|---|
| [![](https://www.tensorflow.org/images/GitHub-Mark-32px.png) View source on GitHub](https://github.com/tensorflow/tensorflow/blob/v2.21.0/tensorflow/python/ops/math_ops.py#L5771-L5818) |

Computes exponential of x element-wise. \\(y = e\^x\\).

#### View aliases


**Main aliases**

[`tf.exp`](https://www.tensorflow.org/api_docs/python/tf/math/exp)
**Compat aliases for migration**

See
[Migration guide](https://www.tensorflow.org/guide/migrate) for
more details.

[`tf.compat.v1.exp`](https://www.tensorflow.org/api_docs/python/tf/math/exp)

<br />

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

### Used in the notebooks

| Used in the guide | Used in the tutorials |
|---|---|
| - [Understanding masking \& padding](https://www.tensorflow.org/guide/keras/understanding_masking_and_padding) | - [Scalable model compression](https://www.tensorflow.org/tutorials/optimization/compression) - [Convolutional Variational Autoencoder](https://www.tensorflow.org/tutorials/generative/cvae) - [Learned data compression](https://www.tensorflow.org/tutorials/generative/data_compression) - [Multiple changepoint detection and Bayesian model selection](https://www.tensorflow.org/probability/examples/Multiple_changepoint_detection_and_Bayesian_model_selection) - [Modeling COVID-19 spread in Europe and the effect of interventions](https://www.tensorflow.org/probability/examples/Estimating_COVID_19_in_11_European_countries) |

This function computes the exponential of the input tensor element-wise.
i.e. [`math.exp(x)`](https://www.tensorflow.org/api_docs/python/tf/math/exp) or \\(e\^x\\), where `x` is the input tensor.
\\(e\\) denotes Euler's number and is approximately equal to 2.718281.
Output is positive for any real input.

    x = tf.constant(2.0)
    tf.math.exp(x)
    <tf.Tensor: shape=(), dtype=float32, numpy=7.389056>

    x = tf.constant([2.0, 8.0])
    tf.math.exp(x)
    <tf.Tensor: shape=(2,), dtype=float32,
    numpy=array([   7.389056, 2980.958   ], dtype=float32)>

For complex numbers, the exponential value is calculated as

\\\[
e\^{x+iy} = {e\^x} {e\^{iy} } = {e\^x} ({\\cos (y) + i \\sin (y)})
\\\]

For `1+1j` the value would be computed as:

\\\[
e\^1 (\\cos (1) + i \\sin (1)) = 2.7182817 \\times (0.5403023+0.84147096j)
\\\]

    x = tf.constant(1 + 1j)
    tf.math.exp(x)
    <tf.Tensor: shape=(), dtype=complex128,
    numpy=(1.4686939399158851+2.2873552871788423j)>

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| ## Args ||
|---|---|
| `x` | A [`tf.Tensor`](https://www.tensorflow.org/api_docs/python/tf/Tensor). Must be one of the following types: `bfloat16`, `half`, `float32`, `float64`, `complex64`, `complex128`. |
| `name` | A name for the operation (optional). |

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| ## Returns ||
|---|---|
| A [`tf.Tensor`](https://www.tensorflow.org/api_docs/python/tf/Tensor). Has the same type as `x`. ||

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## numpy compatibility

<br />

Equivalent to np.exp

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<br />