Pattern-Recognition-A1
所属分类：人工智能/神经网络/深度学习
开发工具：Python
文件大小：13793KB
下载次数：0
上传日期：2018-03-02 13:09:37
上 传 者：
sh-1993
说明： Pattern-Ignition-A1，我们将应用判别分析来识别MNIST数据集中的数字（<http://yann.lecun.com.exdb-MNIST>）...
(We will apply discriminant analysis to recognize the digits in the MNIST data set (<http://yann.lecun.com/exdb/mnist/>). As a bonus problem we will construct "Fisher digits". We will train our model using the training data sets ("train-images-idx3-ubyte.gz" and "train-labels-idx1-ubyte.gz") and test the performance using the test data set)
文件列表:
Outputs (0, 2018-03-02)
Outputs\mean-digits (0, 2018-03-02)
Outputs\mean-digits\0-mean.jpg (28385, 2018-03-02)
Outputs\mean-digits\1-mean.jpg (20843, 2018-03-02)
Outputs\mean-digits\2-mean.jpg (28129, 2018-03-02)
Outputs\mean-digits\3-mean.jpg (27659, 2018-03-02)
Outputs\mean-digits\4-mean.jpg (25759, 2018-03-02)
Outputs\mean-digits\5-mean.jpg (27487, 2018-03-02)
Outputs\mean-digits\6-mean.jpg (26175, 2018-03-02)
Outputs\mean-digits\7-mean.jpg (25717, 2018-03-02)
Outputs\mean-digits\8-mean.jpg (26526, 2018-03-02)
Outputs\mean-digits\9-mean.jpg (25254, 2018-03-02)
Outputs\standard-deviation-digits (0, 2018-03-02)
Outputs\standard-deviation-digits\0-std.jpg (31811, 2018-03-02)
Outputs\standard-deviation-digits\1-std.jpg (28561, 2018-03-02)
Outputs\standard-deviation-digits\2-std.jpg (31855, 2018-03-02)
Outputs\standard-deviation-digits\3-std.jpg (32130, 2018-03-02)
Outputs\standard-deviation-digits\4-std.jpg (31893, 2018-03-02)
Outputs\standard-deviation-digits\5-std.jpg (31261, 2018-03-02)
Outputs\standard-deviation-digits\6-std.jpg (30710, 2018-03-02)
Outputs\standard-deviation-digits\7-std.jpg (31128, 2018-03-02)
Outputs\standard-deviation-digits\8-std.jpg (30807, 2018-03-02)
Outputs\standard-deviation-digits\9-std.jpg (29977, 2018-03-02)
Report (0, 2018-03-02)
Report\50246821.docx (608687, 2018-03-02)
Report\Assignment-1-PR 555.docx (608687, 2018-03-02)
Report\Assignment-1-PR 555.pdf (524473, 2018-03-02)
code (0, 2018-03-02)
code\MNIST_Data (0, 2018-03-02)
code\MNIST_Data\t10k-images-idx3-ubyte.gz (1648877, 2018-03-02)
code\MNIST_Data\t10k-labels-idx1-ubyte.gz (4542, 2018-03-02)
code\MNIST_Data\train-images-idx3-ubyte.gz (9912422, 2018-03-02)
code\MNIST_Data\train-labels-idx1-ubyte.gz (28881, 2018-03-02)
code\__pycache__ (0, 2018-03-02)
code\__pycache__\libs.cpython-36.pyc (1502, 2018-03-02)
code\libs.py (2541, 2018-03-02)
... ...
# Pattern-Recognition-A1
We will apply discriminant analysis to recognize the digits in the MNIST data set (http://yann.lecun.com/exdb/mnist/). As a bonus problem we will construct "Fisher digits". We will train our model using the training data sets ("train-images-idx3-ubyte.gz" and "train-labels-idx1-ubyte.gz") and test the performance using the test data set ("t10k-images-idx3-ubyte.gz" and "t10k-labels-idx1-ubyte.gz")
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