def test_ab_relu_id(self, same_inputs, do_stabilize):
key = random.PRNGKey(1)
X0_1 = random.normal(key, (3, 2))
fc = stax.Dense(5, 1, 0)
X0_2 = None if same_inputs else random.normal(key, (4, 2))
# Test that ABRelu(a, a) == a * Identity
init_fn, apply_id, kernel_fn_id = stax.serial(fc, stax.Identity())
_, params = init_fn(key, input_shape=X0_1.shape)
for a in [-5, -1, -0.5, 0, 0.5, 1, 5]:
with self.subTest(a=a):
_, apply_ab_relu, kernel_fn_ab_relu = stax.serial(
fc, stax.ABRelu(a, a, do_stabilize=do_stabilize))
X1_1_id = a * apply_id(params, X0_1)
X1_1_ab_relu = apply_ab_relu(params, X0_1)
self.assertAllClose(X1_1_id, X1_1_ab_relu)
kernels_id = kernel_fn_id(X0_1 * a, None if X0_2 is None else a * X0_2)
kernels_ab_relu = kernel_fn_ab_relu(X0_1, X0_2)
# Manually correct the value of `is_gaussian` because
# `ab_relu` (incorrectly) sets `is_gaussian=False` when `a==b`.
kernels_ab_relu = kernels_ab_relu.replace(is_gaussian=True)
self.assertAllClose(kernels_id, kernels_ab_relu)
def test_ab_relu_id(self, same_inputs):
key = random.PRNGKey(1)
X0_1 = random.normal(key, (5, 7))
fc = stax.Dense(10, 1, 0)
X0_2 = None if same_inputs else random.normal(key, (9, 7))
# Test that ABRelu(a, a) == a * Identity
init_fn, apply_id, kernel_fn_id = stax.serial(fc, stax.Identity())
params = init_fn(key, input_shape=(-1, 7))
for a in [-5, -1, -0.5, 0, 0.5, 1, 5]:
with self.subTest(a=a):
_, apply_ab_relu, kernel_fn_ab_relu = stax.serial(
fc, stax.ABRelu(a, a))
X1_1_id = a * apply_id(params, X0_1)
X1_1_ab_relu = apply_ab_relu(params, X0_1)
self.assertAllClose(X1_1_id, X1_1_ab_relu, True)
kernels_id = kernel_fn_id(X0_1 * a,
None if X0_2 is None else a * X0_2)
kernels_ab_relu = kernel_fn_ab_relu(X0_1, X0_2,
('nngp', 'ntk'))
self.assertAllClose(kernels_id, kernels_ab_relu, True)
def test_abs(self, same_inputs):
key = random.PRNGKey(1)
X0_1 = random.normal(key, (5, 7))
fc = stax.Dense(10, 1, 0)
X0_2 = None if same_inputs else random.normal(key, (9, 7))
# Test that Abs == ABRelu(-1, 1)
init_fn, apply_leaky_relu, kernel_fn_abs = stax.serial(fc, stax.Abs())
_, apply_ab_relu, kernel_fn_ab_relu = stax.serial(fc, stax.ABRelu(-1, 1))
params = init_fn(key, input_shape=(-1, 7))
X1_1_abs = apply_leaky_relu(params, X0_1)
X1_1_ab_relu = apply_ab_relu(params, X0_1)
self.assertAllClose(X1_1_abs, X1_1_ab_relu, True)
kernels_abs = kernel_fn_abs(X0_1, X0_2, ('nngp', 'ntk'))
kernels_ab_relu = kernel_fn_ab_relu(X0_1, X0_2, ('nngp', 'ntk'))
self.assertAllClose(kernels_abs, kernels_ab_relu, True)
def test_abs(self, same_inputs, do_stabilize):
key = random.PRNGKey(1)
X0_1 = random.normal(key, (3, 2))
fc = stax.Dense(5, 1, 0)
X0_2 = None if same_inputs else random.normal(key, (4, 2))
# Test that Abs == ABRelu(-1, 1)
init_fn, apply_leaky_relu, kernel_fn_abs = stax.serial(
fc, stax.Abs(do_stabilize=do_stabilize))
_, apply_ab_relu, kernel_fn_ab_relu = stax.serial(fc, stax.ABRelu(-1, 1))
_, params = init_fn(key, input_shape=X0_1.shape)
X1_1_abs = apply_leaky_relu(params, X0_1)
X1_1_ab_relu = apply_ab_relu(params, X0_1)
self.assertAllClose(X1_1_abs, X1_1_ab_relu)
kernels_abs = kernel_fn_abs(X0_1, X0_2, ('nngp', 'ntk'))
kernels_ab_relu = kernel_fn_ab_relu(X0_1, X0_2, ('nngp', 'ntk'))
self.assertAllClose(kernels_abs, kernels_ab_relu)
def test_leaky_relu(self, same_inputs):
key = random.PRNGKey(1)
X0_1 = random.normal(key, (5, 7))
fc = stax.Dense(10, 1, 0)
X0_2 = None if same_inputs else random.normal(key, (9, 7))
# Test that ABRelu(alpha, 1) == LeakyRelu(alpha)
for a in [-2, -1, 0, 1, 2]:
with self.subTest(alpha=a):
init_fn, apply_leaky_relu, kernel_fn_leaky_relu = stax.serial(
fc, stax.LeakyRelu(a))
_, apply_ab_relu, kernel_fn_ab_relu = stax.serial(fc, stax.ABRelu(a, 1))
params = init_fn(key, input_shape=(-1, 7))
X1_1_leaky_relu = apply_leaky_relu(params, X0_1)
X1_1_ab_relu = apply_ab_relu(params, X0_1)
self.assertAllClose(X1_1_leaky_relu, X1_1_ab_relu, True)
kernels_leaky_relu = kernel_fn_leaky_relu(X0_1, X0_2)
kernels_ab_relu = kernel_fn_ab_relu(X0_1, X0_2)
self.assertAllClose(kernels_leaky_relu, kernels_ab_relu, True)
def test_ab_relu_relu(self, same_inputs):
key = random.PRNGKey(1)
X0_1 = random.normal(key, (5, 7))
fc = stax.Dense(10, 1, 0)
# Test that ABRelu(0, 1) == ReLU
init_fn, apply_relu, kernel_fn_relu = stax.serial(fc, stax.Relu())
params = init_fn(key, input_shape=(-1, 7))
X0_2 = None if same_inputs else random.normal(key, (9, 7))
for a, b in [(0, 1), (0, -1), (-1, 0), (1, 0)]:
with self.subTest(a=a, b=b):
_, apply_ab_relu, kernel_fn_ab_relu = stax.serial(fc, stax.ABRelu(a, b))
X1_1_relu = (b - a) * apply_relu(params, X0_1 * (-1 if a != 0 else 1))
X1_1_ab_relu = apply_ab_relu(params, X0_1)
self.assertAllClose(X1_1_relu, X1_1_ab_relu, True)
kernels_relu = kernel_fn_relu(X0_1, X0_2)
kernels_ab_relu = kernel_fn_ab_relu(X0_1, X0_2)
self.assertAllClose(kernels_relu, kernels_ab_relu, True)
'SAME',
'VALID',
'CIRCULAR'
]
STRIDES = [
None,
(1, 2),
(2, 1),
]
ACTIVATIONS = {
# TODO: investigate poor erf convergence.
stax.Erf(): 'erf',
stax.Relu(): 'Relu',
stax.ABRelu(-0.5, 0.7): 'ABRelu(-0.5, 0.7)'
}
PROJECTIONS = [
'FLAT',
'POOL',
'ATTN_FIXED',
'ATTN_PARAM'
]
LAYER_NORM = [
(-1,),
(1, 3),
(1, 2, 3)
]
def test_mask_fc(self, same_inputs, get, concat, p, mask_axis,
mask_constant):
width = 512
n_samples = 128
tol = 0.04
key = random.PRNGKey(1)
x1 = random.normal(key, (4, 6, 5, 7))
x1 = test_utils.mask(x1, mask_constant, mask_axis, key, p)
if same_inputs:
x2 = None
else:
x2 = random.normal(key, (2, 6, 5, 7))
x2 = test_utils.mask(x2, mask_constant, mask_axis, key, p)
nn = stax.serial(
stax.Flatten(), stax.FanOut(3),
stax.parallel(
stax.serial(
stax.Dense(width, 1., 0.1),
stax.Abs(),
stax.DotGeneral(lhs=-0.2),
stax.Dense(width, 1.5, 0.01),
),
stax.serial(
stax.Dense(width, 1.1, 0.1),
stax.DotGeneral(rhs=0.7),
stax.Erf(),
stax.Dense(width if concat != 1 else 512, 1.5, 0.1),
),
stax.serial(
stax.DotGeneral(rhs=0.5),
stax.Dense(width, 1.2),
stax.ABRelu(-0.2, 0.4),
stax.Dense(width if concat != 1 else 1024, 1.3, 0.2),
)),
(stax.FanInSum() if concat is None else stax.FanInConcat(concat)),
stax.Dense(width, 2., 0.01), stax.Relu())
if get == 'nngp':
init_fn, apply_fn, kernel_fn = stax.serial(
nn, stax.Dense(width, 1., 0.1))
elif get == 'ntk':
init_fn, apply_fn, kernel_fn = stax.serial(nn,
stax.Dense(1, 1., 0.1))
else:
raise ValueError(get)
kernel_fn_mc = nt.monte_carlo_kernel_fn(
init_fn,
apply_fn,
key,
n_samples,
device_count=0 if concat in (0, -2) else -1,
implementation=_DEFAULT_TESTING_NTK_IMPLEMENTATION,
vmap_axes=None if concat in (0, -2) else 0,
)
kernel_fn = jit(kernel_fn, static_argnames='get')
exact = kernel_fn(x1, x2, get, mask_constant=mask_constant)
empirical = kernel_fn_mc(x1, x2, get=get, mask_constant=mask_constant)
test_utils.assert_close_matrices(self, empirical, exact, tol)
'SAME',
'VALID',
'CIRCULAR'
]
STRIDES = [
None,
(1, 2),
(2, 1),
]
ACTIVATIONS = {
# TODO(romann): investigate poor erf convergence.
stax.Erf(): 'erf',
stax.Relu(): 'Relu',
stax.ABRelu(-3, 2): 'ABRelu(-3, 2)'
}
def _get_inputs(key, is_conv, same_inputs, input_shape, fun=np.cos):
key, split = random.split(key)
shape = input_shape if is_conv else (input_shape[0], np.prod(input_shape[1:]))
x1 = fun(random.normal(key, shape))
x2 = None if same_inputs else 2 * fun(random.normal(split, shape))
return x1, x2
def _get_net(W_std, b_std, filter_shape, is_conv, use_pooling, is_res,
padding, phi, strides, width, is_ntk):
fc = partial(stax.Dense, W_std=W_std, b_std=b_std)
conv = partial(
