tf.keras.metrics.MeanTensor

TensorFlow 1 version View source on GitHub

Class MeanTensor

Computes the element-wise (weighted) mean of the given tensors.

Inherits From: Metric

Aliases:

MeanTensor returns a tensor with the same shape of the input tensors. The mean value is updated by keeping local variables total and count. The total tracks the sum of the weighted values, and count stores the sum of the weighted counts.

Usage:

m = tf.keras.metrics.MeanTensor()
m.update_state([0, 1, 2, 3])
m.update_state([4, 5, 6, 7])
print('Result: ', m.result().numpy())  # Result: [2, 3, 4, 5]
m.update_state([12, 10, 8, 6], sample_weights= [0, 0.2, 0.5, 1])
print('Result: ', m.result().numpy())  # Result: [2, 3.636, 4.8, 5.333]

__init__

View source

__init__(
    name='mean_tensor',
    dtype=None
)

Creates a MeanTensor instance.

Args:

  • name: (Optional) string name of the metric instance.
  • dtype: (Optional) data type of the metric result.

__new__

View source

__new__(
    cls,
    *args,
    **kwargs
)

Create and return a new object. See help(type) for accurate signature.

Properties

count

total

Methods

reset_states

View source

reset_states()

Resets all of the metric state variables.

This function is called between epochs/steps, when a metric is evaluated during training.

result

View source

result()

Computes and returns the metric value tensor.

Result computation is an idempotent operation that simply calculates the metric value using the state variables.

update_state

View source

update_state(
    values,
    sample_weight=None
)

Accumulates statistics for computing the element-wise mean.

Args:

  • values: Per-example value.
  • sample_weight: Optional weighting of each example. Defaults to 1.

Returns:

Update op.