Metadata-Version: 2.1
Name: torchy
Version: 0.0.1
Summary: A pytorch wrapper that makes .fit() possible!
Project-URL: repository, https://github.com/ashimdahal/easy-torch.git
Project-URL: changelog, https://github.com/me/spam/blob/master/CHANGELOG.md
Author: Ashim Dahal
Author-email: codeashim@gmail.com
License: MIT License
        
        Copyright (c) 2023 Ashim  Dahal
        
        Permission is hereby granted, free of charge, to any person obtaining a copy
        of this software and associated documentation files (the "Software"), to deal
        in the Software without restriction, including without limitation the rights
        to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
        copies of the Software, and to permit persons to whom the Software is
        furnished to do so, subject to the following conditions:
        
        The above copyright notice and this permission notice shall be included in all
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        THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
        IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
        FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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        SOFTWARE.
License-File: LICENSE.txt
Keywords: Deep Learning,PyTorch,Tensorflow,Torch
Classifier: Development Status :: 2 - Pre-Alpha
Classifier: Intended Audience :: Developers
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Requires-Python: >=3.8
Requires-Dist: torch
Description-Content-Type: text/markdown

# Welcome to torchy
torchy is a work in progress and will be going through constant changes everyday.
## Introduction
easy-torch is a PyTorch wrapper that has some additional benefits to using plain pytorch. With easy-torch you have everything in pytorch plus
some additional features found on other libraries. 
## Installation using pip
## Additional Functionality
```python
import torchy.nn as nn
import torch
from torchy.utils.data import TensorDataset, DataLoader, random_split, DeviceDL

x = torch.tensor([[12.],[13],[15]])
y = torch.tensor([[2.],[3],[4]])
train = TensorDataset(x,y)
class Model(nn.Module):
    def __init__(self):
        super().__init__()
        self.linear = nn.Linear(1, 1)

    def forward(self,x):
        return self.linear(x)

loss_fn = nn.functional.mse_loss
model = Model()
opt = torch.optim.SGD(model.parameters(),lr=0.001,momentum=.9)
model = model.fit(train, loss_fn,opt,20,valid_pct = 20,batch_size=2)
```
You can also use a dataloader instead of a dataset. If you're using a dataloader be sure to pass additional argument "valid_dataloader" otherwise 
the no model validation would be carried out.

```python
dl = DataLoader(train,batch_size = 2)
model = model.fit(dl, loss_fn,opt,20)

```

## To-do
more documentation and all arguments and their function table comming soon.