0 ×

**KNIME Deep Learning - Keras Integration** version **4.3.0.v202012011122** by **KNIME AG, Zurich, Switzerland**

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.

- Keras Transposed Convolution 2D Layer (36 %)
- Keras Reshape Layer (29 %)
- Keras Upsampling 2D Layer (18 %)
- Merge Variables (7 %)
- Keras Leaky ReLU Layer (4 %)
- Show all 7 recommendations

- Keras Transposed Convolution 2D Layer (45 %)
- Keras Network Learner (27 %)
- Keras Dropout Layer (14 %)
- Keras Convolution 2D Layer (5 %)
- Keras Upsampling 2D Layer (5 %)
- Show all 6 recommendations

- 07_GAN_MNIST_Digit_Generator (KNIME Hub)
- U-Net 2D - Decoding Layer (KNIME Hub)
- UNet_Keras_CellSegmentation (KNIME Hub)

To use this node in KNIME, install KNIME Deep Learning - Keras Integration from the following update site:

KNIME 4.3

A zipped version of the software site can be downloaded here.

You don't know what to do with this link? Read our NodePit Product and Node Installation Guide that explains you in detail how to install nodes to your KNIME Analytics Platform.

You want to see the source code for this node? Click the following button and we’ll use our super-powers to find it for you.

Do you have feedback, questions, comments about NodePit, want to support this platform, or want your own nodes or workflows listed here as well? Do you think, the search results could be improved or something is missing? Then please get in touch! Alternatively, you can send us an email to mail@nodepit.com, follow @NodePit on Twitter, or chat on Gitter!

Please note that this is only about NodePit. We do not provide general support for KNIME — please use the KNIME forums instead.