我不太了解编程不过试着问了chatgpt。
这是chatgpt的代碼纪录:
from chatterbot import ChatBot
from chatterbot.trainers import ListTrainer
from chatterbot.conversation import Statement
import numpy as np
# Define a function to calculate the dot product attention score
def dot_product_attention(query, values):
# Calculate the dot product between the query and each value
scores = np.dot(query, values.T)
# Apply softmax to the scores to get the attention weights
attention_weights = np.softmax(scores)
# Calculate the weighted average of the values using the attention weights
weighted_values = np.dot(attention_weights, values)
return weighted_values
def multihead_attention(query, keys, values, num_heads):
# Split the query, keys, and values into multiple heads
query_heads = np.array_split(query, num_heads, axis=-1)
key_heads = np.array_split(keys, num_heads, axis=-1)
value_heads = np.array_split(values, num_heads, axis=-1)
# Concatenate the heads along the last axis
query_heads = np.concatenate(query_heads, axis=-1)
key_heads = np.concatenate(key_heads, axis=-1)
value_heads = np.concatenate(value_heads, axis=-1)
# Calculate the dot product between the query and each key head
scores = np.dot(query_heads, key_heads.T)
# Scale the scores by the square root of the number of features
scores = scores / np.sqrt(query.shape[-1])
# Apply softmax to the scores to get the attention weights
attention_weights = np.softmax(scores, axis=-1)
# Calculate the weighted average of the value heads using the attention weights
weighted_values = np.dot(attention_weights, value_heads)
# Concatenate the weighted value heads along the last axis
weighted_values = np.concatenate(np.split(weighted_values, num_heads, axis=-1), axis=-1)
return weighted_values
# Read the conversation corpus file
with open('db.txt', 'r', encoding='utf-8') as f:
corpus = f.readlines()
# Create a ChatBot instance and train it
my_bot = ChatBot(input('请输入ChatBot名称：'))
trainer = ListTrainer(my_bot)
print('开始训练！')
# Train the ChatBot instance with each conversation in the corpus
for conversation in corpus:
# Split the conversation into two statements
statements = conversation.strip().split('\t')
if len(statements) == 2:
# Create a Statement object for each statement
statement_1 = Statement(text=statements[0])
statement_2 = Statement(text=statements[1])
# Train the ChatBot with the two statements as a pair
trainer.train([statement_1, statement_2])
else:
# If the conversation is not in the expected format, skip it
continue
print('训练完毕！')
我不太了解编程不过试着问了chatgpt。
这是chatgpt的代碼纪录: