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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
)
Used in the notebooks
| Used in the guide |
|---|
tf.nn.experimental.stateless_dropout instead of this function. The
difference between tf.nn.experimental.stateless_dropout and this
function is analogous to the difference between
tf.random.stateless_uniform and tf.random.uniform. Please see
Random number
generation guide
for a detailed description of the various RNG systems in TF. As the
guide states, legacy stateful RNG ops like tf.random.uniform and
tf.nn.dropout are not deprecated yet but highly discouraged,
because their states are hard to control.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 integer Tensor, 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.
|