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_spancat_model(
tok2vec: Model[List[Doc], List[Floats2d]],
reducer: Model[Ragged, Floats2d],
scorer: Model[Floats2d, Floats2d],
) -> Model[Tuple[List[Doc], Ragged], Floats2d]:
"""Build a span categorizer model, given a token-to-vector model, a
reducer model to map the sequence of vectors for each span down to a single
vector, and a scorer model to map the vectors to probabilities.
tok2vec (Model[List[Doc], List[Floats2d]]): The tok2vec model.
reducer (Model[Ragged, Floats2d]): The reducer model.
scorer (Model[Floats2d, Floats2d]): The scorer model.
"""
model = chain(
cast(
Model[Tuple[List[Doc], Ragged], Tuple[Ragged, Ragged]],
with_getitem(
0,
chain(tok2vec,
cast(Model[List[Floats2d], Ragged], list2ragged()))),
),
extract_spans(),
reducer,
scorer,
)
model.set_ref("tok2vec", tok2vec)
model.set_ref("reducer", reducer)
model.set_ref("scorer", scorer)
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 softmax_tanh_class_vector(nr_class, *, exclusive_classes=True, **cfg):
"""Select features from the class-vectors from the last hidden state,
mean-pool them, and softmax to produce one vector per document.
The gradients of the class vectors are incremented in the backward pass,
to allow fine-tuning.
"""
width = cfg["token_vector_width"]
return chain(get_pytt_class_tokens, flatten_add_lengths,
with_getitem(0, chain(Affine(width, width), tanh)),
Pooling(mean_pool), Softmax(2, width))
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_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 softmax_pooler_output(nr_class, *, exclusive_classes=True, **cfg):
"""Select features from the pooler output, (if necessary) mean-pool them
to produce one vector per item, and then softmax them.
The gradients of the class vectors are incremented in the backward pass,
to allow fine-tuning.
"""
return chain(
get_pooler_output,
flatten_add_lengths,
with_getitem(0, Softmax(nr_class, cfg["token_vector_width"])),
Pooling(mean_pool),
)
def fine_tune_pooler_output(nr_class, *, exclusive_classes=True, **cfg):
"""Select features from the class-vectors from the last hidden state,
softmax them, and then mean-pool them to produce one feature per vector.
The gradients of the class vectors are incremented in the backward pass,
to allow fine-tuning.
"""
return chain(
get_pytt_pooler_output,
flatten_add_lengths,
with_getitem(0, Softmax(nr_class, cfg["token_vector_width"])),
Pooling(mean_pool),
)
def test_with_getitem():
data = (
numpy.asarray([[1, 2, 3, 4]], dtype="f"),
numpy.asarray([[5, 6, 7, 8]], dtype="f"),
)
model = with_getitem(1, Linear())
model.initialize(data, data)
Y, backprop = model.begin_update(data)
assert len(Y) == len(data)
assert numpy.array_equal(Y[0], data[0]) # the other item stayed the same
assert not numpy.array_equal(Y[1], data[1])
dX = backprop(Y)
assert numpy.array_equal(dX[0], data[0])
assert not numpy.array_equal(dX[1], data[1])
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_model(nr_class, width, depth, conv_depth, **kwargs):
with Model.define_operators({'|': concatenate, '>>': chain, '**': clone}):
embed = ((HashEmbed(width, 5000, column=1)
| HashEmbed(width // 2, 750, column=2)
| HashEmbed(width // 2, 750, column=3)
| HashEmbed(width // 2, 750, column=4)) >> Maxout(width))
sent2vec = (
FeatureExtracter([ORTH, LOWER, PREFIX, SUFFIX, SHAPE]) >>
flatten_add_lengths >> with_getitem(
0,
uniqued(embed, column=0) >>
Residual(ExtractWindow(nW=1) >> SELU(width))**conv_depth) >>
ParametricAttention(width) >> Pooling(sum_pool) >> Residual(
SELU(width))**depth)
model = (
foreach_sentence(sent2vec, drop_factor=2.0) >> flatten_add_lengths
>> ParametricAttention(width, hard=False) >> Pooling(sum_pool) >>
Residual(SELU(width))**depth >> Softmax(nr_class))
model.lsuv = False
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_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=100,
depth=4,
vector_length=64,
min_batch_size=4,
max_batch_size=32,
learn_rate=0.001,
momentum=0.9,
dropout=0.0,
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,
'&': concatenate_ragged
}):
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 = (flatten_add_lengths >> with_getitem(
0, (lower_case | shape | prefix | suffix) >> LayerNorm(
Maxout(width, pieces=3))) >> concatenate_ragged(
SelfAttention(nK=16, nO=16, nI=width, nL=1, nR=1),
SelfAttention(nK=16, nO=16, nI=width, nL=1, nR=1),
SelfAttention(nK=16, nO=16, nI=width, nL=1, nR=1),
SelfAttention(nK=16, nO=16, nI=width, nL=1, nR=1)) >>
with_getitem(0, Softmax(nr_tag)) >> unflatten)
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(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(dataset='quora',
width=128,
depth=2,
min_batch_size=128,
max_batch_size=128,
dropout=0.2,
dropout_decay=0.0,
pooling="mean+max",
nb_epoch=20,
pieces=3,
use_gpu=False,
out_loc=None,
quiet=False):
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))
with Model.define_operators({
'>>': chain,
'**': clone,
'|': concatenate,
'*': multiply
}):
# Important trick: text isn't like images, and the best way to use
# convolution is different. Don't use pooling-over-time. Instead,
# use the window to compute one vector per word, and do this N deep.
# In the first layer, we adjust each word vector based on the two
# surrounding words --- this gives us essentially trigram vectors.
# In the next layer, we have a trigram of trigrams --- so we're
# conditioning on information from a five word slice. The third layer
# gives us 7 words. This is like the BiLSTM insight: we're not trying
# to learn a vector for the whole sentence in this step. We're just
# trying to learn better, position-sensitive word features. This simple
# convolution step is much more efficient than BiLSTM, and can be
# computed in parallel for every token in the batch.
mwe_encode = ExtractWindow(nW=1) >> Maxout(
width, width * 3, pieces=pieces)
# Comments indicate the output type and shape at each step of the pipeline.
# * B: Number of sentences in the batch
# * T: Total number of words in the batch
# (i.e. sum(len(sent) for sent in batch))
# * W: Width of the network (input hyper-parameter)
# * ids: ID for each word (integers).
# * lengths: Number of words in each sentence in the batch (integers)
# * floats: Standard dense vector.
# (Dimensions annotated in curly braces.)
sent2vec = ( # List[spacy.token.Doc]{B}
get_word_ids >> flatten_add_lengths # : (ids{T}, lengths{B})
>> with_getitem(
0, # : word_ids{T}
(TokenWeights(Model.ops, nlp) * SpacyVectors('en', width) >>
mwe_encode**depth)) # : (floats{T, W}, lengths{B})
# Useful trick: Why choose between max pool and mean pool?
# We may as well have both representations.
>> pool_layer # : floats{B, 2*W}
)
model = (
diff(sent2vec) # : floats{B, 8*W}
>> Maxout(width, pieces=pieces) # : floats{B, W}
>> Softmax() # : floats{B, 2}
)
print("Read and parse data: %s" % dataset)
if dataset == 'quora':
train, dev = datasets.quora_questions()
elif dataset == 'snli':
train, dev = datasets.snli()
else:
raise ValueError("Unknown dataset: %s" % dataset)
train_X, train_y = preprocess(model.ops, nlp, train)
dev_X, dev_y = preprocess(model.ops, nlp, dev)
assert len(dev_y.shape) == 2
print("Initialize with data (LSUV)")
with model.begin_training(train_X[:5000], train_y[:5000],
**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(dev_X, dev_y))
print("Train")
global epoch_train_acc
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)
# No auto-diff: Just get a callback and pass the data through.
# Hardly a hardship, and it means we don't have to create/maintain
# a computational graph. We just use closures.
backprop(yh - y, optimizer)
epoch_train_acc += (yh.argmax(axis=1) == y.argmax(axis=1)).sum()
# 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(
dataset="quora",
width=64,
depth=2,
min_batch_size=1,
max_batch_size=128,
dropout=0.0,
dropout_decay=0.0,
pooling="mean+max",
nb_epoch=20,
pieces=3,
use_gpu=False,
out_loc=None,
quiet=False,
):
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))
with Model.define_operators({">>": chain, "**": clone, "|": concatenate, "+": add}):
mwe_encode = ExtractWindow(nW=1) >> Maxout(width, width * 3, pieces=pieces)
embed = StaticVectors("en", width) # + Embed(width, width*2, 5000)
# Comments indicate the output type and shape at each step of the pipeline.
# * B: Number of sentences in the batch
# * T: Total number of words in the batch
# (i.e. sum(len(sent) for sent in batch))
# * W: Width of the network (input hyper-parameter)
# * ids: ID for each word (integers).
# * lengths: Number of words in each sentence in the batch (integers)
# * floats: Standard dense vector.
# (Dimensions annotated in curly braces.)
sent2vec = ( # List[spacy.token.Doc]{B}
flatten_add_lengths # : (ids{T}, lengths{B})
>> with_getitem(
0, embed >> mwe_encode ** depth # : word_ids{T}
) # : (floats{T, W}, lengths{B})
>> pool_layer
>> Maxout(width, pieces=pieces)
>> Maxout(width, pieces=pieces)
)
model = (
((Arg(0) >> sent2vec) | (Arg(1) >> sent2vec))
>> Maxout(width, pieces=pieces)
>> Maxout(width, pieces=pieces)
>> Softmax(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)
print("Initialize with data (LSUV)")
print(dev_y.shape)
with model.begin_training(train_X[:5000], train_y[:5000], **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(dev_X, dev_y))
print("Train")
global epoch_train_acc
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)
# No auto-diff: Just get a callback and pass the data through.
# Hardly a hardship, and it means we don't have to create/maintain
# a computational graph. We just use closures.
assert (yh >= 0.0).all()
train_acc = (yh.argmax(axis=1) == y.argmax(axis=1)).sum()
epoch_train_acc += train_acc
backprop(yh - y, optimizer)
# 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(dataset='quora', width=64, depth=2, min_batch_size=1,
max_batch_size=128, dropout=0.0, dropout_decay=0.0, pooling="mean+max",
nb_epoch=20, pieces=3, use_gpu=False, out_loc=None, quiet=False):
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))
with Model.define_operators({'>>': chain, '**': clone, '|': concatenate,
'+': add}):
mwe_encode = ExtractWindow(nW=1) >> Maxout(width, width*3, pieces=pieces)
embed = StaticVectors('en', width)# + Embed(width, width*2, 5000)
# Comments indicate the output type and shape at each step of the pipeline.
# * B: Number of sentences in the batch
# * T: Total number of words in the batch
# (i.e. sum(len(sent) for sent in batch))
# * W: Width of the network (input hyper-parameter)
# * ids: ID for each word (integers).
# * lengths: Number of words in each sentence in the batch (integers)
# * floats: Standard dense vector.
# (Dimensions annotated in curly braces.)
sent2vec = ( # List[spacy.token.Doc]{B}
flatten_add_lengths # : (ids{T}, lengths{B})
>> with_getitem(0, # : word_ids{T}
embed
>> mwe_encode ** depth
) # : (floats{T, W}, lengths{B})
>> pool_layer
>> Maxout(width, pieces=pieces)
>> Maxout(width, pieces=pieces)
)
model = (
((Arg(0) >> sent2vec) | (Arg(1) >> sent2vec))
>> Maxout(width, pieces=pieces)
>> Maxout(width, pieces=pieces)
>> Softmax(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)
print("Initialize with data (LSUV)")
print(dev_y.shape)
with model.begin_training(train_X[:5000], train_y[:5000], **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(dev_X, dev_y))
print("Train")
global epoch_train_acc
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)
# No auto-diff: Just get a callback and pass the data through.
# Hardly a hardship, and it means we don't have to create/maintain
# a computational graph. We just use closures.
assert (yh >= 0.).all()
train_acc = (yh.argmax(axis=1) == y.argmax(axis=1)).sum()
epoch_train_acc += train_acc
backprop(yh-y, optimizer)
# 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(loc=None,
width=128,
depth=2,
max_batch_size=128,
dropout=0.5,
dropout_decay=1e-5,
nb_epoch=30,
use_gpu=False):
cfg = dict(locals())
print("Load spaCy")
nlp = spacy.load('en',
parser=False,
entity=False,
matcher=False,
tagger=False)
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))
with Model.define_operators({'>>': chain, '**': clone, '|': concatenate}):
# Important trick: text isn't like images, and the best way to use
# convolution is different. Don't use pooling-over-time. Instead,
# use the window to compute one vector per word, and do this N deep.
# In the first layer, we adjust each word vector based on the two
# surrounding words --- this gives us essentially trigram vectors.
# In the next layer, we have a trigram of trigrams --- so we're
# conditioning on information from a five word slice. The third layer
# gives us 7 words. This is like the BiLSTM insight: we're not trying
# to learn a vector for the whole sentence in this step. We're just
# trying to learn better, position-sensitive word features. This simple
# convolution step is much more efficient than BiLSTM, and can be
# computed in parallel for every token in the batch.
mwe_encode = ExtractWindow(nW=1) >> Maxout(width, width * 3)
# Comments indicate the output type and shape at each step of the pipeline.
# * B: Number of sentences in the batch
# * T: Total number of words in the batch
# (i.e. sum(len(sent) for sent in batch))
# * W: Width of the network (input hyper-parameter)
# * ids: ID for each word (integers).
# * lengths: Number of words in each sentence in the batch (integers)
# * floats: Standard dense vector.
# (Dimensions annotated in curly braces.)
sent2vec = ( # List[spacy.token.Doc]{B}
#get_word_ids # : List[ids]{B}
flatten_add_lengths # : (ids{T}, lengths{B})
>> with_getitem(
0, # : word_ids{T}
# This class integrates a linear projection layer, and loads
# static embeddings (by default, GloVe common crawl).
SpacyVectors(nlp, width) # : floats{T, W}
>> mwe_encode**depth # : floats{T, W}
) # : (floats{T, W}, lengths{B})
# Useful trick: Why choose between max pool and mean pool?
# We may as well have both representations.
>> Pooling(mean_pool, max_pool) # : floats{B, 2*W}
)
model = ((
(Arg(0) >> sent2vec) | (Arg(1) >> sent2vec)) # : floats{B, 4*W}
>> Maxout(width, width * 4) # : floats{B, W}
>> Maxout(width, width)**depth # : floats{B, W}
>> Softmax(3, width) # : floats{B, 2}
)
print("Read and parse SNLI data")
train, dev = datasets.snli(loc)
train_X, train_y = preprocess(model.ops, nlp, train)
dev_X, dev_y = preprocess(model.ops, nlp, dev)
assert len(dev_y.shape) == 2
print("Initialize with data (LSUV)")
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 = 1
batch_size = 1.
print("Accuracy before training", model.evaluate(dev_X, dev_y))
print("Train")
global epoch_train_acc
for X, y in trainer.iterate(train_X, train_y):
# Slightly useful trick: Decay the dropout as training proceeds.
yh, backprop = model.begin_update(X, drop=trainer.dropout)
# No auto-diff: Just get a callback and pass the data through.
# Hardly a hardship, and it means we don't have to create/maintain
# a computational graph. We just use closures.
backprop(yh - y, optimizer)
epoch_train_acc += (yh.argmax(axis=1) == y.argmax(axis=1)).sum()
# Slightly useful trick: start with low batch size, accelerate.
trainer.batch_size = min(int(batch_size), max_batch_size)
batch_size *= 1.001
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 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