tf.train.slice_input_producer(tensor_list, num_epochs=None, shuffle=True, seed=None, capacity=32, shared_name=None, name=None) Produces a slice of each Tensor in tensor_list. Implemented using a Queue -- a QueueRunner for the Queue is added to the current Graph's QUEUE_RUNNER collection. Args: tensor_list: A list of Tensor objects. Every Tensor in tensor_list must have the same size in the first dimension. num_epochs: An integer (optional). If specified, slice_input_producer produces each sl

tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.name Name prepended to all ops created by this Distribution.

tf.igamma(a, x, name=None) Compute the lower regularized incomplete Gamma function Q(a, x). The lower regularized incomplete Gamma function is defined as: P(a, x) = gamma(a, x) / Gamma(a) = 1 - Q(a, x) where gamma(a, x) = int_{0}^{x} t^{a-1} exp(-t) dt is the lower incomplete Gamma function. Note, above Q(a, x) (Igammac) is the upper regularized complete Gamma function. Args: a: A Tensor. Must be one of the following types: float32, float64. x: A Tensor. Must have the same type as a. name:

tf.contrib.learn.monitors.EveryN.every_n_post_step(step, session) Callback after a step is finished or end() is called. Args: step: int, the current value of the global step. session: Session object.

tf.contrib.distributions.ExponentialWithSoftplusLam.survival_function(value, name='survival_function') Survival function. Given random variable X, the survival function is defined: survival_function(x) = P[X > x] = 1 - P[X <= x] = 1 - cdf(x). Args: value: float or double Tensor. name: The name to give this op. Returns: Tensorof shapesample_shape(x) + self.batch_shapewith values of typeself.dtype`.

tf.contrib.graph_editor.connect(sgv0, sgv1, disconnect_first=False) Connect the outputs of sgv0 to the inputs of sgv1. Args: sgv0: the first subgraph to have its outputs swapped. This argument is converted to a subgraph using the same rules as the function subgraph.make_view. Note that sgv0 is modified in place. sgv1: the second subgraph to have its outputs swapped. This argument is converted to a subgraph using the same rules as the function subgraph.make_view. Note that sgv1 is modified in

tf.contrib.distributions.QuantizedDistribution.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.

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.contrib.framework.assign_from_checkpoint(model_path, var_list) Creates an operation to assign specific variables from a checkpoint. Args: model_path: The full path to the model checkpoint. To get latest checkpoint use model_path = tf.train.latest_checkpoint(checkpoint_dir) var_list: A list of Variable objects or a dictionary mapping names in the checkpoint to the correspoing variables to initialize. If empty or None, it would return no_op(), None. Returns: the restore_op and the feed_d

tf.contrib.distributions.BetaWithSoftplusAB.prob(value, name='prob') Probability density/mass function (depending on is_continuous). Additional documentation from Beta: Note that the argument x must be a non-negative floating point tensor whose shape can be broadcast with self.a and self.b. For fixed leading dimensions, the last dimension represents counts for the corresponding Beta distribution in self.a and self.b. x is only legal if 0 < x < 1. Args: value: float or double Tensor. na