def test_dropout(data):
model = Dropout(0.2)
model.initialize(data, data)
Y, backprop = model(data, is_train=False)
assert_data_match(Y, data)
dX = backprop(Y)
assert_data_match(dX, data)
def build_text_classifier_v2(
tok2vec: Model[List[Doc], List[Floats2d]],
linear_model: Model[List[Doc], Floats2d],
nO: Optional[int] = None,
) -> Model[List[Doc], Floats2d]:
exclusive_classes = not linear_model.attrs["multi_label"]
with Model.define_operators({">>": chain, "|": concatenate}):
width = tok2vec.maybe_get_dim("nO")
attention_layer = ParametricAttention(
width) # TODO: benchmark performance difference of this layer
maxout_layer = Maxout(nO=width, nI=width)
norm_layer = LayerNorm(nI=width)
cnn_model = (
tok2vec >> list2ragged() >> attention_layer >> reduce_sum() >>
residual(maxout_layer >> norm_layer >> Dropout(0.0)))
nO_double = nO * 2 if nO else None
if exclusive_classes:
output_layer = Softmax(nO=nO, nI=nO_double)
else:
output_layer = Linear(nO=nO, nI=nO_double) >> Logistic()
model = (linear_model | cnn_model) >> output_layer
model.set_ref("tok2vec", tok2vec)
if model.has_dim("nO") is not False:
model.set_dim("nO", nO)
model.set_ref("output_layer", linear_model.get_ref("output_layer"))
model.set_ref("attention_layer", attention_layer)
model.set_ref("maxout_layer", maxout_layer)
model.set_ref("norm_layer", norm_layer)
model.attrs["multi_label"] = not exclusive_classes
model.init = init_ensemble_textcat
return model
def test_dropout_gives_zero_gradients(W_b_input):
model = chain(get_model(W_b_input), Dropout(1.0))
nr_batch, nr_out, nr_in = get_shape(W_b_input)
W, b, input_ = W_b_input
for node in model.walk():
if node.name == "dropout":
node.attrs["dropout_rate"] = 1.0
fwd_dropped, finish_update = model.begin_update(input_)
grad_BO = numpy.ones((nr_batch, nr_out), dtype="f")
grad_BI = finish_update(grad_BO)
assert all(val == 0.0 for val in grad_BI.flatten())
def test_chain(ops):
data = numpy.asarray([[1, 2, 3, 4]], dtype="f")
model = chain(Linear(1), Dropout(), Linear(1))
model.ops = ops
model.initialize(data, data)
Y, backprop = model(data, is_train=True)
backprop(Y)
# Layers with and without nO/nI
model = chain(Linear(1), Dropout(), Linear(1, 1))
model.initialize(data, data)
# Setting dim on model
model = chain(Linear(1), Dropout(), Linear(1))
model.set_dim("nO", 1)
model.initialize(data, None)
model = chain(Linear(1, 1), Dropout(), Linear(1, 1))
model.set_dim("nI", 1)
model.initialize(None, data)
# Not enough arguments
with pytest.raises(TypeError):
chain(Linear())
with pytest.raises(TypeError):
chain()
def build_text_classifier_lowdata(
width: int,
dropout: Optional[float],
nO: Optional[int] = None) -> Model[List[Doc], Floats2d]:
# Don't document this yet, I'm not sure it's right.
# Note, before v.3, this was the default if setting "low_data" and "pretrained_dims"
with Model.define_operators({">>": chain, "**": clone}):
model = (StaticVectors(width) >> list2ragged() >>
ParametricAttention(width) >> reduce_sum() >> residual(
Relu(width, width))**2 >> Linear(nO, width))
if dropout:
model = model >> Dropout(dropout)
model = model >> Logistic()
return model
def test_set_dropout():
model = Dropout()
assert model.attrs["dropout_rate"] == 0.0
set_dropout_rate(model, 0.2)
assert model.attrs["dropout_rate"] == 0.2
def test_dropout_gives_zero_activations(W_b_input):
model = chain(get_model(W_b_input), Dropout(1.0))
nr_batch, nr_out, nr_in = get_shape(W_b_input)
W, b, input_ = W_b_input
fwd_dropped, _ = model.begin_update(input_)
assert all(val == 0.0 for val in fwd_dropped.flatten())
def TextCatEnsemble_v1(
width: int,
embed_size: int,
pretrained_vectors: Optional[bool],
exclusive_classes: bool,
ngram_size: int,
window_size: int,
conv_depth: int,
dropout: Optional[float],
nO: Optional[int] = None,
) -> Model:
# Don't document this yet, I'm not sure it's right.
cols = [ORTH, LOWER, PREFIX, SUFFIX, SHAPE, ID]
with Model.define_operators({">>": chain, "|": concatenate, "**": clone}):
lower = HashEmbed(nO=width,
nV=embed_size,
column=cols.index(LOWER),
dropout=dropout,
seed=10)
prefix = HashEmbed(
nO=width // 2,
nV=embed_size,
column=cols.index(PREFIX),
dropout=dropout,
seed=11,
)
suffix = HashEmbed(
nO=width // 2,
nV=embed_size,
column=cols.index(SUFFIX),
dropout=dropout,
seed=12,
)
shape = HashEmbed(
nO=width // 2,
nV=embed_size,
column=cols.index(SHAPE),
dropout=dropout,
seed=13,
)
width_nI = sum(
layer.get_dim("nO") for layer in [lower, prefix, suffix, shape])
trained_vectors = FeatureExtractor(cols) >> with_array(
uniqued(
(lower | prefix | suffix | shape) >> Maxout(
nO=width, nI=width_nI, normalize=True),
column=cols.index(ORTH),
))
if pretrained_vectors:
static_vectors = StaticVectors(width)
vector_layer = trained_vectors | static_vectors
vectors_width = width * 2
else:
vector_layer = trained_vectors
vectors_width = width
tok2vec = vector_layer >> with_array(
Maxout(width, vectors_width, normalize=True) >>
residual((expand_window(window_size=window_size) >> Maxout(
nO=width, nI=width *
((window_size * 2) + 1), normalize=True)))**conv_depth,
pad=conv_depth,
)
cnn_model = (tok2vec >> list2ragged() >> ParametricAttention(width) >>
reduce_sum() >> residual(Maxout(nO=width, nI=width)) >>
Linear(nO=nO, nI=width) >> Dropout(0.0))
linear_model = build_bow_text_classifier(
nO=nO,
ngram_size=ngram_size,
exclusive_classes=exclusive_classes,
no_output_layer=False,
)
nO_double = nO * 2 if nO else None
if exclusive_classes:
output_layer = Softmax(nO=nO, nI=nO_double)
else:
output_layer = Linear(nO=nO,
nI=nO_double) >> Dropout(0.0) >> Logistic()
model = (linear_model | cnn_model) >> output_layer
model.set_ref("tok2vec", tok2vec)
if model.has_dim("nO") is not False:
model.set_dim("nO", nO)
model.set_ref("output_layer", linear_model.get_ref("output_layer"))
model.attrs["multi_label"] = not exclusive_classes
return model