hhy5277 / learning-notes-1

💡 Repo of learning notes in DRL and DL, theory, codes, models and notes maybe.

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
• LogisticRegression
• RegressionTree
• Support Vector Machine
• NeuralNetwork

Natural Language Processing

• Word2Vec
• GloVe

Deep Reinforcement Learning

• PolicyGradient
• DQN
• DoubleDQN
• PPO
• A3C / DPPO

Deep Learning Engineering

• TensorFlow Serving

Docker

• Docker Notes

Codes

• Artifical Neuron Network (ANN)

Requirements

• numpy
• scipy
• sklearn
• matplotlib
• tensorflow==1.8

Instructions for codes

Artifical Neuron Network (ANN)

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)