搜索资源列表
MNIST(tensorflow)
- 基于tensorflow的手写识别，训练后可以识别手写数字-Based on tensorflow handwriting recognition, training can identify handwritten numbers
tensorflow-cnn
- 基于TensorFlow的mnist数据集识别，使用CNN的方法，采用梯度下降学习(MNIST data set recognition based on TensorFlow, using CNN method, using gradient descent learning)
tensorflow_cov_mnist
- 基于tensorflow的mnist数据集卷积神经网络简单代码实现。(MNIST dataset based on tensorflow convolutional neural network simple code implementation)
mnist
- tensorflow demo of mnist by python
CNN_MNIST
- Tensorflow实现基于MNIST数据集的卷积神经网络(Tensorflow implementation of convolutional neural networks based on MNIST data)
mnist.pkl代码原文
- BP算法的实现，其中的手势识别，用python语言，在tensorflow下！(Implementation of BP algorithm)
mnistDemo.py
- 实现tensorflow的mnist实现(The file use tensorflow implement mnist)
Tensorflow：实战Google深度学习框架
- 介绍tensorflow的应用，mnist数据，神经网络的简单例子(Describes the application of tensorflow, MNIST data, a simple example of neural networks)
test
- tensorflow测试 计算mnist识别准确率 以及计算时间(tensorflow test Calculate the MNIST recognition accuracy and calculation time)
dnn
- 用TensorFlow搭建神经网络，识别手写数字(building the neural network by using TensorFlow to identify mnist dataset)
AlexNet
- 使用TensorFlow 实现 AlexNet ，并使用 Mnist 数据集进行训练并测试。(AlexNet is implemented using TensorFlow and trained and tested using the Mnist data set.)
cnn
- 卷积神经网络（CNN），TensorFlow框架下运行，基于MNIST手写体数据集，可直接运行(Convolutional Neural Network (CNN), run under TensorFlow framework, can run directly based on MNIST handwritten dataset)
5.2.2.py
- MNIST数字识别问题 使用验证数据集判断模型结果(tensorflow.examples.tutorials.mnist After 30000 training step(s), test accuracy using average model is 0.9835)
mnist
- 深度学习时间手写数字识别，使用python和tensorflow实现(Handwritten numerals recognition in depth learning time)
SAE
- 使用TensorFlow实现稀疏自编码神经网络，采用数据mnist(Using TensorFlow to realize sparse atuoencoder neural network, using data MNIST)
vae
- 变分自编码结构，用一个mnist数据。。。。。。(Using TensorFlow to realize Variational Auto-Encoder neural network, using data MNIST)
MNIST
- mnist手写体识别，使用tensorflow编写(mnist hand-writing recognition using tensorflow)
LeNet5MNIST
- 使用TensorFlow处理MNIST数据，帮助更好的理解和使用TensorFlow(Using TensorFlow to process MNIST data to help better understand and use TensorFlow)
tensorflow-mnist
- 改进了官方的MNIST进阶demo，准确率提升。(The official MINIST advanced demo is improved and the accuracy is improved.)
分布式tensorflow
- 1.使用distribute.py在分布式tensorflow中进行训练mnist模型 2.使用mnist_test.py进行测试模型，获取输出结果(1. Training MNIST model in distributed tensorflow using distribute.py 2. Use mnist_test.py to test the model and get the output results)