class tf.contrib.learn.monitors.NanLoss NaN Loss monitor. Monitors loss and stops training if loss is NaN. Can either fail with exception or just stop training.

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

tf.contrib.distributions.MultivariateNormalDiagWithSoftplusStDev.mode(name='mode') Mode.

tf.contrib.distributions.NormalWithSoftplusSigma.mode(name='mode') Mode.

tf.contrib.distributions.Gamma.__init__(alpha, beta, validate_args=False, allow_nan_stats=True, name='Gamma') Construct Gamma distributions with parameters alpha and beta. The parameters alpha and beta must be shaped in a way that supports broadcasting (e.g. alpha + beta is a valid operation). Args: alpha: Floating point tensor, the shape params of the distribution(s). alpha must contain only positive values. beta: Floating point tensor, the inverse scale params of the distribution(s). beta

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

tf.contrib.distributions.InverseGamma.log_prob(value, name='log_prob') Log probability density/mass function (depending on is_continuous). 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.

tf.contrib.learn.monitors.ValidationMonitor.step_end(step, output) Overrides BaseMonitor.step_end. When overriding this method, you must call the super implementation. Args: step: int, the current value of the global step. output: dict mapping string values representing tensor names to the value resulted from running these tensors. Values may be either scalars, for scalar tensors, or Numpy array, for non-scalar tensors. Returns: bool, the result of every_n_step_end, if that was called this

tf.contrib.distributions.GammaWithSoftplusAlphaBeta.validate_args Python boolean indicated possibly expensive checks are enabled.

tf.zeros_like(tensor, dtype=None, name=None, optimize=True) Creates a tensor with all elements set to zero. Given a single tensor (tensor), this operation returns a tensor of the same type and shape as tensor with all elements set to zero. Optionally, you can use dtype to specify a new type for the returned tensor. For example: # 'tensor' is [[1, 2, 3], [4, 5, 6]] tf.zeros_like(tensor) ==> [[0, 0, 0], [0, 0, 0]] Args: tensor: A Tensor. dtype: A type for the returned Tensor. Must be float3