**
conv_depth,
) >> ParametricAttention(width) >> Pooling(sum_pool) >> Residual(
LN(Maxout(width)))**depth)
model = (
foreach(sent2vec, drop_factor=2.0) >> flatten_add_lengths
# This block would allow the model to learn some cross-sentence
# features. It's not useful on this problem. It might make more
# sense to use a BiLSTM here, following Liang et al (2016).
# >> with_getitem(0,
# Residual(ExtractWindow(nW=1) >> LN(Maxout(width))) ** conv_depth
# )
>> ParametricAttention(width, hard=False) >> Pooling(sum_pool) >>
Residual(LN(Maxout(width)))**depth >> Softmax(nr_class))
model.lsuv = False
return model
def create_pretraining_model(nlp, tok2vec, objective="basic"):
"""Define a network for the pretraining."""
output_size = nlp.vocab.vectors.data.shape[1]
# This is annoying, but the parser etc have the flatten step after
# the tok2vec. To load the weights in cleanly, we need to match
# the shape of the models' components exactly. So what we cann
# "tok2vec" has to be the same set of processes as what the components do.
with Model.define_operators({">>": chain, "|": concatenate}):
l2r_model = (
tok2vec.l2r
>> flatten
>> LN(Maxout(output_size, tok2vec.l2r.nO, pieces=3))
>> zero_init(Affine(output_size, drop_factor=0.0))
)
r2l_model = (
tok2vec.r2l
>> flatten
>> LN(Maxout(output_size, tok2vec.r2l.nO, pieces=3))
>> zero_init(Affine(output_size, drop_factor=0.0))
)
model = tok2vec.embed >> (l2r_model | r2l_model)
model.tok2vec = tok2vec
model.begin_training([nlp.make_doc("Give it a doc to infer shapes")])
tok2vec.begin_training([nlp.make_doc("Give it a doc to infer shapes")])
tokvecs = tok2vec([nlp.make_doc('hello there'), nlp.make_doc(u'and hello')])
print(tokvecs.shape)
return model
def build_model(nr_class, width, **kwargs):
with Model.define_operators({'|': concatenate, '>>': chain, '**': clone}):
model = (FeatureExtracter([ORTH]) >> flatten_add_lengths >>
with_getitem(0, uniqued(HashEmbed(width, 10000, column=0))) >>
Pooling(mean_pool) >> Softmax(nr_class))
model.lsuv = False
return model
def main(width=100,
depth=4,
vector_length=64,
min_batch_size=1,
max_batch_size=32,
learn_rate=0.001,
momentum=0.9,
dropout=0.5,
dropout_decay=1e-4,
nb_epoch=20,
L2=1e-6):
cfg = dict(locals())
print(cfg)
if cupy is not None:
print("Using GPU")
Model.ops = CupyOps()
train_data, check_data, nr_tag = ancora_pos_tags()
extracter = FeatureExtracter('es', attrs=[LOWER, SHAPE, PREFIX, SUFFIX])
Model.lsuv = True
with Model.define_operators({
'**': clone,
'>>': chain,
'+': add,
'|': concatenate
}):
lower_case = HashEmbed(width, 100, column=0)
shape = HashEmbed(width // 2, 200, column=1)
prefix = HashEmbed(width // 2, 100, column=2)
suffix = HashEmbed(width // 2, 100, column=3)
model = (with_flatten(
(lower_case | shape | prefix | suffix) >> Maxout(width, pieces=3)
>> Residual(ExtractWindow(nW=1) >> Maxout(width, pieces=3))**depth
>> Softmax(nr_tag),
pad=depth))
train_X, train_y = preprocess(model.ops, extracter, train_data, nr_tag)
dev_X, dev_y = preprocess(model.ops, extracter, check_data, nr_tag)
n_train = float(sum(len(x) for x in train_X))
global epoch_train_acc
with model.begin_training(train_X[:5000], train_y[:5000],
**cfg) as (trainer, optimizer):
trainer.each_epoch.append(track_progress(**locals()))
trainer.batch_size = min_batch_size
batch_size = float(min_batch_size)
for X, y in trainer.iterate(train_X, train_y):
yh, backprop = model.begin_update(X, drop=trainer.dropout)
gradient = [yh[i] - y[i] for i in range(len(yh))]
backprop(gradient, optimizer)
trainer.batch_size = min(int(batch_size), max_batch_size)
batch_size *= 1.001
with model.use_params(trainer.optimizer.averages):
print(model.evaluate(dev_X, model.ops.flatten(dev_y)))
with open('/tmp/model.pickle', 'wb') as file_:
pickle.dump(model, file_)
def build_morphologizer_model(class_nums, **cfg):
embed_size = util.env_opt("embed_size", 7000)
if "token_vector_width" in cfg:
token_vector_width = cfg["token_vector_width"]
else:
token_vector_width = util.env_opt("token_vector_width", 128)
pretrained_vectors = cfg.get("pretrained_vectors")
char_embed = cfg.get("char_embed", True)
with Model.define_operators({">>": chain, "+": add, "**": clone}):
if "tok2vec" in cfg:
tok2vec = cfg["tok2vec"]
else:
tok2vec = Tok2Vec(
token_vector_width,
embed_size,
char_embed=char_embed,
pretrained_vectors=pretrained_vectors,
)
softmax = with_flatten(MultiSoftmax(class_nums, token_vector_width))
softmax.out_sizes = class_nums
model = tok2vec >> softmax
model.nI = None
model.tok2vec = tok2vec
model.softmax = softmax
return model
def main(depth=2, width=512, nb_epoch=30):
prefer_gpu()
# Configuration here isn't especially good. But, for demo..
with Model.define_operators({"**": clone, ">>": chain}):
model = ReLu(width) >> ReLu(width) >> Softmax()
train_data, dev_data, _ = datasets.mnist()
train_X, train_y = model.ops.unzip(train_data)
dev_X, dev_y = model.ops.unzip(dev_data)
dev_y = to_categorical(dev_y)
with model.begin_training(train_X, train_y, L2=1e-6) as (trainer, optimizer):
epoch_loss = [0.0]
def report_progress():
with model.use_params(optimizer.averages):
print(epoch_loss[-1], model.evaluate(dev_X, dev_y), trainer.dropout)
epoch_loss.append(0.0)
trainer.each_epoch.append(report_progress)
trainer.nb_epoch = nb_epoch
trainer.dropout = 0.3
trainer.batch_size = 128
trainer.dropout_decay = 0.0
train_X = model.ops.asarray(train_X, dtype="float32")
y_onehot = to_categorical(train_y)
for X, y in trainer.iterate(train_X, y_onehot):
yh, backprop = model.begin_update(X, drop=trainer.dropout)
loss = ((yh - y) ** 2.0).sum() / y.shape[0]
backprop(yh - y, optimizer)
epoch_loss[-1] += loss
with model.use_params(optimizer.averages):
print("Avg dev.: %.3f" % model.evaluate(dev_X, dev_y))
with open("out.pickle", "wb") as file_:
pickle.dump(model, file_, -1)
def build_nel_encoder(embed_width, hidden_width, ner_types, **cfg):
if "entity_width" not in cfg:
raise ValueError(Errors.E144.format(param="entity_width"))
conv_depth = cfg.get("conv_depth", 2)
cnn_maxout_pieces = cfg.get("cnn_maxout_pieces", 3)
pretrained_vectors = cfg.get("pretrained_vectors", None)
context_width = cfg.get("entity_width")
with Model.define_operators({">>": chain, "**": clone}):
# context encoder
tok2vec = Tok2Vec(
width=hidden_width,
embed_size=embed_width,
pretrained_vectors=pretrained_vectors,
cnn_maxout_pieces=cnn_maxout_pieces,
subword_features=True,
conv_depth=conv_depth,
bilstm_depth=0,
)
model = (
tok2vec >> flatten_add_lengths >> Pooling(mean_pool) >> Residual(
zero_init(Maxout(hidden_width, hidden_width))) >> zero_init(
Affine(context_width, hidden_width, drop_factor=0.0)))
model.tok2vec = tok2vec
model.nO = context_width
return model
def Tok2Vec(width, embed_size, **kwargs):
pretrained_vectors = kwargs.get("pretrained_vectors", None)
cnn_maxout_pieces = kwargs.get("cnn_maxout_pieces", 3)
subword_features = kwargs.get("subword_features", True)
conv_depth = kwargs.get("conv_depth", 4)
bilstm_depth = kwargs.get("bilstm_depth", 0)
cols = [ID, NORM, PREFIX, SUFFIX, SHAPE, ORTH]
with Model.define_operators(
{">>": chain, "|": concatenate, "**": clone, "+": add, "*": reapply}
):
norm = HashEmbed(width, embed_size, column=cols.index(NORM), name="embed_norm")
if subword_features:
prefix = HashEmbed(
width, embed_size // 2, column=cols.index(PREFIX), name="embed_prefix"
)
suffix = HashEmbed(
width, embed_size // 2, column=cols.index(SUFFIX), name="embed_suffix"
)
shape = HashEmbed(
width, embed_size // 2, column=cols.index(SHAPE), name="embed_shape"
)
else:
prefix, suffix, shape = (None, None, None)
if pretrained_vectors is not None:
glove = StaticVectors(pretrained_vectors, width, column=cols.index(ID))
if subword_features:
embed = uniqued(
(glove | norm | prefix | suffix | shape)
>> LN(Maxout(width, width * 5, pieces=3)),
column=cols.index(ORTH),
)
else:
embed = uniqued(
(glove | norm) >> LN(Maxout(width, width * 2, pieces=3)),
column=cols.index(ORTH),
)
elif subword_features:
embed = uniqued(
(norm | prefix | suffix | shape)
>> LN(Maxout(width, width * 4, pieces=3)),
column=cols.index(ORTH),
)
else:
embed = norm
convolution = Residual(
ExtractWindow(nW=1)
>> LN(Maxout(width, width * 3, pieces=cnn_maxout_pieces))
)
tok2vec = FeatureExtracter(cols) >> with_flatten(
embed >> convolution ** conv_depth, pad=conv_depth
)
if bilstm_depth >= 1:
tok2vec = tok2vec >> PyTorchBiLSTM(width, width, bilstm_depth)
# Work around thinc API limitations :(. TODO: Revise in Thinc 7
tok2vec.nO = width
tok2vec.embed = embed
return tok2vec
def main(
width=100,
depth=4,
vector_length=64,
min_batch_size=1,
max_batch_size=32,
learn_rate=0.001,
momentum=0.9,
dropout=0.5,
dropout_decay=1e-4,
nb_epoch=20,
L2=1e-6,
):
cfg = dict(locals())
print(cfg)
prefer_gpu()
train_data, check_data, nr_tag = ancora_pos_tags()
extracter = FeatureExtracter("es", attrs=[LOWER, SHAPE, PREFIX, SUFFIX])
Model.lsuv = True
with Model.define_operators({"**": clone, ">>": chain, "+": add, "|": concatenate}):
lower_case = HashEmbed(width, 100, column=0)
shape = HashEmbed(width // 2, 200, column=1)
prefix = HashEmbed(width // 2, 100, column=2)
suffix = HashEmbed(width // 2, 100, column=3)
model = with_flatten(
(lower_case | shape | prefix | suffix)
>> Maxout(width, pieces=3)
>> Residual(ExtractWindow(nW=1) >> Maxout(width, pieces=3)) ** depth
>> Softmax(nr_tag),
pad=depth,
)
train_X, train_y = preprocess(model.ops, extracter, train_data, nr_tag)
dev_X, dev_y = preprocess(model.ops, extracter, check_data, nr_tag)
n_train = float(sum(len(x) for x in train_X))
global epoch_train_acc
with model.begin_training(train_X[:5000], train_y[:5000], **cfg) as (
trainer,
optimizer,
):
trainer.each_epoch.append(track_progress(**locals()))
trainer.batch_size = min_batch_size
batch_size = float(min_batch_size)
for X, y in trainer.iterate(train_X, train_y):
yh, backprop = model.begin_update(X, drop=trainer.dropout)
gradient = [yh[i] - y[i] for i in range(len(yh))]
backprop(gradient, optimizer)
trainer.batch_size = min(int(batch_size), max_batch_size)
batch_size *= 1.001
with model.use_params(trainer.optimizer.averages):
print(model.evaluate(dev_X, model.ops.flatten(dev_y)))
with open("/tmp/model.pickle", "wb") as file_:
pickle.dump(model, file_)
def main(width=128,
depth=1,
vector_length=128,
min_batch_size=16,
max_batch_size=16,
learn_rate=0.001,
momentum=0.9,
dropout=0.5,
dropout_decay=1e-4,
nb_epoch=20,
L2=1e-6):
using_gpu = prefer_gpu()
if using_gpu:
torch.set_default_tensor_type('torch.cuda.FloatTensor')
cfg = dict(locals())
print(cfg)
train_data, check_data, nr_tag = ancora_pos_tags()
train_data = list(train_data)
check_data = list(check_data)
extracter = FeatureExtracter('es', attrs=[LOWER, SHAPE, PREFIX, SUFFIX])
with Model.define_operators({
'**': clone,
'>>': chain,
'+': add,
'|': concatenate
}):
lower_case = HashEmbed(width, 100, column=0)
shape = HashEmbed(width // 2, 200, column=1)
prefix = HashEmbed(width // 2, 100, column=2)
suffix = HashEmbed(width // 2, 100, column=3)
model = (with_flatten(
(lower_case | shape | prefix | suffix) >> Maxout(width, pieces=3))
>> PyTorchBiLSTM(width, width, depth) >> with_flatten(
Softmax(nr_tag)))
train_X, train_y = preprocess(model.ops, extracter, train_data, nr_tag)
dev_X, dev_y = preprocess(model.ops, extracter, check_data, nr_tag)
n_train = float(sum(len(x) for x in train_X))
global epoch_train_acc
with model.begin_training(train_X[:10], train_y[:10],
**cfg) as (trainer, optimizer):
trainer.each_epoch.append(track_progress(**locals()))
trainer.batch_size = min_batch_size
batch_size = float(min_batch_size)
for X, y in trainer.iterate(train_X, train_y):
yh, backprop = model.begin_update(X, drop=trainer.dropout)
gradient = [yh[i] - y[i] for i in range(len(yh))]
backprop(gradient, optimizer)
trainer.batch_size = min(int(batch_size), max_batch_size)
batch_size *= 1.001
print(model.evaluate(dev_X, model.ops.flatten(dev_y)))
with open('/tmp/model.pickle', 'wb') as file_:
pickle.dump(model, file_)
def Tok2Vec(width, embed_size, **kwargs):
pretrained_vectors = kwargs.get("pretrained_vectors", None)
cnn_maxout_pieces = kwargs.get("cnn_maxout_pieces", 3)
subword_features = kwargs.get("subword_features", True)
conv_depth = kwargs.get("conv_depth", 4)
bilstm_depth = kwargs.get("bilstm_depth", 0)
cols = [ID, NORM, PREFIX, SUFFIX, SHAPE, ORTH]
with Model.define_operators(
{">>": chain, "|": concatenate, "**": clone, "+": add, "*": reapply}
):
norm = HashEmbed(width, embed_size, column=cols.index(NORM), name="embed_norm")
if subword_features:
prefix = HashEmbed(
width, embed_size // 2, column=cols.index(PREFIX), name="embed_prefix"
)
suffix = HashEmbed(
width, embed_size // 2, column=cols.index(SUFFIX), name="embed_suffix"
)
shape = HashEmbed(
width, embed_size // 2, column=cols.index(SHAPE), name="embed_shape"
)
else:
prefix, suffix, shape = (None, None, None)
if pretrained_vectors is not None:
glove = StaticVectors(pretrained_vectors, width, column=cols.index(ID))
if subword_features:
embed = uniqued(
(glove | norm | prefix | suffix | shape)
>> LN(Maxout(width, width * 5, pieces=3)),
column=cols.index(ORTH),
)
else:
embed = uniqued(
(glove | norm) >> LN(Maxout(width, width * 2, pieces=3)),
column=cols.index(ORTH),
)
elif subword_features:
embed = uniqued(
(norm | prefix | suffix | shape)
>> LN(Maxout(width, width * 4, pieces=3)),
column=cols.index(ORTH),
)
else:
embed = norm
convolution = Residual(
ExtractWindow(nW=1)
>> LN(Maxout(width, width * 3, pieces=cnn_maxout_pieces))
)
tok2vec = FeatureExtracter(cols) >> with_flatten(
embed >> convolution ** conv_depth, pad=conv_depth
)
if bilstm_depth >= 1:
tok2vec = tok2vec >> PyTorchBiLSTM(width, width, bilstm_depth)
# Work around thinc API limitations :(. TODO: Revise in Thinc 7
tok2vec.nO = width
tok2vec.embed = embed
return tok2vec
def MultiHashEmbed(config):
# For backwards compatibility with models before the architecture registry,
# we have to be careful to get exactly the same model structure. One subtle
# trick is that when we define concatenation with the operator, the operator
# is actually binary associative. So when we write (a | b | c), we're actually
# getting concatenate(concatenate(a, b), c). That's why the implementation
# is a bit ugly here.
cols = config["columns"]
width = config["width"]
rows = config["rows"]
norm = HashEmbed(width,
rows,
column=cols.index("NORM"),
name="embed_norm",
seed=1)
if config["use_subwords"]:
prefix = HashEmbed(width,
rows // 2,
column=cols.index("PREFIX"),
name="embed_prefix",
seed=2)
suffix = HashEmbed(width,
rows // 2,
column=cols.index("SUFFIX"),
name="embed_suffix",
seed=3)
shape = HashEmbed(width,
rows // 2,
column=cols.index("SHAPE"),
name="embed_shape",
seed=4)
if config.get("@pretrained_vectors"):
glove = make_layer(config["@pretrained_vectors"])
mix = make_layer(config["@mix"])
with Model.define_operators({">>": chain, "|": concatenate}):
if config["use_subwords"] and config["@pretrained_vectors"]:
mix._layers[0].nI = width * 5
layer = uniqued(
(glove | norm | prefix | suffix | shape) >> mix,
column=cols.index("ORTH"),
)
elif config["use_subwords"]:
mix._layers[0].nI = width * 4
layer = uniqued((norm | prefix | suffix | shape) >> mix,
column=cols.index("ORTH"))
elif config["@pretrained_vectors"]:
mix._layers[0].nI = width * 2
layer = uniqued(
(glove | norm) >> mix,
column=cols.index("ORTH"),
)
else:
layer = norm
layer.cfg = config
return layer
def build_textcat_model(tok2vec, nr_class, width):
from thinc.v2v import Model, Softmax
from thinc.api import flatten_add_lengths, chain
from thinc.t2v import Pooling, mean_pool
with Model.define_operators({">>": chain}):
model = (tok2vec >> flatten_add_lengths >> Pooling(mean_pool) >>
Softmax(nr_class, width))
model.tok2vec = chain(tok2vec, flatten)
return model
def build_model(nr_class, width, **kwargs):
with Model.define_operators({"|": concatenate, ">>": chain, "**": clone}):
model = (
FeatureExtracter([ORTH])
>> flatten_add_lengths
>> with_getitem(0, uniqued(HashEmbed(width, 10000, column=0)))
>> Pooling(mean_pool)
>> Softmax(nr_class)
)
model.lsuv = False
return model
def build_bow_text_classifier(
nr_class, ngram_size=1, exclusive_classes=False, no_output_layer=False, **cfg
):
with Model.define_operators({">>": chain}):
model = with_cpu(
Model.ops, extract_ngrams(ngram_size, attr=ORTH) >> LinearModel(nr_class)
)
if not no_output_layer:
model = model >> (cpu_softmax if exclusive_classes else logistic)
model.nO = nr_class
return model
def build_text_classifier(nr_class, width=64, **cfg):
nr_vector = cfg.get('nr_vector', 5000)
pretrained_dims = cfg.get('pretrained_dims', 0)
with Model.define_operators({
'>>': chain,
'+': add,
'|': concatenate,
'**': clone
}):
if cfg.get('low_data') and pretrained_dims:
model = (SpacyVectors >> flatten_add_lengths >> with_getitem(
0, Affine(width, pretrained_dims)) >>
ParametricAttention(width) >> Pooling(sum_pool) >>
Residual(ReLu(width, width))**2 >> zero_init(
Affine(nr_class, width, drop_factor=0.0)) >> logistic)
return model
lower = HashEmbed(width, nr_vector, column=1)
prefix = HashEmbed(width // 2, nr_vector, column=2)
suffix = HashEmbed(width // 2, nr_vector, column=3)
shape = HashEmbed(width // 2, nr_vector, column=4)
trained_vectors = (FeatureExtracter(
[ORTH, LOWER, PREFIX, SUFFIX, SHAPE, ID]) >> with_flatten(
uniqued((lower | prefix | suffix | shape) >> LN(
Maxout(width, width + (width // 2) * 3)),
column=0)))
if pretrained_dims:
static_vectors = (
SpacyVectors >> with_flatten(Affine(width, pretrained_dims)))
# TODO Make concatenate support lists
vectors = concatenate_lists(trained_vectors, static_vectors)
vectors_width = width * 2
else:
vectors = trained_vectors
vectors_width = width
static_vectors = None
cnn_model = (
vectors >> with_flatten(
LN(Maxout(width, vectors_width)) >> Residual(
(ExtractWindow(nW=1) >> LN(Maxout(width, width * 3))))**2,
pad=2) >> flatten_add_lengths >> ParametricAttention(width) >>
Pooling(sum_pool) >> Residual(zero_init(Maxout(width, width))) >>
zero_init(Affine(nr_class, width, drop_factor=0.0)))
linear_model = (
_preprocess_doc >> LinearModel(nr_class, drop_factor=0.))
model = ((linear_model | cnn_model) >> zero_init(
Affine(nr_class, nr_class * 2, drop_factor=0.0)) >> logistic)
model.nO = nr_class
model.lsuv = False
return model
def build_bow_text_classifier(
nr_class, ngram_size=1, exclusive_classes=False, no_output_layer=False, **cfg
):
with Model.define_operators({">>": chain}):
model = with_cpu(
Model.ops, extract_ngrams(ngram_size, attr=ORTH) >> LinearModel(nr_class)
)
if not no_output_layer:
model = model >> (cpu_softmax if exclusive_classes else logistic)
model.nO = nr_class
return model
def build_textcat_model(tok2vec, nr_class, width):
from thinc.v2v import Model, Softmax, Maxout
from thinc.api import flatten_add_lengths, chain
from thinc.t2v import Pooling, sum_pool, mean_pool, max_pool
from thinc.misc import Residual, LayerNorm
from spacy._ml import logistic, zero_init
with Model.define_operators({">>": chain}):
model = (tok2vec >> flatten_add_lengths >> Pooling(mean_pool) >>
Softmax(nr_class, width))
model.tok2vec = tok2vec
return model
def Tok2Vec(width, embed_size, **kwargs):
pretrained_vectors = kwargs.get('pretrained_vectors', None)
cnn_maxout_pieces = kwargs.get('cnn_maxout_pieces', 2)
cols = [ID, NORM, PREFIX, SUFFIX, SHAPE, ORTH]
with Model.define_operators({
'>>': chain,
'|': concatenate,
'**': clone,
'+': add,
'*': reapply
}):
norm = HashEmbed(width,
embed_size,
column=cols.index(NORM),
name='embed_norm')
prefix = HashEmbed(width,
embed_size // 2,
column=cols.index(PREFIX),
name='embed_prefix')
suffix = HashEmbed(width,
embed_size // 2,
column=cols.index(SUFFIX),
name='embed_suffix')
shape = HashEmbed(width,
embed_size // 2,
column=cols.index(SHAPE),
name='embed_shape')
if pretrained_vectors is not None:
glove = StaticVectors(pretrained_vectors,
width,
column=cols.index(ID))
embed = uniqued((glove | norm | prefix | suffix | shape) >> LN(
Maxout(width, width * 5, pieces=3)),
column=cols.index(ORTH))
else:
embed = uniqued((norm | prefix | suffix | shape) >> LN(
Maxout(width, width * 4, pieces=3)),
column=cols.index(ORTH))
convolution = Residual(
ExtractWindow(
nW=1) >> LN(Maxout(width, width *
3, pieces=cnn_maxout_pieces)))
tok2vec = (FeatureExtracter(cols) >> with_flatten(
embed >> convolution**4, pad=4))
# Work around thinc API limitations :(. TODO: Revise in Thinc 7
tok2vec.nO = width
tok2vec.embed = embed
return tok2vec
def build_textcat_model(tok2vec, nr_class, width):
from thinc.v2v import Model, Softmax, Maxout
from thinc.api import flatten_add_lengths, chain
from thinc.t2v import Pooling, sum_pool, mean_pool, max_pool
from thinc.misc import Residual, LayerNorm
from spacy._ml import logistic, zero_init
with Model.define_operators({">>": chain}):
model = (
tok2vec
>> flatten_add_lengths
>> Pooling(mean_pool)
>> Softmax(nr_class, width)
)
model.tok2vec = tok2vec
return model
def build_simple_cnn_text_classifier(tok2vec, nr_class, exclusive_classes=False, **cfg):
"""
Build a simple CNN text classifier, given a token-to-vector model as inputs.
If exclusive_classes=True, a softmax non-linearity is applied, so that the
outputs sum to 1. If exclusive_classes=False, a logistic non-linearity
is applied instead, so that outputs are in the range [0, 1].
"""
with Model.define_operators({">>": chain}):
if exclusive_classes:
output_layer = Softmax(nr_class, tok2vec.nO)
else:
output_layer = zero_init(Affine(nr_class, tok2vec.nO, drop_factor=0.0)) >> logistic
model = tok2vec >> flatten_add_lengths >> Pooling(mean_pool) >> output_layer
model.tok2vec = chain(tok2vec, flatten)
model.nO = nr_class
return model
def build_simple_cnn_text_classifier(tok2vec, nr_class, exclusive_classes=False, **cfg):
"""
Build a simple CNN text classifier, given a token-to-vector model as inputs.
If exclusive_classes=True, a softmax non-linearity is applied, so that the
outputs sum to 1. If exclusive_classes=False, a logistic non-linearity
is applied instead, so that outputs are in the range [0, 1].
"""
with Model.define_operators({">>": chain}):
if exclusive_classes:
output_layer = Softmax(nr_class, tok2vec.nO)
else:
output_layer = (
zero_init(Affine(nr_class, tok2vec.nO, drop_factor=0.0)) >> logistic
)
model = tok2vec >> flatten_add_lengths >> Pooling(mean_pool) >> output_layer
model.tok2vec = chain(tok2vec, flatten)
model.nO = nr_class
return model
def build_model(nr_class, width, depth, conv_depth, **kwargs):
with Model.define_operators({'|': concatenate, '>>': chain, '**': clone}):
embed = ((HashEmbed(width, 5000, column=1)
| StaticVectors('spacy_pretrained_vectors', width, column=5)
| HashEmbed(width // 2, 750, column=2)
| HashEmbed(width // 2, 750, column=3)
| HashEmbed(width // 2, 750, column=4)) >> LN(Maxout(width)))
sent2vec = (flatten_add_lengths >> with_getitem(
0, embed >> Residual(ExtractWindow(nW=1) >> LN(Maxout(width)))**
conv_depth) >> ParametricAttention(width) >> Pooling(sum_pool) >>
Residual(LN(Maxout(width)))**depth)
model = (foreach(sent2vec, drop_factor=2.0) >> flatten_add_lengths >>
ParametricAttention(width, hard=False) >> Pooling(sum_pool) >>
Residual(LN(Maxout(width)))**depth >> Softmax(nr_class))
model.lsuv = False
return model
def build_tagger_model(nr_class, **cfg):
embed_size = util.env_opt('embed_size', 7000)
if 'token_vector_width' in cfg:
token_vector_width = cfg['token_vector_width']
else:
token_vector_width = util.env_opt('token_vector_width', 128)
pretrained_dims = cfg.get('pretrained_dims', 0)
with Model.define_operators({'>>': chain, '+': add}):
if 'tok2vec' in cfg:
tok2vec = cfg['tok2vec']
else:
tok2vec = Tok2Vec(token_vector_width,
embed_size,
pretrained_dims=pretrained_dims)
softmax = with_flatten(Softmax(nr_class, token_vector_width))
model = (tok2vec >> softmax)
model.nI = None
model.tok2vec = tok2vec
model.softmax = softmax
return model
def Tok2Vec(width, embed_size, **kwargs):
pretrained_vectors = kwargs.get('pretrained_vectors', None)
cnn_maxout_pieces = kwargs.get('cnn_maxout_pieces', 2)
cols = [ID, NORM, PREFIX, SUFFIX, SHAPE, ORTH]
with Model.define_operators({'>>': chain, '|': concatenate, '**': clone,
'+': add, '*': reapply}):
norm = HashEmbed(width, embed_size, column=cols.index(NORM),
name='embed_norm')
prefix = HashEmbed(width, embed_size//2, column=cols.index(PREFIX),
name='embed_prefix')
suffix = HashEmbed(width, embed_size//2, column=cols.index(SUFFIX),
name='embed_suffix')
shape = HashEmbed(width, embed_size//2, column=cols.index(SHAPE),
name='embed_shape')
if pretrained_vectors is not None:
glove = StaticVectors(pretrained_vectors, width, column=cols.index(ID))
embed = uniqued(
(glove | norm | prefix | suffix | shape)
>> LN(Maxout(width, width*5, pieces=3)), column=cols.index(ORTH))
else:
embed = uniqued(
(norm | prefix | suffix | shape)
>> LN(Maxout(width, width*4, pieces=3)), column=cols.index(ORTH))
convolution = Residual(
ExtractWindow(nW=1)
>> LN(Maxout(width, width*3, pieces=cnn_maxout_pieces))
)
tok2vec = (
FeatureExtracter(cols)
>> with_flatten(
embed
>> convolution ** 4, pad=4
)
)
# Work around thinc API limitations :(. TODO: Revise in Thinc 7
tok2vec.nO = width
tok2vec.embed = embed
return tok2vec
def build_tagger_model(nr_class, **cfg):
embed_size = util.env_opt('embed_size', 7000)
if 'token_vector_width' in cfg:
token_vector_width = cfg['token_vector_width']
else:
token_vector_width = util.env_opt('token_vector_width', 128)
pretrained_vectors = cfg.get('pretrained_vectors')
with Model.define_operators({'>>': chain, '+': add}):
if 'tok2vec' in cfg:
tok2vec = cfg['tok2vec']
else:
tok2vec = Tok2Vec(token_vector_width, embed_size,
pretrained_vectors=pretrained_vectors)
softmax = with_flatten(Softmax(nr_class, token_vector_width))
model = (
tok2vec
>> softmax
)
model.nI = None
model.tok2vec = tok2vec
model.softmax = softmax
return model
def main(depth=2, width=512, nb_epoch=30):
if CupyOps.xp != None:
Model.ops = CupyOps()
Model.Ops = CupyOps
# Configuration here isn't especially good. But, for demo..
with Model.define_operators({'**': clone, '>>': chain}):
model = ReLu(width) >> ReLu(width) >> Softmax()
train_data, dev_data, _ = datasets.mnist()
train_X, train_y = model.ops.unzip(train_data)
dev_X, dev_y = model.ops.unzip(dev_data)
dev_y = to_categorical(dev_y)
with model.begin_training(train_X, train_y,
L2=1e-6) as (trainer, optimizer):
epoch_loss = [0.]
def report_progress():
with model.use_params(optimizer.averages):
print(epoch_loss[-1], model.evaluate(dev_X, dev_y),
trainer.dropout)
epoch_loss.append(0.)
trainer.each_epoch.append(report_progress)
trainer.nb_epoch = nb_epoch
trainer.dropout = 0.3
trainer.batch_size = 128
trainer.dropout_decay = 0.0
train_X = model.ops.asarray(train_X, dtype='float32')
y_onehot = to_categorical(train_y)
for X, y in trainer.iterate(train_X, y_onehot):
yh, backprop = model.begin_update(X, drop=trainer.dropout)
loss = ((yh - y)**2.).sum() / y.shape[0]
backprop(yh - y, optimizer)
epoch_loss[-1] += loss
with model.use_params(optimizer.averages):
print('Avg dev.: %.3f' % model.evaluate(dev_X, dev_y))
with open('out.pickle', 'wb') as file_:
pickle.dump(model, file_, -1)
def build_nel_encoder(embed_width, hidden_width, ner_types, **cfg):
# TODO proper error
if "entity_width" not in cfg:
raise ValueError("entity_width not found")
if "context_width" not in cfg:
raise ValueError("context_width not found")
conv_depth = cfg.get("conv_depth", 2)
cnn_maxout_pieces = cfg.get("cnn_maxout_pieces", 3)
pretrained_vectors = cfg.get(
"pretrained_vectors") # self.nlp.vocab.vectors.name
context_width = cfg.get("context_width")
entity_width = cfg.get("entity_width")
with Model.define_operators({">>": chain, "**": clone}):
model = (
Affine(entity_width, entity_width + context_width + 1 + ner_types)
>> Affine(1, entity_width, drop_factor=0.0) >> logistic)
# context encoder
tok2vec = (Tok2Vec(
width=hidden_width,
embed_size=embed_width,
pretrained_vectors=pretrained_vectors,
cnn_maxout_pieces=cnn_maxout_pieces,
subword_features=True,
conv_depth=conv_depth,
bilstm_depth=0,
) >> flatten_add_lengths >> Pooling(mean_pool) >> Residual(
zero_init(Maxout(hidden_width, hidden_width))) >> zero_init(
Affine(context_width, hidden_width)))
model.tok2vec = tok2vec
model.tok2vec = tok2vec
model.tok2vec.nO = context_width
model.nO = 1
return model
def build_model(nr_class, width, depth, conv_depth, **kwargs):
with Model.define_operators({'|': concatenate, '>>': chain, '**': clone}):
embed = (
(HashEmbed(width, 5000, column=1)
| StaticVectors('spacy_pretrained_vectors', width, column=5)
| HashEmbed(width//2, 750, column=2)
| HashEmbed(width//2, 750, column=3)
| HashEmbed(width//2, 750, column=4))
>> LN(Maxout(width))
)
sent2vec = (
flatten_add_lengths
>> with_getitem(0,
embed
>> Residual(ExtractWindow(nW=1) >> LN(Maxout(width))) ** conv_depth
)
>> ParametricAttention(width)
>> Pooling(sum_pool)
>> Residual(LN(Maxout(width))) ** depth
)
model = (
foreach(sent2vec, drop_factor=2.0)
>> flatten_add_lengths
# This block would allow the model to learn some cross-sentence
# features. It's not useful on this problem. It might make more
# sense to use a BiLSTM here, following Liang et al (2016).
#>> with_getitem(0,
# Residual(ExtractWindow(nW=1) >> LN(Maxout(width))) ** conv_depth
#)
>> ParametricAttention(width, hard=False)
>> Pooling(sum_pool)
>> Residual(LN(Maxout(width))) ** depth
>> Softmax(nr_class)
)
model.lsuv = False
return model
def build_model(nr_class, width, depth, conv_depth, vectors_name, **kwargs):
with Model.define_operators({"|": concatenate, ">>": chain, "**": clone}):
embed = (HashEmbed(width, 5000, column=1)
| StaticVectors(vectors_name, width, column=5)
| HashEmbed(width // 2, 750, column=2)
| HashEmbed(width // 2, 750, column=3)
| HashEmbed(width // 2, 750, column=4)) >> LN(Maxout(width))
sent2vec = (with_flatten(embed) >> Residual(
prepare_self_attention(Affine(width * 3, width), nM=width, nH=4) >>
MultiHeadedAttention() >> with_flatten(
Maxout(width, width, pieces=3))) >> flatten_add_lengths >>
ParametricAttention(width, hard=False) >>
Pooling(mean_pool) >> Residual(LN(Maxout(width))))
model = (foreach(sent2vec, drop_factor=2.0) >> Residual(
prepare_self_attention(Affine(width * 3, width), nM=width, nH=4) >>
MultiHeadedAttention() >> with_flatten(LN(Affine(width, width))))
>> flatten_add_lengths >> ParametricAttention(
width, hard=False) >> Pooling(mean_pool) >> Residual(
LN(Maxout(width)))**2 >> Softmax(nr_class))
model.lsuv = False
return model
def build_tagger_model(nr_class, **cfg):
embed_size = util.env_opt("embed_size", 2000)
if "token_vector_width" in cfg:
token_vector_width = cfg["token_vector_width"]
else:
token_vector_width = util.env_opt("token_vector_width", 96)
pretrained_vectors = cfg.get("pretrained_vectors")
subword_features = cfg.get("subword_features", True)
with Model.define_operators({">>": chain, "+": add}):
if "tok2vec" in cfg:
tok2vec = cfg["tok2vec"]
else:
tok2vec = Tok2Vec(
token_vector_width,
embed_size,
subword_features=subword_features,
pretrained_vectors=pretrained_vectors,
)
softmax = with_flatten(Softmax(nr_class, token_vector_width))
model = tok2vec >> softmax
model.nI = None
model.tok2vec = tok2vec
model.softmax = softmax
return model
def build_tagger_model(nr_class, **cfg):
embed_size = util.env_opt("embed_size", 2000)
if "token_vector_width" in cfg:
token_vector_width = cfg["token_vector_width"]
else:
token_vector_width = util.env_opt("token_vector_width", 96)
pretrained_vectors = cfg.get("pretrained_vectors")
subword_features = cfg.get("subword_features", True)
with Model.define_operators({">>": chain, "+": add}):
if "tok2vec" in cfg:
tok2vec = cfg["tok2vec"]
else:
tok2vec = Tok2Vec(
token_vector_width,
embed_size,
subword_features=subword_features,
pretrained_vectors=pretrained_vectors,
)
softmax = with_flatten(Softmax(nr_class, token_vector_width))
model = tok2vec >> softmax
model.nI = None
model.tok2vec = tok2vec
model.softmax = softmax
return model
def main(
width=128,
depth=1,
vector_length=128,
min_batch_size=16,
max_batch_size=16,
learn_rate=0.001,
momentum=0.9,
dropout=0.5,
dropout_decay=1e-4,
nb_epoch=20,
L2=1e-6,
):
using_gpu = prefer_gpu()
if using_gpu:
torch.set_default_tensor_type("torch.cuda.FloatTensor")
cfg = dict(locals())
print(cfg)
train_data, check_data, nr_tag = ancora_pos_tags()
train_data = list(train_data)
check_data = list(check_data)
extracter = FeatureExtracter("es", attrs=[LOWER, SHAPE, PREFIX, SUFFIX])
with Model.define_operators({"**": clone, ">>": chain, "+": add, "|": concatenate}):
lower_case = HashEmbed(width, 100, column=0)
shape = HashEmbed(width // 2, 200, column=1)
prefix = HashEmbed(width // 2, 100, column=2)
suffix = HashEmbed(width // 2, 100, column=3)
model = (
with_flatten(
(lower_case | shape | prefix | suffix) >> Maxout(width, pieces=3)
)
>> PyTorchBiLSTM(width, width, depth)
>> with_flatten(Softmax(nr_tag))
)
train_X, train_y = preprocess(model.ops, extracter, train_data, nr_tag)
dev_X, dev_y = preprocess(model.ops, extracter, check_data, nr_tag)
n_train = float(sum(len(x) for x in train_X))
global epoch_train_acc
with model.begin_training(train_X[:10], train_y[:10], **cfg) as (
trainer,
optimizer,
):
trainer.each_epoch.append(track_progress(**locals()))
trainer.batch_size = min_batch_size
batch_size = float(min_batch_size)
for X, y in trainer.iterate(train_X, train_y):
yh, backprop = model.begin_update(X, drop=trainer.dropout)
gradient = [yh[i] - y[i] for i in range(len(yh))]
backprop(gradient, optimizer)
trainer.batch_size = min(int(batch_size), max_batch_size)
batch_size *= 1.001
print(model.evaluate(dev_X, model.ops.flatten(dev_y)))
with open("/tmp/model.pickle", "wb") as file_:
pickle.dump(model, file_)
def main(
dataset="quora",
width=200,
depth=2,
min_batch_size=1,
max_batch_size=512,
dropout=0.2,
dropout_decay=0.0,
pooling="mean+max",
nb_epoch=5,
pieces=3,
L2=0.0,
use_gpu=False,
out_loc=None,
quiet=False,
job_id=None,
ws_api_url=None,
rest_api_url=None,
):
cfg = dict(locals())
if out_loc:
out_loc = Path(out_loc)
if not out_loc.parent.exists():
raise IOError("Can't open output location: %s" % out_loc)
print(cfg)
if pooling == "mean+max":
pool_layer = Pooling(mean_pool, max_pool)
elif pooling == "mean":
pool_layer = mean_pool
elif pooling == "max":
pool_layer = max_pool
else:
raise ValueError("Unrecognised pooling", pooling)
print("Load spaCy")
nlp = get_spacy("en")
if use_gpu:
Model.ops = CupyOps()
print("Construct model")
# Bind operators for the scope of the block:
# * chain (>>): Compose models in a 'feed forward' style,
# i.e. chain(f, g)(x) -> g(f(x))
# * clone (**): Create n copies of a model, and chain them, i.e.
# (f ** 3)(x) -> f''(f'(f(x))), where f, f' and f'' have distinct weights.
# * concatenate (|): Merge the outputs of two models into a single vector,
# i.e. (f|g)(x) -> hstack(f(x), g(x))
Model.lsuv = True
# Model.ops = CupyOps()
with Model.define_operators({">>": chain, "**": clone, "|": concatenate, "+": add}):
mwe_encode = ExtractWindow(nW=1) >> LN(
Maxout(width, drop_factor=0.0, pieces=pieces)
)
sent2vec = (
flatten_add_lengths
>> with_getitem(
0,
(HashEmbed(width, 3000) | StaticVectors("en", width))
>> LN(Maxout(width, width * 2))
>> Residual(mwe_encode) ** depth,
) # : word_ids{T}
>> Pooling(mean_pool, max_pool)
>> Residual(LN(Maxout(width * 2, pieces=pieces), nO=width * 2)) ** 2
>> logistic
)
model = Siamese(sent2vec, CauchySimilarity(width * 2))
print("Read and parse data: %s" % dataset)
if dataset == "quora":
train, dev = datasets.quora_questions()
elif dataset == "snli":
train, dev = datasets.snli()
elif dataset == "stackxc":
train, dev = datasets.stack_exchange()
elif dataset in ("quora+snli", "snli+quora"):
train, dev = datasets.quora_questions()
train2, dev2 = datasets.snli()
train.extend(train2)
dev.extend(dev2)
else:
raise ValueError("Unknown dataset: %s" % dataset)
get_ids = get_word_ids(Model.ops)
train_X, train_y = preprocess(model.ops, nlp, train, get_ids)
dev_X, dev_y = preprocess(model.ops, nlp, dev, get_ids)
with model.begin_training(train_X[:10000], train_y[:10000], **cfg) as (
trainer,
optimizer,
):
# Pass a callback to print progress. Give it all the local scope,
# because why not?
trainer.each_epoch.append(track_progress(**locals()))
trainer.batch_size = min_batch_size
batch_size = float(min_batch_size)
print("Accuracy before training", model.evaluate_logloss(dev_X, dev_y))
print("Train")
global epoch_train_acc
n_iter = 0
for X, y in trainer.iterate(train_X, train_y, progress_bar=not quiet):
# Slightly useful trick: Decay the dropout as training proceeds.
yh, backprop = model.begin_update(X, drop=trainer.dropout)
assert yh.shape == y.shape, (yh.shape, y.shape)
assert (yh >= 0.0).all(), yh
train_acc = ((yh >= 0.5) == (y >= 0.5)).sum()
loss = model.ops.xp.abs(yh - y).mean()
epoch_train_acc += train_acc
backprop(yh - y, optimizer)
n_iter += 1
# Slightly useful trick: start with low batch size, accelerate.
trainer.batch_size = min(int(batch_size), max_batch_size)
batch_size *= 1.001
if out_loc:
out_loc = Path(out_loc)
print("Saving to", out_loc)
with out_loc.open("wb") as file_:
pickle.dump(model, file_, -1)
def main(
width=100,
depth=4,
vector_length=64,
min_batch_size=1,
max_batch_size=32,
learn_rate=0.001,
momentum=0.9,
dropout=0.5,
dropout_decay=1e-4,
nb_epoch=20,
L2=1e-6,
):
cfg = dict(locals())
print(cfg)
prefer_gpu()
train_data, check_data, nr_tag = ancora_pos_tags()
extracter = FeatureExtracter("es", attrs=[LOWER, SHAPE, PREFIX, SUFFIX])
Model.lsuv = True
with Model.define_operators({
"**": clone,
">>": chain,
"+": add,
"|": concatenate
}):
lower_case = HashEmbed(width, 100, column=0)
shape = HashEmbed(width // 2, 200, column=1)
prefix = HashEmbed(width // 2, 100, column=2)
suffix = HashEmbed(width // 2, 100, column=3)
model = (with_flatten(
(lower_case | shape | prefix | suffix) >> Maxout(width, pieces=3),
pad=depth) >> with_pad_and_mask(
MultiHeadedAttention(nM=width, nH=4)) >> with_flatten(
Softmax(nr_tag)))
train_X, train_y = preprocess(model.ops, extracter, train_data, nr_tag)
dev_X, dev_y = preprocess(model.ops, extracter, check_data, nr_tag)
n_train = float(sum(len(x) for x in train_X))
global epoch_train_acc
with model.begin_training(train_X[:5000], train_y[:5000], **cfg) as (
trainer,
optimizer,
):
trainer.each_epoch.append(track_progress(**locals()))
trainer.batch_size = min_batch_size
batch_size = float(min_batch_size)
for X, y in trainer.iterate(train_X, train_y):
yh, backprop = model.begin_update(X, drop=trainer.dropout)
gradient = [yh[i] - y[i] for i in range(len(yh))]
backprop(gradient, optimizer)
trainer.batch_size = min(int(batch_size), max_batch_size)
batch_size *= 1.001
with model.use_params(trainer.optimizer.averages):
print(model.evaluate(dev_X, model.ops.flatten(dev_y)))
with open("/tmp/model.pickle", "wb") as file_:
pickle.dump(model, file_)
def build_text_classifier(nr_class, width=64, **cfg):
nr_vector = cfg.get('nr_vector', 5000)
pretrained_dims = cfg.get('pretrained_dims', 0)
with Model.define_operators({'>>': chain, '+': add, '|': concatenate,
'**': clone}):
if cfg.get('low_data') and pretrained_dims:
model = (
SpacyVectors
>> flatten_add_lengths
>> with_getitem(0, Affine(width, pretrained_dims))
>> ParametricAttention(width)
>> Pooling(sum_pool)
>> Residual(ReLu(width, width)) ** 2
>> zero_init(Affine(nr_class, width, drop_factor=0.0))
>> logistic
)
return model
lower = HashEmbed(width, nr_vector, column=1)
prefix = HashEmbed(width//2, nr_vector, column=2)
suffix = HashEmbed(width//2, nr_vector, column=3)
shape = HashEmbed(width//2, nr_vector, column=4)
trained_vectors = (
FeatureExtracter([ORTH, LOWER, PREFIX, SUFFIX, SHAPE, ID])
>> with_flatten(
uniqued(
(lower | prefix | suffix | shape)
>> LN(Maxout(width, width+(width//2)*3)),
column=0
)
)
)
if pretrained_dims:
static_vectors = (
SpacyVectors
>> with_flatten(Affine(width, pretrained_dims))
)
# TODO Make concatenate support lists
vectors = concatenate_lists(trained_vectors, static_vectors)
vectors_width = width*2
else:
vectors = trained_vectors
vectors_width = width
static_vectors = None
cnn_model = (
vectors
>> with_flatten(
LN(Maxout(width, vectors_width))
>> Residual(
(ExtractWindow(nW=1) >> LN(Maxout(width, width*3)))
) ** 2, pad=2
)
>> flatten_add_lengths
>> ParametricAttention(width)
>> Pooling(sum_pool)
>> Residual(zero_init(Maxout(width, width)))
>> zero_init(Affine(nr_class, width, drop_factor=0.0))
)
linear_model = (
_preprocess_doc
>> LinearModel(nr_class)
)
#model = linear_model >> logistic
model = (
(linear_model | cnn_model)
>> zero_init(Affine(nr_class, nr_class*2, drop_factor=0.0))
>> logistic
)
model.nO = nr_class
model.lsuv = False
return model
def build_text_classifier(nr_class, width=64, **cfg):
depth = cfg.get("depth", 2)
nr_vector = cfg.get("nr_vector", 5000)
pretrained_dims = cfg.get("pretrained_dims", 0)
with Model.define_operators({
">>": chain,
"+": add,
"|": concatenate,
"**": clone
}):
if cfg.get("low_data") and pretrained_dims:
model = (SpacyVectors >> flatten_add_lengths >> with_getitem(
0, Affine(width, pretrained_dims)) >>
ParametricAttention(width) >> Pooling(sum_pool) >>
Residual(ReLu(width, width))**2 >> zero_init(
Affine(nr_class, width, drop_factor=0.0)) >> logistic)
return model
lower = HashEmbed(width, nr_vector, column=1)
prefix = HashEmbed(width // 2, nr_vector, column=2)
suffix = HashEmbed(width // 2, nr_vector, column=3)
shape = HashEmbed(width // 2, nr_vector, column=4)
trained_vectors = FeatureExtracter(
[ORTH, LOWER, PREFIX, SUFFIX, SHAPE, ID]) >> with_flatten(
uniqued(
(lower | prefix | suffix | shape) >> LN(
Maxout(width, width + (width // 2) * 3)),
column=0,
))
if pretrained_dims:
static_vectors = SpacyVectors >> with_flatten(
Affine(width, pretrained_dims))
# TODO Make concatenate support lists
vectors = concatenate_lists(trained_vectors, static_vectors)
vectors_width = width * 2
else:
vectors = trained_vectors
vectors_width = width
static_vectors = None
tok2vec = vectors >> with_flatten(
LN(Maxout(width, vectors_width)) >> Residual(
(ExtractWindow(nW=1) >> LN(Maxout(width, width * 3))))**depth,
pad=depth,
)
cnn_model = (
tok2vec >> flatten_add_lengths >> ParametricAttention(width) >>
Pooling(sum_pool) >> Residual(zero_init(Maxout(width, width))) >>
zero_init(Affine(nr_class, width, drop_factor=0.0)))
linear_model = build_bow_text_classifier(nr_class,
ngram_size=cfg.get(
"ngram_size", 1),
exclusive_classes=False)
if cfg.get("exclusive_classes"):
output_layer = Softmax(nr_class, nr_class * 2)
else:
output_layer = (zero_init(
Affine(nr_class, nr_class * 2, drop_factor=0.0)) >> logistic)
model = (linear_model | cnn_model) >> output_layer
model.tok2vec = chain(tok2vec, flatten)
model.nO = nr_class
model.lsuv = False
return model
def main(dataset='quora',
width=50,
depth=2,
min_batch_size=1,
max_batch_size=512,
dropout=0.2,
dropout_decay=0.0,
pooling="mean+max",
nb_epoch=5,
pieces=3,
L2=0.0,
use_gpu=False,
out_loc=None,
quiet=False,
job_id=None,
ws_api_url=None,
rest_api_url=None):
global CTX
if job_id is not None:
CTX = neptune.Context()
width = CTX.params.width
L2 = CTX.params.L2
nb_epoch = CTX.params.nb_epoch
depth = CTX.params.depth
max_batch_size = CTX.params.max_batch_size
cfg = dict(locals())
if out_loc:
out_loc = Path(out_loc)
if not out_loc.parent.exists():
raise IOError("Can't open output location: %s" % out_loc)
print(cfg)
if pooling == 'mean+max':
pool_layer = Pooling(mean_pool, max_pool)
elif pooling == "mean":
pool_layer = mean_pool
elif pooling == "max":
pool_layer = max_pool
else:
raise ValueError("Unrecognised pooling", pooling)
print("Load spaCy")
nlp = get_spacy('en')
if use_gpu:
Model.ops = CupyOps()
print("Construct model")
# Bind operators for the scope of the block:
# * chain (>>): Compose models in a 'feed forward' style,
# i.e. chain(f, g)(x) -> g(f(x))
# * clone (**): Create n copies of a model, and chain them, i.e.
# (f ** 3)(x) -> f''(f'(f(x))), where f, f' and f'' have distinct weights.
# * concatenate (|): Merge the outputs of two models into a single vector,
# i.e. (f|g)(x) -> hstack(f(x), g(x))
Model.lsuv = True
#Model.ops = CupyOps()
with Model.define_operators({
'>>': chain,
'**': clone,
'|': concatenate,
'+': add
}):
mwe_encode = ExtractWindow(nW=1) >> BN(
Maxout(width, drop_factor=0.0, pieces=pieces))
sent2vec = ( # List[spacy.token.Doc]{B}
flatten_add_lengths # : (ids{T}, lengths{B})
>> with_getitem(
0,
#(StaticVectors('en', width)
HashEmbed(width, 3000)
#+ HashEmbed(width, 3000))
#>> Residual(mwe_encode ** 2)
) # : word_ids{T}
>> Pooling(mean_pool, max_pool)
#>> Residual(BN(Maxout(width*2, pieces=pieces), nO=width*2)**2)
>> Maxout(width * 2, pieces=pieces, drop_factor=0.0) >> logistic)
model = Siamese(sent2vec, CauchySimilarity(width * 2))
print("Read and parse data: %s" % dataset)
if dataset == 'quora':
train, dev = datasets.quora_questions()
elif dataset == 'snli':
train, dev = datasets.snli()
elif dataset == 'stackxc':
train, dev = datasets.stack_exchange()
elif dataset in ('quora+snli', 'snli+quora'):
train, dev = datasets.quora_questions()
train2, dev2 = datasets.snli()
train.extend(train2)
dev.extend(dev2)
else:
raise ValueError("Unknown dataset: %s" % dataset)
get_ids = get_word_ids(Model.ops)
train_X, train_y = preprocess(model.ops, nlp, train, get_ids)
dev_X, dev_y = preprocess(model.ops, nlp, dev, get_ids)
with model.begin_training(train_X[:10000], train_y[:10000],
**cfg) as (trainer, optimizer):
# Pass a callback to print progress. Give it all the local scope,
# because why not?
trainer.each_epoch.append(track_progress(**locals()))
trainer.batch_size = min_batch_size
batch_size = float(min_batch_size)
print("Accuracy before training", model.evaluate_logloss(dev_X, dev_y))
print("Train")
global epoch_train_acc
n_iter = 0
for X, y in trainer.iterate(train_X, train_y, progress_bar=not quiet):
# Slightly useful trick: Decay the dropout as training proceeds.
yh, backprop = model.begin_update(X, drop=trainer.dropout)
assert yh.shape == y.shape, (yh.shape, y.shape)
assert (yh >= 0.).all(), yh
train_acc = ((yh >= 0.5) == (y >= 0.5)).sum()
loss = model.ops.xp.abs(yh - y).mean()
epoch_train_acc += train_acc
backprop(yh - y, optimizer)
n_iter += 1
# Slightly useful trick: start with low batch size, accelerate.
trainer.batch_size = min(int(batch_size), max_batch_size)
batch_size *= 1.001
if out_loc:
out_loc = Path(out_loc)
print('Saving to', out_loc)
with out_loc.open('wb') as file_:
pickle.dump(model, file_, -1)
def main(nH=6,
dropout=0.1,
nS=6,
nB=64,
nE=20,
use_gpu=-1,
lim=1000000,
nM=300,
mL=100,
save=False,
nTGT=5000,
save_name="model.pkl"):
if use_gpu != -1:
spacy.require_gpu()
device = 'cuda'
else:
device = 'cpu'
''' Read dataset '''
nlp = spacy.load('en_core_web_sm')
print('English model loaded')
for control_token in ("<eos>", "<bos>", "<pad>", "<cls>", "<mask>"):
nlp.tokenizer.add_special_case(control_token, [{ORTH: control_token}])
train, dev, test = get_iwslt()
print('Dataset loaded')
train, _ = zip(*train)
dev, _ = zip(*dev)
test, _ = zip(*test)
train = train[:lim]
dev = dev[:lim]
test = test[:lim]
''' Tokenize '''
train = spacy_tokenize(nlp.tokenizer, train, mL=mL)
dev = spacy_tokenize(nlp.tokenizer, dev, mL=mL)
test = spacy_tokenize(nlp.tokenizer, test, mL=mL)
print('Tokenization finished')
''' Set rank based on all the docs '''
all_docs = train + dev + test
set_rank(nlp.vocab, all_docs, nTGT=nTGT)
train = set_numeric_ids(nlp.vocab, train)
dev = set_numeric_ids(nlp.vocab, dev)
test = set_numeric_ids(nlp.vocab, test)
print('Numeric ids set')
word2indx, indx2word = get_dicts(nlp.vocab)
print('Vocab dictionaries grabbed')
with Model.define_operators({">>": chain}):
embed_cols = [ORTH, SHAPE, PREFIX, SUFFIX]
extractor = FeatureExtracter(attrs=embed_cols)
position_encode = PositionEncode(mL, nM)
model = (FeatureExtracter(attrs=embed_cols) >> with_flatten(
FancyEmbed(nM, nTGT, cols=embed_cols)) >> Residual(position_encode)
>> create_model_input() >> Encoder(
nM=nM, nS=nS, nH=nH, device=device) >> with_reshape(
Softmax(nO=nTGT, nI=nM)))
''' Progress tracking '''
losses = [0.]
train_accuracies = [0.]
train_totals = [0.]
dev_accuracies = [0.]
dev_loss = [0.]
def track_progress():
correct = 0.
total = 0.
''' Get dev stats '''
for X0 in minibatch(dev, size=nB):
X1, loss_mask = random_mask(X0, nlp, indx2word, nlp.vocab, mL)
Xh = model(X1)
L, C, t = get_loss(Xh, X0, X1, loss_mask)
correct += C
total += t
dev_loss[-1] += (L**2).sum()
dev_accuracies[-1] = correct / total
print(len(losses), losses[-1],
train_accuracies[-1] / train_totals[-1], dev_loss[-1],
dev_accuracies[-1])
dev_loss.append(0.)
losses.append(0.)
train_accuracies.append(0.)
dev_accuracies.append(0.)
train_totals.append(0.)
if save:
model.to_disk('.models/' + save_name)
''' Model training '''
with model.begin_training(batch_size=nB,
nb_epoch=nE) as (trainer, optimizer):
trainer.dropout = dropout
trainer.dropout_decay = 1e-4
optimizer.alpha = 0.001
optimizer.L2 = 1e-6
optimizer.max_grad_norm = 1.0
trainer.each_epoch.append(track_progress)
optimizer.alpha = 0.001
optimizer.L2 = 1e-6
optimizer.max_grad_norm = 1.0
for X0, _ in trainer.iterate(train, train):
X1, loss_mask = random_mask(X0, nlp, indx2word, nlp.vocab, mL)
Xh, backprop = model.begin_update(X1, drop=dropout)
dXh, C, total = get_loss(Xh, X0, X1, loss_mask)
backprop(dXh, sgd=optimizer)
losses[-1] += (dXh**2).sum()
train_accuracies[-1] += C
train_totals[-1] += total
def main(nH=6, dropout=0.1, nS=6, nB=15, nE=20, use_gpu=-1, lim=2000):
if use_gpu != -1:
# TODO: Make specific to different devices, e.g. 1 vs 0
spacy.require_gpu()
train, dev, test = get_iwslt()
train_X, train_Y = zip(*train)
dev_X, dev_Y = zip(*dev)
test_X, test_Y = zip(*test)
''' Read dataset '''
nlp_en = spacy.load('en_core_web_sm')
nlp_de = spacy.load('de_core_news_sm')
print('Models loaded')
for control_token in ("<eos>", "<bos>", "<pad>"):
nlp_en.tokenizer.add_special_case(control_token, [{
ORTH: control_token
}])
nlp_de.tokenizer.add_special_case(control_token, [{
ORTH: control_token
}])
train_X, train_Y = spacy_tokenize(nlp_en.tokenizer, nlp_de.tokenizer,
train_X[-lim:], train_Y[-lim:],
MAX_LENGTH)
dev_X, dev_Y = spacy_tokenize(nlp_en.tokenizer, nlp_de.tokenizer,
dev_X[-lim:], dev_Y[-lim:], MAX_LENGTH)
test_X, test_Y = spacy_tokenize(nlp_en.tokenizer, nlp_de.tokenizer,
test_X[-lim:], test_Y[-lim:], MAX_LENGTH)
train_X = set_numeric_ids(nlp_en.vocab, train_X, vocab_size=VOCAB_SIZE)
train_Y = set_numeric_ids(nlp_de.vocab, train_Y, vocab_size=VOCAB_SIZE)
nTGT = VOCAB_SIZE
with Model.define_operators({">>": chain}):
embed_cols = [ORTH, SHAPE, PREFIX, SUFFIX]
extractor = FeatureExtracter(attrs=embed_cols)
position_encode = PositionEncode(MAX_LENGTH, MODEL_SIZE)
model = (apply_layers(extractor, extractor) >> apply_layers(
with_flatten(FancyEmbed(MODEL_SIZE, 5000, cols=embed_cols)),
with_flatten(FancyEmbed(MODEL_SIZE, 5000, cols=embed_cols)),
) >> apply_layers(Residual(position_encode), Residual(position_encode))
>> create_batch() >> EncoderDecoder(nS=nS, nH=nH, nTGT=nTGT))
losses = [0.]
train_accuracies = [0.]
train_totals = [0.]
dev_accuracies = [0.]
dev_loss = [0.]
def track_progress():
correct = 0.
total = 0.
for batch in minibatch(zip(dev_X, dev_Y), size=1024):
X, Y = zip(*batch)
Yh, Y_mask = model((X, Y))
L, C = get_loss(model.ops, Yh, Y, Y_mask)
correct += C
dev_loss[-1] += (L**2).sum()
total += len(Y)
dev_accuracies[-1] = correct / total
n_train = train_totals[-1]
print(len(losses), losses[-1], train_accuracies[-1] / n_train,
dev_loss[-1], dev_accuracies[-1])
dev_loss.append(0.)
losses.append(0.)
train_accuracies.append(0.)
dev_accuracies.append(0.)
train_totals.append(0.)
with model.begin_training(batch_size=nB,
nb_epoch=nE) as (trainer, optimizer):
trainer.dropout = dropout
trainer.dropout_decay = 1e-4
trainer.each_epoch.append(track_progress)
optimizer.alpha = 0.001
optimizer.L2 = 1e-6
optimizer.max_grad_norm = 1.0
for X, Y in trainer.iterate(train_X, train_Y):
(Yh, X_mask), backprop = model.begin_update((X, Y), drop=dropout)
dYh, C = get_loss(model.ops, Yh, Y, X_mask)
backprop(dYh, sgd=optimizer)
losses[-1] += (dYh**2).sum()
train_accuracies[-1] += C
train_totals[-1] += sum(len(y) for y in Y)
def main(nH=6,
dropout=0.0,
nS=6,
nB=32,
nE=20,
use_gpu=-1,
lim=2000,
nM=300,
mL=20,
nTGT=3500,
save=False,
load=False,
save_name="model.pkl",
load_name="model.pkl"):
if use_gpu != -1:
# TODO: Make specific to different devices, e.g. 1 vs 0
spacy.require_gpu()
device = 'cuda'
else:
device = 'cpu'
train, dev, test = get_iwslt()
train_X, train_Y = zip(*train)
dev_X, dev_Y = zip(*dev)
test_X, test_Y = zip(*test)
''' Read dataset '''
nlp_en = spacy.load('en_core_web_sm')
nlp_de = spacy.load('de_core_news_sm')
print('Models loaded')
for control_token in ("<eos>", "<bos>", "<pad>"):
nlp_en.tokenizer.add_special_case(control_token, [{
ORTH: control_token
}])
nlp_de.tokenizer.add_special_case(control_token, [{
ORTH: control_token
}])
train_lim = min(lim, len(train_X))
dev_lim = min(lim, len(dev_X))
test_lim = min(lim, len(test_X))
train_X, train_Y = spacy_tokenize(nlp_en.tokenizer, nlp_de.tokenizer,
train_X[:train_lim], train_Y[:train_lim],
mL)
dev_X, dev_Y = spacy_tokenize(nlp_en.tokenizer, nlp_de.tokenizer,
dev_X[:dev_lim], dev_Y[:dev_lim], mL)
test_X, test_Y = spacy_tokenize(nlp_en.tokenizer, nlp_de.tokenizer,
test_X[:test_lim], test_Y[:test_lim], mL)
all_X_docs = train_X + dev_X + test_X
all_y_docs = train_Y + dev_Y + test_Y
set_rank(nlp_en.vocab, all_X_docs, nTGT=nTGT)
set_rank(nlp_de.vocab, all_y_docs, nTGT=nTGT)
train_X = set_numeric_ids(nlp_en.vocab, train_X)
dev_X = set_numeric_ids(nlp_en.vocab, dev_X)
test_X = set_numeric_ids(nlp_en.vocab, test_X)
train_Y = set_numeric_ids(nlp_de.vocab, train_Y)
dev_Y = set_numeric_ids(nlp_de.vocab, dev_Y)
test_Y = set_numeric_ids(nlp_de.vocab, test_Y)
en_word2indx, en_indx2word = get_dicts(nlp_en.vocab)
de_word2indx, de_indx2word = get_dicts(nlp_de.vocab)
nTGT += 1
if not load:
with Model.define_operators({">>": chain}):
embed_cols = [ORTH, SHAPE, PREFIX, SUFFIX]
extractor = FeatureExtracter(attrs=embed_cols)
position_encode = PositionEncode(mL, nM)
model = (apply_layers(extractor, extractor) >> apply_layers(
with_flatten(FancyEmbed(nM, 5000, cols=embed_cols)),
with_flatten(FancyEmbed(nM, 5000, cols=embed_cols)),
) >> apply_layers(Residual(position_encode),
Residual(position_encode)) >> create_batch() >>
EncoderDecoder(
nS=nS, nH=nH, nTGT=nTGT, nM=nM, device=device))
else:
model = Model.from_disk(load_name)
losses = [0.]
train_accuracies = [0.]
train_totals = [0.]
dev_accuracies = [0.]
dev_loss = [0.]
def track_progress():
correct = 0.
total = 0.
for batch in minibatch(zip(dev_X, dev_Y), size=1024):
X, Y = zip(*batch)
Yh, Y_mask = model((X, Y))
L, C, total = get_loss(model.ops, Yh, Y, Y_mask)
correct += C
dev_loss[-1] += (L**2).sum()
dev_accuracies[-1] = correct / total
n_train = train_totals[-1]
print(len(losses), losses[-1], train_accuracies[-1] / n_train,
dev_loss[-1], dev_accuracies[-1])
dev_loss.append(0.)
losses.append(0.)
train_accuracies.append(0.)
dev_accuracies.append(0.)
train_totals.append(0.)
with model.begin_training(batch_size=nB,
nb_epoch=nE) as (trainer, optimizer):
trainer.dropout = dropout
trainer.dropout_decay = 1e-4
optimizer.alpha = 0.001
optimizer.L2 = 1e-6
optimizer.max_grad_norm = 1.0
trainer.each_epoch.append(track_progress)
optimizer.alpha = 0.001
optimizer.L2 = 1e-6
optimizer.max_grad_norm = 1.0
for X, Y in trainer.iterate(train_X, train_Y):
(Yh, X_mask), backprop = model.begin_update((X, Y))
dYh, C, total = get_loss(model.ops, Yh, Y, X_mask)
backprop(dYh, sgd=optimizer)
losses[-1] += (dYh**2).sum()
train_accuracies[-1] += C
train_totals[-1] += total
if save:
model.to_disk(save_name)
def build_text_classifier(nr_class, width=64, **cfg):
depth = cfg.get("depth", 2)
nr_vector = cfg.get("nr_vector", 5000)
pretrained_dims = cfg.get("pretrained_dims", 0)
with Model.define_operators({">>": chain, "+": add, "|": concatenate, "**": clone}):
if cfg.get("low_data") and pretrained_dims:
model = (
SpacyVectors
>> flatten_add_lengths
>> with_getitem(0, Affine(width, pretrained_dims))
>> ParametricAttention(width)
>> Pooling(sum_pool)
>> Residual(ReLu(width, width)) ** 2
>> zero_init(Affine(nr_class, width, drop_factor=0.0))
>> logistic
)
return model
lower = HashEmbed(width, nr_vector, column=1)
prefix = HashEmbed(width // 2, nr_vector, column=2)
suffix = HashEmbed(width // 2, nr_vector, column=3)
shape = HashEmbed(width // 2, nr_vector, column=4)
trained_vectors = FeatureExtracter(
[ORTH, LOWER, PREFIX, SUFFIX, SHAPE, ID]
) >> with_flatten(
uniqued(
(lower | prefix | suffix | shape)
>> LN(Maxout(width, width + (width // 2) * 3)),
column=0,
)
)
if pretrained_dims:
static_vectors = SpacyVectors >> with_flatten(
Affine(width, pretrained_dims)
)
# TODO Make concatenate support lists
vectors = concatenate_lists(trained_vectors, static_vectors)
vectors_width = width * 2
else:
vectors = trained_vectors
vectors_width = width
static_vectors = None
tok2vec = vectors >> with_flatten(
LN(Maxout(width, vectors_width))
>> Residual((ExtractWindow(nW=1) >> LN(Maxout(width, width * 3)))) ** depth,
pad=depth,
)
cnn_model = (
tok2vec
>> flatten_add_lengths
>> ParametricAttention(width)
>> Pooling(sum_pool)
>> Residual(zero_init(Maxout(width, width)))
>> zero_init(Affine(nr_class, width, drop_factor=0.0))
)
linear_model = build_bow_text_classifier(
nr_class, ngram_size=cfg.get("ngram_size", 1), exclusive_classes=False
)
if cfg.get("exclusive_classes"):
output_layer = Softmax(nr_class, nr_class * 2)
else:
output_layer = (
zero_init(Affine(nr_class, nr_class * 2, drop_factor=0.0)) >> logistic
)
model = (linear_model | cnn_model) >> output_layer
model.tok2vec = chain(tok2vec, flatten)
model.nO = nr_class
model.lsuv = False
return model
def Tok2Vec(width, embed_size, **kwargs):
# Circular imports :(
from .._ml import CharacterEmbed
from .._ml import PyTorchBiLSTM
pretrained_vectors = kwargs.get("pretrained_vectors", None)
cnn_maxout_pieces = kwargs.get("cnn_maxout_pieces", 3)
subword_features = kwargs.get("subword_features", True)
char_embed = kwargs.get("char_embed", False)
if char_embed:
subword_features = False
conv_depth = kwargs.get("conv_depth", 4)
bilstm_depth = kwargs.get("bilstm_depth", 0)
cols = [ID, NORM, PREFIX, SUFFIX, SHAPE, ORTH]
with Model.define_operators({">>": chain, "|": concatenate, "**": clone}):
norm = HashEmbed(width,
embed_size,
column=cols.index(NORM),
name="embed_norm",
seed=6)
if subword_features:
prefix = HashEmbed(width,
embed_size // 2,
column=cols.index(PREFIX),
name="embed_prefix",
seed=7)
suffix = HashEmbed(width,
embed_size // 2,
column=cols.index(SUFFIX),
name="embed_suffix",
seed=8)
shape = HashEmbed(width,
embed_size // 2,
column=cols.index(SHAPE),
name="embed_shape",
seed=9)
else:
prefix, suffix, shape = (None, None, None)
if pretrained_vectors is not None:
glove = StaticVectors(pretrained_vectors,
width,
column=cols.index(ID))
if subword_features:
embed = uniqued(
(glove | norm | prefix | suffix | shape) >> LN(
Maxout(width, width * 5, pieces=3)),
column=cols.index(ORTH),
)
elif char_embed:
embed = concatenate_lists(
CharacterEmbed(nM=64, nC=8),
FeatureExtracter(cols) >> with_flatten(glove),
)
reduce_dimensions = LN(
Maxout(width, 64 * 8 + width, pieces=cnn_maxout_pieces))
else:
embed = uniqued(
(glove | norm) >> LN(Maxout(width, width * 2, pieces=3)),
column=cols.index(ORTH),
)
elif subword_features:
embed = uniqued(
(norm | prefix | suffix | shape) >> LN(
Maxout(width, width * 4, pieces=3)),
column=cols.index(ORTH),
)
elif char_embed:
embed = concatenate_lists(
CharacterEmbed(nM=64, nC=8),
FeatureExtracter(cols) >> with_flatten(norm),
)
reduce_dimensions = LN(
Maxout(width, 64 * 8 + width, pieces=cnn_maxout_pieces))
else:
embed = norm
convolution = Residual(
ExtractWindow(
nW=1) >> LN(Maxout(width, width *
3, pieces=cnn_maxout_pieces)))
if char_embed:
tok2vec = embed >> with_flatten(
reduce_dimensions >> convolution**conv_depth, pad=conv_depth)
else:
tok2vec = FeatureExtracter(cols) >> with_flatten(
embed >> convolution**conv_depth, pad=conv_depth)
if bilstm_depth >= 1:
tok2vec = tok2vec >> PyTorchBiLSTM(width, width, bilstm_depth)
# Work around thinc API limitations :(. TODO: Revise in Thinc 7
tok2vec.nO = width
tok2vec.embed = embed
return tok2vec
def main(nH=6,
dropout=0.0,
nS=6,
nB=32,
nE=20,
use_gpu=-1,
lim=2000,
nM=300,
mL=100,
save=False,
save_name="model.pkl"):
if use_gpu != -1:
# TODO: Make specific to different devices, e.g. 1 vs 0
spacy.require_gpu()
device = 'cuda'
else:
device = 'cpu'
''' Read dataset '''
nlp = spacy.load('en_core_web_sm')
for control_token in ("<eos>", "<bos>", "<pad>", "<cls>"):
nlp.tokenizer.add_special_case(control_token, [{ORTH: control_token}])
train, dev = imdb(limit=lim)
print('Loaded imdb dataset')
train = train[:lim]
dev = dev[:lim]
train_X, train_Y = zip(*train)
dev_X, dev_Y = zip(*dev)
train_X = spacy_tokenize(nlp.tokenizer, train_X, mL=mL)
dev_X = spacy_tokenize(nlp.tokenizer, dev_X, mL=mL)
print('Tokenized dataset')
train_X = set_numeric_ids(nlp.vocab, train_X)
dev_X = set_numeric_ids(nlp.vocab, dev_X)
print('Numeric ids ready')
with Model.define_operators({">>": chain}):
embed_cols = [ORTH, SHAPE, PREFIX, SUFFIX]
extractor = FeatureExtracter(attrs=embed_cols)
position_encode = PositionEncode(mL, nM)
model = (FeatureExtracter(attrs=embed_cols) >> with_flatten(
FancyEmbed(nM, 5000, cols=embed_cols)) >> Residual(position_encode)
>> create_model_input() >> Categorizer(
nM=nM, nS=nS, nH=nH, device=device))
losses = [0.]
train_accuracies = [0.]
train_totals = [0.]
dev_accuracies = [0.]
dev_loss = [0.]
def track_progress():
correct = 0.
total = 0.
for batch in minibatch(zip(dev_X, dev_Y), size=1024):
X, Y = zip(*batch)
Yh = model(X)
L, C = get_loss(Yh, Y)
correct += C
dev_loss[-1] += (L**2).sum()
total += len(X)
dev_accuracies[-1] = correct / total
n_train = train_totals[-1]
print(len(losses), losses[-1], train_accuracies[-1] / n_train,
dev_loss[-1], dev_accuracies[-1])
dev_loss.append(0.)
losses.append(0.)
train_accuracies.append(0.)
dev_accuracies.append(0.)
train_totals.append(0.)
with model.begin_training(batch_size=nB,
nb_epoch=nE) as (trainer, optimizer):
trainer.dropout = dropout
trainer.dropout_decay = 1e-4
optimizer.alpha = 0.001
optimizer.L2 = 1e-6
optimizer.max_grad_norm = 1.0
trainer.each_epoch.append(track_progress)
optimizer.alpha = 0.001
optimizer.L2 = 1e-6
optimizer.max_grad_norm = 1.0
other_pipes = [pipe for pipe in nlp.pipe_names if pipe != "textcat"]
for X, Y in trainer.iterate(train_X, train_Y):
Yh, backprop = model.begin_update(X)
dYh, C = get_loss(Yh, Y)
backprop(dYh, sgd=optimizer)
losses[-1] += (dYh**2).sum()
train_accuracies[-1] += C
train_totals[-1] += len(Y)
if save:
model.to_disk(save_name)
