tf.estimator.ModeKeys

TensorFlow 1 version

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Creates a Head for multi-objective learning.

Inherits From: Head

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Compat aliases for migration

See Migration guide for more details.

tf.compat.v1.estimator.MultiHead

tf.estimator.MultiHead(
    heads, head_weights=None
)

This class merges the output of multiple Head objects. Specifically:

For training, sums losses of each head, calls train_op_fn with this final loss.
For eval, merges metrics by adding head.name suffix to the keys in eval metrics, such as precision/head1.name, precision/head2.name.
For prediction, merges predictions and updates keys in prediction dict to a 2-tuple, (head.name, prediction_key). Merges export_outputs such that by default the first head is served.

Usage:

head1 = tf.estimator.MultiLabelHead(n_classes=2, name='head1')
head2 = tf.estimator.MultiLabelHead(n_classes=3, name='head2')
multi_head = tf.estimator.MultiHead([head1, head2])
logits = {
   'head1': np.array([[-10., 10.], [-15., 10.]], dtype=np.float32),
   'head2': np.array([[20., -20., 20.], [-30., 20., -20.]],
   dtype=np.float32),}
labels = {
   'head1': np.array([[1, 0], [1, 1]], dtype=np.int64),
   'head2': np.array([[0, 1, 0], [1, 1, 0]], dtype=np.int64),}
features = {'x': np.array(((42,),), dtype=np.float32)}
# For large logits, sigmoid cross entropy loss is approximated as:
# loss = labels * (logits < 0) * (-logits) +
#        (1 - labels) * (logits > 0) * logits =>
# head1: expected_unweighted_loss = [[10., 10.], [15., 0.]]
# loss1 = ((10 + 10) / 2 + (15 + 0) / 2) / 2 = 8.75
# head2: expected_unweighted_loss = [[20., 20., 20.], [30., 0., 0]]
# loss2 = ((20 + 20 + 20) / 3 + (30 + 0 + 0) / 3) / 2 = 15.00
# loss = loss1 + loss2 = 8.75 + 15.00 = 23.75
loss = multi_head.loss(labels, logits, features=features)
print('{:.2f}'.format(loss.numpy()))
23.75
eval_metrics = multi_head.metrics()
updated_metrics = multi_head.update_metrics(
  eval_metrics, features, logits, labels)
for k in sorted(updated_metrics):
 print('{} : {:.2f}'.format(k, updated_metrics[k].result().numpy()))
auc/head1 : 0.17
auc/head2 : 0.33
auc_precision_recall/head1 : 0.60
auc_precision_recall/head2 : 0.40
average_loss/head1 : 8.75
average_loss/head2 : 15.00
loss/head1 : 8.75
loss/head2 : 15.00
preds = multi_head.predictions(logits)
print(preds[('head1', 'logits')])
tf.Tensor(
  [[-10.  10.]
   [-15.  10.]], shape=(2, 2), dtype=float32)

Usage with a canned estimator:

# In `input_fn`, specify labels as a dict keyed by head name:
def input_fn():
  features = ...
  labels1 = ...
  labels2 = ...
  return features, {'head1.name': labels1, 'head2.name': labels2}

# In `model_fn`, specify logits as a dict keyed by head name:
def model_fn(features, labels, mode):
  # Create simple heads and specify head name.
  head1 = tf.estimator.MultiClassHead(n_classes=3, name='head1')
  head2 = tf.estimator.BinaryClassHead(name='head2')
  # Create MultiHead from two simple heads.
  head = tf.estimator.MultiHead([head1, head2])
  # Create logits for each head, and combine them into a dict.
  logits1, logits2 = logit_fn()
  logits = {'head1.name': logits1, 'head2.name': logits2}
  # Return the merged EstimatorSpec
  return head.create_estimator_spec(..., logits=logits, ...)

# Create an estimator with this model_fn.
estimator = tf.estimator.Estimator(model_fn=model_fn)
estimator.train(input_fn=input_fn)

Also supports logits as a Tensor of shape [D0, D1, ... DN, logits_dimension]. It will split the Tensor along the last dimension and distribute it appropriately among the heads. E.g.:

# Input logits.
logits = np.array([[-1., 1., 2., -2., 2.], [-1.5, 1., -3., 2., -2.]],
                  dtype=np.float32)
# Suppose head1 and head2 have the following logits dimension.
head1.logits_dimension = 2
head2.logits_dimension = 3
# After splitting, the result will be:
logits_dict = {'head1_name': [[-1., 1.], [-1.5, 1.]],
               'head2_name':  [[2., -2., 2.], [-3., 2., -2.]]}

Usage:

def model_fn(features, labels, mode):
  # Create simple heads and specify head name.
  head1 = tf.estimator.MultiClassHead(n_classes=3, name='head1')
  head2 = tf.estimator.BinaryClassHead(name='head2')
  # Create multi-head from two simple heads.
  head = tf.estimator.MultiHead([head1, head2])
  # Create logits for the multihead. The result of logits is a `Tensor`.
  logits = logit_fn(logits_dimension=head.logits_dimension)
  # Return the merged EstimatorSpec
  return head.create_estimator_spec(..., logits=logits, ...)

Args
`heads`	List or tuple of `Head` instances. All heads must have `name` specified. The first head in the list is the default used at serving time.
`head_weights`	Optional list of weights, same length as `heads`. Used when merging losses to calculate the weighted sum of losses from each head. If `None`, all losses are weighted equally.

Attributes
`logits_dimension`	See `base_head.Head` for details.
`loss_reduction`	See `base_head.Head` for details.
`name`	See `base_head.Head` for details.

Methods

`create_estimator_spec`

View source

create_estimator_spec(
    features, mode, logits, labels=None, optimizer=None, trainable_variables=None,
    train_op_fn=None, update_ops=None, regularization_losses=None
)

Returns a model_fn.EstimatorSpec.

Args
`features`	Input `dict` of `Tensor` or `SparseTensor` objects.
`mode`	Estimator's `ModeKeys`.
`logits`	Input `dict` keyed by head name, or logits `Tensor` with shape `[D0, D1, ... DN, logits_dimension]`. For many applications, the `Tensor` shape is `[batch_size, logits_dimension]`. If logits is a `Tensor`, it will split the `Tensor` along the last dimension and distribute it appropriately among the heads. Check `MultiHead` for examples.
`labels`	Input `dict` keyed by head name. For each head, the label value can be integer or string `Tensor` with shape matching its corresponding `logits`.`labels` is a required argument when `mode` equals `TRAIN` or `EVAL`.
`optimizer`	An `tf.keras.optimizers.Optimizer` instance to optimize the loss in TRAIN mode. Namely, sets `train_op = optimizer.get_updates(loss, trainable_variables)`, which updates variables to minimize `loss`.
`trainable_variables`	A list or tuple of `Variable` objects to update to minimize `loss`. In Tensorflow 1.x, by default these are the list of variables collected in the graph under the key `GraphKeys.TRAINABLE_VARIABLES`. As Tensorflow 2.x doesn't have collections and GraphKeys, trainable_variables need to be passed explicitly here.
`train_op_fn`	Function that takes a scalar loss `Tensor` and returns `train_op`. Used if `optimizer` is `None`.
`update_ops`	A list or tuple of update ops to be run at training time. For example, layers such as BatchNormalization create mean and variance update ops that need to be run at training time. In Tensorflow 1.x, these are thrown into an UPDATE_OPS collection. As Tensorflow 2.x doesn't have collections, update_ops need to be passed explicitly here.
`regularization_losses`	A list of additional scalar losses to be added to the training loss, such as regularization losses. These losses are usually expressed as a batch average, so for best results, in each head, users need to use the default `loss_reduction=SUM_OVER_BATCH_SIZE` to avoid scaling errors. Compared to the regularization losses for each head, this loss is to regularize the merged loss of all heads in multi head, and will be added to the overall training loss of multi head.

Returns
A `model_fn.EstimatorSpec` instance.

Raises
`ValueError`	If both `train_op_fn` and `optimizer` are `None` in TRAIN mode, or if both are set. If `mode` is not in Estimator's `ModeKeys`.

`loss`

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loss(
    labels, logits, features=None, mode=None, regularization_losses=None
)

Returns regularized training loss. See base_head.Head for details.

`metrics`

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metrics(
    regularization_losses=None
)

Creates metrics. See base_head.Head for details.

`predictions`

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predictions(
    logits, keys=None
)

Create predictions. See base_head.Head for details.

`update_metrics`

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update_metrics(
    eval_metrics, features, logits, labels, regularization_losses=None
)

Updates eval metrics. See base_head.Head for details.

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tf.estimator / ModeKeys

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Usage:

Usage:

Args

Attributes

Methods

create_estimator_spec

loss

metrics

predictions

update_metrics

`create_estimator_spec`

`loss`

`metrics`

`predictions`

`update_metrics`