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 )
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.
inputs: The tensor to pass to the nn.dropout op.
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.
is_training: A bool
Tensorindicating 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
A tensor representing the output of the operation.