Added some MNIST stuff

configs/data.yaml

@@ -76,7 +76,7 @@ straightline:
     split: "train"
     vertical_horizontal_only: True
     aug_range: 0
-    num_images: 600
+    num_images: 300
     num_aug_versions: 0
   valid_dataset_params:
     split: "val"

configs/s1_toy_sample3.yaml

@@ -0,0 +1,76 @@
+dataset:
+  name: mnist
+  augmentation: None
+  loader: torch
+  batch_size: 1
+  num_workers: 0
+
+run:
+  n_epochs: 15
+  current_epoch: 0
+  plots:
+    enable: True
+    only_last_epoch: False
+    store_path: "../tmp/test/"
+  store_state_path: None
+  load_state_path: None
+
+
+metrics:
+  - mse:
+      type: MeanSquaredError
+      meter: avg
+  - mae:
+      type: MeanAbsoluteError
+      meter: avg
+
+optimizers:
+  l2_opt:
+    type: Adam
+    params:
+      lr: 0.05
+      betas: [ 0.9, 0.999 ]
+      eps: 0.00000001
+      weight_decay: 0
+      amsgrad: False
+    scheduler:
+      type: ReduceLROnPlateau
+      params:
+        mode: min
+        factor: 0.1
+        patience: 5
+        threshold: 0.0001
+        threshold_mode: rel
+        cooldown: 0
+        min_lr: 0.000001
+        eps: 0.00000001
+
+logging:
+  wandb:
+    active: False,
+    save_dir: "../wandb"
+    project: "lateral_connections_toy_example"
+    log_model: True
+    group: null # "hebbian_learning"
+    job_type: null # "train"
+  console:
+    active: True
+
+feature_extractor:
+  out_channels: 4
+  add_bg_channel: False
+  bin_threshold: 0.  # set to 0.5 to obtain better features
+  optimized_filter_lines: True # set to True to obtain better features
+
+lateral_model:
+  channels: 4
+  max_timesteps: 1  # 1 = only one forward pass without recurrent connection
+  min_k: 2
+  max_k: 3
+  l1_type: 'lateral_flex'
+  l1_params:
+    locality_size: 3
+    lr: 20.0
+    hebbian_rule: 'vanilla'
+    neg_corr: True
+    act_threshold: 0.15  # 'bernoulli'