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```
#' 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.
#'
#' @inheritParams layer_conv_2d
#'
#' @param filters Integer, the dimensionality of the output space (i.e. the
#' number output of filters in the convolution).
#' @param kernel_size An integer or list of a single integer, specifying the
#' length of the 1D convolution window.
#' @param 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.
#' @param padding Currently only supports `"valid"` (case-insensitive). `"same"`
#' may be supported in the future.
#' @param 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`.
#'
#' @section Input shape: 3D tensor with shape: `(batch_size, steps, input_dim)`
#'
#' @section Output shape: 3D tensor with shape: `(batch_size, new_steps,
#' filters)` `steps` value might have changed due to padding or strides.
#'
#' @family locally connected layers
#'
#' @export
layer_locally_connected_1d <- function(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,
implementation = 1L,
batch_size = NULL, name = NULL, trainable = NULL, weights = NULL) {
create_layer(keras$layers$LocallyConnected1D, object, list(
filters = as.integer(filters),
kernel_size = as_integer_tuple(kernel_size),
strides = as_integer_tuple(strides),
padding = padding,
data_format = data_format,
activation = activation,
use_bias = use_bias,
kernel_initializer = kernel_initializer,
bias_initializer = bias_initializer,
kernel_regularizer = kernel_regularizer,
bias_regularizer = bias_regularizer,
activity_regularizer = activity_regularizer,
kernel_constraint = kernel_constraint,
bias_constraint = bias_constraint,
implementation = as.integer(implementation),
batch_size = as_nullable_integer(batch_size),
name = name,
trainable = trainable,
weights = weights
))
}
#' Locally-connected layer for 2D inputs.
#'
#' `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.
#'
#' @inheritParams layer_locally_connected_1d
#'
#' @param filters Integer, the dimensionality of the output space (i.e. the
#' number output of filters in the convolution).
#' @param 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.
#' @param 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.
#' @param 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".
#' @param 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`.
#'
#' @section 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'.
#'
#' @section 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.
#'
#' @family locally connected layers
#'
#' @export
layer_locally_connected_2d <- function(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) {
create_layer(keras$layers$LocallyConnected2D, object, list(
filters = as.integer(filters),
kernel_size = as_integer_tuple(kernel_size),
strides = as_integer_tuple(strides),
padding = padding,
data_format = data_format,
activation = activation,
use_bias = use_bias,
kernel_initializer = kernel_initializer,
bias_initializer = bias_initializer,
kernel_regularizer = kernel_regularizer,
bias_regularizer = bias_regularizer,
activity_regularizer = activity_regularizer,
kernel_constraint = kernel_constraint,
bias_constraint = bias_constraint,
implementation = as.integer(implementation),
batch_size = as_nullable_integer(batch_size),
name = name,
trainable = trainable,
weights = weights
))
}
```

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