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'. See
here
for more information.
|
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.
|