tf.contrib.distributions.Bernoulli.log_pdf(value, name='log_pdf') Log probability density function. Args: value: float or double Tensor. name: The name to give this op. Returns: log_prob: a Tensor of shape sample_shape(x) + self.batch_shape with values of type self.dtype. Raises: TypeError: if not is_continuous.

tf.contrib.bayesflow.stochastic_tensor.BernoulliTensor.dtype

tf.contrib.learn.TensorFlowRNNRegressor.save(path) Saves checkpoints and graph to given path. Args: path: Folder to save model to.

tf.contrib.graph_editor.OpMatcher.__call__(op) Evaluate if the op matches or not.

Static helper routines for TensorShape. Includes a few common predicates on a tensor shape. Member Details static bool tensorflow::TensorShapeUtils::IsScalar(const TensorShape &shape) static bool tensorflow::TensorShapeUtils::IsVector(const TensorShape &shape) static bool tensorflow::TensorShapeUtils::IsVectorOrHigher(const TensorShape &shape) static bool tensorflow::TensorShapeUtils::IsMatrix(const TensorShape &shape) static bool tensorflow::TensorShapeUtils::IsSquareMatrix(con

tf.contrib.distributions.Mixture.get_event_shape() Shape of a single sample from a single batch as a TensorShape. Same meaning as event_shape. May be only partially defined. Returns: event_shape: TensorShape, possibly unknown.

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

tf.contrib.distributions.InverseGamma.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.distributions.StudentTWithAbsDfSoftplusSigma.is_reparameterized

tf.contrib.distributions.MultivariateNormalCholesky.dtype The DType of Tensors handled by this Distribution.