雖說最新版的 Up-Ubuntu 突然有 HDMI Audio 之問題？
[ 12.012421] Audio Port: ASoC: no backend DAIs enabled for Audio Port
不過眼下關心的卻是『張量流』哩！樂見『TensorFlow』二十行內完成之『手寫阿拉伯數字』辨識程式，可以正確且快速執行也︰
up@up-UP-CHT01:~$ python3
Python 3.5.2 (default, Nov 23 2017, 16:37:01)
[GCC 5.4.0 20160609] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> from tensorflow.examples.tutorials.mnist import input_data
>>> mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)
Successfully downloaded train-images-idx3-ubyte.gz 9912422 bytes.
Extracting MNIST_data/train-images-idx3-ubyte.gz
Successfully downloaded train-labels-idx1-ubyte.gz 28881 bytes.
Extracting MNIST_data/train-labels-idx1-ubyte.gz
Successfully downloaded t10k-images-idx3-ubyte.gz 1648877 bytes.
Extracting MNIST_data/t10k-images-idx3-ubyte.gz
Successfully downloaded t10k-labels-idx1-ubyte.gz 4542 bytes.
Extracting MNIST_data/t10k-labels-idx1-ubyte.gz
>>> import tensorflow as tf
>>> x = tf.placeholder(tf.float32, [None, 784])
>>> W = tf.Variable(tf.zeros([784, 10]))
>>> b = tf.Variable(tf.zeros([10]))
>>> y = tf.nn.softmax(tf.matmul(x, W) + b)
>>> y_ = tf.placeholder(tf.float32, [None, 10])
>>> cross_entropy = -tf.reduce_sum(y_*tf.log(y))
>>> train_step = tf.train.GradientDescentOptimizer(0.01).minimize(cross_entropy)
>>> init = tf.initialize_all_variables()
WARNING:tensorflow:From /usr/local/lib/python3.5/dist-packages/tensorflow/python/util/tf_should_use.py:118: initialize_all_variables (from tensorflow.python.ops.variables) is deprecated and will be removed after 2017-03-02.
Instructions for updating:
Use `tf.global_variables_initializer` instead.
>>> sess = tf.Session()
2018-02-24 19:12:57.709052: I tensorflow/core/platform/cpu_feature_guard.cc:137] Your CPU supports instructions that this TensorFlow binary was not compiled to use: SSE4.1 SSE4.2
>>> sess.run(init)
>>> for i in range(1000):
... batch_xs, batch_ys = mnist.train.next_batch(100)
... sess.run(train_step, feed_dict={x: batch_xs, y_: batch_ys})
...
>>> correct_prediction = tf.equal(tf.argmax(y,1), tf.argmax(y_,1))
>>> accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
>>> print(sess.run(accuracy, feed_dict={x: mnist.test.images, y_: mnist.test.labels}))
0.9166
>>>
故希望能乘
Keras: The Python Deep Learning library
You have just found Keras.
Keras is a high-level neural networks API, written in Python and capable of running on top of TensorFlow, CNTK, or Theano. It was developed with a focus on enabling fast experimentation. Being able to go from idea to result with the least possible delay is key to doing good research.
Use Keras if you need a deep learning library that:
- Allows for easy and fast prototyping (through user friendliness, modularity, and extensibility).
- Supports both convolutional networks and recurrent networks, as well as combinations of the two.
- Runs seamlessly on CPU and GPU.
Read the documentation at Keras.io.
Keras is compatible with: Python 2.7-3.6.
Guiding principles
- User friendliness. Keras is an API designed for human beings, not machines. It puts user experience front and center. Keras follows best practices for reducing cognitive load: it offers consistent & simple APIs, it minimizes the number of user actions required for common use cases, and it provides clear and actionable feedback upon user error.
- Modularity. A model is understood as a sequence or a graph of standalone, fully-configurable modules that can be plugged together with as little restrictions as possible. In particular, neural layers, cost functions, optimizers, initialization schemes, activation functions, regularization schemes are all standalone modules that you can combine to create new models.
- Easy extensibility. New modules are simple to add (as new classes and functions), and existing modules provide ample examples. To be able to easily create new modules allows for total expressiveness, making Keras suitable for advanced research.
- Work with Python. No separate models configuration files in a declarative format. Models are described in Python code, which is compact, easier to debug, and allows for ease of extensibility.
的羽翼，再顯 Michael Nielsen 先生
對一本小巧完整而且寫的好的書，又該多說些什麼的呢？於是幾經思慮，就講講過去之閱讀隨筆與念頭雜記的吧！終究面對一個既舊也新的議題，尚待火石電光激發創意和發想。也許只需一個洞見或將改變人工智慧的未來乎？？
Michael Nielsen 先生開宗明義在首章起頭
The human visual system is one of the wonders of the world. Consider the following sequence of handwritten digits:
The difficulty of visual pattern recognition becomes apparent if you attempt to write a computer program to recognize digits like those above. What seems easy when we do it ourselves suddenly becomes extremely difficult. Simple intuitions about how we recognize shapes – “a 9 has a loop at the top, and a vertical stroke in the bottom right” – turn out to be not so simple to express algorithmically. When you try to make such rules precise, you quickly get lost in a morass of exceptions and caveats and special cases. It seems hopeless.
Neural networks approach the problem in a different way. The idea is to take a large number of handwritten digits, known as training examples,
and then develop a system which can learn from those training examples. In other words, the neural network uses the examples to automatically infer rules for recognizing handwritten digits. Furthermore, by increasing the number of training examples, the network can learn more about handwriting, and so improve its accuracy. So while I’ve shown just 100 training digits above, perhaps we could build a better handwriting recognizer by using thousands or even millions or billions of training examples.
In this chapter we’ll write a computer program implementing a neural network that learns to recognize handwritten digits. The program is just 74 lines long, and uses no special neural network libraries. But this short program can recognize digits with an accuracy over 96 percent, without human intervention. Furthermore, in later chapters we’ll develop ideas which can improve accuracy to over 99 percent. In fact, the best commercial neural networks are now so good that they are used by banks to process cheques, and by post offices to recognize addresses.
We’re focusing on handwriting recognition because it’s an excellent prototype problem for learning about neural networks in general. As a prototype it hits a sweet spot: it’s challenging – it’s no small feat to recognize handwritten digits – but it’s not so difficult as to require an extremely complicated solution, or tremendous computational power. Furthermore, it’s a great way to develop more advanced techniques, such as deep learning. And so throughout the book we’ll return repeatedly to the problem of handwriting recognition. Later in the book, we’ll discuss how these ideas may be applied to other problems in computer vision, and also in speech, natural language processing, and other domains.
Of course, if the point of the chapter was only to write a computer program to recognize handwritten digits, then the chapter would be much shorter! But along the way we’ll develop many key ideas about neural networks, including two important types of artificial neuron (the perceptron and the sigmoid neuron), and the standard learning algorithm for neural networks, known as stochastic gradient descent. Throughout, I focus on explaining why things are done the way they are, and on building your neural networks intuition. That requires a lengthier discussion than if I just presented the basic mechanics of what’s going on, but it’s worth it for the deeper understanding you’ll attain. Amongst the payoffs, by the end of the chapter we’ll be in position to understand what deep learning is, and why it matters.
………
之理念矣◎
說明這本書的主旨。是用『手寫阿拉伯數字辨識』這一主題貫串『神經網絡』以及『深度學習』之點滴，希望讀者能夠藉著最少的文本一窺全豹、聞一知十。因此他盡量少用『數學』，盡可能白話描述重要的『原理』與『概念』。
─── 摘自《W!o+ 的《小伶鼬工坊演義》︰神經網絡與深度學習【發凡】》