00de689e6624b4b3c312c921226ad23f49a79229,deeppavlov/core/models/keras_model.py,KerasModel,load,#KerasModel#Any#Any#Any#Any#Any#Any#Any#Any#Any#Any#Any#,121

Before Change

        metrics_names = metrics_names.split(" ")
        metrics_funcs = []
        for i in range(len(metrics_names)):
            metrics_func = getattr(keras.metrics, metrics_names[i], None)
            if callable(metrics_func):
                metrics_funcs.append(metrics_func)
            else:
                metrics_func = getattr(add_metrics_file, metrics_names[i], None)
                if callable(metrics_func):
                    metrics_funcs.append(metrics_func)
                else:
                    raise AttributeError("Metric {} is not defined".format(metrics_names[i]))

        model.compile(optimizer=optimizer_,
                      loss=loss,
                      metrics=metrics_funcs,
                      loss_weights=loss_weights,

After Change

            opt_path = "{}_opt.json".format(self.model_path)
            weights_path = "{}.h5".format(self.model_path)

        if Path(opt_path).exists() and Path(weights_path).exists():

            self.opt = read_json(opt_path)

            model_func = getattr(self, model_name, None)
            if callable(model_func):
                model = model_func(params=self.opt)
            else:
                raise AttributeError("Model {} is not defined".format(model_name))

            print("Loading weights from `{}{}`".format(self._model_file, ".h5"))
            model.load_weights(weights_path)

            optimizer_func = getattr(keras.optimizers, optimizer_name, None)
            if callable(optimizer_func):
                optimizer_ = optimizer_func(lr=lr, decay=decay)
            else:
                raise AttributeError("Optimizer {} is not callable".format(optimizer_name))

            loss_func = getattr(keras.losses, loss_name, None)
            if callable(loss_func):
                loss = loss_func
            else:
                raise AttributeError("Loss {} is not defined".format(loss_name))

            metrics_names = metrics_names.split(" ")
            metrics_funcs = []
            for i in range(len(metrics_names)):
                metrics_func = getattr(keras.metrics, metrics_names[i], None)
                if callable(metrics_func):
                    metrics_funcs.append(metrics_func)
                else:
                    metrics_func = getattr(add_metrics_file, metrics_names[i], None)
                    if callable(metrics_func):
                        metrics_funcs.append(metrics_func)
                    else:
                        raise AttributeError("Metric {} is not defined".format(metrics_names[i]))

            model.compile(optimizer=optimizer_,
                          loss=loss,
                          metrics=metrics_funcs,
                          loss_weights=loss_weights,
                          sample_weight_mode=sample_weight_mode,
                          weighted_metrics=weighted_metrics,
                          target_tensors=target_tensors)
            return model
        else:
            self.init_model_from_scratch(model_name, optimizer_name,
                                         lr, decay, loss_name)

    @abstractmethod
    def train_on_batch(self, batch):
        
        Method trains the model on a single batch of data

Instances