tf.contrib.bayesflow.stochastic_tensor.InverseGammaWithSoftplusAlphaBetaTensor.input_dict

Status tensorflow::PartialTensorShape::MergeWith(const PartialTensorShape &shape, PartialTensorShape *result) const Merges all the dimensions from shape. Returns InvalidArgument error if either shape has a different rank or if any of the dimensions are incompatible.

tf.contrib.learn.TensorFlowEstimator.__repr__()

tf.python_io.TFRecordWriter.__exit__(unused_type, unused_value, unused_traceback) Exit a with block, closing the file.

tf.contrib.distributions.Gamma.param_shapes(cls, sample_shape, name='DistributionParamShapes') Shapes of parameters given the desired shape of a call to sample(). Subclasses should override static method _param_shapes. Args: sample_shape: Tensor or python list/tuple. Desired shape of a call to sample(). name: name to prepend ops with. Returns: dict of parameter name to Tensor shapes.

tf.contrib.distributions.BernoulliWithSigmoidP.sample_n(n, seed=None, name='sample_n') Generate n samples. 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.LaplaceWithSoftplusScale.is_reparameterized

tf.contrib.distributions.StudentTWithAbsDfSoftplusSigma.__init__(df, mu, sigma, validate_args=False, allow_nan_stats=True, name='StudentTWithAbsDfSoftplusSigma')

tf.contrib.distributions.ExponentialWithSoftplusLam.is_reparameterized

tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.param_shapes(cls, sample_shape, name='DistributionParamShapes') Shapes of parameters given the desired shape of a call to sample(). Subclasses should override static method _param_shapes. Args: sample_shape: Tensor or python list/tuple. Desired shape of a call to sample(). name: name to prepend ops with. Returns: dict of parameter name to Tensor shapes.