def _RunAndVerify(self, dtype):
with self.test_session():
# random shape
shape = np.random.randint(1, 16, size=4)
# Make depth at least 2 to make it meaningful
shape[3] += 1
p = array_ops.placeholder(dtype, shape=shape)
# random depth_radius, bias, alpha, beta
lrn_depth_radius = np.random.randint(1, shape[3])
bias = 1.0 + np.random.rand()
alpha = 2.0 * np.random.rand()
beta = 2.0 * np.random.rand()
with self.test_scope():
lrn_t = nn.local_response_normalization(
p,
name="lrn",
depth_radius=lrn_depth_radius,
bias=bias,
alpha=alpha,
beta=beta)
params = {p: np.random.rand(*shape).astype("f")}
result = lrn_t.eval(feed_dict=params)
expected = self._LRN(params[p],
lrn_depth_radius=lrn_depth_radius,
bias=bias,
alpha=alpha,
beta=beta)
err = np.amax(np.abs(result - expected))
print("LRN error for bias ", bias, "alpha ", alpha, " beta ", beta,
" is ", err)
if dtype == dtypes.float32:
self.assertTrue(err < 1e-4)
else:
self.assertTrue(err < 1e-2)
self.assertShapeEqual(expected, lrn_t)
def _RunAndVerifyGradients(self, dtype):
with self.test_session(use_gpu=True):
# random shape
shape = np.random.randint(1, 5, size=4)
# Make depth at least 2 to make it meaningful
shape[3] += 1
# random depth_radius, bias, alpha, beta. cuDNN requires depth_radius to
# be in [1, 7].
lrn_depth_radius = np.random.randint(1, min(8, shape[3]))
bias = 1.0 + np.random.rand()
alpha = 1.0 * np.random.rand()
# cuDNN requires beta >= 0.01.
beta = 0.01 + 1.0 * np.random.rand()
if dtype == dtypes.float32:
inp_array = np.random.rand(*shape).astype(np.float32)
else:
inp_array = np.random.rand(*shape).astype(np.float16)
inp = constant_op.constant(
list(inp_array.ravel(order="C")), shape=shape, dtype=dtype)
lrn_op = nn.local_response_normalization(
inp,
name="lrn",
depth_radius=lrn_depth_radius,
bias=bias,
alpha=alpha,
beta=beta)
err = gradient_checker.compute_gradient_error(inp, shape, lrn_op, shape)
print("LRN Gradient error for bias ", bias, "alpha ", alpha, " beta ", beta,
" is ", err)
if dtype == dtypes.float32:
self.assertLess(err, 1e-4)
else:
self.assertLess(err, 1.0)
def _RunAndVerifyGradients(self, dtype):
with self.cached_session(use_gpu=True):
# random shape
shape = np.random.randint(1, 5, size=4)
# Make depth at least 2 to make it meaningful
shape[3] += 1
# random depth_radius, bias, alpha, beta. cuDNN requires depth_radius to
# be in [1, 7].
lrn_depth_radius = np.random.randint(1, min(8, shape[3]))
bias = 1.0 + np.random.rand()
alpha = 1.0 * np.random.rand()
# cuDNN requires beta >= 0.01.
beta = 0.01 + 1.0 * np.random.rand()
if dtype == dtypes.float32:
inp_array = np.random.rand(*shape).astype(np.float32)
else:
inp_array = np.random.rand(*shape).astype(np.float16)
inp = constant_op.constant(
list(inp_array.ravel(order="C")), shape=shape, dtype=dtype)
lrn_op = nn.local_response_normalization(
inp,
name="lrn",
depth_radius=lrn_depth_radius,
bias=bias,
alpha=alpha,
beta=beta)
err = gradient_checker.compute_gradient_error(inp, shape, lrn_op, shape)
print("LRN Gradient error for bias ", bias, "alpha ", alpha, " beta ", beta,
" is ", err)
if dtype == dtypes.float32:
self.assertLess(err, 1e-4)
else:
self.assertLess(err, 1.0)
def testGradientsZeroInput(self):
with self.test_session(use_gpu=True):
shape = [4, 4, 4, 4]
p = array_ops.placeholder(dtypes.float32, shape=shape)
inp_array = np.zeros(shape).astype("f")
lrn_op = nn.local_response_normalization(p, 2, 1.0, 0.0, 1.0, name="lrn")
grad = gradients_impl.gradients([lrn_op], [p])[0]
params = {p: inp_array}
r = grad.eval(feed_dict=params)
expected = np.ones(shape).astype("f")
self.assertAllClose(r, expected)
self.assertShapeEqual(expected, grad)
def testGradientsZeroInput(self):
with self.session(use_gpu=True):
shape = [4, 4, 4, 4]
p = array_ops.placeholder(dtypes.float32, shape=shape)
inp_array = np.zeros(shape).astype("f")
lrn_op = nn.local_response_normalization(p, 2, 1.0, 0.0, 1.0, name="lrn")
grad = gradients_impl.gradients([lrn_op], [p])[0]
params = {p: inp_array}
r = grad.eval(feed_dict=params)
expected = np.ones(shape).astype("f")
self.assertAllClose(r, expected)
self.assertShapeEqual(expected, grad)
def _RunAndVerify(self, dtype):
with self.test_session(use_gpu=True):
# random shape
shape = np.random.randint(1, 16, size=4)
# Make depth at least 2 to make it meaningful
shape[3] += 1
p = array_ops.placeholder(dtype, shape=shape)
# random depth_radius, bias, alpha, beta. cuDNN requires depth_radius to
# be in [1, 7].
lrn_depth_radius = np.random.randint(1, min(8, shape[3]))
bias = 1.0 + np.random.rand()
alpha = 2.0 * np.random.rand()
# cuDNN requires beta >= 0.01.
beta = 0.01 + 2.0 * np.random.rand()
lrn_t = nn.local_response_normalization(
p,
name="lrn",
depth_radius=lrn_depth_radius,
bias=bias,
alpha=alpha,
beta=beta)
params = {p: np.random.rand(*shape).astype("f")}
result = lrn_t.eval(feed_dict=params)
expected = self._LRN(
params[p],
lrn_depth_radius=lrn_depth_radius,
bias=bias,
alpha=alpha,
beta=beta)
err = np.amax(np.abs(result - expected))
print("LRN error for bias ", bias, "alpha ", alpha, " beta ", beta, " is ",
err)
if dtype == dtypes.float32:
self.assertTrue(err < 1e-4)
else:
self.assertTrue(err < 1e-2)
self.assertShapeEqual(expected, lrn_t)