TensorShapeIter tensorflow::TensorShape::begin() const For iterating through the dimensions.

tf.contrib.learn.monitors.EveryN.end(session=None)

tf.contrib.learn.LinearRegressor.weights_

tf.contrib.distributions.MultivariateNormalDiag.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.BernoulliWithSigmoidP.__init__(p=None, dtype=tf.int32, validate_args=False, allow_nan_stats=True, name='BernoulliWithSigmoidP')

tf.contrib.learn.TensorFlowEstimator.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.

Status tensorflow::EnvWrapper::GetSymbolFromLibrary(void *handle, const char *symbol_name, void **symbol) override

tf.contrib.distributions.GammaWithSoftplusAlphaBeta.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'

tf.contrib.bayesflow.stochastic_tensor.MultivariateNormalFullTensor.value_type

tf.contrib.distributions.BernoulliWithSigmoidP.name Name prepended to all ops created by this Distribution.