layer_locally_connected_2d works similarly to layer_conv_2d(), except that weights are unshared, that is, a different set of filters is applied at each different patch of the input.

layer_locally_connected_2d(
  object,
  filters,
  kernel_size,
  strides = c(1L, 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

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 2 integers, specifying the width and height of the 2D convolution window. Can be a single integer to specify the same value for all spatial dimensions.

strides

An integer or list of 2 integers, specifying the strides of the convolution along the width and height. Can be a single integer to specify the same value for all spatial dimensions. 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, width, height, channels) while channels_first corresponds to inputs with shape (batch, channels, width, height). 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

4D tensor with shape: (samples, channels, rows, cols) if data_format='channels_first' or 4D tensor with shape: (samples, rows, cols, channels) if data_format='channels_last'.

Output shape

4D tensor with shape: (samples, filters, new_rows, new_cols) if data_format='channels_first' or 4D tensor with shape: (samples, new_rows, new_cols, filters) if data_format='channels_last'. rows and cols values might have changed due to padding.

See also

Other locally connected layers: layer_locally_connected_1d()