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_shape + [in_width, in_channels]
if data_format is "NWC", or
batch_shape + [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_shape + [in_width, in_channels]
is reshaped to
batch_shape + [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_shape + [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 Tensor of rank at least 3. Must be of type float16, float32, or
float64.
|
filters
|
A Tensor of rank at least 3. 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_shape + [in_width, in_channels]. The "NCW" format stores data
as batch_shape + [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.
|
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TensorFlow 1 version
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