Apply 1D conv with un-shared weights.

`k_local_conv1d(inputs, kernel, kernel_size, strides, data_format = NULL)`

the tensor after 1d conv with un-shared weights, with shape (batch_size, output_length, filters)

- inputs
3D tensor with shape: (batch_size, steps, input_dim)

- kernel
the unshared weight for convolution, with shape (output_length, feature_dim, filters)

- kernel_size
a list of a single integer, specifying the length of the 1D convolution window

- strides
a list of a single integer, specifying the stride length of the convolution

- data_format
the data format, channels_first or channels_last

This function is part of a set of Keras backend functions that enable lower level access to the core operations of the backend tensor engine (e.g. TensorFlow, CNTK, Theano, etc.).

You can see a list of all available backend functions here: https://tensorflow.rstudio.com/reference/keras/index.html#backend.