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 test_PrecomputableAffine(nO=4, nI=5, nF=3, nP=2):
model = PrecomputableAffine(nO=nO, nI=nI, nF=nF, nP=nP)
assert model.W.shape == (nF, nO, nP, nI)
tensor = model.ops.allocate((10, nI))
Y, get_dX = model.begin_update(tensor)
assert Y.shape == (tensor.shape[0] + 1, nF, nO, nP)
assert model.d_pad.shape == (1, nF, nO, nP)
dY = model.ops.allocate((15, nO, nP))
ids = model.ops.allocate((15, nF))
ids[1, 2] = -1
dY[1] = 1
assert model.d_pad[0, 2, 0, 0] == 0.0
model._backprop_padding(dY, ids)
assert model.d_pad[0, 2, 0, 0] == 1.0
model.d_pad.fill(0.0)
ids.fill(0.0)
dY.fill(0.0)
ids[1, 2] = -1
ids[1, 1] = -1
ids[1, 0] = -1
dY[1] = 1
assert model.d_pad[0, 2, 0, 0] == 0.0
model._backprop_padding(dY, ids)
assert model.d_pad[0, 2, 0, 0] == 3.0
def test_PrecomputableAffine(nO=4, nI=5, nF=3, nP=2):
model = PrecomputableAffine(nO=nO, nI=nI, nF=nF, nP=nP)
assert model.W.shape == (nF, nO, nP, nI)
tensor = model.ops.allocate((10, nI))
Y, get_dX = model.begin_update(tensor)
assert Y.shape == (tensor.shape[0] + 1, nF, nO, nP)
assert model.d_pad.shape == (1, nF, nO, nP)
dY = model.ops.allocate((15, nO, nP))
ids = model.ops.allocate((15, nF))
ids[1, 2] = -1
dY[1] = 1
assert model.d_pad[0, 2, 0, 0] == 0.0
model._backprop_padding(dY, ids)
assert model.d_pad[0, 2, 0, 0] == 1.0
model.d_pad.fill(0.0)
ids.fill(0.0)
dY.fill(0.0)
ids[1, 2] = -1
ids[1, 1] = -1
ids[1, 0] = -1
dY[1] = 1
assert model.d_pad[0, 2, 0, 0] == 0.0
model._backprop_padding(dY, ids)
assert model.d_pad[0, 2, 0, 0] == 3.0
def precompute_hiddens(nO, nI, nF, nP, **cfg):
return PrecomputableAffine(hidden_width,
nF=nr_feature,
nI=token_vector_width,
nP=parser_maxout_pieces)