132 lines
3.8 KiB
Python
132 lines
3.8 KiB
Python
#imports
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import os
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import torch
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import torch.nn as nn
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import torch.optim as optim #optimisation algorithmes
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import torch.nn.functional as F
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from torch.utils.data import DataLoader
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from numpy import mean, std
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import torchvision.datasets as datasets #import datsets
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import torchvision.transforms as transforms
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import torch
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import torch.nn as nn
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import torch.optim as optim
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from torchvision import datasets, models, transforms
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import time
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import os
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import copy
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#create fully connected network
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class NN(nn.Module):
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def __init__(self, input_size, num_classes): #1 layer (28x28 = 784 nodes)
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super(NN,self).__init__()
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self.fc1 = nn.Linear(input_size, 50)
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self.fc2 = nn.Linear(50,num_classes)
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def forward(self, x):
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x = F.relu(self.fc1(x))
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x = self.fc2(x)
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return x
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#set device
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device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
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#hyperparameters
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input_size = 784
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num_classes = 10
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learning_rate = 0.001
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batch_size = 64
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num_epochs = 1
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# Define data transformations
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data_transforms = {
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"train": transforms.Compose([
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transforms.RandomResizedCrop(224),
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transforms.RandomHorizontalFlip(),
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transforms.ToTensor(),
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transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
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]),
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"validation": transforms.Compose([
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transforms.Resize(256),
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transforms.CenterCrop(224),
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transforms.ToTensor(),
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transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
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])
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}
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# Set the path to your vegetable images folder
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data_dir = "neural_network/dataset/vegetables"
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# Load the dataset from the folder
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image_datasets = {x: datasets.ImageFolder(f"{data_dir}/{x}", data_transforms[x])
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for x in ["train", "validation"]}
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dataloaders = {x: torch.utils.data.DataLoader(image_datasets[x], batch_size=4, shuffle=True, num_workers=4)
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for x in ["train", "validation"]}
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dataset_sizes = {x: len(image_datasets[x]) for x in ["train", "validation"]}
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class_names = image_datasets["train"].classes
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num_classes = len(class_names)
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#initialize network
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#model = NN(input_size=input_size, num_classes=num_classes).to(device)
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model = models.resnet18(pretrained=True)
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num_ftrs = model.fc.in_features
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model.fc = nn.Linear(num_ftrs, num_classes)
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model = model.to(device)
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#loss and optimizer
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criterion = nn.CrossEntropyLoss()
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optimizer = optim.Adam(model.parameters(), lr=learning_rate)
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#train network
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for epoch in range(num_epochs): #epoch is a number of all pictures in dataset
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for batch_idx, (data, targets) in enumerate(train_loader):
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#get data to cuda if possible
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data = data.to(device = device)
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targets = targets.to(device = device)
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#get to correct shape
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data = data.reshape(data.shape[0], -1)
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# forward
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scores = model(data)
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loss = criterion(scores, targets)
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#backward
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optimizer.zero_grad()
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loss.backward()
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#gradient descent or adam step
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optimizer.step()
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#check accuracy on training and test to see how good our model
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def check_accuracy(loader, model):
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if loader.dataset.train:
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print("Checking accuracy on training data")
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else:
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print("Checking accuracy on test data")
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num_correct = 0
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num_samples = 0
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model.eval()
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with torch.no_grad():
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for x,y in loader:
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x = x.to(device = device)
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y = y.to(device = device)
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x = x.reshape(x.shape[0], -1)
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scores = model(x)
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_, predictions = scores.max(1)
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num_correct += (predictions == y).sum()
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num_samples += predictions.size(0)
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print(f'Got {num_correct} / {num_samples} with accuracy {float(num_correct)/float(num_samples)*100:.2f}')
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model.train()
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check_accuracy(train_loader, model)
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check_accuracy(test_loader, model)
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