# Tensorflow 2: Convert Arrays to Tensors (2 Examples)

A tensor is a multi-dimensional array with a uniform type. It is the standard data format used in Tensorflow. Below are a few examples of creating tensors from Numpy arrays by using tf.convert_to_tensor and tf.constant functions.

## Example 1: Using tf.convert_to_tensor

The code:

``````import tensorflow as tf
import numpy as np

np_array = np.random.randint(low=0, high=100, size=(3, 4, 5))
tf_tensor = tf.convert_to_tensor(np_array, dtype=tf.float32)
print(tf_tensor)``````

Output:

``````tf.Tensor(
[[[40. 68. 54. 50.  9.]
[28. 80.  9. 43.  6.]
[89. 11. 69. 23. 19.]
[45. 30. 33. 39. 64.]]

[[65. 91. 16. 76. 93.]
[33. 22. 84. 79. 25.]
[37.  2. 28.  5. 66.]
[87. 76.  8. 86. 21.]]

[[84. 83. 27. 13. 62.]
[89. 58. 49. 93. 57.]
[69. 89. 75.  2. 74.]
[47. 91. 48. 79. 64.]]], shape=(3, 4, 5), dtype=float32)``````

## Example 2: Using tf.constant

The code:

``````import tensorflow as tf
import numpy as np

# A normal Python list
x = [
[4., 3., 2.],
[1., 2., 5.],
[11., 9., 0.]
]

# Turn the Python list into a Numpy array
np_arr = np.asarray(x, np.float32)

# Convert the Numpy array to a tensor
tensor = tf.constant(np_arr, np.float32)

print(tensor)``````

Output:

``````tf.Tensor(
[[ 4.  3.  2.]
[ 1.  2.  5.]
[11.  9.  0.]], shape=(3, 3), dtype=float32)``````