Hands-On Meta Learning with Python

Reptile

Now we'll see how can we find the optimal parameters of our neural network with Reptile.

First, we initialize the necessary variables:

#number of epochs i.e training iterationsnum_epochs = 100#number of samples i.e number of shotsnum_samples = 50 #number of tasksnum_tasks = 2#number of times we want to perform optimizationnum_iterations = 10#mini btach sizemini_batch = 10 

Then, we start the TensorFlow session:

with tf.Session() as sess:        sess.run(init)    

For the number of epochs:

    for e in range(num_epochs):                #for each task in batch of tasks        for task in range(num_tasks):

We get the initial parameters of the model:

            old_w1, old_b1, old_w2, old_b2 = sess.run([w1, b1, w2, b2,])

Then, we sample x and y:

 x_sample, y_sample = sample_points(num_samples) ...