# Model Exporting ## Introduction Export mode is used to convert the trained TensorFlow model into a format that can be used for deployment onto Ambiq's family of SoCs. Currently, the command will convert the TensorFlow model into both TensorFlow Lite (TFL) and TensorFlow Lite for micro-controller (TFLM) variants. The command will also verify the models' outputs match. The activations and weights can be quantized by configuring the `quantization` section in the configuration file or by setting the `quantization` parameter in the code. 1. Load the configuration data (e.g. `configuration.json`) 1. Load the test data (e.g. `test.tfds`) 1. Load the trained model (e.g. `model.keras`) 1. Quantize the model (e.g. `16x8`) 1. Convert the model (e.g. `TFL`, `TFLM`) 1. Verify the models' outputs match 1. Save artifacts (e.g. `model.tflite`) **Example configuration** ```json title="modes-export-1.json" { "name": "sd-2-tcn-sm", "job_dir": "./results/sd-2-tcn-sm", "verbose": 2, "datasets": [{ "name": "cmidss", "params": { "path": "./datasets/cmidss" } }], "feature": { "name": "FS-W-A-5", "sampling_rate": 0.2, "frame_size": 12, "loader": "hdf5", "feat_key": "features", "label_key": "detect_labels", "mask_key": "mask", "feat_cols": null, "save_path": "./datasets/store/fs-w-a-5-60", "params": {} }, "sampling_rate": 0.0083333, "frame_size": 240, "num_classes": 2, "class_map": { "0": 0, "1": 1, "2": 1, "3": 1, "4": 1, "5": 1 }, "class_names": ["WAKE", "SLEEP"], "samples_per_subject": 100, "val_samples_per_subject": 100, "test_samples_per_subject": 50, "val_size": 4000, "test_size": 2500, "val_subjects": 0.20, "batch_size": 128, "buffer_size": 10000, "epochs": 200, "steps_per_epoch": 25, "val_steps_per_epoch": 25, "val_metric": "loss", "lr_rate": 1e-3, "lr_cycles": 1, "label_smoothing": 0, "test_metric": "f1", "test_metric_threshold": 0.02, "tflm_var_name": "sk_detect_flatbuffer", "tflm_file": "sk_detect_flatbuffer.h", "backend": "pc", "display_report": true, "quantization": { "qat": false, "mode": "INT8", "io_type": "int8", "concrete": true, "debug": false }, "model_file": "model.keras", "use_logits": false, "architecture": { "name": "tcn", "params": { "input_kernel": [1, 5], "input_norm": "batch", "blocks": [ {"depth": 1, "branch": 1, "filters": 16, "kernel": [1, 5], "dilation": [1, 1], "dropout": 0.10, "ex_ratio": 1, "se_ratio": 4, "norm": "batch"}, {"depth": 1, "branch": 1, "filters": 32, "kernel": [1, 5], "dilation": [1, 2], "dropout": 0.10, "ex_ratio": 1, "se_ratio": 4, "norm": "batch"}, {"depth": 1, "branch": 1, "filters": 48, "kernel": [1, 5], "dilation": [1, 4], "dropout": 0.10, "ex_ratio": 1, "se_ratio": 4, "norm": "batch"}, {"depth": 1, "branch": 1, "filters": 64, "kernel": [1, 5], "dilation": [1, 8], "dropout": 0.10, "ex_ratio": 1, "se_ratio": 4, "norm": "batch"} ], "output_kernel": [1, 5], "include_top": true, "use_logits": true, "model_name": "tcn" } } } ``` ```mermaid graph TD A("`Load configuration __TaskParams__ `") B("`Load test data `") C("`Load trained __model__ `") D("`Quantize __model__ `") E("`Convert __model__ `") F("`Verify __outputs__ `") G("`Save __artifacts__ `") A ==> B B ==> C subgraph CF["Export"] C ==> D D ==> E E ==> F end F ==> G ``` --- ## Usage ### CLI The following command will export a detect model using the reference configuration. ```bash title="Terminal" sleepkit --task detect --mode export --config ./configuration.json ``` ### Python The model can be exported using the following snippet: ```py title="Python example" linenums="1" from pathlib import Path import sleepkit as sk task = sk.TaskFactory.get("detect") params = sk.TaskParams.model_validate_json( Path("configuration.json").read_text() ) task.export(params) ``` **Example configuration** ```json title="modes-export-4.json" { "name": "sd-2-tcn-sm", "job_dir": "./results/sd-2-tcn-sm", "verbose": 2, "datasets": [{ "name": "cmidss", "params": { "path": "./datasets/cmidss" } }], "feature": { "name": "FS-W-A-5", "sampling_rate": 0.2, "frame_size": 12, "loader": "hdf5", "feat_key": "features", "label_key": "detect_labels", "mask_key": "mask", "feat_cols": null, "save_path": "./datasets/store/fs-w-a-5-60", "params": {} }, "sampling_rate": 0.0083333, "frame_size": 240, "num_classes": 2, "class_map": { "0": 0, "1": 1, "2": 1, "3": 1, "4": 1, "5": 1 }, "class_names": ["WAKE", "SLEEP"], "samples_per_subject": 100, "val_samples_per_subject": 100, "test_samples_per_subject": 50, "val_size": 4000, "test_size": 2500, "val_subjects": 0.20, "batch_size": 128, "buffer_size": 10000, "epochs": 200, "steps_per_epoch": 25, "val_steps_per_epoch": 25, "val_metric": "loss", "lr_rate": 1e-3, "lr_cycles": 1, "label_smoothing": 0, "test_metric": "f1", "test_metric_threshold": 0.02, "tflm_var_name": "sk_detect_flatbuffer", "tflm_file": "sk_detect_flatbuffer.h", "backend": "pc", "display_report": true, "quantization": { "qat": false, "mode": "INT8", "io_type": "int8", "concrete": true, "debug": false }, "model_file": "model.keras", "use_logits": false, "architecture": { "name": "tcn", "params": { "input_kernel": [1, 5], "input_norm": "batch", "blocks": [ {"depth": 1, "branch": 1, "filters": 16, "kernel": [1, 5], "dilation": [1, 1], "dropout": 0.10, "ex_ratio": 1, "se_ratio": 4, "norm": "batch"}, {"depth": 1, "branch": 1, "filters": 32, "kernel": [1, 5], "dilation": [1, 2], "dropout": 0.10, "ex_ratio": 1, "se_ratio": 4, "norm": "batch"}, {"depth": 1, "branch": 1, "filters": 48, "kernel": [1, 5], "dilation": [1, 4], "dropout": 0.10, "ex_ratio": 1, "se_ratio": 4, "norm": "batch"}, {"depth": 1, "branch": 1, "filters": 64, "kernel": [1, 5], "dilation": [1, 8], "dropout": 0.10, "ex_ratio": 1, "se_ratio": 4, "norm": "batch"} ], "output_kernel": [1, 5], "include_top": true, "use_logits": true, "model_name": "tcn" } } } ``` --- ## Arguments Please refer to [TaskParams](https://ambiqai.github.io/sleepkit/modes/configuration/#taskparams) for the list of arguments that can be used with the `export` command. ---