aryan-jadon

aryan-jadon

aryan-jadon

aryan-jadon

aryan-jadon

aryan-jadon

aryan-jadon

aryan-jadon

from sklearn import metrics

# Measure RMSE error.  RMSE is common for regression.
pred = model(x_test)
score = torch.sqrt(torch.nn.functional.mse_loss(pred.flatten(),y_test))
print(f"Final score (RMSE): {score}")

## Final score (RMSE): 3.3968639373779297

aryan-jadon

import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score

import torch.nn as nn
import torch.nn.functional as F
import numpy as np
from torch.autograd import Variable
from sklearn import preprocessing
from torch.utils.data import DataLoader, TensorDataset

aryan-jadon

from sklearn.metrics import accuracy_score

pred = model(x_test)
vloss = loss_fn(pred, y_test)
print(f"Loss = {vloss}")

## Loss = 0.5756629109382629

pred = model(x_test)
_, predict_classes = torch.max(pred, 1)