- 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=[4031425291 3501487998]), b_initializer=ZeroInit, mask=None, mode=None, name=None)#
in_channels (int) – The number of input channels.
out_channels (int) – The number of output channels.
padding (str, int, sequence of int, sequence of tuple) – 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 (int, sequence of 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 (int, sequence of 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 (Callable, ArrayType, Initializer) – The initializer for the convolutional kernel.
b_initializer (Callable, ArrayType, Initializer) – The initializer for the bias.
mask (ArrayType, Optional) – The optional mask of the weights.
mode (Mode) – The computation mode of the current object. Default it is training.
name (str, Optional) – The name of the object.