(self, callback_data, model, epochs)
Called when training is about to begin

        Arguments:
            callback_data (HDF5 dataset): shared data between callbacks
            model (Model): model object
            epochs (int): Total epochs
Called when training is about to begin
Called when training is about to begin
def on_train_begin(self, callback_data, model, epochs):
        """
        Called when training is about to begin

        Arguments:
            callback_data (HDF5 dataset): shared data between callbacks
            model (Model): model object
            epochs (int): Total epochs
        """
        self.minibatches = callback_data['config'].attrs['total_minibatches']

        hist_grp = callback_data.create_group("hist")
        hist_grp.attrs['bins'] = self.be.hist_bins
        hist_grp.attrs['offset'] = self.be.hist_offset
        hist_grp.attrs['time_markers'] = 'minibatch' if self.plot_per_mini else 'epoch'
        hist_grp.attrs['time_steps'] = self.minibatches if self.plot_per_mini else epochs
def on_train_begin ( self , callback_data , model , epochs ) : self . minibatches = callback_data [ 'config' ] . attrs [ 'total_minibatches' ] hist_grp = callback_data . create_group ( "hist" ) hist_grp . attrs [ 'bins' ] = self . be . hist_bins hist_grp . attrs [ 'offset' ] = self . be . hist_offset hist_grp . attrs [ 'time_markers' ] = 'minibatch' if self . plot_per_mini else 'epoch' hist_grp . attrs [ 'time_steps' ] = self . minibatches if self . plot_per_mini else epochs