A generic Python and Tensorflow function that implements a simple version of the "Model-Agnostic Meta-Learning (MAML) Algorithm for Fast Adaptation of Deep Networks" as designed by Chelsea Finn et al. 2017
Project description
MAML
A generic Python/Tensorflow function that implements a simple version of the "Model-Agnostic Meta-Learning (MAML) Algorithm for Fast Adaptation of Deep Networks" as designed by Chelsea Finn et al. 2017 [1]. Especially, this implementation focuses on regression and prediction problems.
Original algorithm adapted for regression
Usage
- Install with
pip install simplemaml
- In your python code:
from simplemaml import MAML
MAML(model=your_model, tasks=your_array_of_tasks, etc.)
More about the algorithm
- Chelsea Finn explains well her algorithm in this Standford lecture: https://www.youtube.com/watch?v=Gj5SEpFIv8I&list=PLoROMvodv4rNjRoawgt72BBNwL2V7doGI
- Original repository with a more complete version of the code: https://github.com/cbfinn/maml
Tools needed
- tensorflow>=2.13.0: https://www.tensorflow.org/
- numpy>=1.24.3: https://numpy.org/
Refer to this Repository in scientific document
@misc{simplemaml,
author = {Anas Neumann},
title = {Simple MAML},
year = {2023},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {\url{https://github.com/AnasNeumann/simplemaml}},
commit = {main}
}
Complete code
# MAML generic function
def MAML(model, alpha=0.005, beta=0.005, optimizer=keras.optimizers.Adam, c_loss=keras.losses.mse, f_loss=keras.losses.MeanSquaredError(), meta_epochs=100, meta_tasks_per_epoch=[10, 30], train_split=0.2, tasks=[]):
log_step = meta_epochs // 10 if meta_epochs > 10 else 1
optim_test=optimizer(learning_rate=alpha)
optim_test.build(model.trainable_variables)
model.compile(loss=f_loss, optimizer=optim_test)
losses=[]
total_l=0.
for step in range (meta_epochs):
task_gradients = []
model_copy = tf.keras.models.clone_model(model)
model_copy.build(model.input_shape)
model_copy.set_weights(model.get_weights())
optim_train=optimizer(learning_rate=beta)
optim_train.build(model_copy.trainable_variables)
model_copy.compile(loss=f_loss, optimizer=optim_train)
for _ in range(random.randint(meta_tasks_per_epoch[0], meta_tasks_per_epoch[1])):
t = tasks[random.randint(0, len(tasks)-1)]
split_idx = int(len(t["inputs"]) * train_split)
train_input = t["inputs"][:split_idx]
test_input = t["inputs"][split_idx:]
train_target = t["target"][:split_idx]
test_target = t["target"][split_idx:]
# Inner loop: Update the model copy on the current task
with tf.GradientTape(watch_accessed_variables=False) as train_tape:
train_tape.watch(model_copy.trainable_variables)
train_pred = model_copy(train_input)
train_loss = tf.reduce_mean(c_loss(train_target, train_pred))
g = train_tape.gradient(train_loss, model_copy.trainable_variables)
optim_train.apply_gradients(zip(g, model_copy.trainable_variables))
# Compute gradients with respect to the test data
with tf.GradientTape(watch_accessed_variables=False) as test_tape:
test_tape.watch(model_copy.trainable_variables)
test_pred = model_copy(test_input)
test_loss = tf.reduce_mean(c_loss(test_target, test_pred))
g = test_tape.gradient(test_loss, model_copy.trainable_variables)
task_gradients.append(g)
# Meta-update: apply the accumulated gradients to the original model
if task_gradients:
sum_gradients = [tf.reduce_mean(tf.stack([grads[layer] for grads in task_gradients]), axis=0)
for layer in range(len(model.trainable_variables))]
optim_test.apply_gradients(zip(sum_gradients, model.trainable_variables))
total_l += test_loss.numpy()
loss_evol = total_l/(step+1)
losses.append(loss_evol)
if step % log_step == 0:
print(f'Meta step: {step}. Loss: {loss_evol}')
return model, losses
REFERENCES
[1] Finn, C., Abbeel, P. & Levine, S.. (2017). Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks. Proceedings of the 34th International Conference on Machine Learning, in Proceedings of Machine Learning Research 70:1126-1135 Available from https://proceedings.mlr.press/v70/finn17a.html and https://proceedings.mlr.press/v70/finn17a/finn17a.pdf.
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