tf.contrib.distributions.WishartCholesky.log_cdf(value, name='log_cdf') Log cumulative distribution function. Given random variable X, the cumulative distribution function cdf is: log_cdf(x) := Log[ P[X <= x] ] Often, a numerical approximation can be used for log_cdf(x) that yields a more accurate answer than simply taking the logarithm of the cdf when x << -1. Args: value: float or double Tensor. name: The name to give this op. Returns: logcdf: a Tensor of shape sample_shape(x)

tf.sub(x, y, name=None) Returns x - y element-wise. NOTE: Sub supports broadcasting. More about broadcasting here Args: x: A Tensor. Must be one of the following types: half, float32, float64, int32, int64, complex64, complex128. y: A Tensor. Must have the same type as x. name: A name for the operation (optional). Returns: A Tensor. Has the same type as x.

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

Options to configure a Thread . Note that the options are all hints, and the underlying implementation may choose to ignore it. Member Details size_t tensorflow::ThreadOptions::stack_size Thread stack size to use (in bytes). size_t tensorflow::ThreadOptions::guard_size Guard area size to use near thread stacks to use (in bytes)

tf.contrib.graph_editor.copy(sgv, dst_graph=None, dst_scope='', src_scope='', reuse_dst_scope=False) Copy a subgraph. Args: sgv: the source subgraph-view. This argument is converted to a subgraph using the same rules than the function subgraph.make_view. dst_graph: the destination graph. dst_scope: the destination scope. src_scope: the source scope. reuse_dst_scope: if True the dst_scope is re-used if it already exists. Otherwise, the scope is given a unique name based on the one given by

tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.param_shapes(cls, sample_shape, name='DistributionParamShapes') Shapes of parameters given the desired shape of a call to sample(). Subclasses should override static method _param_shapes. Args: sample_shape: Tensor or python list/tuple. Desired shape of a call to sample(). name: name to prepend ops with. Returns: dict of parameter name to Tensor shapes.

tf.contrib.metrics.streaming_recall_at_k(*args, **kwargs) Computes the recall@k of the predictions with respect to dense 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). The streaming_recall_at_k function creates two local variables, total and count, that are used to compute the rec

tf.contrib.distributions.MultivariateNormalCholesky.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.metrics.streaming_mean_squared_error(predictions, labels, weights=None, metrics_collections=None, updates_collections=None, name=None) Computes the mean squared error between the labels and predictions. The streaming_mean_squared_error function creates two local variables, total and count that are used to compute the mean squared error. This average is weighted by weights, and it is ultimately returned as mean_squared_error: an idempotent operation that simply divides total by count

tf.nn.rnn_cell.MultiRNNCell.__init__(cells, state_is_tuple=True) Create a RNN cell composed sequentially of a number of RNNCells. Args: cells: list of RNNCells that will be composed in this order. state_is_tuple: If True, accepted and returned states are n-tuples, where n = len(cells). If False, the states are all concatenated along the column axis. This latter behavior will soon be deprecated. Raises: ValueError: if cells is empty (not allowed), or at least one of the cells returns a sta