tf.keras.layers.LocallyConnected1D

Locally-connected layer for 1D inputs.

Inherits From: Layer, Module

The LocallyConnected1D layer works similarly to the Conv1D layer, except that weights are unshared, that is, a different set of filters is applied at each different patch of the input.

Example:

    # apply a unshared weight convolution 1d of length 3 to a sequence with
    # 10 timesteps, with 64 output filters
    model = Sequential()
    model.add(LocallyConnected1D(64, 3, input_shape=(10, 32)))
    # now model.output_shape == (None, 8, 64)
    # add a new conv1d on top
    model.add(LocallyConnected1D(32, 3))
    # now model.output_shape == (None, 6, 32)

filters Integer, the dimensionality of the output space (i.e. the number of output filters in the convolution).
kernel_size An integer or tuple/list of a single integer, specifying the length of the 1D convolution window.
strides An integer or tuple/list of a single integer, specifying the stride length of the convolution.
padding Currently only supports "valid" (case-insensitive). "same" may be supported in the future. "valid" means no padding.
data_format A string, one of channels_last (default) or channels_first. The ordering of the dimensions in the inputs. channels_last corresponds to inputs with shape (batch, length, channels) while channels_first corresponds to inputs with shape (batch, channels, length). It defaults to the image_data_format value found in your Keras config file at ~/.keras/keras.json. If you never set it, then it will be "