tf.nn.conv1d

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Computes a 1-D convolution given 3-D input and filter tensors.

tf.nn.conv1d(
    input, filters, stride, padding, data_format='NWC', dilations=None, name=None
)

Given an input tensor of shape [batch, in_width, in_channels] if data_format is "NWC", or [batch, in_channels, in_width] if data_format is "NCW", 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 tf.nn.conv2d. 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] (where out_width is a function of the stride and padding as in conv2d) and returned to the caller.

Args:

• input: A 3D Tensor. Must be of type float16, float32, or float64.
• filters: A 3D Tensor. Must have the same type as input.
• stride: An int or list of ints that has length 1 or 3. The number of entries by which the filter is moved right at each step.
• padding: 'SAME' or 'VALID'
• data_format: An optional string from "NWC", "NCW". Defaults to "NWC", the data is stored in the order of [batch, in_width, in_channels]. The "NCW" format stores data as [batch, in_channels, in_width].
• dilations: An int or list of ints that has length 1 or 3 which defaults to 1. The dilation factor for each dimension of input. If set to k > 1, there will be k-1 skipped cells between each filter element on that dimension. Dilations in the batch and depth dimensions must be 1.
• name: A name for the operation (optional).

Returns:

A Tensor. Has the same type as input.

Raises:

• ValueError: if data_format is invalid.