Applies Dropout to the input. It works also with complex inputs! The Dropout layer randomly sets input units to 0 with a frequency of
rateat each step during training time, which helps prevent overfitting. Inputs not set to 0 are scaled up by
1/(1 - rate)such that the sum over all inputs is unchanged. Note that the Dropout layer only applies when
trainingis set to True such that no values are dropped during inference. When using
trainingwill be appropriately set to True automatically, and in other contexts, you can set the
Truewhen calling the layer. This is in contrast to setting
trainable=Falsefor a Dropout layer.
trainabledoes not affect the layer’s behavior, as Dropout does not have any variables/weights that can be frozen during training.
Dropout [CIT2014-SRIVASTAVA] consists in randomly setting a fraction
rateof input units to 0 at each update during training time, which helps prevent overfitting.
__init__(self, rate, noise_shape=None, seed=None)¶
- rate – Float between 0 and 1. Fraction of the input units to drop.
- noise_shape – 1D integer tensor representing the shape of the binary dropout mask that will be multiplied with the input.
For instance, if your inputs have shape
(batch_size, timesteps, features)and you want the dropout mask to be the same for all timesteps, you can use
noise_shape=(batch_size, 1, features).
- seed – A Python integer to use as random seed.
call(self, inputs, training=None)¶
- inputs – Input tensor (of any rank).
- training – Python boolean indicating whether the layer should behave in training mode (adding dropout) or in inference mode (doing nothing).
Let’s first create some data:
import tensorflow as tf import numpy as np import cvnn.layers as complex_layers tf.random.set_seed(0) layer = complex_layers.ComplexDropout(.2, input_shape=(2,)) data = np.arange(10).reshape(5, 2).astype(np.float32) data = tf.complex(data, data) print(data)
Data will therefore be:
<tf.Tensor: shape=(5, 2), dtype=complex64, numpy= array([[0.+0.j, 1.+1.j], [2.+2.j, 3.+3.j], [4.+4.j, 5.+5.j], [6.+6.j, 7.+7.j], [8.+8.j, 9.+9.j]], dtype=complex64)>
Now when we apply the dropout layer:
outputs = layer(data, training=True) print(output)
<tf.Tensor: shape=(5, 2), dtype=complex64, numpy= array([[ 0. +0.j , 1.25 +1.25j], [ 2.5 +2.5j , 3.75 +3.75j], [ 5. +5.j , 6.25 +6.25j], [ 7.5 +7.5j , 8.75 +8.75j], [10. +10.j , 0. +0.j ]], dtype=complex64)>
However, if you use
training=False, you will get the data unchanged:
assert np.all(data == layer(data, training=False))