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Computes dropout: randomly sets elements to zero to prevent overfitting.

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

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.

See also: `tf.keras.layers.Dropout`

for a dropout layer.

Dropout is useful for regularizing DNN models. Inputs elements are randomly set to zero (and the other elements are rescaled). This encourages each node to be independently useful, as it cannot rely on the output of other nodes.

More precisely: With probability `rate`

elements of `x`

are set to `0`

. The remaining elements are scaled up by `1.0 / (1 - rate)`

, so that the expected value is preserved.

tf.random.set_seed(0) x = tf.ones([3,5]) tf.nn.dropout(x, rate = 0.5, seed = 1).numpy() array([[2., 0., 0., 2., 2.], [2., 2., 2., 2., 2.], [2., 0., 2., 0., 2.]], dtype=float32)

tf.random.set_seed(0) x = tf.ones([3,5]) tf.nn.dropout(x, rate = 0.8, seed = 1).numpy() array([[0., 0., 0., 5., 5.], [0., 5., 0., 5., 0.], [5., 0., 5., 0., 5.]], dtype=float32)

tf.nn.dropout(x, rate = 0.0) == x <tf.Tensor: shape=(3, 5), dtype=bool, numpy= array([[ True, True, True, True, True], [ True, True, True, True, True], [ True, True, True, True, True]])>

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. This is useful for dropping whole channels from an image or sequence. For example:

tf.random.set_seed(0) x = tf.ones([3,10]) tf.nn.dropout(x, rate = 2/3, noise_shape=[1,10], seed=1).numpy() array([[0., 0., 0., 3., 3., 0., 3., 3., 3., 0.], [0., 0., 0., 3., 3., 0., 3., 3., 3., 0.], [0., 0., 0., 3., 3., 0., 3., 3., 3., 0.]], dtype=float32)

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.random.set_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. `rate=1` is disallowed, because the output would be all zeros, which is likely not what was intended. |

© 2020 The TensorFlow Authors. All rights reserved.

Licensed under the Creative Commons Attribution License 3.0.

Code samples licensed under the Apache 2.0 License.

https://www.tensorflow.org/versions/r2.4/api_docs/python/tf/nn/dropout