tf.nn.dropout( x, keep_prob, noise_shape=None, seed=None, name=None )
Defined in tensorflow/python/ops/nn_ops.py
.
See the guides: Layers (contrib) > Higher level ops for building neural network layers, Neural Network > Activation Functions
Computes dropout.
With probability keep_prob
, outputs the input element scaled up by 1 / keep_prob
, otherwise outputs 0
. The scaling is so 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.
x
: A floating point tensor.keep_prob
: A scalar Tensor
with the same type as x. The probability that each element is kept.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.set_random_seed
for behavior.name
: A name for this operation (optional).A Tensor of the same shape of x
.
ValueError
: If keep_prob
is not in (0, 1]
or if x
is not a floating point tensor.
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Licensed under the Creative Commons Attribution License 3.0.
Code samples licensed under the Apache 2.0 License.
https://www.tensorflow.org/api_docs/python/tf/nn/dropout