def normalize_batch_in_training(x, gamma, beta, reduction_axes, epsilon=0.001):
 if (not hasattr(T.nnet.bn, 'batch_normalization_train')):
  return _old_normalize_batch_in_training(x, gamma, beta, reduction_axes, epsilon)
 (normed, mean, stdinv) = T.nnet.bn.batch_normalization_train(x, gamma, beta, reduction_axes, epsilon)
 return (normed, mean, T.inv((stdinv ** 2)))

def normalize_batch_in_training ( x , gamma , beta , reduction_axes , epsilon = 0.001 ) : if ( not hasattr ( T . nnet . bn , 'batch_normalization_train' ) ) : return _old_normalize_batch_in_training ( x , gamma , beta , reduction_axes , epsilon ) ( normed , mean , stdinv ) = T . nnet . bn . batch_normalization_train ( x , gamma , beta , reduction_axes , epsilon ) return ( normed , mean , T . inv ( ( stdinv ** 2 ) ) )
computes mean and std for batch then apply batch_normalization on batch .