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

Inherits From: Metric, Layer, Module

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.

name (Optional) string name of the metric instance.
dtype (Optional) data type of the metric result.
shape (Optional) A list of integers, a tuple of integers, or a 1-D Tensor of type int32. If not specified, the shape is inferred from the values at the first call of update_state.

Standalone usage:

m = tf.keras.metrics.MeanTensor()
m.update_state([0, 1, 2, 3])
m.update_state([4, 5, 6, 7])
array([2., 3., 4., 5.], dtype=float32)
m.update_state([12, 10, 8, 6], sample_weight= [0, 0.2, 0.5, 1])
array([2.       , 3.6363635, 4.8      , 5.3333335], dtype=float32)
m = tf.keras.metrics.MeanTensor(dtype=tf.float64, shape=(1, 4))
array([[0., 0., 0., 0.]])
m.update_state([[0, 1, 2, 3]])
m.update_state([[4, 5, 6, 7]])
array([[2., 3., 4., 5.]])





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Merges the state from one or more metrics.

This method can be used by distributed systems to merge the state computed by different metric instances. Typically the state will be stored in the form of the metric's weights. For example, a tf.keras.metrics.Mean metric contains a list of two weight values: a total and a count. If there were two instances of a tf.keras.metrics.Accuracy that each independently aggregated partial state for an overall accuracy calculation, these two metric's states could be combined as follows:

m1 = tf.keras.metrics.Accuracy()
_ = m1.update_state([[1], [2]], [[0], [2]])
m2 = tf.keras.metrics.Accuracy()
_ = m2.update_state([[3], [4]], [[3], [4]])

metrics an iterable of metrics. The metrics must have compatible state.

ValueError If the provided iterable does not contain metrics matching the metric's required specifications.


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Resets all of the metric state variables.

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


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Computes and returns the metric value tensor.

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


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Accumulates statistics for computing the element-wise mean.