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import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt

import torch
import torchvision
import torch.nn as nn
import torch.nn.functional as F
from torchvision import datasets, transforms
import mlflow
import mlflow.pytorch
class Params(object):
    def __init__(self, batch_size, epochs, seed, interval):
        self.batch_size = batch_size
        self.epochs = epochs
        self.seed = seed
        self.interval = interval
args = Params(256, 4, 0, 20)
C:\ProgramData\Anaconda3\envs\torchEnv\lib\site-packages\ipykernel\ipkernel.py:287: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above. and should_run_async(code)
transform = transforms.Compose([
    transforms.ToTensor(),
    transforms.Normalize((0.1307,), (0.3081, ))
])

trainset = datasets.MNIST(root='./', train=True, download=True, transform=transform)
testset = datasets.MNIST(root='./', train=False, download=True, transform=transform)

trainloader = torch.utils.data.DataLoader(trainset, batch_size=args.batch_size, shuffle=True)
testloader = torch.utils.data.DataLoader(testset, batch_size=args.batch_size, shuffle=False)
class Model(nn.Module):
    def __init__(self, nh=32):
        super(Model, self).__init__()
        self.classifier = nn.Sequential(
            nn.Linear(784, nh),
            nn.ReLU(),
            nn.Linear(nh, 10))
            
    def forward(self, x):
        x = x.view(x.size(0), -1)
        return self.classifier(x)