tf.contrib.learn.DNNRegressor.evaluate(x=None, y=None, input_fn=None, feed_fn=None, batch_size=None, steps=None, metrics=None, name=None) See Evaluable. Raises: ValueError: If at least one of x or y is provided, and at least one of input_fn or feed_fn is provided. Or if metrics is not None or dict.

tf.ReaderBase.read(queue, name=None) Returns the next record (key, value pair) produced by a reader. Will dequeue a work unit from queue if necessary (e.g. when the Reader needs to start reading from a new file since it has finished with the previous file). Args: queue: A Queue or a mutable string Tensor representing a handle to a Queue, with string work items. name: A name for the operation (optional). Returns: A tuple of Tensors (key, value). key: A string scalar Tensor. value: A strin

tf.contrib.learn.infer(restore_checkpoint_path, output_dict, feed_dict=None) Restore graph from restore_checkpoint_path and run output_dict tensors. If restore_checkpoint_path is supplied, restore from checkpoint. Otherwise, init all variables. Args: restore_checkpoint_path: A string containing the path to a checkpoint to restore. output_dict: A dict mapping string names to Tensor objects to run. Tensors must all be from the same graph. feed_dict: dict object mapping Tensor objects to input

tf.sparse_maximum(sp_a, sp_b, name=None) Returns the element-wise max of two SparseTensors. Assumes the two SparseTensors have the same shape, i.e., no broadcasting. Example: sp_zero = ops.SparseTensor([[0]], [0], [7]) sp_one = ops.SparseTensor([[1]], [1], [7]) res = tf.sparse_maximum(sp_zero, sp_one).eval() # "res" should be equal to SparseTensor([[0], [1]], [0, 1], [7]). Args: sp_a: a SparseTensor operand whose dtype is real, and indices lexicographically ordered. sp_b: the other SparseTe

class tf.errors.OutOfRangeError Raised when an operation iterates past the valid input range. This exception is raised in "end-of-file" conditions, such as when a queue.dequeue() operation is blocked on an empty queue, and a queue.close() operation executes.