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layers-convolutional.R
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layers-convolutional.R
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#' 1D convolution layer (e.g. temporal convolution).
#'
#' This layer creates a convolution kernel that is convolved with the layer
#' input over a single spatial (or temporal) dimension to produce a tensor of
#' outputs. If `use_bias` is TRUE, a bias vector is created and added to the
#' outputs. Finally, if `activation` is not `NULL`, it is applied to the outputs
#' as well. When using this layer as the first layer in a model, provide an
#' `input_shape` argument (list of integers or `NULL `, e.g. `(10, 128)` for
#' sequences of 10 vectors of 128-dimensional vectors, or `(NULL, 128)` for
#' variable-length sequences of 128-dimensional vectors.
#'
#' @inheritParams layer_dense
#'
#' @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 One of `"valid"`, `"causal"` or `"same"` (case-insensitive).
#' `"causal"` results in causal (dilated) convolutions, e.g. `output[t]` does
#' not depend on `input[t+1:]`. Useful when modeling temporal data where the
#' model should not violate the temporal order. See [WaveNet: A Generative
#' Model for Raw Audio, section 2.1](https://arxiv.org/abs/1609.03499).
#' @param dilation_rate an integer or list of a single integer, specifying the
#' dilation rate to use for dilated convolution. Currently, specifying any
#' `dilation_rate` value != 1 is incompatible with specifying any `strides`
#' value != 1.
#' @param activation Activation function to use. If you don't specify anything,
#' no activation is applied (ie. "linear" activation: `a(x) = x`).
#' @param use_bias Boolean, whether the layer uses a bias vector.
#' @param kernel_initializer Initializer for the `kernel` weights matrix.
#' @param bias_initializer Initializer for the bias vector.
#' @param kernel_regularizer Regularizer function applied to the `kernel`
#' weights matrix.
#' @param bias_regularizer Regularizer function applied to the bias vector.
#' @param activity_regularizer Regularizer function applied to the output of the
#' layer (its "activation")..
#' @param kernel_constraint Constraint function applied to the kernel matrix.
#' @param bias_constraint Constraint function applied to the bias vector.
#'
#' @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 convolutional layers
#'
#' @export
layer_conv_1d <- function(x, filters, kernel_size, strides = 1L, padding = "valid",
dilation_rate = 1L, 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, input_shape = NULL,
batch_input_shape = NULL, batch_size = NULL, dtype = NULL,
name = NULL, trainable = NULL, weights = NULL) {
call_layer(keras$layers$Conv1D, x, list(
filters = as.integer(filters),
kernel_size = as_integer_tuple(kernel_size),
strides = as_integer_tuple(strides),
padding = padding,
dilation_rate = as_integer_tuple(dilation_rate),
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,
input_shape = normalize_shape(input_shape),
batch_input_shape = normalize_shape(batch_input_shape),
batch_size = as_nullable_integer(batch_size),
dtype = dtype,
name = name,
trainable = trainable,
weights = weights
))
}
#' 2D convolution layer (e.g. spatial convolution over images).
#'
#' This layer creates a convolution kernel that is convolved with the layer
#' input to produce a tensor of outputs. If `use_bias` is TRUE, a bias vector is
#' created and added to the outputs. Finally, if `activation` is not `NULL`, it
#' is applied to the outputs as well. When using this layer as the first layer
#' in a model, provide the keyword argument `input_shape` (list of integers,
#' does not include the sample axis), e.g. `input_shape=c(128, 128, 3)` for
#' 128x128 RGB pictures in `data_format="channels_last"`.
#'
#' @inheritParams layer_conv_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 padding one of `"valid"` or `"same"` (case-insensitive).
#' @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, 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".
#' @param dilation_rate an integer or list of 2 integers, specifying the
#' dilation rate to use for dilated convolution. Can be a single integer to
#' specify the same value for all spatial dimensions. Currently, specifying
#' any `dilation_rate` value != 1 is incompatible with specifying any stride
#' value != 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 convolutional layers
#'
#' @export
layer_conv_2d <- function(x, filters, kernel_size, strides = c(1L, 1L), padding = "valid", data_format = NULL,
dilation_rate = c(1L, 1L), 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, input_shape = NULL,
batch_input_shape = NULL, batch_size = NULL, dtype = NULL,
name = NULL, trainable = NULL, weights = NULL) {
call_layer(keras$layers$Conv2D, x, list(
filters = as.integer(filters),
kernel_size = as_integer_tuple(kernel_size),
strides = as_integer_tuple(strides),
padding = padding,
data_format = data_format,
dilation_rate = dilation_rate,
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,
input_shape = normalize_shape(input_shape),
batch_input_shape = normalize_shape(batch_input_shape),
batch_size = as_nullable_integer(batch_size),
dtype = dtype,
name = name,
trainable = trainable,
weights = weights
))
}
#' 3D convolution layer (e.g. spatial convolution over volumes).
#'
#' This layer creates a convolution kernel that is convolved with the layer
#' input to produce a tensor of outputs. If `use_bias` is TRUE, a bias vector is
#' created and added to the outputs. Finally, if `activation` is not `NULL`, it
#' is applied to the outputs as well. When using this layer as the first layer
#' in a model, provide the keyword argument `input_shape` (list of integers,
#' does not include the sample axis), e.g. `input_shape=c(128L, 128L, 128L, 3L)`
#' for 128x128x128 volumes with a single channel, in
#' `data_format="channels_last"`.
#'
#' @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 3 integers, specifying the width and
#' height of the 3D convolution window. Can be a single integer to specify the
#' same value for all spatial dimensions.
#' @param strides An integer or list of 3 integers, specifying the strides of
#' the convolution along each spatial dimension. 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 padding one of `"valid"` or `"same"` (case-insensitive).
#' @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, spatial_dim1,
#' spatial_dim2, spatial_dim3, channels)` while `channels_first` corresponds
#' to inputs with shape `(batch, channels, spatial_dim1, spatial_dim2,
#' spatial_dim3)`. 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 dilation_rate an integer or list of 3 integers, specifying the
#' dilation rate to use for dilated convolution. Can be a single integer to
#' specify the same value for all spatial dimensions. Currently, specifying
#' any `dilation_rate` value != 1 is incompatible with specifying any stride
#' value != 1.
#'
#' @section Input shape: 5D tensor with shape: `(samples, channels, conv_dim1,
#' conv_dim2, conv_dim3)` if data_format='channels_first' or 5D tensor with
#' shape: `(samples, conv_dim1, conv_dim2, conv_dim3, channels)` if
#' data_format='channels_last'.
#'
#' @section Output shape: 5D tensor with shape: `(samples, filters,
#' new_conv_dim1, new_conv_dim2, new_conv_dim3)` if
#' data_format='channels_first' or 5D tensor with shape: `(samples,
#' new_conv_dim1, new_conv_dim2, new_conv_dim3, filters)` if
#' data_format='channels_last'. `new_conv_dim1`, `new_conv_dim2` and
#' `new_conv_dim3` values might have changed due to padding.
#'
#' @family convolutional layers
#'
#' @export
layer_conv_3d <- function(x, filters, kernel_size, strides = c(1L, 1L, 1L), padding = "valid",
data_format = NULL, dilation_rate = c(1L, 1L, 1L), 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, input_shape = NULL,
batch_input_shape = NULL, batch_size = NULL, dtype = NULL,
name = NULL, trainable = NULL, weights = NULL) {
call_layer(keras$layers$Conv3D, x, list(
filters = as.integer(filters),
kernel_size = as_integer_tuple(kernel_size),
strides = as_integer_tuple(strides),
padding = padding,
data_format = data_format,
dilation_rate = dilation_rate,
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,
input_shape = normalize_shape(input_shape),
batch_input_shape = normalize_shape(batch_input_shape),
batch_size = as_nullable_integer(batch_size),
dtype = dtype,
name = name,
trainable = trainable,
weights = weights
))
}
#' Transposed convolution layer (sometimes called Deconvolution).
#'
#' The need for transposed convolutions generally arises from the desire to use
#' a transformation going in the opposite direction of a normal convolution,
#' i.e., from something that has the shape of the output of some convolution to
#' something that has the shape of its input while maintaining a connectivity
#' pattern that is compatible with said convolution. When using this layer as
#' the first layer in a model, provide the keyword argument `input_shape` (list
#' of integers, does not include the sample axis), e.g. `input_shape=c(128L,
#' 128L, 3L)` for 128x128 RGB pictures in `data_format="channels_last"`.
#'
#' @inheritParams layer_conv_2d
#'
#' @param filters Integer, the dimensionality of the output space (i.e. the
#' number of output 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 padding one of `"valid"` or `"same"` (case-insensitive).
#'
#' @section Input shape: 4D tensor with shape: `(batch, channels, rows, cols)`
#' if data_format='channels_first' or 4D tensor with shape: `(batch, rows,
#' cols, channels)` if data_format='channels_last'.
#'
#' @section Output shape: 4D tensor with shape: `(batch, filters, new_rows,
#' new_cols)` if data_format='channels_first' or 4D tensor with shape:
#' `(batch, new_rows, new_cols, filters)` if data_format='channels_last'.
#' `rows` and `cols` values might have changed due to padding.
#'
#' @section References:
#' - [A guide to convolution arithmetic for deep learning](https://arxiv.org/abs/1603.07285v1)
#' - [Deconvolutional Networks](http://www.matthewzeiler.com/pubs/cvpr2010/cvpr2010.pdf)
#'
#' @family convolutional layers
#'
#' @export
layer_conv_2d_transpose <- function(x, 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, input_shape = NULL,
batch_input_shape = NULL, batch_size = NULL, dtype = NULL,
name = NULL, trainable = NULL, weights = NULL) {
call_layer(keras$layers$Conv2DTranspose, x, 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,
input_shape = normalize_shape(input_shape),
batch_input_shape = normalize_shape(batch_input_shape),
batch_size = as_nullable_integer(batch_size),
dtype = dtype,
name = name,
trainable = trainable,
weights = weights
))
}
#' Depthwise separable 2D convolution.
#'
#' Separable convolutions consist in first performing a depthwise spatial
#' convolution (which acts on each input channel separately) followed by a
#' pointwise convolution which mixes together the resulting output channels. The
#' `depth_multiplier` argument controls how many output channels are generated
#' per input channel in the depthwise step. Intuitively, separable convolutions
#' can be understood as a way to factorize a convolution kernel into two smaller
#' kernels, or as an extreme version of an Inception block.
#'
#' @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 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 padding one of `"valid"` or `"same"` (case-insensitive).
#' @param depth_multiplier The number of depthwise convolution output channels
#' for each input channel. The total number of depthwise convolution output
#' channels will be equal to `filterss_in * depth_multiplier`.
#' @param depthwise_initializer Initializer for the depthwise kernel matrix.
#' @param pointwise_initializer Initializer for the pointwise kernel matrix.
#' @param depthwise_regularizer Regularizer function applied to the depthwise
#' kernel matrix.
#' @param pointwise_regularizer Regularizer function applied to the depthwise
#' kernel matrix.
#' @param depthwise_constraint Constraint function applied to the depthwise
#' kernel matrix.
#' @param pointwise_constraint Constraint function applied to the pointwise
#' kernel matrix.
#'
#' @section Input shape: 4D tensor with shape: `(batch, channels, rows, cols)`
#' if data_format='channels_first' or 4D tensor with shape: `(batch, rows,
#' cols, channels)` if data_format='channels_last'.
#'
#' @section Output shape: 4D tensor with shape: `(batch, filters, new_rows,
#' new_cols)` if data_format='channels_first' or 4D tensor with shape:
#' `(batch, new_rows, new_cols, filters)` if data_format='channels_last'.
#' `rows` and `cols` values might have changed due to padding.
#'
#' @family convolutional layers
#'
#' @export
layer_separable_conv_2d <- function(x, filters, kernel_size, strides = c(1L, 1L), padding = "valid", data_format = NULL,
depth_multiplier = 1L, activation = NULL, use_bias = TRUE,
depthwise_initializer = "glorot_uniform", pointwise_initializer = "glorot_uniform", bias_initializer = "zeros",
depthwise_regularizer = NULL, pointwise_regularizer = NULL, bias_regularizer = NULL, activity_regularizer = NULL,
depthwise_constraint = NULL, pointwise_constraint = NULL, bias_constraint = NULL,
batch_size = NULL, name = NULL, trainable = NULL, weights = NULL) {
call_layer(keras$layers$SeparableConv2D, x, list(
filters = as.integer(filters),
kernel_size = as_integer_tuple(kernel_size),
strides = as_integer_tuple(strides),
padding = padding,
data_format = data_format,
depth_multiplier = as.integer(depth_multiplier),
activation = activation,
use_bias = use_bias,
depthwise_initializer = depthwise_initializer,
pointwise_initializer = pointwise_initializer,
bias_initializer = bias_initializer,
depthwise_regularizer = depthwise_regularizer,
pointwise_regularizer = pointwise_regularizer,
bias_regularizer = bias_regularizer,
activity_regularizer = activity_regularizer,
depthwise_constraint = depthwise_constraint,
pointwise_constraint = pointwise_constraint,
bias_constraint = bias_constraint,
batch_size = as_nullable_integer(batch_size),
name = name,
trainable = trainable,
weights = weights
))
}
#' Upsampling layer for 1D inputs.
#'
#' Repeats each temporal step `size` times along the time axis.
#'
#' @inheritParams layer_dense
#'
#' @param size integer. Upsampling factor.
#'
#' @section Input shape: 3D tensor with shape: `(batch, steps, features)`.
#'
#' @section Output shape: 3D tensor with shape: `(batch, upsampled_steps,
#' features)`.
#'
#' @family convolutional layers
#'
#' @export
layer_upsampling_1d <- function(x, size = 2L,
batch_size = NULL, name = NULL, trainable = NULL, weights = NULL) {
call_layer(keras$layers$UpSampling1D, x, list(
size = as.integer(size),
batch_size = as_nullable_integer(batch_size),
name = name,
trainable = trainable,
weights = weights
))
}
#' Upsampling layer for 2D inputs.
#'
#' Repeats the rows and columns of the data by `size[[0]]` and `size[[1]]` respectively.
#'
#' @inheritParams layer_conv_2d
#'
#' @param size int, or list of 2 integers. The upsampling factors for rows and
#' columns.
#'
#' @section Input shape:
#' 4D tensor with shape:
#' - If `data_format` is `"channels_last"`: `(batch, rows, cols, channels)`
#' - If `data_format` is `"channels_first"`: `(batch, channels, rows, cols)`
#'
#' @section Output shape:
#' 4D tensor with shape:
#' - If `data_format` is `"channels_last"`: `(batch, upsampled_rows, upsampled_cols, channels)`
#' - If `data_format` is `"channels_first"`: `(batch, channels, upsampled_rows, upsampled_cols)`
#'
#' @family convolutional layers
#'
#' @export
layer_upsampling_2d <- function(x, size = c(2L, 2L), data_format = NULL,
batch_size = NULL, name = NULL, trainable = NULL, weights = NULL) {
call_layer(keras$layers$UpSampling2D, x, list(
size = as.integer(size),
data_format = data_format,
batch_size = as_nullable_integer(batch_size),
name = name,
trainable = trainable,
weights = weights
))
}
#' Upsampling layer for 3D inputs.
#'
#' Repeats the 1st, 2nd and 3rd dimensions of the data by `size[[0]]`, `size[[1]]` and
#' `size[[2]]` respectively.
#'
#' @inheritParams layer_upsampling_1d
#'
#' @param size int, or list of 3 integers. The upsampling factors for dim1, dim2
#' and dim3.
#' @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, spatial_dim1,
#' spatial_dim2, spatial_dim3, channels)` while `channels_first` corresponds
#' to inputs with shape `(batch, channels, spatial_dim1, spatial_dim2,
#' spatial_dim3)`. 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".
#'
#' @section Input shape:
#' 5D tensor with shape:
#' - If `data_format` is `"channels_last"`: `(batch, dim1, dim2, dim3, channels)`
#' - If `data_format` is `"channels_first"`: `(batch, channels, dim1, dim2, dim3)`
#'
#' @section Output shape:
#' 5D tensor with shape:
#' - If `data_format` is `"channels_last"`: `(batch, upsampled_dim1, upsampled_dim2, upsampled_dim3, channels)`
#' - If `data_format` is `"channels_first"`: `(batch, channels, upsampled_dim1, upsampled_dim2, upsampled_dim3)`
#'
#' @family convolutional layers
#'
#' @export
layer_upsampling_3d <- function(x, size= c(2L, 2L, 2L), data_format = NULL,
batch_size = NULL, name = NULL, trainable = NULL, weights = NULL) {
call_layer(keras$layers$UpSampling3D, x, list(
size = as.integer(size),
data_format = data_format,
batch_size = as_nullable_integer(batch_size),
name = name,
trainable = trainable,
weights = weights
))
}
#' Zero-padding layer for 1D input (e.g. temporal sequence).
#'
#' @inheritParams layer_conv_2d
#'
#' @param padding int, or list of int (length 2)
#' - If int: How many zeros to add at the beginning and end of the padding dimension (axis 1).
#' - If list of int (length 2): How many zeros to add at the beginning and at the end of the padding dimension (`(left_pad, right_pad)`).
#'
#' @section Input shape:
#' 3D tensor with shape `(batch, axis_to_pad, features)`
#'
#' @section Output shape:
#' 3D tensor with shape `(batch, padded_axis, features)`
#'
#' @family convolutional layers
#'
#' @export
layer_zero_padding_1d <- function(x, padding = 1L,
batch_size = NULL, name = NULL, trainable = NULL, weights = NULL) {
call_layer(keras$layers$ZeroPadding1D, x, list(
padding = as.integer(padding),
batch_size = as_nullable_integer(batch_size),
name = name,
trainable = trainable,
weights = weights
))
}
#' Zero-padding layer for 2D input (e.g. picture).
#'
#' This layer can add rows and columns or zeros at the top, bottom, left and
#' right side of an image tensor.
#'
#' @inheritParams layer_conv_2d
#' @inheritParams layer_zero_padding_1d
#'
#' @param padding int, or list of 2 ints, or list of 2 lists of 2 ints.
#' - If int: the same symmetric padding is applied to width and height.
#' - If list of 2 ints: interpreted as two different symmetric padding values for height
#' and width: `(symmetric_height_pad, symmetric_width_pad)`.
#' - If list of 2 lists of 2 ints: interpreted as `((top_pad, bottom_pad), (left_pad,
#' right_pad))`
#'
#' @section Input shape: 4D tensor with shape:
#' - If `data_format` is `"channels_last"`: `(batch, rows, cols, channels)`
#' - If `data_format` is `"channels_first"`: `(batch, channels, rows, cols)`
#'
#' @section Output shape: 4D tensor with shape:
#' - If `data_format` is `"channels_last"`: `(batch, padded_rows, padded_cols, channels)`
#' - If `data_format` is `"channels_first"`: `(batch, channels, padded_rows, padded_cols)`
#'
#' @family convolutional layers
#'
#' @export
layer_zero_padding_2d <- function(x, padding = c(1L, 1L), data_format = NULL,
batch_size = NULL, name = NULL, trainable = NULL, weights = NULL) {
call_layer(keras$layers$ZeroPadding2D, x, list(
padding = normalize_padding(padding, 2L),
data_format = data_format,
batch_size = as_nullable_integer(batch_size),
name = name,
trainable = trainable,
weights = weights
))
}
#' Zero-padding layer for 3D data (spatial or spatio-temporal).
#'
#' @inheritParams layer_zero_padding_1d
#'
#' @param padding int, or list of 3 ints, or list of 3 lists of 2 ints.
#' - If int: the same symmetric padding is applied to width and height.
#' - If list of 3 ints: interpreted as three different symmetric padding values:
#' `(symmetric_dim1_pad, symmetric_dim2_pad, symmetric_dim3_pad)`.
#' - If list of 3 lists of 2 ints: interpreted as `((left_dim1_pad,
#' right_dim1_pad), (left_dim2_pad, right_dim2_pad), (left_dim3_pad,
#' right_dim3_pad))`
#' @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, spatial_dim1,
#' spatial_dim2, spatial_dim3, channels)` while `channels_first` corresponds
#' to inputs with shape `(batch, channels, spatial_dim1, spatial_dim2,
#' spatial_dim3)`. 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".
#'
#' @section Input shape: 5D tensor with shape:
#' - If `data_format` is `"channels_last"`: `(batch, first_axis_to_pad, second_axis_to_pad,
#' third_axis_to_pad, depth)`
#' - If `data_format` is `"channels_first"`: `(batch, depth, first_axis_to_pad, second_axis_to_pad, third_axis_to_pad)`
#'
#' @section Output shape: 5D tensor with shape:
#' - If `data_format` is `"channels_last"`: `(batch, first_padded_axis, second_padded_axis,
#' third_axis_to_pad, depth)`
#' - If `data_format` is `"channels_first"`: `(batch, depth, first_padded_axis, second_padded_axis, third_axis_to_pad)`
#'
#' @family convolutional layers
#'
#' @export
layer_zero_padding_3d <- function(x, padding = c(1L, 1L, 1L), data_format = NULL,
batch_size = NULL, name = NULL, trainable = NULL, weights = NULL) {
call_layer(keras$layers$ZeroPadding3D, x, list(
padding = normalize_padding(padding, 3L),
data_format = data_format,
batch_size = as_nullable_integer(batch_size),
name = name,
trainable = trainable,
weights = weights
))
}
#' Cropping layer for 1D input (e.g. temporal sequence).
#'
#' It crops along the time dimension (axis 1).
#'
#' @inheritParams layer_dense
#'
#' @param cropping int or list of int (length 2) How many units should be
#' trimmed off at the beginning and end of the cropping dimension (axis 1). If
#' a single int is provided, the same value will be used for both.
#'
#' @section Input shape: 3D tensor with shape `(batch, axis_to_crop, features)`
#'
#' @section Output shape: 3D tensor with shape `(batch, cropped_axis, features)`
#'
#' @family convolutional layers
#'
#' @export
layer_cropping_1d <- function(x, cropping = c(1L, 1L),
batch_size = NULL, name = NULL, trainable = NULL, weights = NULL) {
call_layer(keras$layers$Cropping1D, x, list(
cropping = as.integer(cropping),
batch_size = as_nullable_integer(batch_size),
name = name,
trainable = trainable,
weights = weights
))
}
#' Cropping layer for 2D input (e.g. picture).
#'
#' It crops along spatial dimensions, i.e. width and height.
#'
#' @inheritParams layer_conv_2d
#' @inheritParams layer_cropping_1d
#'
#' @param cropping int, or list of 2 ints, or list of 2 lists of 2 ints.
#' - If int: the same symmetric cropping is applied to width and height.
#' - If list of 2 ints: interpreted as two different symmetric cropping values for
#' height and width: `(symmetric_height_crop, symmetric_width_crop)`.
#' - If list of 2 lists of 2 ints: interpreted as `((top_crop, bottom_crop), (left_crop,
#' right_crop))`
#'
#' @section Input shape: 4D tensor with shape:
#' - If `data_format` is `"channels_last"`: `(batch, rows, cols, channels)`
#' - If `data_format` is `"channels_first"`: `(batch, channels, rows, cols)`
#'
#' @section Output shape: 4D tensor with shape:
#' - If `data_format` is `"channels_last"`: `(batch, cropped_rows, cropped_cols, channels)`
#' - If `data_format` is `"channels_first"`: `(batch, channels, cropped_rows, cropped_cols)`
#'
#' @family convolutional layers
#'
#' @export
layer_cropping_2d <- function(x, cropping = list(c(0L, 0L), c(0L, 0L)), data_format = NULL,
batch_size = NULL, name = NULL, trainable = NULL, weights = NULL) {
call_layer(keras$layers$Cropping2D, x, list(
cropping = normalize_cropping(cropping, 2L),
data_format = data_format,
batch_size = as_nullable_integer(batch_size),
name = name,
trainable = trainable,
weights = weights
))
}
#' Cropping layer for 3D data (e.g. spatial or spatio-temporal).
#'
#' @inheritParams layer_cropping_1d
#'
#' @param cropping int, or list of 3 ints, or list of 3 lists of 2 ints.
#' - If int: the same symmetric cropping is applied to width and height.
#' - If list of 3 ints: interpreted as two different symmetric cropping values for
#' height and width: `(symmetric_dim1_crop, symmetric_dim2_crop,
#' symmetric_dim3_crop)`.
#' - If list of 3 lists of 2 ints: interpreted as
#' `((left_dim1_crop, right_dim1_crop), (left_dim2_crop, right_dim2_crop),
#' (left_dim3_crop, right_dim3_crop))`
#' @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, spatial_dim1,
#' spatial_dim2, spatial_dim3, channels)` while `channels_first` corresponds
#' to inputs with shape `(batch, channels, spatial_dim1, spatial_dim2,
#' spatial_dim3)`. 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".
#'
#' @section Input shape: 5D tensor with shape:
#' - If `data_format` is `"channels_last"`: `(batch, first_axis_to_crop, second_axis_to_crop,
#' third_axis_to_crop, depth)`
#' - If `data_format` is `"channels_first"`:
#' `(batch, depth, first_axis_to_crop, second_axis_to_crop,
#' third_axis_to_crop)`
#'
#' @section Output shape: 5D tensor with shape:
#' - If `data_format` is `"channels_last"`: `(batch, first_cropped_axis, second_cropped_axis,
#' third_cropped_axis, depth)`
#' - If `data_format` is `"channels_first"`: `(batch, depth, first_cropped_axis, second_cropped_axis,
#' third_cropped_axis)`
#'
#' @family convolutional layers
#'
#' @export
layer_cropping_3d <- function(x, cropping = list(c(1L, 1L), c(1L, 1L), c(1L, 1L)), data_format = NULL,
batch_size = NULL, name = NULL, trainable = NULL, weights = NULL) {
call_layer(keras$layers$Cropping3D, x, list(
cropping = normalize_cropping(cropping, 3L),
data_format = data_format,
batch_size = as_nullable_integer(batch_size),
name = name,
trainable = trainable,
weights = weights
))
}
#' Convolutional LSTM.
#'
#' It is similar to an LSTM layer, but the input transformations and recurrent
#' transformations are both convolutional.
#'
#' @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 n integers, specifying the
#' dimensions of the convolution window.
#' @param strides An integer or list of n integers, specifying the strides of
#' the convolution. Specifying any stride value != 1 is incompatible with
#' specifying any `dilation_rate` value != 1.
#' @param padding One of `"valid"` or `"same"` (case-insensitive).
#' @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, time, ...,
#' channels)` while `channels_first` corresponds to inputs with shape `(batch,
#' time, channels, ...)`. 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 dilation_rate An integer or list of n integers, specifying the
#' dilation rate to use for dilated convolution. Currently, specifying any
#' `dilation_rate` value != 1 is incompatible with specifying any `strides`
#' value != 1.
#' @param activation Activation function to use. If you don't specify anything,
#' no activation is applied (ie. "linear" activation: `a(x) = x`).
#' @param recurrent_activation Activation function to use for the recurrent
#' step.
#' @param use_bias Boolean, whether the layer uses a bias vector.
#' @param kernel_initializer Initializer for the `kernel` weights matrix, used
#' for the linear transformation of the inputs..
#' @param recurrent_initializer Initializer for the `recurrent_kernel` weights
#' matrix, used for the linear transformation of the recurrent state..
#' @param bias_initializer Initializer for the bias vector.
#' @param unit_forget_bias Boolean. If TRUE, add 1 to the bias of the forget
#' gate at initialization. Use in combination with `bias_initializer="zeros"`.
#' This is recommended in [Jozefowicz et
#' al.](http://www.jmlr.org/proceedings/papers/v37/jozefowicz15.pdf)
#' @param kernel_regularizer Regularizer function applied to the `kernel`
#' weights matrix.
#' @param recurrent_regularizer Regularizer function applied to the
#' `recurrent_kernel` weights matrix.
#' @param bias_regularizer Regularizer function applied to the bias vector.
#' @param activity_regularizer Regularizer function applied to the output of the
#' layer (its "activation")..
#' @param kernel_constraint Constraint function applied to the `kernel` weights
#' matrix.
#' @param recurrent_constraint Constraint function applied to the
#' `recurrent_kernel` weights matrix.
#' @param bias_constraint Constraint function applied to the bias vector.
#' @param return_sequences Boolean. Whether to return the last output in the
#' output sequence, or the full sequence.
#' @param go_backwards Boolean (default FALSE). If TRUE, rocess the input
#' sequence backwards.
#' @param stateful Boolean (default FALSE). If TRUE, the last state for each
#' sample at index i in a batch will be used as initial state for the sample
#' of index i in the following batch.
#' @param dropout Float between 0 and 1. Fraction of the units to drop for the
#' linear transformation of the inputs.
#' @param recurrent_dropout Float between 0 and 1. Fraction of the units to drop
#' for the linear transformation of the recurrent state.
#'
#' @section Input shape:
#' - if data_format='channels_first' 5D tensor with shape:
#' `(samples,time, channels, rows, cols)`
#' - if data_format='channels_last' 5D
#' tensor with shape: `(samples,time, rows, cols, channels)`
#'
#' @section References:
#' - [Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting](http://arxiv.org/abs/1506.04214v1)
#' The current implementation does not include the feedback loop on the cells
#' output
#'
#' @family convolutional layers
#'
#' @export
layer_conv_lstm_2d <- function(x, filters, kernel_size, strides = c(1L, 1L), padding = "valid", data_format = NULL,
dilation_rate = c(1L, 1L), activation = "tanh", recurrent_activation = "hard_sigmoid", use_bias = TRUE,
kernel_initializer = "glorot_uniform", recurrent_initializer = "orthogonal", bias_initializer = "zeros",
unit_forget_bias = TRUE, kernel_regularizer = NULL, recurrent_regularizer = NULL, bias_regularizer = NULL,
activity_regularizer = NULL, kernel_constraint = NULL, recurrent_constraint = NULL, bias_constraint = NULL,
return_sequences = FALSE, go_backwards = FALSE, stateful = FALSE, dropout = 0.0, recurrent_dropout = 0.0,
batch_size = NULL, name = NULL, trainable = NULL, weights = NULL) {
call_layer(keras$layers$ConvLSTM2D, x, list(
filters = as.integer(filters),
kernel_size = as_integer_tuple(kernel_size),
strides = as_integer_tuple(strides),
data_format = data_format,
dilation_rate = as.integer(dilation_rate),
activation = activation,
recurrent_activation = recurrent_activation,
use_bias = use_bias,
kernel_initializer = kernel_initializer,
recurrent_initializer = recurrent_initializer,
bias_initializer = bias_initializer,
unit_forget_bias = unit_forget_bias,
kernel_regularizer = kernel_regularizer,
recurrent_regularizer = recurrent_regularizer,
bias_regularizer = bias_regularizer,
activity_regularizer = activity_regularizer,
kernel_constraint = kernel_constraint,
recurrent_constraint = recurrent_constraint,
bias_constraint = bias_constraint,
return_sequences = return_sequences,
go_backwards = go_backwards,
stateful = stateful,
dropout = dropout,
recurrent_dropout = recurrent_dropout,
batch_size = as_nullable_integer(batch_size),
name = name,
trainable = trainable,
weights = weights
))
}
normalize_padding <- function(padding, dims) {
normalize_scale("padding", padding, dims)
}
normalize_cropping <- function(cropping, dims) {
normalize_scale("cropping", cropping, dims)
}
normalize_scale <- function(name, scale, dims) {
# validate and marshall scale argument
throw_invalid_scale <- function() {
stop(name, " must be a list of ", dims, " integers or list of ", dims, " lists of 2 integers",
call. = FALSE)
}
# if all of the individual items are numeric then cast to integer vector
if (all(sapply(scale, function(x) length(x) == 1 && is.numeric(x)))) {
as.integer(scale)
} else if (is.list(scale)) {
lapply(scale, function(x) {
if (length(x) != 2)
throw_invalid_scale()
as.integer(x)
})
} else {
throw_invalid_scale()
}
}