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),
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 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. Integer, the dimensionality of the output space (i.e. the number output of filters in the convolution). 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. 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. Currently only supports "valid" (case-insensitive). "same" may be supported in the future. 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 function to use. If you don't specify anything, no activation is applied (ie. "linear" activation: a(x) = x). Boolean, whether the layer uses a bias vector. Initializer for the kernel weights matrix. Initializer for the bias vector. Regularizer function applied to the kernel weights matrix. Regularizer function applied to the bias vector. Regularizer function applied to the output of the layer (its "activation").. Constraint function applied to the kernel matrix. Constraint function applied to the bias vector. 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. Fixed batch size for layer 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. Whether the layer weights will be updated during training. 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.

Other locally connected layers: layer_locally_connected_1d()