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.Multinomial.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

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

tf.contrib.distributions.Normal.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.framework.get_unique_variable(var_op_name) Gets the variable uniquely identified by that var_op_name. Args: var_op_name: the full name of the variable op, including the scope. Returns: a tensorflow variable. Raises: ValueError: if no variable uniquely identified by the name exists.

tf.contrib.distributions.Beta.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. name: The name t

tf.contrib.distributions.MultivariateNormalFull.sigma Dense (batch) covariance matrix, if available.

tf.contrib.distributions.InverseGammaWithSoftplusAlphaBeta.sample(sample_shape=(), seed=None, name='sample') Generate samples of the specified shape. Note that a call to sample() without arguments will generate a single sample. Args: sample_shape: 0D or 1D int32 Tensor. Shape of the generated samples. seed: Python integer seed for RNG name: name to give to the op. Returns: samples: a Tensor with prepended dimensions sample_shape.

tf.assert_rank(x, rank, data=None, summarize=None, message=None, name=None) Assert x has rank equal to rank. Example of adding a dependency to an operation: with tf.control_dependencies([tf.assert_rank(x, 2)]): output = tf.reduce_sum(x) Example of adding dependency to the tensor being checked: x = tf.with_dependencies([tf.assert_rank(x, 2)], x) Args: x: Numeric Tensor. rank: Scalar integer Tensor. data: The tensors to print out if the condition is False. Defaults to error message and fi

tf.contrib.distributions.GammaWithSoftplusAlphaBeta.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.