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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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In 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)])
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"
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"
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"
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"
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"
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!

TrueSight is an AIOps platform, powered by machine learning and analytics, that elevates IT operations to address multi-cloud complexity and the speed of digital transformation.

Topics:
big data ,python ,tensors ,tensorflow ,tutorial

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