def get_single_state_feed_dict(self, single_state): xs = self.get_single_state_dict(single_state) feed_dict = create_x_feed_dict( self.action_model.get_input_vars(), xs) feed_dict.update(self.hidden_state_vals) feed_dict.update(create_supp_test_feed_dict(self.action_model)) return feed_dict
def compute_preds(self, xs, sess): xs = self.pred_xs_preprocessor(xs) feed_dict = create_x_feed_dict(self.input_vars, xs) feed_dict.update(create_supp_test_feed_dict(self)) preds = self.y_hat.eval(feed_dict=feed_dict, session=sess) return preds
def make_single_feed_dict(self, model, xs, y_var, y_val, train=True): feed_dict = create_x_feed_dict(model.get_input_vars(), xs) feed_dict.update(create_y_feed_dict(y_var, y_val)) if train: feed_dict.update(create_supp_train_feed_dict(model)) else: feed_dict.update(create_supp_test_feed_dict(model)) return feed_dict
def make_feed_dict(self, xs, y, mask, train=True): feed_dict = create_x_feed_dict(self.q_model.get_input_vars(), xs) feed_dict.update(create_y_feed_dict(self.y, y)) feed_dict.update({self.mask: mask}) if train: feed_dict.update(create_supp_train_feed_dict(self.q_model)) else: feed_dict.update(create_supp_test_feed_dict(self.q_model)) return feed_dict
def make_feed_dict(self, models, y_vars, inputs, train=True): feed_dict = {} for model_name, model in models.items(): feed_dict.update(create_x_feed_dict(model.get_input_vars(), inputs[0][model_name])) feed_dict.update(create_y_feed_dict(y_vars[model_name], inputs[1][model_name])) if train: for model_name, model in models.items(): feed_dict.update(create_supp_train_feed_dict(model)) else: for model_name, model in models.items(): feed_dict.update(create_supp_test_feed_dict(model)) return feed_dict
def compute(self, model, xs, y_var, y_val): feed_dict = create_x_feed_dict(model.input_vars, xs) feed_dict.update(create_y_feed_dict(y_var, y_val)) feed_dict.update(create_supp_test_feed_dict(model)) loss = self.loss.eval(feed_dict=feed_dict) return loss