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341 lines
7.5 KiB
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Translating by ghsgz | Simple TensorFlow Examples
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======
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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.
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## What is TensorFlow?
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TensorFlow is a library which was developed by Google for solving complicated mathematical problems which takes much time.
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Actually, TensorFlow can do many things like:
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* Solving complex mathematical expressions.
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* 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 the artificial intelligence!!
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* 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.
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Before we start working with TensorFlow examples, we need to know some basics.
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## What is a Tensor?
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The tensor is the main blocks 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.
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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.
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The type is the data type for the elements of the tensor.
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## Define one-dimensional Tensor
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To define a tensor, we will create a NumPy array or a [Python list][1] and convert it to a tensor using the tf_convert_to_tensor function.
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We will use NumPy to create an array like this:
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```
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import numpy as np arr = np.array([1, 5.5, 3, 15, 20])
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arr = np.array([1, 5.5, 3, 15, 20])
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```
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You can see from the results the dimension and shape of the array.
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```
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import numpy as np
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arr = np.array([1, 5.5, 3, 15, 20])
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print(arr)
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print (arr.ndim)
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print (arr.shape)
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print (arr.dtype)
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```
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It looks like the Python list but here there is no comma between the items.
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Now we will convert this array to a tensor using tf_convert_to_tensor function.
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```
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import numpy as np
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import tensorflow as tf
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arr = np.array([1, 5.5, 3, 15, 20])
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tensor = tf.convert_to_tensor(arr,tf.float64)
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print(tensor)
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```
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From the results, you can see the tensor definition, but you can’t see the tensor elements.
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Well, to see the tensor elements, you can run a session like this:
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```
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import numpy as np
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import tensorflow as tf
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arr = np.array([1, 5.5, 3, 15, 20])
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tensor = tf.convert_to_tensor(arr,tf.float64)
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sess = tf.Session()
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print(sess.run(tensor))
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print(sess.run(tensor[1]))
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```
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## Define Two-dimensional Tensor
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The same way as the one-dimensional array, but this time we will define the array like this:
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```
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arr = np.array([(1, 5.5, 3, 15, 20),(10, 20, 30, 40, 50), (60, 70, 80, 90, 100)])
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```
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And you can convert it to a tensor like this:
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```
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import numpy as np
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import tensorflow as tf
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arr = np.array([(1, 5.5, 3, 15, 20),(10, 20, 30, 40, 50), (60, 70, 80, 90, 100)])
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tensor = tf.convert_to_tensor(arr)
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sess = tf.Session()
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print(sess.run(tensor))
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```
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Now you know how to define tensors, what about performing some math operations between them?
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## Performing Math on Tensors
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Suppose that we have 2 arrays like this:
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```
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arr1 = np.array([(1,2,3),(4,5,6)])
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arr2 = np.array([(7,8,9),(10,11,12)])
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```
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We need to get the sum of them. You can perform many math operations using TensorFlow.
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You can use the add function like this:
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```
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import numpy as np
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import tensorflow as tf
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arr1 = np.array([(1,2,3),(4,5,6)])
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arr2 = np.array([(7,8,9),(10,11,12)])
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arr3 = tf.add(arr1,arr2)
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sess = tf.Session()
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tensor = sess.run(arr3)
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print(tensor)
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```
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You can multiply arrays like this:
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```
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import numpy as np
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import tensorflow as tf
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arr1 = np.array([(1,2,3),(4,5,6)])
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arr2 = np.array([(7,8,9),(10,11,12)])
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arr3 = tf.multiply(arr1,arr2)
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sess = tf.Session()
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tensor = sess.run(arr3)
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print(tensor)
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```
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Now you got the idea.
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## Three-dimensional Tensor
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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.
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These coordinates are the width, height, and color depth.
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First, let’s import the image using matplotlib. You can install matplotlib [using pip][2] if it’s not installed on your system.
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Now, put your file in the same directory with your Python file and import the image using matplotlib like this:
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```
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import matplotlib.image as img
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myfile = "likegeeks.png"
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myimage = img.imread(myfile)
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print(myimage.ndim)
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print(myimage.shape)
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```
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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.
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You can view the image like this:
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```
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import matplotlib.image as img
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import matplotlib.pyplot as plot
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myfile = "likegeeks.png"
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myimage = img.imread(myfile)
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plot.imshow(myimage)
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plot.show()
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```
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Cool!!
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What about manipulating the image using TensorFlow? Super easy.
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## Crop Or Slice Image Using TensorFlow
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First, we put the values on a placeholder like this:
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```
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myimage = tf.placeholder("int32",[None,None,3])
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```
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To slice the image, we will use the slice operator like this:
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```
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cropped = tf.slice(myimage,[10,0,0],[16,-1,-1])
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```
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Finally, run the session:
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```
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result = sess.run(cropped, feed\_dict={slice: myimage})
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```
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Then you can see the result image using matplotlib.
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So the whole code will be like this:
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```
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import tensorflow as tf
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import matplotlib.image as img
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import matplotlib.pyplot as plot
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myfile = "likegeeks.png"
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myimage = img.imread(myfile)
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slice = tf.placeholder("int32",[None,None,3])
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cropped = tf.slice(myimage,[10,0,0],[16,-1,-1])
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sess = tf.Session()
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result = sess.run(cropped, feed_dict={slice: myimage})
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plot.imshow(result)
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plot.show()
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```
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Awesome!!
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## Transpose Images using TensorFlow
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In this TensorFlow example, we will do a simple transformation using TensorFlow.
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First, specify the input image and initialize TensorFlow variables:
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```
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myfile = "likegeeks.png"
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myimage = img.imread(myfile)
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image = tf.Variable(myimage,name='image')
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vars = tf.global_variables_initializer()
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```
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Then we will use the transpose function which flips the 0 and 1 axes of the input grid:
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```
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sess = tf.Session()
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flipped = tf.transpose(image, perm=[1,0,2])
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sess.run(vars)
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result=sess.run(flipped)
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```
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Then you can show the resulting image using matplotlib.
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```
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import tensorflow as tf
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import matplotlib.image as img
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import matplotlib.pyplot as plot
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myfile = "likegeeks.png"
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myimage = img.imread(myfile)
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image = tf.Variable(myimage,name='image')
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vars = tf.global_variables_initializer()
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sess = tf.Session()
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flipped = tf.transpose(image, perm=[1,0,2])
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sess.run(vars)
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result=sess.run(flipped)
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plot.imshow(result)
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plot.show()
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```
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All these TensorFlow examples show you how easy it’s to work with TensorFlow.
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--------------------------------------------------------------------------------
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via: https://www.codementor.io/likegeeks/define-and-use-tensors-using-simple-tensorflow-examples-ggdgwoy4u
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作者：[LikeGeeks][a]
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译者：[译者ID](https://github.com/译者ID)
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校对：[校对者ID](https://github.com/校对者ID)
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本文由 [LCTT](https://github.com/LCTT/TranslateProject) 原创编译，[Linux中国](https://linux.cn/) 荣誉推出
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[a]:https://www.codementor.io/likegeeks
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[1]:https://likegeeks.com/python-list-functions/
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[2]:https://likegeeks.com/import-create-install-reload-alias-python-modules/#Install-Python-Modules-Using-pip
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