forked from s444420/AL-2020
108 lines
3.2 KiB
Python
108 lines
3.2 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
|
|
import cv2
|
|
|
|
# IMG transform
|
|
transform = transforms.Compose([transforms.ToTensor(),
|
|
transforms.Normalize((0.5,), (0.5,)),
|
|
])
|
|
|
|
# dataset download
|
|
train_set = datasets.MNIST('PATH_TO_STORE_TRAINSET', download=True, train=True, transform=transform)
|
|
val_set = datasets.MNIST('PATH_TO_STORE_TESTSET', download=True, train=False, transform=transform)
|
|
train_loader = torch.utils.data.DataLoader(train_set, batch_size=64, shuffle=True)
|
|
val_loader = torch.utils.data.DataLoader(val_set, batch_size=64, shuffle=True)
|
|
print(train_set[0])
|
|
|
|
# building nn model
|
|
input_size = 784 # = 28*28
|
|
hidden_sizes = [128, 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], hidden_sizes[2]),
|
|
nn.ReLU(),
|
|
nn.Linear(hidden_sizes[2], output_size),
|
|
nn.LogSoftmax(dim=-1))
|
|
# print(model)
|
|
|
|
criterion = nn.NLLLoss()
|
|
images, labels = next(iter(train_loader))
|
|
images = images.view(images.shape[0], -1)
|
|
|
|
logps = model(images) # log probabilities
|
|
loss = criterion(logps, labels) # calculate the NLL loss
|
|
|
|
|
|
# training
|
|
|
|
optimizer = optim.SGD(model.parameters(), lr=0.003, momentum=0.9)
|
|
time0 = time()
|
|
epochs = 1
|
|
for e in range(epochs):
|
|
running_loss = 0
|
|
for images, labels in train_loader:
|
|
# Flatten MNIST images into a 784 long vector
|
|
images = images.view(images.shape[0], -1)
|
|
|
|
# Training pass
|
|
optimizer.zero_grad()
|
|
|
|
output = model(images)
|
|
loss = criterion(output, labels)
|
|
|
|
# This is where the model learns by backpropagating
|
|
loss.backward()
|
|
|
|
# And optimizes its weights here
|
|
optimizer.step()
|
|
|
|
running_loss += loss.item()
|
|
else:
|
|
print("Epoch {} - Training loss: {}".format(e + 1, running_loss / len(train_loader)))
|
|
|
|
print("\nTraining Time (in minutes) =", (time() - time0) / 60)
|
|
|
|
# testing
|
|
|
|
images, labels = next(iter(val_loader))
|
|
img = images[0].view(1, 784)
|
|
with torch.no_grad():
|
|
logps = model(img)
|
|
ps = torch.exp(logps)
|
|
probab = list(ps.numpy()[0])
|
|
print("Predicted Digit =", probab.index(max(probab)))
|
|
# view_classify(img.view(1, 28, 28), ps)
|
|
|
|
# accuracy
|
|
correct_count, all_count = 0, 0
|
|
for images, labels in val_loader:
|
|
for i in range(len(labels)):
|
|
img = images[i].view(1, 784)
|
|
with torch.no_grad():
|
|
logps = model(img)
|
|
|
|
ps = torch.exp(logps)
|
|
probab = list(ps.numpy()[0])
|
|
pred_label = probab.index(max(probab))
|
|
true_label = labels.numpy()[i]
|
|
if true_label == pred_label:
|
|
correct_count += 1
|
|
all_count += 1
|
|
|
|
print("Number Of Images Tested =", all_count)
|
|
print("\nModel Accuracy =", (correct_count / all_count))
|
|
|
|
|
|
# saving model
|
|
|
|
# torch.save(model.state_dict(), './digit_reco_model.pt')
|
|
# torch.save(model.state_dict(), './digit_reco_model2.pt') |