69 lines
2.0 KiB
Python
69 lines
2.0 KiB
Python
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 torch.utils.data import DataLoader, Dataset
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import pandas as pd
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from sklearn.model_selection import train_test_split
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from sklearn.preprocessing import LabelEncoder
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import torch.nn.functional as F
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device = (
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"cuda"
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if torch.cuda.is_available()
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else "cpu"
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)
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class Model(nn.Module):
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def __init__(self, input_features=54, hidden_layer1=25, hidden_layer2=30, output_features=8):
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super().__init__()
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self.fc1 = nn.Linear(input_features,output_features)
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self.bn1 = nn.BatchNorm1d(hidden_layer1) # Add batch normalization
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self.fc2 = nn.Linear(hidden_layer1, hidden_layer2)
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self.bn2 = nn.BatchNorm1d(hidden_layer2) # Add batch normalization
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self.out = nn.Linear(hidden_layer2, output_features)
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def forward(self, x):
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x = F.relu(self.fc1(x))
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return x
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def load_model(model, model_path):
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model.load_state_dict(torch.load(model_path))
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model.eval()
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def predict(model, input_data):
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# Convert input data to PyTorch tensor
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# Perform forward pass
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with torch.no_grad():
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output = model(input_data)
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_, predicted_class = torch.max(output, 0)
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return predicted_class.item() # Return the predicted class label
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def main():
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forest_test = pd.read_csv('forest_test.csv')
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X_test = forest_test.drop(columns=['Cover_Type']).values
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y_test = forest_test['Cover_Type'].values
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X_test = torch.tensor(X_test, dtype=torch.float32).to(device)
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model = Model().to(device)
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model_path = 'model.pth' # Path to your saved model file
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load_model(model, model_path)
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predictions = []
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for input_data in X_test:
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predicted_class = predict(model, input_data)
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predictions.append(predicted_class)
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with open(r'predictions.txt', 'w') as fp:
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for item in predictions:
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# write each item on a new line
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fp.write("%s\n" % item)
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if __name__ == "__main__":
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main() |