tf.reduce_prod(input_tensor, reduction_indices=None, keep_dims=False, name=None) Computes the product of elements across dimensions of a tensor. Reduces input_tensor along the dimensions given in reduction_indices. Unless keep_dims is true, the rank of the tensor is reduced by 1 for each entry in reduction_indices. If keep_dims is true, the reduced dimensions are retained with length 1. If reduction_indices has no entries, all dimensions are reduced, and a tensor with a single element is retur

tf.contrib.distributions.Categorical.sample_n(n, seed=None, name='sample_n') Generate n samples. Args: n: Scalar Tensor of type int32 or int64, the number of observations to sample. seed: Python integer seed for RNG name: name to give to the op. Returns: samples: a Tensor with a prepended dimension (n,). Raises: TypeError: if n is not an integer type.

tf.contrib.bayesflow.stochastic_tensor.TransformedDistributionTensor.value_type

tf.contrib.distributions.Beta.allow_nan_stats Python boolean describing behavior when a stat is undefined. Stats return +/- infinity when it makes sense. E.g., the variance of a Cauchy distribution is infinity. However, sometimes the statistic is undefined, e.g., if a distribution's pdf does not achieve a maximum within the support of the distribution, the mode is undefined. If the mean is undefined, then by definition the variance is undefined. E.g. the mean for Student's T for df = 1 is unde

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.StudentT.sample_n(n, seed=None, name='sample_n') Generate n samples. Args: n: Scalar Tensor of type int32 or int64, the number of observations to sample. seed: Python integer seed for RNG name: name to give to the op. Returns: samples: a Tensor with a prepended dimension (n,). Raises: TypeError: if n is not an integer type.

tf.contrib.layers.safe_embedding_lookup_sparse(embedding_weights, sparse_ids, sparse_weights=None, combiner=None, default_id=None, name=None, partition_strategy='div') Lookup embedding results, accounting for invalid IDs and empty features. The partitioned embedding in embedding_weights must all be the same shape except for the first dimension. The first dimension is allowed to vary as the vocabulary size is not necessarily a multiple of P. Invalid IDs (< 0) are pruned from input IDs and we

tf.contrib.framework.deprecated_arg_values(date, instructions, **deprecated_kwargs) Decorator for marking specific function argument values as deprecated. This decorator logs a deprecation warning whenever the decorated function is called with the deprecated argument values. It has the following format: Calling (from ) with = is deprecated and will be removed after . Instructions for updating: will include the class name if it is a method. It also edits the docstring of the function: ' (dep

tf.contrib.learn.NanLossDuringTrainingError.__str__()

tf.contrib.graph_editor.get_within_boundary_ops(ops, seed_ops, boundary_ops=(), inclusive=True, control_inputs=False, control_outputs=None, control_ios=None) Return all the tf.Operation within the given boundary. Args: ops: an object convertible to a list of tf.Operation. those ops define the set in which to perform the operation (if a tf.Graph is given, it will be converted to the list of all its operations). seed_ops: the operations from which to start expanding. boundary_ops: the ops for