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# Imports
import torch
import jovian
import torchvision
import torch.nn as nn
import matplotlib.pyplot as plt
import torch.nn.functional as F
import torchvision.transforms as transforms
from torchvision.datasets import FashionMNIST
from torch.utils.data import random_split
from torch.utils.data import DataLoader
from IPython.core.display import display, HTML
display(HTML("<style>.container { width:100% height:120% !important; }</style>"))
# Hyperparmeters
batch_size = 128
learning_rate = 0.001

# Other constants
input_size = 28*28
num_classes = 10
jovian.reset()
jovian.log_hyperparams(batch_size=batch_size, learning_rate=learning_rate)
[jovian] Hyperparams logged.
# Download dataset
dataset = FashionMNIST(root='D:\PyTorch\data', train=True, transform=transforms.ToTensor(), download=True)

# Training validation & test dataset
train_ds, val_ds = random_split(dataset, [50000, 10000])
test_ds = FashionMNIST(root='D:\PyTorch\data', train=False, transform=transforms.ToTensor())