Apply 1D conv with un-shared weights.
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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'. |