Conv1d

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Conv1d#

class brainpy.dnn.Conv1d(in_channels, out_channels, kernel_size, stride=None, strides=None, padding='SAME', lhs_dilation=1, rhs_dilation=1, groups=1, w_initializer=XavierNormal(scale=1.0, mode=fan_avg, in_axis=-2, out_axis=-1, distribution=truncated_normal, rng=RandomState(Array((), dtype=key<fry>) overlaying: [ 216744582 1008666480])), b_initializer=ZeroInit, mask=None, mode=None, name=None)[source]#

One-dimensional convolution.

The input should a 2d array with the shape of [H, C], or a 3d array with the shape of [B, H, C], where H is the feature size.

Parameters:

  • in_channels (int) – The number of input channels.

  • out_channels (int) – The number of output channels.

  • kernel_size (Union[int, Tuple[int, ...]]) – The shape of the convolutional kernel. For 1D convolution, the kernel size can be passed as an integer. For all other cases, it must be a sequence of integers.

  • strides (Union[int, Tuple[int, ...]]) – An integer or a sequence of n integers, representing the inter-window strides (default: 1).

  • padding (Union[str, int, Tuple[int, int], Sequence[Tuple[int, int]]]) – Either the string ‘SAME’, the string ‘VALID’, or a sequence of n (low, high) integer pairs that give the padding to apply before and after each spatial dimension.

  • lhs_dilation (Union[int, Tuple[int, ...]]) – An integer or a sequence of n integers, giving the dilation factor to apply in each spatial dimension of inputs (default: 1). Convolution with input dilation d is equivalent to transposed convolution with stride d.

  • rhs_dilation (Union[int, Tuple[int, ...]]) – An integer or a sequence of n integers, giving the dilation factor to apply in each spatial dimension of the convolution kernel (default: 1). Convolution with kernel dilation is also known as ‘atrous convolution’.

  • groups (int) – If specified, divides the input features into groups. default 1.

  • w_initializer (Union[Callable, TypeVar(ArrayType, Array, Variable, TrainVar, Array, ndarray), Initializer]) – The initializer for the convolutional kernel.

  • b_initializer (Union[Callable, TypeVar(ArrayType, Array, Variable, TrainVar, Array, ndarray), Initializer, None]) – The initializer for the bias.

  • mask (Optional[TypeVar(ArrayType, Array, Variable, TrainVar, Array, ndarray)]) – The optional mask of the weights.

  • mode (Optional[Mode]) – The computation mode of the current object. Default it is training.

  • name (Optional[str]) – The name of the object.

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