def __init__(self, freq, activation, input, target_idx, task_loss, surrogate_loss,
hyperparameter, learning_rate, batch_generator, n_batches,
factor=1.5, n_updates=10):
Extension.__init__(self, 'adapt_zloss', freq)
self.batch_generator = batch_generator
self.n_batches = n_batches
self.learning_rate = learning_rate
self.hyperparameter = hyperparameter
self.factor = factor
self.n_updates = n_updates
# grad = theano.grad(surrogate_loss, activation)
# new_activation = activation - learning_rate * grad
self.fun_activation = theano.function([input], activation)
activation_bis = tensor.matrix()
surr_loss_bis = theano.clone(surrogate_loss,
replace={activation: activation_bis})
grad = theano.grad(surr_loss_bis, activation_bis)
new_activation = activation_bis - 100*learning_rate * grad
task_loss_bis = theano.clone(task_loss,
replace={activation: new_activation})
self.fun_update_task_loss = theano.function(
[activation_bis, target_idx], [task_loss_bis, new_activation])
def __init__(self, freq, words, embedding_matrix, knn, vocab, inv_vocab):
Extension.__init__(self, 'Nearest neighbours of words', freq)
self.words = words
self.embedding_matrix = embedding_matrix
self.knn = knn
self.vocab = vocab
self.inv_vocab = inv_vocab
self.word_ids = []
for word in words:
self.word_ids.append(self.vocab[word])
def __init__(self, freq, words, embedding_matrix, knn, vocab, inv_vocab):
Extension.__init__(self, 'Nearest neighbours of words', freq)
self.words = words
self.embedding_matrix = embedding_matrix
self.knn = knn
self.vocab = vocab
self.inv_vocab = inv_vocab
self.word_ids = []
for word in words:
self.word_ids.append(self.vocab[word])
def __init__(self,
freq,
activation,
input,
target_idx,
task_loss,
surrogate_loss,
hyperparameter,
learning_rate,
batch_generator,
n_batches,
factor=1.5,
n_updates=10):
Extension.__init__(self, 'adapt_zloss', freq)
self.batch_generator = batch_generator
self.n_batches = n_batches
self.learning_rate = learning_rate
self.hyperparameter = hyperparameter
self.factor = factor
self.n_updates = n_updates
# grad = theano.grad(surrogate_loss, activation)
# new_activation = activation - learning_rate * grad
self.fun_activation = theano.function([input], activation)
activation_bis = tensor.matrix()
surr_loss_bis = theano.clone(surrogate_loss,
replace={activation: activation_bis})
grad = theano.grad(surr_loss_bis, activation_bis)
new_activation = activation_bis - 100 * learning_rate * grad
task_loss_bis = theano.clone(task_loss,
replace={activation: new_activation})
self.fun_update_task_loss = theano.function(
[activation_bis, target_idx], [task_loss_bis, new_activation])
