layer_separable_conv_2d

Separable 2D convolution.

Description

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.

Usage

 
layer_separable_conv_2d( 
  object, 
  filters, 
  kernel_size, 
  strides = c(1, 1), 
  padding = "valid", 
  data_format = NULL, 
  dilation_rate = 1, 
  depth_multiplier = 1, 
  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, 
  input_shape = NULL, 
  batch_input_shape = NULL, 
  batch_size = NULL, 
  dtype = NULL, 
  name = NULL, 
  trainable = NULL, 
  weights = NULL 
)

Arguments

Arguments	Description
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.
filters	Integer, the dimensionality of the output space (i.e. the number of output 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	one of `"valid"` or `"same"` (case-insensitive).
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.
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`.
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.
pointwise_initializer	Initializer for the pointwise kernel matrix.
bias_initializer	Initializer for the bias vector.
depthwise_regularizer	Regularizer function applied to the depthwise kernel matrix.
pointwise_regularizer	Regularizer function applied to the pointwise 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.
pointwise_constraint	Constraint function applied to the pointwise 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.

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’.

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.