Complex Convolution 2D Transpose

class ComplexConv2DTranspose

Complex Transposed convolution layer. Sometimes (wrongly) called Deconvolution.

The need for transposed convolutions generally arises from the desire to use a transformation going in the opposite direction of a normal convolution, i.e., from something that has the shape of the output of some convolution to something that has the shape of its input while maintaining a connectivity pattern that is compatible with said convolution.

__init__(self, filters, kernel_size, strides, padding, dtype, output_padding, data_format, dilation_rate, activation, use_bias, kernel_initializer, bias_initializer, kernel_regularizer, bias_regularizer, activity_regularizer, kernel_constraint, bias_constraint, **kwargs)
Parameters:
  • filters – Integer, the dimensionality of the output space (i.e. the number of output filters in the convolution).
  • kernel_size – An integer or tuple/list of 2 integers, specifying the height and width of the 2D convolution window. Can be a single integer to specify the same value for all spatial dimensions.
  • strides – An integer or tuple/list of 2 integers, specifying the strides of the convolution along the height and width. 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). "valid" means no padding. "same" results in padding evenly to the left/right or up/down of the input such that output has the same height/width dimension as the input.
  • output_padding – An integer or tuple/list of 2 integers, specifying the amount of padding along the height and width of the output tensor. Can be a single integer to specify the same value for all spatial dimensions. The amount of output padding along a given dimension must be lower than the stride along that same dimension. If set to None (default), the output shape is inferred.
  • data_format – A string, one of :code:channels_last (default) or channels_first. The ordering of the dimensions in the inputs. channels_last corresponds to inputs with shape (batch_size, height, width, channels) while channels_first corresponds to inputs with shape (batch_size, 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 tuple/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 (see keras.activations).
  • use_bias – Boolean, whether the layer uses a bias vector.
  • kernel_initializer – Initializer for the kernel weights matrix (see keras.initializers).
  • bias_initializer – Initializer for the bias vector (see keras.initializers).
  • kernel_regularizer – Regularizer function applied to the kernel weights matrix (see keras.regularizers).
  • bias_regularizer – Regularizer function applied to the bias vector (see keras.regularizers).
  • activity_regularizer – Regularizer function applied to the output of the layer (its “activation”) (see keras.regularizers).
  • kernel_constraint – Constraint function applied to the kernel matrix (see keras.constraints).
  • bias_constraint – Constraint function applied to the bias vector (see keras.constraints).