tf.nn.dropout( x, keep_prob, noise_shape=None, seed=None, name=None )
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
Tensorwith the same type as x. The probability that each element is kept.
noise_shape: A 1-D
int32, representing the shape for randomly generated keep/drop flags.
seed: A Python integer. Used to create random seeds. See
name: A name for this operation (optional).
A Tensor of the same shape of
keep_probis not in
(0, 1]or if
xis 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.