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.


  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, 
  implementation = 1L, 
  batch_size = NULL, 
  name = NULL, 
  trainable = NULL, 
  weights = NULL 


Arguments Description
object What to compose the new Layer instance with. Typically a Sequential model or a Tensor (e.g., as returned by layer_input()). The return value depends on object. If object is:
- missing or NULL, the Layer instance is returned.
- a Sequential model, the model with an additional layer is returned.
- a Tensor, the output tensor from layer_instance(object) is returned.
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.
implementation 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.
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()