tf.div(x, y, name=None) Returns x / y element-wise. NOTE: Div supports broadcasting. More about broadcasting here Args: x: A Tensor. Must be one of the following types: half, float32, float64, uint8, int8, uint16, int16, int32, int64, complex64, complex128. y: A Tensor. Must have the same type as x. name: A name for the operation (optional). Returns: A Tensor. Has the same type as x.

tf.digamma(x, name=None) Computes Psi, the derivative of Lgamma (the log of the absolute value of Gamma(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.diag_part(input, name=None) Returns the diagonal part of the tensor. This operation returns a tensor with the diagonal part of the input. The diagonal part is computed as follows: Assume input has dimensions [D1,..., Dk, D1,..., Dk], then the output is a tensor of rank k with dimensions [D1,..., Dk] where: diagonal[i1,..., ik] = input[i1, ..., ik, i1,..., ik]. For example: # 'input' is [[1, 0, 0, 0] [0, 2, 0, 0] [0, 0, 3, 0] [0, 0, 0, 4]] tf.diag_p

tf.diag(diagonal, name=None) Returns a diagonal tensor with a given diagonal values. Given a diagonal, this operation returns a tensor with the diagonal and everything else padded with zeros. The diagonal is computed as follows: Assume diagonal has dimensions [D1,..., Dk], then the output is a tensor of rank 2k with dimensions [D1,..., Dk, D1,..., Dk] where: output[i1,..., ik, i1,..., ik] = diagonal[i1, ..., ik] and 0 everywhere else. For example: # 'diagonal' is [1, 2, 3, 4] tf.diag(diagonal)

tf.delete_session_tensor(handle, name=None) Delete the tensor for the given tensor handle. This is EXPERIMENTAL and subject to change. Delete the tensor of a given tensor handle. The tensor is produced in a previous run() and stored in the state of the session. Args: handle: The string representation of a persistent tensor handle. name: Optional name prefix for the return tensor. Returns: A pair of graph elements. The first is a placeholder for feeding a tensor handle and the second is a d

tf.decode_raw(bytes, out_type, little_endian=None, name=None) Reinterpret the bytes of a string as a vector of numbers. Args: bytes: A Tensor of type string. All the elements must have the same length. out_type: A tf.DType from: tf.float32, tf.float64, tf.int32, tf.uint8, tf.int16, tf.int8, tf.int64. little_endian: An optional bool. Defaults to True. Whether the input bytes are in little-endian order. Ignored for out_type values that are stored in a single byte like uint8. name: A name for

tf.decode_json_example(json_examples, name=None) Convert JSON-encoded Example records to binary protocol buffer strings. This op translates a tensor containing Example records, encoded using the standard JSON mapping, into a tensor containing the same records encoded as binary protocol buffers. The resulting tensor can then be fed to any of the other Example-parsing ops. Args: json_examples: A Tensor of type string. Each string is a JSON object serialized according to the JSON mapping of the

tf.decode_csv(records, record_defaults, field_delim=None, name=None) Convert CSV records to tensors. Each column maps to one tensor. RFC 4180 format is expected for the CSV records. (https://tools.ietf.org/html/rfc4180) Note that we allow leading and trailing spaces with int or float field. Args: records: A Tensor of type string. Each string is a record/row in the csv and all records should have the same format. record_defaults: A list of Tensor objects with types from: float32, int32, int64

tf.decode_base64(input, name=None) Decode web-safe base64-encoded strings. Input may or may not have padding at the end. See EncodeBase64 for padding. Web-safe means that input must use - and _ instead of + and /. Args: input: A Tensor of type string. Base64 strings to decode. name: A name for the operation (optional). Returns: A Tensor of type string. Decoded strings.

tf.cumsum(x, axis=0, exclusive=False, reverse=False, name=None) Compute the cumulative sum of the tensor x along axis. By default, this op performs an inclusive cumsum, which means that the first element of the input is identical to the first element of the output: prettyprint tf.cumsum([a, b, c]) ==> [a, a + b, a + b + c] By setting the exclusive kwarg to True, an exclusive cumsum is performed instead: prettyprint tf.cumsum([a, b, c], exclusive=True) ==> [0, a, a + b] By setting the rev