Locally-connected layer for 1D inputs.

    layer_locally_connected_1d() works similarly to layer_conv_1d() , except that weights are unshared, that is, a different set of filters is applied at each different patch of the input.

    layer_locally_connected_1d(
      object,
      filters,
      kernel_size,
      strides = 1L,
      padding = "valid",
      data_format = NULL,
      activation = NULL,
      use_bias = TRUE,
      kernel_initializer = "glorot_uniform",
      bias_initializer = "zeros",
      kernel_regularizer = NULL,
      bias_regularizer = NULL,
      activity_regularizer = NULL,
      kernel_constraint = NULL,
      bias_constraint = NULL,
      batch_size = NULL,
      name = NULL,
      trainable = NULL,
      weights = NULL
    )

    Arguments

    object

    Model or layer object

    filters

    Integer, the dimensionality of the output space (i.e. the number output of filters in the convolution).

    kernel_size

    An integer or list of a single integer, specifying the length of the 1D convolution window.

    strides

    An integer or list of a single integer, specifying the stride length of the convolution. Specifying any stride value != 1 is incompatible with specifying any dilation_rate value != 1.

    padding

    Currently only supports "valid" (case-insensitive). "same" may be supported in the future.

    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, height, width, channels) while channels_first corresponds to inputs with shape (batch, channels, height, width). 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 "channels_last".

    activation

    Activation function to use. If you don't specify anything, no activation is applied (ie. "linear" activation: a(x) = x).

    use_bias

    Boolean, whether the layer uses a bias vector.

    kernel_initializer

    Initializer for the kernel weights matrix.

    bias_initializer

    Initializer for the bias vector.

    kernel_regularizer

    Regularizer function applied to the kernel weights matrix.

    bias_regularizer

    Regularizer function applied to the bias vector.

    activity_regularizer

    Regularizer function applied to the output of the layer (its "activation")..

    kernel_constraint

    Constraint function applied to the kernel matrix.

    bias_constraint

    Constraint function applied to the bias vector.

    batch_size

    Fixed batch size for layer

    name

    An optional name string for the layer. Should be unique in a model (do not reuse the same name twice). It will be autogenerated if it isn't provided.

    trainable

    Whether the layer weights will be updated during training.

    weights

    Initial weights for layer.

    Input shape

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

    Output shape

    3D tensor with shape: (batch_size, new_steps, filters) steps value might have changed due to padding or strides.

    See also

    Other locally connected layers: layer_locally_connected_2d()