Fixed a bug in logging objectives and circuits. Working on template

configs some more

resources/config_templates/user/brain/deep-autoencoder.yaml

@@ -12,10 +12,11 @@ connections:
   - ["vision", "retina"]  # Input to retina
   - ["retina","thalamus"]  # Retina to thalamus
   - ["thalamus","visual_cortex"]  # Thalamus to visual cortex
-  - ["visual_cortex", "pfc"]  # Visual cortex to prefrontal cortex
-  - ["pfc", "classifier"]  # Prefrontal cortex to classifier
-  - ["pfc", "pfc_decoder"]  # pfc to FC decoder
-  - ["pfc_decoder", "decoder"]  # FC decoder to convoluational decoder
+  - ["visual_cortex", "inferotemporal"]  # Visual cortex to inferotemporal cortex
+  - ["inferotemporal", "prefrontal"] 
+  - ["prefrontal", "classifier"]  # Prefrontal cortex to classifier
+  - ["inferotemporal", "inferotemporal_decoder"]  # prefrontal to FC decoder
+  - ["inferotemporal_decoder", "decoder"]  # FC decoder to convoluational decoder
 
 # Define the individual nodes (neural circuits) of the network. Many circuit
 # parameters are interpolated from the experiment config.
@@ -50,22 +51,32 @@ circuits:
     act_name: ${activation}
     layer_names: ["v1"]  # Primary Visual Cortex
 
-  # Prefrontal Cortex: high-level cognitive processing
-  pfc:
+  # Inferotemporal Cortex: Associations
+  inferotemporal:
     _target_: retinal_rl.models.circuits.fully_connected.FullyConnectedEncoder
     output_shape:
-      - 128  # Size of the latent representation
+      - 64  # Size of the latent representation
     hidden_units: 
       - 64  # Number of hidden units
     act_name: ${activation}
 
   # Prefrontal Cortex: high-level cognitive processing
-  pfc_decoder:
+  prefrontal:
+    _target_: retinal_rl.models.circuits.fully_connected.FullyConnectedEncoder
+    output_shape:
+      - 32  # Size of the latent representation
+    hidden_units: 
+      - 32  # Number of hidden units
+    act_name: ${activation}
+
+  # Prefrontal Cortex: high-level cognitive processing
+  inferotemporal_decoder:
     _target_: retinal_rl.models.circuits.fully_connected.FullyConnectedDecoder
-    output_shape: "pfc.input_shape"  # Size of the latent representation
+    output_shape: "inferotemporal.input_shape"  # Size of the latent representation
     hidden_units: 
       - 64  # Number of hidden units
     act_name: ${activation}
+
   # Decoder: for reconstructing the input from the latent representation
   decoder:
     _target_: retinal_rl.models.circuits.convolutional.ConvolutionalDecoder

resources/config_templates/user/dataset/cifar10.yaml

@@ -24,8 +24,8 @@ imageset:
         - ${eval:"1.5 if ${brightness_noise_transform} else 1"}
     - _target_: retinal_rl.datasets.transforms.BlurTransform
       blur_range:
-        - ${eval:"0 if ${blur_noise_transform} else 1"}
-        - ${eval:"2 if ${blur_noise_transform} else 1"}
+        - ${eval:"0 if ${blur_noise_transform} else 0"}
+        - ${eval:"2 if ${blur_noise_transform} else 0"}
   apply_normalization: true
   fixed_transformation: false
   multiplier: 1

resources/config_templates/user/experiment/cifar10-class-recon.yaml → resources/config_templates/user/experiment/classification.yaml

@@ -3,7 +3,7 @@ defaults:
   - _self_
   - override /dataset: cifar10
   - override /brain: shallow-autoencoder
-  - override /optimizer: recon-weight
+  - override /optimizer: class-recon
 
 # This is the main entry point for control of a retinal-rl experiment. Variables
 # created here will be top-level, and defaults can be set for the various parts
@@ -18,6 +18,7 @@ activation: "elu"
 
 recon_weight_retina: 1
 recon_weight_thalamus: 0.99
+recon_weight_cortex: 0.9
 
 shot_noise_transform: True
 contrast_noise_transform: True

resources/config_templates/user/optimizer/recon-weight.yaml → resources/config_templates/user/optimizer/class-recon.yaml

@@ -21,6 +21,7 @@ goal:
         weight: 1
     target_circuits: # Circuit parameters to optimize with this optimizer.  We train the retina and the decoder exclusively to maximize reconstruction
       - decoder
+      - inferotemporal_decoder
   mixed:
     min_epoch: 0
     max_epoch: 100
@@ -31,6 +32,17 @@ goal:
         weight: ${eval:'1-${recon_weight_thalamus}'}
     target_circuits: # The thalamus is somewhat sensitive to task losses
       - thalamus
+  cortex:
+    min_epoch: 0
+    max_epoch: 100
+    losses:
+      - _target_: retinal_rl.models.loss.ReconstructionLoss
+        weight: ${recon_weight_cortex}
+      - _target_: retinal_rl.classification.loss.ClassificationLoss
+        weight: ${eval:'1-${recon_weight_cortex}'}
+    target_circuits: # Visual cortex and downstream layers are driven by the task
+      - visual_cortex
+      - inferotemporal
   class:
     min_epoch: 0
     max_epoch: 100
@@ -40,6 +52,5 @@ goal:
       - _target_: retinal_rl.classification.loss.PercentCorrect
         weight: 0
     target_circuits: # Visual cortex and downstream layers are driven by the task
-      - visual_cortex
-      - pfc
+      - prefrontal
       - classifier

retinal_rl/models/goal.py

@@ -75,7 +75,7 @@ def __init__(self, brain: Brain, objective_configs: Dict[str, DictConfig]):
                 instantiate(obj_config) for obj_config in config.losses
             ]
             logger.info(
-                f"Initalized objective: {objective}, with losses: {[obj.key_name for obj in self.losses[objective]]}, and target circuits: {[circuit_name for circuit_name in self.target_circuits]}"
+                f"Initialized objective: {objective}, with losses: {[obj.key_name for obj in self.losses[objective]]}, and target circuits: {[circuit_name for circuit_name in self.target_circuits[objective]]}"
             )
 
     def evaluate_objective(