From 630741b0a29bb86b1be15e6cff6eb5582bc9886d Mon Sep 17 00:00:00 2001 From: ghsgz <34056529+ghsgz@users.noreply.github.com> Date: Sun, 11 Feb 2018 17:11:04 +0800 Subject: [PATCH] Delete 20180206 Simple TensorFlow Examples.md MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit 删除源文件 --- .../20180206 Simple TensorFlow Examples.md | 340 ------------------ 1 file changed, 340 deletions(-) delete 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 deleted file mode 100644 index fa5b633b2b..0000000000 --- a/sources/tech/20180206 Simple TensorFlow Examples.md +++ /dev/null @@ -1,340 +0,0 @@ -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