tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.log_cdf(value, name='log_cdf') Log cumulative distribution function. Given random variable X, the cumulative distribution function cdf is: log_cdf(x) := Log[ P[X <= x] ] Often, a numerical approximation can be used for log_cdf(x) that yields a more accurate answer than simply taking the logarithm of the cdf when x << -1. Args: value: float or double Tensor. name: The name to give this op. Returns: logcdf: a Tensor of shape

bool tensorflow::Tensor::CopyFrom(const Tensor &other, const TensorShape &shape) TF_MUST_USE_RESULT Copy the other tensor into this tensor and reshape it. This tensor shares other's underlying storage. Returns true iff other.shape() has the same number of elements of the given shape.

tf.contrib.distributions.Dirichlet.__init__(alpha, validate_args=False, allow_nan_stats=True, name='Dirichlet') Initialize a batch of Dirichlet distributions. Args: alpha: Positive floating point tensor with shape broadcastable to [N1,..., Nm, k] m >= 0. Defines this as a batch of N1 x ... x Nm different k class Dirichlet distributions. validate_args: Boolean, default False. Whether to assert valid values for parameters alpha and x in prob and log_prob. If False, correct behavior is not g

tf.contrib.util.stripped_op_list_for_graph(graph_def) Collect the stripped OpDefs for ops used by a graph. This function computes the stripped_op_list field of MetaGraphDef and similar protos. The result can be communicated from the producer to the consumer, which can then use the C++ function RemoveNewDefaultAttrsFromGraphDef to improve forwards compatibility. Args: graph_def: A GraphDef proto, as from graph.as_graph_def(). Returns: An OpList of ops used by the graph. Raises: ValueError:

tf.contrib.distributions.Normal.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.distributions.GammaWithSoftplusAlphaBeta.log_pmf(value, name='log_pmf') Log probability mass function. Args: value: float or double Tensor. name: The name to give this op. Returns: log_pmf: a Tensor of shape sample_shape(x) + self.batch_shape with values of type self.dtype. Raises: TypeError: if is_continuous.

tf.contrib.distributions.StudentT.__init__(df, mu, sigma, validate_args=False, allow_nan_stats=True, name='StudentT') Construct Student's t distributions. The distributions have degree of freedom df, mean mu, and scale sigma. The parameters df, mu, and sigma must be shaped in a way that supports broadcasting (e.g. df + mu + sigma is a valid operation). Args: df: Floating point tensor, the degrees of freedom of the distribution(s). df must contain only positive values. mu: Floating point tens

tf.contrib.distributions.Chi2.parameters Dictionary of parameters used by this Distribution.

tf.contrib.learn.TensorFlowRNNRegressor.partial_fit(x, y) Incremental fit on a batch of samples. This method is expected to be called several times consecutively on different or the same chunks of the dataset. This either can implement iterative training or out-of-core/online training. This is especially useful when the whole dataset is too big to fit in memory at the same time. Or when model is taking long time to converge, and you want to split up training into subparts. Args: x: matrix or t

tf.contrib.distributions.Chi2WithAbsDf.log_pdf(value, name='log_pdf') Log probability density function. Args: value: float or double Tensor. name: The name to give this op. Returns: log_prob: a Tensor of shape sample_shape(x) + self.batch_shape with values of type self.dtype. Raises: TypeError: if not is_continuous.