class tf.errors.CancelledError Raised when an operation or step is cancelled. For example, a long-running operation (e.g. queue.enqueue() may be cancelled by running another operation (e.g. queue.close(cancel_pending_enqueues=True), or by closing the session. A step that is running such a long-running operation will fail by raising CancelledError.

tf.errors.AlreadyExistsError.__init__(node_def, op, message) Creates an AlreadyExistsError.

class tf.errors.AlreadyExistsError Raised when an entity that we attempted to create already exists. For example, running an operation that saves a file (e.g. tf.train.Saver.save()) could potentially raise this exception if an explicit filename for an existing file was passed.

tf.errors.AbortedError.__init__(node_def, op, message) Creates an AbortedError.

class tf.errors.AbortedError The operation was aborted, typically due to a concurrent action. For example, running a queue.enqueue() operation may raise AbortedError if a queue.close() operation previously ran.

tf.erfc(x, name=None) Computes the complementary error function of x element-wise. Args: x: A Tensor. Must be one of the following types: half, float32, float64. name: A name for the operation (optional). Returns: A Tensor. Has the same type as x.

tf.erf(x, name=None) Computes the Gauss error function of x element-wise. Args: x: A Tensor of SparseTensor. Must be one of the following types: half, float32, float64. name: A name for the operation (optional). Returns: A Tensor or SparseTensor, respectively. Has the same type as x.

tf.encode_base64(input, pad=None, name=None) Encode strings into web-safe base64 format. Refer to the following article for more information on base64 format: en.wikipedia.org/wiki/Base64. Base64 strings may have padding with '=' at the end so that the encoded has length multiple of 4. See Padding section of the link above. Web-safe means that the encoder uses - and _ instead of + and /. Args: input: A Tensor of type string. Strings to be encoded. pad: An optional bool. Defaults to False. Bo

tf.einsum(axes, *inputs) A generalized contraction between tensors of arbitrary dimension. Like numpy.einsum.

tf.edit_distance(hypothesis, truth, normalize=True, name='edit_distance') Computes the Levenshtein distance between sequences. This operation takes variable-length sequences (hypothesis and truth), each provided as a SparseTensor, and computes the Levenshtein distance. You can normalize the edit distance by length of truth by setting normalize to true. For example, given the following input: # 'hypothesis' is a tensor of shape `[2, 1]` with variable-length values: # (0,0) = ["a"] # (1,0) =