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fann_train_epoch

(PECL fann >= 1.0.0)

fann_train_epoch — 使用一组训练数据训练一个周期。

说明

fann_train_epoch ( resource $ann , resource $data ) : float

使用保存在 data 中训练数据训练一个周期。一个训练周期表示所有的训练数据正好使用了一次。

这个函数将会返回在其实际计算之前或当中被计算的 MSE 错误。但是因为计算需要再次走一遍整个训练集，所有训练周期之后的不是真正的 MSE。在训练中使用这个值是绰绰有余的。

该函数使用的是被 fann_set_training_algorithm() 函数选中的训练算法。

参数

ann: Neural network resource.
data: Neural network training data resource.

返回值

成功，则返回 MSE, 错误则返回 FALSE .

参见

fann_train_on_data() - 在整个数据集上训练一段时间。
fann_test_data() - 使用训练数据来测试并且计算出 MSE .
fann_get_MSE() - 从网络中读取均方误差。
fann_set_training_algorithm() - 设置训练算法。

User Contributed Notes

geekgirljoy at gmail dot com 29-Oct-2018 02:26


This code demonstrates training XOR using fann_train_epoch and will let you watch the training process by observing a psudo MSE (mean squared error).



Other training functions: fann_train_on_data, fann_train_on_file, fann_train.



fann_train_epoch is useful when you want to observe the ANN while it is training and perhaps save snapshots or compare competing networks during training. 



fann_train_epoch is different from fann_train in that it takes a data resource (training file) whereas fann_train takes an array of inputs and a separate array of outputs so use fann_train_epoch for observing training on data files (callback training resources) and use fann_train when observing manually specified data. 



Example code: 



<?php

$num_input = 2;

$num_output = 1;

$num_layers = 3; 

$num_neurons_hidden = 3; 

$desired_error = 0.0001;

$max_epochs = 500000;

$current_epoch = 0;

$epochs_between_saves = 100; // Minimum number of epochs between saves

$epochs_since_last_save = 0;

$filename = dirname(__FILE__) . "/xor.data";



// Initialize psudo mse (mean squared error) to a number greater than the desired_error

// this is what the network is trying to minimize.

$psudo_mse_result = $desired_error * 10000; // 1

$best_mse = $psudo_mse_result; // keep the last best seen MSE network score here



// Initialize ANN

$ann = fann_create_standard($num_layers, $num_input, $num_neurons_hidden, $num_output);



if ($ann) {

  echo 'Training ANN... ' . PHP_EOL; 

  

  // Configure the ANN

  fann_set_training_algorithm ($ann , FANN_TRAIN_BATCH);

  fann_set_activation_function_hidden($ann, FANN_SIGMOID_SYMMETRIC);

  fann_set_activation_function_output($ann, FANN_SIGMOID_SYMMETRIC);

  

  // Read training data

  $train_data = fann_read_train_from_file($filename);

  

  

  // Check if psudo_mse_result is greater than our desired_error 

  // if so keep training so long as we are also under max_epochs

  while(($psudo_mse_result > $desired_error) && ($current_epoch <= $max_epochs)){

    $current_epoch++;

    $epochs_since_last_save++;  

  

    // See: http://php.net/manual/en/function.fann-train-epoch.php

    // Train one epoch with the training data stored in data. 

    //

    // One epoch is where all of the training data is considered 

    // exactly once.

    //

    // This function returns the MSE error as it is calculated 

    // either before or during the actual training. This is not the 

    // actual MSE after the training epoch, but since calculating this 

    // will require to go through the entire training set once more. 

    // It is more than adequate to use this value during training.

    $psudo_mse_result = fann_train_epoch ($ann , $train_data );

    echo 'Epoch ' . $current_epoch . ' : ' . $psudo_mse_result . PHP_EOL; // report

    

    

    // If we haven't saved the ANN in a while...

    // and the current network is better then the previous best network

    // as defined by the current MSE being less than the last best MSE

    // Save it!

    if(($epochs_since_last_save >= $epochs_between_saves) && ($psudo_mse_result < $best_mse)){

      

      $best_mse = $psudo_mse_result; // we have a new best_mse

      

      // Save a Snapshot of the ANN

      fann_save($ann, dirname(__FILE__) . "/xor.net");

      echo 'Saved ANN.' . PHP_EOL; // report the save

      $epochs_since_last_save = 0; // reset the count

    }

  

  } // While we're training



  echo 'Training Complete! Saving Final Network.'  . PHP_EOL;

  

  // Save the final network

  fann_save($ann, dirname(__FILE__) . "/xor.net");  

  fann_destroy($ann); // free memory

}

echo 'All Done!' . PHP_EOL;

?>

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fann_train_epoch

说明

参数

返回值

参见

User Contributed Notes

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