SeparableConv2D

 parameters : layer parameters.

 n_filters : integer, the dimensionality of the output space.
Default value “3”.
size : array integer,  specify the height and width of the 2D convolution window. Can be a single integer to specify the same value for all spatial dimensions.
Default value “[3,3]”.
stride : array integer, specify the strides of the convolution along the height and width. Can be a single integer to specify the same value for all spatial dimensions.  Current implementation only supports equal length strides in the row and column dimensions.
Default value “[1,1]”.
 activation : enum, activation function to use.
Default value “relu”.
 optimizer :

 algorithm : enum, (name of optimizer) for optimizer instance.
Default value “adam”.
 learning_rate : float, define the learning rate to use.
Default value “0.001”.
 beta_1 : float, define the exponential decay rate for the 1st moment estimates.
Default value “0.9”.
 beta_2 : float, define the exponential decay rate for the 2nd moment estimates.
Default value “0.999”.

use_bias? : boolean, whether the layer uses a bias vector.
Default value “True”.
 padding : boolean, False = “valid” means no padding. True = “same” results in padding with zeros evenly to the left/right or up/down of the input such that output has the same height/width dimension as the input.
Default value “False”.
 data_format : enum, one of channels_last or channels_first (default) . The ordering of the dimensions in the inputs. channel_last corresponds to inputs with shape (batch, steps, features) while channels_first corresponds to inputs with shape (batch, features, steps).
Default value “channels_first”.
depthwise_filter_initializer : enum, for the depthwise convolution kernel.
Default value “glorot_uniform”.
pointwise_filter_initializer : enum, for the pointwise convolution kernel.
Default value “glorot_uniform”.
 bias_initializer : enum, initializer for the bias vector.
Default value “zero”.
 training? : boolean, whether the layer is in training mode (can store data for backward).
Default value “True”.
 store? : boolean, whether the layer stores the last iteration gradient (accessible via the “get_gradients” function).
Default value “False”.
 update? : boolean, whether the layer’s variables should be updated during backward. Equivalent to freeze the layer.
Default value “True”.
 lda_coeff : float, defines the coefficient by which the loss derivative will be multiplied before being sent to the previous layer (since during the backward run we go backwards).
Default value “1”.