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)
whilechannels_first
corresponds to inputs with shape(batch_size, channels, height, width)
. It defaults to theimage_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).