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TensorFlow 2 Network Reader

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.

Reads a TensorFlow 2 model from a file or folder. The file/folder must be:

  • A HDF5 (.h5) file containing a (TensorFlow) Keras Model.
  • A SavedModel file containing a TensorFlow Keras Model.
  • A ZIP file of the directory of a SavedModel containing a TensorFlow Keras Model.

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

Read from
Select a file system which stores the model you want to read. There are four default file system options to choose from:
  • Local File System: Allows you to select a file/folder from your local system.
  • Mountpoint: Allows you to read from 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 two 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 read timeout in milliseconds. Browsing is disabled for this option.
To read from other file systems, click on ... in the bottom left corner of the node icon followed by Add File System Connection port. Afterwards, connect the desired file system connector node to the newly added input port. 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 the network you want to read is stored as a file (HDF5 or ZIP) or as a folder (SavedModel).
File/Folder/URL
Enter a URL when reading from 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.

Output 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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