To understand the content of this chapter, we need to review the concept about gradient and gradient descent. Gradient descent algorithm was introduced in chapter 4. In that chapter, a […]
In Linux, you can compute the md5 of a file using the md5 command. md5 file You can compare the output of the md5 with the md5 published on the […]
Knowing the version of Python and tensorflow currently installed on your PC is important as tensorflow may not run on the latest version of python, or you installed a 32-bit […]
The python with statement is a little strange to programmers of other languages. It is frequently occurred in tensorflow related codes like: with tf.Session() as sess: do something A new […]
When you debug a QtWebEngine application, you may use qDebug to print information here and there. You need to turn on the console by adding a line “CONFIG += console” […]
If you ever upgrade php5 to php7 for your server/vps/shared hosting, you probably encounter the following errors: Fatal error: Uncaught Error: Call to undefined function mysql_connect() Fatal error: Uncaught Error: […]
The tensorflow.constant is one of the functions to create a tensor with known values. Its usage is flexible and may not be what you think. The simplest usage would be: […]
LTU(Linear Threshold Unit) \(\vec{w}=\{w_1,w_2,…,w_n\}\) is the weight vector, \(\vec{x}=\{x_1,x_2,…,x_n\}\) is an input feature vector. step(z) can be a heaviside step function which outputs 1 for z>=0, and outputs 0 for […]
To understand tensorflow computation graph, you need to know how to print or plot it. If you can visualize the computation graph, it would be easy to understand it. The […]
np.random.seed(4) m = 60 w1, w2 = 0.1, 0.3 noise = 0.1 angles = np.random.rand(m) * 3 * np.pi / 2 – 0.5 X = np.empty((m, 3)) X = np.empty((m, […]