def testOpScaleDifferentDtypes(self):
x_1 = tf.placeholder(tf.float16, shape=())
snt.scale_gradient(x_1, 0.1)
# clip_gradient throws here if the Defun func_name does not use the dtype.
x_2 = tf.placeholder(tf.float32, shape=())
snt.scale_gradient(x_2, 0.1)
def testOpScaleDifferentDtypes(self):
x_1 = tf.placeholder(tf.float16, shape=())
snt.scale_gradient(x_1, 0.1)
# clip_gradient throws here if the Defun func_name does not use the dtype.
x_2 = tf.placeholder(tf.float32, shape=())
snt.scale_gradient(x_2, 0.1)
def testTwoOps(self):
"""Tests that the op can be instantiated twice with appropriate results.
Implementations with inappropriate global registration of gradients will
fail this test.
"""
x = tf.placeholder(tf.float32, [1])
y = x * x
y = snt.scale_gradient(y, 0.1)
y = snt.scale_gradient(y, 0.1)
dydx = tf.gradients([y], [x])[0]
with self.test_session() as sess:
dydx_, y_ = sess.run([dydx, y], feed_dict={x: [3.0]})
self.assertAlmostEqual(dydx_[0], 2 * 0.1**2 * 3.0, places=6)
self.assertAlmostEqual(y_[0], 3.0 ** 2, places=6)
def testTwoOps(self):
"""Tests that the op can be instantiated twice with appropriate results.
Implementations with inappropriate global registration of gradients will
fail this test.
"""
x = tf.placeholder(tf.float32, [1])
y = x * x
y = snt.scale_gradient(y, 0.1)
y = snt.scale_gradient(y, 0.1)
dydx = tf.gradients([y], [x])[0]
with self.test_session() as sess:
dydx_, y_ = sess.run([dydx, y], feed_dict={x: [3.0]})
self.assertAlmostEqual(dydx_[0], 2 * 0.1**2 * 3.0, places=6)
self.assertAlmostEqual(y_[0], 3.0 ** 2, places=6)
def testOpScale(self, x_, scale):
x = tf.placeholder(tf.float32, [1])
y = x * x
y = snt.scale_gradient(y, scale)
dydx = tf.gradients([y], [x])[0]
if scale == 0.0:
self.assertEqual(y.op.type, "StopGradient")
self.assertIs(dydx, None)
else:
if scale == 1.0:
self.assertEqual(y.op.type, "Identity")
else:
self.assertEqual(y.op.type, "ScaleGradient_float32")
with self.test_session() as sess:
dydx_, y_ = sess.run([dydx, y], feed_dict={x: [x_]})
self.assertAlmostEqual(dydx_[0], 2 * scale * x_, places=6)
self.assertAlmostEqual(y_[0], x_**2, places=6)
def testOpScale(self, x_, scale):
x = tf.placeholder(tf.float32, [1])
y = x * x
y = snt.scale_gradient(y, scale)
dydx = tf.gradients([y], [x])[0]
if scale == 0.0:
self.assertEqual(y.op.type, "StopGradient")
self.assertIs(dydx, None)
else:
if scale == 1.0:
self.assertEqual(y.op.type, "Identity")
else:
self.assertEqual(y.op.type, "ScaleGradient_float32")
with self.test_session() as sess:
dydx_, y_ = sess.run([dydx, y], feed_dict={x: [x_]})
self.assertAlmostEqual(dydx_[0], 2 * scale * x_, places=6)
self.assertAlmostEqual(y_[0], x_ ** 2, places=6)
def testShape(self): x = tf.placeholder(tf.float32, [None, 10, 13]) y = snt.scale_gradient(x, 0.1) shape = tuple(y.get_shape().as_list()) self.assertEqual(shape, (None, 10, 13))
def testShape(self): x = tf.placeholder(tf.float32, [None, 10, 13]) y = snt.scale_gradient(x, 0.1) shape = tuple(y.get_shape().as_list()) self.assertEqual(shape, (None, 10, 13))
