tf.contrib.learn.TensorFlowEstimator.set_params(**params) Set the parameters of this estimator. The method works on simple estimators as well as on nested objects (such as pipelines). The former have parameters of the form <component>__<parameter> so that it's possible to update each component of a nested object. Args: **params: Parameters. Returns: self Raises: ValueError: If params contain invalid names.

tf.contrib.distributions.Chi2WithAbsDf.log_pdf(value, name='log_pdf') Log probability density function. Args: value: float or double Tensor. name: The name to give this op. Returns: log_prob: a Tensor of shape sample_shape(x) + self.batch_shape with values of type self.dtype. Raises: TypeError: if not is_continuous.

tf.contrib.training.NextQueuedSequenceBatch.context A dict mapping keys of input_context to batched context. Returns: A dict mapping keys of input_context to tensors. If we had at input: context["name"].get_shape() == [d1, d2, ...] then for this property: context["name"].get_shape() == [batch_size, d1, d2, ...]

tf.ReaderBase.read_up_to(queue, num_records, name=None) Returns up to num_records (key, value pairs) produced by a reader. Will dequeue a work unit from queue if necessary (e.g., when the Reader needs to start reading from a new file since it has finished with the previous file). It may return less than num_records even before the last batch. Args: queue: A Queue or a mutable string Tensor representing a handle to a Queue, with string work items. num_records: Number of records to read. name

tf.contrib.learn.monitors.GraphDump.compare(other_dump, step, atol=1e-06) Compares two GraphDump monitors and returns differences. Args: other_dump: Another GraphDump monitor. step: int, step to compare on. atol: float, absolute tolerance in comparison of floating arrays. Returns: Returns tuple: matched: list of keys that matched. non_matched: dict of keys to tuple of 2 mismatched values. Raises: ValueError: if a key in data is missing from other_dump at step.

TensorShapeIter tensorflow::TensorShape::end() const

tf.contrib.distributions.InverseGammaWithSoftplusAlphaBeta.param_static_shapes(cls, sample_shape) param_shapes with static (i.e. TensorShape) shapes. Args: sample_shape: TensorShape or python list/tuple. Desired shape of a call to sample(). Returns: dict of parameter name to TensorShape. Raises: ValueError: if sample_shape is a TensorShape and is not fully defined.

tf.contrib.distributions.WishartFull.param_static_shapes(cls, sample_shape) param_shapes with static (i.e. TensorShape) shapes. Args: sample_shape: TensorShape or python list/tuple. Desired shape of a call to sample(). Returns: dict of parameter name to TensorShape. Raises: ValueError: if sample_shape is a TensorShape and is not fully defined.

tf.contrib.bayesflow.stochastic_tensor.TransformedDistributionTensor.loss(final_loss, name='Loss')

tf.TextLineReader.read(queue, name=None) Returns the next record (key, value pair) produced by a reader. Will dequeue a work unit from queue if necessary (e.g. when the Reader needs to start reading from a new file since it has finished with the previous file). Args: queue: A Queue or a mutable string Tensor representing a handle to a Queue, with string work items. name: A name for the operation (optional). Returns: A tuple of Tensors (key, value). key: A string scalar Tensor. value: A s