Main: basic one-run scoring

src/main.py

@@ -8,14 +8,14 @@ import optax
 import torch
 import torchvision
 from functools import partial
-from jaxtyping import Array, Float, Int, PyTree
+from jaxtyping import Array, Float, Int, Bool, PyTree
 from typing import Tuple, ForwardRef
 
 
 DATA_ROOT="data"
-BATCH_SIZE = 64
+BATCH_SIZE = 1#64
-LEARNING_RATE = 3e-4
+LEARNING_RATE = 0.03 #3e-4
-STEPS = 300
+STEPS = 10#300
 PRINT_EVERY = 30
 SEED = 5678
 
@@ -150,7 +150,33 @@ def evaluate_model(params: PyTree, statics: PyTree, test_dl: torch.utils.data.Da
     return avg_loss, avg_acc
 
 
-def load_data(m, key):
+def score_model(model: PyTree, attack_ds: torch.utils.data.TensorDataset, membership: Bool[Array, "m"]) -> Float[Array, "m"]:
+    assert len(attack_ds) == len(membership)
+    k = 800
+
+    params, statics = eqx.partition(model, eqx.is_array)
+    scores = list()
+
+    for i in range(len(attack_ds)):
+        x = np.expand_dims(attack_ds[i][0].numpy(), axis=0)
+        y = np.expand_dims(attack_ds[i][1].numpy(), axis=0)
+
+        loss, acc = get_stats(params, statics, x, y)
+        scores.append(loss.item())
+
+    scores = np.array(scores)
+    perm = np.argsort(scores)
+    scores = scores[perm]
+    membership = membership[perm]
+
+    correct = np.sum(membership[:k]) + np.sum(~membership[-k:])
+
+    print(scores[:10])
+    print(perm[:10])
+    print(f"Accuracy = {correct} / {2*k}")
+
+
+def load_data(m: int, key):
     normalise_data = torchvision.transforms.Compose([
         torchvision.transforms.ToTensor(),
         torchvision.transforms.Normalize((0.5,), (0.5,)),
@@ -198,7 +224,7 @@ def load_data(m, key):
 
         attack_ds = torch.utils.data.TensorDataset(
             torch.from_numpy(x_attack),
-            torch.from_numpy(y_attack)
+            torch.from_numpy(y_attack),
         )
         x_train = np.concatenate([x_train, x_attack[membership]])
         y_train = np.concatenate([y_train, y_attack[membership]])
@@ -236,9 +262,12 @@ if __name__ == '__main__':
     key = jax.random.PRNGKey(SEED)
     key, key1, key2 = jax.random.split(key, 3)
 
-    train_dl, test_dl, attack_ds, membership = load_data(1000, key2)
+    train_dl, test_dl, attack_ds, membership = load_data(5000, key2)
 
     model = CNN(key1)
-    optim = optax.adamw(LEARNING_RATE)
+    optim = optax.adamw(3e-4)
+    #optim = optax.sgd(0.03)
 
     model = CNN.train(model, train_dl, test_dl, optim, STEPS, 10)
+
+    scores = score_model(model, attack_ds, membership)