tf.nn.dropout | TensorFlow v2.0.0 (original) (raw)
Computes 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.
By default, each element is kept or dropped independently. If noise_shapeis specified, it must bebroadcastableto 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. Seetf.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. |