Layer factory function to create a 2D convolution transpose layer with optional non-linearity. Same as ConvolutionTranspose() except that filter_shape is verified to be 2-dimensional. See ConvolutionTranspose() for extensive documentation.
1 2 3 4 | ConvolutionTranspose2D(filter_shape, num_filters = NULL,
activation = activation_identity, init = init_glorot_uniform(),
pad = FALSE, strides = 1, bias = TRUE, init_bias = 0,
output_shape = NULL, name = "")
|
filter_shape |
int or list of int - shape (spatial extent) of the receptive field, not including the input feature-map depth. E.g. (3,3) for a 2D convolution. |
num_filters |
(int, defaults to None) – number of filters (output feature-map depth), or () to denote scalar output items (output shape will have no depth axis). |
activation |
(Function) - optional activation Function |
init |
(scalar or matrix or initializer, defaults to init_glorot_uniform()) – initial value of weights W |
pad |
(bool or list of bools) – if False, then the operation will be shifted over the “valid” area of input, that is, no value outside the area is used. If pad=True on the other hand, the operation will be applied to all input positions, and positions outside the valid region will be considered containing zero. Use a list to specify a per-axis value. |
strides |
(int or tuple of ints, defaults to 1) – stride of the operation. Use a list of ints to specify a per-axis value. |
bias |
(bool) – whether to include bias |
init_bias |
(scalar or matrix or initializer, defaults to 0) – initial value of weights b |
output_shape |
(int or tuple of ints) – output shape. When strides > 2, the output shape is non-deterministic. User can specify the wanted output shape. Note the specified shape must satisify the condition that if a convolution is perform from the output with the same setting, the result must have same shape as the input. |
name |
string (optional) the name of the Function instance in the network |
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