tf.contrib.layers.dropout

Returns a dropout op applied to the input.

tf.contrib.layers.dropout(
    inputs, keep_prob=0.5, noise_shape=None, is_training=True,
    outputs_collections=None, scope=None, seed=None
)

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.

Args
`inputs`	The tensor to pass to the nn.dropout op.
`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.
`is_training`	A bool `Tensor` indicating whether or not the model is in training mode. If so, dropout is applied and values scaled. Otherwise, inputs is returned.
`outputs_collections`	Collection to add the outputs.
`scope`	Optional scope for name_scope.
`seed`	A Python integer. Used to create random seeds. See `tf.compat.v1.set_random_seed` for behavior.

Returns
A tensor representing the output of the operation.

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Last updated 2020-10-01 UTC.

tf.contrib.layers.dropout

Args

Returns