6c91204831b4c7628ed6b0975b26df039f866c2c,deeppavlov/models/classifiers/intents/intent_model.py,KerasIntentModel,train,#KerasIntentModel#Any#,159

Before Change

        print("\n____Training over {} samples____\n\n".format(n_train_samples))

        while epochs_done < self.opt["epochs"]:
            batch_gen = dataset.batch_generator(batch_size=self.opt["batch_size"],
                                                data_type="train")
            for step, batch in enumerate(batch_gen):
                metrics_values = self.train_on_batch(batch)
                updates += 1

                if self.opt["verbose"] and step % 50 == 0:
                    log_metrics(names=self.metrics_names,
                                values=metrics_values,
                                updates=updates,
                                mode="train")

            epochs_done += 1
            if epochs_done % self.opt["val_every_n_epochs"] == 0:
                if "valid" in dataset.data.keys():
                    valid_metrics_values = self.model.test_on_batch(x=valid_x, y=valid_y)

                    log_metrics(names=self.metrics_names,
                                values=valid_metrics_values,
                                mode="valid")
                    if valid_metrics_values[0] > val_loss:
                        val_increase += 1
                        print("__Validation impatience {} out of {}".format(
                            val_increase, self.opt["val_patience"]))
                        if val_increase == self.opt["val_patience"]:
                            print("___Stop training: validation is out of patience___")
                            break
                    else:
                        val_increase = 0
                        val_loss = valid_metrics_values[0]
            print("epochs_done: {}".format(epochs_done))

        self.save()

    def infer(self, data, return_proba=False, *args):
        

After Change

        print("\n____Training over {} samples____\n\n".format(n_train_samples))

        try:
            while epochs_done < self.opt["epochs"]:
                batch_gen = dataset.batch_generator(batch_size=self.opt["batch_size"],
                                                    data_type="train")
                for step, batch in enumerate(batch_gen):
                    metrics_values = self.train_on_batch(batch)
                    updates += 1

                    if self.opt["verbose"] and step % 50 == 0:
                        log_metrics(names=self.metrics_names,
                                    values=metrics_values,
                                    updates=updates,
                                    mode="train")

                epochs_done += 1
                if epochs_done % self.opt["val_every_n_epochs"] == 0:
                    if "valid" in dataset.data.keys():
                        valid_metrics_values = self.model.test_on_batch(x=valid_x, y=valid_y)

                        log_metrics(names=self.metrics_names,
                                    values=valid_metrics_values,
                                    mode="valid")
                        if valid_metrics_values[0] > val_loss:
                            val_increase += 1
                            print("__Validation impatience {} out of {}".format(
                                val_increase, self.opt["val_patience"]))
                            if val_increase == self.opt["val_patience"]:
                                print("___Stop training: validation is out of patience___")
                                break
                        else:
                            val_increase = 0
                            val_loss = valid_metrics_values[0]
                print("epochs_done: {}".format(epochs_done))
        except KeyboardInterrupt:
            print("Interrupted", file=sys.stderr)

        self.save()

    def infer(self, data, return_proba=False, *args):
        

Instances