Updated thesis
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@@ -2,7 +2,7 @@ from src.utils.clearml import ClearMLHelper
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clearml_helper = ClearMLHelper(project_name="Thesis/NrvForecast")
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clearml_helper = ClearMLHelper(project_name="Thesis/NrvForecast")
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task = clearml_helper.get_task(
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task = clearml_helper.get_task(
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task_name="Diffusion Training (GRUs): hidden_sizes=[1024, 1024] (300 steps), lr=0.0001, time_dim=8 + NRV + L + W + PV + NP",
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task_name="Diffusion Training (GRU - 2048): hidden_sizes=[512, 512] (300 steps), lr=0.0001, time_dim=8 + NRV + L + W + PV + NP",
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)
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)
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task.execute_remotely(queue_name="default", exit_process=True)
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task.execute_remotely(queue_name="default", exit_process=True)
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@@ -42,7 +42,7 @@ print("Input dim: ", inputDim)
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model_parameters = {
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model_parameters = {
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"epochs": 15000,
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"epochs": 15000,
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"learning_rate": 0.0001,
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"learning_rate": 0.0001,
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"hidden_sizes": [1024, 1024],
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"hidden_sizes": [512, 512],
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"time_dim": 8,
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"time_dim": 8,
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}
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}
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@@ -61,7 +61,7 @@ model = GRUDiffusionModel(
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model_parameters["hidden_sizes"],
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model_parameters["hidden_sizes"],
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other_inputs_dim=inputDim[2],
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other_inputs_dim=inputDim[2],
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time_dim=model_parameters["time_dim"],
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time_dim=model_parameters["time_dim"],
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gru_hidden_size=512,
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gru_hidden_size=2048,
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)
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)
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### Policy Evaluator ###
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### Policy Evaluator ###
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