tfsnippet

tfsnippet Package

Functions

as_distribution(distribution)	Convert a supported type of distribution into Distribution type.
reduce_group_ndims(operation, tensor, …[, …])	Reduce the last group_ndims dimensions in tensor, using operation.
early_stopping(*args, **kwargs)	Deprecated since version 0.1.
summarize_variables(variables[, title, …])	Get a formatted summary about the variables.
train_loop(*args, **kwargs)	Deprecated since version 0.1.
auto_batch_weight(*batch_arrays)	Automatically inspect the metric weight for an evaluation mini-batch.
merge_feed_dict(*feed_dicts)	Merge all feed dicts into one.
resolve_feed_dict(feed_dict[, inplace])	Resolve all dynamic values in feed_dict into fixed values.
elbo_objective(log_joint, latent_log_prob[, …])	Derive the ELBO objective.
importance_sampling_log_likelihood(…[, …])	Compute \(\log p(\mathbf{x})\) by importance sampling.
iwae_estimator(log_values, axis[, keepdims, …])	Derive the gradient estimator for \(\mathbb{E}_{q(\mathbf{z}^{(1:K)}|\mathbf{x})}\Big[\log \frac{1}{K} \sum_{k=1}^K f\big(\mathbf{x},\mathbf{z}^{(k)}\big)\Big]\), by IWAE (Burda, Y., Grosse, R.
monte_carlo_objective(log_joint, latent_log_prob)	Derive the Monte-Carlo objective.
sgvb_estimator(values[, axis, keepdims, name])	Derive the gradient estimator for \(\mathbb{E}_{q(\mathbf{z}|\mathbf{x})}\big[f(\mathbf{x},\mathbf{z})\big]\), by SGVB (Kingma, D.P.
model_variable(name[, shape, dtype, …])	Get or create a model variable.
get_model_variables([scope])	Get all model variables (i.e., variables in MODEL_VARIABLES collection).
get_config_defaults(config)	Get the default config values of config.
register_config_arguments(config, parser[, …])	Register config to the specified argument parser.
get_reuse_stack_top()	Get the top of the reuse scope stack.
instance_reuse([method_or_scope, _sentinel, …])	Decorate an instance method to reuse a variable scope automatically.
global_reuse([method_or_scope, _sentinel, scope])	Decorate a function to reuse a variable scope automatically.

Classes

Bernoulli(logits[, dtype])	Univariate Bernoulli distribution.
Categorical(logits[, dtype])	Univariate Categorical distribution.
Concrete(temperature, logits[, …])	The class of Concrete (or Gumbel-Softmax) distribution from (Maddison, 2016; Jang, 2016), served as the continuous relaxation of the OnehotCategorical.
Discrete	alias of tfsnippet.distributions.univariate.Categorical
Distribution	Base class for probability distributions.
ExpConcrete(temperature, logits[, …])	The class of ExpConcrete distribution from (Maddison, 2016), transformed from Concrete by taking logarithm.
FlowDistribution(distribution, flow)	Transform a Distribution by a BaseFlow, as a new distribution.
Normal(mean[, std, logstd, …])	Univariate Normal distribution.
OnehotCategorical(logits[, dtype])	One-hot multivariate Categorical distribution.
Uniform([minval, maxval, …])	Univariate Uniform distribution.
DefaultMetricFormatter	Default training metric formatter.
EarlyStopping(param_vars[, initial_metric, …])	Early-stopping context object.
EarlyStoppingContext	alias of tfsnippet.scaffold.early_stopping_.EarlyStopping
MetricFormatter	Base class for a training metrics formatter.
MetricLogger([summary_writer, …])	Logger for the training metrics.
TrainLoop(param_vars[, var_groups, …])	Training loop object.
TrainLoopContext	alias of tfsnippet.scaffold.train_loop_.TrainLoop
VariableSaver(variables, save_dir[, …])	Version controlled saving and restoring TensorFlow variables.
AnnealingVariable(name, initial_value, ratio)	A non-trainable tf.Variable, whose value will be annealed as training goes by.
BaseTrainer(loop)	Base class for all trainers.
Evaluator(loop, metrics, inputs, data_flow)	Class to compute evaluation metrics.
HookEntry(callback, freq, priority, birth)	Configurations of a hook entry in HookList.
HookList()	Class for managing hooks in BaseTrainer and Evaluator.
HookPriority	Pre-defined hook priorities for BaseTrainer and Evaluator.
LossTrainer(**kwargs)	A subclass of BaseTrainer, which optimizes a single loss.
ScheduledVariable(name, initial_value[, …])	A non-trainable tf.Variable, whose value might need to be changed as training goes by.
Trainer(loop, train_op, inputs, data_flow[, …])	A subclass of BaseTrainer, executing a training operation per step.
Validator(**kwargs)	Class to compute validation loss and other metrics.
VariationalChain(variational, model[, …])	Chain of the variational and model nets for variational inference.
VariationalEvaluation(vi)	Factory for variational evaluation outputs.
VariationalInference(log_joint, latent_log_probs)	Class for variational inference.
VariationalLowerBounds(vi)	Factory for variational lower-bounds.
VariationalTrainingObjectives(vi)	Factory for variational training objectives.
BayesianNet([observed])	Bayesian networks.
DataFlow	Data flows are objects for constructing mini-batch iterators.
DataMapper	Base class for all data mappers.
SlidingWindow(data_array, window_size)	DataMapper for producing sliding windows according to indices.
Config()	Base class for defining config values.
ConfigField(type[, default, description, …])	A config field.
VarScopeObject([name, scope])	Base class for objects that own a variable scope.
StochasticTensor(distribution, tensor[, …])	Samples or observations of a stochastic variable.

Class Inheritance Diagram