tf.contrib.distributions.Bernoulli.log_pmf(value, name='log_pmf') Log probability mass function. Args: value: float or double Tensor. name: The name to give this op. Returns: log_pmf: a Tensor of shape sample_shape(x) + self.batch_shape with values of type self.dtype. Raises: TypeError: if is_continuous.

tf.sparse_concat(concat_dim, sp_inputs, name=None, expand_nonconcat_dim=False) Concatenates a list of SparseTensor along the specified dimension. Concatenation is with respect to the dense versions of each sparse input. It is assumed that each inputs is a SparseTensor whose elements are ordered along increasing dimension number. If expand_nonconcat_dim is False, all inputs' shapes must match, except for the concat dimension. If expand_nonconcat_dim is True, then inputs' shapes are allowd to va

TTypes<T>::Flat tensorflow::Tensor::flat() Return the tensor data as an Eigen::Tensor of the data type and a specified shape. These methods allow you to access the data with the dimensions and sizes of your choice. You do not need to know the number of dimensions of the Tensor to call them. However, they CHECK that the type matches and the dimensions requested creates an Eigen::Tensor with the same number of elements as the tensor. Example: Tensor my_ten(...built with Shape{planes: 4, ro

tf.contrib.graph_editor.assign_renamed_collections_handler(info, elem, elem_) Add the transformed elem to the (renamed) collections of elem. Args: info: Transform._Info instance. elem: the original element (tf.Tensor or tf.Operation) elem_: the transformed element

tf.contrib.distributions.LaplaceWithSoftplusScale.variance(name='variance') Variance.

tf.contrib.distributions.WishartFull.sample(sample_shape=(), seed=None, name='sample') Generate samples of the specified shape. Note that a call to sample() without arguments will generate a single sample. Args: sample_shape: 0D or 1D int32 Tensor. Shape of the generated samples. seed: Python integer seed for RNG name: name to give to the op. Returns: samples: a Tensor with prepended dimensions sample_shape.

tf.contrib.distributions.DirichletMultinomial.__init__(n, alpha, validate_args=False, allow_nan_stats=True, name='DirichletMultinomial') Initialize a batch of DirichletMultinomial distributions. Args: n: Non-negative floating point tensor, whose dtype is the same as alpha. The shape is broadcastable to [N1,..., Nm] with m >= 0. Defines this as a batch of N1 x ... x Nm different Dirichlet multinomial distributions. Its components should be equal to integer values. alpha: Positive floating

tf.contrib.distributions.Laplace.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.framework.get_unique_variable(var_op_name) Gets the variable uniquely identified by that var_op_name. Args: var_op_name: the full name of the variable op, including the scope. Returns: a tensorflow variable. Raises: ValueError: if no variable uniquely identified by the name exists.

tf.contrib.learn.LinearClassifier.get_variable_names()