I am doing a code for binary classification in pytorch , & training accuracy and test accuracy reached 100 %, i am sharing the code here !!

[tedzz12]

from torch.autograd.grad_mode import inference_mode
import torch
from torch import nn
import sklearn
from sklearn.datasets import make_circles
from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt

n_samples = 1000

x,y = make_circles(n_samples,
noise=0.03,
random_state=42)

X = torch.from_numpy(x).type(torch.float)
Y = torch.from_numpy(y).type(torch.float)

x_train,x_test,y_train,y_test = train_test_split(X,
Y,
test_size=0.2,
random_state=42)

len(x_train),len(x_test),len(y_train),len(y_test)

class CirclemodX0(nn.Module):
def init(self):
super().init()
self.linear1=nn.Linear(in_features=2,out_features=10)
self.linear2=nn.Linear(in_features=10,out_features=10)
self.linear3=nn.Linear(in_features=10,out_features=10)
self.linear4=nn.Linear(in_features=10,out_features=10)
self.linear5=nn.Linear(in_features=10,out_features=1)
self.relu = nn.ReLU()
def forward(self,x):
return self.linear5(self.relu(self.linear4(self.relu(self.linear3(self.relu(self.linear2(self.relu(self.linear1(x)))))))))

model_vx= CirclemodX0()
model_vx.state_dict()

losfn = nn.BCEWithLogitsLoss()

optimizer = torch.optim.SGD(params=model_vx.parameters(),
lr=0.001)

def accuracy_fn(y_true,y_pred):
correct = torch.eq(y_true,y_pred).sum().item()
acc = correct/len(y_pred)*100
return acc

torch.manual_seed(42)

epochs =5000

for epoch in range(epochs):
model_vx.train()
y_logits = model_vx(x_train).squeeze()
y_pred =torch.round(torch.sigmoid(y_logits))
loss = losfn(y_logits,y_train)
acc = accuracy_fn(y_true=y_train,
y_pred = y_pred)

optimizer.zero_grad()
loss.backward()
optimizer.step()
model_vx.eval()
with inference_mode():
test_logits = model_vx(x_test).squeeze()
test_pred = torch.round(torch.sigmoid(test_logits))
test_loss = losfn(test_logits,y_test)
test_acc = accuracy_fn(y_true=y_test,
y_pred = test_pred)

if epoch % 100==0:
print(f"Epoch {epoch} | Loss {loss:.5f} | Accuracy {acc:.2f}% | Test Loss {test_loss:.5f} | test accuracy {test_acc:.2f}%")

[Sharwesh05]

can you try without random state/seed and send