keras_utils.py 文件源码

def calc_metrics(self, data_gen, history, dataset, logs):
        y_true = []
        predictions = []
        for i in range(data_gen.steps):
            if self.verbose == 1:
                print "\r\tdone {}/{}".format(i, data_gen.steps),
            (x,y) = next(data_gen)
            pred = self.model.predict(x, batch_size=self.batch_size)
            if isinstance(x, list) and len(x) == 2: # deep supervision
                for m, t, p in zip(x[1].flatten(), y.flatten(), pred.flatten()):
                    if np.equal(m, 1):
                        y_true.append(t)
                        predictions.append(p)
            else:
                y_true += list(y.flatten())
                predictions += list(pred.flatten())
        print "\n"
        predictions = np.array(predictions)
        predictions = np.stack([1-predictions, predictions], axis=1)
        ret = metrics.print_metrics_binary(y_true, predictions)
        for k, v in ret.iteritems():
            logs[dataset + '_' + k] = v
        history.append(ret)