tensorplay.nn.functional.conv1d

tensorplay.nn.functional.conv1d(input, weight, bias=None, stride=1, padding=0, dilation=1, groups=1)[source]

Applies a 1D convolution over an input signal composed of several input planes.

See Conv1d for details and output shape.

Parameters:

• input – input tensor of shape $(\text{minibatch} , \text{in\_channels} , iW)$

• weight – filters of shape $(\text{out\_channels} , \frac{\text{in\_channels}}{\text{groups}} , kW)$

• bias – optional bias of shape $(\text{out\_channels})$. Default: None

• stride – the stride of the convolving kernel. Can be a single number or a one-element tuple (sW,). Default: 1

• padding – implicit paddings on both sides of the input. Can be a single number, a one-element tuple (padW,), 'valid' for no padding, or 'same' to keep every output position (unit stride only). Default: 0

• dilation – the spacing between kernel elements. Can be a single number or a one-element tuple (dW,). Default: 1

• groups – split input into groups, $\text{in\_channels}$ should be divisible by the number of groups. Default: 1

Examples:

>>> inputs = tp.randn(33, 16, 30)
>>> filters = tp.randn(20, 16, 5)
>>> F.conv1d(inputs, filters)