dropout1d

paddle.nn.functional. dropout1d ( input: Tensor, p: float = 0.5, training: bool = True, inplace: bool = False ) → Tensor [source]

Randomly zero out entire 1D channels (feature maps) during training.

Parameters

input – Input tensor of shape [C, L] (2D) or [N, C, L] (3D)
p – Probability of a channel being zeroed. Default: 0.5
training – If False, returns input unchanged. Default: True
inplace – If True, modifies input tensor in-place. Default: False WARNING: Currently not implemented (will behave as False). TODO: Implement in-place operation in future versions. Default: False

Returns

Tensor with the same shape as input, where entire channels are zeroed with probability p

Examples

           >>> import paddle

# Case 1: 3D input (batched)
>>> x = paddle.randn([2, 3, 10])  # [N, C, L]
>>> y_train = paddle.nn.functional.dropout1d(x, p=0.2)  # Training mode
>>> y_test = paddle.nn.functional.dropout1d(x, p=0.2, training=False)  # Test mode
>>> print("Original first channel:", x[0, 0, :])
>>> print("Train output (may be zeroed):", y_train[0, 0, :])
>>> print("Test output (always unchanged):", y_test[0, 0, :])

# Case 2: 2D input (single sample)
>>> x = paddle.randn([3, 8])  # [C, L]
>>> y = paddle.nn.functional.dropout1d(x, p=0.5)
>>> print("Input shape:", x.shape)
>>> print("Output shape:", y.shape)
>>> print("Zeroed channels count:", paddle.sum(y == 0).item())

          