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https://github.com/Ceruleanacg/Learning-Notes
💡 Repo of learning notes in DRL and DL, theory, codes, models and notes maybe.
https://github.com/Ceruleanacg/Learning-Notes
artifical-neuron-network deep-learning deep-reinforcement-learning
Last synced: about 2 months ago
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💡 Repo of learning notes in DRL and DL, theory, codes, models and notes maybe.
- Host: GitHub
- URL: https://github.com/Ceruleanacg/Learning-Notes
- Owner: Ceruleanacg
- License: mit
- Created: 2018-04-28T07:11:22.000Z (over 6 years ago)
- Default Branch: master
- Last Pushed: 2018-12-09T10:07:11.000Z (almost 6 years ago)
- Last Synced: 2024-06-04T01:45:34.148Z (4 months ago)
- Topics: artifical-neuron-network, deep-learning, deep-reinforcement-learning
- Language: Jupyter Notebook
- Homepage:
- Size: 1.87 MB
- Stars: 98
- Watchers: 5
- Forks: 18
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
[](/LICENSE)
[](https://www.tensorflow.org/)
[]()
# Learning Notes of DRL & DL
A repo of Learning notes of DRL & DL, theory, codes, models and notes maybe.
# Content
## Notes
### Deep Learning Basic
- [LinearRegression](/note/LinearRegression.ipynb)
- [LogisticRegression](/note/LogisticRegression.ipynb)
- [RegressionTree](/note/RegressionTree.ipynb)
- [Support Vector Machine](/note/SVM.ipynb)
- [NeuralNetwork](/note/NeuralNetwork.ipynb)
### Natural Language Processing
- [Word2Vec](/note/Word2Vec.ipynb)
- [GloVe](/note/GloVe.ipynb)
### Deep Reinforcement Learning
- [PolicyGradient](/note/PolicyGradient.ipynb)
- [DQN](/note/DQN.ipynb)
- [DoubleDQN](/note/DoubleDQN.ipynb)
- [PPO](/note/PPO.ipynb)
- [A3C / DPPO](/note/A3C.ipynb)
### Deep Learning Engineering
- [TensorFlow Serving](/note/TensorFlowServing.ipynb)
### Docker
- [Docker Notes](/note/Docker.ipynb)
## Codes
- [Artifical Neuron Network (ANN)](/ann/Dense.py)
# Requirements
- numpy
- scipy
- sklearn
- matplotlib
- tensorflow==1.8
# Instructions for codes
### [Artifical Neuron Network (ANN)](/ann/Dense.py)
1. Load your data, for example, iris data set.
```
from sklearn.datasets import load_iris
iris = load_iris()
```
2. Standardize your data.
```
scaler = StandardScaler()
scaler.fit(iris.data)
x_data = scaler.transform(iris.data)
y_data = np.zeros((150, 3))
y_data[np.arange(150), iris.target] = 1
```
3. Initialize activations, which are configurable.
```
activation_funcs = [function.relu] * 1
# activation_funcs = [function.tanh] * 1
# activation_funcs = [function.sigmoid] * 1
activation_funcs.append(function.linear)
```
4. Initialize model, option parameters are configurable.
```
dense = Dense(x_space=4, y_space=3, neuron_count_list=[10], **{
"loss_func": function.softmax_cross_entropy,
"activation_funcs": activation_funcs,
"learning_rate": 0.01,
"enable_logger": True,
"model_name": 'iris',
"batch_size": 30,
'model': 'train'
)
```
5. Train or Restore & Evaluate.
```
dense.train(x_data, y_data)
# dense.restore()
dense.evaluate(x_data, y_data)
```