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
)

Defined in contrib/layers/python/layers/layers.py.

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.