Keras Transposed Convolution 2D Layer

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.

Options

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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Links

• KNIME Deep Learning Keras Integration

  www.knime.com/deeplearning/keras

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