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…ir_pollution_estimation into transfer_learning
…tion_estimation into transfer_learning
…tion_estimation into transfer_learning
…tion_estimation into transfer_learning
…tion_estimation into transfer_learning
Maren-Eckhoff
requested changes
Aug 15, 2019
| return true_positive_dict, true_labels_dict, pred_labels_dict | ||
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| def evaluate_on_gpu(sess, gpu_nms_op, pred_boxes_flag, pred_scores_flag, |
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this seems very different than what we had at the start of the file.
Let's break the file down into multiple files with names that make clear what type of task the functions do that live inside the file.
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broken down into four utils files: tensorflow_evaluation_utils, tensorflow_image_formatting_utis, tensorflow_plotting_utils, tensorflow_training_utils
src/traffic_analysis/d04_modelling/transfer_learning/train_tensorflow_model.py
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| from traffic_analysis.d04_modelling.transfer_learning.tensorflow_detection_utils import read_class_names | ||
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| def transfer_learn(paths, params, train_params, train_file, test_file, selected_labels): |
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very big function. Can we modularise this at all?
src/traffic_analysis/d04_modelling/transfer_learning/train_tensorflow_model.py
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src/traffic_analysis/d04_modelling/transfer_learning/training_data_loader.py
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…tion_estimation into transfer_learning
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includes all the requisite functions to run transfer learning. to use, python run_transfer_learning.py. training_parameters.yml has some of the hyperparams that can be adjusted to tune the training. saves model with highest performance in data/ref/detection_model/TRAINING_PARAMETERS['TRAINED_MODEL_NAME'].