def prepare(self, params, train_size): self.params = params self.train_size = train_size self.resampler = WeightedResampler(train_size) self.D = self.resampler.weights self.alphas = [] self.member_number = 0
def prepare(self, params, dataset): self.params = params self.dataset = dataset self.resampler = WeightedResampler(dataset) self.D = self.resampler.weights self.alphas = [] self.member_number = 0
def prepare(self, params, dataset): self.params = params self.dataset = dataset self.resampler = WeightedResampler(dataset) self.D = self.resampler.weights # <--- sets the initial weights (step 1 in [1]) self.alphas = [] self.member_number = 0
def prepare(self, params, dataset): self.params = params self.dataset = dataset self.resampler = WeightedResampler(dataset) self.D = self.resampler.weights self.weights = None self.member_number = 0 self.alphas = [] model_yaml = read_yaml_file(params.model_file) self.model_config = keras.models.model_from_yaml( model_yaml).get_config()
def test_weighted_resampler(self): r = WeightedResampler(self.dataset) print numpy.asarray(r.make_new_train(10)) r.update_weights(self.weights) print numpy.asarray(r.make_new_train(10))