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. Corresponds to the Keras Transposed Convolution 2D Layer.

- Name prefix
- The name prefix of the layer. The prefix is complemented by an index suffix to obtain a unique layer name. If this option is unchecked, the name prefix is derived from the layer type.
- Filters
- The dimensionality of the output space (i.e. the number of output filters in the convolution).
- Kernel size
- A tuple of 2 integers, specifying the height and width of the 2D convolution window.
- Strides
- A tuple of 2 integers, specifying the strides of the convolution along the height and width. Specifying any stride value != 1 is incompatible with specifying any dilation_rate value != 1.
- Padding
- Different padding modes to apply to the spatial dimensions
(excluding the batch and channel dimensions)
of the inputs before the
pooling operation. The padding will be done
with zeroes. A detailed
explanation of the different modes can be
found
here
.
- Valid: No padding
- Same: Padding such that the spatial output dimension do not change.
- Full: Padding with kernel size - 1

- Dilation rate
- A tuple/list of 2 integers, specifying the dilation rate to use for dilated convolution. Currently, specifying any dilation_rate value != 1 is incompatible with specifying any stride value != 1.
- Activation function
- The activation function to use.
- Use bias?
- If checked, a bias vector will be used.
- Kernel initializer
- Initializer for the kernel weights matrix.
- Bias initializer
- Initializer for the bias vector.
- Kernel regularizer
- Regularizer function applied to the kernel weights matrix.
- Bias regularizer
- Regularizer function applied to the bias vector.
- Activation regularizer
- Regularizer function applied to the output of the layer (its "activation").
- Kernel constraint
- Constraint function applied to the kernel matrix.
- Bias constraint
- Constraint function applied to the bias vector.

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