decompose_graph
Applicability
Product |
Supported |
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Atlas 350 Accelerator Card |
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Description
Fine-tunes a decomposed model by decomposing the graph in the training code.
Prototype
1 | add_ops = decompose_graph(save_path, graph=None) |
Parameters
Parameter |
Input/Output |
Description |
|---|---|---|
save_path |
Input |
Path for storing the file after tensor decomposition using the auto_decomposition API. A string. |
graph |
Input |
(Optional) Graph to be decomposed. If it is not specified or it is set to None, decomposes the current graph. A tf.Graph. Default: None |
add_ops |
Returns |
List of names of new convolutional layers after tensor decomposition. A list. |
Returns
Returns a list of names of new convolutional layers after tensor decomposition.
Restrictions
- The auto_decomposition API has been called to decompose the model.
- This API is used based on the training code. The model file passed to the auto_decomposition API must be obtained based on the same training code.
- This API modifies only the graph, but does not modify a variable whose convolution kernel has been referenced. If a convolution kernel has been referenced by a variable before being decomposed, the variable should not be used.
Example
In the user training code:
1 2 3 4 5 6 | from amct_tensorflow.tensor_decompose import decompose_graph # User code for constructing the network graph... decompose_graph(save_path='decomposed_path/model') # User code for building an optimizer and applying the optimizer... # ... # Code for loading the model weights generated after decomposition before training. (Update the sample code based on your situation.) |