# Learn TensorFlow: Vectors

# Learn TensorFlow: Vectors

### Learn about TensorFlow and explore vectors.

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## TensorFlow

TensorFlow is an open-source library that was developed by the Google Brain team, and it was released in November 2015. Before working with TensorFlow, we need to understand the following basic concepts:

**Graph**: Layout of the learning process. It does not include data.**Data**: Examples that are used to train. It has two kinds, which are inputs and targets.**Session**: Where we feed the graph with data or**Session = Graph + Data**. We can do this by using**placeholders**— gates to introduce examples.

We can install Anaconda to use TensorFlow.

**Vectors**

In Machine Learning, vectors can be used as a good way to represent numeric data. When using vectors, we can meet the following basic operations:

- Add two vectors
- Subtract two vectors
- Mutilply a vector with a scalar (i.e., a number)
- Norm (i.e, magnitude or length of a vector)
- Dot product of two vectors

The operations on vectors can be implemented by using the functions from the TensorFlow library, but before using this library, we must:

`import tensorflow as tf`

Next step is to create the graph:

```
#####GRAPH
vec_1 = tf.placeholder(tf.float32)
vec_2 = tf.placeholder(tf.float32)
scalar = tf.placeholder(tf.float32)
vector_add = tf.add(vec_1,vec_2)
vector_subtract = tf.subtract(vec_1,vec_2)
scalar_multiply = tf.multiply(scalar,vec_1)
norm = tf.norm(vec_1)
dot = tf.tensordot(vec_1, vec_2, 1)
```

We can feed the graph with data through the session. Data can be declared as lists of numbers:

```
#############DATA
v = [1,2]
w = [2,3]
c = 3
```

Session can be created:

```
##########SESSION
with tf.Session() as sess:
result_add = sess.run(vector_add, feed_dict={vec_1:v,vec_2:w})
result_sub = sess.run(vector_subtract, feed_dict={vec_1:v,vec_2:w})
result_mul = sess.run(scalar_multiply, feed_dict={scalar:c,vec_1:v})
result_norm = sess.run(norm , feed_dict={vec_1:v})
result_dot = sess.run(dot, feed_dict={vec_1:v,vec_2:w})
```

Finally, we can create some outputs:

```
###########OUTPUT
print(result_add.tolist())
print(result_sub.tolist())
print(result_mul.tolist())
print(result_norm)
print(result_dot)
```

The result can look like this:

```
[3.0, 5.0]
[-1.0, -1.0]
[3.0, 6.0]
2.236068
8.0
```

## Conclusion

Starting from the simplest things is one of the best ways to learn the TensorFlow. Through operations on vectors, I hope you (and both me) — TensorFlow beginners — will understand how to use the TensorFlow before using it for more complex tasks in the future.

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