Simple question, i wanted to experiment with the simplest possible.network, but i kept running into RuntimeError: expected scalar type Float but found Double
unless i casted data
into .float()
(see below code with comment)
What i dont understand is, why is this casting needed? data
is already a torch.float64
type. Whys the explicit re-casting in the output = model(data.float())
line needed?
Code
from __future__ import print_function
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torchvision import datasets, transforms
from torch.optim.lr_scheduler import StepLR
from sklearn.datasets import make_classification
from torch.utils.data import TensorDataset, DataLoader
# =============================================================================
# Simplest Example
# =============================================================================
X, y = make_classification()
X, y = torch.tensor(X), torch.tensor(y)
print("X Shape :{}".format(X.shape))
print("y Shape :{}".format(y.shape))
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
self.fc1 = nn.Linear(X.shape[1], 128)
self.fc2 = nn.Linear(128, 10)
self.fc3 = nn.Linear(10, 2)
def forward(self, x):
x = F.relu(self.fc1(x))
x = F.relu(self.fc2(x))
x = self.fc3(x)
return x
device = torch.device("cuda")
lr = 1
batch_size = 32
gamma = 0.7
epochs = 14
args = {'log_interval': 10, 'dry_run':False}
kwargs = {'batch_size': batch_size}
kwargs.update({'num_workers': 1,
'pin_memory': True,
'shuffle': True},
)
model = Net().to(device)
optimizer = optim.Adam(model.parameters(), lr=lr)
scheduler = StepLR(optimizer, step_size=1, gamma=gamma)
my_dataset = TensorDataset(X,y) # create dataset
train_loader = DataLoader(my_dataset,**kwargs) #generate dataloader
cross_entropy_loss = torch.nn.CrossEntropyLoss()
for epoch in range(1, epochs + 1):
## Train step ##
model.train()
for batch_idx, (data, target) in enumerate(train_loader):
data, target = data.to(device), target.to(device)
optimizer.zero_grad()
output = model(data.float()) #HERE: why is .float() needed here?
loss = cross_entropy_loss(output, target)
loss.backward()
optimizer.step()
if batch_idx % args['log_interval'] == 0:
print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format(
epoch, batch_idx * len(data), len(train_loader.dataset),
100. * batch_idx / len(train_loader), loss.item()))
if args['dry_run']:
break
scheduler.step()
In PyTorch, 64-bit floating point corresponds to torch.float64
or torch.double
. While, 32-bit floating point corresponds to torch.float32
or torch.float
.
Thus,
data
is already atorch.float64
type
ie data
is a 64 floating point type ( torch.double
).
By casting it using .float()
, you convert it into 32-bit floating point.
a = torch.tensor([[1., -1.], [1., -1.]], dtype=torch.double)
print(a.dtype)
# torch.float64
print(a.float().dtype)
# torch.float32
Check different data types in PyTorch.
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