import unittest import numpy from paddle.v2.framework.op import Operator from gradient_checker import GradientChecker from gradient_checker import get_numeric_gradient class GetNumericGradientTest(unittest.TestCase): def test_add_op(self): add_op = Operator('add', X="X", Y="Y", Out="Z") x = numpy.random.random((10, 1)).astype("float32") y = numpy.random.random((10, 1)).astype("float32") arr = get_numeric_gradient(add_op, {'X': x, "Y": y}, 'Z', 'X') self.assertAlmostEqual(arr.mean(), 1.0, delta=1e-4) def test_softmax_op(self): def stable_softmax(x): """Compute the softmax of vector x in a numerically stable way.""" shiftx = x - numpy.max(x) exps = numpy.exp(shiftx) return exps / numpy.sum(exps) def label_softmax_grad(Y, dY): dX = Y * 0.0 for i in range(Y.shape[0]): d = numpy.dot(Y[i, :], dY[i, :]) dX[i, :] = Y[i, :] * (dY[i, :] - d) return dX softmax_op = Operator("softmax", X="X", Y="Y") X = numpy.random.random((2, 2)).astype("float32") Y = numpy.apply_along_axis(stable_softmax, 1, X) dY = numpy.ones(Y.shape) dX = label_softmax_grad(Y, dY) arr = get_numeric_gradient(softmax_op, {"X": X}, 'Y', 'X') numpy.testing.assert_almost_equal(arr, dX, decimal=1e-2) if __name__ == '__main__': unittest.main()