def get_loss(self, data):
with self.graph.as_default():
loss = self.sess.run(self.loss,
feed_dict={
self.features: process_x(data['x']),
self.labels: process_y(data['y'])
})
return loss
def get_softmax(self, data):
with self.graph.as_default():
soft_max = self.sess.run(self.soft_max,
feed_dict={
self.features: process_x(data['x']),
self.labels: process_y(data['y'])
})
return soft_max
def get_kl_gradients(self, data, output2):
with self.graph.as_default():
kl_grads = self.sess.run(self.kl_grads,
feed_dict={
self.features: process_x(data['x']),
self.labels: process_y(data['y']),
self.output2: output2
})
return kl_grads
def test(self, data):
'''
Args:
data: dict of the form {'x': [list], 'y': [list]}
'''
with self.graph.as_default():
tot_correct, loss = self.sess.run(
[self.eval_metric_ops, self.loss],
feed_dict={
self.features: process_x(data['x']),
self.labels: process_y(data['y'])
})
return tot_correct, loss