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 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 float16, float32 or float64.

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

• training (bool, optional) – 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 . When it is NCHW , the data is stored in the order of: [batch_size, input_channels, input_height, input_width]. Default: NCHW .

• name (str, optional) – Name for the operation, Default: 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
>>> y_test = paddle.nn.functional.dropout2d(x, training=False) #test
>>> for i in range(2):
...     for j in range(3):
...         print(x[i,j,:,:])
...         print(y_train[i,j,:,:]) # may all 0
...         print(y_test[i,j,:,:])
[[-0.30557564,  0.11855337,  0.41220093, -0.09968963,  1.50014710],
[ 1.24004936, -0.92485696,  0.08612321,  1.15149164, -0.09276631],
[ 1.22873247, -1.46587241, -1.30802727,  0.19496460,  1.73776841],
[ 0.40092674,  0.67630458,  0.72265440,  1.31720388, -1.41899264]])
[[-0.61115128,  0.23710674,  0.82440186, -0.19937925,  3.00029421],
[ 2.48009872, -1.84971392,  0.17224643,  2.30298328, -0.18553263],
[ 2.45746493, -2.93174481, -2.61605453,  0.38992921,  3.47553682],
[ 0.80185348,  1.35260916,  1.44530880,  2.63440776, -2.83798528]])
[[-0.30557564,  0.11855337,  0.41220093, -0.09968963,  1.50014710],
[ 1.24004936, -0.92485696,  0.08612321,  1.15149164, -0.09276631],
[ 1.22873247, -1.46587241, -1.30802727,  0.19496460,  1.73776841],
[ 0.40092674,  0.67630458,  0.72265440,  1.31720388, -1.41899264]])
[[ 0.88350385, -1.14767575,  0.51043051, -0.10051888, -0.61305630],
[-0.12084112,  0.48506257, -1.13189507,  0.62806708, -0.80003673],
[ 0.51513153, -0.08890446,  0.22753835,  0.11557858,  0.78117645],
[ 1.47505593,  0.84618902, -0.38528305, -1.05887091,  0.16592593]])
[[ 1.76700771, -2.29535151,  1.02086103, -0.20103776, -1.22611260],
[-0.24168225,  0.97012514, -2.26379013,  1.25613415, -1.60007346],
[ 1.03026307, -0.17780893,  0.45507669,  0.23115715,  1.56235290],
[ 2.95011187,  1.69237804, -0.77056611, -2.11774182,  0.33185187]])
[[ 0.88350385, -1.14767575,  0.51043051, -0.10051888, -0.61305630],
[-0.12084112,  0.48506257, -1.13189507,  0.62806708, -0.80003673],
[ 0.51513153, -0.08890446,  0.22753835,  0.11557858,  0.78117645],
[ 1.47505593,  0.84618902, -0.38528305, -1.05887091,  0.16592593]])
[[-1.46668839, -0.38117948,  1.18678427,  0.38740095,  0.29117522],
[-0.13538910, -0.14527084, -0.04912176, -0.26063353,  0.23640174],
[ 0.45643106,  0.60587281, -1.03242552, -0.45319262, -1.57911122],
[-0.08732958, -0.75898546,  0.14563090, -1.73751652, -0.89109969]])
[[-0., -0., 0. , 0. , 0. ],
[-0., -0., -0., -0., 0. ],
[0. , 0. , -0., -0., -0.],
[-0., -0., 0. , -0., -0.]])
[[-1.46668839, -0.38117948,  1.18678427,  0.38740095,  0.29117522],
[-0.13538910, -0.14527084, -0.04912176, -0.26063353,  0.23640174],
[ 0.45643106,  0.60587281, -1.03242552, -0.45319262, -1.57911122],
[-0.08732958, -0.75898546,  0.14563090, -1.73751652, -0.89109969]])
[[-0.32110816, -0.76044011,  0.34456784, -0.39410326,  0.37896338],
[ 0.52747023,  0.72711533,  0.29204839,  0.72493637,  0.31128070],
[ 0.58046782, -1.78499067, -1.67504823, -0.38590902, -0.26243693],
[ 0.96669912,  0.43670532, -0.38109761,  0.78405094, -2.17882323]])
[[-0., -0., 0. , -0., 0. ],
[0. , 0. , 0. , 0. , 0. ],
[0. , -0., -0., -0., -0.],
[0. , 0. , -0., 0. , -0.]])
[[-0.32110816, -0.76044011,  0.34456784, -0.39410326,  0.37896338],
[ 0.52747023,  0.72711533,  0.29204839,  0.72493637,  0.31128070],
[ 0.58046782, -1.78499067, -1.67504823, -0.38590902, -0.26243693],
[ 0.96669912,  0.43670532, -0.38109761,  0.78405094, -2.17882323]])
[[ 0.17168395,  0.45112833,  0.63307828,  2.38763475, -1.27247131],
[ 0.56171960, -1.09584677,  0.38300961, -0.57512099,  0.31011426],
[-0.95336407, -1.04852903, -0.21312937, -0.53549880, -0.00074209],
[ 2.22819090,  1.12403083, -0.04198794, -1.51167727, -0.42699185]])
[[0. , 0. , 0. , 0. , -0.],
[0. , -0., 0. , -0., 0. ],
[-0., -0., -0., -0., -0.],
[0. , 0. , -0., -0., -0.]])
[[ 0.17168395,  0.45112833,  0.63307828,  2.38763475, -1.27247131],
[ 0.56171960, -1.09584677,  0.38300961, -0.57512099,  0.31011426],
[-0.95336407, -1.04852903, -0.21312937, -0.53549880, -0.00074209],
[ 2.22819090,  1.12403083, -0.04198794, -1.51167727, -0.42699185]])
[[ 0.62503546, -0.20989063, -0.22046235, -0.38679042, -1.02590704],
[ 1.04561794,  1.08428383, -0.52219963, -1.56003857,  0.89213932],
[-0.16578521,  0.14524542, -0.45563069,  0.48180851,  1.35843253],
[ 1.07669640, -0.84535235, -1.18651557,  0.79144061, -0.45565742]])