I am relatively new to Pytorch and have been training an LSTM model. Any feedback on code in general would be appreciated.
When I train the model I receive the following warning
UserWarning: Using a target size (torch.Size([4050, 1, 1])) that is different to the input size (torch.Size([1])). This will likely lead to incorrect results due to broadcasting. Please ensure they have the same size.
Can anyone help me with this problem?
My goal is to train a model on a time series containing multiple features. Please see the code below:
LSTM:
import torch
import torch.nn as nn
import pandas as pd
import numpy as np
from Tools.data_loader import CreateDataset
class LSTMModel(nn.Module):
def __init__(self, input_dim, hidden_dim, num_layers, output_dim):
super(LSTMModel, self).__init__()
self.input_dim = input_dim
self.hidden_dim = hidden_dim
self.output_dim = output_dim
self.num_layers = num_layers
self.lstm = nn.LSTM(input_dim, hidden_dim, num_layers)
self.forecast = nn.Linear(hidden_dim, output_dim)
def forward(self, x):
batch_size = 1
h0 = torch.zeros(self.num_layers, batch_size, self.hidden_dim).requires_grad_()
c0 = torch.zeros(self.num_layers, batch_size, self.hidden_dim).requires_grad_()
out, (hn,cn) = self.lstm(x, (h0.detach(), c0.detach()))
y_pred = self.forecast(out[-1].view(batch_size,-1))
return y_pred.view(-1)
dataset = CreateDataset('Data/Bloomberg_PV_weather.csv', 'Datetime', 0.8, 'Power') #PLACEHOLDER
input_dim = dataset.num_features
hidden_dim = 50
num_layers = 1
output_dim = 1
num_epochs = 50
X_train = dataset.X_train
y_train = dataset.y_train
X_test = dataset.X_test
y_test = dataset.y_test
model = LSTMModel(input_dim, hidden_dim, num_layers, output_dim)
criterion = nn.MSELoss()
optimizer=torch.optim.Adam(model.parameters())
hist = np.zeros(num_epochs)
for epoch in range(num_epochs):
optimizer.zero_grad()
output=model(X_train)
loss = criterion(output,y_train)
if epoch % 100 == 0:
print('Epoch ', epoch, 'Loss: ',loss.item())
hist[epoch] = loss.item()
loss.backward()
optimizer.step()
Data Loader:
and here is my CreateDataset class:
'''
Loads data and creates train and test sets
'''
import torch
import math
import pandas as pd
class CreateDataset():
def __init__(self, file, datetime_col, train_proportion, target):
self.file = file
self.datetime_col = datetime_col
self.train_proportion = train_proportion
self.target = target
self.data = self.load_data()
self.train_set, self.test_set = self.split_data()
self.X_train, self.y_train,self.X_test, self.y_test, self.num_features = self.reshape_data()
def load_data(self):
'''
Reads in data
'''
data = pd.read_csv(self.file, header=0)
data.drop(columns=self.datetime_col, inplace=True)
return data
def split_data(self):
'''
Creates test and train sets
'''
train_length = math.ceil(len(self.data)*self.train_proportion)
train_set = self.data[0:train_length]
test_set = self.data[train_length:]
return train_set, test_set
def reshape_data(self):
'''
Splits datasets into X and y sets and reshapes into 3D tensor
'''
num_features = (len(self.test_set.columns)-1)
y_train = torch.tensor(self.train_set[self.target].values).float()
X_train = torch.tensor(self.train_set.drop(columns=self.target).values).float()
y_test = torch.tensor(self.test_set[self.target].values).float()
X_test = torch.tensor(self.test_set.drop(columns=self.target).values).float()
X_train = X_train.view(-1,1,num_features)
y_train = y_train.view(-1,1,1)
X_test = X_test.view(-1,1,num_features)
y_test = y_test.view(-1,1,1)
return X_train, y_train, X_test, y_test, num_features