def test_behler_parrinello_network_neighbor_list(self, N_types, dtype):
key = random.PRNGKey(1)
R = np.array([[0, 0, 0], [1, 1, 1], [1, 1, 0]], dtype)
species = np.array([1, 1, N_types]) if N_types > 1 else None
box_size = f32(1.5)
displacement, _ = space.periodic(box_size)
neighbor_fn, nn_init, nn_apply = energy.behler_parrinello_neighbor_list(
displacement, box_size, species)
nbrs = neighbor_fn(R)
params = nn_init(key, R, nbrs)
nn_force_fn = grad(nn_apply, argnums=1)
nn_force = jit(nn_force_fn)(params, R, nbrs)
nn_energy = jit(nn_apply)(params, R, nbrs)
self.assertAllClose(np.any(np.isnan(nn_energy)), False)
self.assertAllClose(np.any(np.isnan(nn_force)), False)
self.assertAllClose(nn_force.shape, [3, 3])
def testGammaGrad(self, alpha):
rng = random.PRNGKey(0)
alphas = onp.full((100, ), alpha)
z = random.gamma(rng, alphas)
actual_grad = api.grad(lambda x: random.gamma(rng, x).sum())(alphas)
eps = 0.01 * alpha / (1.0 + onp.sqrt(alpha))
cdf_dot = (scipy.stats.gamma.cdf(z, alpha + eps) -
scipy.stats.gamma.cdf(z, alpha - eps)) / (2 * eps)
pdf = scipy.stats.gamma.pdf(z, alpha)
expected_grad = -cdf_dot / pdf
self.assertAllClose(
actual_grad,
expected_grad,
check_dtypes=True,
rtol=2e-2 if jtu.device_under_test() == "tpu" else 5e-4)
def main(unused_argv):
# Build data pipelines.
print('Loading data.')
x_train, y_train, x_test, y_test = \
datasets.mnist(FLAGS.train_size, FLAGS.test_size)
# Build the network
init_fn, f, _ = stax.serial(stax.Dense(2048, 1., 0.05), stax.Erf(),
stax.Dense(10, 1., 0.05))
key = random.PRNGKey(0)
_, params = init_fn(key, (-1, 784))
# Create and initialize an optimizer.
opt_init, opt_apply, get_params = optimizers.sgd(FLAGS.learning_rate)
state = opt_init(params)
# Create an mse loss function and a gradient function.
loss = lambda fx, y_hat: 0.5 * np.mean((fx - y_hat)**2)
grad_loss = jit(grad(lambda params, x, y: loss(f(params, x), y)))
# Create an MSE predictor to solve the NTK equation in function space.
ntk = batch(get_ntk_fun_empirical(f), batch_size=4, device_count=0)
g_dd = ntk(x_train, None, params)
g_td = ntk(x_test, x_train, params)
predictor = predict.gradient_descent_mse(g_dd, y_train, g_td)
# Get initial values of the network in function space.
fx_train = f(params, x_train)
fx_test = f(params, x_test)
# Train the network.
train_steps = int(FLAGS.train_time // FLAGS.learning_rate)
print('Training for {} steps'.format(train_steps))
for i in range(train_steps):
params = get_params(state)
state = opt_apply(i, grad_loss(params, x_train, y_train), state)
# Get predictions from analytic computation.
print('Computing analytic prediction.')
fx_train, fx_test = predictor(FLAGS.train_time, fx_train, fx_test)
# Print out summary data comparing the linear / nonlinear model.
util.print_summary('train', y_train, f(params, x_train), fx_train, loss)
util.print_summary('test', y_test, f(params, x_test), fx_test, loss)
def test_custom_root_scalar(self):
# TODO(shoyer): Figure out why this fails and re-enable it, if possible. My
# best guess is that TPUs use less stable numerics for pow().
if jtu.device_under_test() == "tpu":
raise SkipTest("Test fails on TPU")
def scalar_solve(f, y):
return y / f(1.0)
def binary_search(func, x0, low=0.0, high=100.0, tolerance=1e-6):
del x0 # unused
def cond(state):
low, high = state
return high - low > tolerance
def body(state):
low, high = state
midpoint = 0.5 * (low + high)
update_upper = func(midpoint) > 0
low = np.where(update_upper, low, midpoint)
high = np.where(update_upper, midpoint, high)
return (low, high)
solution, _ = lax.while_loop(cond, body, (low, high))
return solution
def sqrt_cubed(x, tangent_solve=scalar_solve):
f = lambda y: y ** 2. - np.array(x) ** 3.
return lax.custom_root(f, 0.0, binary_search, tangent_solve)
value, grad = api.value_and_grad(sqrt_cubed)(5.0)
self.assertAllClose(value, 5 ** 1.5, check_dtypes=False, rtol=1e-6)
self.assertAllClose(grad, api.grad(pow)(5.0, 1.5), check_dtypes=False,
rtol=1e-7)
jtu.check_grads(sqrt_cubed, (5.0,), order=2, rtol=1e-3)
# TODO(shoyer): reenable when batching works
# inputs = np.array([4.0, 5.0])
# results = api.vmap(sqrt_cubed)(inputs)
# self.assertAllClose(results, inputs ** 1.5, check_dtypes=False)
results = api.jit(sqrt_cubed)(5.0)
self.assertAllClose(results, 5.0 ** 1.5, check_dtypes=False,
rtol={onp.float64:1e-7})
def main(unused_argv):
# Build data pipelines.
print('Loading data.')
x_train, y_train, x_test, y_test = \
datasets.mnist(FLAGS.train_size, FLAGS.test_size)
# Build the network
init_fn, f = stax.serial(layers.Dense(4096), stax.Tanh, layers.Dense(10))
key = random.PRNGKey(0)
_, params = init_fn(key, (-1, 784))
# Create and initialize an optimizer.
opt_init, opt_apply, get_params = optimizers.sgd(FLAGS.learning_rate)
state = opt_init(params)
# Create an mse loss function and a gradient function.
loss = lambda fx, y_hat: 0.5 * np.mean((fx - y_hat)**2)
grad_loss = jit(grad(lambda params, x, y: loss(f(params, x), y)))
# Create an MSE predictor to solve the NTK equation in function space.
theta = tangents.ntk(f, batch_size=32)
g_dd = theta(params, x_train)
g_td = theta(params, x_test, x_train)
predictor = tangents.analytic_mse_predictor(g_dd, y_train, g_td)
# Get initial values of the network in function space.
fx_train = f(params, x_train)
fx_test = f(params, x_test)
# Train the network.
train_steps = int(FLAGS.train_time // FLAGS.learning_rate)
print('Training for {} steps'.format(train_steps))
for i in range(train_steps):
params = get_params(state)
state = opt_apply(i, grad_loss(params, x_train, y_train), state)
# Get predictions from analytic computation.
print('Computing analytic prediction.')
fx_train, fx_test = predictor(fx_train, fx_test, FLAGS.train_time)
# Print out summary data comparing the linear / nonlinear model.
util.print_summary('train', y_train, f(params, x_train), fx_train, loss)
util.print_summary('test', y_test, f(params, x_test), fx_test, loss)
def testGatherGradBatchedBoth(self, op_axis, idxs_axis, shape, dtype, idxs,
dnums, slice_sizes, rng_factory,
rng_idx_factory):
rng = rng_factory()
rng_idx = rng_idx_factory()
fun = partial(lax.gather,
dimension_numbers=dnums,
slice_sizes=slice_sizes)
gfun = grad(lambda x, idx: np.sum(np.sin(fun(x, idx))))
operand = rng(shape, dtype)
assert operand.shape[op_axis] == idxs.shape[idxs_axis]
ans = vmap(gfun, (op_axis, idxs_axis))(operand, idxs)
expected = onp.stack([
gfun(operand[(slice(None), ) * op_axis + (i, )],
idxs[(slice(None), ) * idxs_axis + (i, )])
for i in range(idxs.shape[idxs_axis])
])
self.assertAllClose(ans, expected, check_dtypes=False)
def testNpMaximumPerExampleGrad(self):
R = onp.random.RandomState(0).randn
x = R(10, 5)
W = R(5, 5)
fun = lambda W, x: np.sum(np.maximum(np.dot(x, W), 0.0) ** 2)
ans = vmap(partial(grad(fun), W))(x)
W_t = np.transpose(W)
for i in range(10):
x_ex = x[i:i + 1]
expected_ans = 2.0 * np.dot(
np.maximum(np.dot(W_t, np.transpose(x_ex)), 0.0), x_ex)
expected_ans = np.transpose(expected_ans)
self.assertAllClose(ans[i], expected_ans, check_dtypes=False)
def hvp(loss, params, batch, v):
"""Computes the hessian vector product Hv.
This implementation uses forward-over-reverse mode for computing the hvp.
Args:
loss: function computing the loss with signature
loss(params, batch).
params: pytree for the parameters of the model.
batch: A batch of data. Any format is fine as long as it is a valid input
to loss(params, batch).
v: pytree of the same structure as params.
Returns:
hvp: array of shape [num_params] equal to Hv where H is the hessian.
"""
loss_fn = lambda x: loss(x, batch)
return jvp(grad(loss_fn), [params], [v])[1]
def test_defvjp_all_multiple_arguments(self):
# also tests passing in symbolic zero tangents b/c we differentiate wrt only
# the first argument in one case
foo_p = Primitive('foo')
def foo(x, y): return foo_p.bind(x, y)
def vjpfun(x, y):
out = x**2 + y**3
vjp = lambda g: (g + x + y, g * x * 9.)
return out, vjp
ad.defvjp_all(foo_p, vjpfun)
val_ans, grad_ans = api.value_and_grad(foo)(3., 4.)
self.assertAllClose(val_ans, 3.**2 + 4.**3, check_dtypes=False)
self.assertAllClose(grad_ans, 1. + 3. + 4., check_dtypes=False)
ans = api.grad(foo, (0, 1))(3., 4.)
self.assertAllClose(ans, (1. + 3. + 4., 1. * 3. * 9.), check_dtypes=False)
def test_sparse_grad(self):
rng_sparse = rand_sparse(self.rng())
rng = jtu.rand_default(self.rng())
y = rng(5, "float32")
X = rng_sparse((10, 5), "float32")
Xsp = sparse.BCOO.fromdense(X)
def f(X, y):
return jnp.sum(X @ y)
grad_dense = api.grad(f, argnums=0)(X, y)
grad_sparse = sparse.grad(f, argnums=0)(Xsp, y)
# extract sparse gradient from dense gradient
indices = tuple(Xsp.indices)
grad_sparse_from_dense = jnp.zeros_like(grad_dense).at[indices].set(grad_dense[indices])
self.assertArraysEqual(grad_sparse.todense(), grad_sparse_from_dense)
def test_remat_grad_python_control_flow(self):
@partial(api.remat, concrete=True)
def g(x):
if x > 0:
return lax.sin(x), 3.
else:
return lax.cos(x), 4.
def f(x):
x, _ = g(x)
return x
ans = f(2.)
expected = onp.sin(2.)
self.assertAllClose(ans, expected, check_dtypes=False)
ans = api.grad(f)(2.)
expected = onp.cos(2.)
self.assertAllClose(ans, expected, check_dtypes=False)
def testNpMaximumPerExampleGrad(self):
R = np.random.RandomState(0).randn
x = R(10, 5)
W = R(5, 5)
fun = lambda W, x: jnp.sum(jnp.maximum(jnp.dot(x, W), 0.0) ** 2)
ans = vmap(partial(grad(fun), W))(x)
W_t = jnp.transpose(W)
for i in range(10):
x_ex = x[i:i + 1]
expected_ans = 2.0 * jnp.dot(
jnp.maximum(jnp.dot(W_t, jnp.transpose(x_ex)), 0.0), x_ex)
expected_ans = jnp.transpose(expected_ans)
self.assertAllClose(
ans[i], expected_ans, check_dtypes=False,
atol={np.float32:5e-2} if jtu.device_under_test() == "tpu" else None)
def test_grad_simple(self):
def func(x):
y = hcb.id_print(x * 2., what="x * 2", output_stream=testing_stream)
return x * hcb.id_print(y * 3., what="y * 3", output_stream=testing_stream)
grad_func = api.grad(func)
#assertMultiLineStrippedEqual(self, "", str(api.make_jaxpr(grad_func)(5.)))
with hcb.outfeed_receiver():
res_grad = grad_func(jnp.float32(5.))
self.assertAllClose(2. * 5. * 6., res_grad, check_dtypes=False)
assertMultiLineStrippedEqual(self, """
what: x * 2
10.00
what: y * 3
30.00
transforms: ({'name': 'jvp'}, {'name': 'transpose'}) what: y * 3
5.00
transforms: ({'name': 'jvp'}, {'name': 'transpose'}) what: x * 2
15.00""", testing_stream.output)
testing_stream.reset()
def test_soft_sphere(self, spatial_dimension, alpha, dtype):
key = random.PRNGKey(0)
alpha = f32(alpha)
for _ in range(STOCHASTIC_SAMPLES):
key, split_sigma, split_epsilon = random.split(key, 3)
sigma = np.array(random.uniform(
split_sigma, (1,), minval=0.0, maxval=3.0)[0], dtype=dtype)
epsilon = np.array(
random.uniform(split_epsilon, (1,), minval=0.0, maxval=4.0)[0],
dtype=dtype)
self.assertAllClose(
energy.soft_sphere(
dtype(0), sigma, epsilon, alpha), epsilon / alpha, True)
self.assertAllClose(
energy.soft_sphere(dtype(sigma), sigma, epsilon, alpha),
np.array(0.0, dtype=dtype), True)
if alpha == 3.0:
grad_energy = grad(energy.soft_sphere)
g = grad_energy(dtype(sigma), sigma, epsilon, alpha)
self.assertAllClose(g, np.array(0, dtype=dtype), True)
def test_root_scalar(self):
def scalar_solve(f, y):
return y / f(1.0)
def binary_search(func, x0, low=0.0, high=100.0, tolerance=1e-6):
del x0 # unused
def cond(state):
low, high = state
return high - low > tolerance
def body(state):
low, high = state
midpoint = 0.5 * (low + high)
update_upper = func(midpoint) > 0
low = np.where(update_upper, low, midpoint)
high = np.where(update_upper, midpoint, high)
return (low, high)
solution, _ = lax.while_loop(cond, body, (low, high))
return solution
def sqrt_cubed(x, tangent_solve=scalar_solve):
f = lambda y: y ** 2 - x ** 3
return lax.root(f, 0.0, binary_search, tangent_solve)
value, grad = api.value_and_grad(sqrt_cubed)(5.0)
self.assertAllClose(value, 5 ** 1.5, check_dtypes=False)
self.assertAllClose(grad, api.grad(pow)(5.0, 1.5), check_dtypes=False)
jtu.check_grads(sqrt_cubed, (5.0,), order=2, rtol=1e-3)
# TODO(shoyer): reenable when batching works
# inputs = np.array([4.0, 5.0])
# results = api.vmap(sqrt_cubed)(inputs)
# self.assertAllClose(results, inputs ** 1.5, check_dtypes=False)
results = api.jit(sqrt_cubed)(5.0)
self.assertAllClose(results, 5.0 ** 1.5, check_dtypes=False)
def test_morse(self, spatial_dimension, dtype):
key = random.PRNGKey(0)
for _ in range(STOCHASTIC_SAMPLES):
key, split_sigma, split_epsilon, split_alpha = random.split(key, 4)
sigma = dtype(
random.uniform(split_sigma, (1, ), minval=0., maxval=3.0)[0])
epsilon = dtype(
random.uniform(split_epsilon, (1, ), minval=0.0,
maxval=4.0)[0])
alpha = dtype(
random.uniform(split_alpha, (1, ), minval=1.0, maxval=30.0)[0])
dr = dtype(sigma)
self.assertAllClose(energy.morse(dr, sigma, epsilon, alpha),
np.array(-epsilon, dtype=dtype))
g = grad(energy.morse)(dr, sigma, epsilon, alpha)
self.assertAllClose(g, np.array(0, dtype=dtype))
# if dr = a/alpha + sigma, then V_morse(dr, sigma, epsilon, alpha)/epsilon
# should be independent of sigma, epsilon, and alpha, depending only on a.
key, split_sigma, split_epsilon, split_alpha = random.split(key, 4)
sigmas = random.uniform(split_sigma, (STOCHASTIC_SAMPLES, ),
minval=0.,
maxval=3.0)
epsilons = random.uniform(split_epsilon, (STOCHASTIC_SAMPLES, ),
minval=0.1,
maxval=4.0)
alphas = random.uniform(split_alpha, (STOCHASTIC_SAMPLES, ),
minval=1.0,
maxval=30.0)
for sigma, epsilon, alpha in zip(sigmas, epsilons, alphas):
a = np.linspace(max(-2.5, -alpha * sigma), 8.0, 100)
dr = np.array(a / alpha + sigma, dtype=dtype)
U = energy.morse(dr, sigma, epsilon, alpha) / dtype(epsilon)
Ucomp = np.array((dtype(1) - np.exp(-a))**dtype(2) - dtype(1),
dtype=dtype)
self.assertAllClose(U, Ucomp)
def testStopGradient(self):
def f(x):
return lax.sin(x) * lax.cos(lax.stop_gradient(x))
def f2(x, y):
return lax.sin(x) * lax.cos(y)
x = 3.14
ans = api.grad(f)(x)
expected = api.grad(f2)(x, x)
self.assertAllClose(ans, expected)
ans = api.grad(api.grad(f))(x)
expected = api.grad(api.grad(f2))(x, x)
self.assertAllClose(ans, expected)
ans = api.grad(lambda x: lax.stop_gradient({'foo':x})['foo'])(3.)
expected = onp.array(0.0)
self.assertAllClose(ans, expected, check_dtypes=False)
with core.skipping_checks():
with self.assertRaises(TypeError):
lax.stop_gradient(lambda x: x)
def main(unused_argv):
from jax.api import grad, jit, vmap, pmap, device_put
"The following is required to use TPU Driver as JAX's backend."
if FLAGS.TPU:
config.FLAGS.jax_xla_backend = "tpu_driver"
config.FLAGS.jax_backend_target = "grpc://" + os.environ[
'TPU_ADDR'] + ':8470'
TPU_ADDR = os.environ['TPU_ADDR']
ndevices = xla_bridge.device_count()
if not FLAGS.TPU:
ndevices = 1
pmap = partial(pmap, axis_name='i')
"""Setup some experiment parameters."""
meas_step = FLAGS.meas_step
training_epochs = int(FLAGS.epochs)
tmult = 1.0
if FLAGS.physical:
tmult = FLAGS.lr
if FLAGS.physicalL2:
tmult = FLAGS.L2 * tmult
if FLAGS.physical:
training_epochs = 1 + int(FLAGS.epochs / tmult)
print('Evolving for {:}e'.format(training_epochs))
losst = FLAGS.losst
learning_rate = FLAGS.lr
batch_size_per_device = FLAGS.bs
N = FLAGS.N
K = FLAGS.K
batch_size = batch_size_per_device * ndevices
steps_per_epoch = 50000 // batch_size
training_steps = training_epochs * steps_per_epoch
"Filename from FLAGS"
filename = 'wrnL2_' + losst + '_n' + str(N) + '_k' + str(K)
if FLAGS.momentum:
filename += '_mom'
if FLAGS.L2_sch:
filename += '_L2sch' + '_decay' + str(FLAGS.L2dec) + '_del' + str(
FLAGS.delay)
if FLAGS.seed != 1:
filename += 'seed' + str(FLAGS.seed)
filename += '_L2' + str(FLAGS.L2)
if FLAGS.std_wrn_sch:
filename += '_stddec'
if FLAGS.physical:
filename += 'phys'
else:
filename += '_ctlr'
if not FLAGS.augment:
filename += '_noaug'
if not FLAGS.mix:
filename += '_nomixup'
filename += '_bs' + str(batch_size) + '_lr' + str(learning_rate)
if FLAGS.jobdir is not None:
filedir = os.path.join('wrnlogs', FLAGS.jobdir)
else:
filedir = 'wrnlogs'
if not os.path.exists(filedir):
os.makedirs(filedir)
filedir = os.path.join(filedir, filename + '.csv')
print('Saving log to ', filename)
print('Found {} cores.'.format(ndevices))
"""Load CIFAR10 data and create a minimal pipeline."""
train_images, train_labels, test_images, test_labels = utils.load_data(
'cifar10')
train_images = np.reshape(train_images, (-1, 32, 32 * 3))
train = (train_images, train_labels)
test = (test_images, test_labels)
k = train_labels.shape[-1]
train = utils.shard_data(train, ndevices)
test = utils.shard_data(test, ndevices)
"""Create a Wide Resnet and replicate its parameters across the devices."""
initparams, f, _ = utils.WideResnetnt(N, K, k)
"Loss and optimizer definitions"
l2_norm = lambda params: tree_map(lambda x: np.sum(x**2), params)
l2_reg = lambda params: tree_reduce(lambda x, y: x + y, l2_norm(params))
currL2 = FLAGS.L2
L2p = pmap(lambda x: x)(currL2 * np.ones((ndevices, )))
def xentr(params, images_and_labels):
images, labels = images_and_labels
return -np.mean(stax.logsoftmax(f(params, images)) * labels)
def mse(params, data_tuple):
"""MSE loss."""
x, y = data_tuple
return 0.5 * np.mean((y - f(params, x))**2)
if losst == 'xentr':
print('Using xentr')
lossm = xentr
else:
print('Using mse')
lossm = mse
loss = lambda params, data, L2: lossm(params, data) + L2 * l2_reg(params)
def accuracy(params, images_and_labels):
images, labels = images_and_labels
return np.mean(
np.array(np.argmax(f(params, images), axis=1) == np.argmax(labels,
axis=1),
dtype=np.float32))
"Define optimizer"
if FLAGS.std_wrn_sch:
lr = learning_rate
first_epoch = int(60 / 200 * training_epochs)
learning_rate_fn = optimizers.piecewise_constant(
np.array([1, 2, 3]) * first_epoch * steps_per_epoch,
np.array([lr, lr * 0.2, lr * 0.2**2, lr * 0.2**3]))
else:
learning_rate_fn = optimizers.make_schedule(learning_rate)
if FLAGS.momentum:
momentum = 0.9
else:
momentum = 0
@pmap
def update_step(step, state, batch_state, L2):
batch, batch_state = batch_fn(batch_state)
params = get_params(state)
dparams = grad_loss(params, batch, L2)
dparams = tree_map(lambda x: lax.psum(x, 'i') / ndevices, dparams)
return step + 1, apply_fn(step, dparams, state), batch_state
@pmap
def evaluate(state, data, L2):
params = get_params(state)
lossmm = lossm(params, data)
l2mm = l2_reg(params)
return lossmm + L2 * l2mm, accuracy(params, data), lossmm, l2mm
"Initialization and loading"
_, params = initparams(random.PRNGKey(0), (-1, 32, 32, 3))
replicate_array = lambda x: \
np.broadcast_to(x, (ndevices,) + x.shape)
replicated_params = tree_map(replicate_array, params)
grad_loss = jit(grad(loss))
init_fn, apply_fn, get_params = optimizers.momentum(
learning_rate_fn, momentum)
apply_fn = jit(apply_fn)
key = random.PRNGKey(FLAGS.seed)
batchinit_fn, batch_fn = utils.sharded_minibatcher(batch_size,
ndevices,
transform=FLAGS.augment,
k=k,
mix=FLAGS.mix)
batch_state = pmap(batchinit_fn)(random.split(key, ndevices), train)
state = pmap(init_fn)(replicated_params)
if FLAGS.checkpointing:
## Loading of checkpoint if available/provided.
single_state = init_fn(params)
i0, load_state, load_params, filename0, batch_stateb = utils.load_weights(
filename,
single_state,
params,
full_file=FLAGS.load_w,
ndevices=ndevices)
if i0 is not None:
filename = filename0
if batch_stateb is not None:
batch_state = batch_stateb
if load_params is not None:
state = pmap(init_fn)(load_params)
else:
state = load_state
else:
i0 = 0
else:
i0 = 0
if FLAGS.steps_from_load:
training_steps = i0 + training_steps
batch_xs, _ = pmap(batch_fn)(batch_state)
train_loss = []
train_accuracy = []
lrL = []
test_loss = []
test_accuracy = []
test_L2, test_lm, train_lm, train_L2 = [], [], [], []
L2_t = []
idel0 = i0
start = time.time()
step = pmap(lambda x: x)(i0 * np.ones((ndevices, )))
"Start training loop"
if FLAGS.checkpointing:
print('Evolving for {:}e and saving every {:}s'.format(
training_epochs, FLAGS.checkpointing))
print(
'Epoch\tLearning Rate\tTrain bareLoss\t L2_norm \tTest Loss\tTrain Error\tTest Error\tTime / Epoch'
)
for i in range(i0, training_steps):
if i % meas_step == 0:
# Make Measurement
l, a, lm, L2m = evaluate(state, test, L2p)
test_loss += [np.mean(l)]
test_accuracy += [np.mean(a)]
test_lm += [np.mean(lm)]
test_L2 += [np.mean(L2m)]
train_batch, _ = pmap(batch_fn)(batch_state)
l, a, lm, L2m = evaluate(state, train_batch, L2p)
train_loss += [np.mean(l)]
train_accuracy += [np.mean(a)]
train_lm += [np.mean(lm)]
train_L2 += [np.mean(L2m)]
L2_t.append(currL2)
lrL += [learning_rate_fn(i)]
if FLAGS.L2_sch and i > FLAGS.delay / currL2 + idel0 and len(
train_lm) > 2 and ((minloss <= train_lm[-1]
and minloss <= train_lm[-2]) or
(maxacc >= train_accuracy[-1]
and maxacc >= train_accuracy[-2])):
# If AutoL2 is on and we are beyond the refractory period, decay if the loss or error have increased in the last two measurements.
print('Decaying L2 to', currL2 / FLAGS.L2dec)
currL2 = currL2 / FLAGS.L2dec
L2p = pmap(lambda x: x)(currL2 * np.ones((ndevices, )))
idel0 = i
elif FLAGS.L2_sch and len(train_lm) >= 2:
# Update the minimum values.
try:
maxacc = max(train_accuracy[-2], maxacc)
minloss = min(train_lm[-2], minloss)
except:
maxacc, minloss = train_accuracy[-2], train_lm[-2]
if i % (meas_step * 10) == 0 or i == i0:
# Save measurements to csv
epoch = batch_size * i / 50000
dt = (time.time() - start) / (meas_step * 10) * steps_per_epoch
print(('{}\t' + ('{: .4f}\t' * 7)).format(
epoch, learning_rate_fn(i), train_lm[-1], train_L2[-1],
test_loss[-1], train_accuracy[-1], test_accuracy[-1], dt))
start = time.time()
data = {
'train_loss': train_loss,
'test_loss': test_loss,
'train_acc': train_accuracy,
'test_acc': test_accuracy
}
data['train_bareloss'] = train_lm
data['train_L2'] = train_L2
data['test_bareloss'] = test_lm
data['test_L2'] = test_L2
data['L2_t'] = L2_t
df = pd.DataFrame(data)
df['learning_rate'] = lrL
df['width'] = K
df['batch_size'] = batch_size
df['step'] = i0 + onp.arange(0, len(train_loss)) * meas_step
df.to_csv(filedir, index=False)
if FLAGS.checkpointing:
### SAVE MODEL
if i % FLAGS.checkpointing == 0 and i > i0:
if not os.path.exists('weights/'):
os.makedirs('weights/')
saveparams = tree_flatten(state[0])[0]
if ndevices > 1:
saveparams = [el[0] for el in saveparams]
saveparams = np.concatenate(
[el.reshape(-1) for el in saveparams])
step0 = i
print('Step', i)
print('saving at', filename, step0, 'size:', saveparams.shape)
utils.save_weights(filename, step0, saveparams, batch_state)
## UPDATE
step, state, batch_state = update_step(step, state, batch_state, L2p)
print('Training done')
if FLAGS.TPU:
with open('done/' + TPU_ADDR, 'w') as fp:
fp.write(filedir)
pass
def update(i, opt_state):
params = minmax.get_params(opt_state)
gradient = grad(objective)(params, i)
return opt_update(i, gradient, opt_state)
def apply_carry(carry, _):
i, x = carry
new_x = x - 0.1 * api.grad(energy_fn)(x)
new_carry = (i + 1, new_x)
return new_carry, _
def apply_carry(x, i):
return api.grad(fn, argnums=(0, ))(x)[0], i
st.header("Comparison against finite SGD-NNs")
"""
Finally, let's bring back the practioner loved Neural Networks for a comparison.
"""
learning_rate = st.slider("Learning rate",
1e-4,
1.0,
0.1,
step=1e-4,
format="%.4f")
opt_init, opt_update, get_params = optimizers.sgd(learning_rate)
opt_update = jit(opt_update)
loss = jit(lambda params, x, y: 0.5 * np.mean((apply_fn(params, x) - y)**2))
grad_loss = jit(lambda state, x, y: grad(loss)(get_params(state), x, y))
train_losses = []
test_losses = []
opt_state = opt_init(params)
for i in range(training_steps):
opt_state = opt_update(i, grad_loss(opt_state, *train), opt_state)
train_losses += [loss(get_params(opt_state), *train)]
test_losses += [loss(get_params(opt_state), *test)]
# NTK loss
plt.loglog(ts, ntk_train_loss_mean, linewidth=3)
plt.loglog(ts, ntk_test_loss_mean, linewidth=3)
def testScanRnn(self):
r = npr.RandomState(0)
n_in = 4
n_hid = 2
n_out = 1
length = 3
W_trans = r.randn(n_hid, n_hid + n_in)
W_out = r.randn(n_out, n_hid + n_in)
params = W_trans, W_out
inputs = r.randn(length, n_in)
targets = r.randn(length, n_out)
def step(params, state, input):
W_trans, W_out = params
stacked = np.concatenate([state, input])
output = np.tanh(np.dot(W_out, stacked))
next_state = np.tanh(np.dot(W_trans, stacked))
return next_state, output
def rnn(params, inputs):
init_state = np.zeros(n_hid)
_, outputs = lax.scan(partial(step, params), init_state, inputs)
return outputs
def loss(params, inputs, targets):
predictions = rnn(params, inputs)
return np.sum((predictions - targets)**2)
# evaluation doesn't crash
loss(params, inputs, targets)
# jvp evaluation doesn't crash
api.jvp(lambda params: loss(params, inputs, targets), (params, ),
(params, ))
# jvp numerical check passes
jtu.check_grads(loss, (params, inputs, targets),
order=2,
modes=["fwd"])
# linearize works
_, expected = api.jvp(loss, (params, inputs, targets),
(params, inputs, targets))
_, linfun = api.linearize(loss, params, inputs, targets)
ans = linfun(params, inputs, targets)
self.assertAllClose(ans, expected, check_dtypes=False)
# gradient evaluation doesn't crash
api.grad(loss)(params, inputs, targets)
# gradient check passes
jtu.check_grads(loss, (params, inputs, targets), order=2)
# we can vmap to batch things
batch_size = 7
batched_inputs = r.randn(batch_size, length, n_in)
batched_targets = r.randn(batch_size, length, n_out)
batched_loss = api.vmap(lambda x, y: loss(params, x, y))
losses = batched_loss(batched_inputs, batched_targets)
expected = onp.stack(
list(
map(lambda x, y: loss(params, x, y), batched_inputs,
batched_targets)))
self.assertAllClose(losses, expected, check_dtypes=False)
def update(_, i, opt_state, batch):
params = get_params(opt_state)
return opt_update(i, grad(loss)(params, batch), opt_state)
def update(params, batch):
grads = grad(loss)(params, batch)
return [(w - step_size * dw, b - step_size * db)
for (w, b), (dw, db) in zip(params, grads)]
def update(params, batch):
grads = grad(loss)(params, batch)
return [(w - learning_rate * dw, b - learning_rate * db)
for (w, b), (dw, db) in zip(params, grads)]
#new define of loss function
def computation(params, inputs, targets):
logits = predict(params, inputs)
preds = stax.logsoftmax(logits)
return -np.mean(np.sum(preds * targets, axis=1))
#set up of index
tl = test_labels
index7 = tl.tolist().index([0, 0, 0, 0, 0, 0, 0, 1, 0, 0])
print(test_labels[index7])
#computing process to the new x
input_image, input_label = shape_as_image(test_images[index7],
test_labels[index7])
grad_newx = grad(computation, 1)(params, input_image, input_label)
newx = input_image + hyper * np.sign(grad_newx)
#start plot and its predicted vector
target_class = np.argmax(input_label)
predicted_class = np.argmax(predict(params, newx))
#predicted vector
predict_vector = predict(params, newx)
print('the target class is :', target_class)
print('the predict class is :', predicted_class)
print('the predicted vector is :', predict_vector)
image = np.array(newx)
image = image * 255
image = image.reshape(28, 28)
plt.imshow(image)
"""## From here is Part 2"""
def testGradOfXlog1pyAtZero(self):
partial_xlog1py = functools.partial(lsp_special.xlog1py, 0.)
self.assertAllClose(api.grad(partial_xlog1py)(-1.),
0.,
check_dtypes=False)
def testTrainedEnsemblePredCov(self, train_shape, test_shape, network,
out_logits):
if xla_bridge.get_backend().platform == 'gpu' and config.read(
'jax_enable_x64'):
raise jtu.SkipTest('Not running GPU x64 to save time.')
training_steps = 5000
learning_rate = 1.0
ensemble_size = 50
init_fn, apply_fn, ker_fn = stax.serial(
stax.Dense(1024, W_std=1.2, b_std=0.05), stax.Erf(),
stax.Dense(out_logits, W_std=1.2, b_std=0.05))
opt_init, opt_update, get_params = optimizers.sgd(learning_rate)
opt_update = jit(opt_update)
key = random.PRNGKey(0)
key, = random.split(key, 1)
key, split = random.split(key)
x_train = np.cos(random.normal(split, train_shape))
key, split = random.split(key)
y_train = np.array(
random.bernoulli(split, shape=(train_shape[0], out_logits)),
np.float32)
train = (x_train, y_train)
key, split = random.split(key)
x_test = np.cos(random.normal(split, test_shape))
ensemble_key = random.split(key, ensemble_size)
loss = jit(lambda params, x, y: 0.5 * np.mean(
(apply_fn(params, x) - y)**2))
grad_loss = jit(lambda state, x, y: grad(loss)
(get_params(state), x, y))
def train_network(key):
_, params = init_fn(key, (-1, ) + train_shape[1:])
opt_state = opt_init(params)
for i in range(training_steps):
opt_state = opt_update(i, grad_loss(opt_state, *train),
opt_state)
return get_params(opt_state)
params = vmap(train_network)(ensemble_key)
ensemble_fx = vmap(apply_fn, (0, None))(params, x_test)
ensemble_loss = vmap(loss, (0, None, None))(params, x_train, y_train)
ensemble_loss = np.mean(ensemble_loss)
self.assertLess(ensemble_loss, 1e-5, True)
mean_emp = np.mean(ensemble_fx, axis=0)
mean_subtracted = ensemble_fx - mean_emp
cov_emp = np.einsum(
'ijk,ilk->jl', mean_subtracted, mean_subtracted, optimize=True) / (
mean_subtracted.shape[0] * mean_subtracted.shape[-1])
reg = 1e-7
ntk_predictions = predict.gp_inference(ker_fn,
x_train,
y_train,
x_test,
'ntk',
reg,
compute_cov=True)
self.assertAllClose(mean_emp, ntk_predictions.mean, True, RTOL, ATOL)
self.assertAllClose(cov_emp, ntk_predictions.covariance, True, RTOL,
ATOL)
def testNTKMomentumPrediction(self, train_shape, test_shape, network,
out_logits, fn_and_kernel):
key = random.PRNGKey(0)
key, split = random.split(key)
x_train = random.normal(split, train_shape)
key, split = random.split(key)
y_train = np.array(
random.bernoulli(split, shape=(train_shape[0], out_logits)),
np.float32)
key, split = random.split(key)
x_test = random.normal(split, test_shape)
params, f, ntk = fn_and_kernel(key, train_shape[1:], network,
out_logits)
# Regress to an MSE loss.
loss = lambda y, y_hat: 0.5 * np.mean((y - y_hat)**2)
grad_loss = jit(grad(lambda params, x: loss(f(params, x), y_train)))
g_dd = ntk(x_train, None, 'ntk')
g_td = ntk(x_test, x_train, 'ntk')
atol = ATOL
rtol = RTOL
step_size = 0.5
if len(train_shape) > 2:
# Hacky way to up the tolerance just for convolutions.
atol = ATOL * 2
rtol = RTOL * 2
step_size = 0.1
train_time = 100.0
steps = int(train_time / np.sqrt(step_size))
init, predictor, get = predict.momentum(g_dd, y_train, loss, step_size,
g_td)
opt_init, opt_update, get_params = momentum(step_size, 0.9)
opt_state = opt_init(params)
fx_initial_train = f(params, x_train)
fx_initial_test = f(params, x_test)
lin_state = init(fx_initial_train, fx_initial_test)
fx_pred_train, fx_pred_test = get(lin_state)
self.assertAllClose(fx_initial_train, fx_pred_train, True)
self.assertAllClose(fx_initial_test, fx_pred_test, True)
for i in range(steps):
params = get_params(opt_state)
opt_state = opt_update(i, grad_loss(params, x_train), opt_state)
params = get_params(opt_state)
fx_train = f(params, x_train)
fx_test = f(params, x_test)
lin_state = predictor(lin_state, train_time)
fx_pred_train, fx_pred_test = get(lin_state)
fx_disp_train = np.sqrt(np.mean((fx_train - fx_initial_train)**2))
fx_disp_test = np.sqrt(np.mean((fx_test - fx_initial_test)**2))
fx_error_train = (fx_train - fx_pred_train) / fx_disp_train
fx_error_test = (fx_test - fx_pred_test) / fx_disp_test
self.assertAllClose(fx_error_train, np.zeros_like(fx_error_train),
True, rtol, atol)
self.assertAllClose(fx_error_test, np.zeros_like(fx_error_test), True,
rtol, atol)