tf.nn.rnn_cell.EmbeddingWrapper.output_size

tf.contrib.bayesflow.stochastic_tensor.CategoricalTensor.name

tf.contrib.graph_editor.SubGraphView.__str__()

tf.contrib.distributions.Chi2.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 unde

class tf.contrib.learn.TensorFlowEstimator Base class for all TensorFlow estimators.

tf.contrib.distributions.Chi2.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.InverseGamma.mode(name='mode') Mode. Additional documentation from InverseGamma: The mode of an inverse gamma distribution is beta / (alpha + 1).

tf.contrib.bayesflow.stochastic_tensor.UniformTensor.mean(name='mean')

tf.contrib.bayesflow.stochastic_tensor.MultinomialTensor.mean(name='mean')

Status tensorflow::Env::NewRandomAccessFile(const string &fname, std::unique_ptr< RandomAccessFile > *result) Creates a brand new random access read-only file with the specified name. On success, stores a pointer to the new file in *result and returns OK. On failure stores NULL in *result and returns non-OK. If the file does not exist, returns a non-OK status. The returned file may be concurrently accessed by multiple threads. The ownership of the returned RandomAccessFile is passed