# Tensorflow 2 – Arithmetic Operations on Tensors

This guide shows you how to perform the four arithmetic operations on tensors in Tensorflow 2: addition, subtraction, multiplication, and division.

You can add values to a tensor using the Python math addition operator or Tensorflow’s built-in tf.add function.

Example:

``````import tensorflow as tf

x1 = tf.constant([
[1, 2],
[2, 1]
])

x2 = tf.constant([
[0, 1],
[1, 1]
])

print(x1 + x2)
print(x1 + 5)

Output:

``````tf.Tensor(
[[1 3]
[3 2]], shape=(2, 2), dtype=int32)
tf.Tensor(
[[6 7]
[7 6]], shape=(2, 2), dtype=int32)

tf.Tensor(
[[1 3]
[3 2]], shape=(2, 2), dtype=int32)
tf.Tensor(
[[11 12]
[12 11]], shape=(2, 2), dtype=int32)``````

Note that the built-in function helps your code run faster on a GPU (or TPU), especially with large tensors.

## Subtraction

With subtraction, we have the Python math subtraction operator and the tf.subtract function.

Example:

``````import tensorflow as tf

x1 = tf.constant([
[1, 2],
[3, 4]
])

x2 = tf.constant([
[0, 1],
[1, 0]
])

print(x1 - x2)
print(x1 - 15)

print(tf.subtract(x1, x2))
print(tf.subtract(x1, 10))``````

Output:

``````tf.Tensor(
[[1 1]
[2 4]], shape=(2, 2), dtype=int32)
tf.Tensor(
[[-14 -13]
[-12 -11]], shape=(2, 2), dtype=int32)

tf.Tensor(
[[1 1]
[2 4]], shape=(2, 2), dtype=int32)
tf.Tensor(
[[-9 -8]
[-7 -6]], shape=(2, 2), dtype=int32)``````

## Multiplication

You can use the Python multiplication operator or the tf.multiply function.

Example:

``````import tensorflow as tf

x1 = tf.constant([
[0.5, 2.1],
[3.2, 4.5]
])

x2 = tf.constant([
[0., 1.],
[1., 0.]
])

print(x1 * x2)
print(x1 * 2)

print(tf.multiply(x1, x2))
print(tf.multiply(x1, 1.5))``````

Output:

``````tf.Tensor(
[[0.  2.1]
[3.2 0. ]], shape=(2, 2), dtype=float32)
tf.Tensor(
[[1.  4.2]
[6.4 9. ]], shape=(2, 2), dtype=float32)

tf.Tensor(
[[0.  2.1]
[3.2 0. ]], shape=(2, 2), dtype=float32)
tf.Tensor(
[[0.75      3.1499999]
[4.8       6.75     ]], shape=(2, 2), dtype=float32)``````

## Division

You can use the math division operator or the tf.divide function.

Example:

``````import tensorflow as tf

x1 = tf.constant([
[1, 2, 3],
[4, 5, 6]
])

x2 = tf.constant([
[1, 2, 3],
[3, 2, 1]
])

print(x1 / x2)
print(x1 / 2)

print(tf.divide(x1, x2))
print(tf.divide(x1, 0))``````

Output:

``````tf.Tensor(
[[1.         1.         1.        ]
[1.33333333 2.5        6.        ]], shape=(2, 3), dtype=float64)
tf.Tensor(
[[0.5 1.  1.5]
[2.  2.5 3. ]], shape=(2, 3), dtype=float64)

tf.Tensor(
[[1.         1.         1.        ]
[1.33333333 2.5        6.        ]], shape=(2, 3), dtype=float64)
tf.Tensor(
[[inf inf inf]
[inf inf inf]], shape=(2, 3), dtype=float64)``````

## Conclusion

We’ve gone through the four basic math operations in Tensorflow 2. If you would like to explore more about machine learning and Python stuff, take a look at the following articles:

You can also check out our Machine Learning category page or Python category page for more tutorials and examples.

## References

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