tf.nn.dropout(x, keep_prob, noise_shape=None, seed=None, name=None)

tf.nn.dropout(x, keep_prob, noise_shape=None, seed=None, name=None)

See the guides: Layers (contrib) > Higher level ops for building neural network layers, Neural Network > Activation Functions

Computes dropout.

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.

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 tensor.
• 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.
• seed: A Python integer. Used to create random seeds. See tf.set_random_seed for behavior.
• name: A name for this operation (optional).

Returns:

A Tensor of the same shape of x.

Raises:

• ValueError: If keep_prob is not in (0, 1].

Defined in tensorflow/python/ops/nn_ops.py.