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.Normal.cdf(value, name='cdf') Cumulative distribution function. Given random variable X, the cumulative distribution function cdf is: cdf(x) := P[X <= x] Args: value: float or double Tensor. name: The name to give this op. Returns: cdf: a Tensor of shape sample_shape(x) + self.batch_shape with values of type self.dtype.

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.Dirichlet.alpha Shape parameter.

tf.python_io.tf_record_iterator(path, options=None) An iterator that read the records from a TFRecords file. Args: path: The path to the TFRecords file. options: (optional) A TFRecordOptions object. Yields: Strings. Raises: IOError: If path cannot be opened for reading.

tf.contrib.graph_editor.ControlOutputs.__init__(graph) Create a dictionary of control-output dependencies. Args: graph: a tf.Graph. Returns: A dictionary where a key is a tf.Operation instance and the corresponding value is a list of all the ops which have the key as one of their control-input dependencies. Raises: TypeError: graph is not a tf.Graph.