tf.tan(x, name=None) Computes tan of x element-wise. Args: x: A Tensor. Must be one of the following types: half, float32, float64, int32, int64, complex64, complex128. name: A name for the operation (optional). Returns: A Tensor. Has the same type as x.

tf.svd(tensor, compute_uv=True, full_matrices=False, name=None) Computes the singular value decompositions of one or more matrices. Computes the SVD of each inner matrix in tensor such that tensor[..., :, :] = u[..., :, :] * diag(s[..., :, :]) * transpose(v[..., :, :]) # a is a tensor. # s is a tensor of singular values. # u is a tensor of left singular vectors. # v is a tensor of right singular vectors. s, u, v = svd(a) s = svd(a, compute_uv=False) Args: matrix: Tensor of shape [..., M, N].

tf.summary.tensor_summary(display_name, tensor, description='', labels=None, collections=None, name=None) Outputs a Summary protocol buffer with a serialized tensor.proto. The generated Summary has one summary value containing the input tensor. Args: display_name: A name to associate with the data series. Will be used to organize output data and as a name in visualizers. tensor: A tensor of any type and shape to serialize. description: An optional long description of the data being output.

tf.summary.scalar(display_name, tensor, description='', labels=None, collections=None, name=None) Outputs a Summary protocol buffer containing a single scalar value. The generated Summary has a Tensor.proto containing the input Tensor. Args: display_name: A name to associate with the data series. Will be used to organize output data and as a name in visualizers. tensor: A tensor containing a single floating point or integer value. description: An optional long description of the data being

tf.sub(x, y, name=None) Returns x - y element-wise. NOTE: Sub supports broadcasting. More about broadcasting here Args: x: A Tensor. Must be one of the following types: half, float32, float64, 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.string_to_hash_bucket_strong(input, num_buckets, key, name=None) Converts each string in the input Tensor to its hash mod by a number of buckets. The hash function is deterministic on the content of the string within the process. The hash function is a keyed hash function, where attribute key defines the key of the hash function. key is an array of 2 elements. A strong hash is important when inputs may be malicious, e.g. URLs with additional components. Adversaries could try to make their i

tf.string_to_hash_bucket_fast(input, num_buckets, name=None) Converts each string in the input Tensor to its hash mod by a number of buckets. The hash function is deterministic on the content of the string within the process and will never change. However, it is not suitable for cryptography. This function may be used when CPU time is scarce and inputs are trusted or unimportant. There is a risk of adversaries constructing inputs that all hash to the same bucket. To prevent this problem, use a

tf.string_to_hash_bucket(string_tensor, num_buckets, name=None) Converts each string in the input Tensor to its hash mod by a number of buckets. The hash function is deterministic on the content of the string within the process. Note that the hash function may change from time to time. This functionality will be deprecated and it's recommended to use tf.string_to_hash_bucket_fast() or tf.string_to_hash_bucket_strong(). Args: string_tensor: A Tensor of type string. num_buckets: An int that is

tf.string_split(source, delimiter=' ') Split elements of source based on delimiter into a SparseTensor. Let N be the size of source (typically N will be the batch size). Split each element of source based on delimiter and return a SparseTensor containing the splitted tokens. Empty tokens are ignored. If delimiter is an empty string, each element of the source is split into individual 1 character strings. For example: N = 2, source[0] is 'hello world' and source[1] is 'a b c', then the output w

tf.string_join(inputs, separator=None, name=None) Joins the strings in the given list of string tensors into one tensor; with the given separator (default is an empty separator). Args: inputs: A list of at least 1 Tensor objects of type string. A list of string tensors. The tensors must all have the same shape, or be scalars. Scalars may be mixed in; these will be broadcast to the shape of non-scalar inputs. separator: An optional string. Defaults to "". string, an optional join separator.