layer_depthwise_conv_2d: Depthwise separable 2D convolution.
In keras: R Interface to 'Keras'

layer_depthwise_conv_2d

R Documentation

Depthwise separable 2D convolution.

Description

Depthwise Separable convolutions consists in performing just the first step in a depthwise spatial convolution (which acts on each input channel separately). The depth_multiplier argument controls how many output channels are generated per input channel in the depthwise step.

Usage

layer_depthwise_conv_2d(
  object,
  kernel_size,
  strides = c(1, 1),
  padding = "valid",
  depth_multiplier = 1,
  data_format = NULL,
  dilation_rate = c(1, 1),
  activation = NULL,
  use_bias = TRUE,
  depthwise_initializer = "glorot_uniform",
  bias_initializer = "zeros",
  depthwise_regularizer = NULL,
  bias_regularizer = NULL,
  activity_regularizer = NULL,
  depthwise_constraint = NULL,
  bias_constraint = NULL,
  input_shape = NULL,
  batch_input_shape = NULL,
  batch_size = NULL,
  dtype = 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.
`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`	one of `"valid"` or `"same"` (case-insensitive).
`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 `filters_in * depth_multiplier`.
`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".
`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.
`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.
`depthwise_initializer`	Initializer for the depthwise kernel matrix.
`bias_initializer`	Initializer for the bias vector.
`depthwise_regularizer`	Regularizer function applied to the depthwise kernel matrix.
`bias_regularizer`	Regularizer function applied to the bias vector.
`activity_regularizer`	Regularizer function applied to the output of the layer (its "activation")..
`depthwise_constraint`	Constraint function applied to the depthwise kernel matrix.
`bias_constraint`	Constraint function applied to the bias vector.
`input_shape`	Dimensionality of the input (integer) not including the samples axis. This argument is required when using this layer as the first layer in a model.
`batch_input_shape`	Shapes, including the batch size. For instance, `batch_input_shape=c(10, 32)` indicates that the expected input will be batches of 10 32-dimensional vectors. `batch_input_shape=list(NULL, 32)` indicates batches of an arbitrary number of 32-dimensional vectors.
`batch_size`	Fixed batch size for layer
`dtype`	The data type expected by the input, as a string (`float32`, `float64`, `int32`...)
`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.

Other convolutional layers: layer_conv_1d(), layer_conv_1d_transpose(), layer_conv_2d(), layer_conv_2d_transpose(), layer_conv_3d(), layer_conv_3d_transpose(), layer_conv_lstm_2d(), layer_cropping_1d(), layer_cropping_2d(), layer_cropping_3d(), layer_depthwise_conv_1d(), layer_separable_conv_1d(), layer_separable_conv_2d(), layer_upsampling_1d(), layer_upsampling_2d(), layer_upsampling_3d(), layer_zero_padding_1d(), layer_zero_padding_2d(), layer_zero_padding_3d()