tf.contrib.rnn.GridLSTMCell.zero_state(batch_size, dtype) Return zero-filled state tensor(s). Args: batch_size: int, float, or unit Tensor representing the batch size. dtype: the data type to use for the state. Returns: If state_size is an int or TensorShape, then the return value is a N-D tensor of shape [batch_size x state_size] filled with zeros. If state_size is a nested list or tuple, then the return value is a nested list or tuple (of the same structure) of 2-D tensors with the shape

tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.std(name='std') Standard deviation.

tf.contrib.bayesflow.stochastic_tensor.StochasticTensor.clone(name=None, **dist_args)

tf.contrib.distributions.NormalWithSoftplusSigma.sigma Distribution parameter for standard deviation.

tf.contrib.bayesflow.stochastic_tensor.GammaWithSoftplusAlphaBetaTensor.loss(final_loss, name='Loss')

tf.contrib.graph_editor.ControlOutputs.get_all()

tf.contrib.bayesflow.stochastic_tensor.NormalTensor.value_type

virtual uint64 tensorflow::Env::NowMicros()=0 Returns the number of micro-seconds since some fixed point in time. Only useful for computing deltas of time.

tf.contrib.distributions.Beta.a_b_sum Sum of parameters.

tf.contrib.bayesflow.monte_carlo.expectation_importance_sampler(f, log_p, sampling_dist_q, z=None, n=None, seed=None, name='expectation_importance_sampler') Monte Carlo estimate of E_p[f(Z)] = E_q[f(Z) p(Z) / q(Z)]. With p(z) := exp{log_p(z)}, this Op returns n^{-1} sum_{i=1}^n [ f(z_i) p(z_i) / q(z_i) ], z_i ~ q, \approx E_q[ f(Z) p(Z) / q(Z) ] = E_p[f(Z)] This integral is done in log-space with max-subtraction to better handle the often extreme values that f(z) p(z) / q(z) can take o