# Complex Dropout¶

class ComplexDropout

Applies Dropout to the input. It works also with complex inputs! The Dropout layer randomly sets input units to 0 with a frequency of rate at 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 training is set to True such that no values are dropped during inference. When using model.fit, training will be appropriately set to True automatically, and in other contexts, you can set the kwarg explicitly to True when calling the layer. This is in contrast to setting trainable=False for a Dropout layer. trainable does 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 rate of input units to 0 at each update during training time, which helps prevent overfitting.

__init__(self, rate, noise_shape=None, seed=None)
Parameters: 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)
Parameters: 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).

Code example

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)


It outputs:

<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))

 [CIT2014-SRIVASTAVA] Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov, “Dropout: a simple way to prevent neural networks from overfitting,” J. Mach. Learn. Res., vol. 15, no. 1, pp. 1929–1958, Jan. 2014