Python mxnet.initializer() Examples

The following are 6 code examples of mxnet.initializer(). You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. You may also want to check out all available functions/classes of the module mxnet , or try the search function .
Example #1
Source File: train.py    From dynamic-training-with-apache-mxnet-on-aws with Apache License 2.0 5 votes vote down vote up 
def get_initializer(args):
    init_type = getattr(mx.initializer, args.config.get('train', 'initializer'))
    init_scale = args.config.getfloat('train', 'init_scale')
    if init_type is mx.initializer.Xavier:
        return mx.initializer.Xavier(magnitude=init_scale, factor_type=args.config.get('train', 'factor_type'))
    return init_type(init_scale)
Example #2
Source File: train.py    From training_results_v0.6 with Apache License 2.0 5 votes vote down vote up 
def get_initializer(args):
    init_type = getattr(mx.initializer, args.config.get('train', 'initializer'))
    init_scale = args.config.getfloat('train', 'init_scale')
    if init_type is mx.initializer.Xavier:
        return mx.initializer.Xavier(magnitude=init_scale, factor_type=args.config.get('train', 'factor_type'))
    return init_type(init_scale)
Example #3
Source File: detection_module.py    From simpledet with Apache License 2.0 5 votes vote down vote up 
def set_params(self, arg_params, aux_params, allow_missing=False, force_init=True,
                   allow_extra=False):
        """Assigns parameter and aux state values.

        Parameters
        ----------
        arg_params : dict
            Dictionary of name to `NDArray`.
        aux_params : dict
            Dictionary of name to `NDArray`.
        allow_missing : bool
            If ``True``, params could contain missing values, and the initializer will be
            called to fill those missing params.
        force_init : bool
            If ``True``, will force re-initialize even if already initialized.
        allow_extra : boolean, optional
            Whether allow extra parameters that are not needed by symbol.
            If this is True, no error will be thrown when arg_params or aux_params
            contain extra parameters that is not needed by the executor.
        Examples
        --------
        >>> # An example of setting module parameters.
        >>> sym, arg_params, aux_params = mx.model.load_checkpoint(model_prefix, n_epoch_load)
        >>> mod.set_params(arg_params=arg_params, aux_params=aux_params)
        """
        if not allow_missing:
            self.init_params(initializer=None, arg_params=arg_params, aux_params=aux_params,
                             allow_missing=allow_missing, force_init=force_init,
                             allow_extra=allow_extra)
            return

        if self.params_initialized and not force_init:
            warnings.warn("Parameters already initialized and force_init=False. "
                          "set_params call ignored.", stacklevel=2)
            return

        self._exec_group.set_params(arg_params, aux_params, allow_extra=allow_extra)

        # because we didn't update self._arg_params, they are dirty now.
        self._params_dirty = True
        self.params_initialized = True
Example #4
Source File: train.py    From deepspeech.mxnet with Apache License 2.0 5 votes vote down vote up 
def get_initializer(args):
    init_type = getattr(mx.initializer, args.config.get('train', 'initializer'))
    init_scale = args.config.getfloat('train', 'init_scale')
    if init_type is mx.initializer.Xavier:
        return mx.initializer.Xavier(magnitude=init_scale, factor_type=args.config.get('train', 'factor_type'))
    return init_type(init_scale)
Example #5
Source File: detection_module.py    From groupsoftmax-simpledet with Apache License 2.0 5 votes vote down vote up 
def set_params(self, arg_params, aux_params, allow_missing=False, force_init=True,
                   allow_extra=False):
        """Assigns parameter and aux state values.

        Parameters
        ----------
        arg_params : dict
            Dictionary of name to `NDArray`.
        aux_params : dict
            Dictionary of name to `NDArray`.
        allow_missing : bool
            If ``True``, params could contain missing values, and the initializer will be
            called to fill those missing params.
        force_init : bool
            If ``True``, will force re-initialize even if already initialized.
        allow_extra : boolean, optional
            Whether allow extra parameters that are not needed by symbol.
            If this is True, no error will be thrown when arg_params or aux_params
            contain extra parameters that is not needed by the executor.
        Examples
        --------
        >>> # An example of setting module parameters.
        >>> sym, arg_params, aux_params = mx.model.load_checkpoint(model_prefix, n_epoch_load)
        >>> mod.set_params(arg_params=arg_params, aux_params=aux_params)
        """
        if not allow_missing:
            self.init_params(initializer=None, arg_params=arg_params, aux_params=aux_params,
                             allow_missing=allow_missing, force_init=force_init,
                             allow_extra=allow_extra)
            return

        if self.params_initialized and not force_init:
            warnings.warn("Parameters already initialized and force_init=False. "
                          "set_params call ignored.", stacklevel=2)
            return

        self._exec_group.set_params(arg_params, aux_params, allow_extra=allow_extra)

        # because we didn't update self._arg_params, they are dirty now.
        self._params_dirty = True
        self.params_initialized = True
Example #6
Source File: train.py    From SNIPER-mxnet with Apache License 2.0 5 votes vote down vote up 
def get_initializer(args):
    init_type = getattr(mx.initializer, args.config.get('train', 'initializer'))
    init_scale = args.config.getfloat('train', 'init_scale')
    if init_type is mx.initializer.Xavier:
        return mx.initializer.Xavier(magnitude=init_scale, factor_type=args.config.get('train', 'factor_type'))
    return init_type(init_scale)