文件名称:mnist
介绍说明－－下载内容来自于网络，使用问题请自行百度
使用了全连接网络，卷积神经网络，循环神经网络分别构建不同的分类器，如何通过模型保存原理进行保存。(Using the fully connected network and convolution neural network, recurrent neural network builds different classifiers respectively, and how to save them through the preservation principle of the model.)
相关搜索： 分类器
(系统自动生成,下载前可以参看下载内容)
下载文件列表
| 文件名 | 大小 | 更新时间 |
|---|---|---|
| mnist | 0 | 2018-04-22 |
| mnist\.DS_Store | 8196 | 2018-04-15 |
| __MACOSX | 0 | 2018-04-27 |
| __MACOSX\mnist | 0 | 2018-04-27 |
| __MACOSX\mnist\._.DS_Store | 120 | 2018-04-15 |
| mnist\302_simple_classification.py | 2164 | 2018-02-12 |
| __MACOSX\mnist\._302_simple_classification.py | 212 | 2018-02-12 |
| mnist\401_CNN.py | 3737 | 2018-02-12 |
| __MACOSX\mnist\._401_CNN.py | 212 | 2018-02-12 |
| mnist\eval.py | 2289 | 2018-04-15 |
| mnist\mnist | 0 | 2018-04-22 |
| mnist\mnist\t10k-images-idx3-ubyte.gz | 1648877 | 2018-04-22 |
| mnist\mnist\t10k-labels-idx1-ubyte.gz | 4542 | 2018-04-22 |
| mnist\mnist\train-images-idx3-ubyte.gz | 9912422 | 2018-04-22 |
| mnist\mnist\train-labels-idx1-ubyte.gz | 28881 | 2018-04-22 |
| mnist\MNIST_data | 0 | 2018-04-07 |
| mnist\MNIST_data\t10k-images-idx3-ubyte.gz | 1648877 | 2018-04-07 |
| mnist\MNIST_data\t10k-labels-idx1-ubyte.gz | 4542 | 2018-04-07 |
| mnist\MNIST_data\train-images-idx3-ubyte.gz | 9912422 | 2018-04-07 |
| mnist\MNIST_data\train-labels-idx1-ubyte.gz | 28881 | 2018-04-07 |
| mnist\mnist_eval.py | 1658 | 2018-04-09 |
| mnist\mnist_inference.py | 949 | 2017-03-04 |
| __MACOSX\mnist\._mnist_inference.py | 222 | 2017-03-04 |
| mnist\mnist_inference.pyc | 1285 | 2018-04-07 |
| mnist\MNIST_model | 0 | 2018-04-15 |
| mnist\MNIST_model\checkpoint | 277 | 2018-04-15 |
| mnist\MNIST_model\model.ckpt-25001.data-00000-of-00001 | 3180084 | 2018-04-15 |
| mnist\MNIST_model\model.ckpt-25001.index | 470 | 2018-04-15 |
| mnist\MNIST_model\model.ckpt-25001.meta | 65160 | 2018-04-15 |
| mnist\MNIST_model\model.ckpt-26001.data-00000-of-00001 | 3180084 | 2018-04-15 |
| mnist\MNIST_model\model.ckpt-26001.index | 470 | 2018-04-15 |
| mnist\MNIST_model\model.ckpt-26001.meta | 65160 | 2018-04-15 |
| mnist\MNIST_model\model.ckpt-27001.data-00000-of-00001 | 3180084 | 2018-04-15 |
| mnist\MNIST_model\model.ckpt-27001.index | 470 | 2018-04-15 |
| mnist\MNIST_model\model.ckpt-27001.meta | 65160 | 2018-04-15 |
| mnist\MNIST_model\model.ckpt-28001.data-00000-of-00001 | 3180084 | 2018-04-15 |
| mnist\MNIST_model\model.ckpt-28001.index | 470 | 2018-04-15 |
| mnist\MNIST_model\model.ckpt-28001.meta | 65160 | 2018-04-15 |
| mnist\MNIST_model\model.ckpt-29001.data-00000-of-00001 | 3180084 | 2018-04-15 |
| mnist\MNIST_model\model.ckpt-29001.index | 470 | 2018-04-15 |
| mnist\MNIST_model\model.ckpt-29001.meta | 65160 | 2018-04-15 |
| mnist\mnist_train.py | 2351 | 2018-04-08 |
| __MACOSX\mnist\._mnist_train.py | 222 | 2018-04-08 |
| mnist\mnist_train.pyc | 2792 | 2018-04-08 |
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