def predictions(self, logits, keys=None):
"""Return predictions based on keys. See `base_head.Head` for details.
Args:
logits: logits `Tensor` with shape `[D0, D1, ... DN, logits_dimension]`.
For many applications, the shape is `[batch_size, logits_dimension]`.
keys: a list or tuple of prediction keys. Each key can be either the class
variable of prediction_keys.PredictionKeys or its string value, such as:
prediction_keys.PredictionKeys.CLASSES or 'classes'. If not specified,
it will return the predictions for all valid keys.
Returns:
A dict of predictions.
"""
pred_keys = prediction_keys.PredictionKeys
valid_keys = [
pred_keys.LOGITS, pred_keys.PROBABILITIES, pred_keys.CLASS_IDS,
pred_keys.CLASSES
]
if keys:
base_head.check_prediction_keys(keys, valid_keys)
else:
keys = valid_keys
logits = base_head.check_logits_final_dim(logits,
self.logits_dimension)
predictions = {}
with ops.name_scope('predictions', values=(logits, )):
if pred_keys.LOGITS in keys:
predictions[pred_keys.LOGITS] = logits
if pred_keys.PROBABILITIES in keys:
probabilities = nn.softmax(logits,
name=pred_keys.PROBABILITIES)
predictions[pred_keys.PROBABILITIES] = probabilities
if pred_keys.CLASS_IDS in keys or pred_keys.CLASSES in keys:
# class_ids's shape is [D0, D1, ... DN].
class_ids = math_ops.argmax(logits,
axis=-1,
name=pred_keys.CLASS_IDS)
# Expand to [batch_size, 1].
class_ids = array_ops.expand_dims(class_ids, axis=-1)
if pred_keys.CLASS_IDS in keys:
predictions[pred_keys.CLASS_IDS] = class_ids
if pred_keys.CLASSES in keys:
if self._label_vocabulary:
classes = self._class_string_table.lookup(class_ids)
else:
classes = string_ops.as_string(class_ids,
name='str_classes')
predictions[pred_keys.CLASSES] = classes
return predictions
def predictions(self, logits, keys=None):
"""Return predictions based on keys.
See `base_head.Head` for details.
Args:
logits: logits `Tensor` with shape `[D0, D1, ... DN, logits_dimension]`.
For many applications, the shape is `[batch_size, logits_dimension]`.
keys: a list of prediction keys. Key can be either the class variable
of prediction_keys.PredictionKeys or its string value, such as:
prediction_keys.PredictionKeys.LOGITS or 'logits'.
Returns:
A dict of predictions.
"""
pred_keys = prediction_keys.PredictionKeys
valid_keys = [
pred_keys.LOGITS, pred_keys.PROBABILITIES, pred_keys.CLASSES
]
if keys:
base_head.check_prediction_keys(keys, valid_keys)
else:
keys = valid_keys
logits = base_head.check_logits_final_dim(logits,
self.logits_dimension)
predictions = {}
with ops.name_scope('predictions', values=(logits, )):
if pred_keys.LOGITS in keys:
predictions[pred_keys.LOGITS] = logits
if pred_keys.PROBABILITIES in keys:
probabilities = tf.math.sigmoid(logits,
name=pred_keys.PROBABILITIES)
predictions[pred_keys.PROBABILITIES] = probabilities
if pred_keys.CLASSES in keys:
predictions[pred_keys.CLASSES] = base_head.all_classes(
logits, self._n_classes, self._label_vocabulary)
return predictions
def predictions(self, logits, keys=None):
"""Return predictions based on keys..
Args:
logits: for single event, indepdent event, logits is a tensor of shape
[batch_size, 1]; for correlated event, a dict with event_name as key,
value as tensor of shape [batch_size, 1].
keys: a list of prediction keys. Key can be either the class variable
of prediction_keys.PredictionKeys or its string value, such as:
prediction_keys.PredictionKeys.LOGITS or 'logits'.
Returns:
predictions: see base_head.Head.
"""
pred_keys = prediction_keys.PredictionKeys
valid_keys = [pred_keys.LOGITS, pred_keys.PROBABILITIES]
if keys:
base_head.check_prediction_keys(keys, valid_keys)
else:
keys = valid_keys
processed_logits = self._processed_logits(logits)
predictions = {}
with ops.name_scope('predictions', values=(processed_logits,)):
if pred_keys.LOGITS in keys:
predictions[pred_keys.LOGITS] = processed_logits
if pred_keys.PROBABILITIES in keys:
model = self._survival_model(
params=processed_logits,
labels=None,
event_index=self._event_index,
model_hparams=self._model_hparams)
predictions[pred_keys.PROBABILITIES] = model.probability()
predictions[HAZARD_RATE] = model.params()
predictions[PREDICTED_TIME] = model.predicted_time()
return predictions
def predictions(self, logits, keys=None):
"""Return predictions based on keys.
See `base_head.Head` for details.
Args:
logits: logits `Tensor` with shape `[D0, D1, ... DN, logits_dimension]`.
For many applications, the shape is `[batch_size, logits_dimension]`.
keys: a list or tuple of prediction keys. Each key can be either the class
variable of prediction_keys.PredictionKeys or its string value, such as:
prediction_keys.PredictionKeys.CLASSES or 'classes'. If not specified,
it will return the predictions for all valid keys.
Returns:
A dict of predictions.
"""
pred_keys = prediction_keys.PredictionKeys
valid_keys = [
pred_keys.LOGITS, pred_keys.LOGISTIC, pred_keys.PROBABILITIES,
pred_keys.CLASS_IDS, pred_keys.CLASSES, pred_keys.ALL_CLASS_IDS,
pred_keys.ALL_CLASSES
]
if keys:
base_head.check_prediction_keys(keys, valid_keys)
else:
keys = valid_keys
logits = base_head.check_logits_final_dim(logits,
self.logits_dimension)
predictions = {}
with ops.name_scope('predictions', values=(logits, )):
if pred_keys.LOGITS in keys:
predictions[pred_keys.LOGITS] = logits
if pred_keys.LOGISTIC in keys:
logistic = tf.math.sigmoid(logits, name=pred_keys.LOGISTIC)
predictions[pred_keys.LOGISTIC] = logistic
two_class_logits = tf.concat(
(tf.compat.v1.zeros_like(logits), logits),
axis=-1,
name='two_class_logits')
if pred_keys.PROBABILITIES in keys:
probabilities = tf.compat.v1.nn.softmax(
two_class_logits, name=pred_keys.PROBABILITIES)
predictions[pred_keys.PROBABILITIES] = probabilities
if pred_keys.CLASS_IDS in keys or pred_keys.CLASSES in keys:
class_ids = tf.compat.v1.math.argmax(two_class_logits,
axis=-1,
name=pred_keys.CLASS_IDS)
class_ids = tf.compat.v1.expand_dims(class_ids, axis=-1)
if pred_keys.CLASS_IDS in keys:
predictions[pred_keys.CLASS_IDS] = class_ids
if pred_keys.CLASSES in keys:
if self._label_vocabulary is not None:
classes = self._class_string_table.lookup(class_ids)
else:
classes = tf.strings.as_string(class_ids,
name='str_classes')
predictions[pred_keys.CLASSES] = classes
if pred_keys.ALL_CLASS_IDS in keys:
predictions[pred_keys.ALL_CLASS_IDS] = base_head.all_class_ids(
logits, n_classes=2)
if pred_keys.ALL_CLASSES in keys:
predictions[pred_keys.ALL_CLASSES] = base_head.all_classes(
logits,
n_classes=2,
label_vocabulary=self._label_vocabulary)
return predictions
