GNN Model

poision_edible_classification.py

+# -*- coding: utf-8 -*-
+"""Poision Edible Classification.ipynb
+
+Automatically generated by Colaboratory.
+
+Original file is located at
+    https://colab.research.google.com/drive/1r40VuKZJbBTeFVASnAhetlId5x-OjmBQ
+"""
+
+! pip install torch_geometric
+
+! pip install pyg_lib torch_scatter torch_sparse torch_cluster torch_spline_conv -f https://data.pyg.org/whl/torch-2.0.0+cpu.html
+
+import torch
+import torch.nn as nn
+import torch.optim as optim
+from torch.utils.data import DataLoader
+from torchvision.datasets import ImageFolder
+from torchvision.transforms import ToTensor
+from torch_geometric.data import Data
+
+# Define the Graph Convolutional Network (GCN) layer
+class GCNConv(nn.Module):
+    def __init__(self, in_channels, out_channels):
+        super(GCNConv, self).__init__()
+        self.linear = nn.Linear(in_channels, out_channels)
+
+    def forward(self, x, edge_index):
+        x = self.linear(x)
+        return torch.matmul(x, edge_index)
+
+# Define the Graph Neural Network model
+class GNNModel(nn.Module):
+    def __init__(self, in_channels, hidden_channels, num_classes):
+        super(GNNModel, self).__init__()
+        self.conv1 = GCNConv(in_channels, hidden_channels)
+        self.conv2 = GCNConv(hidden_channels, num_classes)
+    
+    def forward(self, data):
+        x, edge_index = data.x, data.edge_index
+        x = self.conv1(x, edge_index)
+        x = x.relu()
+        x = self.conv2(x, edge_index)
+        return x.log_softmax(dim=1)
+
+# Set the device
+device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+
+# Mount Google Drive
+from google.colab import drive
+drive.mount('/content/drive')
+
+from torchvision.transforms import Resize, ToTensor
+from torchvision import transforms
+
+# Define the dataset and data loaders
+
+# transform = nn.Sequential(Resize((224, 224)), ToTensor())
+
+transform=transforms.Compose([Resize((64,3)),ToTensor()])
+
+train_dataset = ImageFolder('/content/drive/MyDrive/Mushroom Classification Project/component poision edible /poisonousmushroomdetection/train', transform=transform)
+val_dataset = ImageFolder('/content/drive/MyDrive/Mushroom Classification Project/component poision edible /poisonousmushroomdetection/validation', transform=transform)
+
+train_loader = DataLoader(train_dataset, batch_size=16, shuffle=False)
+val_loader = DataLoader(val_dataset, batch_size=16, shuffle=True)
+
+train_dataset
+
+val_dataset
+
+# Define the model and optimizer
+model = GNNModel(in_channels=3, hidden_channels=64, num_classes=len(train_dataset.classes)).to(device)
+optimizer = optim.Adam(model.parameters(), lr=0.001)
+
+import torch
+
+tensor = torch.randn(64,2)
+tensor = tensor.t()
+tensor = tensor.mm(torch.ones( 64,3072))
+
+print(tensor)
+
+edge_index = torch.randn(64,3072)
+
+# Training loop
+model.train()
+for epoch in range(10):
+    for data in train_loader:
+        x = data[0].to(device)
+        y = data[1].to(device)
+        
+        edge_index = edge_index 
+        graph_data = Data(x=x, edge_index=edge_index, y=y)
+        graph_data = graph_data.to(device)
+        
+        optimizer.zero_grad()
+        output = model(graph_data)
+        loss = nn.functional.nll_loss(output, graph_data.y)
+        loss.backward()
+        optimizer.step()
+