# tf.nn.dropout

Computes dropout.

### Aliases:

• `tf.compat.v2.nn.dropout`
``````tf.nn.dropout(
x,
rate,
noise_shape=None,
seed=None,
name=None
)
``````

### Used in the tutorials:

With probability `rate`, drops elements of `x`. Input that are kept are scaled up by `1 / (1 - rate)`, otherwise outputs `0`. The scaling is so that the expected sum is unchanged.

Note: The behavior of dropout has changed between TensorFlow 1.x and 2.x. When converting 1.x code, please use named arguments to ensure behavior stays consistent.

By default, each element is kept or dropped independently. If `noise_shape` is specified, it must be broadcastable to the shape of `x`, and only dimensions with `noise_shape[i] == shape(x)[i]` will make independent decisions. For example, if `shape(x) = [k, l, m, n]` and `noise_shape = [k, 1, 1, n]`, each batch and channel component will be kept independently and each row and column will be kept or not kept together.

#### Args:

• `x`: A floating point tensor.
• `rate`: A scalar `Tensor` with the same type as x. The probability that each element is dropped. For example, setting rate=0.1 would drop 10% of input elements.
• `noise_shape`: A 1-D `Tensor` of type `int32`, representing the shape for randomly generated keep/drop flags.
• `seed`: A Python integer. Used to create random seeds. See `tf.compat.v1.set_random_seed` for behavior.
• `name`: A name for this operation (optional).

#### Returns:

A Tensor of the same shape of `x`.

#### Raises:

• `ValueError`: If `rate` is not in `(0, 1]` or if `x` is not a floating point tensor.