def forward(self, tensors, mode: str = None):
"""Forward method of the layer
(details: https://arxiv.org/pdf/1706.03847.pdf)
Parameters
----------
tensors : Tuple[tf.Tensor]
- positives : shape = (batch, num_events)
- negatives : shape = (batch, num_events, num_negatives)
- mask : shape = (batch, num_events, num_negatives)
- weights : shape = (batch, num_events)
Returns
-------
tf.Tensor
TopOne Max loss
"""
positives, negatives, mask, weights = tensors
positives, negatives = make_same_shape([positives, negatives], broadcast=False)
softmax_scores = Softmax()((negatives, tf.to_float(mask)))
losses = tf.multiply(softmax_scores, tf.nn.sigmoid(negatives - positives) + tf.nn.sigmoid(tf.square(negatives)))
# One loss per event, average of scores : (batch, num_events)
event_scores = WeightedAverage()((losses, tf.to_float(mask)))
# Each event contributes according to its weight
event_weights = weights * tf.to_float(tf.reduce_any(mask, axis=-1))
event_losses = event_scores * event_weights
return tf.div_no_nan(tf.reduce_sum(event_losses), tf.reduce_sum(event_weights))
def forward(self, tensors, mode: str = None):
"""Forward method of the layer
Parameters
----------
tensors : Tuple[tf.Tensor]
- positives : shape = (batch, num_events)
- negatives : shape = (batch, num_events, num_negatives)
Returns
-------
tf.Tensor
BPR Max loss
"""
positives, negatives = tensors
positives, negatives = make_same_shape([positives, negatives],
broadcast=False)
softmax_scores = Softmax()((negatives, tf.ones_like(negatives)))
losses = -tf.log(
tf.reduce_sum(
tf.multiply(softmax_scores,
tf.nn.sigmoid(positives - negatives)), -1))
# add bpr_max regularisation
bpr_regularization = tf.multiply(
tf.constant(self.bpr_max_regularizer, dtype=tf.float32),
tf.reduce_sum(tf.multiply(softmax_scores, tf.square(negatives)),
-1),
)
scores = losses + bpr_regularization
return Average()(scores, mode)
def forward(self, tensors, mode: str = None):
"""Forward method of the layer
(details: https://arxiv.org/pdf/1706.03847.pdf)
Parameters
----------
tensors : Tuple[tf.Tensor]
- positives : shape = (batch, num_events)
- negatives : shape = (batch, num_events, num_negatives)
- mask : shape = (batch, num_events, num_negatives)
- weights : shape = (batch, num_events)
Returns
-------
tf.Tensor
BPR Max loss
"""
positives, negatives, mask, weights = tensors
mask = tf.cast(mask, tf.float32)
positives, negatives = make_same_shape([positives, negatives],
broadcast=False)
no_sampled_logits = tf.cast(
tf.greater_equal(tf.reduce_sum(mask, -1), 0), tf.float32)
softmax_scores = Softmax()((negatives, mask))
# compute bpr_max losses
losses = -tf.multiply(
no_sampled_logits,
tf.log(
tf.reduce_sum(
tf.multiply(
tf.multiply(softmax_scores,
tf.nn.sigmoid(positives -
negatives)), mask), 2) +
1e-8),
)
# compute regularization part
bpr_regularization = tf.multiply(
tf.constant(self.bpr_max_regularizer, dtype=tf.float32),
tf.reduce_sum(
tf.multiply(tf.multiply(softmax_scores, tf.square(negatives)),
mask), 2),
)
losses_with_regularization = losses + bpr_regularization
# One loss per event, average of scores : (batch, num_events)
# TODO: fix this line, it seems it's doing averaging twice
# event_scores = WeightedAverage()((losses_with_regularization, mask))
event_scores = losses_with_regularization
# Each event contributes according to its weight
event_weights = weights * tf.to_float(
tf.reduce_any(tf.cast(mask, tf.bool), axis=-1))
event_losses = event_scores * event_weights
return tf.div_no_nan(tf.reduce_sum(event_losses),
tf.reduce_sum(event_weights))
def forward(self, tensors, mode: str = None):
"""Forward method of the layer
Parameters
----------
tensors : Tuple[tf.Tensor]
- positives : shape = (batch, num_events)
- negatives : shape = (batch, num_events, num_negatives)
Returns
-------
tf.Tensor
TopOne Max loss
"""
positives, negatives = tensors
positives, negatives = make_same_shape([positives, negatives], broadcast=False)
softmax_scores = Softmax()((negatives, tf.ones_like(negatives)))
losses = tf.multiply(softmax_scores, tf.nn.sigmoid(negatives - positives) + tf.nn.sigmoid(tf.square(negatives)))
return Average()(losses, mode)