The np.ones() function returns a one-dimensional matrix. It can be used to initialize the weights in TensorFlow and other statistical tasks during the first iteration.
Numpy is a popular Python library for numerical computing, and the numpy.ones() function is useful for creating arrays filled with ones.
The basic syntax for numpy.ones() is as follows::
numpy.ones(shape, dtype=float, order='C')
Parameters:
- Shape: is the shape of the array
- Dtype: is the datatype. It is optional. The default value is float64
- Order: The default is C, which is an essential row style.
Return Value :
The np.ones() function returns an array with element values as ones.
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How To use numpy.ones() Method
Here’s an example usage of numpy.ones():
import numpy as np
b = np.ones(2, dtype = int)
print("Matrix b : \n", b)
a = np.ones([2, 2], dtype = int)
print("\nMatrix a : \n", a)
c = np.ones([3, 3])
print("\nMatrix c : \n", c)
d = np.ones((1,2,3), dtype=np.int16)
print("\nMatrix d : \n", c)In the above example, the numpy.ones() function is used to create arrays of different shapes and data types.
Output:
Matrix b : [1 1] Matrix a : [[1 1] [1 1]] Matrix c : [[ 1. 1. 1.] [ 1. 1. 1.] [ 1. 1. 1.]] Matrix d : [[[1 1 1] [1 1 1]]]
Int filled Array
Create an array of type int filled with ones
d = np.ones(4, dtype=int) print(d)
Output:
[1 1 1 1]
Reference :
https://numpy.org/devdocs/reference/generated/numpy.ones.html
