tf.VarLenFeature.dtype Alias for field number 0

tf.contrib.distributions.GammaWithSoftplusAlphaBeta.is_continuous

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

tf.TensorArray.pack(name=None) Return the values in the TensorArray as a packed Tensor. All of the values must have been written and their shapes must all match. Args: name: A name for the operation (optional). Returns: All the tensors in the TensorArray packed into one tensor.

tf.contrib.distributions.Chi2WithAbsDf.event_shape(name='event_shape') Shape of a single sample from a single batch as a 1-D int32 Tensor. Args: name: name to give to the op Returns: event_shape: Tensor.

tf.contrib.learn.LinearClassifier.predict_proba(x=None, input_fn=None, batch_size=None, outputs=None, as_iterable=False) Runs inference to determine the class probability predictions.

tf.contrib.distributions.Chi2WithAbsDf.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 =

tf.InteractiveSession.__init__(target='', graph=None, config=None) Creates a new interactive TensorFlow session. If no graph argument is specified when constructing the session, the default graph will be launched in the session. If you are using more than one graph (created with tf.Graph() in the same process, you will have to use different sessions for each graph, but each graph can be used in multiple sessions. In this case, it is often clearer to pass the graph to be launched explicitly to

tf.contrib.distributions.Binomial.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 is

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