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Jan Świątek 2023-06-30 08:10:55 +02:00
parent 3d936915dc
commit 5c9692c1ba
4 changed files with 971 additions and 5 deletions
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51
eval.py
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@ -1,13 +1,60 @@
# Import dependencies
import numpy as np
import pandas as pd
import torch
from torch import nn
from torch.utils.data import DataLoader, TensorDataset
# Load data
test_data = pd.read_csv('spambase.data.test.csv')
# X, Y
x_test = np.array(test_data.drop('is_spam', axis=1).values)
y_test = np.array(test_data['is_spam'].values)
test = TensorDataset(torch.Tensor(x_test), torch.LongTensor(y_test))
test_loader = DataLoader(test, batch_size=64)
# Load model
model = torch.jit.load("model.pth")
model.eval()
print(model)
# Evaluate
def test_loop(dataloader, model, loss_fn):
open("results.txt", "w").close() # Create empty file
model.eval()
size = len(dataloader.dataset)
num_batches = len(dataloader)
test_loss, correct = 0, 0
f = open("results.txt", "a")
with torch.no_grad():
for X, y in dataloader:
pred = model(X)
test_loss += loss_fn(pred, y).item()
correct += (pred.argmax(1) == y).type(torch.float).sum().item()
for val in pred:
f.write(str(torch.argmax(val)) + "\n")
test_loss /= num_batches
correct /= size
print(f"Avg loss: {test_loss:>8f}")
loss_fn = nn.CrossEntropyLoss()
test_loop(test_loader, model, loss_fn)

4
main.py
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@ -11,9 +11,6 @@ from torch.utils.data import DataLoader, TensorDataset
# Load data
train_data = pd.read_csv('spambase.data.train.csv')
val_data = pd.read_csv('spambase.data.val.csv')
test_data = pd.read_csv('spambase.data.test.csv')
# X, Y
@ -35,6 +32,7 @@ class NeuralNetwork(nn.Module):
nn.Linear(128, 128),
nn.ReLU(),
nn.Linear(128, 2),
nn.Softmax(1)
)
def forward(self, x):

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model.pth
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results.txt Normal file
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