tf.contrib.learn.TensorFlowRNNClassifier.restore(cls, path, config=None) Restores model from give path. Args: path: Path to the checkpoints and other model information. config: RunConfig object that controls the configurations of the session, e.g. num_cores, gpu_memory_fraction, etc. This is allowed to be reconfigured. Returns: Estimator, object of the subclass of TensorFlowEstimator. Raises: ValueError: if path does not contain a model definition.

tf.squared_difference(x, y, name=None) Returns (x - y)(x - y) element-wise. NOTE: SquaredDifference supports broadcasting. More about broadcasting here Args: x: A Tensor. Must be one of the following types: half, float32, float64, int32, int64, complex64, complex128. y: A Tensor. Must have the same type as x. name: A name for the operation (optional). Returns: A Tensor. Has the same type as x.

tf.log(x, name=None) Computes natural logarithm of x element-wise. I.e., \(y = \log_e x\). Args: x: A Tensor. Must be one of the following types: half, float32, float64, complex64, complex128. name: A name for the operation (optional). Returns: A Tensor. Has the same type as x.

tf.contrib.graph_editor.get_generating_ops(ts) Return all the generating ops of the tensors in ts. Args: ts: a list of tf.Tensor Returns: A list of all the generating tf.Operation of the tensors in ts. Raises: TypeError: if ts cannot be converted to a list of tf.Tensor.

class tf.contrib.distributions.Mixture Mixture distribution. The Mixture object implements batched mixture distributions. The mixture model is defined by a Categorical distribution (the mixture) and a python list of Distribution objects. Methods supported include log_prob, prob, mean, sample, and entropy_lower_bound.

tf.contrib.distributions.Binomial.cdf(value, name='cdf') Cumulative distribution function. Given random variable X, the cumulative distribution function cdf is: cdf(x) := P[X <= x] Args: value: float or double Tensor. name: The name to give this op. Returns: cdf: a Tensor of shape sample_shape(x) + self.batch_shape with values of type self.dtype.

tf.contrib.distributions.MultivariateNormalCholesky.is_reparameterized

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

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

tf.igammac(a, x, name=None) Compute the upper regularized incomplete Gamma function Q(a, x). The upper regularized incomplete Gamma function is defined as: Q(a, x) = Gamma(a, x) / Gamma(a) = 1 - P(a, x) where Gamma(a, x) = int_{x}^{\infty} t^{a-1} exp(-t) dt is the upper incomplete Gama function. Note, above P(a, x) (Igamma) is the lower regularized complete Gamma function. Args: a: A Tensor. Must be one of the following types: float32, float64. x: A Tensor. Must have the same type as a. n