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TestModel.py
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TestModel.py
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import torch
import torch.nn as nn
import torchvision.datasets as dsets
import torchvision.transforms as transforms
import random
import matplotlib.pyplot as plt
from MLP import MLP
from torch.utils.data import DataLoader
device = 'cuda' if torch.cuda.is_available() else 'cpu'
batch_size = 100
mnist_test = dsets.MNIST(root='MNIST_data/',
train=False,
transform=transforms.ToTensor(),
download=True)
test_loader = DataLoader(dataset=mnist_test,
batch_size=batch_size,
shuffle=True)
# Model Load
model = torch.load('model.pth')
# Evaluate Model
model.eval()
correct = 0
total = 0
with torch.no_grad() :
for images, labels in test_loader :
images = images.view(-1, 28*28).to(device)
labels = labels.to(device)
outputs = model(images).to(device)
_, predicted = torch.max(outputs.data, 1)
total += labels.size(0)
correct += (predicted == labels).sum().item()
print('accuracy:', 100 * correct / total)
# Model Test with Test Image
data_number = 10
with torch.no_grad() :
sample_images = random.sample(list(mnist_test), data_number)
for images, labels in sample_images :
# Print label
print("Label:", labels)
# Image visualize
plt.imshow(images[0], cmap='gray')
plt.show()
# Predict Number with Model
images = images.view(-1, 28*28).to(device)
labels = labels
outputs = model(images).to(device)
_, predicted = torch.max(outputs.data, 1)
print("Predicted:", predicted.item())
print("")