tf.nn.dropout

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Computes dropout.

Aliases:

  • tf.compat.v2.nn.dropout
tf.nn.dropout(
    x,
    rate,
    noise_shape=None,
    seed=None,
    name=None
)

With probability rate, drops elements of x. Input that are kept are scaled up by 1 / (1 - rate), otherwise outputs 0. The scaling is so that the expected sum is unchanged.

Note: The behavior of dropout has changed between TensorFlow 1.x and 2.x. When converting 1.x code, please use named arguments to ensure behavior stays consistent.

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.

Args:

  • x: A floating point tensor.
  • rate: A scalar Tensor with the same type as x. The probability that each element is dropped. For example, setting rate=0.1 would drop 10% of input elements.
  • noise_shape: A 1-D Tensor of type int32, representing the shape for randomly generated keep/drop flags.
  • seed: A Python integer. Used to create random seeds. See tf.compat.v1.set_random_seed for behavior.
  • name: A name for this operation (optional).

Returns:

A Tensor of the same shape of x.

Raises:

  • ValueError: If rate is not in (0, 1] or if x is not a floating point tensor.

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