Using the above code, got from PyTorch Tutorial: How to Develop Deep Learning Models with Python
The example code is working but i just want to change the predict to last 2 fields (acutally i got predict for only one field (last)).
# pytorch mlp for regression
from numpy import vstack
from numpy import sqrt
from pandas import read_csv
from sklearn.metrics import mean_squared_error
from torch.utils.data import Dataset
from torch.utils.data import DataLoader
from torch.utils.data import random_split
from torch import Tensor
from torch.nn import Linear
from torch.nn import Sigmoid
from torch.nn import Module
from torch.optim import SGD
from torch.nn import MSELoss
from torch.nn.init import xavier_uniform_
# dataset definition
class CSVDataset(Dataset):
# load the dataset
def __init__(self, path):
# load the csv file as a dataframe
df = read_csv(path, header=None)
# store the inputs and outputs
self.X = df.values[:, :-1].astype('float32')
self.y = df.values[:, -1].astype('float32')
# ensure target has the right shape
self.y = self.y.reshape((len(self.y), 1))
# number of rows in the dataset
def __len__(self):
return len(self.X)
# get a row at an index
def __getitem__(self, idx):
return [self.X[idx], self.y[idx]]
# get indexes for train and test rows
def get_splits(self, n_test=0.33):
# determine sizes
test_size = round(n_test * len(self.X))
train_size = len(self.X) - test_size
# calculate the split
return random_split(self, [train_size, test_size])
# model definition
class MLP(Module):
# define model elements
def __init__(self, n_inputs):
super(MLP, self).__init__()
# input to first hidden layer
self.hidden1 = Linear(n_inputs, 10)
xavier_uniform_(self.hidden1.weight)
self.act1 = Sigmoid()
# second hidden layer
self.hidden2 = Linear(10, 8)
xavier_uniform_(self.hidden2.weight)
self.act2 = Sigmoid()
# third hidden layer and output
self.hidden3 = Linear(8, 1)
xavier_uniform_(self.hidden3.weight)
# forward propagate input
def forward(self, X):
# input to first hidden layer
X = self.hidden1(X)
X = self.act1(X)
# second hidden layer
X = self.hidden2(X)
X = self.act2(X)
# third hidden layer and output
X = self.hidden3(X)
return X
# prepare the dataset
def prepare_data(path):
# load the dataset
dataset = CSVDataset(path)
# calculate split
train, test = dataset.get_splits()
# prepare data loaders
train_dl = DataLoader(train, batch_size=32, shuffle=True)
test_dl = DataLoader(test, batch_size=1024, shuffle=False)
return train_dl, test_dl
# train the model
def train_model(train_dl, model):
# define the optimization
criterion = MSELoss()
optimizer = SGD(model.parameters(), lr=0.01, momentum=0.9)
# enumerate epochs
for epoch in range(100):
# enumerate mini batches
for i, (inputs, targets) in enumerate(train_dl):
# clear the gradients
optimizer.zero_grad()
# compute the model output
yhat = model(inputs)
# calculate loss
loss = criterion(yhat, targets)
# credit assignment
loss.backward()
# update model weights
optimizer.step()
# evaluate the model
def evaluate_model(test_dl, model):
predictions, actuals = list(), list()
for i, (inputs, targets) in enumerate(test_dl):
# evaluate the model on the test set
yhat = model(inputs)
# retrieve numpy array
yhat = yhat.detach().numpy()
actual = targets.numpy()
actual = actual.reshape((len(actual), 1))
# store
predictions.append(yhat)
actuals.append(actual)
predictions, actuals = vstack(predictions), vstack(actuals)
# calculate mse
mse = mean_squared_error(actuals, predictions)
return mse
# make a class prediction for one row of data
def predict(row, model):
# convert row to data
row = Tensor([row])
# make prediction
yhat = model(row)
# retrieve numpy array
yhat = yhat.detach().numpy()
return yhat
# prepare the data
path = 'https://raw.githubusercontent.com/jbrownlee/Datasets/master/housing.csv'
train_dl, test_dl = prepare_data(path)
print(len(train_dl.dataset), len(test_dl.dataset))
# define the network
model = MLP(13)
# train the model
train_model(train_dl, model)
# evaluate the model
mse = evaluate_model(test_dl, model)
print('MSE: %.3f, RMSE: %.3f' % (mse, sqrt(mse)))
# make a single prediction (expect class=1)
row = [0.00632,18.00,2.310,0,0.5380,6.5750,65.20,4.0900,1,296.0,15.30,396.90,4.98]
yhat = predict(row, model)
print('Predicted: %.3f' % yhat)