tf.math.softplus

TensorFlow 1 version

Computes square of x element-wise.

View aliases

Main aliases

tf.square

Compat aliases for migration

See Migration guide for more details.

tf.compat.v1.math.square, tf.compat.v1.square

tf.math.square(
    x, name=None
)

Used in the notebooks

Used in the guide	Used in the tutorials
Training and evaluation with the built-in methods Making new Layers and Models via subclassing Basic training loops Eager execution	Customization basics: tensors and operations

I.e., \(y = x * x = x^2\).

tf.math.square([-2., 0., 3.])
<tf.Tensor: shape=(3,), dtype=float32, numpy=array([4., 0., 9.], dtype=float32)>

Args
`x`	A `Tensor`. Must be one of the following types: `bfloat16`, `half`, `float32`, `float64`, `int8`, `int16`, `int32`, `int64`, `complex64`, `complex128`.
`name`	A name for the operation (optional).

Returns
A `Tensor`. Has the same type as `x`. If `x` is a `SparseTensor`, returns `SparseTensor(x.indices, tf.math.square(x.values, ...), x.dense_shape)`

TensorFlow

tf

tf.audio

tf.autograph

tf.bitwise

tf.compat

tf.config

tf.data

tf.debugging

tf.distribute

tf.dtypes

tf.errors

tf.estimator

tf.experimental

tf.feature_column

tf.graph_util

tf.image

tf.initializers

tf.io

tf.keras

tf.linalg

tf.lite

tf.lookup

tf.losses

tf.math

tf.metrics

tf.nest

tf.nn

tf.optimizers

tf.quantization

tf.queue

tf.ragged

tf.random

tf.raw_ops

tf.saved_model

tf.sets

tf.signal

tf.sparse

tf.strings

tf.summary

tf.sysconfig

tf.test

tf.tpu

tf.train

tf.version

tf.xla

tf.math / softplus

View aliases

Used in the notebooks

Args

Returns