# An Introduction to TensorFlow

# An Introduction to TensorFlow

### TensorFlow is a library that was developed by Google for solving complicated mathematical problems. This introduction to TensorFlow contains all you need to know!

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Join For FreeIn this post, we are going to see some TensorFlow examples, define tensors, perform math operations using tensors, and see other machine learning examples.

## What Is TensorFlow?

TensorFlow is a library that was developed by Google for solving complicated mathematical problems, which takes a lot of time.

TensorFlow can do many things, like:

**Solve complex mathematical expressions**.**Perform machine learning techniques**in which you give it a sample of data for training, then you give another sample of data to predict the result based on the training data. This is the artificial intelligence component!**Provide GPU support**. You can use GPU (Graphical Processing Unit) instead of CPU for faster processing. There are two versions of TensorFlow: the CPU version and the GPU version.

Before we start working with TensorFlow examples, we need to know some basics.

## What Is a Tensor?

A tensor is the main blocks of data that TensorFlow uses. They're like the variables that TensorFlow uses to work with data. Each tensor has a dimension and a type.

The dimension refers to the rows and columns of the tensor. You can define one-dimensional tensors, two-dimensional tensors, and three-dimensional tensors, as we will see later.

The type refers to the data type for the elements of the tensor.

## Define One-Dimensional Tensor

To define a tensor, we will create a NumPy array or a Python list and convert it to a tensor using the `tf_convert_to_tensor`

function.

We will use NumPy to create an array like this:

```
import numpy as np
arr = np.array([1, 5.5, 3, 15, 20])
```

The results show the dimension and the shape of the array.

```
import numpy as np
arr = np.array([1, 5.5, 3, 15, 20])
print(arr)
print (arr.ndim)
print (arr.shape)
print (arr.dtype)
```

It looks like the Python list but here there is no comma between the items.

Now we will convert this array to a tensor using the `tf_convert_to_tensor`

function.

```
import numpy as np
import tensorflow as tf
arr = np.array([1, 5.5, 3, 15, 20])
tensor = tf.convert_to_tensor(arr,tf.float64)
print(tensor)
```

From the results, you can see the tensor definition, but you can’t see the tensor elements.

To see the tensor elements, you can run a session like this:

```
import numpy as np
import tensorflow as tf
arr = np.array([1, 5.5, 3, 15, 20])
tensor = tf.convert_to_tensor(arr,tf.float64)
sess = tf.Session()
print(sess.run(tensor))
print(sess.run(tensor[1]))
```

## Define Two-Dimensional Tensor

This is done the same way as the one-dimensional array, but this time, we will define the array like this:

`arr = np.array([(1, 5.5, 3, 15, 20),(10, 20, 30, 40, 50), (60, 70, 80, 90,100)])`

And you can convert it to a tensor like this:

```
import numpy as np
import tensorflow as tf
arr = np.array([(1, 5.5, 3, 15, 20),(10, 20, 30, 40, 50), (60, 70, 80, 90, 100)])
tensor = tf.convert_to_tensor(arr)
sess = tf.Session()
print(sess.run(tensor))
```

Now you know how to define tensors. What about performing some math operations between them?

## Performing Math on Tensors

Suppose that we have two arrays like this:

```
arr1 = np.array([(1,2,3),(4,5,6)])
arr2 = np.array([(7,8,9),(10,11,12)])
```

We need to get the sum of them. You can perform many math operations using TensorFlow.

You can use the add function like this:

`arr3 = tf.add(arr1,arr2)`

So the whole code will be like this:

```
import numpy as np
import tensorflow as tf
arr1 = np.array([(1,2,3),(4,5,6)])
arr2 = np.array([(7,8,9),(10,11,12)])
arr3 = tf.add(arr1,arr2)
sess = tf.Session()
tensor = sess.run(arr3)
print(tensor)
```

You can multiply arrays like this:

```
import numpy as np
import tensorflow as tf
arr1 = np.array([(1,2,3),(4,5,6)])
arr2 = np.array([(7,8,9),(10,11,12)])
arr3 = tf.multiply(arr1,arr2)
sess = tf.Session()
tensor = sess.run(arr3)
print(tensor)
```

Now you got the idea.

## Three-Dimensional Tensor

We've seen how to work with one and two-dimensional tensors. Now, we will see the three-dimensional tensors. But this time, we won’t use numbers; we will use an RGB image where each piece of the image is specified by x, y, and z coordinates.

These coordinates are the width, height, and color depth.

First, let’s import the image using `matplotlib`

. You can install `matplotlib`

using `pip`

if it’s not installed on your system.

Now, put your file in the same directory as your Python file and import the image using `matplotlib`

like this:

```
import matplotlib.image as img
myfile = "likegeeks.png"
myimage = img.imread(myfile)
print(myimage.ndim)
print(myimage.shape)
```

As you can see, it’s a three-dimensional image where the width is 150, the height is 150, and the color depth is 3.

You can view the image like this:

```
import matplotlib.image as img
import matplotlib.pyplot as plot
myfile = "likegeeks.png"
myimage = img.imread(myfile)
plot.imshow(myimage)
plot.show()
```

Cool!

What about manipulating the image using TensorFlow? Super easy.

## Crop or Slice Image Using TensorFlow

First, we put the values on a placeholder like this:

`myimage = tf.placeholder("int32",[None,None,3])`

To slice the image, we will use the slice operator like this:

`cropped = tf.slice(myimage,[10,0,0],[16,-1,-1])`

Finally, run the session:

`result = sess.run(cropped, feed_dict={slice: myimage})`

Then you can see the result image using `matplotlib`

.

So the whole code will be like this:

```
import tensorflow as tf
import matplotlib.image as img
import matplotlib.pyplot as plot
myfile = "likegeeks.png"
myimage = img.imread(myfile)
slice = tf.placeholder("int32",[None,None,3])
cropped = tf.slice(myimage,[10,0,0],[16,-1,-1])
sess = tf.Session()
result = sess.run(cropped, feed_dict={slice: myimage})
plot.imshow(result)
plot.show()
```

Awesome!

## Transpose Images Using Tensorflow

In this TensorFlow example, we will do a simple transformation using TensorFlow.

First, specify the input image and initialize TensorFlow variables:

```
myfile = "likegeeks.png"
myimage = img.imread(myfile)
image = tf.Variable(myimage,name='image')
vars = tf.global_variables_initializer()
```

Then we will use the `transpose`

function, which flips the 0 and 1 axes of the input grid:

```
sess = tf.Session()
flipped = tf.transpose(image, perm=[1,0,2])
sess.run(vars)
result=sess.run(flipped)
```

Then, you can show the resulting image using `matplotlib`

.

```
import tensorflow as tf
import matplotlib.image as img
import matplotlib.pyplot as plot
myfile = "likegeeks.png"
myimage = img.imread(myfile)
image = tf.Variable(myimage,name='image')
vars = tf.global_variables_initializer()
sess = tf.Session()
flipped = tf.transpose(image, perm=[1,0,2])
sess.run(vars)
result=sess.run(flipped)
plot.imshow(result)
plot.show()
```

All of these TensorFlow examples show you how easy it os to work with TensorFlow.

I hope you find the post useful. Keep coming back!

Published at DZone with permission of Mokhtar Ebrahim , DZone MVB. See the original article here.

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