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TensorFlow 2 Network Writer

KNIME Deep Learning - TensorFlow 2 Integration version 4.3.1.v202101261634 by KNIME AG, Zurich, Switzerland

This node supports the path flow variable. For further information about file handling in general see the File Handling Guide.

Writes a TensorFlow 2 Model to a file or directory. The file extension determines the format of the written file. If no file extension is given the model is saved in the SavedModel format. If the file extension .h5 is given the model is saved in the HDF5 format. If the file extension .zip is given the model is in the SavedModel format and compressed as a ZIP file .

The KNIME Deep Learning - TensorFlow 2 Integration is developed by KNIME and uses the TensorFlow 2 library. The KNIME Deep Learning - TensorFlow 2 Integration is not endorsed by or otherwise affiliated with Google. TensorFlow, the TensorFlow logo and any related marks are trademarks of Google Inc.

Options

Write to
Select a file system in which you want to store the network. There are four default file system options to choose from:
  • Local File System: Allows you to select a location in your local system.
  • Mountpoint: Allows you to write to a mountpoint. When selected, a new drop-down menu appears to choose the mountpoint. Unconnected mountpoints are greyed out but can still be selected (note that browsing is disabled in this case). Go to the KNIME Explorer and connect to the mountpoint to enable browsing. A mountpoint is displayed in red if it was previously selected but is no longer available. You won't be able to save the dialog as long as you don't select a valid i.e. known mountpoint.
  • Relative to: Allows you to choose whether to resolve the path relative to the current mountpoint, current workflow or the current workflow's data area. When selected, a new drop-down menu appears to choose which of the three options to use.
  • Custom/KNIME URL: Allows to specify a URL (e.g. file://, http:// or knime:// protocol). When selected, a spinner appears that allows you to specify the desired connection and write timeout in milliseconds. In case it takes longer to connect to the host / write the file, the node fails to execute. Browsing is disabled for this option.
It is possible to use other file systems with this node. Therefore, you have to enable the file system connection input port of this node by clicking the ... in the bottom left corner of the node's icon and choose Add File System Connection port .
Afterwards, you can simply connect the desired connector node to this node. The file system connection will then be shown in the drop-down menu. It is greyed out if the file system is not connected in which case you have to (re)execute the connector node first. Note: The default file systems listed above can't be selected if a file system is provided via the input port.
Mode
Select whether you want to store the network as a file (HDF5, ZIP) or folder (SavedModel).
File/Folder/URL
Enter a URL when writing to Custom/KNIME URL, otherwise enter a path to a file. The required syntax of a path depends on the chosen file system, such as "C:\path\to\file" (Local File System on Windows) or "/path/to/file" (Local File System on Linux/MacOS and Mountpoint). For file systems connected via input port, the node description of the respective connector node describes the required path format. You can also choose a previously selected file from the drop-down list, or select a location from the "Browse..." dialog. Note that browsing is disabled in some cases:
  • Custom/KNIME URL: Browsing is never enabled.
  • Mountpoint: Browsing is disabled if the selected mountpoint isn't connected. Go to the KNIME Explorer and connect to the mountpoint to enable browsing.
  • File systems provided via input port: Browsing is disabled if the connector node hasn't been executed since the workflow has been opened. (Re)execute the connector node to enable browsing.
The location can be exposed as or automatically set via a path flow variable.
Create missing folders
Select if the folders of the selected output location should be created if they do not already exist. If this option is unchecked, the node will fail if a folder does not exist.
If exists
Specify the behavior of the node in case the output file already exists.
  • Overwrite: Will replace any existing file.
  • Fail: Will issue an error during the node's execution (to prevent unintentional overwrite).
Save optimizer state
Check this option to save the optimizer state with the network. Note: Depending on the optimizer this can increase the model size considerably.

Input Ports

Icon
The TensorFlow 2 network.

Best Friends (Incoming)

Best Friends (Outgoing)

Workflows

Installation

To use this node in KNIME, install KNIME Deep Learning - TensorFlow 2 Integration from the following update site:

KNIME 4.3

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

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Developers

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