def evaluate_example(self, example: SingleExample,
prediction: SinglePrediction) -> MeanStat:
"""Computes token accuracy for a single sequence example.
Args:
example: One example with target in range [0, num_classes) of shape [1].
prediction: Unnormalized prediction for ``example`` of shape [num_classes]
Returns:
MeanStat for token accuracy for a single sequence example or at each
token position if ``per_position`` is ``True``.
"""
target = example[self.target_key]
pred = prediction if self.pred_key is None else prediction[
self.pred_key]
if self.logits_mask is not None:
logits_mask = jnp.array(self.logits_mask)
pred += logits_mask
target_weight = get_target_weight(target, self.masked_target_values)
correct = (target == jnp.argmax(pred, axis=-1)).astype(jnp.float32)
if self.per_position:
return MeanStat.new(correct * target_weight, target_weight)
return MeanStat.new(jnp.sum(correct * target_weight),
jnp.sum(target_weight))
def test_argmax_consistent(self):
rngs = PRNGSequence(13)
vec = jax.random.normal(next(rngs), shape=(5,))
mat = jax.random.normal(next(rngs), shape=(3, 5))
ten = jax.random.normal(next(rngs), shape=(3, 5, 7))
self.assertEqual(
argmax(next(rngs), vec), jnp.argmax(vec, axis=-1))
self.assertArrayAlmostEqual(
argmax(next(rngs), mat), jnp.argmax(mat, axis=-1))
self.assertArrayAlmostEqual(
argmax(next(rngs), mat, axis=0), jnp.argmax(mat, axis=0))
self.assertArrayAlmostEqual(
argmax(next(rngs), ten), jnp.argmax(ten, axis=-1))
self.assertArrayAlmostEqual(
argmax(next(rngs), ten, axis=0), jnp.argmax(ten, axis=0))
self.assertArrayAlmostEqual(
argmax(next(rngs), ten, axis=1), jnp.argmax(ten, axis=1))
def extract_best_search_space(scores_dict, centrality_key,
search_spaces_reduced):
"""Select the arg max score search space."""
return search_spaces_reduced[jnp.argmax(scores_dict[centrality_key]), :, :]
mdbM = moldb.MdbExomol('.database/H2O/1H2-16O/POKAZATEL', nus,
crit=1.e-45) # loading molecular dat
molmassM = molinfo.molmass('H2O') # molecular mass (H2O)
q = mdbM.qr_interp(1500.0)
S = SijT(1500.0, mdbM.logsij0, mdbM.nu_lines, mdbM.elower, q)
mask = S > 1.e-25
mdbM.masking(mask)
Tarr = jnp.logspace(jnp.log10(800), jnp.log10(1600), 100)
qt = vmap(mdbM.qr_interp)(Tarr)
SijM = jit(vmap(SijT,
(0, None, None, None, 0)))(Tarr, mdbM.logsij0, mdbM.nu_lines,
mdbM.elower, qt)
imax = jnp.argmax(SijM, axis=0)
Tmax = Tarr[imax]
print(jnp.min(Tmax))
pl = planck.piBarr(jnp.array([1100.0, 1000.0]), nus)
print(pl[1] / pl[0])
pl = planck.piBarr(jnp.array([1400.0, 1200.0]), nus)
print(pl[1] / pl[0])
lsa = ['solid', 'dashed', 'dotted', 'dashdot']
lab = ['A', 'B', 'C']
fac = 1.e22
fig = plt.figure(figsize=(12, 6))
# for j,i in enumerate(range(len(mdbM.A))):
# for j,i in enumerate([56,72,141,147,173,236,259,211,290]):
def _per_batch(inputs, labels): target_class = jnp.argmax(labels, axis=1) predicted_class = jnp.argmax(predict(params, inputs), axis=1) return jnp.mean(predicted_class == target_class)
def accuracy(params, mask):
logits = model.apply(params, graph, train=False)
correct = jnp.argmax(logits, -1) == jnp.argmax(labels, -1)
return jnp.sum(correct * mask) / jnp.sum(mask)
def ground_truth_label(sentences, lengths): scores = batch_score(sentences, lengths) return jnp.argmax(scores, axis=1)
def accuracy(params, batch, model_predict): """Calculate accuracy.""" inputs, targets = batch predicted_class = np.argmax(model_predict(params, inputs), axis=1) return np.mean(predicted_class == targets)
def loss(
params: networks_lib.Params, target_params: networks_lib.Params,
key_grad: networks_lib.PRNGKey, sample: reverb.ReplaySample
) -> Tuple[jnp.ndarray, Tuple[jnp.ndarray, jnp.ndarray]]:
"""Computes mean transformed N-step loss for a batch of sequences."""
# Convert sample data to sequence-major format [T, B, ...].
data = utils.batch_to_sequence(sample.data)
# Get core state & warm it up on observations for a burn-in period.
if use_core_state:
# Replay core state.
online_state = jax.tree_map(lambda x: x[0],
data.extras['core_state'])
else:
online_state = initial_state
target_state = online_state
# Maybe burn the core state in.
if burn_in_length:
burn_obs = jax.tree_map(lambda x: x[:burn_in_length],
data.observation)
key_grad, key1, key2 = jax.random.split(key_grad, 3)
_, online_state = unroll.apply(params, key1, burn_obs,
online_state)
_, target_state = unroll.apply(target_params, key2, burn_obs,
target_state)
# Only get data to learn on from after the end of the burn in period.
data = jax.tree_map(lambda seq: seq[burn_in_length:], data)
# Unroll on sequences to get online and target Q-Values.
key1, key2 = jax.random.split(key_grad)
online_q, _ = unroll.apply(params, key1, data.observation,
online_state)
target_q, _ = unroll.apply(target_params, key2, data.observation,
target_state)
# Get value-selector actions from online Q-values for double Q-learning.
selector_actions = jnp.argmax(online_q, axis=-1)
# Preprocess discounts & rewards.
discounts = (data.discount * discount).astype(online_q.dtype)
rewards = data.reward
if clip_rewards:
rewards = jnp.clip(rewards, -max_abs_reward, max_abs_reward)
rewards = rewards.astype(online_q.dtype)
# Get N-step transformed TD error and loss.
batch_td_error_fn = jax.vmap(functools.partial(
rlax.transformed_n_step_q_learning,
n=bootstrap_n,
tx_pair=tx_pair),
in_axes=1,
out_axes=1)
# TODO(b/183945808): when this bug is fixed, truncations of actions,
# rewards, and discounts will no longer be necessary.
batch_td_error = batch_td_error_fn(online_q[:-1], data.action[:-1],
target_q[1:],
selector_actions[1:],
rewards[:-1], discounts[:-1])
batch_loss = 0.5 * jnp.square(batch_td_error).sum(axis=0)
# Importance weighting.
probs = sample.info.probability
importance_weights = (1. / (probs + 1e-6)).astype(online_q.dtype)
importance_weights **= importance_sampling_exponent
importance_weights /= jnp.max(importance_weights)
mean_loss = jnp.mean(importance_weights * batch_loss)
# Calculate priorities as a mixture of max and mean sequence errors.
abs_td_error = jnp.abs(batch_td_error).astype(online_q.dtype)
max_priority = max_priority_weight * jnp.max(abs_td_error, axis=0)
mean_priority = (1 - max_priority_weight) * jnp.mean(abs_td_error,
axis=0)
priorities = (max_priority + mean_priority)
return mean_loss, priorities
def hmm_viterbi_log(params, obs_seq, length=None):
'''
Computes, for each time step, the marginal conditional probability that the Hidden Markov Model was
in each possible state given the observations that were made at each time step, i.e.
P(z[i] | x[0], ..., x[num_steps - 1]) for all i from 0 to num_steps - 1
It is based on https://github.com/deepmind/distrax/blob/master/distrax/_src/utils/hmm.py
Parameters
----------
params : HMM
Hidden Markov Model
obs_seq: array(seq_len)
History of observed states
Returns
-------
* array(seq_len, n_states)
Alpha values
* array(seq_len, n_states)
Beta values
* array(seq_len, n_states)
Marginal conditional probability
* float
The loglikelihood giving log(p(x|model))
'''
seq_len = len(obs_seq)
if length is None:
length = seq_len
trans_dist, obs_dist, init_dist = params.trans_dist, params.obs_dist, params.init_dist
trans_log_probs = log_softmax(trans_dist.logits)
init_log_probs = log_softmax(init_dist.logits)
n_states = obs_dist.batch_shape[0]
first_log_prob = init_log_probs + obs_dist.log_prob(obs_seq[0])
if seq_len == 1:
return jnp.expand_dims(jnp.argmax(first_log_prob), axis=0)
def viterbi_forward(prev_logp, t):
obs_logp = obs_dist.log_prob(obs_seq[t])
logp = jnp.where(
t <= length,
prev_logp[..., None] + trans_log_probs + obs_logp[..., None, :],
-jnp.inf + jnp.zeros_like(trans_log_probs))
max_logp_given_successor = jnp.where(t <= length, jnp.max(logp,
axis=-2),
prev_logp)
most_likely_given_successor = jnp.where(t <= length,
jnp.argmax(logp, axis=-2), -1)
return max_logp_given_successor, most_likely_given_successor
ts = jnp.arange(1, seq_len)
final_log_prob, most_likely_sources = lax.scan(viterbi_forward,
first_log_prob, ts)
most_likely_initial_given_successor = jnp.argmax(trans_log_probs +
first_log_prob,
axis=-2)
most_likely_sources = jnp.concatenate([
jnp.expand_dims(most_likely_initial_given_successor, axis=0),
most_likely_sources
],
axis=0)
def viterbi_backward(state, t):
state = jnp.where(
t <= length,
jnp.sum(most_likely_sources[t] * one_hot(state, n_states)).astype(
jnp.int64), state)
most_likely = jnp.where(t <= length, state, -1)
return state, most_likely
final_state = jnp.argmax(final_log_prob)
_, most_likely_path = lax.scan(viterbi_backward,
final_state,
ts,
reverse=True)
final_state = jnp.where(length == seq_len, final_state, -1)
return jnp.append(most_likely_path, final_state)
def categorical_logits(key, logits, shape=()):
shape = shape or logits.shape[:-1]
return np.argmax(
random.gumbel(key, shape + logits.shape[-1:], logits.dtype) + logits,
axis=-1)
def loss_fn(
model,
padded_example_and_rng,
static_metadata,
regularization_weights = None,
reinforce_weight = 1.0,
baseline_weight = 0.001,
):
"""Loss function for multi-pointer task.
Args:
model: The model to evaluate.
padded_example_and_rng: Padded example to evaluate on, with a PRNGKey.
static_metadata: Padding configuration for the example, since this may vary
for different examples.
regularization_weights: Associates side output key regexes with
regularization penalties.
reinforce_weight: Weight to give to the reinforce term.
baseline_weight: Weight to give to the baseline.
Returns:
Tuple of loss and metrics.
"""
padded_example, rng = padded_example_and_rng
# Run the model.
with side_outputs.collect_side_outputs() as collected_side_outputs:
with flax.nn.stochastic(rng):
joint_log_probs = model(padded_example, static_metadata)
# Computing the loss:
# Extract logits for the correct location.
log_probs_at_bug = joint_log_probs[padded_example.bug_node_index, :]
# Compute p(repair) = sum[ p(node) p(repair | node) ]
# -> log p(repair) = logsumexp[ log p(node) + log p (repair | node) ]
log_prob_joint = jax.scipy.special.logsumexp(
log_probs_at_bug + jnp.log(padded_example.repair_node_mask))
# Metrics:
# Marginal log probabilities:
log_prob_bug = jax.scipy.special.logsumexp(log_probs_at_bug)
log_prob_repair = jax.scipy.special.logsumexp(
jax.scipy.special.logsumexp(joint_log_probs, axis=0) +
jnp.log(padded_example.repair_node_mask))
# Conditional log probabilities:
log_prob_repair_given_bug = log_prob_joint - log_prob_bug
log_prob_bug_given_repair = log_prob_joint - log_prob_repair
# Majority accuracy (1 if we assign the correct tuple > 50%):
# (note that this is easier to compute, since we can't currently aggregate
# probability separately for each candidate.)
log_half = jnp.log(0.5)
majority_acc_joint = log_prob_joint > log_half
# Probabilities associated with each node.
node_node_probs = jnp.exp(joint_log_probs)
# Accumulate across unique candidates by identifier. This has the same shape,
# but only the first few values will be populated.
node_candidate_probs = padded_example.unique_candidate_operator.apply_add(
in_array=node_node_probs,
out_array=jnp.zeros_like(node_node_probs),
in_dims=[1],
out_dims=[1])
# Classify: 50% decision boundary
only_buggy_probs = node_candidate_probs.at[0, :].set(0).at[:, 0].set(0)
p_buggy = jnp.sum(only_buggy_probs)
pred_nobug = p_buggy <= 0.5
# Localize/repair: take most likely bug position, conditioned on being buggy
pred_bug_loc, pred_cand_id = jnp.unravel_index(
jnp.argmax(only_buggy_probs), only_buggy_probs.shape)
actual_nobug = jnp.array(padded_example.bug_node_index == 0)
actual_bug = jnp.logical_not(actual_nobug)
pred_bug = jnp.logical_not(pred_nobug)
metrics = {
'nll/joint':
-log_prob_joint,
'nll/marginal_bug':
-log_prob_bug,
'nll/marginal_repair':
-log_prob_repair,
'nll/repair_given_bug':
-log_prob_repair_given_bug,
'nll/bug_given_repair':
-log_prob_bug_given_repair,
'inaccuracy/legacy_overall':
1 - majority_acc_joint,
'inaccuracy/overall':
(~((actual_nobug & pred_nobug) |
(actual_bug & pred_bug &
(pred_bug_loc == padded_example.bug_node_index) &
(pred_cand_id == padded_example.repair_id)))),
'inaccuracy/classification_overall': (actual_nobug != pred_nobug),
'inaccuracy/classification_given_nobug':
train_util.RatioMetric(
numerator=(actual_nobug & ~pred_nobug), denominator=actual_nobug),
'inaccuracy/classification_given_bug':
train_util.RatioMetric(
numerator=(actual_bug & ~pred_bug), denominator=actual_bug),
'inaccuracy/localized_given_bug':
train_util.RatioMetric(
numerator=(actual_bug
& ~(pred_bug_loc == padded_example.bug_node_index)),
denominator=actual_bug),
'inaccuracy/repaired_given_bug':
train_util.RatioMetric(
numerator=(actual_bug
& ~(pred_cand_id == padded_example.repair_id)),
denominator=actual_bug),
'inaccuracy/localized_repaired_given_bug':
train_util.RatioMetric(
numerator=(actual_bug
& ~((pred_bug_loc == padded_example.bug_node_index) &
(pred_cand_id == padded_example.repair_id))),
denominator=actual_bug),
'inaccuracy/overall_given_bug':
train_util.RatioMetric(
numerator=(actual_bug
& ~(pred_bug &
(pred_bug_loc == padded_example.bug_node_index) &
(pred_cand_id == padded_example.repair_id))),
denominator=actual_bug),
}
loss = -log_prob_joint
for k, v in collected_side_outputs.items():
# Flax collection keys will start with "/".
if v.shape == (): # pylint: disable=g-explicit-bool-comparison
metrics['side' + k] = v
if regularization_weights:
total_regularization = 0
for query, weight in regularization_weights.items():
logging.info('Regularizing side outputs matching query %s', query)
found = False
for k, v in collected_side_outputs.items():
if re.search(query, k):
found = True
logging.info('Regularizing %s with weight %f', k, weight)
total_regularization += weight * v
if not found:
raise ValueError(
f'Regularization query {query} did not match any side output. '
f'Side outputs were {set(collected_side_outputs.keys())}')
loss = loss + total_regularization
is_single_sample = any(
k.endswith('one_sample_log_prob_per_edge_per_node')
for k in collected_side_outputs)
if is_single_sample:
log_prob, = [
v for k, v in collected_side_outputs.items()
if k.endswith('one_sample_log_prob_per_edge_per_node')
]
baseline, = [
v for k, v in collected_side_outputs.items()
if k.endswith('one_sample_reward_baseline')
]
num_real_nodes = padded_example.input_graph.bundle.graph_metadata.num_nodes
valid_mask = (
jnp.arange(static_metadata.bundle_padding.static_max_metadata.num_nodes)
< num_real_nodes)
log_prob = jnp.where(valid_mask[None, :], log_prob, 0)
total_log_prob = jnp.sum(log_prob)
reinforce_virtual_cost = (
total_log_prob * jax.lax.stop_gradient(loss - baseline))
baseline_penalty = jnp.square(loss - baseline)
reinforce_virtual_cost_zeroed = reinforce_virtual_cost - jax.lax.stop_gradient(
reinforce_virtual_cost)
loss = (
loss + reinforce_weight * reinforce_virtual_cost_zeroed +
baseline_weight * baseline_penalty)
metrics['reinforce_virtual_cost'] = reinforce_virtual_cost
metrics['baseline_penalty'] = baseline_penalty
metrics['baseline'] = baseline
metrics['total_log_prob'] = total_log_prob
metrics = jax.tree_map(lambda x: x.astype(jnp.float32), metrics)
return loss, metrics
bias_coef=args.bias_coef,
activation=args.activation,
norm=args.norm)
_, params = net_init(rng=random.PRNGKey(42), input_shape=(-1, 2))
else:
raise ValueError
# loss functions
if args.dataset == 'sinusoid':
loss = lambda fx, y_hat: 0.5 * np.mean((fx - y_hat)**2)
elif args.dataset in ['omniglot', 'circle']:
loss = lambda fx, targets: -np.sum(logsoftmax(fx) * targets
) / targets.shape[0]
acc = lambda fx, targets: np.mean(
np.argmax(logsoftmax(fx), axis=-1) == np.argmax(targets, axis=-1))
param_acc = jit(lambda p, x, y: acc(f(p, x), y))
else:
raise ValueError
grad_loss = jit(grad(lambda p, x, y: loss(f(p, x), y)))
param_loss = jit(lambda p, x, y: loss(f(p, x), y))
# optimizers #TODO: separate optimizers for nonlinear and linear?
outer_opt_init, outer_opt_update, outer_get_params = select_opt(
args.outer_opt_alg, args.outer_step_size)()
inner_opt_init, inner_opt_update, inner_get_params = select_opt(
args.inner_opt_alg, args.inner_step_size)()
# consistent task for plotting eval
if args.dataset == 'sinusoid':
def collect_trajectories(env,
policy_net_apply,
policy_net_params,
num_trajectories=1,
policy="greedy",
max_timestep=None,
epsilon=0.1):
"""Collect trajectories with the given policy net and behaviour."""
trajectories = []
for t in range(num_trajectories):
t_start = time.time()
rewards = []
actions = []
done = False
observation = env.reset()
# This is currently shaped (1, 1) + OBS, but new observations will keep
# getting added to it, making it eventually (1, T+1) + OBS
observation_history = observation[np.newaxis, np.newaxis, :]
# Run either till we're done OR if max_timestep is defined only till that
# timestep.
ts = 0
while ((not done)
and (not max_timestep
or observation_history.shape[1] < max_timestep)):
ts_start = time.time()
# Run the policy, to pick an action, shape is (1, t, A) because
# observation_history is shaped (1, t) + OBS
predictions = policy_net_apply(observation_history,
policy_net_params)
# We need the predictions for the last time-step, so squeeze the batch
# dimension and take the last time-step.
predictions = np.squeeze(predictions, axis=0)[-1]
# Policy can be run in one of the following ways:
# - Greedy
# - Epsilon-Greedy
# - Categorical-Sampling
action = None
if policy == "greedy":
action = np.argmax(predictions)
elif policy == "epsilon-greedy":
# A schedule for epsilon is 1/k where k is the episode number sampled.
if onp.random.random() < epsilon:
# Choose an action at random.
action = onp.random.randint(0, high=len(predictions))
else:
# Return the best action.
action = np.argmax(predictions)
elif policy == "categorical-sampling":
# NOTE: The predictions aren't probabilities but log-probabilities
# instead, since they were computed with LogSoftmax.
# So just np.exp them to make them probabilities.
predictions = np.exp(predictions)
action = onp.argwhere(
onp.random.multinomial(1, predictions) == 1)
else:
raise ValueError("Unknown policy: %s" % policy)
# NOTE: Assumption, single batch.
try:
action = int(action)
except TypeError as err:
# Let's dump some information before we die off.
logging.error("Cannot convert action into an integer: [%s]",
err)
logging.error("action.shape: [%s]", action.shape)
logging.error("action: [%s]", action)
logging.error("predictions.shape: [%s]", predictions.shape)
logging.error("predictions: [%s]", predictions)
logging.error("observation_history: [%s]", observation_history)
logging.error("policy_net_params: [%s]", policy_net_params)
log_params(policy_net_params, "policy_net_params")
raise err
observation, reward, done, _ = env.step(action)
# observation is of shape OBS, so add extra dims and concatenate on the
# time dimension.
observation_history = np.concatenate(
[observation_history, observation[np.newaxis, np.newaxis, :]],
axis=1)
rewards.append(reward)
actions.append(action)
ts += 1
logging.vlog(
2,
" Collected time-step[ %5d] of trajectory[ %5d] in [%0.2f] msec.",
ts, t, get_time(ts_start))
logging.vlog(2, " Collected trajectory[ %5d] in [%0.2f] msec.", t,
get_time(t_start))
# This means we are done we're been terminated early.
assert done or (max_timestep
and max_timestep >= observation_history.shape[1])
# observation_history is (1, T+1) + OBS, lets squeeze out the batch dim.
observation_history = np.squeeze(observation_history, axis=0)
trajectories.append(
(observation_history, np.stack(actions), np.stack(rewards)))
return trajectories
def accuracy(params: hk.Params, batch: Batch) -> jnp.ndarray:
predictions = net.apply(params, batch)
return jnp.mean(jnp.argmax(predictions, axis=-1) == batch["label"])
def __call__(self, inputs, is_training):
"""Connects the module to some inputs.
Args:
inputs: Tensor, final dimension must be equal to ``embedding_dim``. All
other leading dimensions will be flattened and treated as a large batch.
is_training: boolean, whether this connection is to training data. When
this is set to ``False``, the internal moving average statistics will
not be updated.
Returns:
dict: Dictionary containing the following keys and values:
* ``quantize``: Tensor containing the quantized version of the input.
* ``loss``: Tensor containing the loss to optimize.
* ``perplexity``: Tensor containing the perplexity of the encodings.
* ``encodings``: Tensor containing the discrete encodings, ie which
element of the quantized space each input element was mapped to.
* ``encoding_indices``: Tensor containing the discrete encoding indices,
ie which element of the quantized space each input element was mapped
to.
"""
flat_inputs = jnp.reshape(inputs, [-1, self.embedding_dim])
embeddings = self.embeddings
distances = (jnp.sum(flat_inputs**2, 1, keepdims=True) -
2 * jnp.matmul(flat_inputs, embeddings) +
jnp.sum(embeddings**2, 0, keepdims=True))
encoding_indices = jnp.argmax(-distances, 1)
encodings = jax.nn.one_hot(encoding_indices,
self.num_embeddings,
dtype=distances.dtype)
# NB: if your code crashes with a reshape error on the line below about a
# Tensor containing the wrong number of values, then the most likely cause
# is that the input passed in does not have a final dimension equal to
# self.embedding_dim. Ideally we would catch this with an Assert but that
# creates various other problems related to device placement / TPUs.
encoding_indices = jnp.reshape(encoding_indices, inputs.shape[:-1])
quantized = self.quantize(encoding_indices)
e_latent_loss = jnp.mean(
(jax.lax.stop_gradient(quantized) - inputs)**2)
if is_training:
cluster_size = jnp.sum(encodings, axis=0)
if self.cross_replica_axis:
cluster_size = jax.lax.psum(cluster_size,
axis_name=self.cross_replica_axis)
updated_ema_cluster_size = self.ema_cluster_size(cluster_size)
dw = jnp.matmul(flat_inputs.T, encodings)
if self.cross_replica_axis:
dw = jax.lax.psum(dw, axis_name=self.cross_replica_axis)
updated_ema_dw = self.ema_dw(dw)
n = jnp.sum(updated_ema_cluster_size)
updated_ema_cluster_size = (
(updated_ema_cluster_size + self.epsilon) /
(n + self.num_embeddings * self.epsilon) * n)
normalised_updated_ema_w = (
updated_ema_dw /
jnp.reshape(updated_ema_cluster_size, [1, -1]))
hk.set_state("embeddings", normalised_updated_ema_w)
loss = self.commitment_cost * e_latent_loss
else:
loss = self.commitment_cost * e_latent_loss
# Straight Through Estimator
quantized = inputs + jax.lax.stop_gradient(quantized - inputs)
avg_probs = jnp.mean(encodings, 0)
if self.cross_replica_axis:
avg_probs = jax.lax.pmean(avg_probs,
axis_name=self.cross_replica_axis)
perplexity = jnp.exp(-jnp.sum(avg_probs * jnp.log(avg_probs + 1e-10)))
return {
"quantize": quantized,
"loss": loss,
"perplexity": perplexity,
"encodings": encodings,
"encoding_indices": encoding_indices,
"distances": distances,
}
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"""
#initial setup
images = np.array(test_images[0:1000])
def accuracy(y_true, y_pred):
return jnp.mean(jnp.argmax(y_pred, axis=-1) == y_true)
num_complete_batches, leftover = divmod(X.shape[0], batch_size)
num_batches = num_complete_batches + bool(leftover)
while True:
temp, rng = random.split(rng)
perm = random.permutation(temp, X.shape[0])
for i in range(num_batches):
batch_idx = perm[i * batch_size:(i + 1) * batch_size]
yield X[batch_idx], y[batch_idx]
if __name__ == "__main__":
rng = random.PRNGKey(0)
X, y, X_test, y_test = mnist()
X, X_test = X.reshape(-1, 28, 28, 1), X_test.reshape(-1, 28, 28, 1)
y, y_test = (np.argmax(y, 1) % 2 == 1).astype(
np.float32), (np.argmax(y_test, 1) % 1 == 1).astype(np.float32)
temp, rng = random.split(rng)
params, predict = model(temp)
def loss(params, batch, l2=0.05):
X, y = batch
y_hat = predict(params, X).reshape(-1)
return -np.mean(y * np.log(y_hat) + (1. - y) * np.log(1. - y_hat))
@jit
def update(i, opt_state, batch):
params = get_params(opt_state)
return opt_update(i, grad(loss)(params, batch), opt_state)
def accuracy(trainable_params, untrainable_params, batch):
inputs, targets = batch
target_class = jnp.argmax(targets, axis=1)
params = merge_params(trainable_params, untrainable_params)
predicted_class = jnp.argmax(net.apply(params, inputs), axis=1)
return jnp.mean(predicted_class == target_class)
def test_change_point_x64():
# Ref: https://forum.pyro.ai/t/i-dont-understand-why-nuts-code-is-not-working-bayesian-hackers-mail/696
warmup_steps, num_samples = 500, 3000
def model(data):
alpha = 1 / np.mean(data)
lambda1 = numpyro.sample('lambda1', dist.Exponential(alpha))
lambda2 = numpyro.sample('lambda2', dist.Exponential(alpha))
tau = numpyro.sample('tau', dist.Uniform(0, 1))
lambda12 = np.where(
np.arange(len(data)) < tau * len(data), lambda1, lambda2)
numpyro.sample('obs', dist.Poisson(lambda12), obs=data)
count_data = np.array([
13,
24,
8,
24,
7,
35,
14,
11,
15,
11,
22,
22,
11,
57,
11,
19,
29,
6,
19,
12,
22,
12,
18,
72,
32,
9,
7,
13,
19,
23,
27,
20,
6,
17,
13,
10,
14,
6,
16,
15,
7,
2,
15,
15,
19,
70,
49,
7,
53,
22,
21,
31,
19,
11,
18,
20,
12,
35,
17,
23,
17,
4,
2,
31,
30,
13,
27,
0,
39,
37,
5,
14,
13,
22,
])
kernel = NUTS(model=model)
mcmc = MCMC(kernel, warmup_steps, num_samples)
mcmc.run(random.PRNGKey(4), count_data)
samples = mcmc.get_samples()
tau_posterior = (samples['tau'] * len(count_data)).astype(np.int32)
tau_values, counts = onp.unique(tau_posterior, return_counts=True)
mode_ind = np.argmax(counts)
mode = tau_values[mode_ind]
assert mode == 44
if 'JAX_ENABLE_X64' in os.environ:
assert samples['lambda1'].dtype == np.float64
assert samples['lambda2'].dtype == np.float64
assert samples['tau'].dtype == np.float64
def sample_categorical(key, logits):
minval = jnp.finfo(logits.dtype).tiny
unif = jax.random.uniform(key, logits.shape, minval=minval, maxval=1.0)
gumbel = -jnp.log(-jnp.log(unif) + minval)
category = jnp.argmax(logits + gumbel, -1)
return category
def gumbel_max_sampler(logits_1, logits_2, rng):
"""Samples from a Gumbel-max coupling."""
gumbels = jax.random.gumbel(rng, logits_1.shape)
x = jnp.argmax(gumbels + logits_1)
y = jnp.argmax(gumbels + logits_2)
return jnp.zeros([10, 10]).at[x, y].set(1.)
def main(_):
sns.set()
sns.set_palette(sns.color_palette('hls', 10))
npr.seed(FLAGS.seed)
logging.info('Starting experiment.')
# Create model folder for outputs
try:
gfile.MakeDirs(FLAGS.work_dir)
except gfile.GOSError:
pass
stdout_log = gfile.Open('{}/stdout.log'.format(FLAGS.work_dir), 'w+')
# use mean/std of svhn train
train_images, _, _ = datasets.get_dataset_split(
name=FLAGS.train_split.split('-')[0],
split=FLAGS.train_split.split('-')[1],
shuffle=False)
train_mu, train_std = onp.mean(train_images), onp.std(train_images)
del train_images
# BEGIN: fetch test data and candidate pool
test_images, test_labels, _ = datasets.get_dataset_split(
name=FLAGS.test_split.split('-')[0],
split=FLAGS.test_split.split('-')[1],
shuffle=False)
pool_images, pool_labels, _ = datasets.get_dataset_split(
name=FLAGS.pool_split.split('-')[0],
split=FLAGS.pool_split.split('-')[1],
shuffle=False)
n_pool = len(pool_images)
test_images = (test_images -
train_mu) / train_std # normalize w train mu/std
pool_images = (pool_images -
train_mu) / train_std # normalize w train mu/std
# augmentation for train/pool data
if FLAGS.augment_data:
augmentation = data.chain_transforms(data.RandomHorizontalFlip(0.5),
data.RandomCrop(4), data.ToDevice)
else:
augmentation = None
# END: fetch test data and candidate pool
# BEGIN: load ckpt
opt_init, opt_update, get_params = optimizers.sgd(FLAGS.learning_rate)
if FLAGS.pretrained_dir is not None:
with gfile.Open(FLAGS.pretrained_dir, 'rb') as fpre:
pretrained_opt_state = optimizers.pack_optimizer_state(
pickle.load(fpre))
fixed_params = get_params(pretrained_opt_state)[:7]
ckpt_dir = '{}/{}'.format(FLAGS.root_dir, FLAGS.ckpt_idx)
with gfile.Open(ckpt_dir, 'wr') as fckpt:
opt_state = optimizers.pack_optimizer_state(pickle.load(fckpt))
params = get_params(opt_state)
# combine fixed pretrained params and dpsgd trained last layers
params = fixed_params + params
opt_state = opt_init(params)
else:
ckpt_dir = '{}/{}'.format(FLAGS.root_dir, FLAGS.ckpt_idx)
with gfile.Open(ckpt_dir, 'wr') as fckpt:
opt_state = optimizers.pack_optimizer_state(pickle.load(fckpt))
params = get_params(opt_state)
stdout_log.write('finetune from: {}\n'.format(ckpt_dir))
logging.info('finetune from: %s', ckpt_dir)
test_acc, test_pred = accuracy(params,
shape_as_image(test_images, test_labels),
return_predicted_class=True)
logging.info('test accuracy: %.2f', test_acc)
stdout_log.write('test accuracy: {}\n'.format(test_acc))
stdout_log.flush()
# END: load ckpt
# BEGIN: setup for dp model
@jit
def update(_, i, opt_state, batch):
params = get_params(opt_state)
return opt_update(i, grad_loss(params, batch), opt_state)
@jit
def private_update(rng, i, opt_state, batch):
params = get_params(opt_state)
rng = random.fold_in(rng, i) # get new key for new random numbers
return opt_update(
i,
private_grad(params, batch, rng, FLAGS.l2_norm_clip,
FLAGS.noise_multiplier, FLAGS.batch_size), opt_state)
# END: setup for dp model
### BEGIN: prepare extra points picked from pool data
# BEGIN: on pool data
pool_embeddings = [apply_fn_0(params[:-1],
pool_images[b_i:b_i + FLAGS.batch_size]) \
for b_i in range(0, n_pool, FLAGS.batch_size)]
pool_embeddings = np.concatenate(pool_embeddings, axis=0)
pool_logits = apply_fn_1(params[-1:], pool_embeddings)
pool_true_labels = np.argmax(pool_labels, axis=1)
pool_predicted_labels = np.argmax(pool_logits, axis=1)
pool_correct_indices = \
onp.where(pool_true_labels == pool_predicted_labels)[0]
pool_incorrect_indices = \
onp.where(pool_true_labels != pool_predicted_labels)[0]
assert len(pool_correct_indices) + \
len(pool_incorrect_indices) == len(pool_labels)
pool_probs = stax.softmax(pool_logits)
if FLAGS.uncertain == 0 or FLAGS.uncertain == 'entropy':
pool_entropy = -onp.sum(pool_probs * onp.log(pool_probs), axis=1)
stdout_log.write('all {} entropy: min {}, max {}\n'.format(
len(pool_entropy), onp.min(pool_entropy), onp.max(pool_entropy)))
pool_entropy_sorted_indices = onp.argsort(pool_entropy)
# take the n_extra most uncertain points
pool_uncertain_indices = \
pool_entropy_sorted_indices[::-1][:FLAGS.n_extra]
stdout_log.write('uncertain {} entropy: min {}, max {}\n'.format(
len(pool_entropy[pool_uncertain_indices]),
onp.min(pool_entropy[pool_uncertain_indices]),
onp.max(pool_entropy[pool_uncertain_indices])))
elif FLAGS.uncertain == 1 or FLAGS.uncertain == 'difference':
# 1st_prob - 2nd_prob
assert len(pool_probs.shape) == 2
sorted_pool_probs = onp.sort(pool_probs, axis=1)
pool_probs_diff = sorted_pool_probs[:, -1] - sorted_pool_probs[:, -2]
assert min(pool_probs_diff) > 0.
stdout_log.write('all {} difference: min {}, max {}\n'.format(
len(pool_probs_diff), onp.min(pool_probs_diff),
onp.max(pool_probs_diff)))
pool_uncertain_indices = onp.argsort(pool_probs_diff)[:FLAGS.n_extra]
stdout_log.write('uncertain {} difference: min {}, max {}\n'.format(
len(pool_probs_diff[pool_uncertain_indices]),
onp.min(pool_probs_diff[pool_uncertain_indices]),
onp.max(pool_probs_diff[pool_uncertain_indices])))
elif FLAGS.uncertain == 2 or FLAGS.uncertain == 'random':
pool_uncertain_indices = npr.permutation(n_pool)[:FLAGS.n_extra]
# END: on pool data
### END: prepare extra points picked from pool data
finetune_images = copy.deepcopy(pool_images[pool_uncertain_indices])
finetune_labels = copy.deepcopy(pool_labels[pool_uncertain_indices])
stdout_log.write('Starting fine-tuning...\n')
logging.info('Starting fine-tuning...')
stdout_log.flush()
stdout_log.write('{} points picked via {}\n'.format(
len(finetune_images), FLAGS.uncertain))
logging.info('%d points picked via %s', len(finetune_images),
FLAGS.uncertain)
assert FLAGS.n_extra == len(finetune_images)
for epoch in range(1, FLAGS.epochs + 1):
# BEGIN: finetune model with extra data, evaluate and save
num_extra = len(finetune_images)
num_complete_batches, leftover = divmod(num_extra, FLAGS.batch_size)
num_batches = num_complete_batches + bool(leftover)
finetune = data.DataChunk(X=finetune_images,
Y=finetune_labels,
image_size=32,
image_channels=3,
label_dim=1,
label_format='numeric')
batches = data.minibatcher(finetune,
FLAGS.batch_size,
transform=augmentation)
itercount = itertools.count()
key = random.PRNGKey(FLAGS.seed)
start_time = time.time()
for _ in range(num_batches):
# tmp_time = time.time()
b = next(batches)
if FLAGS.dpsgd:
opt_state = private_update(
key, next(itercount), opt_state,
shape_as_image(b.X, b.Y, dummy_dim=True))
else:
opt_state = update(key, next(itercount), opt_state,
shape_as_image(b.X, b.Y))
# stdout_log.write('single update in {:.2f} sec\n'.format(
# time.time() - tmp_time))
epoch_time = time.time() - start_time
stdout_log.write('Epoch {} in {:.2f} sec\n'.format(epoch, epoch_time))
logging.info('Epoch %d in %.2f sec', epoch, epoch_time)
# accuracy on test data
params = get_params(opt_state)
test_pred_0 = test_pred
test_acc, test_pred = accuracy(params,
shape_as_image(test_images,
test_labels),
return_predicted_class=True)
test_loss = loss(params, shape_as_image(test_images, test_labels))
stdout_log.write(
'Eval set loss, accuracy (%): ({:.2f}, {:.2f})\n'.format(
test_loss, 100 * test_acc))
logging.info('Eval set loss, accuracy: (%.2f, %.2f)', test_loss,
100 * test_acc)
stdout_log.flush()
# visualize prediction difference between 2 checkpoints.
if FLAGS.visualize:
utils.visualize_ckpt_difference(test_images,
np.argmax(test_labels, axis=1),
test_pred_0,
test_pred,
epoch - 1,
epoch,
FLAGS.work_dir,
mu=train_mu,
sigma=train_std)
# END: finetune model with extra data, evaluate and save
stdout_log.close()
def accuracy(params, batch): inputs, targets = batch target_class = np.argmax(targets, axis=1) predicted_class = np.argmax(predict(params, inputs), axis=1) return np.mean(predicted_class == target_class)
def _accuracy(y, y_hat):
"""Compute the accuracy of the predictions with respect to one-hot labels."""
return np.mean(np.argmax(y, axis=1) == np.argmax(y_hat, axis=1))
def _gumbel_max(rng, logit_probs):
return np.argmax(random.gumbel(rng, logit_probs.shape, logit_probs.dtype) +
logit_probs,
axis=0)
def mode(self):
return jp.argmax(self._probs, axis=-1)
def accuracy(params, images, targets):
target_class = jnp.argmax(targets, axis=1)
predicted_class = jnp.argmax(batched_predict(params, images), axis=1)
return jnp.mean(predicted_class == target_class)
def sample(self, sample_shape, seed):
return jp.argmax(
self._logits +
jax.random.gumbel(seed, shape=sample_shape + self._logits.shape),
axis=-1,
)