0 ×

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

Fully-connected RNN where the output is to be fed back to input. Corresponds to the SimpleRNN Keras 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.
- Input tensor
- The tensor to use as input for the layer.
- Hidden state tensor
- The tensor to use as initial hidden state in case the corresponding port is connected.
- Units
- Dimensionality of the output space.
- Activation
- The activation function to use on the input transformations.
- Use bias
- If checked, a bias vector will be used.
- Return sequences
- Whether to return the last output in the output sequence or the full output sequence. If selected the output will have shape [time, units] otherwise the output will have shape [units].
- Return state
- Whether to return the hidden state in addition to the layer output. If selected the layer returns two tensors, the normal output and the hidden state of the layer. If sequences are returned, this also applies to the hidden state.
- Dropout
- Fraction of the units to drop for the linear transformation of the input.
- Recurrent dropout
- Fraction of the units to drop for the linear transformation of the recurrent state.
- Go backwards
- Whether to go backwards in time i.e. read the input sequence backwards.
- Unroll
- Whether to unroll the network i.e. convert it in a feed-forward network that reuses the layer's weights for each timestep. Unrolling can speed up an RNN but it's more memory-expensive and only suitable for short sequences. If the layer is not unrolled, a symbolic loop is used.

- Kernel initializer
- Initializer for the weight matrix used for the linear transformations of the input. See initializers for details on the available initializers.
- Recurrent initializer
- Initializer for the weight matrix used for the linear transformation of the recurrent connection. See initializers for details on the available initializers.
- Bias initializer
- Initializer for the bias vector (if a bias is used). See initializers for details on the available initializers.

- Kernel regularizer
- Regularizer function applied to the weight matrix. See regularizers for details on the available regularizers.
- Recurrent regularizer
- Regularizer function applied to the weight matrix for the recurrent connection. See regularizers for details on the available regularizers.
- Bias regularizer
- Regularizer function applied to the bias vector. See regularizers for details on the available regularizers.
- Activity regularizer
- Regularizer function applied to the output of the layer i.e. its activation. See regularizers for details on the available regularizers.

- Kernel constraint
- Constraint on the weight matrix for the input connection. See constraints for details on the available constraints.
- Recurrent constraint
- Constraint on the weight matrix for the recurrent connection. See constraints for details on the available constraints.
- Bias constraint
- Constraint on the bias vector. See constraints for details on the available constraints.

- The Keras deep learning network to which to add a
`Simple RNN`

layer. - An optional Keras deep learning network that provides the initial state for this
`Simple RNN`

layer. The hidden state must have shape [units], where units must correspond to the number of units this layer uses.

- Keras Activation Layer (40 %)
- Keras Input Layer (20 %)
~~Keras LSTM Layer~~(20 %) Deprecated- Keras Network Reader (20 %)

- Keras Network Learner (63 %)
~~Keras LSTM Layer~~(13 %) Deprecated- Keras Activation Layer (13 %)
~~Keras Dense Layer~~(13 %) Deprecated

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.