我们从Python开源项目中，提取了以下2个代码示例，用于说明如何使用keras.optimizers.Optimizer()。
def compile(self, *args, **kwargs): '''Refer to Model.compile docstring for parameters. Override functionality is documented below. :override compile: Override Model.compile method to check for options that the optimizer is multi-gpu enabled, and synchronize initial variables. ''' initsync = self._initsync usenccl = self._usenccl opt = kwargs['optimizer'] # if isinstance(opt, str): if not isinstance(opt, KO.Optimizer): opt = KO.get(opt) kwargs['optimizer'] = opt if self._syncopt and not getattr(opt, 'ismgpu', False): raise RuntimeError( 'Multi-GPU synchronization model requires a multi-GPU ' 'optimizer. Instead got: {}'.format(opt)) opt.usenccl = usenccl if self._enqueue_ops: # Produces a warning that kwargs are ignored for Tensorflow. Patch # Function in tensorflow_backend to use the enqueue_ops option. kwargs['fetches'] = self._enqueue_ops super(ModelMGPU, self).compile(*args, **kwargs) if initsync: self._run_initsync()
def __init__(self, input_size_per_time_step: int, allowed_characters: List[chr], use_raw_wave_input: bool = False, activation: str = "relu", output_activation: str = "softmax", optimizer: Optimizer = Adam(1e-4), dropout: Optional[float] = None, load_model_from_directory: Optional[Path] = None, load_epoch: Optional[int] = None, allowed_characters_for_loaded_model: Optional[List[chr]] = None, frozen_layer_count: int = 0, reinitialize_trainable_loaded_layers: bool = False, use_asg: bool = False, asg_transition_probabilities: Optional[ndarray] = None, asg_initial_probabilities: Optional[ndarray] = None, kenlm_directory: Path = None): if frozen_layer_count > 0 and load_model_from_directory is None: raise ValueError("Layers cannot be frozen if model is trained from scratch.") self.kenlm_directory = kenlm_directory self.grapheme_encoding = AsgGraphemeEncoding(allowed_characters=allowed_characters) \ if use_asg else CtcGraphemeEncoding(allowed_characters=allowed_characters) self.asg_transition_probabilities = self._default_asg_transition_probabilities( self.grapheme_encoding.grapheme_set_size) \ if asg_transition_probabilities is None else asg_transition_probabilities self.asg_initial_probabilities = self._default_asg_initial_probabilities( self.grapheme_encoding.grapheme_set_size) \ if asg_initial_probabilities is None else asg_initial_probabilities self.use_asg = use_asg self.frozen_layer_count = frozen_layer_count self.output_activation = output_activation self.activation = activation self.use_raw_wave_input = use_raw_wave_input self.input_size_per_time_step = input_size_per_time_step self.optimizer = optimizer self.load_epoch = load_epoch self.dropout = dropout self.predictive_net = self.create_predictive_net() self.prediction_phase_flag = 0. if self.kenlm_directory is not None: expected_characters = list( single(read_text(self.kenlm_directory / "vocabulary", encoding='utf8').splitlines()).lower()) if allowed_characters != expected_characters: raise ValueError("Allowed characters {} differ from those expected by kenlm decoder: {}". format(allowed_characters, expected_characters)) if load_model_from_directory is not None: self.load_weights( allowed_characters_for_loaded_model, load_epoch, load_model_from_directory, loaded_first_layers_count=frozen_layer_count if reinitialize_trainable_loaded_layers else None)
