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 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 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 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_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 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 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 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 __init__(self, ngram_size, attr=LOWER):
Model.__init__(self)
self.ngram_size = ngram_size
self.attr = attr
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 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(
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 __init__(self, nO=None, nI=None, nF=None, nP=None, **kwargs):
Model.__init__(self, **kwargs)
self.nO = nO
self.nP = nP
self.nI = nI
self.nF = nF
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
