'''
1) Design model(input, output, forward pass)
2) Construct loss and optimizer
3) Traning loop
- forward pass : compute prediction and loss
- backward pass : gradients
- updata weights
'''
import torch
import torch.nn as nn
import numpy as np
from sklearn import datasets
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import train_test_split
# prepare data
bc = datasets.load_breast_cancer()
X, y = bc.data, bc.target
n_samples, n_features = X.shape
print(n_samples, n_features)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=1234)
# scale
sc = StandardScaler()
X_train = sc.fit_transform(X_train)
X_test = sc.transform(X_test)
X_train = torch.from_numpy(X_train.astype(np.float32))
X_test = torch.from_numpy(X_test.astype(np.float32))
y_train = torch.from_numpy(y_train.astype(np.float32))
y_test = torch.from_numpy(y_test.astype(np.float32))
y_train = y_train.view(y_train.shape[0], 1)
y_test = y_test.view(y_test.shape[0], 1)
# model
#f = wx+b , sigmoid at the end
class LogisticRegression(nn.Module):
def __init__(self, n_input_features):
super(LogisticRegression, self).__init__()
self.linear = nn.Linear(n_input_features, 1)
def forward(self,x):
y_predicted = torch.sigmoid(self.linear(x))
return y_predicted
model = LogisticRegression(n_features)
# loss and optimizer
learning_reate = 0.01
criterion = nn.BCELoss()
optimizer = torch.optim.SGD(model.parameters(), lr = learning_reate)
# training loop
num_epochs = 100
for epoch in range(num_epochs):
# foward pass and loss
y_predicted = model(X_train)
loss = criterion(y_predicted, y_train)
# backward pass
loss.backward()
# updates
optimizer.step()
#zero_gradients
optimizer.zero_grad()
if (epoch +1) % 10 == 0:
print(f'epoch: {epoch+1}, loss = {loss.item():.4f}')
with torch.no_grad():
y_predicted = model(X_test)
y_predicted_cls = y_predicted.round()
acc = y_predicted_cls.eq(y_test).sum()/ float(y_test.shape[0])
print(f'accuracy = {acc:.4f}')
