Matrices

A matrix is a two-dimensional array of numbers where each element is identified by two indices instead of just one. If a real matrix X has a height of m and a width of n, then we say that X ∈ Rm × n. Here, R is a set of real numbers.

The following example shows how different matrices are converted to tensor objects:

# convert matrices to tensor objects
import numpy as np
import tensorflow as tf

# create a 2x2 matrix in various forms
matrix1 = [[1.0, 2.0], [3.0, 40]]
matrix2 = np.array([[1.0, 2.0], [3.0, 40]], dtype=np.float32)
matrix3 = tf.constant([[1.0, 2.0], [3.0, 40]])

print(type(matrix1))
print(type(matrix2))
print(type(matrix3))

tensorForM1 = tf.convert_to_tensor(matrix1, dtype=tf.float32)
tensorForM2 = tf.convert_to_tensor(matrix2, dtype=tf.float32)
tensorForM3 = tf.convert_to_tensor(matrix3, dtype=tf.float32)

print(type(tensorForM1))
print(type(tensorForM2))
print(type(tensorForM3))

The output of the listing is shown in the following code:

<class 'list'>
<class 'numpy.ndarray'>
<class 'tensorflow.python.framework.ops.Tensor'>
<class 'tensorflow.python.framework.ops.Tensor'>
<class 'tensorflow.python.framework.ops.Tensor'>
<class 'tensorflow.python.framework.ops.Tensor'>