tf.train.match_filenames_once(pattern, name=None) Save the list of files matching pattern, so it is only computed once. Args: pattern: A file pattern (glob). name: A name for the operations (optional). Returns: A variable that is initialized to the list of files matching pattern.

tf.train.limit_epochs(tensor, num_epochs=None, name=None) Returns tensor num_epochs times and then raises an OutOfRange error. Args: tensor: Any Tensor. num_epochs: A positive integer (optional). If specified, limits the number of steps the output tensor may be evaluated. name: A name for the operations (optional). Returns: tensor or OutOfRange. Raises: ValueError: if num_epochs is invalid.

tf.train.input_producer(input_tensor, element_shape=None, num_epochs=None, shuffle=True, seed=None, capacity=32, shared_name=None, summary_name=None, name=None) Output the rows of input_tensor to a queue for an input pipeline. Args: input_tensor: A tensor with the rows to produce. Must be at least one-dimensional. Must either have a fully-defined shape, or element_shape must be defined. element_shape: (Optional.) A TensorShape representing the shape of a row of input_tensor, if it cannot be

tf.train.batch_join(tensors_list, batch_size, capacity=32, enqueue_many=False, shapes=None, dynamic_pad=False, allow_smaller_final_batch=False, shared_name=None, name=None) Runs a list of tensors to fill a queue to create batches of examples. The tensors_list argument is a list of tuples of tensors, or a list of dictionaries of tensors. Each element in the list is treated similarly to the tensors argument of tf.train.batch(). Enqueues a different list of tensors in different threads. Implement

tf.train.batch(tensors, batch_size, num_threads=1, capacity=32, enqueue_many=False, shapes=None, dynamic_pad=False, allow_smaller_final_batch=False, shared_name=None, name=None) Creates batches of tensors in tensors. The argument tensors can be a list or a dictionary of tensors. The value returned by the function will be of the same type as tensors. This function is implemented using a queue. A QueueRunner for the queue is added to the current Graph's QUEUE_RUNNER collection. If enqueue_many i

tf.trace(x, name=None) Compute the trace of a tensor x. trace(x) returns the sum of along the diagonal. For example: # 'x' is [[1, 1], # [1, 1]] tf.trace(x) ==> 2 # 'x' is [[1,2,3], # [4,5,6], # [7,8,9]] tf.trace(x) ==> 15 Args: x: 2-D tensor. name: A name for the operation (optional). Returns: The trace of input tensor.

tf.TFRecordReader.__init__(name=None, options=None) Create a TFRecordReader. Args: name: A name for the operation (optional). options: A TFRecordOptions object (optional).

tf.TFRecordReader.supports_serialize Whether the Reader implementation can serialize its state.

tf.TFRecordReader.serialize_state(name=None) Produce a string tensor that encodes the state of a reader. Not all Readers support being serialized, so this can produce an Unimplemented error. Args: name: A name for the operation (optional). Returns: A string Tensor.

tf.TFRecordReader.restore_state(state, name=None) Restore a reader to a previously saved state. Not all Readers support being restored, so this can produce an Unimplemented error. Args: state: A string Tensor. Result of a SerializeState of a Reader with matching type. name: A name for the operation (optional). Returns: The created Operation.