dropout2d

paddle.nn.functional. dropout2d ( x, p=0.5, training=True, data_format='NCHW', name=None ) [source]

Randomly zero out entire channels (in the batched input 4d tensor with the shape NCHW , a channel is a 2D feature map with the shape HW ). Each channel will be zeroed out independently on every forward call with probability p using samples from a Bernoulli distribution.

See paddle.nn.functional.dropout for more details.

Parameters

  • x (Tensor) – The input is 4-D Tensor with shape [N, C, H, W] or [N, H, W, C]. The data type is float32 or float64.

  • p (float) – Probability of setting units to zero. Default 0.5.

  • training (bool) – A flag indicating whether it is in train phrase or not. Default True.

  • data_format (str, optional) – Specify the data format of the input, and the data format of the output will be consistent with that of the input. An optional string from NCHW or NHWC . The default is NCHW . When it is NCHW , the data is stored in the order of: [batch_size, input_channels, input_height, input_width].

  • name (str, optional) – Name for the operation (optional, default is None). For more information, please refer to Name.

Returns

A Tensor representing the dropout2d, has same shape and data type as x .

Examples

import paddle
import numpy as np

x = np.random.random(size=(2, 3, 4, 5)).astype('float32')
x = paddle.to_tensor(x)
y_train = paddle.nn.functional.dropout2d(x)  #train
y_test = paddle.nn.functional.dropout2d(x, training=False) #test
for i in range(2):
    for j in range(3):
        print(x.numpy()[i,j,:,:])
        print(y_train.numpy()[i,j,:,:]) # may all 0
        print(y_test.numpy()[i,j,:,:])