src/core/train.py
def predict_sum(predict_var, x_unlabeled, inputs, y_true, batch_sizes, x_labeled=None, y_labeled=None):
    '''
    Convenience function: sums over all the points to return a single value
    per tensor in predict_var
    '''
    y = predict(predict_var, x_unlabeled, inputs, y_true, batch_sizes,
            x_labeled=x_labeled, y_labeled=y_labeled)
    return np.sum(y)

    Convenience function: sums over all the points to return a single value
    per tensor in predict_var
    
Convenience function: sums over all the points to return a single value
per tensor in predict_var
Convenience function : sums over all the points to return a single value per tensor in predict_var
def predict_sum(predict_var, x_unlabeled, inputs, y_true, batch_sizes, x_labeled=None, y_labeled=None):
    y = predict(predict_var, x_unlabeled, inputs, y_true, batch_sizes,
            x_labeled=x_labeled, y_labeled=y_labeled)
    return np.sum(y)
def predict_sum ( predict_var , x_unlabeled , inputs , y_true , batch_sizes , x_labeled = None , y_labeled = None ) : y = predict ( predict_var , x_unlabeled , inputs , y_true , batch_sizes , x_labeled = x_labeled , y_labeled = y_labeled ) return np . sum ( y )
Convenience function: sums over all the points to return a single value
per tensor in predict_var
Convenience function : sums over all the points to return a single value per tensor in predict_var
'''
    Convenience function: sums over all the points to return a single value
    per tensor in predict_var
    '''
predict_var
x_unlabeled
inputs
y_true
batch_sizes
x_labeled
y_labeled