tf.math.reduce_max  |  TensorFlow v2.16.1 (original) (raw)

tf.math.reduce_max

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Computes tf.math.maximum of elements across dimensions of a tensor.

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Main aliases

tf.reduce_max

tf.math.reduce_max(
    input_tensor, axis=None, keepdims=False, name=None
)

Used in the notebooks

Used in the guide Used in the tutorials
Advanced automatic differentiation Introduction to Tensors Matrix approximation with Core APIs Ragged tensors Working with sparse tensors Intro to Autoencoders Integrated gradients Client-efficient large-model federated learning via `federated_select` and sparse aggregation Preprocess data with TensorFlow Transform Tutorial on Multi Armed Bandits in TF-Agents

This is the reduction operation for the elementwise tf.math.maximum op.

Reduces input_tensor along the dimensions given in axis. Unless keepdims is true, the rank of the tensor is reduced by 1 for each of the entries in axis, which must be unique. If keepdims is true, the reduced dimensions are retained with length 1.

If axis is None, all dimensions are reduced, and a tensor with a single element is returned.

Usage example
>>> x = tf.constant([5, 1, 2, 4]) >>> tf.reduce_max(x) <tf.Tensor: shape=(), dtype=int32, numpy=5> >>> x = tf.constant([-5, -1, -2, -4]) >>> tf.reduce_max(x) <tf.Tensor: shape=(), dtype=int32, numpy=-1> >>> x = tf.constant([4, float('nan')]) >>> tf.reduce_max(x) <tf.Tensor: shape=(), dtype=float32, numpy=nan> >>> x = tf.constant([float('nan'), float('nan')]) >>> tf.reduce_max(x) <tf.Tensor: shape=(), dtype=float32, numpy=nan> >>> x = tf.constant([float('-inf'), float('inf')]) >>> tf.reduce_max(x) <tf.Tensor: shape=(), dtype=float32, numpy=inf>

See the numpy docs for np.amax and np.nanmax behavior.

Args
input_tensor The tensor to reduce. Should have real numeric type.
axis The dimensions to reduce. If None (the default), reduces all dimensions. Must be in the range [-rank(input_tensor), rank(input_tensor)).
keepdims If true, retains reduced dimensions with length 1.
name A name for the operation (optional).
Returns
The reduced tensor.