tf.nn.dropout

Computes dropout. (deprecated arguments)

Aliases:

``````tf.nn.dropout(
x,
keep_prob=None,
noise_shape=None,
seed=None,
name=None,
rate=None
)
``````

For each element of `x`, with probability `rate`, outputs `0`, and otherwise scales up the input by `1 / (1-rate)`. The scaling is such that the expected sum is unchanged.

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.
• `keep_prob`: (deprecated) A deprecated alias for `(1-rate)`.
• `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).
• `rate`: A scalar `Tensor` with the same type as `x`. The probability that each element of `x` is discarded.

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.