【机器学习】机器学习与大模型在人工智能领域的融合应用与性能优化新探索

import openai

openai.api_key = 'YOUR_API_KEY'

def generate_text(prompt):
    response = openai.Completion.create(
        engine="davinci-codex",
        prompt=prompt,
        max_tokens=100
    )
    return response.choices[0].text.strip()

prompt = "Explain the significance of machine learning in modern technology."
generated_text = generate_text(prompt)
print(generated_text)

from transformers import BertTokenizer, BertForSequenceClassification
from transformers import TextClassificationPipeline

tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
model = BertForSequenceClassification.from_pretrained('bert-base-uncased')

pipeline = TextClassificationPipeline(model=model, tokenizer=tokenizer)
result = pipeline("I love this product, it's amazing!")

print(result)

from transformers import MarianMTModel, MarianTokenizer

model_name = 'Helsinki-NLP/opus-mt-en-de'
tokenizer = MarianTokenizer.from_pretrained(model_name)
model = MarianMTModel.from_pretrained(model_name)

def translate(text):
    inputs = tokenizer(text, return_tensors="pt", padding=True)
    translated = model.generate(**inputs)
    translated_text = tokenizer.batch_decode(translated, skip_special_tokens=True)
    return translated_text[0]

text = "Machine learning is transforming the way we approach problems in various fields."
translated_text = translate(text)
print(translated_text)

import torch
import torchvision.models as models
import torchvision.transforms as transforms
from PIL import Image

model = models.resnet50(pretrained=True)
model.eval()

def predict_image(image_path):
    img = Image.open(image_path)
    preprocess = transforms.Compose([
        transforms.Resize(256),
        transforms.CenterCrop(224),
        transforms.ToTensor(),
        transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
    ])
    img_tensor = preprocess(img)
    img_tensor = img_tensor.unsqueeze(0)

    with torch.no_grad():
        output = model(img_tensor)
    
    _, predicted = torch.max(output, 1)
    return predicted.item()

image_path = 'path/to/your/image.jpg'
predicted_class = predict_image(image_path)
print(predicted_class)

from fastai.vision.all import *

def load_learner():
    learner = load_learner('path/to/your/exported/learner.pkl')
    return learner

def predict_image(image_path):
    learner = load_learner()
    img = PILImage.create(image_path)
    pred, pred_idx, probs = learner.predict(img)
    return pred, probs[pred_idx]

image_path = 'path/to/your/image.jpg'
predicted_class, probability = predict_image(image_path)
print(f'Predicted class: {predicted_class}, Probability: {probability}')

import speech_recognition as sr

def recognize_speech():
    recognizer = sr.Recognizer()
    with sr.Microphone() as source:
        print("Say something:")
        audio = recognizer.listen(source)

    try:
        text = recognizer.recognize_google(audio)
        print("You said: " + text)
    except sr.UnknownValueError:
        print("Google Speech Recognition could not understand audio")
    except sr.RequestError as e:
        print("Could not request results from Google Speech Recognition service; {0}".format(e))

recognize_speech()

from transformers import Wav2Vec2ForCTC, Wav2Vec2Tokenizer
import torch
import librosa

tokenizer = Wav2Vec2Tokenizer.from_pretrained("facebook/wav2vec2-base-960h")
model = Wav2Vec2ForCTC.from_pretrained("facebook/wav2vec2-base-960h")

def speech_to_text(audio_path):
    speech, _ = librosa.load(audio_path, sr=16000)
    input_values = tokenizer(speech, return_tensors="pt").input_values
    logits = model(input_values).logits
    predicted_ids = torch.argmax(logits, dim=-1)
    transcription = tokenizer.batch_decode(predicted_ids)[0]
    return transcription

audio_path = 'path/to/your/audio.wav'
transcription = speech_to_text(audio_path)
print(transcription)

import torch
import torch.nn.utils.prune as prune

model = models.resnet50(pretrained=True)
parameters_to_prune = [(module, 'weight') for module in model.modules() if isinstance(module, torch.nn.Conv2d)]

for module, param in parameters_to_prune:
    prune.l1_unstructured(module, name=param, amount=0.2)

# Remove pruning reparameterization to enable inference
for module, param in parameters_to_prune:
    prune.remove(module, param)

model = models.resnet50(pretrained=True)
model.eval()

quantized_model = torch.quantization.quantize_dynamic(
    model, {torch.nn.Linear}, dtype=torch.qint8
)

# Test the quantized model
input_tensor = torch.randn(1, 3, 224, 224)
output = quantized_model(input_tensor)
print(output)

import torch.nn.functional as F

# Define teacher and student models
teacher_model = models.resnet50(pretrained=True)
student_model = models.resnet18()

# Define a knowledge distillation loss function
def distillation_loss(student_outputs, teacher_outputs, labels, alpha=0.5, temperature=2.0):
    loss_ce = F.cross_entropy(student_outputs, labels)
    loss_kd = F.kl_div(
        F.log_softmax(student_outputs / temperature, dim=1),
        F.softmax(teacher_outputs / temperature, dim=1),
        reduction='batchmean'
    ) * (temperature ** 2)
    return alpha * loss_ce + (1 - alpha) * loss_kd

# Example training loop
optimizer = torch.optim.SGD(student_model.parameters(), lr=0.01, momentum=0.9)

for epoch in range(10):
    for inputs, labels in dataloader:
        optimizer.zero_grad()
        student_outputs = student_model(inputs)
        with torch.no_grad():
            teacher_outputs = teacher_model(inputs)
        loss = distillation_loss(student_outputs, teacher_outputs, labels)
        loss.backward()
        optimizer.step()

import torch
import torch.distributed as dist
import torch.nn as nn
import torch.optim as optim
from torch.nn.parallel import DistributedDataParallel as DDP

dist.init_process_group(backend='nccl')
model = models.resnet50().cuda()
ddp_model = DDP(model)
optimizer = optim.SGD(ddp_model.parameters(), lr=0.01)

for epoch in range(10):
    for inputs, labels in dataloader:
        inputs, labels = inputs.cuda(), labels.cuda()
        optimizer.zero_grad()
        outputs = ddp_model(inputs)
        loss = F.cross_entropy(outputs, labels)
        loss.backward()
        optimizer.step()

import torch
import torch.nn as nn
import torch.optim as optim
from torch.nn.parallel import DistributedDataParallel as DDP

class ModelParallelResNet50(nn.Module):
    def __init__(self):
        super(ModelParallelResNet50, self).__init__()
        self.model = nn.Sequential(
            models.resnet50().layer1.to('cuda:0'),
            models.resnet50().layer2.to('cuda:1'),
            models.resnet50().layer3.to('cuda:2'),
            models.resnet50().layer4.to('cuda:3')
        )

    def forward(self, x):
        for layer in self.model:
            x = layer(x)
        return x

model = ModelParallelResNet50()
optimizer = optim.SGD(model.parameters(), lr=0.01)

for epoch in range(10):
    for inputs, labels in dataloader:
        inputs, labels = inputs.to('cuda:0'), labels.to('cuda:3')
        optimizer.zero_grad()
        outputs = model(inputs)
        loss = F.cross_entropy(outputs, labels)
        loss.backward()
        optimizer.step()

import torch
import torch.distributed as dist
import torch.nn as nn
import torch.optim as optim
from torch.multiprocessing import Process

def train(rank, world_size):
    dist.init_process_group(backend='gloo', rank=rank, world_size=world_size)
    model = models.resnet50().to(rank)
    ddp_model = nn.parallel.DistributedDataParallel(model, device_ids=[rank])
    optimizer = optim.SGD(ddp_model.parameters(), lr=0.01)

    for epoch in range(10):
        for inputs, labels in dataloader:
            inputs, labels = inputs.to(rank), labels.to(rank)
            optimizer.zero_grad()
            outputs = ddp_model(inputs)
            loss = F.cross_entropy(outputs, labels)
            loss.backward()
            optimizer.step()

def main():
    world_size = 4
    processes = []
    for rank in range(world_size):
        p = Process(target=train, args=(rank, world_size))
        p.start()
        processes.append(p)

    for p in processes:
        p.join()

if __name__ == "__main__":
    main()

import torch

class DynamicPruningModel(nn.Module):
    def __init__(self, model):
        super(DynamicPruningModel, self).__init__()
        self.model = model

    def forward(self, x):
        # Example of dynamic pruning logic
        if x.size(0) > 1:
            self.model.layer4[0].conv1 = nn.Identity()
        return self.model(x)

model = models.resnet50(pretrained=True)
pruned_model = DynamicPruningModel(model)

input_tensor = torch.randn(1, 3, 224, 224)
output = pruned_model(input_tensor)
print(output)

import torch
import torch.nn as nn

class CachedModel(nn.Module):
    def __init__(self, model):
        super(CachedModel, self).__init__()
        self.model = model
        self.cache = {}

    def forward(self, x):
        if x in self.cache:
            return self.cache[x]
        output = self.model(x)
        self

.cache[x] = output
        return output

model = models.resnet50(pretrained=True)
cached_model = CachedModel(model)

input_tensor = torch.randn(1, 3, 224, 224)
output = cached_model(input_tensor)
print(output)

import torch
import torch.nn as nn

# Check if a GPU is available
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
model = models.resnet50(pretrained=True).to(device)

input_tensor = torch.randn(1, 3, 224, 224).to(device)
output = model(input_tensor)
print(output)