somewhat working

config.yaml

@@ -1,5 +1,5 @@
 name: DEFAULT
-project: Spikey
+project: Spikey_1
 
 slurm:
   name: 'Spikey_{config[name]}'
@@ -22,7 +22,7 @@ runner: spikey
 
 scheduler:
   reps_per_version: 1
-  agents_per_job: 1
+  agents_per_job: 100
   reps_per_agent: 1
 
 wandb:
@@ -37,35 +37,6 @@ wandb:
   monitor_gym: False
   save_code: False
 
----
-name: Test
-import: $
-
-latent_projector:
-  type: rnn  # Options: 'fc', 'rnn'
-  input_size: 195 # =0.01s 19531  # =1s Input size for the Latent Projector (length of snippets).
-  latent_size: 4  # Size of the latent representation before message passing.
-  #layer_shapes: [256, 32]  # List of layer sizes for the latent projector (if type is 'fc').
-  #activations: ['ReLU', 'ReLU']  # Activation functions for the latent projector layers (if type is 'fc').
-  rnn_hidden_size: 4  # Hidden size for the RNN projector (if type is 'rnn').
-  rnn_num_layers: 1  # Number of layers for the RNN projector (if type is 'rnn').
-
-middle_out:
-  output_size: 4  # Size of the latent representation after message passing.
-  num_peers: 3  # Number of most correlated peers to consider.
-
-predictor:
-  layer_shapes: [4]  # List of layer sizes for the predictor.
-  activations: ['ELU']  # Activation functions for the predictor layers.
-
-training:
-  epochs: 1024  # Number of training epochs.
-  batch_size: 16 # 64  # Batch size for training.
-  num_batches: 4 # Batches per epoch
-  learning_rate: 0.05  # Learning rate for the optimizer.
-  eval_freq: -1 # 8  # Frequency of evaluation during training (in epochs).
-  save_path: models  # Directory to save the best model and encoder.
-
 evaluation:
   full_compression: false  # Perform full compression during evaluation
 
@@ -79,4 +50,86 @@ data:
   cut_length: null  # Optional length to cut sequences to.
 
 profiler:
-  enable: false
+  enable: false
+
+training:
+  eval_freq: -1 # 8  # Frequency of evaluation during training (in epochs).
+  save_path: models  # Directory to save the best model and encoder.
+---
+name: FC
+import: $
+
+latent_projector:
+  type: fc  # Options: 'fc', 'rnn'
+  input_size: 1953 # =0.1s 19531  # =1s Input size for the Latent Projector (length of snippets).
+  latent_size: 4  # Size of the latent representation before message passing.
+  layer_shapes: [32, 8]  # List of layer sizes for the latent projector (if type is 'fc').
+  activations: ['ReLU', 'ReLU']  # Activation functions for the latent projector layers (if type is 'fc').
+  #rnn_hidden_size: 4  # Hidden size for the RNN projector (if type is 'rnn').
+  #rnn_num_layers: 1  # Number of layers for the RNN projector (if type is 'rnn').
+
+middle_out:
+  output_size: 4  # Size of the latent representation after message passing.
+  num_peers: 3  # Number of most correlated peers to consider.
+
+predictor:
+  layer_shapes: [3]  # List of layer sizes for the predictor.
+  activations: ['ReLU']  # Activation functions for the predictor layers.
+
+training:
+  epochs: 1024  # Number of training epochs.
+  batch_size: 32  # Batch size for training.
+  num_batches: 1 # Batches per epoch
+  learning_rate: 0.01  # Learning rate for the optimizer.
+---
+name: FC6
+import: $
+
+latent_projector:
+  type: fc  # Options: 'fc', 'rnn'
+  input_size: 195 # =0.1s 19531  # =1s Input size for the Latent Projector (length of snippets).
+  latent_size: 4  # Size of the latent representation before message passing.
+  layer_shapes: [16]  # List of layer sizes for the latent projector (if type is 'fc').
+  activations: ['ReLU']  # Activation functions for the latent projector layers (if type is 'fc').
+  #rnn_hidden_size: 4  # Hidden size for the RNN projector (if type is 'rnn').
+  #rnn_num_layers: 1  # Number of layers for the RNN projector (if type is 'rnn').
+
+middle_out:
+  output_size: 8  # Size of the latent representation after message passing.
+  num_peers: 3  # Number of most correlated peers to consider.
+
+predictor:
+  layer_shapes: [3]  # List of layer sizes for the predictor.
+  activations: ['ReLU']  # Activation functions for the predictor layers.
+
+training:
+  epochs: 1024  # Number of training epochs.
+  batch_size: 16  # Batch size for training.
+  num_batches: 1 # Batches per epoch
+  learning_rate: 0.01  # Learning rate for the optimizer.
+---
+name: RNN
+import: $
+
+latent_projector:
+  type: rnn  # Options: 'fc', 'rnn'
+  input_size: 1953 # =0.1s 19531  # =1s Input size for the Latent Projector (length of snippets).
+  latent_size: 4  # Size of the latent representation before message passing.
+  #layer_shapes: [32, 8]  # List of layer sizes for the latent projector (if type is 'fc').
+  #activations: ['ReLU', 'ReLU']  # Activation functions for the latent projector layers (if type is 'fc').
+  rnn_hidden_size: 3  # Hidden size for the RNN projector (if type is 'rnn').
+  rnn_num_layers: 2  # Number of layers for the RNN projector (if type is 'rnn').
+
+middle_out:
+  output_size: 4  # Size of the latent representation after message passing.
+  num_peers: 3  # Number of most correlated peers to consider.
+
+predictor:
+  layer_shapes: [3]  # List of layer sizes for the predictor.
+  activations: ['ReLU']  # Activation functions for the predictor layers.
+
+training:
+  epochs: 1024  # Number of training epochs.
+  batch_size: 64  # Batch size for training.
+  num_batches: 2 # Batches per epoch
+  learning_rate: 0.01  # Learning rate for the optimizer.