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# tf.math.cumprod

Compute the cumulative product of the tensor `x` along `axis`.

### Aliases:

• `tf.compat.v1.cumprod`
• `tf.compat.v1.math.cumprod`
• `tf.compat.v2.math.cumprod`
• `tf.cumprod`
``````tf.math.cumprod(
x,
axis=0,
exclusive=False,
reverse=False,
name=None
)
``````

By default, this op performs an inclusive cumprod, which means that the first element of the input is identical to the first element of the output:

``````tf.math.cumprod([a, b, c])  # [a, a * b, a * b * c]
``````

By setting the `exclusive` kwarg to `True`, an exclusive cumprod is performed instead:

``````tf.math.cumprod([a, b, c], exclusive=True)  # [1, a, a * b]
``````

By setting the `reverse` kwarg to `True`, the cumprod is performed in the opposite direction:

``````tf.math.cumprod([a, b, c], reverse=True)  # [a * b * c, b * c, c]
``````

This is more efficient than using separate `tf.reverse` ops. The `reverse` and `exclusive` kwargs can also be combined:

``````tf.math.cumprod([a, b, c], exclusive=True, reverse=True)  # [b * c, c, 1]
``````

#### Args:

• `x`: A `Tensor`. Must be one of the following types: `float32`, `float64`, `int64`, `int32`, `uint8`, `uint16`, `int16`, `int8`, `complex64`, `complex128`, `qint8`, `quint8`, `qint32`, `half`.
• `axis`: A `Tensor` of type `int32` (default: 0). Must be in the range `[-rank(x), rank(x))`.
• `exclusive`: If `True`, perform exclusive cumprod.
• `reverse`: A `bool` (default: False).
• `name`: A name for the operation (optional).

#### Returns:

A `Tensor`. Has the same type as `x`.