From 227019b9f3b581f44eeb5910b738aac3c4a4afa1 Mon Sep 17 00:00:00 2001 From: darksun Date: Thu, 8 Feb 2018 14:30:45 +0800 Subject: [PATCH] =?UTF-8?q?=E9=80=89=E9=A2=98:=20Simple=20TensorFlow=20Exa?= =?UTF-8?q?mples?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../20180206 Simple TensorFlow Examples.md | 340 ++++++++++++++++++ 1 file changed, 340 insertions(+) create mode 100644 sources/tech/20180206 Simple TensorFlow Examples.md diff --git a/sources/tech/20180206 Simple TensorFlow Examples.md b/sources/tech/20180206 Simple TensorFlow Examples.md new file mode 100644 index 0000000000..fa5b633b2b --- /dev/null +++ b/sources/tech/20180206 Simple TensorFlow Examples.md @@ -0,0 +1,340 @@ +Simple TensorFlow Examples +====== + +![](https://process.filestackapi.com/cache=expiry:max/resize=width:700/compress/XWiMrodDQb2Qg6RxyDDG) + +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 which was developed by Google for solving complicated mathematical problems which takes much time. + +Actually, 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 the 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 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. + +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][1] 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]) + +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 2 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: +``` +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][2] if it’s not installed on your system. + +Now, put your file in the same directory with 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 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 these TensorFlow examples show you how easy it’s to work with TensorFlow. + +-------------------------------------------------------------------------------- + +via: https://www.codementor.io/likegeeks/define-and-use-tensors-using-simple-tensorflow-examples-ggdgwoy4u + +作者：[LikeGeeks][a] +译者：[译者ID](https://github.com/译者ID) +校对：[校对者ID](https://github.com/校对者ID) + +本文由 [LCTT](https://github.com/LCTT/TranslateProject) 原创编译，[Linux中国](https://linux.cn/) 荣誉推出 + +[a]:https://www.codementor.io/likegeeks +[1]:https://likegeeks.com/python-list-functions/ +[2]:https://likegeeks.com/import-create-install-reload-alias-python-modules/#Install-Python-Modules-Using-pip