Define and Use Tensors Using Simple TensorFlow Examples

In this post, we are going to see some TensorFlow examples, and see how it’s easy to define tensors, perform math operations using tensors, and other machine learning examples.

 

 

What is TensorFlow?

TensorFlow is a library that was developed by Google to solve complicated mathematical problems, which takes much time.

TensorFlow can do many things like:

• Solving complex mathematical expressions.
• Machine learning techniques, where 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 artificial intelligence!!
• GPU support. You can use GPU (Graphical Processing Unit) instead of CPU for faster processing. There are two versions of TensorFlow, CPU version, and GPU version.

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

 

What is a tensor?

The tensor is the main block of data that TensorFlow uses; it’s like the variables that TensorFlow uses to work with data. Each tensor has a dimension and a type.

The dimension is the rows and columns of the tensor; you can define one-dimensional tensor, two-dimensional tensor, and three-dimensional tensor as we will see later.

The type is 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])

You can see from the results the dimension and 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 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.

Well, 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

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 saw 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, and 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 resulting 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 these TensorFlow examples show you how easy it’s to work with TensorFlow.

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