dropout2d¶
根据丢弃概率 p，在训练过程中随机将某些通道特征图置 0 (对一个形状为 NCHW 的 4 维 Tensor，通道特征图指的是其中的形状为 HW 的 2 维特征图)。
基于 paddle.nn.functional.dropout 实现，如您想了解更多，请参见 dropout 。
参数¶
x (Tensor) - 形状为 [N, C, H, W] 或 [N, H, W, C] 的 4D Tensor。数据类型为 float16、float32 或 float64。
p (float，可选) - 将输入通道置 0 的概率，即丢弃概率。默认值为 0.5。
training (bool，可选) - 标记是否为训练阶段。默认值为 True。
data_format (str，可选) - 指定输入的数据格式，输出的数据格式将与输入保持一致，可以是 NCHW 和 NHWC。其中 N 是批尺寸，C 是通道数，H 是特征高度，W 是特征宽度。默认值为 NCHW 。
name (str，可选) - 具体用法请参见 Name，一般无需设置，默认值为 None。
返回¶
经过 dropout2d 之后的结果，与输入 x 形状相同的 Tensor 。
代码示例¶
>>> import paddle
>>> paddle.seed(1)
>>> x = paddle.randn(shape=(2, 3, 4, 5)).astype(paddle.float32)
>>> 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[i,j,:,:])
... print(y_train[i,j,:,:]) # may all 0
... print(y_test[i,j,:,:])
Tensor(shape=[4, 5], dtype=float32, place=Place(cpu), stop_gradient=True,
[[-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]])
Tensor(shape=[4, 5], dtype=float32, place=Place(cpu), stop_gradient=True,
[[-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]])
Tensor(shape=[4, 5], dtype=float32, place=Place(cpu), stop_gradient=True,
[[-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]])
Tensor(shape=[4, 5], dtype=float32, place=Place(cpu), stop_gradient=True,
[[ 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]])
Tensor(shape=[4, 5], dtype=float32, place=Place(cpu), stop_gradient=True,
[[ 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]])
Tensor(shape=[4, 5], dtype=float32, place=Place(cpu), stop_gradient=True,
[[ 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]])
Tensor(shape=[4, 5], dtype=float32, place=Place(cpu), stop_gradient=True,
[[-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]])
Tensor(shape=[4, 5], dtype=float32, place=Place(cpu), stop_gradient=True,
[[-0., -0., 0. , 0. , 0. ],
[-0., -0., -0., -0., 0. ],
[0. , 0. , -0., -0., -0.],
[-0., -0., 0. , -0., -0.]])
Tensor(shape=[4, 5], dtype=float32, place=Place(cpu), stop_gradient=True,
[[-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]])
Tensor(shape=[4, 5], dtype=float32, place=Place(cpu), stop_gradient=True,
[[-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]])
Tensor(shape=[4, 5], dtype=float32, place=Place(cpu), stop_gradient=True,
[[-0., -0., 0. , -0., 0. ],
[0. , 0. , 0. , 0. , 0. ],
[0. , -0., -0., -0., -0.],
[0. , 0. , -0., 0. , -0.]])
Tensor(shape=[4, 5], dtype=float32, place=Place(cpu), stop_gradient=True,
[[-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]])
Tensor(shape=[4, 5], dtype=float32, place=Place(cpu), stop_gradient=True,
[[ 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]])
Tensor(shape=[4, 5], dtype=float32, place=Place(cpu), stop_gradient=True,
[[0. , 0. , 0. , 0. , -0.],
[0. , -0., 0. , -0., 0. ],
[-0., -0., -0., -0., -0.],
[0. , 0. , -0., -0., -0.]])
Tensor(shape=[4, 5], dtype=float32, place=Place(cpu), stop_gradient=True,
[[ 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]])
Tensor(shape=[4, 5], dtype=float32, place=Place(cpu), stop_gradient=True,
[[ 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]])
Tensor(shape=[4, 5], dtype=float32, place=Place(cpu), stop_gradient=True,
[[0. , -0., -0., -0., -0.],
[0. , 0. , -0., -0., 0. ],
[-0., 0. , -0., 0. , 0. ],
[0. , -0., -0., 0. , -0.]])
Tensor(shape=[4, 5], dtype=float32, place=Place(cpu), stop_gradient=True,
[[ 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]])