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Updated thesis

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Victor Mylle
2024-05-11 02:06:07 +02:00
parent 1b2b3518e2
commit 934d4951ff
6 changed files with 24 additions and 17 deletions
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24
src/training_scripts/autoregressive_quantiles.py
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@@ -2,7 +2,7 @@ from src.utils.clearml import ClearMLHelper
#### ClearML ####
clearml_helper = ClearMLHelper(project_name="Thesis/NrvForecast")
task = clearml_helper.get_task(task_name="AQR: Linear + Load + Wind + PV + QE + NP")
task = clearml_helper.get_task(task_name="AQR: Non Linear + Load + Wind + PV + QE + NP")
task.execute_remotely(queue_name="default", exit_process=True)
from src.policies.PolicyEvaluator import PolicyEvaluator
@@ -68,9 +68,9 @@ else:
model_parameters = {
"learning_rate": 0.0001,
"hidden_size": 256,
"num_layers": 16,
"num_layers": 8,
"dropout": 0.2,
"time_feature_embedding": 2,
"time_feature_embedding": 5,
}
model_parameters = task.connect(model_parameters, name="model_parameters")
@@ -89,17 +89,17 @@ time_embedding = TimeEmbedding(
# dropout=model_parameters["dropout"],
# )
# non_linear_model = NonLinearRegression(
# time_embedding.output_dim(inputDim),
# len(quantiles),
# hiddenSize=model_parameters["hidden_size"],
# numLayers=model_parameters["num_layers"],
# dropout=model_parameters["dropout"],
# )
non_linear_model = NonLinearRegression(
time_embedding.output_dim(inputDim),
len(quantiles),
hiddenSize=model_parameters["hidden_size"],
numLayers=model_parameters["num_layers"],
dropout=model_parameters["dropout"],
)
linear_model = LinearRegression(time_embedding.output_dim(inputDim), len(quantiles))
# linear_model = LinearRegression(time_embedding.output_dim(inputDim), len(quantiles))
model = nn.Sequential(time_embedding, linear_model)
model = nn.Sequential(time_embedding, non_linear_model)
model.output_size = 1
optimizer = torch.optim.Adam(model.parameters(), lr=model_parameters["learning_rate"])

4
src/training_scripts/diffusion_training.py
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@@ -2,7 +2,7 @@ from src.utils.clearml import ClearMLHelper
clearml_helper = ClearMLHelper(project_name="Thesis/NrvForecast")
task = clearml_helper.get_task(
task_name="Diffusion Training: hidden_sizes=[2048, 2048, 2048, 2048] (300 steps), lr=0.0001, time_dim=8"
task_name="Diffusion Training: hidden_sizes=[2048, 2048, 2048] (300 steps), lr=0.0001, time_dim=8"
)
task.execute_remotely(queue_name="default", exit_process=True)
@@ -42,7 +42,7 @@ print("Input dim: ", inputDim)
model_parameters = {
"epochs": 15000,
"learning_rate": 0.0001,
"hidden_sizes": [2048, 2048, 2048, 2048],
"hidden_sizes": [2048, 2048, 2048],
"time_dim": 8,
}