tessl/pypi-tensorflow
An end-to-end open source platform for machine learning
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math.mddocs/
Math Operations
Comprehensive mathematical operations including arithmetic, trigonometric, linear algebra, and statistical functions. These operations provide the mathematical foundation for machine learning computations and numerical analysis.
Capabilities
Basic Arithmetic
Fundamental arithmetic operations for tensor computations.
def add(x, y, name=None):
"""
Returns x + y element-wise.
Parameters:
- x: A Tensor. Must be one of the following types: bfloat16, half, float32, float64, uint8, int8, int16, int32, int64, complex64, complex128, string
- y: A Tensor. Must have the same type as x
- name: A name for the operation
Returns:
A Tensor. Has the same type as x
"""
def subtract(x, y, name=None):
"""
Returns x - y element-wise.
Parameters:
- x: A Tensor. Must be one of the following types: bfloat16, half, float32, float64, int8, int16, int32, int64, complex64, complex128
- y: A Tensor. Must have the same type as x
- name: A name for the operation
Returns:
A Tensor. Has the same type as x
"""
def multiply(x, y, name=None):
"""
Returns x * y element-wise.
Parameters:
- x: A Tensor. Must be one of the following types: bfloat16, 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
Returns:
A Tensor. Has the same type as x
"""
def divide(x, y, name=None):
"""
Computes Python style division of x by y.
Parameters:
- x: A Tensor. Must be one of the following types: bfloat16, 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
Returns:
A Tensor. Has the same type as x
"""
def floordiv(x, y, name=None):
"""
Divides x / y elementwise, rounding toward the most negative integer.
Parameters:
- x: A Tensor. Must be one of the following types: bfloat16, half, float32, float64, uint8, int8, uint16, int16, int32, int64
- y: A Tensor. Must have the same type as x
- name: A name for the operation
Returns:
A Tensor. Has the same type as x
"""
def mod(x, y, name=None):
"""
Returns element-wise remainder of division.
Parameters:
- x: A Tensor. Must be one of the following types: int8, int16, int32, int64, uint8, uint16, uint32, uint64, bfloat16, half, float32, float64
- y: A Tensor. Must have the same type as x
- name: A name for the operation
Returns:
A Tensor. Has the same type as x
"""
def pow(x, y, name=None):
"""
Computes the power of one value to another.
Parameters:
- x: A Tensor of type float16, float32, float64, int32, int64, complex64, or complex128
- y: A Tensor of type float16, float32, float64, int32, int64, complex64, or complex128
- name: A name for the operation
Returns:
A Tensor
"""Mathematical Functions
Common mathematical functions and operations.
def abs(x, name=None):
"""
Computes the absolute value of a tensor.
Parameters:
- x: A Tensor or SparseTensor of type float16, float32, float64, int8, int16, int32, int64, complex64 or complex128
- name: A name for the operation
Returns:
A Tensor or SparseTensor the same size, type, and sparsity as x with absolute values
"""
def sign(x, name=None):
"""
Returns an element-wise indication of the sign of a number.
Parameters:
- x: A Tensor. Must be one of the following types: bfloat16, half, float32, float64, int8, int16, int32, int64, complex64, complex128
- name: A name for the operation
Returns:
A Tensor. Has the same type as x
"""
def sqrt(x, name=None):
"""
Computes element-wise square root of the input tensor.
Parameters:
- x: A Tensor. Must be one of the following types: bfloat16, half, float32, float64, complex64, complex128
- name: A name for the operation
Returns:
A Tensor. Has the same type as x
"""
def square(x, name=None):
"""
Computes square of x element-wise.
Parameters:
- x: A Tensor. Must be one of the following types: bfloat16, half, float32, float64, int8, int16, int32, int64, complex64, complex128
- name: A name for the operation
Returns:
A Tensor. Has the same type as x
"""
def exp(x, name=None):
"""
Computes exponential of x element-wise.
Parameters:
- x: A Tensor. Must be one of the following types: bfloat16, half, float32, float64, complex64, complex128
- name: A name for the operation
Returns:
A Tensor. Has the same type as x
"""
def log(x, name=None):
"""
Computes natural logarithm of x element-wise.
Parameters:
- x: A Tensor. Must be one of the following types: bfloat16, half, float32, float64, complex64, complex128
- name: A name for the operation
Returns:
A Tensor. Has the same type as x
"""
def log10(x, name=None):
"""
Computes element-wise log base 10 of x.
Parameters:
- x: A Tensor. Must be one of the following types: bfloat16, half, float32, float64, complex64, complex128
- name: A name for the operation
Returns:
A Tensor. Has the same type as x
"""Trigonometric Functions
Trigonometric and hyperbolic functions for mathematical computations.
def sin(x, name=None):
"""
Computes sine of x element-wise.
Parameters:
- x: A Tensor. Must be one of the following types: bfloat16, half, float32, float64, complex64, complex128
- name: A name for the operation
Returns:
A Tensor. Has the same type as x
"""
def cos(x, name=None):
"""
Computes cosine of x element-wise.
Parameters:
- x: A Tensor. Must be one of the following types: bfloat16, half, float32, float64, complex64, complex128
- name: A name for the operation
Returns:
A Tensor. Has the same type as x
"""
def tan(x, name=None):
"""
Computes tan of x element-wise.
Parameters:
- x: A Tensor. Must be one of the following types: bfloat16, half, float32, float64, complex64, complex128
- name: A name for the operation
Returns:
A Tensor. Has the same type as x
"""
def asin(x, name=None):
"""
Computes the trignometric inverse sine of x element-wise.
Parameters:
- x: A Tensor. Must be one of the following types: bfloat16, half, float32, float64, int8, int16, int32, int64, complex64, complex128
- name: A name for the operation
Returns:
A Tensor. Has the same type as x
"""
def acos(x, name=None):
"""
Computes acos of x element-wise.
Parameters:
- x: A Tensor. Must be one of the following types: bfloat16, half, float32, float64, int8, int16, int32, int64, complex64, complex128
- name: A name for the operation
Returns:
A Tensor. Has the same type as x
"""
def atan(x, name=None):
"""
Computes the trignometric inverse tangent of x element-wise.
Parameters:
- x: A Tensor. Must be one of the following types: bfloat16, half, float32, float64, int8, int16, int32, int64, complex64, complex128
- name: A name for the operation
Returns:
A Tensor. Has the same type as x
"""
def sinh(x, name=None):
"""
Computes hyperbolic sine of x element-wise.
Parameters:
- x: A Tensor. Must be one of the following types: bfloat16, half, float32, float64, complex64, complex128
- name: A name for the operation
Returns:
A Tensor. Has the same type as x
"""
def cosh(x, name=None):
"""
Computes hyperbolic cosine of x element-wise.
Parameters:
- x: A Tensor. Must be one of the following types: bfloat16, half, float32, float64, complex64, complex128
- name: A name for the operation
Returns:
A Tensor. Has the same type as x
"""
def tanh(x, name=None):
"""
Computes hyperbolic tangent of x element-wise.
Parameters:
- x: A Tensor. Must be one of the following types: bfloat16, half, float32, float64, complex64, complex128
- name: A name for the operation
Returns:
A Tensor. Has the same type as x
"""Linear Algebra
Matrix operations and linear algebra functions.
def matmul(a, b, transpose_a=False, transpose_b=False, adjoint_a=False,
adjoint_b=False, a_is_sparse=False, b_is_sparse=False, output_type=None,
grad_a=False, grad_b=False, name=None):
"""
Multiplies matrix a by matrix b, producing a * b.
Parameters:
- a: A Tensor of type float16, float32, float64, int32, int64, complex64, complex128 and rank > 1
- b: A Tensor with same type and rank as a
- transpose_a: If True, a is transposed before multiplication
- transpose_b: If True, b is transposed before multiplication
- adjoint_a: If True, a is conjugated and transposed before multiplication
- adjoint_b: If True, b is conjugated and transposed before multiplication
- a_is_sparse: If True, a is treated as a sparse matrix
- b_is_sparse: If True, b is treated as a sparse matrix
- output_type: The output type of the operation (float16, float32, etc.)
- grad_a: Whether to use gradient optimized version for matrix a
- grad_b: Whether to use gradient optimized version for matrix b
- name: Name for the operation
Returns:
A Tensor of the same type as a and b where each inner-most matrix is the product of the corresponding matrices in a and b
"""
def transpose(a, perm=None, conjugate=False, name="transpose"):
"""
Transposes a.
Parameters:
- a: A Tensor
- perm: A permutation of the dimensions of a
- conjugate: Setting it to True is mathematically equivalent to tf.math.conj(tf.transpose(input))
- name: A name for the operation
Returns:
A transposed Tensor
"""
def trace(x, name=None):
"""
Compute the trace of a tensor x.
Parameters:
- x: A Tensor. Must be one of the following types: bfloat16, half, float32, float64, int32, int64, complex64, complex128
- name: A name for the operation
Returns:
A Tensor. Has the same type as x
"""
def det(input, name=None):
"""
Computes the determinant of one or more square matrices.
Parameters:
- input: A Tensor of type float32, float64, complex64 or complex128 of shape [..., M, M]
- name: A name for the operation
Returns:
A Tensor of the same type as input with shape [...]
"""
def inv(input, adjoint=False, name=None):
"""
Computes the inverse of one or more square invertible matrices or their adjoints (conjugate transposes).
Parameters:
- input: A Tensor. Must be one of the following types: float64, float32, half, complex64, complex128
- adjoint: An optional bool. Defaults to False
- name: A name for the operation
Returns:
A Tensor. Has the same type as input
"""
def norm(tensor, ord='euclidean', axis=None, keepdims=None, name=None):
"""
Computes the norm of vectors, matrices, and tensors.
Parameters:
- tensor: A Tensor
- ord: Order of the norm. Supported values are 'fro', 'euclidean', 1, 2, np.inf and any positive real number yielding the corresponding p-norm
- axis: If axis is None (the default), the input is considered a vector and a single vector norm is computed over the entire set of values in the tensor
- keepdims: If True, the axis indicated in axis are kept with size 1
- name: The name of the op
Returns:
A Tensor with the same type as tensor, containing the vector or matrix norms
"""Reduction Operations
Operations that reduce tensor dimensions through aggregation.
def reduce_sum(input_tensor, axis=None, keepdims=None, name=None):
"""
Computes the sum of elements across dimensions of a tensor.
Parameters:
- input_tensor: The tensor to reduce. Should have numeric type
- axis: The dimensions to reduce. If None (the default), reduces all dimensions
- keepdims: If true, retains reduced dimensions with length 1
- name: A name for the operation
Returns:
The reduced tensor
"""
def reduce_mean(input_tensor, axis=None, keepdims=None, name=None):
"""
Computes the mean of elements across dimensions of a tensor.
Parameters:
- input_tensor: The tensor to reduce. Should have numeric type
- axis: The dimensions to reduce. If None (the default), reduces all dimensions
- keepdims: If true, retains reduced dimensions with length 1
- name: A name for the operation
Returns:
The reduced tensor
"""
def reduce_max(input_tensor, axis=None, keepdims=None, name=None):
"""
Computes the maximum of elements across dimensions of a tensor.
Parameters:
- input_tensor: The tensor to reduce. Should have numeric type
- axis: The dimensions to reduce. If None (the default), reduces all dimensions
- keepdims: If true, retains reduced dimensions with length 1
- name: A name for the operation
Returns:
The reduced tensor
"""
def reduce_min(input_tensor, axis=None, keepdims=None, name=None):
"""
Computes the minimum of elements across dimensions of a tensor.
Parameters:
- input_tensor: The tensor to reduce. Should have numeric type
- axis: The dimensions to reduce. If None (the default), reduces all dimensions
- keepdims: If true, retains reduced dimensions with length 1
- name: A name for the operation
Returns:
The reduced tensor
"""
def reduce_prod(input_tensor, axis=None, keepdims=None, name=None):
"""
Computes the product of elements across dimensions of a tensor.
Parameters:
- input_tensor: The tensor to reduce. Should have numeric type
- axis: The dimensions to reduce. If None (the default), reduces all dimensions
- keepdims: If true, retains reduced dimensions with length 1
- name: A name for the operation
Returns:
The reduced tensor
"""
def reduce_all(input_tensor, axis=None, keepdims=None, name=None):
"""
Computes the "logical and" of elements across dimensions of a tensor.
Parameters:
- input_tensor: The boolean tensor to reduce
- axis: The dimensions to reduce. If None (the default), reduces all dimensions
- keepdims: If true, retains reduced dimensions with length 1
- name: A name for the operation
Returns:
The reduced tensor
"""
def reduce_any(input_tensor, axis=None, keepdims=None, name=None):
"""
Computes the "logical or" of elements across dimensions of a tensor.
Parameters:
- input_tensor: The boolean tensor to reduce
- axis: The dimensions to reduce. If None (the default), reduces all dimensions
- keepdims: If true, retains reduced dimensions with length 1
- name: A name for the operation
Returns:
The reduced tensor
"""Element-wise Comparisons
Operations for comparing tensor elements.
def equal(x, y, name=None):
"""
Returns the truth value of (x == y) element-wise.
Parameters:
- x: A Tensor
- y: A Tensor. Must have the same type as x
- name: A name for the operation
Returns:
A Tensor of type bool
"""
def not_equal(x, y, name=None):
"""
Returns the truth value of (x != y) element-wise.
Parameters:
- x: A Tensor
- y: A Tensor. Must have the same type as x
- name: A name for the operation
Returns:
A Tensor of type bool
"""
def less(x, y, name=None):
"""
Returns the truth value of (x < y) element-wise.
Parameters:
- x: A Tensor
- y: A Tensor. Must have the same type as x
- name: A name for the operation
Returns:
A Tensor of type bool
"""
def less_equal(x, y, name=None):
"""
Returns the truth value of (x <= y) element-wise.
Parameters:
- x: A Tensor
- y: A Tensor. Must have the same type as x
- name: A name for the operation
Returns:
A Tensor of type bool
"""
def greater(x, y, name=None):
"""
Returns the truth value of (x > y) element-wise.
Parameters:
- x: A Tensor
- y: A Tensor. Must have the same type as x
- name: A name for the operation
Returns:
A Tensor of type bool
"""
def greater_equal(x, y, name=None):
"""
Returns the truth value of (x >= y) element-wise.
Parameters:
- x: A Tensor
- y: A Tensor. Must have the same type as x
- name: A name for the operation
Returns:
A Tensor of type bool
"""
def maximum(x, y, name=None):
"""
Returns the max of x and y (i.e. x > y ? x : y) element-wise.
Parameters:
- x: A Tensor
- y: A Tensor. Must have the same type as x
- name: A name for the operation
Returns:
A Tensor. Has the same type as x
"""
def minimum(x, y, name=None):
"""
Returns the min of x and y (i.e. x < y ? x : y) element-wise.
Parameters:
- x: A Tensor
- y: A Tensor. Must have the same type as x
- name: A name for the operation
Returns:
A Tensor. Has the same type as x
"""Usage Examples
import tensorflow as tf
# Basic arithmetic
a = tf.constant([1.0, 2.0, 3.0])
b = tf.constant([4.0, 5.0, 6.0])
sum_result = tf.add(a, b) # [5.0, 7.0, 9.0]
diff_result = tf.subtract(b, a) # [3.0, 3.0, 3.0]
prod_result = tf.multiply(a, b) # [4.0, 10.0, 18.0]
div_result = tf.divide(b, a) # [4.0, 2.5, 2.0]
# Mathematical functions
sqrt_result = tf.sqrt(a) # [1.0, 1.414, 1.732]
exp_result = tf.exp(a) # [2.718, 7.389, 20.086]
log_result = tf.log(b) # [1.386, 1.609, 1.792]
# Trigonometric functions
sin_result = tf.sin(a) # [0.841, 0.909, 0.141]
cos_result = tf.cos(a) # [0.540, -0.416, -0.990]
# Matrix operations
matrix_a = tf.constant([[1.0, 2.0], [3.0, 4.0]])
matrix_b = tf.constant([[5.0, 6.0], [7.0, 8.0]])
matmul_result = tf.matmul(matrix_a, matrix_b) # [[19.0, 22.0], [43.0, 50.0]]
transpose_result = tf.transpose(matrix_a) # [[1.0, 3.0], [2.0, 4.0]]
# Reduction operations
tensor = tf.constant([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]])
sum_all = tf.reduce_sum(tensor) # 21.0
sum_axis0 = tf.reduce_sum(tensor, axis=0) # [5.0, 7.0, 9.0]
mean_result = tf.reduce_mean(tensor) # 3.5
max_result = tf.reduce_max(tensor) # 6.0
# Comparisons
x = tf.constant([1, 2, 3])
y = tf.constant([1, 4, 2])
eq_result = tf.equal(x, y) # [True, False, False]
gt_result = tf.greater(x, y) # [False, False, True]
max_xy = tf.maximum(x, y) # [1, 4, 3]Install with Tessl CLI
npx tessl i tessl/pypi-tensorflow