tf.contrib.distributions.NormalWithSoftplusSigma.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.Normal.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.Normal.get_event_shape() Shape of a single sample from a single batch as a TensorShape. Same meaning as event_shape. May be only partially defined. Returns: event_shape: TensorShape, possibly unknown.

tf.contrib.learn.monitors.SummarySaver.step_begin(step) Overrides BaseMonitor.step_begin. When overriding this method, you must call the super implementation. Args: step: int, the current value of the global step. Returns: A list, the result of every_n_step_begin, if that was called this step, or an empty list otherwise. Raises: ValueError: if called more than once during a step.

tf.contrib.metrics.streaming_sparse_recall_at_k(*args, **kwargs) Computes recall@k of the predictions with respect to sparse labels. (deprecated arguments) SOME ARGUMENTS ARE DEPRECATED. They will be removed after 2016-10-19. Instructions for updating: ignore_mask is being deprecated. Instead use weights with values 0.0 and 1.0 to mask values. For example, weights=tf.logical_not(mask). If class_id is specified, we calculate recall by considering only the entries in the batch for which class_id

tf.contrib.distributions.Uniform.a

tf.decode_base64(input, name=None) Decode web-safe base64-encoded strings. Input may or may not have padding at the end. See EncodeBase64 for padding. Web-safe means that input must use - and _ instead of + and /. Args: input: A Tensor of type string. Base64 strings to decode. name: A name for the operation (optional). Returns: A Tensor of type string. Decoded strings.

Status tensorflow::Env::GetFileSize(const string &fname, uint64 *file_size) Stores the size of fname in *file_size.

tf.contrib.bayesflow.stochastic_tensor.Chi2WithAbsDfTensor.value(name='value')

tf.self_adjoint_eigvals(tensor, name=None) Computes the eigenvalues of one or more self-adjoint matrices. Args: tensor: Tensor of shape [..., N, N]. name: string, optional name of the operation. Returns: e: Eigenvalues. Shape is [..., N]. The vector e[..., :] contains the N eigenvalues of tensor[..., :, :].