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hyperparameter_ranges = {
    "lr": ContinuousParameter(0.01, 0.1),
    "embedding-dim": CategoricalParameter([6, 12]),
    "hidden-dim": CategoricalParameter([6, 12])
}

objective_metric_name = "validation loss"
objective_type = "Minimize"
metric_definitions = [
    {"Name": objective_metric_name,

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estimator = PyTorch(entry_point="tuning.py",
                    source_dir="../../allennlp-sagemaker-tuning",
                    dependencies=[from_root("example"), from_root(".venv")],
                    role=role,
                    framework_version="1.0.0",
                    train_instance_count=1,
                    train_instance_type="ml.p2.8xlarge",
                    hyperparameters={
                        "train-file-name": os.path.basename(s3_paths[0]),
                        "validation": os.path.basename(s3_paths[1]),

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estimator = PyTorch(entry_point="tuning.py",
                    role=role,
                    framework_version="1.0.0",
                    train_instance_count=1,
                    train_instance_type="ml.p2.8xlarge",
                    hyperparameters={
                        "train-file-name": os.path.basename(s3_paths[0]),
                        "validation": os.path.basename(s3_paths[1]),
                        "epochs": 10
                    })

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model = LstmTagger(word_embeddings, lstm, vocab)
optimizer = optim.SGD(model.parameters(), lr=0.1)
iterator = BucketIterator(batch_size=2, sorting_keys=[("sentence", "num_tokens")])
iterator.index_with(vocab)

trainer = Trainer(model=model,
                  optimizer=optimizer,
                  iterator=iterator,
                  train_dataset=train_dataset,
                  validation_dataset=validation_dataset,

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class LstmTagger(Model):

    def __init__(self,
                 word_embeddings: TextFieldEmbedder,
                 encoder: Seq2SeqEncoder,
                 vocab: Vocabulary) -> None:
        super().__init__(vocab)
        self.word_embeddings = word_embeddings
        self.encoder = encoder
        self.hidden2tag = torch.nn.Linear(in_features=encoder.get_output_dim(),

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class EDINETGetDocumentSensor(BaseSensorOperator):

    @apply_defaults
    def __init__(self, document_type="xbrl", *args, **kwargs):
        self.document_type = document_type
        self._next_document_index = -1
        super().__init__(*args, **kwargs)

    def poke(self, context):
        document_ids = context["task_instance"].xcom_pull(

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class EDINETGetDocumentsOperator(BaseOperator):

    @apply_defaults
    def __init__(self, filter_func=None, *args, **kwargs):
        self.filter_func = filter_func
        super().__init__(*args, **kwargs)

    def execute(self, context):
        self.log.info("Retreave list of documents from EDINET @ {}.".format(
            self.start_date.strftime("%Y/%m/%d")))

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def update(self, states, actions, rewards, values):
    # Calculate values (or advantage) at outside of update process.
    advantage = reward - values
    action_probs = self.actor(states)
    selected_action_probs = action_probs[self.to_one_hot(actions)]
    neg_logs = - log(selected_action_probs)
    policy_loss = reduce_mean(neg_logs * advantages)
 

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def update(self, states, actions, rewards):
    values = self.critic(states)
    advantage = reward - tf.stop_gradient(values)  # Prevent gradient flows to critic
    action_probs = self.actor(states)
    selected_action_probs = action_probs[self.to_one_hot(actions)]
    neg_logs = - log(selected_action_probs)
    policy_loss = reduce_mean(neg_logs * advantages)