small fixes
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source/NN/__pycache__/model.cpython-311.pyc
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source/NN/__pycache__/model.cpython-311.pyc
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@ -1,4 +1,6 @@
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import torch.nn as nn
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import torch
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import torch.nn.functional as F
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class Neural_Network_Model(nn.Module):
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@ -16,5 +18,4 @@ class Neural_Network_Model(nn.Module):
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x = self.fc2(x)
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x = torch.relu(x)
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x = self.out(x)
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F.log_softmax(x, dim=-1)
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return x
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return F.log_softmax(x, dim=-1)
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@ -1,15 +1,16 @@
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import torch
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import torch.nn as nn
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from torch.utils.data import DataLoader
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from torchvision import datasets, transforms
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from torchvision import datasets, transforms, utils
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from torchvision.transforms import Compose, Lambda, ToTensor
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import matplotlib.pyplot as plt
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import numpy as np
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from model import *
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device = torch.device('cuda')
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#data transform to tensors:
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data_transformer = transforms.Compose
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([
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data_transformer = transforms.Compose([
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transforms.Resize((150, 150)),
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transforms.ToTensor(),
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transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
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@ -46,6 +47,9 @@ def train(model, dataset, iter=100, batch_size=64):
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loss = criterion(output, labels.to(device))
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loss.backward()
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optimizer.step()
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if epoch % 10 == 0:
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print('epoch: %3d loss: %.4f' % (epoch, loss))
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#function for getting accuracy
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def accuracy(model, dataset):
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model.eval()
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@ -57,6 +61,9 @@ def accuracy(model, dataset):
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return correct.float() / len(dataset)
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model = Neural_Network_Model()
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model.to(device)
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train(model, train_set)
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print(accuracy(model, test_set))
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