Tweaked datasets and list of configs

dflat/metasurface/ckpt/Nanocylinders_TiO2_U200H600/config.yaml

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+model:
+  target: dflat.metasurface.optical_model.NeuralCells
+  ckpt_path: metasurface/ckpt/Nanocylinders_TiO2_U200H600/model.ckpt
+  params:
+    trainable_model: False
+    param_bounds:
+      - [15e-9, 85e-9]
+      - [310e-9, 750e-9]
+    nn_config:
+      target: dflat.metasurface.nn_siren.SirenNet
+      ckpt_path: None
+      loss: torch.nn.MSELoss
+      params:
+        dim_in: 2
+        dim_hidden: 256
+        dim_out: 3
+        num_layers: 2
+        w0: 1.
+        w0_initial: 30.
+        use_bias: True
+        dropout: 0
+
+trainer:
+  target: dflat.metasurface.trainer.Trainer_v1
+  data: dflat.metasurface.datasets.Nanocylinders_TiO2_U200nm_H600nm
+  ckpt_path: metasurface/ckpt/Nanocylinders_TiO2_U200H600/model.ckpt
+  relative_ckpt: True
+  params:
+    test_split: 0.10
+    learning_rate: .001
+    epochs: 5000
+    batch_size: 65536
+    checkpoint_every_n: 100
+    update_figure_every_epoch: True
+    gradient_accumulation_steps: 1
+    cosine_anneal_warm_restart: False
+    cosine_anneal_minLR: .001
+    cosine_anneal_T_0: 10
+    cosine_anneal_T_mult: 3

dflat/metasurface/ckpt/Nanocylinders_TiO2_U250H600/config.yaml

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+model:
+  target: dflat.metasurface.optical_model.NeuralCells
+  ckpt_path: metasurface/ckpt/Nanocylinders_TiO2_U250H600/model.ckpt
+  params:
+    trainable_model: False
+    param_bounds:
+      - [15e-9, 110e-9]
+      - [310e-9, 750e-9]
+    nn_config:
+      target: dflat.metasurface.nn_siren.SirenNet
+      ckpt_path: None
+      loss: torch.nn.MSELoss
+      params:
+        dim_in: 2
+        dim_hidden: 256
+        dim_out: 3
+        num_layers: 2
+        w0: 1.
+        w0_initial: 30.
+        use_bias: True
+        dropout: 0
+
+trainer:
+  target: dflat.metasurface.trainer.Trainer_v1
+  data: dflat.metasurface.datasets.Nanocylinders_TiO2_U250nm_H600nm
+  ckpt_path: metasurface/ckpt/Nanocylinders_TiO2_U250H600/model.ckpt
+  relative_ckpt: True
+  params:
+    test_split: 0.10
+    learning_rate: .001
+    epochs: 5000
+    batch_size: 65536
+    checkpoint_every_n: 100
+    update_figure_every_epoch: True
+    gradient_accumulation_steps: 1
+    cosine_anneal_warm_restart: False
+    cosine_anneal_minLR: .001
+    cosine_anneal_T_0: 10
+    cosine_anneal_T_mult: 3

dflat/metasurface/ckpt/Nanocylinders_TiO2_U300H600/config.yaml

@@ -0,0 +1,39 @@
+model:
+  target: dflat.metasurface.optical_model.NeuralCells
+  ckpt_path: metasurface/ckpt/Nanocylinders_TiO2_U300H600/model.ckpt
+  params:
+    trainable_model: False
+    param_bounds:
+      - [15e-9, 135e-9]
+      - [310e-9, 750e-9]
+    nn_config:
+      target: dflat.metasurface.nn_siren.SirenNet
+      ckpt_path: None
+      loss: torch.nn.MSELoss
+      params:
+        dim_in: 2
+        dim_hidden: 256
+        dim_out: 3
+        num_layers: 2
+        w0: 1.
+        w0_initial: 30.
+        use_bias: True
+        dropout: 0
+
+trainer:
+  target: dflat.metasurface.trainer.Trainer_v1
+  data: dflat.metasurface.datasets.Nanocylinders_TiO2_U300nm_H600nm
+  ckpt_path: metasurface/ckpt/Nanocylinders_TiO2_U300H600/model.ckpt
+  relative_ckpt: True
+  params:
+    test_split: 0.10
+    learning_rate: .001
+    epochs: 6000
+    batch_size: 65536
+    checkpoint_every_n: 100
+    update_figure_every_epoch: True
+    gradient_accumulation_steps: 1
+    cosine_anneal_warm_restart: False
+    cosine_anneal_minLR: .001
+    cosine_anneal_T_0: 10
+    cosine_anneal_T_mult: 3

dflat/metasurface/ckpt/Nanocylinders_TiO2_U350H600_Medium/config.yaml

@@ -0,0 +1,39 @@
+model:
+  target: dflat.metasurface.optical_model.NeuralCells
+  ckpt_path: metasurface/ckpt/Nanocylinders_TiO2_U350H600/model.ckpt
+  params:
+    trainable_model: False
+    param_bounds:
+      - [15e-9, 160e-9]
+      - [310e-9, 750e-9]
+    nn_config:
+      target: dflat.metasurface.nn_siren.SirenNet
+      ckpt_path: None
+      loss: torch.nn.MSELoss
+      params:
+        dim_in: 2
+        dim_hidden: 256
+        dim_out: 3
+        num_layers: 2
+        w0: 1.
+        w0_initial: 30.
+        use_bias: True
+        dropout: 0
+
+trainer:
+  target: dflat.metasurface.trainer.Trainer_v1
+  data: dflat.metasurface.datasets.Nanocylinders_TiO2_U350nm_H600nm
+  ckpt_path: metasurface/ckpt/Nanocylinders_TiO2_U350H600/model.ckpt
+  relative_ckpt: True
+  params:
+    test_split: 0.10
+    learning_rate: .001
+    epochs: 5000
+    batch_size: 65536
+    checkpoint_every_n: 100
+    update_figure_every_epoch: True
+    gradient_accumulation_steps: 1
+    cosine_anneal_warm_restart: False
+    cosine_anneal_minLR: .001
+    cosine_anneal_T_0: 10
+    cosine_anneal_T_mult: 3