dropout¶

dropout(data=None, p=_Null, mode=_Null, axes=_Null, cudnn_off=_Null, out=None, name=None, **kwargs)¶

Applies dropout operation to input array.

During training, each element of the input is set to zero with probability p. The whole array is rescaled by \(1/(1-p)\) to keep the expected sum of the input unchanged.
During testing, this operator does not change the input if mode is ‘training’. If mode is ‘always’, the same computaion as during training will be applied.

Example:

random.seed(998)
input_array = array([[3., 0.5,  -0.5,  2., 7.],
                    [2., -0.4,   7.,  3., 0.2]])
a = symbol.Variable('a')
dropout = symbol.Dropout(a, p = 0.2)
executor = dropout.simple_bind(a = input_array.shape)

## If training
executor.forward(is_train = True, a = input_array)
executor.outputs
[[ 3.75   0.625 -0.     2.5    8.75 ]
 [ 2.5   -0.5    8.75   3.75   0.   ]]

## If testing
executor.forward(is_train = False, a = input_array)
executor.outputs
[[ 3.     0.5   -0.5    2.     7.   ]
 [ 2.    -0.4    7.     3.     0.2  ]]

Defined in src/operator/nn/dropout.cc:L96

Parameters:

data : ndarray: Input array to which dropout will be applied.
p : float, optional, default=0.5: Fraction of the input that gets dropped out during training time.
mode : {‘always’, ‘training’},optional, default=’training’: Whether to only turn on dropout during training or to also turn on for inference.
axes : Shape(tuple), optional, default=[]: Axes for variational dropout kernel.
cudnn_off : boolean or None, optional, default=0: Whether to turn off cudnn in dropout operator. This option is ignored if axes is specified.
out : ndarray, optional: The output ndarray to hold the result.

Returns:

out : ndarray or list of ndarrays: The output of this function.