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Class LocallyConnected1D

Locally-connected layer for 1D inputs.

Inherits From: Layer


  • Class tf.compat.v1.keras.layers.LocallyConnected1D
  • Class tf.compat.v2.keras.layers.LocallyConnected1D

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.


    # 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. 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, 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 "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.
  • implementation: implementation mode, either 1, 2, or 3. 1 loops over input spatial locations to perform the forward pass. It is memory-efficient but performs a lot of (small) ops.

    2 stores layer weights in a dense but sparsely-populated 2D matrix and implements the forward pass as a single matrix-multiply. It uses a lot of RAM but performs few (large) ops.

    3 stores layer weights in a sparse tensor and implements the forward pass as a single sparse matrix-multiply.

    How to choose:

    1: large, dense models, 2: small models, 3: large, sparse models,

    where "large" stands for large input/output activations (i.e. many filters, input_filters, large input_size, output_size), and "sparse" stands for few connections between inputs and outputs, i.e. small ratio filters * input_filters * kernel_size / (input_size * strides), where inputs to and outputs of the layer are assumed to have shapes (input_size, input_filters), (output_size, filters) respectively.

    It is recommended to benchmark each in the setting of interest to pick the most efficient one (in terms of speed and memory usage). Correct choice of implementation can lead to dramatic speed improvements (e.g. 50X), potentially at the expense of RAM.

    Also, only padding="valid" is supported by implementation=1.

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.


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