# tf.contrib.layers.dropout

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


Returns a dropout op applied to the input.

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.set_random_seed for behavior.

#### Returns:

A tensor representing the output of the operation.