tf.contrib.distributions.Mixture.cat

tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.__init__(mu, diag_large, v, diag_small=None, validate_args=False, allow_nan_stats=True, name='MultivariateNormalDiagPlusVDVT') Multivariate Normal distributions on R^k. For every batch member, this distribution represents k random variables (X_1,...,X_k), with mean E[X_i] = mu[i], and covariance matrix C_{ij} := E[(X_i - mu[i])(X_j - mu[j])] The user initializes this class by providing the mean mu, and a lightweight definition of C: C = S

tf.contrib.distributions.TransformedDistribution.mean(name='mean') Mean.

tf.contrib.bayesflow.stochastic_tensor.ExponentialTensor.dtype

tf.TextLineReader.reset(name=None) Restore a reader to its initial clean state. Args: name: A name for the operation (optional). Returns: The created Operation.

tf.train.shuffle_batch_join(tensors_list, batch_size, capacity, min_after_dequeue, seed=None, enqueue_many=False, shapes=None, allow_smaller_final_batch=False, shared_name=None, name=None) Create batches by randomly shuffling tensors. The tensors_list argument is a list of tuples of tensors, or a list of dictionaries of tensors. Each element in the list is treated similarly to the tensors argument of tf.train.shuffle_batch(). This version enqueues a different list of tensors in different threa

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

tf.contrib.bayesflow.stochastic_tensor.BaseStochasticTensor.dtype

tf.contrib.distributions.Beta.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.bayesflow.stochastic_tensor.BaseStochasticTensor.loss(sample_loss) Returns the term to add to the surrogate loss. This method is called by surrogate_loss. The input sample_loss should have already had stop_gradient applied to it. This is because the surrogate_loss usually provides a Monte Carlo sample term of the form differentiable_surrogate * sample_loss where sample_loss is considered constant with respect to the input for purposes of the gradient. Args: sample_loss: Tensor, sam