tf.contrib.bayesflow.stochastic_tensor.MixtureTensor.input_dict

tf.Session.__exit__(exec_type, exec_value, exec_tb)

tf.QueueBase.size(name=None) Compute the number of elements in this queue. Args: name: A name for the operation (optional). Returns: A scalar tensor containing the number of elements in this queue.

tf.contrib.distributions.Normal.validate_args Python boolean indicated possibly expensive checks are enabled.

tf.errors.AbortedError.__init__(node_def, op, message) Creates an AbortedError.

class tf.contrib.distributions.BaseDistribution Simple abstract base class for probability distributions. Implementations of core distributions to be included in the distributions module should subclass Distribution. This base class may be useful to users that want to fulfill a simpler distribution contract.

tf.contrib.distributions.Gamma.dtype The DType of Tensors handled by this Distribution.

tf.contrib.bayesflow.stochastic_tensor.StochasticTensor.graph

tf.nn.rnn_cell.LSTMCell.__init__(num_units, input_size=None, use_peepholes=False, cell_clip=None, initializer=None, num_proj=None, proj_clip=None, num_unit_shards=1, num_proj_shards=1, forget_bias=1.0, state_is_tuple=True, activation=tanh) Initialize the parameters for an LSTM cell. Args: num_units: int, The number of units in the LSTM cell input_size: Deprecated and unused. use_peepholes: bool, set True to enable diagonal/peephole connections. cell_clip: (optional) A float value, if provi

tf.contrib.bayesflow.stochastic_tensor.ObservedStochasticTensor.loss(final_loss, name=None)