Neural_network #4

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s481894 merged 12 commits from Neural_network into master 2024-06-04 16:59:03 +02:00
2 changed files with 56 additions and 8 deletions
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source/NN/model.py Normal file
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@ -0,0 +1,20 @@
import torch.nn as nn
class Neural_Network_Model(nn.Module):
def __init__(self, num_classes=5,hidden_layer1 = 100,hidden_layer2 = 100):
super(Neural_Network_Model, self).__init__()
self.fc1 = nn.Linear(150*150*3,hidden_layer1)
self.fc2 = nn.Linear(hidden_layer1,hidden_layer2)
self.out = nn.Linear(hidden_layer2,num_classes)
# two hidden layers
def forward(self, x):
x = x.view(-1, 150*150*3)
x = self.fc1(x)
x = torch.relu(x)
x = self.fc2(x)
x = torch.relu(x)
x = self.out(x)
F.log_softmax(x, dim=-1)
return x

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@ -5,7 +5,7 @@ from torchvision import datasets, transforms
from torchvision.transforms import Compose, Lambda, ToTensor
import matplotlib.pyplot as plt
import numpy as np
from model import *
#data transform to tensors:
data_transformer = transforms.Compose
@ -17,18 +17,46 @@ data_transformer = transforms.Compose
#loading data:
train_set = datasets.ImageFolder(root='source/resources/train', transform=data_transformer)
test_set = datasets.ImageFolder(root='source/resources/test', transform=data_transformer)
train_set = datasets.ImageFolder(root='resources/train', transform=data_transformer)
test_set = datasets.ImageFolder(root='resources/test', transform=data_transformer)
#to mozna nawet przerzucic do funkcji train:
train_loader = DataLoader(train_set, batch_size=32, shuffle=True, num_workers=2)
test_loader = DataLoader(test_set, batch_size=32, shuffle=True, num_workers=2)
#train_loader = DataLoader(train_set, batch_size=32, shuffle=True, num_workers=2)
#test_loader = DataLoader(test_set, batch_size=32, shuffle=True, num_workers=2)
#test if classes work properly:
print(train_set.classes)
print(train_set.class_to_idx)
print(train_set.targets[3002])
#print(train_set.classes)
#print(train_set.class_to_idx)
#print(train_set.targets[3002])
#loading your own image: <-- zrobię to na koniec - wrzucanie konkretnego obrazka aby uzyskac wynik
#function for training model
def train(model, dataset, iter=100, batch_size=64):
optimizer = torch.optim.SGD(model.parameters(), lr=0.001)
criterion = nn.NLLLoss()
train_loader = DataLoader(dataset, batch_size=batch_size, shuffle=True)
model.train()
for epoch in range(iter):
for inputs, labels in train_loader:
optimizer.zero_grad()
output = model(inputs.to(device))
loss = criterion(output, labels.to(device))
loss.backward()
optimizer.step()
#function for getting accuracy
def accuracy(model, dataset):
model.eval()
correct = sum([
(model(inputs.to(device)).argmax(dim=1) == labels.to(device)).sum()
for inputs, labels in DataLoader(dataset, batch_size=64, shuffle=True)
])
return correct.float() / len(dataset)
model = Neural_Network_Model()
train(model, train_set)
print(accuracy(model, test_set))