tf.OpError.error_code The integer error code that describes the error.

class tf.OpError A generic error that is raised when TensorFlow execution fails. Whenever possible, the session will raise a more specific subclass of OpError from the tf.errors module.

tf.ones_like(tensor, dtype=None, name=None, optimize=True) Creates a tensor with all elements set to 1. Given a single tensor (tensor), this operation returns a tensor of the same type and shape as tensor with all elements set to 1. Optionally, you can specify a new type (dtype) for the returned tensor. For example: # 'tensor' is [[1, 2, 3], [4, 5, 6]] tf.ones_like(tensor) ==> [[1, 1, 1], [1, 1, 1]] Args: tensor: A Tensor. dtype: A type for the returned Tensor. Must be float32, float64,

tf.ones(shape, dtype=tf.float32, name=None) Creates a tensor with all elements set to 1. This operation returns a tensor of type dtype with shape shape and all elements set to 1. For example: tf.ones([2, 3], int32) ==> [[1, 1, 1], [1, 1, 1]] Args: shape: Either a list of integers, or a 1-D Tensor of type int32. dtype: The type of an element in the resulting Tensor. name: A name for the operation (optional). Returns: A Tensor with all elements set to 1.

tf.nn.rnn_cell.RNNCell.__call__(inputs, state, scope=None) Run this RNN cell on inputs, starting from the given state. Args: inputs: 2-D tensor with shape [batch_size x input_size]. state: if self.state_size is an integer, this should be a 2-D Tensor with shape [batch_size x self.state_size]. Otherwise, if self.state_size is a tuple of integers, this should be a tuple with shapes [batch_size x s] for s in self.state_size. scope: VariableScope for the created subgraph; defaults to class name

tf.nn.rnn_cell.RNNCell.zero_state(batch_size, dtype) Return zero-filled state tensor(s). Args: batch_size: int, float, or unit Tensor representing the batch size. dtype: the data type to use for the state. Returns: If state_size is an int or TensorShape, then the return value is a N-D tensor of shape [batch_size x state_size] filled with zeros. If state_size is a nested list or tuple, then the return value is a nested list or tuple (of the same structure) of 2-D tensors with the shapes [ba

tf.nn.rnn_cell.RNNCell.state_size size(s) of state(s) used by this cell. It can be represented by an Integer, a TensorShape or a tuple of Integers or TensorShapes.

tf.nn.rnn_cell.RNNCell.output_size Integer or TensorShape: size of outputs produced by this cell.

class tf.nn.rnn_cell.RNNCell Abstract object representing an RNN cell. The definition of cell in this package differs from the definition used in the literature. In the literature, cell refers to an object with a single scalar output. The definition in this package refers to a horizontal array of such units. An RNN cell, in the most abstract setting, is anything that has a state and performs some operation that takes a matrix of inputs. This operation results in an output matrix with self.outp

tf.nn.rnn_cell.OutputProjectionWrapper.__init__(cell, output_size) Create a cell with output projection. Args: cell: an RNNCell, a projection to output_size is added to it. output_size: integer, the size of the output after projection. Raises: TypeError: if cell is not an RNNCell. ValueError: if output_size is not positive.