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
)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.
Integer, the dimensionality of the output space (i.e. the number of output filters in the convolution).
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.
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.
one of "valid" or "same" (case-insensitive).
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".
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.
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 function to use. If you don't specify anything,
no activation is applied (ie. "linear" activation: a(x) = x).
Boolean, whether the layer uses a bias vector.
Initializer for the depthwise kernel matrix.
Initializer for the pointwise kernel matrix.
Initializer for the bias vector.
Regularizer function applied to the depthwise kernel matrix.
Regularizer function applied to the pointwise kernel matrix.
Regularizer function applied to the bias vector.
Regularizer function applied to the output of the layer (its "activation")..
Constraint function applied to the depthwise kernel matrix.
Constraint function applied to the pointwise kernel matrix.
Constraint function applied to the bias vector.
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.
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.
Fixed batch size for layer
The data type expected by the input, as a string (float32,
float64, int32...)
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.
Whether the layer weights will be updated during training.
Initial weights for layer.
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'.
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.
Other convolutional layers:
layer_conv_1d_transpose(),
layer_conv_1d(),
layer_conv_2d_transpose(),
layer_conv_2d(),
layer_conv_3d_transpose(),
layer_conv_3d(),
layer_conv_lstm_2d(),
layer_cropping_1d(),
layer_cropping_2d(),
layer_cropping_3d(),
layer_depthwise_conv_1d(),
layer_depthwise_conv_2d(),
layer_separable_conv_1d(),
layer_upsampling_1d(),
layer_upsampling_2d(),
layer_upsampling_3d(),
layer_zero_padding_1d(),
layer_zero_padding_2d(),
layer_zero_padding_3d()