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Neural_network #4

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s481894 merged 12 commits from Neural_network into master 2024-06-04 16:59:03 +02:00
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3 changed files with 13 additions and 5 deletions
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source/NN/__pycache__/model.cpython-311.pyc Normal file
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source/NN/model.py
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@ -1,4 +1,6 @@
import torch.nn as nn import torch.nn as nn
import torch
import torch.nn.functional as F
class Neural_Network_Model(nn.Module): class Neural_Network_Model(nn.Module):
@ -16,5 +18,4 @@ class Neural_Network_Model(nn.Module):
x = self.fc2(x) x = self.fc2(x)
x = torch.relu(x) x = torch.relu(x)
x = self.out(x) x = self.out(x)
F.log_softmax(x, dim=-1) return F.log_softmax(x, dim=-1)
return x

13
source/NN/neural_network.py
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@ -1,15 +1,16 @@
import torch import torch
import torch.nn as nn import torch.nn as nn
from torch.utils.data import DataLoader from torch.utils.data import DataLoader
from torchvision import datasets, transforms from torchvision import datasets, transforms, utils
from torchvision.transforms import Compose, Lambda, ToTensor from torchvision.transforms import Compose, Lambda, ToTensor
import matplotlib.pyplot as plt import matplotlib.pyplot as plt
import numpy as np import numpy as np
from model import * from model import *
device = torch.device('cuda')
#data transform to tensors: #data transform to tensors:
data_transformer = transforms.Compose data_transformer = transforms.Compose([
([
transforms.Resize((150, 150)), transforms.Resize((150, 150)),
transforms.ToTensor(), transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)) transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
@ -46,6 +47,9 @@ def train(model, dataset, iter=100, batch_size=64):
loss = criterion(output, labels.to(device)) loss = criterion(output, labels.to(device))
loss.backward() loss.backward()
optimizer.step() optimizer.step()
if epoch % 10 == 0:
print('epoch: %3d loss: %.4f' % (epoch, loss))
#function for getting accuracy #function for getting accuracy
def accuracy(model, dataset): def accuracy(model, dataset):
model.eval() model.eval()
@ -57,6 +61,9 @@ def accuracy(model, dataset):
return correct.float() / len(dataset) return correct.float() / len(dataset)
model = Neural_Network_Model() model = Neural_Network_Model()
model.to(device)
train(model, train_set) train(model, train_set)
print(accuracy(model, test_set)) print(accuracy(model, test_set))