Python allennlp.training.metrics.Average() Examples

def __init__(self, vocab: Vocabulary,
                       regularizer: RegularizerApplicator = None):
        super().__init__(vocab, regularizer)

        self.nsp_loss_function = torch.nn.CrossEntropyLoss(ignore_index=-1)
        self.lm_loss_function = torch.nn.CrossEntropyLoss(ignore_index=0)

        self._metrics = {
            "total_loss_ema": ExponentialMovingAverage(alpha=0.5),
            "nsp_loss_ema": ExponentialMovingAverage(alpha=0.5),
            "lm_loss_ema": ExponentialMovingAverage(alpha=0.5),
            "total_loss": Average(),
            "nsp_loss": Average(),
            "lm_loss": Average(),
            "lm_loss_wgt": WeightedAverage(),
            "mrr": MeanReciprocalRank(),
        }
        self._accuracy = CategoricalAccuracy() 

def __init__(self,
                 vocab            ,
                 sentence_embedder                   ,
                 action_embedding_dim     ,
                 encoder                ,
                 dropout        = 0.0,
                 rule_namespace      = u'rule_labels')        :
        super(NlvrSemanticParser, self).__init__(vocab=vocab)

        self._sentence_embedder = sentence_embedder
        self._denotation_accuracy = Average()
        self._consistency = Average()
        self._encoder = encoder
        if dropout > 0:
            self._dropout = torch.nn.Dropout(p=dropout)
        else:
            self._dropout = lambda x: x
        self._rule_namespace = rule_namespace

        self._action_embedder = Embedding(num_embeddings=vocab.get_vocab_size(self._rule_namespace),
                                          embedding_dim=action_embedding_dim)

        # This is what we pass as input in the first step of decoding, when we don't have a
        # previous action.
        self._first_action_embedding = torch.nn.Parameter(torch.FloatTensor(action_embedding_dim))
        torch.nn.init.normal_(self._first_action_embedding)

    #overrides 

def __init__(
        self,
        vocab: Vocabulary,
        sentence_embedder: TextFieldEmbedder,
        action_embedding_dim: int,
        encoder: Seq2SeqEncoder,
        dropout: float = 0.0,
        rule_namespace: str = "rule_labels",
    ) -> None:
        super(NlvrSemanticParser, self).__init__(vocab=vocab)

        self._sentence_embedder = sentence_embedder
        self._denotation_accuracy = Average()
        self._consistency = Average()
        self._encoder = encoder
        if dropout > 0:
            self._dropout = torch.nn.Dropout(p=dropout)
        else:
            self._dropout = lambda x: x
        self._rule_namespace = rule_namespace

        self._action_embedder = Embedding(
            num_embeddings=vocab.get_vocab_size(self._rule_namespace),
            embedding_dim=action_embedding_dim,
        )

        # This is what we pass as input in the first step of decoding, when we don't have a
        # previous action.
        self._first_action_embedding = torch.nn.Parameter(torch.FloatTensor(action_embedding_dim))
        torch.nn.init.normal_(self._first_action_embedding) 

def __init__(self, vocab: Vocabulary,
                 text_field_embedder: TextFieldEmbedder,
                 phrase_layer: Seq2SeqEncoder,
                 projected_layer: Seq2SeqEncoder,
                 flow_layer: Seq2SeqEncoder,
                 contextual_passage: Seq2SeqEncoder,
                 contextual_question: Seq2SeqEncoder,
                 dropout: float = 0.2,
                 regularizer: Optional[RegularizerApplicator] = None,
                 initializer: InitializerApplicator = InitializerApplicator(),
                 ):

        super(MultiGranularityHierarchicalAttentionFusionNetworks, self).__init__(vocab, regularizer)
        self._text_field_embedder = text_field_embedder
        self._phrase_layer = phrase_layer
        self._encoding_dim = self._phrase_layer.get_output_dim()
        self.projected_layer = torch.nn.Linear(self._encoding_dim + 1024, self._encoding_dim)
        self.fuse = FusionLayer(self._encoding_dim)
        self.projected_lstm = projected_layer
        self.flow = flow_layer
        self.contextual_layer_p = contextual_passage
        self.contextual_layer_q = contextual_question
        self.linear_self_align = torch.nn.Linear(self._encoding_dim, 1)
        self.bilinear_layer_s = BilinearSeqAtt(self._encoding_dim, self._encoding_dim)
        self.bilinear_layer_e = BilinearSeqAtt(self._encoding_dim, self._encoding_dim)
        self.yesno_predictor = torch.nn.Linear(self._encoding_dim, 3)
        self.relu = torch.nn.ReLU()

        self._max_span_length = 30

        self._span_start_accuracy = CategoricalAccuracy()
        self._span_end_accuracy = CategoricalAccuracy()
        self._span_accuracy = BooleanAccuracy()
        self._squad_metrics = SquadEmAndF1()
        self._span_yesno_accuracy = CategoricalAccuracy()
        self._official_f1 = Average()
        self._variational_dropout = InputVariationalDropout(dropout)

        self._loss = torch.nn.CrossEntropyLoss()
        initializer(self) 

def __init__(
        self,
        vocabulary: Vocabulary,
        input_size: int = 256,
        hidden_size: int = 128,
        num_layers: int = 2,
        dropout: float = 0.0,
    ):
        super().__init__()
        self._start_index = vocabulary.get_token_index("@start@", namespace="programs")
        self._end_index = vocabulary.get_token_index("@end@", namespace="programs")
        self._pad_index = vocabulary.get_token_index("@@PADDING@@", namespace="programs")
        self._unk_index = vocabulary.get_token_index("@@UNKNOWN@@", namespace="programs")

        vocab_size = vocabulary.get_vocab_size(namespace="programs")
        embedder_inner = Embedding(vocab_size, input_size, padding_index=self._pad_index)
        self._embedder = BasicTextFieldEmbedder({"programs": embedder_inner})

        self._encoder = PytorchSeq2SeqWrapper(
            nn.LSTM(
                input_size, hidden_size, num_layers=num_layers, dropout=dropout, batch_first=True
            )
        )
        # Project and tie input and output embeddings
        self._projection_layer = nn.Linear(hidden_size, input_size, bias=False)
        self._output_layer = nn.Linear(input_size, vocab_size, bias=False)
        self._output_layer.weight = embedder_inner.weight

        # Record average log2 (perplexity) for calculating final perplexity.
        self._log2_perplexity = Average() 

def __init__(self,
                 vocab            ,
                 sentence_embedder                   ,
                 action_embedding_dim     ,
                 encoder                ,
                 attention           ,
                 beam_size     ,
                 max_decoding_steps     ,
                 max_num_finished_states      = None,
                 dropout        = 0.0,
                 normalize_beam_score_by_length       = False,
                 checklist_cost_weight        = 0.6,
                 dynamic_cost_weight                               = None,
                 penalize_non_agenda_actions       = False,
                 initial_mml_model_file      = None)        :
        super(NlvrCoverageSemanticParser, self).__init__(vocab=vocab,
                                                         sentence_embedder=sentence_embedder,
                                                         action_embedding_dim=action_embedding_dim,
                                                         encoder=encoder,
                                                         dropout=dropout)
        self._agenda_coverage = Average()
        self._decoder_trainer: DecoderTrainer[Callable[[NlvrDecoderState], torch.Tensor]] =\
                ExpectedRiskMinimization(beam_size=beam_size,
                                         normalize_by_length=normalize_beam_score_by_length,
                                         max_decoding_steps=max_decoding_steps,
                                         max_num_finished_states=max_num_finished_states)

        # Instantiating an empty NlvrWorld just to get the number of terminals.
        self._terminal_productions = set(NlvrWorld([]).terminal_productions.values())
        self._decoder_step = NlvrDecoderStep(encoder_output_dim=self._encoder.get_output_dim(),
                                             action_embedding_dim=action_embedding_dim,
                                             input_attention=attention,
                                             dropout=dropout,
                                             use_coverage=True)
        self._checklist_cost_weight = checklist_cost_weight
        self._dynamic_cost_wait_epochs = None
        self._dynamic_cost_rate = None
        if dynamic_cost_weight:
            self._dynamic_cost_wait_epochs = dynamic_cost_weight[u"wait_num_epochs"]
            self._dynamic_cost_rate = dynamic_cost_weight[u"rate"]
        self._penalize_non_agenda_actions = penalize_non_agenda_actions
        self._last_epoch_in_forward: int = None
        # TODO (pradeep): Checking whether file exists here to avoid raising an error when we've
        # copied a trained ERM model from a different machine and the original MML model that was
        # used to initialize it does not exist on the current machine. This may not be the best
        # solution for the problem.
        if initial_mml_model_file is not None:
            if os.path.isfile(initial_mml_model_file):
                archive = load_archive(initial_mml_model_file)
                self._initialize_weights_from_archive(archive)
            else:
                # A model file is passed, but it does not exist. This is expected to happen when
                # you're using a trained ERM model to decode. But it may also happen if the path to
                # the file is really just incorrect. So throwing a warning.
                logger.warning(u"MML model file for initializing weights is passed, but does not exist."
                               u" This is fine if you're just decoding.") 

def __init__(self,
                 vocab            ,
                 question_embedder                   ,
                 action_embedding_dim     ,
                 encoder                ,
                 entity_encoder                ,
                 max_decoding_steps     ,
                 use_neighbor_similarity_for_linking       = False,
                 dropout        = 0.0,
                 num_linking_features      = 10,
                 rule_namespace      = u'rule_labels',
                 tables_directory      = u'/wikitables/')        :
        super(WikiTablesSemanticParser, self).__init__(vocab)
        self._question_embedder = question_embedder
        self._encoder = encoder
        self._entity_encoder = TimeDistributed(entity_encoder)
        self._max_decoding_steps = max_decoding_steps
        self._use_neighbor_similarity_for_linking = use_neighbor_similarity_for_linking
        if dropout > 0:
            self._dropout = torch.nn.Dropout(p=dropout)
        else:
            self._dropout = lambda x: x
        self._rule_namespace = rule_namespace
        self._denotation_accuracy = WikiTablesAccuracy(tables_directory)
        self._action_sequence_accuracy = Average()
        self._has_logical_form = Average()

        self._action_padding_index = -1  # the padding value used by IndexField
        num_actions = vocab.get_vocab_size(self._rule_namespace)
        self._action_embedder = Embedding(num_embeddings=num_actions, embedding_dim=action_embedding_dim)
        self._output_action_embedder = Embedding(num_embeddings=num_actions, embedding_dim=action_embedding_dim)
        self._action_biases = Embedding(num_embeddings=num_actions, embedding_dim=1)

        # This is what we pass as input in the first step of decoding, when we don't have a
        # previous action, or a previous question attention.
        self._first_action_embedding = torch.nn.Parameter(torch.FloatTensor(action_embedding_dim))
        self._first_attended_question = torch.nn.Parameter(torch.FloatTensor(encoder.get_output_dim()))
        torch.nn.init.normal_(self._first_action_embedding)
        torch.nn.init.normal_(self._first_attended_question)

        check_dimensions_match(entity_encoder.get_output_dim(), question_embedder.get_output_dim(),
                               u"entity word average embedding dim", u"question embedding dim")

        self._num_entity_types = 4  # TODO(mattg): get this in a more principled way somehow?
        self._num_start_types = 5  # TODO(mattg): get this in a more principled way somehow?
        self._embedding_dim = question_embedder.get_output_dim()
        self._type_params = torch.nn.Linear(self._num_entity_types, self._embedding_dim)
        self._neighbor_params = torch.nn.Linear(self._embedding_dim, self._embedding_dim)

        if num_linking_features > 0:
            self._linking_params = torch.nn.Linear(num_linking_features, 1)
        else:
            self._linking_params = None

        if self._use_neighbor_similarity_for_linking:
            self._question_entity_params = torch.nn.Linear(1, 1)
            self._question_neighbor_params = torch.nn.Linear(1, 1)
        else:
            self._question_entity_params = None
            self._question_neighbor_params = None 

def __init__(
        self,
        vocab: Vocabulary,
        utterance_embedder: TextFieldEmbedder,
        action_embedding_dim: int,
        encoder: Seq2SeqEncoder,
        decoder_beam_search: BeamSearch,
        max_decoding_steps: int,
        input_attention: Attention,
        add_action_bias: bool = True,
        dropout: float = 0.0,
        initializer: InitializerApplicator = InitializerApplicator(),
        regularizer: Optional[RegularizerApplicator] = None,
    ) -> None:
        super().__init__(vocab, regularizer)

        self._utterance_embedder = utterance_embedder
        self._encoder = encoder
        self._max_decoding_steps = max_decoding_steps
        self._add_action_bias = add_action_bias
        self._dropout = torch.nn.Dropout(p=dropout)

        self._exact_match = Average()
        self._valid_sql_query = Average()
        self._action_similarity = Average()
        self._denotation_accuracy = Average()

        # the padding value used by IndexField
        self._action_padding_index = -1
        num_actions = vocab.get_vocab_size("rule_labels")
        input_action_dim = action_embedding_dim
        if self._add_action_bias:
            input_action_dim += 1
        self._action_embedder = Embedding(
            num_embeddings=num_actions, embedding_dim=input_action_dim
        )
        self._output_action_embedder = Embedding(
            num_embeddings=num_actions, embedding_dim=action_embedding_dim
        )

        # This is what we pass as input in the first step of decoding, when we don't have a
        # previous action, or a previous utterance attention.
        self._first_action_embedding = torch.nn.Parameter(torch.FloatTensor(action_embedding_dim))
        self._first_attended_utterance = torch.nn.Parameter(
            torch.FloatTensor(encoder.get_output_dim())
        )
        torch.nn.init.normal_(self._first_action_embedding)
        torch.nn.init.normal_(self._first_attended_utterance)

        self._beam_search = decoder_beam_search
        self._decoder_trainer = MaximumMarginalLikelihood(beam_size=1)
        self._transition_function = BasicTransitionFunction(
            encoder_output_dim=self._encoder.get_output_dim(),
            action_embedding_dim=action_embedding_dim,
            input_attention=input_attention,
            add_action_bias=self._add_action_bias,
            dropout=dropout,
        )
        initializer(self) 

def __init__(self, vocab: Vocabulary,
                 text_field_embedder: TextFieldEmbedder,
                 phrase_layer: Seq2SeqEncoder,
                 residual_encoder: Seq2SeqEncoder,
                 span_start_encoder: Seq2SeqEncoder,
                 span_end_encoder: Seq2SeqEncoder,
                 initializer: InitializerApplicator,
                 dropout: float = 0.2,
                 pair2vec_dropout: float = 0.15,
                 max_span_length: int = 30,
                 pair2vec_model_file: str = None,
                 pair2vec_config_file: str = None
                 ) -> None:
        super().__init__(vocab)
        self._max_span_length = max_span_length
        self._text_field_embedder = text_field_embedder
        self._phrase_layer = phrase_layer
        self._encoding_dim = phrase_layer.get_output_dim()

        self.pair2vec = pair2vec_util.get_pair2vec(pair2vec_config_file, pair2vec_model_file)
        self._pair2vec_dropout = torch.nn.Dropout(pair2vec_dropout)

        self._matrix_attention = LinearMatrixAttention(self._encoding_dim, self._encoding_dim, 'x,y,x*y')

        # atten_dim = self._encoding_dim * 4 + 600 if ablation_type == 'attn_over_rels' else self._encoding_dim * 4
        atten_dim = self._encoding_dim * 4 + 600
        self._merge_atten = TimeDistributed(torch.nn.Linear(atten_dim, self._encoding_dim))

        self._residual_encoder = residual_encoder

        self._self_attention = LinearMatrixAttention(self._encoding_dim, self._encoding_dim, 'x,y,x*y')

        self._merge_self_attention = TimeDistributed(torch.nn.Linear(self._encoding_dim * 3,
                                                                     self._encoding_dim))

        self._span_start_encoder = span_start_encoder
        self._span_end_encoder = span_end_encoder

        self._span_start_predictor = TimeDistributed(torch.nn.Linear(self._encoding_dim, 1))
        self._span_end_predictor = TimeDistributed(torch.nn.Linear(self._encoding_dim, 1))
        self._squad_metrics = SquadEmAndF1()
        initializer(self)

        self._span_start_accuracy = CategoricalAccuracy()
        self._span_end_accuracy = CategoricalAccuracy()
        self._official_em = Average()
        self._official_f1 = Average()

        self._span_accuracy = BooleanAccuracy()
        self._variational_dropout = InputVariationalDropout(dropout) 

def __init__(self, vocab: Vocabulary,
                 elmo_embedder: TextFieldEmbedder,
                 tokens_embedder: TextFieldEmbedder,
                 features_embedder: TextFieldEmbedder,
                 phrase_layer: Seq2SeqEncoder,
                 projected_layer: Seq2SeqEncoder,
                 contextual_passage: Seq2SeqEncoder,
                 contextual_question: Seq2SeqEncoder,
                 dropout: float = 0.2,
                 regularizer: Optional[RegularizerApplicator] = None,
                 initializer: InitializerApplicator = InitializerApplicator(),
                 ):

        super(MultiGranularityHierarchicalAttentionFusionNetworks, self).__init__(vocab, regularizer)
        self.elmo_embedder = elmo_embedder
        self.tokens_embedder = tokens_embedder
        self.features_embedder = features_embedder
        self._phrase_layer = phrase_layer
        self._encoding_dim = self._phrase_layer.get_output_dim()
        self.projected_layer = torch.nn.Linear(self._encoding_dim + 1024, self._encoding_dim)
        self.fuse_p = FusionLayer(self._encoding_dim)
        self.fuse_q = FusionLayer(self._encoding_dim)
        self.fuse_s = FusionLayer(self._encoding_dim)
        self.projected_lstm = projected_layer
        self.contextual_layer_p = contextual_passage
        self.contextual_layer_q = contextual_question
        self.linear_self_align = torch.nn.Linear(self._encoding_dim, 1)
        # self._self_attention = LinearMatrixAttention(self._encoding_dim, self._encoding_dim, 'x,y,x*y')
        self._self_attention = BilinearMatrixAttention(self._encoding_dim, self._encoding_dim)
        self.bilinear_layer_s = BilinearSeqAtt(self._encoding_dim, self._encoding_dim)
        self.bilinear_layer_e = BilinearSeqAtt(self._encoding_dim, self._encoding_dim)
        self.yesno_predictor = FeedForward(self._encoding_dim, self._encoding_dim, 3)
        self.relu = torch.nn.ReLU()

        self._max_span_length = 30

        self._span_start_accuracy = CategoricalAccuracy()
        self._span_end_accuracy = CategoricalAccuracy()
        self._span_accuracy = BooleanAccuracy()
        self._squad_metrics = SquadEmAndF1()
        self._span_yesno_accuracy = CategoricalAccuracy()
        self._official_f1 = Average()
        self._variational_dropout = InputVariationalDropout(dropout)

        self._loss = torch.nn.CrossEntropyLoss()
        initializer(self) 

def __init__(self, vocab: Vocabulary,
                 text_field_embedder: TextFieldEmbedder,
                 phrase_layer: Seq2SeqEncoder,
                 projected_layer: Seq2SeqEncoder,
                 contextual_passage: Seq2SeqEncoder,
                 contextual_question: Seq2SeqEncoder,
                 dropout: float = 0.2,
                 regularizer: Optional[RegularizerApplicator] = None,
                 initializer: InitializerApplicator = InitializerApplicator(),
                 ):

        super(MultiGranularityHierarchicalAttentionFusionNetworks, self).__init__(vocab, regularizer)
        self._text_field_embedder = text_field_embedder
        self._phrase_layer = phrase_layer
        self._encoding_dim = self._phrase_layer.get_output_dim()
        self.projected_layer = torch.nn.Linear(self._encoding_dim + 1024, self._encoding_dim)
        self.fuse_p = FusionLayer(self._encoding_dim)
        self.fuse_q = FusionLayer(self._encoding_dim)
        self.fuse_s = FusionLayer(self._encoding_dim)
        self.projected_lstm = projected_layer
        self.contextual_layer_p = contextual_passage
        self.contextual_layer_q = contextual_question
        self.linear_self_align = torch.nn.Linear(self._encoding_dim, 1)
        # self.bilinear_self_align = BilinearSelfAlign(self._encoding_dim)
        # self._self_attention = LinearMatrixAttention(self._encoding_dim, self._encoding_dim, 'x,y,x*y')
        self._self_attention = BilinearMatrixAttention(self._encoding_dim, self._encoding_dim)
        self.bilinear_layer_s = BilinearSeqAtt(self._encoding_dim, self._encoding_dim)
        self.bilinear_layer_e = BilinearSeqAtt(self._encoding_dim, self._encoding_dim)
        self.yesno_predictor = torch.nn.Linear(self._encoding_dim, 3)
        self.relu = torch.nn.ReLU()

        self._max_span_length = 30

        self._span_start_accuracy = CategoricalAccuracy()
        self._span_end_accuracy = CategoricalAccuracy()
        self._span_accuracy = BooleanAccuracy()
        self._squad_metrics = SquadEmAndF1()
        self._span_yesno_accuracy = CategoricalAccuracy()
        self._official_f1 = Average()
        self._variational_dropout = InputVariationalDropout(dropout)

        self._loss = torch.nn.CrossEntropyLoss()
        initializer(self) 

def __init__(
        self,
        vocabulary: Vocabulary,
        source_namespace: str,
        target_namespace: str,
        input_size: int = 256,
        hidden_size: int = 256,
        num_layers: int = 2,
        dropout: float = 0.0,
        max_decoding_steps: int = 30,
    ):

        # @@PADDING@@, @@UNKNOWN@@, @start@, @end@ have same indices in all namespaces.
        self._pad_index = vocabulary.get_token_index("@@PADDING@@", namespace=source_namespace)
        self._unk_index = vocabulary.get_token_index("@@UNKNOWN@@", namespace=source_namespace)
        self._end_index = vocabulary.get_token_index("@end@", namespace=source_namespace)
        self._start_index = vocabulary.get_token_index("@start@", namespace=source_namespace)

        # Short-hand notations.
        __source_vocab_size = vocabulary.get_vocab_size(namespace=source_namespace)
        __target_vocab_size = vocabulary.get_vocab_size(namespace=target_namespace)

        # Source embedder converts tokenized source sequences to dense embeddings.
        __source_embedder = BasicTextFieldEmbedder(
            {"tokens": Embedding(__source_vocab_size, input_size, padding_index=self._pad_index)}
        )

        # Encodes the sequence of source embeddings into a sequence of hidden states.
        __encoder = PytorchSeq2SeqWrapper(
            nn.LSTM(input_size, hidden_size, num_layers, dropout=dropout, batch_first=True)
        )

        # Attention mechanism between decoder context and encoder hidden states at each time step.
        __attention = DotProductAttention()

        super().__init__(
            vocabulary,
            source_embedder=__source_embedder,
            encoder=__encoder,
            max_decoding_steps=max_decoding_steps,
            attention=__attention,
            target_namespace=target_namespace,
            use_bleu=True,
        )
        # Record four metrics - perplexity, sequence accuracy, word error rate and BLEU score.
        # super().__init__() already declared "self._bleu",
        # perplexity = 2 ** average_val_loss
        # word error rate = 1 - unigram recall
        self._log2_perplexity = Average()
        self._sequence_accuracy = SequenceAccuracy()
        self._unigram_recall = UnigramRecall() 

def __init__(self, vocab: Vocabulary,
                 text_field_embedder: TextFieldEmbedder,
                 sentence_encoder: Seq2VecEncoder,
                 classifier_feedforward: FeedForward,
                 label_weight: Dict[str, float] = None,
                 use_label_distribution: bool = False,
                 image_classification_ratio: float = 0.0,
                 decay_every_i_step=100000,
                 decay_ratio=0.8,
                 instance_count=100000,
                 max_epoch=10,
                 initializer: InitializerApplicator = InitializerApplicator(),
                 regularizer: Optional[RegularizerApplicator] = None
                 ) -> None:
        super(BasicClassifier, self).__init__(vocab, regularizer)

        self.text_field_embedder = text_field_embedder
        self.num_classes = self.vocab.get_vocab_size("labels")
        self.sentence_encoder = sentence_encoder
        self.classifier_feedforward = classifier_feedforward

        if text_field_embedder.get_output_dim() != sentence_encoder.get_input_dim():
            raise ConfigurationError("The output dimension of the text_field_embedder must match the "
                                     "input dimension of the title_encoder. Found {} and {}, "
                                     "respectively.".format(text_field_embedder.get_output_dim(),
                                                            sentence_encoder.get_input_dim()))
        self.metrics = {
                "accuracy": CategoricalAccuracy(),
                "cnn_loss": Average()
        }
        if not use_label_distribution:
            self.loss = torch.nn.CrossEntropyLoss()
        else:
            self.loss = torch.nn.CrossEntropyLoss()
        self.image_classification_ratio = image_classification_ratio
        self.decay_every_i_step = decay_every_i_step
        self.decay_ratio = decay_ratio
        self.training_step = 0
        self.current_ratio = image_classification_ratio
        self.total_steps = max_epoch*instance_count//64
        self.step_every_epoch = instance_count // 64

        print("每个epoch的step数量", self.step_every_epoch)

        initializer(self)