Apply 1D conv with un-shared weights.
tf.keras.backend.local_conv1d(
inputs, kernel, kernel_size, strides, data_format=None
)
Arguments |
|---|
inputs
|
3D tensor with shape:
(batch_size, steps, input_dim)
if data_format is "channels_last" or
(batch_size, input_dim, steps)
if data_format is "channels_first".
|
kernel
|
the unshared weight for convolution,
with shape (output_length, feature_dim, filters).
|
kernel_size
|
a tuple of a single integer,
specifying the length of the 1D convolution window.
|
strides
|
a tuple of a single integer,
specifying the stride length of the convolution.
|
data_format
|
the data format, channels_first or channels_last.
|
Returns |
|---|
|
A 3d tensor with shape:
(batch_size, output_length, filters)
if data_format='channels_first'
or 3D tensor with shape:
(batch_size, filters, output_length)
if data_format='channels_last'.
|
TensorFlow 2 version
View source on GitHub