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Creating custom architectures using
Model Training with Baseline Architecture
Before diving into custom architectures, it’s important to start with a baseline model. Here, we’ll use a simple CNN to train on the MNIST dataset.
python
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import torch
from torch import nn, optim
import torchvision
from torchvision import datasets, transforms
import torch.nn.functional as F
from torch.utils.data import DataLoader
Load dataset
train_dataset = datasets.MNIST(root=./data, train=True, download=True, transform=transforms.ToTensor())
DataLoader setup
batch_size = 64
num_workers = 2
train_dataloader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True, num_workers=num_workers)
Define simple CNN model
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
self.conv1 = nn.Conv2d(1, 10, kernel_size=5)
self.conv2 = nn.Conv2d(10, 20, kernel_size=5)
self.conv2_drop = nn.Dropout2d()
self.fc1 = nn.Linear(320, 50)
self.fc2 = nn.Linear(50, 10)
self.softmax = nn.Softmax(dim=-1)
def forward(self, x):
x = F.relu(F.max_pool2d(self.conv1(x), 2))
x = F.relu(F.max_pool2d(self.conv2_drop(self.conv2(x)), 2))
x = x.view(-1, 320)
x = F.relu(self.fc1(x))
x = F.dropout(x, training=self.training)
x = self.fc2(x)
output = self.softmax(x)
return output
Training setup
model = Net()
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters(), lr=0.01)
Training loop
epochs = 10
for epoch in range(epochs):
running_loss = 0.0
for i, data in enumerate(train_dataloader, 0):
inputs, labels = data[0], data[1]
optimizer.zero_grad()
outputs = model(inputs)
loss = criterion(outputs, labels)
loss.backward()
optimizer.step()
running_loss += loss.item()
if i % 20 == 19:
print(f’Epoch [{epoch + 1}/{epochs}], Step [{i + 1}/{len(train_dataloader)}], Loss: {running_loss / 20:.3f}’)
running_loss = 0.0
Save model weights
torch.save({state_dict: model.state_dict()}, model.pth)
With the model trained, we can move on to creating a custom architecture that HuggingFace can use for image classification.
Creating Custom Architectures for HuggingFace
To create a model that is compatible with HuggingFace, we need to define three key components: a configuration file, a model definition, and a custom pipeline. This will ensure that our custom model integrates seamlessly with the HuggingFace ecosystem.
1. The Configuration File
The configuration file contains model parameters and is essential for defining the architecture. It extends PretrainedConfig from HuggingFace and allows us to set hyperparameters for layers such as conv1 and conv2 in our case.
python
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from transformers import PretrainedConfig
class MnistConfig(PretrainedConfig):
model_type = MobileNetV1
def __init__(self, conv1=10, conv2=20, kwargs):
self.conv1 = conv1
self.conv2 = conv2
super().__init__(kwargs)
2. Defining the Model
The model itself needs to inherit from PreTrainedModel. The configuration parameters are passed to the model at instantiation, ensuring flexibility in changing layer configurations.
python
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from transformers import PreTrainedModel
import torch.nn.functional as F
from torch import nn
class MnistModel(PreTrainedModel):
config_class = MnistConfig
def __init__(self, config):
super().__init__(config)
self.conv1 = nn.Conv2d(1, config.conv1, kernel_size=5)
self.conv2 = nn.Conv2d(config.conv1, config.conv2, kernel_size=5)
self.conv2_drop = nn.Dropout2d()
self.fc1 = nn.Linear(320, 50)
self.fc2 = nn.Linear(50, 10)
self.softmax = nn.Softmax(dim=-1)
self.criterion = nn.CrossEntropyLoss()
def forward(self, x, labels=None):
x = F.relu(F.max_pool2d(self.conv1(x), 2))
x = F.relu(F.max_pool2d(self.conv2_drop(self.conv2(x)), 2))
x = x.view(-1, 320)
x = F.relu(self.fc1(x))
x = F.dropout(x, training=self.training)
x = self.fc2(x)
logits = self.softmax(x)
if labels is not None:
loss = self.criterion(logits, labels)
return {loss: loss, logits: logits}
return logits
3. Push Model to HuggingFace Hub
Once the model and configuration are defined, we push both to HuggingFace’s repository for use across different tasks.
python
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from huggingface_hub import notebook_login
from transformers import AutoModelForImageClassification
notebook_login()
conf = MnistConfig()
HF_Model = MnistModel(conf)
weights = torch.load(model.pth)
HF_Model.load_state_dict(weights[state_dict])
conf.push_to_hub(MyRepo)
HF_Model.push_to_hub(MyRepo)
This will make the model and its configuration available on the HuggingFace Hub for later use.
What Undercode Says:
The journey from baseline to custom architecture in HuggingFace is a great example of how flexible and powerful the ecosystem is for fine-tuning and deploying models. By starting with a simple CNN, we can gradually build towards a more specialized architecture for tasks like image classification.
The beauty of using HuggingFace lies in the ability to decouple the configuration, model, and pipeline. This modular approach makes the entire system more scalable and reusable. With HuggingFace’s infrastructure, users can easily upload their trained models to the Hub, where they can be accessed and used by others or deployed into production.
One important takeaway here is the critical role of the configuration file. It allows users to tweak key hyperparameters without needing to modify the model’s code itself. This is a huge benefit for experimentation, as it enables users to explore different configurations rapidly.
Additionally, the custom pipeline feature is an excellent way to automate the process of loading data, passing it through the model, and handling the outputs. This kind of automation reduces the need for repetitive code and makes model inference much cleaner and more efficient.
Fact Checker Results
✅ Model Architecture: The CNN model architecture used here is a standard and simple approach for tasks like MNIST classification.
✅ Pipeline Integration: HuggingFace’s Pipeline class is designed to integrate smoothly with custom models, making the deployment process more streamlined.
✅ Deployment to Hub: The process of pushing models to the HuggingFace Hub is correct, with proper usage of the push_to_hub method.
Prediction 📊
The future of custom architectures in the HuggingFace ecosystem looks incredibly promising. As more models are uploaded to the Hub, the use of pre-built and fine-tuned models will become even more accessible. Additionally, the automation of model inference through custom pipelines will make it easier for developers to integrate AI models into their applications without extensive setup.
We expect HuggingFace’s offerings to evolve further, making it even more seamless to build, deploy, and share custom models at scale. As the community grows, so will the number of pre-trained models available for use, enriching the ecosystem for researchers and developers alike.
🕵️📝✔️Let’s dive deep and fact‑check.
References:
Reported By: huggingface.co
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