개념글 모음

어제 오후 4시경 GPT가 터졌었음.. 한 1주일 전에도 이랬는데 한번 또 터져서 샘 알트먼 욕했는데, 오늘 갑자기 코드 해석 문제가 있어서 물어봄. 근데 내가 알던 GPT4가 아님. 20$로 클황으로 옮기려 했던 내자신이 부끄러워짐

우선 생성 속도가 진짜 빨라짐 -> 1000개가량 코드 해석 후 변환(.ts -> .py)하는 작업 수행시 한 10-15초 정도 걸린듯.

초당 토큰은 최소 50t 정도로 보임.

또 항상 고질적으로 지적되는 delve(이게 ... 기초 알려줘 할때 항상 뜸)가 전혀 안나옴.

그다음에 GPT 계열이 질문 답할때 'Sure! let's dive(delve) into ..." 로 의미없이 질질 끌면서 답을 해서 정작 '코드짜줘' 하면 절반도 못 생성하거나 아예 기능이 삭제된채로 나오는 문제점이 있었는데, 이것도 저렇게 의미없는 말을 싹 줄이고 그냥 담백하게 바로 들어가서 편안함.

일단 많이 써보진 않았고, 이놈들 한 새벽 2시쯤까지는 똑같았는데 갑자기 바뀐걸 보니 진짜 뭔가임..

몰래몰래 업글해주다니 고맙다 알트먼형 욕해서 미안해

글고 혹시 궁금한거나 물어보고 싶은거 있으면 댓글로 달아주셈.. 테스트를 잘하는 편은 아니라서

테스트 1: 리스의 하누라이 메모리 타입스크립트를 파이썬으로 변환하기

프롬프트는 그냥 코드 때려박고 convert this typescript code to python 한줄만 작성함

결과: 

The TypeScript code provided is a function that manages memory for a chat system using a vector search engine and token count management. It processes chat history, calculates similarity scores for recent messages, and adjusts the context to fit within a maximum token limit. Here's a breakdown of the code:

  1. Imports: The code imports several modules and types, including an error alert function, a memory processor (HypaProcesser), a tokenizer, and a database.

  2. Function Signature: The hanuraiMemory function takes an array of chats and an argument object with the current token count, maximum context token count, and a tokenizer.

  3. Variable Initialization: The function initializes several variables, including a HypaProcesser instance, an array for additional texts, and a query index for recent chat queries.

  4. Chat Processing: The function loops through recent chats, processes them, and adds them to the memory processor.

  5. Similarity Search: The function calculates similarity scores for recent chat messages and aggregates them.

  6. Token Management: The function adjusts the token count by removing older chat messages until the current token count fits within the maximum context token count.

  7. Result Assembly: The function assembles result texts based on similarity scores and adds a system memory message to the chat history.

  8. Return: The function returns the updated token count and chat history.(이밑 코드 생략)


스스로 코드 설명까지 해주고 전체적으로 파이썬이랑 일치하는거 같음




테스트2: 코딩 설명해주기

프롬(영어): XOR 게이트를 3개의 레이어(입출력 포함)으로 구현하는 법을 각각 4개의 단계(앞에 자기 스스로 만듬)로 쪼개서 설명해줘

결과:

Sure, let’s set up a simple neural network in Python to solve the XOR problem. We will implement each of the four steps you mentioned.


### Step 1: Initializing Weights and Biases


We start by setting up the architecture of the neural network and initializing the weights and biases. We'll use NumPy for this purpose since it provides efficient array operations.


```python

import numpy as np


# XOR inputs and outputs

X = np.array([[0, 0], [0, 1], [1, 0], [1, 1]])

y = np.array([[0], [1], [1], [0]])


# Initialize weights and biases

input_size = 2

hidden_size = 2

output_size = 1


np.random.seed(42)  # For reproducibility

W1 = np.random.uniform(size=(input_size, hidden_size))

b1 = np.random.uniform(size=(1, hidden_size))

W2 = np.random.uniform(size=(hidden_size, output_size))

b2 = np.random.uniform(size=(1, output_size))

```


### Step 2: Forward Propagation


Next, we compute the outputs of the network given the inputs. The outputs of each layer are calculated using matrix multiplication followed by applying an activation function.


```python

def sigmoid(x):

    return 1 / (1 + np.exp(-x))


def forward(X, W1, b1, W2, b2):

    # Calculate hidden layer activation

    hidden_input = np.dot(X, W1) + b1

    hidden_output = sigmoid(hidden_input)

    

    # Calculate output layer activation

    final_input = np.dot(hidden_output, W2) + b2

    final_output = sigmoid(final_input)

    

    return hidden_output, final_output


hidden_output, final_output = forward(X, W1, b1, W2, b2)

```


### Step 3: Loss Calculation


To measure the error, we can use binary cross-entropy, which is suitable for binary classification problems like XOR.


```python

def binary_cross_entropy(y_true, y_pred):

    return -np.mean(y_true * np.log(y_pred) + (1 - y_true) * np.log(1 - y_pred))


loss = binary_cross_entropy(y, final_output)

```


### Step 4: Backpropagation and Optimization


We now calculate the gradients using backpropagation and adjust the weights and biases accordingly. We use the chain rule to propagate errors backward through the network.


```python

def backward(X, y, hidden_output, final_output, W1, b1, W2, b2, learning_rate):

    # Output layer error

    output_error = final_output - y

    output_delta = output_error * final_output * (1 - final_output)

    

    # Hidden layer error

    hidden_error = output_delta.dot(W2.T)

    hidden_delta = hidden_error * hidden_output * (1 - hidden_output)

    

    # Update weights and biases

    W2 -= hidden_output.T.dot(output_delta) * learning_rate

    b2 -= np.sum(output_delta, axis=0, keepdims=True) * learning_rate

    W1 -= X.T.dot(hidden_delta) * learning_rate

    b1 -= np.sum(hidden_delta, axis=0, keepdims=True) * learning_rate


    return W1, b1, W2, b2


# Hyperparameters

learning_rate = 0.1

epochs = 10000


for _ in range(epochs):

    hidden_output, final_output = forward(X, W1, b1, W2, b2)

    W1, b1, W2, b2 = backward(X, y, hidden_output, final_output, W1, b1, W2, b2, learning_rate)


# After training, the final output should approximate the expected results

hidden_output, final_output = forward(X, W1, b1, W2, b2)

print("Final outputs after training:\n", final_output)

```


### Summary


In this example, you’ve seen how to implement a neural network for the XOR problem using the following steps:


1. **Initialize Weights and Biases**: We set up the network architecture and initialized the weights and biases.

2. **Forward Propagation**: We calculated the hidden and final outputs using matrix multiplication and a sigmoid activation function.

3. **Loss Calculation**: We used binary cross-entropy to calculate the error.

4. **Backpropagation and Optimization**: We computed the gradients and adjusted the weights and biases using gradient descent.


With this foundation in place, you can now explore more complex networks and problems. How comfortable do you feel with this implementation, and are there any parts you'd like to delve into further or clarify?

완전히 delve 문제가 해결된건 아닌데, 대충 봤을때 앞에서 delve 나오는 짜증나는 스타트는 아님

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