def __init__(self, storage, vecs, drop_factor=0.0, column=0):
Model.__init__(self)
self.storage = storage
self.vecs = vecs
self.nV = 300
self.drop_factor = drop_factor
self.column = column
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 Model(cls, nr_class, **cfg):
depth = util.env_opt('parser_hidden_depth', cfg.get('hidden_depth', 1))
subword_features = util.env_opt('subword_features',
cfg.get('subword_features', True))
conv_depth = util.env_opt('conv_depth', cfg.get('conv_depth', 4))
conv_window = util.env_opt('conv_window', cfg.get('conv_depth', 1))
t2v_pieces = util.env_opt('cnn_maxout_pieces',
cfg.get('cnn_maxout_pieces', 3))
bilstm_depth = util.env_opt('bilstm_depth', cfg.get('bilstm_depth', 0))
self_attn_depth = util.env_opt('self_attn_depth',
cfg.get('self_attn_depth', 0))
assert depth == 1
parser_maxout_pieces = util.env_opt('parser_maxout_pieces',
cfg.get('maxout_pieces', 2))
token_vector_width = util.env_opt('token_vector_width',
cfg.get('token_vector_width', 96))
hidden_width = util.env_opt('hidden_width',
cfg.get('hidden_width', 64))
embed_size = util.env_opt('embed_size', cfg.get('embed_size', 2000))
tok2vec = get_t2v(token_vector_width,
embed_size,
conv_depth=conv_depth,
conv_window=conv_window,
cnn_maxout_pieces=t2v_pieces,
subword_features=subword_features,
bilstm_depth=bilstm_depth)
tok2vec = chain(tok2vec, flatten)
tok2vec.nO = token_vector_width
lower = PrecomputableAffine(hidden_width,
nF=cls.nr_feature,
nI=token_vector_width,
nP=parser_maxout_pieces)
lower.nP = parser_maxout_pieces
with Model.use_device('cpu'):
upper = Affine(nr_class, hidden_width, drop_factor=0.0)
upper.W *= 0
cfg = {
'nr_class': nr_class,
'hidden_depth': depth,
'token_vector_width': token_vector_width,
'hidden_width': hidden_width,
'maxout_pieces': parser_maxout_pieces,
'pretrained_vectors': None,
'bilstm_depth': bilstm_depth,
'self_attn_depth': self_attn_depth,
'conv_depth': conv_depth,
'conv_window': conv_window,
'embed_size': embed_size,
'cnn_maxout_pieces': t2v_pieces
}
return ParserModel(tok2vec, lower, upper), cfg
def my_tok_to_vec(width, embed_size, pretrained_vectors, **kwargs):
# Circular imports :(
from spacy._ml import PyTorchBiLSTM
cnn_maxout_pieces = kwargs.get("cnn_maxout_pieces", 3)
conv_depth = kwargs.get("conv_depth", 4)
bilstm_depth = kwargs.get("bilstm_depth", 0)
cols = [ID, NORM, PREFIX, SUFFIX, SHAPE, ORTH]
storage = []
with Model.define_operators({">>": chain, "|": concatenate, "**": clone}):
# 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"
)
glove = Vectors(storage, pretrained_vectors, width, column=cols.index(NORM), )
vec_width = glove.nV
embed = uniqued(
(glove | shape)
>> LN(Maxout(width, width + vec_width, pieces=3)),
column=cols.index(ORTH),
)
convolution = Residual(
ExtractWindow(nW=1)
>> LN(Maxout(width, width * 3, pieces=cnn_maxout_pieces))
)
tok2vec = SaveDoc(storage) >> 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 build_model(n_tags, n_words, word_width, tag_width, hidden_width):
with Model.define_operators({'|': concatenate, '>>': chain}):
words_model = (
with_flatten(
Embed(word_width, word_width, n_words), pad=0
)
>> BiLSTM(word_width, word_width)
>> flatten_add_lengths
>> getitem(0)
>> Affine(hidden_width, word_width * 2)
>> pad_and_reshape
)
tags_model = (
Embed(tag_width, tag_width, n_tags)
>> Affine(hidden_width, tag_width)
)
state_model = Affine(hidden_width, hidden_width)
output_model = Softmax(n_tags, hidden_width)
words_model.nO = hidden_width
state_model.nO = hidden_width
output_model.nO = n_tags
def fwd_step(features, drop=0.):
word_feats, prev_tags, prev_state = features
tag_feats, bp_tags = tags_model.begin_update(prev_tags, drop=drop)
state_feats, bp_state = state_model.begin_update(prev_state, drop=drop)
preact = word_feats + tag_feats + state_feats
nonlin = preact > 0
state = preact * nonlin
scores, bp_scores = output_model.begin_update(state, drop=drop)
def bwd_step(d_scores, d_next_state, sgd=None):
d_state = d_next_state + bp_scores(d_scores, sgd=sgd)
d_state *= nonlin
bp_tags(d_state, sgd=sgd)
d_prev_state = bp_state(d_state, sgd=sgd)
return d_state, d_prev_state
(state, scores), bwd_step
return words_model, fwd_step
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 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 __init__(self, length):
Model.__init__(self)
self.nO = length
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()
track_stat('loss', n_iter, loss)
track_stat('train acc', n_iter, train_acc)
track_stat('LR', n_iter, optimizer.lr(n_iter + 1))
epoch_train_acc += train_acc
backprop(yh - y, optimizer)
optimizer.set_loss(loss)
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
track_stat('Batch size', n_iter, y.shape[0])
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=1,
min_batch_size=128,
max_batch_size=128,
dropout=0.0,
dropout_decay=0.0,
pooling="mean+max",
nb_epoch=20,
pieces=2,
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, 5000)
sent2mat = (get_word_ids(Model.ops) >>
with_flatten(embed >> mwe_encode**depth))
model = Siamese(sent2mat, WordMoversSimilarity(Model.ops))
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)
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.
train_acc = ((yh >= 0.5) == (y >= 0.5)).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 __init__(self, length):
Model.__init__(self)
self.nO = length
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 __init__(self, storage):
Model.__init__(self)
self.storage = storage
