torch.nn.Conv1d

torch.nn.Conv1d is a class in the PyTorch library that represents a 1-dimensional convolutional layer. The Conv1d layer applies a 1D convolution operation on the input tensor. It is commonly used in deep learning models for processing one-dimensional sequential data such as time series, audio signals, or text data.

The Conv1d layer takes as input a 3D tensor with dimensions (batch_size, input_channels, input_length) and applies a convolution operation using a set of learnable filters. The filters slide over the input tensor along one dimension to produce a set of output channels. The output tensor has dimensions (batch_size, output_channels, output_length), where output_length depends on the padding and stride parameters.

The Conv1d layer has several parameters that can be set, including the number of input and output channels, the size of the convolutional kernel, the stride, padding, and dilation rates. These parameters allow the Conv1d layer to be customized for different applications.

Example usage:

import torch

# Define a Conv1d layer with 16 input channels, 32 output channels, and a kernel size of 3
conv1d_layer = torch.nn.Conv1d(in_channels=16, out_channels=32, kernel_size=3)

# Define an input tensor with dimensions (batch_size=4, input_channels=16, input_length=100)
input_tensor = torch.randn(4, 16, 100)

# Apply the Conv1d layer to the input tensor
output_tensor = conv1d_layer(input_tensor)

# The output tensor has dimensions (batch_size=4, output_channels=32, output_length=98)
print(output_tensor.shape)