# TensorFlow Matrix and Vector Arithmetic

### Dot Product

The dot product between two tensors can be performed using:

```tf.matmul(a, b)
```

A full example is given below:

```# Build a graph
graph = tf.Graph()
with graph.as_default():
# A 2x3 matrix
a = tf.constant(np.array([[1, 2, 3],
[2, 4, 6]]),
dtype=tf.float32)
# A 3x2 matrix
b = tf.constant(np.array([[1, 10],
[2, 20],
[3, 30]]),
dtype=tf.float32)

# Perform dot product
c = tf.matmul(a, b)

# Run a Session
with tf.Session(graph=graph) as session:
output = session.run(c)
print(output)
```

prints out

```[[  14.  140.]
[  28.  280.]]
```

### Elementwise Multiplication

To perform elementwise multiplication on tensors, you can use either of the following:

• `a*b`
• `tf.multiply(a, b)`

Here is a full example of elementwise multiplication using both methods.

```import tensorflow as tf
import numpy as np

# Build a graph
graph = tf.Graph()
with graph.as_default():
# A 2x3 matrix
a = tf.constant(np.array([[ 1, 2, 3],
[10,20,30]]),
dtype=tf.float32)
# Another 2x3 matrix
b = tf.constant(np.array([[2, 2, 2],
[3, 3, 3]]),
dtype=tf.float32)

# Elementwise multiplication
c =  a * b
d = tf.multiply(a, b)

# Run a Session
with tf.Session(graph=graph) as session:
(output_c, output_d) = session.run([c, d])
print("output_c")
print(output_c)
print("\noutput_d")
print(output_d)
```

Prints out the following:

```output_c
[[  2.   4.   6.]
[ 30.  60.  90.]]

output_d
[[  2.   4.   6.]
[ 30.  60.  90.]]
```

### Scalar Times a Tensor

In the following example a 2 by 3 tensor is multiplied by a scalar value (2).

```# Build a graph
graph = tf.Graph()
with graph.as_default():
# A 2x3 matrix
a = tf.constant(np.array([[ 1, 2, 3],
[10,20,30]]),
dtype=tf.float32)

# Scalar times Matrix
c =  2 * a

# Run a Session
with tf.Session(graph=graph) as session:
output = session.run(c)
print(output)
```

This prints out

```[[  2.   4.   6.]
[ 20.  40.  60.]]
```

This article is an extract of the original Stack Overflow Documentation created by contributors and released under CC BY-SA 3.0. This website is not affiliated with Stack Overflow

### About This Page

This page was last modified on 9 November 2020, at 06:13.