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MNIST_data
MNIST_data
 
 
Overfitting
Overfitting
 
 
Tensorflow例程
Tensorflow例程
 
 
data
data
 
 
logs
logs
 
 
my_net
my_net
 
 
1.1-tf求解线性回归.py
1.1-tf求解线性回归.py
 
 
1.2-tf-Session会话控制.py
1.2-tf-Session会话控制.py
 
 
1.3-tf变量Variable.py
1.3-tf变量Variable.py
 
 
1.4-tf-Placeholder传入值.py
1.4-tf-Placeholder传入值.py
 
 
2.1-tf添加层add_layer.py
2.1-tf添加层add_layer.py
 
 
2.2-tf建立神经网络.py
2.2-tf建立神经网络.py
 
 
2.3-tf结果可视化.py
2.3-tf结果可视化.py
 
 
3.1-tf-Tensorboard可视化好助手.py
3.1-tf-Tensorboard可视化好助手.py
 
 
3.2-tf-Tensorboard可视化好助手.py
3.2-tf-Tensorboard可视化好助手.py
 
 
4.1-tf中的classification分类学习.py
4.1-tf中的classification分类学习.py
 
 
4.2-tf过拟合Overfitting.py
4.2-tf过拟合Overfitting.py
 
 
4.3-tf-Dropout解决overfitting.py
4.3-tf-Dropout解决overfitting.py
 
 
4.4-tf-卷积神经网络CNN.py
4.4-tf-卷积神经网络CNN.py
 
 
4.5-tf-Saver提取保存.py
4.5-tf-Saver提取保存.py
 
 
4.6-tf-RNN-LSTM回归例子.py
4.6-tf-RNN-LSTM回归例子.py
 
 
4.7-tf中的变量作用域.py
4.7-tf中的变量作用域.py
 
 
4.8-tf-使用LSTM结构的RNN前向传播.py
4.8-tf-使用LSTM结构的RNN前向传播.py
 
 
4.9-tf-基本RNN前向传播过程.py
4.9-tf-基本RNN前向传播过程.py
 
 
5.0-tf-深层循环神经网络实现.py
5.0-tf-深层循环神经网络实现.py
 
 
5.1-tf-RNN中的dropout.py
5.1-tf-RNN中的dropout.py
 
 
5.5-tf-中的命令行参数.py
5.5-tf-中的命令行参数.py
 
 
MNIST_demo.py
MNIST_demo.py
 
 
MNIST_demo1.py
MNIST_demo1.py
 
 
MNIST_demo2.py
MNIST_demo2.py
 
 
README.md
README.md
 
 
conv2d_demo1.py
conv2d_demo1.py
 
 
conv2d_demo2.py
conv2d_demo2.py
 
 
demo1.py
demo1.py
 
 
demo2.py
demo2.py
 
 
demo3.py
demo3.py
 
 
demo4.py
demo4.py
 
 
demo5.py
demo5.py
 
 
demo6.py
demo6.py
 
 
demo7.py
demo7.py
 
 
tensorflow_demo.py
tensorflow_demo.py
 
 
variable_demo.py
variable_demo.py
 
 
已经dropout.png
已经dropout.png
 
 
没有dropout.png
没有dropout.png
 
 

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tensorflow_tutorials

神经网络解决分类问题的步骤: 1.提取问题中实体的特征向量作为神经网络的输入 2.定义神经网络的结构，并定义如何从神经网络的输入得到输出 3.通过训练数据来调整神经网络的参数（即训练神经网络） 4.使用训练好的神经网络来预测未知数据

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