GDA, NB and EM September 27, 2023 6 / 122 Conditional Probability (Contd.) Real valued random variable is a function of the outcome of a ran- domized experiment X : S → R Examples: Discrete random variables valued random variable is a function of the outcome of a ran- domized experiment X : S → R For continuous random variable X P(a < X < b) = P({s ∈ S : a < X(s) < b}) For discrete random variable X P(X = Distribution Probability distribution for discrete random variables Suppose X is a discrete random variable X : S → A Probability mass function (PMF) of X: the probability of X = x pX(x) = P(X = x) Since

behavior by setting the environment variable VBOX_PAM_ALLOW_INACTIVE which will suppress failures when unable to read the shadow pass- word file. Please use this variable carefully, and only if you fully may not be located on your system classpath, and you may have to adjust the CLASSPATH environment variable. Something like this: export CLASSPATH="/path-to-axis-1_4/lib/*":$CLASSPATH Use the directory decode that response message and put the return value of the remote procedure into the “result” variable. Service descriptions in WSDL In the above explanations about SOAP, it was left open how the programming

behavior by setting the environment variable VBOX_PAM_ALLOW_INACTIVE which will suppress failures when unable to read the shadow pass- word file. Please use this variable carefully, and only if you fully may not be located on your system classpath, and you may have to adjust the CLASSPATH environment variable. Something like this: export CLASSPATH="/path-to-axis-1_4/lib/*":$CLASSPATH Use the directory decode that response message and put the return value of the remote procedure into the “result” variable. Service descriptions in WSDL In the above explanations about SOAP, it was left open how the programming

behavior by setting the environment variable VBOX_PAM_ALLOW_INACTIVE which will suppress failures when unable to read the shadow pass- word file. Please use this variable carefully, and only if you fully may not be located on your system classpath, and you may have to adjust the CLASSPATH environment variable. Something like this: export CLASSPATH="/path-to-axis-1_4/lib/*":$CLASSPATH Use the directory decode that response message and put the return value of the remote procedure into the “result” variable. Service descriptions in WSDL In the above explanations about SOAP, it was left open how the programming

behavior by setting the environment variable VBOX_PAM_ALLOW_INACTIVE which will suppress failures when unable to read the shadow pass- word file. Please use this variable carefully, and only if you fully may not be located on your system classpath, and you may have to adjust the CLASSPATH environment variable. Something like this: export CLASSPATH="/path-to-axis-1_4/lib/*":$CLASSPATH Use the directory decode that response message and put the return value of the remote procedure into the “result” variable. Service descriptions in WSDL In the above explanations about SOAP, it was left open how the programming

read privileges of the shadow password file by setting the environment variable VBOX_PAM_ALLOW_INACTIVE. Please use this variable carefully and only if you fully understand what you’re doing. 6 2 Environment-specific may not be located on your system classpath, and you may have to adjust the CLASSPATH environment variable. Something like this: export CLASSPATH="/path-to-axis-1_4/lib/*":$CLASSPATH Use the directory Decode that response message and put the return value of the remote procedure into the “result” variable. 15 2 Environment-specific notes Service descriptions in WSDL In the above explanations about

super(MyLayer, self).__init__(**kwargs) def build(self, input_shape): # Create a trainable weight variable for this layer. self.kernel = self.add_weight(name='kernel', shape=(input_shape[1], self.output_dim) inputs = K.placeholder(ndim=3) 下面的代码实例化一个变量。它等价于 tf.Variable() 或 th.shared()。 import numpy as np val = np.random.random((3, 4, 5)) var = K.variable(value=val) # 全 0 变量： var = K.zeros(shape=(3, 4, 5)) 使用随机数初始化张量 b = K.random_uniform_variable(shape=(3, 4), low=0, high=1) # 均匀分布 c = K.random_normal_variable(shape=(3, 4), mean=0, scale=1) # 高斯分布 d = K.random_normal_variable(shape=(3, 4), mean=0, scale=1)

a sample 2D weight matrix with randomly initialized float values. We also define a sparsity_rate variable initialized with the value 0.4 to sparsify 40% of the total number of weights. Finally, we compute that you are convinced that sparsity helps with improving compression. Increasing the sparsity_rate variable’s value will further reduce the size of the sparsified and compressed size. To take a step back 5-2 uses a fixed pruning rate $$p$$. However, we could use variable pruning rates across the pruning rounds. The motivation behind using variable sparsity is that a pre-trained model’s weights will get disrupted

1.7.x or greater (http://www.oracle.com/technetwork/java/javase/). • The JAVA_HOME environment variable must be set to the directory where the Java runtime is installed, USING APACHE KARAF BINARY DISTRIBUTIONS download the features from Internet. Installation on Windows platform NB: the JAVA_HOME environment variable has to be correctly defined. To accomplish that, press Windows key and Break key together, switch works for sure and is short to type. Installation on Unix platforms NB: the JAVA_HOME environment variable has to be correctly defined. Check the current value using echo $JAVA_HOME If it's not correct

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 4.1 自定义 Variable 数据与网络训练 19 4.2 准确率的可视化 22 4.3 分类结果的可视化 23 4.4 自定义 Dataset 数据集 25 3 4.5 总结 27 Literature . chapter3-3.py。 4. 构建自己的数据集 4.1 自定义 Variable 数据与网络训练 19 4.2 准确率的可视化 22 4.3 分类结果的可视化 23 4.4 自定义 Dataset 数据集 25 4.5 总结 27 本章我们的目标是把构建自己的数据集，并来测试和可视化。 4.1 自定义 Variable 数据与网络训练 假如我们并没有图像数据，我们自己创造一些数据，并用它们来分类。 import torch import numpy as np # 生 成 数 据 def dataGenerate ( data , l a b e l ) : 19 20 4.1. 自定义 Variable 数据与网络训练 f o r idata in data : i f idata [ 0 ] < 0 . 5 : # 把 小 于0 .5 的 值 压 缩 到 [ 0 , 1 ] 之 间