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)
- mask : shape = (batch, num_events, num_negatives)
- weights : shape = (batch, num_events)
Returns
-------
tf.Tensor
Negative Sampling loss
"""
positives, negatives, mask, weights = tensors
true_losses = tf.nn.sigmoid_cross_entropy_with_logits(
labels=tf.ones_like(positives), logits=positives)
sampled_losses = tf.nn.sigmoid_cross_entropy_with_logits(
labels=tf.zeros_like(negatives), logits=negatives)
event_scores = true_losses + WeightedAverage()(
(sampled_losses, tf.cast(mask, dtype=tf.float32)))
event_weights = weights * tf.cast(
tf.reduce_any(input_tensor=mask, axis=-1), dtype=tf.float32)
return tf.math.divide_no_nan(
tf.reduce_sum(input_tensor=event_scores * event_weights),
tf.reduce_sum(input_tensor=event_weights))
def forward(self, tensors, mode: str = None):
"""Computes Triplet Precision
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 loss
"""
# Retrieve positives and negatives logits
positives, negatives, mask, weights = tensors
positives, negatives = make_same_shape([positives, negatives],
broadcast=False)
# One triplet precision per event
event_triplet = WeightedAverage()((tf.cast(
positives > negatives, tf.float32), tf.cast(mask, tf.float32)),
mode)
# Each event contributes according to its weight
event_weights = weights * tf.cast(
tf.reduce_any(input_tensor=mask, axis=-1), dtype=tf.float32)
return tf.math.divide_no_nan(
tf.reduce_sum(input_tensor=event_triplet * event_weights),
tf.reduce_sum(input_tensor=event_weights))
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)
- mask : shape = (batch, num_events, num_negatives)
- weights : shape = (batch, num_events)
Returns
-------
tf.Tensor
BPR loss
"""
positives, negatives, mask, weights = tensors
positives, negatives = make_same_shape([positives, negatives],
broadcast=False)
# One score per negative : (batch, num_events, num_negatives)
scores = -tf.log_sigmoid(positives - negatives)
# One loss per event, average of scores : (batch, num_events)
event_scores = WeightedAverage()((scores, 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)
- mask : shape = (batch, num_events, num_negatives)
- weights : shape = (batch, num_events)
Returns
-------
tf.Tensor
BPR loss
"""
# Retrieve positives and negatives logits
positives, negatives, mask, weights = tensors
positives, negatives = make_same_shape([positives, negatives],
broadcast=False)
# One score per event
event_scores = WeightedAverage()(
(-tf.math.log_sigmoid(positives - negatives),
tf.cast(mask, dtype=tf.float32)))
# Each event contributes according to its weight
event_weights = weights * tf.cast(
tf.reduce_any(input_tensor=mask, axis=-1), dtype=tf.float32)
return tf.math.divide_no_nan(
tf.reduce_sum(input_tensor=event_scores * event_weights),
tf.reduce_sum(input_tensor=event_weights))
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
Top1 loss
"""
positives, negatives, mask, weights = tensors
positives, negatives = make_same_shape([positives, negatives],
broadcast=False)
losses = tf.nn.sigmoid(negatives - positives) + tf.nn.sigmoid(
tf.square(negatives))
event_scores = WeightedAverage()(
(losses, tf.cast(mask, dtype=tf.float32)))
# Each event contributes according to its weight
event_weights = weights * tf.cast(
tf.reduce_any(input_tensor=mask, axis=-1), dtype=tf.float32)
event_losses = event_scores * event_weights
return tf.math.divide_no_nan(tf.reduce_sum(input_tensor=event_losses),
tf.reduce_sum(input_tensor=event_weights))