def testSigmoidGrad(self):
with ops.device("/device:IPU:0"):
pa = array_ops.placeholder(np.float32, [3], name="grad")
pb = array_ops.placeholder(np.float32, [3], name="in")
c = gen_math_ops.sigmoid_grad(pa, pb)
with ops.device('cpu'):
report = gen_ipu_ops.ipu_event_trace()
tu.configure_ipu_system()
with tu.ipu_session() as sess:
sess.run(report)
fd = {pa: [2.0, 0.5, 1.0], pb: [-1.0, 1.0, 6.0]}
result = sess.run(c, fd)
self.assertAllClose(result, [2.0, 0.25, 0.0])
result = sess.run(report)
self.assertTrue(len(result) == 3)
s = tu.extract_all_strings_from_event_trace(result)
cs_list = tu.get_compute_sets_from_report(s)
ok = ['__seed*', 'SigmoidGrad/custom-call/NonLinearityGrad']
self.assertTrue(tu.check_all_compute_sets_and_list(cs_list, ok))
def test_sigmoidgrad_3d(self):
y = constant_op.constant(self.generate_random_numbers(450, 1.0, 15.0),
shape=[10, 3, 15])
y_delta = constant_op.constant(self.generate_random_numbers(
450, -3.0, 10.0),
shape=[10, 3, 15])
out = sigmoid_grad(y, y_delta)
def run_test(sess):
return sess.run(out)
assert np.allclose(self.with_ngraph(run_test),
self.without_ngraph(run_test))
def testSigmoidGrad(self):
with self.session() as sess:
with ops.device("/device:IPU:0"):
pa = array_ops.placeholder(np.float32, [3], name="grad")
pb = array_ops.placeholder(np.float32, [3], name="in")
c = gen_math_ops.sigmoid_grad(pa, pb)
report = tu.ReportJSON(self, sess)
report.reset()
fd = {pa: [2.0, 0.5, 1.0], pb: [-1.0, 1.0, 6.0]}
result = sess.run(c, fd)
self.assertAllClose(result, [2.0, 0.25, 0.0])
report.parse_log(assert_len=4)
ok = ['__seed*', 'SigmoidGrad/sigmoid-grad/NonLinearityGrad']
report.assert_all_compute_sets_and_list(ok)
def _SigmoidGradGrad(op, grad):
with ops.control_dependencies([grad]):
a = math_ops.conj(op.inputs[0])
b = math_ops.conj(op.inputs[1])
gb = grad * b
return gb - 2.0 * gb * a, gen_math_ops.sigmoid_grad(a, grad)
def _SigmoidGrad(op, grad):
"""Returns grad * sigmoid(x) * (1 - sigmoid(x))."""
y = op.outputs[0] # y = sigmoid(x)
with ops.control_dependencies([grad]):
y = math_ops.conj(y)
return gen_math_ops.sigmoid_grad(y, grad)
def _SigmoidGradGrad(op, grad):
with ops.control_dependencies([grad]):
a = math_ops.conj(op.inputs[0])
b = math_ops.conj(op.inputs[1])
gb = grad * b
return gb - 2.0 * gb * a, gen_math_ops.sigmoid_grad(a, grad)
def _SigmoidGrad(op, grad):
"""Returns grad * sigmoid(x) * (1 - sigmoid(x))."""
y = op.outputs[0] # y = sigmoid(x)
with ops.control_dependencies([grad]):
y = math_ops.conj(y)
return gen_math_ops.sigmoid_grad(y, grad)
