def testLogPoissonLoss(self): x_shape = [5, 10] x_np = np.random.randn(*x_shape).astype(np.float32) z_np = np.random.randint(0, 5, size=x_shape).astype(np.float32) y_np = self._log_poisson_loss(x_np, z_np, compute_full_loss=False) y_np_stirling = self._log_poisson_loss(x_np, z_np, compute_full_loss=True) y_tf = nn_impl.log_poisson_loss(z_np, x_np, compute_full_loss=False) y_tf_stirling = nn_impl.log_poisson_loss(z_np, x_np, compute_full_loss=True) y_tf_np = self.evaluate(y_tf) y_tf_np_stirling = self.evaluate(y_tf_stirling) eps = 1e-3 self.assertAllClose(y_tf_np, y_np, eps) self.assertAllClose(y_tf_np_stirling, y_np_stirling, eps)
def testLogPoissonLoss(self): x_shape = [5, 10] x_np = np.random.randn(*x_shape).astype(np.float32) z_np = np.random.randint(0, 5, size=x_shape).astype(np.float32) y_np = self._log_poisson_loss(x_np, z_np, compute_full_loss=False) y_np_stirling = self._log_poisson_loss(x_np, z_np, compute_full_loss=True) y_tf = nn_impl.log_poisson_loss(z_np, x_np, compute_full_loss=False) y_tf_stirling = nn_impl.log_poisson_loss(z_np, x_np, compute_full_loss=True) y_tf_np = self.evaluate(y_tf) y_tf_np_stirling = self.evaluate(y_tf_stirling) eps = 1e-3 self.assertAllClose(y_tf_np, y_np, eps) self.assertAllClose(y_tf_np_stirling, y_np_stirling, eps)
def testGradient(self):
x_shape = [5, 10]
x_np = np.random.randn(*x_shape).astype(np.float64)
z_np = np.random.randint(0, 5, size=x_shape).astype(np.float64)
with self.test_session():
x_tf = constant_op.constant(x_np)
y_tf = nn_impl.log_poisson_loss(z_np, x_tf, compute_full_loss=False)
y_tf_stirling = nn_impl.log_poisson_loss(
z_np, x_tf, compute_full_loss=True)
err = gradient_checker.compute_gradient_error(x_tf, x_shape, y_tf,
x_shape)
err_stirling = gradient_checker.compute_gradient_error(
x_tf, x_shape, y_tf_stirling, x_shape)
eps = 1e-6
self.assertLess(err, eps)
self.assertLess(err_stirling, eps)
