forked from s444420/AL-2020
40 lines
1.2 KiB
Python
40 lines
1.2 KiB
Python
|
import numpy as np
|
||
|
|
import torch
|
||
|
|
import torchvision
|
||
|
|
import matplotlib.pyplot as plt
|
||
|
|
from time import time
|
||
|
|
from torchvision import datasets, transforms
|
||
|
|
from torch import nn, optim
|
||
|
|
|
||
|
|
transform = transforms.Compose([transforms.ToTensor(),
|
||
|
|
transforms.Normalize((0.5,), (0.5,)),
|
||
|
|
])
|
||
|
|
|
||
|
|
trainset = datasets.MNIST('PATH_TO_STORE_TRAINSET', download=True, train=True, transform=transform)
|
||
|
|
valset = datasets.MNIST('PATH_TO_STORE_TESTSET', download=True, train=False, transform=transform)
|
||
|
|
trainloader = torch.utils.data.DataLoader(trainset, batch_size=64, shuffle=True)
|
||
|
|
valloader = torch.utils.data.DataLoader(valset, batch_size=64, shuffle=True)
|
||
|
|
|
||
|
|
dataiter = iter(trainloader)
|
||
|
|
images, labels = dataiter.next()
|
||
|
|
|
||
|
|
print(images.shape)
|
||
|
|
print(labels.shape)
|
||
|
|
|
||
|
|
plt.imshow(images[0].numpy().squeeze(), cmap='gray_r')
|
||
|
|
plt.show()
|
||
|
|
|
||
|
|
# building nn model
|
||
|
|
input_size = 784
|
||
|
|
hidden_sizes = [128, 64]
|
||
|
|
output_size = 10
|
||
|
|
|
||
|
|
model = nn.Sequential(nn.Linear(input_size, hidden_sizes[0]),
|
||
|
|
nn.ReLU(),
|
||
|
|
nn.Linear(hidden_sizes[0], hidden_sizes[1]),
|
||
|
|
nn.ReLU(),
|
||
|
|
nn.Linear(hidden_sizes[1], output_size),
|
||
|
|
nn.LogSoftmax(dim=1))
|
||
|
|
print(model)
|
||
|
|
|