Merge pull request #39 from AdityaD16/updated_maml

Added fas and maml_lr as arguments.

Author: veds12

jeta/maml.py

@@ -3,15 +3,16 @@
 import jax
 import jax.numpy as jnp
 from flax import core
-
-# from loss import mse
+from sympy import Float, Integer
 
 
 def maml_adapt(
     params: core.FrozenDict[str, Any],
     apply_fn: Callable[[core.FrozenDict[str, Any], jnp.ndarray], jnp.ndarray],
     loss_fn: Callable[[jnp.ndarray, jnp.ndarray], jnp.ndarray],
     support_set: Tuple[jnp.ndarray, jnp.ndarray],
+    maml_lr: Float,
+    fas: Integer,
 ) -> core.FrozenDict[str, Any]:
 
     """Adapts with respect to the support set using the MAML algorithm.
@@ -23,17 +24,15 @@ def maml_adapt(
         apply_fn: A function that applies the model to a batch of data.
         loss_fn: A function that computes the loss of a batch of data.
         support_set: A tuple of (x_train, y_train).
-
+        maml_lr : Inner learning rate.
+        fas: Fast adaption step.
     Returns:
         adapted_params: adapted parameters
     """
 
     theta = params["params"]
     mutable_params = [key for key in params if key != "params"]
 
-    maml_lr = 0.01  # Inner Learning rate. TODO: take this parameter as an argument
-    fas = 1  # Fast adaptation steps. TODO: take this parameter as an argument
-
     def loss(theta, batch):
         x_train, y_train = batch
         logits, new_mutable_param_values = apply_fn(
@@ -49,29 +48,3 @@ def loss(theta, batch):
         theta = jax.tree_util.tree_map(lambda t, g: t - maml_lr * g, theta, grads)
 
     return theta
-
-
-# def maml_init(model: nn.Module, init_key, arr: jnp.ndarray):
-#     """Initializes the parameters of the model.
-
-#     The default parameters initilized by flax don't convege for
-#     optimization based meta learning algorithms.
-#     Hence they are scaled to match a normal distribution with mean 0 and std 0.01.
-
-#     Args:
-#         model (nn.Module): model whose parameters are to be initialised
-#         init_key (random.PRNGKey): PRNG Key used for initialisation
-#         arr (jnp.ndarray): a random array used to initialize the parameters
-
-#     Returns:
-#         Parameters: A frozen dict of model parameters.
-#     """
-
-#     EPSILON = 1e-8  # to avoid division by zero
-#     params = model.init(init_key, arr).unfreeze()
-#     # Paramters are scaled to match a normal distribution with mean 0 and std 0.01
-#     params = jax.tree_util.tree_map(
-#         lambda p: 0.01 * (p - p.mean()) / (p.std() + EPSILON), params
-#     )
-#     params = core.frozen_dict.freeze(params)
-#     return params