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.

    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

    object

    Model or layer object

    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.

    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.

    See also