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eugene 2023-06-06 22:38:32 +02:00
parent 1c183a567a
commit c1271fb458
1 changed files with 1 additions and 9 deletions
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script5_4.py
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@ -4,7 +4,7 @@ import torch.nn as nn
from torch.utils.data import DataLoader, Dataset from torch.utils.data import DataLoader, Dataset
from sklearn.model_selection import train_test_split from sklearn.model_selection import train_test_split
from sklearn.preprocessing import LabelEncoder from sklearn.preprocessing import LabelEncoder
from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, mean_squared_error from sklearn.metrics import mean_squared_error
import pickle import pickle
# Define the neural network model # Define the neural network model
@ -75,14 +75,6 @@ for inputs, targets_batch in test_dataloader:
predictions = torch.FloatTensor(predictions).squeeze() predictions = torch.FloatTensor(predictions).squeeze()
targets = torch.FloatTensor(targets).squeeze() targets = torch.FloatTensor(targets).squeeze()
accuracy = accuracy_score(targets, torch.round(predictions))
precision = precision_score(targets, torch.round(predictions), average='micro')
recall = recall_score(targets, torch.round(predictions), average='micro')
f1 = f1_score(targets, torch.round(predictions), average='micro')
rmse = mean_squared_error(targets, predictions, squared=False) rmse = mean_squared_error(targets, predictions, squared=False)
print("Accuracy:", accuracy)
print("Micro-average Precision:", precision)
print("Micro-average Recall:", recall)
print("F1 Score:", f1)
print("RMSE:", rmse) print("RMSE:", rmse)