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What is the input shape for Conv1d?
The current PyTorch 2.14 Conv1d API reference accepts either a batched tensor shaped (N, C_in, L_in) or an unbatched tensor shaped (C_in, L_in).
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| Dimension | Meaning |
|---|---|
N |
Number of samples in the batch. Omit this dimension for unbatched input. |
C_in |
Input channels, often the number of features at each sequence position. |
L_in |
Length of the ordered one-dimensional signal—the axis the convolution scans. |
The output retains the batch dimension when present, replaces the input channel count with out_channels, and uses the calculated output length: (N, C_out, L_out) or, for unbatched input, (C_out, L_out).
Reorder sequence data when features are last
Many datasets arrive as (batch, sequence, features), but Conv1d places channels before length. If the sequence is the axis you want to scan, use x = x.permute(0, 2, 1) to turn (N, L, features) into (N, features, L). Do not permute automatically: first determine which axis is the ordered signal and which represents channels.
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A two-dimensional tensor is interpreted as unbatched (channels, length), not batched single-channel (batch, length). Add a channel dimension deliberately when each sample has one channel, for example by shaping it as (N, 1, L).
How do I calculate the Conv1d output shape?
For integer padding, calculate the output length with:
L_out = floor((L_in + 2 * padding - dilation * (kernel_size - 1) - 1) / stride + 1)
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Here, kernel_size is the number of sampled positions per filter window, stride is the distance between window starts, and dilation spaces the sampled positions apart. Compute each layer’s output length before stacking layers so the next layer receives a compatible input.
Worked output-length examples
- With
L_in=50,kernel_size=3,stride=2,padding=0, anddilation=1, the result isfloor((50 - 2 - 1) / 2 + 1) = 25. Thus the API examplenn.Conv1d(16, 33, 3, stride=2)maps an input of(20, 16, 50)to(20, 33, 25). - For a length of 50 with the same kernel and no padding but stride 2, the result is
floor((50 - 3) / 2 + 1) = 24. This is the setting used in the following sequence-data example.
The string padding option 'valid' means no padding. 'same' preserves input length only when stride=1; with another stride, use the formula to determine the resulting length.
What does the Conv1d weight shape mean?
The learned weight tensor has shape (out_channels, in_channels / groups, kernel_size). With the default groups=1, that becomes (out_channels, in_channels, kernel_size): every output filter has weights for every input channel at each kernel position. If bias is enabled (the default), its shape is (out_channels,), with one bias value per output channel.
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For example, nn.Conv1d(4, 16, kernel_size=3) has a weight shape of (16, 4, 3). Each of its 16 output filters spans 4 input channels and 3 sampled positions. The operation is cross-correlation, as described by the PyTorch API; the weight dimensions describe the parameters, not a promise about what patterns a trained layer will learn.
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How do groups change channel connections?
The groups argument partitions channel connections. Both in_channels and out_channels must be divisible by groups.
groups=1(default): every input channel can contribute to every output channel.groups=2: the channel connections are split into two groups instead of being fully mixed.groups=in_channels: each input channel is handled independently. Whenout_channelsis an integer multiple ofin_channels, PyTorch documents this as depthwise convolution.
Grouping changes the weight tensor’s second dimension to in_channels / groups; it does not change the output-length formula.
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Example: apply Conv1d to sequence data
This example starts with features in the last dimension, rearranges the tensor, and then applies a convolution along the sequence:
import torch
from torch import nn
x = torch.randn(8, 50, 4) # batch, sequence, features
x = x.permute(0, 2, 1) # (8, 4, 50): batch, channels, sequence
conv = nn.Conv1d(4, 16, kernel_size=3, stride=2)
y = conv(x) # (8, 16, 24)
print(conv.weight.shape) # (16, 4, 3)
print(y.shape) # (8, 16, 24)The output length is 24 from the formula with L_in=50, kernel_size=3, stride=2, and zero padding. The code illustrates the shapes; its output is derived from the documented dimensions and formula.
Why do I get a channels mismatch error?
Check the input axes against the layer constructor. The first constructor argument, in_channels, must match the tensor’s channel dimension—not its batch dimension or sequence length.
- For data shaped
(N, L, features)whereLis the ordered sequence axis, permute to(N, features, L). - For a single unbatched signal, use
(C_in, L_in). A two-dimensional tensor will not be treated as a batch of single-channel signals. - Check that the input’s channel count equals
in_channelsand that both channel counts are divisible bygroups.
Permuting fixes a layout mismatch only when the axis meanings support it. If rows are independent observations or the features have no meaningful order, treating them as a sequence may be the wrong modeling assumption.
Which Conv1d settings should I choose?
Conv1d is suited to data with meaningful neighboring positions along one ordered axis, such as a sequence or signal. The arguments control different trade-offs rather than offering one universally best configuration:
- Channel mixing: use
groups=1for full channel connections, or larger groups to restrict which input channels connect to each output. Depthwise convolution keeps input channels separate. - Receptive field: increase
kernel_sizeto sample more positions per window. Increasedilationto spread those samples farther apart without increasing the number of kernel positions. - Resolution: change
strideto move the window more or less densely; the output length changes accordingly. - Boundaries: integer
paddingadds values at both ends. The documented padding modes arezeros,reflect,replicate, andcircular.
Before tuning these settings, establish what the length axis means for the task. Convolution assumes neighboring positions have a useful relationship; independent rows or unordered feature vectors do not automatically satisfy that assumption.
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Repair common Windows errors and clear accumulated junk for a smoother, more stable PC - no reinstall needed.Free scan · no reinstallDoes Conv1d behave deterministically on CUDA?
PyTorch notes that CUDA/CuDNN may select nondeterministic algorithms for Conv1d in some circumstances. Setting torch.backends.cudnn.deterministic = True requests deterministic behavior, potentially at a performance cost. Consult the PyTorch 2.14 API reference for the documented details and current caveats.
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