tf.SparseTensorValue.__repr__() Return a nicely formatted representation string

tf.FixedLenSequenceFeature.allow_missing Alias for field number 2

tf.contrib.graph_editor.filter_ops_from_regex(ops, regex) Get all the operations that match the given regex. Args: ops: an object convertible to a list of tf.Operation. regex: a regular expression matching the operation's name. For example, "^foo(/.*)?$" will match all the operations in the "foo" scope. Returns: A list of tf.Operation. Raises: TypeError: if ops cannot be converted to a list of tf.Operation.

tf.contrib.graph_editor.make_list_of_t(ts, check_graph=True, allow_graph=True, ignore_ops=False) Convert ts to a list of tf.Tensor. Args: ts: can be an iterable of tf.Tensor, a tf.Graph or a single tensor. check_graph: if True check if all the tensors belong to the same graph. allow_graph: if False a tf.Graph cannot be converted. ignore_ops: if True, silently ignore tf.Operation. Returns: A newly created list of tf.Tensor. Raises: TypeError: if ts cannot be converted to a list of tf.Ten

tf.contrib.learn.TensorFlowRNNRegressor.get_variable_names() Returns list of all variable names in this model. Returns: List of names.

tf.contrib.distributions.Poisson.__init__(lam, validate_args=False, allow_nan_stats=True, name='Poisson') Construct Poisson distributions. Args: lam: Floating point tensor, the rate parameter of the distribution(s). lam must be positive. validate_args: Boolean, default False. Whether to assert that lam > 0 as well as inputs to pmf computations are non-negative integers. If validate_args is False, then pmf computations might return NaN, but can be evaluated at any real value. allow_nan_st

tf.contrib.learn.LinearRegressor.get_params(deep=True) Get parameters for this estimator. Args: deep: boolean, optional If True, will return the parameters for this estimator and contained subobjects that are estimators. Returns: params : mapping of string to any Parameter names mapped to their values.

tf.contrib.distributions.MultivariateNormalFull.mu

tf.contrib.learn.Estimator.get_params(deep=True) Get parameters for this estimator. Args: deep: boolean, optional If True, will return the parameters for this estimator and contained subobjects that are estimators. Returns: params : mapping of string to any Parameter names mapped to their values.

tf.contrib.distributions.LaplaceWithSoftplusScale.__init__(loc, scale, validate_args=False, allow_nan_stats=True, name='LaplaceWithSoftplusScale')