modified: Figure_1.png

modified:   checkpoints/forward/best_tcn_model.pt
	modified:   checkpoints/forward/training_history.csv
	new file:   checkpoints_mlp/task1_feature_mlp/best_feature_mlp.pt
	new file:   checkpoints_mlp/task1_feature_mlp/training_history.csv
	new file:   checkpoints_rms/forward_rms/best_rms_model.pt
	new file:   checkpoints_rms/forward_rms/training_history.csv
	modified:   evaluation_outputs/forward/evaluation_forward_test_b0_s0.png
	new file:   evaluation_outputs/forward/evaluation_forward_val_b0_s0.png
	new file:   evaluation_outputs/forward/evaluation_forward_val_b3_s0.png
	new file:   evaluation_outputs/forward_rms/evaluation_test_all_samples.csv
	new file:   evaluation_outputs/forward_rms/evaluation_test_s0.png
	new file:   evaluation_outputs/forward_rms/evaluation_train_all_samples.csv
	new file:   evaluation_outputs/forward_rms/evaluation_val_all_samples.csv
	new file:   evaluation_outputs/forward_rms/evaluation_val_s0.png
	new file:   evaluation_outputs/forward_rms/evaluation_val_s0_waveform.png
	new file:   evaluation_outputs/task1_feature_mlp/evaluation_train_all_samples.csv
	new file:   evaluation_outputs/task1_feature_mlp/evaluation_train_curve.png
	new file:   evaluation_outputs/task1_feature_mlp/evaluation_val_all_samples.csv
	new file:   evaluation_outputs/task1_feature_mlp/evaluation_val_curve.png
	new file:   evaluation_outputs/task1_feature_mlp/evaluation_val_s0.png
	new file:   evaluation_outputs/task1_feature_mlp/harmonic_5mm_0.75Hz_prediction.png
	new file:   evaluation_outputs/task1_feature_mlp/harmonic_5mm_1.55Hz_prediction.png
	new file:   scripts/__pycache__/config.cpython-310.pyc
	new file:   scripts/__pycache__/dataset.cpython-310.pyc
	new file:   scripts/__pycache__/model.cpython-310.pyc
	new file:   scripts/config.py
	new file:   scripts/dataset.py
	new file:   scripts/evaluate.py
	new file:   scripts/model.py
	new file:   scripts/predict_single.py
	new file:   scripts/train.py
	modified:   src/__pycache__/config.cpython-310.pyc
	modified:   src/__pycache__/dataset.cpython-310.pyc
	modified:   src/__pycache__/model.cpython-310.pyc
	modified:   src/config.py
	modified:   src/dataset.py
	modified:   src/model.py
	new file:   src_new/__pycache__/config.cpython-310.pyc
	new file:   src_new/__pycache__/dataset.cpython-310.pyc
	new file:   src_new/__pycache__/evaluate.cpython-310.pyc
	new file:   src_new/__pycache__/model.cpython-310.pyc
	new file:   src_new/__pycache__/train.cpython-310.pyc
	new file:   src_new/config.py
	new file:   src_new/dataset.py
	new file:   src_new/evaluate.py
	new file:   src_new/model.py
	new file:   src_new/train.py

scripts/model.py

@@ -0,0 +1,31 @@
+from __future__ import annotations
+
+import torch
+from torch import nn
+
+try:
+    from .config import ExperimentConfig, ModelConfig
+except ImportError:
+    from config import ExperimentConfig, ModelConfig
+
+
+class FeatureMLP(nn.Module):
+    def __init__(self, config: ModelConfig) -> None:
+        super().__init__()
+        layers: list[nn.Module] = []
+        input_dim = config.input_dim
+        for hidden_dim in config.hidden_dims:
+            layers.append(nn.Linear(input_dim, hidden_dim))
+            layers.append(nn.GELU())
+            layers.append(nn.Dropout(config.dropout))
+            input_dim = hidden_dim
+        layers.append(nn.Linear(input_dim, 1))
+        self.network = nn.Sequential(*layers)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        return self.network(x)
+
+
+def build_model(config: ExperimentConfig | ModelConfig) -> FeatureMLP:
+    model_config = config.model if isinstance(config, ExperimentConfig) else config
+    return FeatureMLP(model_config)