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.

tf.contrib.distributions.GammaWithSoftplusAlphaBeta.batch_shape(name='batch_shape') Shape of a single sample from a single event index as a 1-D Tensor. The product of the dimensions of the batch_shape is the number of independent distributions of this kind the instance represents. Args: name: name to give to the op Returns: batch_shape: Tensor.

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

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

tf.contrib.learn.Estimator.fit(x=None, y=None, input_fn=None, steps=None, batch_size=None, monitors=None, max_steps=None) See Trainable. Raises: ValueError: If x or y are not None while input_fn is not None. ValueError: If both steps and max_steps are not None.