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A much more detailed description of the optimized computational graph, showing all the nodes and the corrisponding operations, is provided as follows:
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Op: BiasAdd -- Name: predictions/BiasAdd
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The accuracy of the '''baseline model''' over the test dataset after applying all transformations:
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Graph accuracy with test dataset: 0.7083
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The accuracy of the '''pruned model''' over the test dataset after applying all transformations:
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In the quantize calibration process, only a small set of images are required to analyze the distribution of activations. Since we are not performing any backpropagation, there is no need to provide any labels either. Depending on the size of the neural network the running time of quantize calibration varies from a few seconds to several minutes.
After calibration, the quantized model is transformed into a DPU deployable model (named as deploy_model.pb for vai_q_tensorflow) which follows the data format of a DPU. This model can be compiled by the Vitis AI compiler and deployed to the DPU. This quantized model cannot be used by the standard TensorFlow framework to evaluate the loss of accuracy; hence in order to do so, a second file is produced (named as quantize_eval_model.pb for vai_q_tensorflow).
For the current application, 100 images are sampled from the train dataset and augmented, resulting in a total number of 1000 images used for calibration. Furthermore, the graph is calibrated providing a batch of 10 images for 100 iterations. Following, the log of vai_q_tensorflow shows the result of the whole quantization process: