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'''
Keras implementation of Decomposable Attention (https://arxiv.org/pdf/1606.01933v1.pdf)
taken from spaCy examples (https://github.com/explosion/spaCy/tree/master/examples)
with modifications.
The MIT License (MIT)
Copyright (C) 2016-2020 ExplosionAI GmbH, 2016 spaCy GmbH, 2015 Matthew Honnibal
'''
from tensorflow.keras.optimizers import Adam
from tensorflow.keras.metrics import Precision, Recall
from tensorflow.keras import layers, Model, models
from tensorflow.keras import backend as K
import numpy as np
try:
from .losses import KDLoss
from .metrics import MatthewsCorrCoef
from .layers import TokenAndPositionEmbedding
except (SystemError, ImportError):
from losses import KDLoss
from metrics import MatthewsCorrCoef
from layers import TokenAndPositionEmbedding
def build_model(vectors, settings, compile=True):
max_length = settings["maxlen"]
nr_hidden = settings["n_hidden"]
nr_class = settings["n_classes"]
input1 = layers.Input(shape=(max_length,), dtype="int32", name="words1")
input2 = layers.Input(shape=(max_length,), dtype="int32", name="words2")
# embeddings (projected)
embed = create_embedding(vectors, settings["emb_dim"],
settings["vocab_size"],
max_length, nr_hidden,
settings["emb_trainable"])
a = embed(input1)
b = embed(input2)
# step 1: attend
# self-attend
if settings["self_attention"]:
S_a = create_feedforward(nr_hidden, dropout=settings["dropout"])
S_b = create_feedforward(nr_hidden, dropout=settings["dropout"])
a_p = layers.Attention()([S_a(a), S_a(a)])
b_p = layers.Attention()([S_b(b), S_b(b)])
# self_att_a = layers.dot([S_a(a), S_a(a)], axes=-1)
# self_att_b = layers.dot([S_b(b), S_b(b)], axes=-1)
# self_norm_a = layers.Lambda(normalizer(1))(self_att_a)
# self_norm_b = layers.Lambda(normalizer(1))(self_att_b)
# a_p = layers.dot([self_norm_a, a], axes=1)
# b_p = layers.dot([self_norm_b, b], axes=1)
else:
a_p = a
b_p = b
# attend
F = create_feedforward(nr_hidden, dropout=settings["dropout"])
att_weights = layers.dot([F(a_p), F(b_p)], axes=-1)
G = create_feedforward(nr_hidden)
if settings["entail_dir"] == "both":
norm_weights_a = layers.Lambda(normalizer(1))(att_weights)
norm_weights_b = layers.Lambda(normalizer(2))(att_weights)
alpha = layers.dot([norm_weights_a, a_p], axes=1)
beta = layers.dot([norm_weights_b, b_p], axes=1)
# step 2: compare
comp1 = layers.concatenate([a_p, beta])
comp2 = layers.concatenate([b_p, alpha])
v1 = layers.TimeDistributed(G)(comp1)
v2 = layers.TimeDistributed(G)(comp2)
# step 3: aggregate
v1_sum = layers.Lambda(sum_word)(v1)
v2_sum = layers.Lambda(sum_word)(v2)
concat = layers.concatenate([v1_sum, v2_sum])
elif settings["entail_dir"] == "left":
norm_weights_a = layers.Lambda(normalizer(1))(att_weights)
alpha = layers.dot([norm_weights_a, a], axes=1)
comp2 = layers.concatenate([b, alpha])
v2 = layers.TimeDistributed(G)(comp2)
v2_sum = layers.Lambda(sum_word)(v2)
concat = v2_sum
else:
norm_weights_b = layers.Lambda(normalizer(2))(att_weights)
beta = layers.dot([norm_weights_b, b], axes=1)
comp1 = layers.concatenate([a, beta])
v1 = layers.TimeDistributed(G)(comp1)
v1_sum = layers.Lambda(sum_word)(v1)
concat = v1_sum
H = create_feedforward(nr_hidden, dropout=settings["dropout"])
out = H(concat)
if settings['distilled']:
out = layers.Dense(nr_class)(out)
loss = KDLoss(settings["batch_size"])
else:
out = layers.Dense(nr_class)(out)
out = layers.Activation('sigmoid', dtype='float32')(out)
loss = settings["loss"]
model = Model([input1, input2], out)
if compile:
model.compile(optimizer=settings["optimizer"],
loss=loss,
metrics=settings["metrics"](), # Call get_metrics
experimental_run_tf_function=False,)
return model
def create_embedding(vectors, emb_dim, vocab_size, max_length, projected_dim, trainable=False):
return models.Sequential(
[
# layers.Embedding(
# vectors.shape[0],
# vectors.shape[1],
# input_length=max_length,
# weights=[vectors],
# trainable=trainable,
# mask_zero=True,
# ),
TokenAndPositionEmbedding(vocab_size,
emb_dim,
max_length,
vectors,
trainable),
layers.TimeDistributed(
layers.Dense(projected_dim, activation=None, use_bias=False,
kernel_regularizer=None)
),
]
)
def create_feedforward(num_units=200, activation="relu", dropout=0.2):
return models.Sequential(
[
layers.Dense(num_units, activation=activation),
layers.Dropout(dropout),
layers.Dense(num_units, activation=activation),
layers.Dropout(dropout),
]
)
def normalizer(axis):
def _normalize(att_weights):
exp_weights = K.exp(att_weights)
sum_weights = K.sum(exp_weights, axis=axis, keepdims=True)
return exp_weights / sum_weights
return _normalize
def sum_word(x):
return K.sum(x, axis=1)
def f1(y_true, y_pred): #taken from old keras source code
true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1)))
possible_positives = K.sum(K.round(K.clip(y_true, 0, 1)))
predicted_positives = K.sum(K.round(K.clip(y_pred, 0, 1)))
precision = true_positives / (predicted_positives+K.epsilon())
recall = true_positives / (possible_positives+K.epsilon())
f1_val = 2*(precision*recall) / (precision+recall+K.epsilon())
return f1_val
def test_build_model():
vectors = np.ndarray((100, 8), dtype="float32")
shape = (10, 16, 3)
settings = {"lr": 0.001, "dropout": 0.2, "gru_encode": True, "entail_dir": "both"}
model = build_model(vectors, shape, settings)
def test_fit_model():
def _generate_X(nr_example, length, nr_vector):
X1 = np.ndarray((nr_example, length), dtype="int32")
X1 *= X1 < nr_vector
X1 *= 0 <= X1
X2 = np.ndarray((nr_example, length), dtype="int32")
X2 *= X2 < nr_vector
X2 *= 0 <= X2
return [X1, X2]
def _generate_Y(nr_example, nr_class):
ys = np.zeros((nr_example, nr_class), dtype="int32")
for i in range(nr_example):
ys[i, i % nr_class] = 1
return ys
vectors = np.ndarray((100, 8), dtype="float32")
shape = (10, 16, 3)
settings = {"lr": 0.001, "dropout": 0.2, "gru_encode": True, "entail_dir": "both"}
model = build_model(vectors, shape, settings)
train_X = _generate_X(20, shape[0], vectors.shape[0])
train_Y = _generate_Y(20, shape[2])
dev_X = _generate_X(15, shape[0], vectors.shape[0])
dev_Y = _generate_Y(15, shape[2])
model.fit(train_X, train_Y, validation_data=(dev_X, dev_Y), epochs=5, batch_size=4)
__all__ = [build_model]
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