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Top Forums UNIX for Dummies Questions & Answers Does formatting slice 2 (backup) destroy disk geometry? Post 302561748 by mia-chan on Wednesday 5th of October 2011 06:09:23 AM
Old 10-05-2011
Thank you for the response, fpmurphy. Based on your response, I am assuming that the way to use this disk in the future is to start fresh by formatting and rebuilding partitions?

I really appreciate your response; do you by any chance know where I can find some documentation that supports your assertion that the disk is usable? I would like to bring this information to my trainer. Have you ever heard of anyone else making a claim such as his? I really want to track down some more info on this case but haven't had much luck on my own.

If anyone has any resources describing disk architecture (and its non-volatile nature) I would find them most helpful; I can only seem to find elementary drawings and explanations of the physical disk parts while, based on the training I have received, there is supposedly some sort of software or firmware which allows the system to communicate with the disk and which is able to be deleted inadvertently by inexperienced admins.

Thanks again!
 

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FANN_CASCADETRAIN_ON_DATA(3)						 1					      FANN_CASCADETRAIN_ON_DATA(3)

fann_cascadetrain_on_data - Trains on an entire dataset, for a period of time using the Cascade2 training algorithm

SYNOPSIS
bool fann_cascadetrain_on_data (resource $ann, resource $data, int $max_neurons, int $neurons_between_reports, float $desired_error) DESCRIPTION
The cascade output change fraction is a number between 0 and 1 determining how large a fraction the fann_get_MSE(3) value should change within fann_get_cascade_output_stagnation_epochs(3) during training of the output connections, in order for the training not to stagnate. If the training stagnates, the training of the output connections will be ended and new candidates will be prepared. This training uses the parameters set using the fann_set_cascade_..., but it also uses another training algorithm as it's internal train- ing algorithm. This algorithm can be set to either FANN_TRAIN_RPROP or FANN_TRAIN_QUICKPROP by fann_set_training_algorithm(3), and the parameters set for these training algorithms will also affect the cascade training. PARAMETERS
o $ann -Neural network resource. o $data -Neural network training data resource. o $max_neurons - The maximum number of neurons to be added to neural network. o $neurons_between_reports - The number of neurons between printing a status report. A value of zero means no reports should be printed. o $desired_error - The desired fann_get_MSE(3) or fann_get_bit_fail(3), depending on which stop function is chosen by fann_set_train_stop_func- tion(3) RETURN VALUES
Returns TRUE on success, or FALSE otherwise. SEE ALSO
fann_train_on_data(3), fann_cascadetrain_on_file(3). PHP Documentation Group FANN_CASCADETRAIN_ON_DATA(3)
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