Hi, I am having a tensor-type issue with my GRU. I know that similar issues have been solved here before but I cant seem to bring the same solution to my problem. Any help would be greatly appreciated.
import pandas as pd
import numpy as np
import torch
import torchvision # torch package for vision related things
import torch.nn.functional as F # Parameterless functions, like (some) activation functions
import torchvision.datasets as datasets # Standard datasets
import torchvision.transforms as transforms # Transformations we can perform on our dataset for augmentation
from torch import optim # For optimizers like SGD, Adam, etc.
from torch import nn # All neural network modules
from torch.utils.data import Dataset, DataLoader # Gives easier dataset managment by creating mini batches etc.
from tqdm import tqdm # For a nice progress bar
from sklearn.preprocessing import StandardScaler
# Set device
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
# Hyperparameters
input_size = 24
hidden_size = 128
num_layers = 1
num_classes = 2
sequence_length = 1
learning_rate = 0.005
batch_size = 8
num_epochs = 3
# Recurrent neural network with GRU (many-to-one)
class RNN_GRU(nn.Module):
def __init__(self, input_size, hidden_size, num_layers, num_classes):
super(RNN_GRU, self).__init__()
self.hidden_size = hidden_size
self.num_layers = num_layers
self.gru = nn.GRU(input_size, hidden_size, num_layers, batch_first=True)
self.fc = nn.Linear(hidden_size * sequence_length, num_classes)
def forward(self, x):
# Set initial hidden and cell states
x = x.unsqueeze(1)
h0 = torch.zeros(self.num_layers, x.size(0), self.hidden_size).to(device)
# Forward propagate GRU
out, _ = self.gru(x, h0)
out = out[:, -1, :]
# Decode the hidden state of the last time step
out = self.fc(out)
return out
class MyDataset(Dataset):
def __init__(self,file_name):
stats_df=pd.read_csv(file_name)
x=stats_df.iloc[:,0:24].values
y=stats_df.iloc[:,24].values
self.x_train=torch.tensor(x,dtype=torch.int64)
self.y_train=torch.tensor(y,dtype=torch.int64)
def __len__(self):
return len(self.y_train)
def __getitem__(self,idx):
return self.x_train[idx],self.y_train[idx]
nomDs=MyDataset("nomStats.csv")
atkDs=MyDataset("atkStats.csv")
train_loader=DataLoader(dataset=nomDs,batch_size=batch_size)
test_loader=DataLoader(dataset=atkDs,batch_size=batch_size)
# Initialize network
model = RNN_GRU(input_size, hidden_size, num_layers, num_classes).to(device)
# Loss and optimizer
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters(), lr=learning_rate)
# Train Network
for epoch in range(num_epochs):
for batch_idx, (data, targets) in enumerate(tqdm(train_loader)):
# Get data to cuda if possible
data = data.to(device=device).squeeze(1)
targets = targets.to(device=device)
# forward
scores = model(data)
loss = criterion(scores, targets)
# backward
optimizer.zero_grad()
loss.backward()
# gradient descent update step/adam step
optimizer.step()
# Check accuracy on training & test to see how good our model
def check_accuracy(loader, model):
num_correct = 0
num_samples = 0
# Set model to eval
model.eval()
with torch.no_grad():
for x, y in loader:
x = x.to(device=device).squeeze(1)
y = y.to(device=device)
scores = model(x)
_, predictions = scores.max(1)
num_correct += (predictions == y).sum()
num_samples += predictions.size(0)
# Toggle model back to train
model.train()
return num_correct / num_samples
print(f"Accuracy on training set: {check_accuracy(train_loader, model)*100:2f}")
print(f"Accuracy on test set: {check_accuracy(test_loader, model)*100:.2f}")
Here is the error
(FYP) C:\Users\steph\FYP>python TESTGRU.py
0%| | 0/8 [00:00<?, ?it/s]
Traceback (most recent call last):
File "TESTGRU.py", line 86, in <module>
scores = model(data)
File "C:\Users\steph\anaconda3\envs\FYP\lib\site-packages\torch\nn\modules\module.py", line 889, in _call_impl
result = self.forward(*input, **kwargs)
File "TESTGRU.py", line 42, in forward
out, _ = self.gru(x, h0)
File "C:\Users\steph\anaconda3\envs\FYP\lib\site-packages\torch\nn\modules\module.py", line 889, in _call_impl
result = self.forward(*input, **kwargs)
File "C:\Users\steph\anaconda3\envs\FYP\lib\site-packages\torch\nn\modules\rnn.py", line 821, in forward
result = _VF.gru(input, hx, self._flat_weights, self.bias, self.num_layers,
RuntimeError: expected scalar type Long but found Float