def predict_heinsfeld_autoencoder():
trn_x, trn_y = read_data('rois_cc200')
tst_x, tst_y = read_data('rois_cc200', training=False)
clf = Autoencoder(num_classes=2,
dropout=(0.6, 0.8),
learning_rate=(0.0001, 0.0001, 0.0005),
momentum=0.9,
noise=(0.2, 0.3),
batch_size=(100, 10, 10),
num_epochs=(700, 2000, 100))
clf.predict(trn_x, trn_y, tst_x, tst_y)
class Agent(agents.random.RandomAgent(epochs)):
def __init__(self, *args):
super().__init__(*args)
self.model = Autoencoder(encoder=self.encode,
shape=self.shape,
beta=1.)
def act(self, seqs):
return list(
zip(*sorted(zip(self.model.predict(seqs), seqs))
[-self.batch:]))[1]
def observe(self, data):
super().observe(data)
self.model.fit(*zip(*self.seen.items()), epochs=epochs)