tf.contrib.distributions.Bernoulli.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.learn.monitors.GraphDump.compare(other_dump, step, atol=1e-06) Compares two GraphDump monitors and returns differences. Args: other_dump: Another GraphDump monitor. step: int, step to compare on. atol: float, absolute tolerance in comparison of floating arrays. Returns: Returns tuple: matched: list of keys that matched. non_matched: dict of keys to tuple of 2 mismatched values. Raises: ValueError: if a key in data is missing from other_dump at step.

tf.contrib.graph_editor.SubGraphView.remap_default(remove_input_map=True, remove_output_map=True) Remap the inputs and/or outputs to the default mapping. Args: remove_input_map: if True the input map is reset to the default one. remove_output_map: if True the output map is reset to the default one. Returns: A new modified instance of the original subgraph view with its input and/or output mapping reset to the default one.

TTypes< T, NDIMS >::UnalignedTensor tensorflow::Tensor::unaligned_shaped(gtl::ArraySlice< int64 > new_sizes)

tf.contrib.learn.read_batch_examples(file_pattern, batch_size, reader, randomize_input=True, num_epochs=None, queue_capacity=10000, num_threads=1, read_batch_size=1, parse_fn=None, name=None) Adds operations to read, queue, batch Example protos. Given file pattern (or list of files), will setup a queue for file names, read Example proto using provided reader, use batch queue to create batches of examples of size batch_size. All queue runners are added to the queue runners collection, and may b

tf.contrib.bayesflow.stochastic_tensor.GammaTensor.input_dict

tf.contrib.distributions.Poisson.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 u

tf.contrib.distributions.ExponentialWithSoftplusLam.sample_n(n, seed=None, name='sample_n') Generate n samples. Additional documentation from Gamma: See the documentation for tf.random_gamma for more details. 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.Chi2.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 sample_shape(x) + self.bat

tf.contrib.bayesflow.stochastic_tensor.NormalWithSoftplusSigmaTensor.value(name='value')