Neural Network

The NeuralNetwork struct provides a basic feedforward neural network implementation.

Initialization

To initialize a new neural network, use the NewNeuralNetwork function:

model := NewNeuralNetwork(inputSize, hiddenLayers, outputSize, activationType, learningRate)

• inputSize: Number of input neurons.
• hiddenLayers: Slice specifying the number of neurons in each hidden layer.
• outputSize: Number of output neurons.
• activationType: Activation function type (e.g., "relu", "sigmoid").
• learningRate: Learning rate for training.

Example Usage

// Start of Selection Refer to nn_try.go for a complete example:

package main

import (
    "fmt"
)

func main() {
    // Initialize the neural network
    nn := NewNeuralNetwork(10, []int{24, 16}, 4, "relu", 0.01)

    // Example input
    input := []float64{0.5, 0.1, -0.3, 0.8, 0.2, 0.9, -0.5, 0.4, 0.7, -0.2}

    // Perform a forward pass
    output := nn.Forward(input)

    // Print the output
    fmt.Println("Neural Network Output:", output)
}

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Recurrent Neural Network (RNN)

The RecurrentNeuralNetwork struct extends the basic neural network with recurrent connections, enabling it to handle sequential data.

Initialization

Use NewRecurrentNeuralNetwork to create an RNN:

rnn := NewRecurrentNeuralNetwork(inputSize, hiddenLayers, outputSize, activationType, learningRate, dropoutRate)

• dropoutRate: Rate for dropout regularization to prevent overfitting.

// Start of Selection

Example Usage

Refer to rnn_try.go for a complete example:

package main

import (
    "fmt"
)

func main() {
    // Initialize the RNN
    rnn := NewRecurrentNeuralNetwork(10, []int{24, 16}, 4, "tanh", 0.01, 0.2)

    // Example input: 5 time steps with 10 features each
    inputs := [][]float64{
        {0.1, 0.2, -0.1, 0.3, 0.0, 0.5, -0.2, 0.1, 0.4, 0.2},
        {0.0, 0.1, 0.3, -0.2, 0.4, 0.1, 0.0, 0.3, 0.2, -0.1},
        {0.2, -0.1, 0.0, 0.1, 0.3, 0.2, 0.4, -0.3, 0.1, 0.0},
        {0.1, 0.0, -0.2, 0.2, 0.1, 0.3, 0.0, 0.2, -0.1, 0.4},
        {0.3, 0.1, 0.2, 0.0, 0.2, 0.1, 0.3, 0.0, 0.2, 0.1},
    }

    // Perform a forward pass
    output := rnn.forward(inputs)

    // Print the output
    fmt.Println("RNN Output:", output)
}

---

Long Short-Term Memory (LSTM) Network

The LongShortTermMemory struct builds upon the RNN by adding gates to better capture long-term dependencies.

Initialization

Create an LSTM network using NewLongShortTermMemory:

lstm := NewLongShortTermMemory(inputSize, hiddenLayers, outputSize, activationType, learningRate, dropoutRate)

// Start of Selection

Example Usage

See lstm_try.go for a practical example:

package main

import (
    "fmt"
)

func main() {
    lstm := NewLongShortTermMemory(10, []int{24, 16}, 4, "relu", 0.01, 0.2)

    // Example input: 5 time steps with 10 features each
    inputs := [][]float64{
        {0.1, 0.2, -0.1, 0.3, 0.0, 0.5, -0.2, 0.1, 0.4, 0.2},
        {0.0, 0.1, 0.3, -0.2, 0.4, 0.1, 0.0, 0.3, 0.2, -0.1},
        {0.2, -0.1, 0.0, 0.1, 0.3, 0.2, 0.4, -0.3, 0.1, 0.0},
        {0.1, 0.0, -0.2, 0.2, 0.1, 0.3, 0.0, 0.2, -0.1, 0.4},
        {0.3, 0.1, 0.2, 0.0, 0.2, 0.1, 0.3, 0.0, 0.2, 0.1},
    }

    // Perform a forward pass
    output := lstm.forward(inputs)

    // Print the output
    fmt.Println("LSTM Output:", output)
}

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Deep Q-Network (DQN) Agent

The DQNAgent struct implements a DQN for reinforcement learning tasks.

Initialization

Initialize the DQN agent with the environment:

agent := NewDQNAgent(env)

• env: An instance of the Environment interface.

Example Usage

Refer to dqn_try.go for an example setup:

package main

import (
    "fmt"
)

func main() {
    env := &SimpleEnv{
        stateSize: 4,
        actionSize: 2,
    }
    agent := NewDQNAgent(env)
    episodes := 100

    for e := 1; e <= episodes; e++ {
        state := env.Reset()
        totalReward := 0.0
        done := false

        for !done {
            action := agent.Act(state, true)
            nextState, reward, done := env.Step(action)
            agent.Remember(state, action, reward, nextState, done)
            agent.Replay(32)
            state = nextState
            totalReward += reward
        }

        fmt.Printf("Episode %d: Total Reward: %.2f, Epsilon: %.4f\n", e, totalReward, agent.epsilon)
    }
}

---

Additional Resources

For more detailed information and advanced usage, please refer to the respective .go files in this repository:

• nn.go
• rnn.go
• lstm.go
• dqn.go

Each file contains comprehensive implementations and additional comments to help you understand and extend the functionality of the neural networks and agents.