tf.nn.conv1d(value, filters, stride, padding, use_cudnn_on_gpu=None, data_format=None, name=None)
See the guide: Neural Network > Convolution
Computes a 1-D convolution given 3-D input and filter tensors.
Given an input tensor of shape [batch, in_width, in_channels] if data_format is "NHWC", or [batch, in_channels, in_width] if data_format is "NCHW", and a filter / kernel tensor of shape [filter_width, in_channels, out_channels], this op reshapes the arguments to pass them to conv2d to perform the equivalent convolution operation.
Internally, this op reshapes the input tensors and invokes
For example, if
data_format does not start with "NC", a tensor of shape
[batch, in_width, in_channels]
is reshaped to
[batch, 1, in_width, in_channels],
and the filter is reshaped to
[1, filter_width, in_channels, out_channels].
The result is then reshaped back to
batch, out_width, out_channels
returned to the caller.
value: A 3D
Tensor. Must be of type
filters: A 3D
Tensor. Must have the same type as
integer. The number of entries by which the filter is moved right at each step.
padding: 'SAME' or 'VALID'
use_cudnn_on_gpu: An optional
bool. Defaults to
data_format: An optional
"NHWC", "NCHW". Defaults to
"NHWC", the data is stored in the order of [batch, in_width, in_channels]. The
"NCHW"format stores data as [batch, in_channels, in_width].
name: A name for the operation (optional).
Tensor. Has the same type as input.