tools/utilities/pythonlibs/audio/training/train_classifier.py
def batch_accuracy(self, scores, labels):
        """ Compute the training accuracy of the results of a single mini-batch """
        batch_size = scores.shape[0]
        passed = 0
        for i in range(batch_size):
            expected = labels[i]
            actual = scores[i].argmax()
            if expected == actual:
                passed += 1

        return (float(passed) * 100.0 / float(batch_size), passed)
 Compute the training accuracy of the results of a single mini-batch 
Compute the training accuracy of the results of a single mini-batch
Compute the training accuracy of the results of a single mini - batch
def batch_accuracy(self, scores, labels):
        batch_size = scores.shape[0]
        passed = 0
        for i in range(batch_size):
            expected = labels[i]
            actual = scores[i].argmax()
            if expected == actual:
                passed += 1
        return (float(passed) * 100.0 / float(batch_size), passed)
def batch_accuracy ( self , scores , labels ) : batch_size = scores . shape [ 0 ] passed = 0 for i in range ( batch_size ) : expected = labels [ i ] actual = scores [ i ] . argmax ( ) if expected == actual : passed += 1 return ( float ( passed ) * 100.0 / float ( batch_size ) , passed )
Compute the training accuracy of the results of a single mini-batch
Compute the training accuracy of the results of a single mini - batch
""" Compute the training accuracy of the results of a single mini-batch """
self
scores
labels