def test_logreg():
X_train, Y_train, X_test, Y_test = import_census(CENSUS_FILE_PATH)
num_features = X_train.shape[1]
# Add a bias
X_train_b = np.append(X_train, np.ones((len(X_train), 1)), axis=1)
X_test_b = np.append(X_test, np.ones((len(X_test), 1)), axis=1)
# my_x=np.array([[3,4],[5,6],[7,8],[9,10],[22,22],[12,23]])
# my_y=np.array([0,1,2,0,2,1]).reshape(6,)
# #print(my_y)
# my_x = np.append(my_x, np.ones((len(my_x), 1)), axis=1)
# test_model = LogisticRegression(2, 3, 2, CONV_THRESHOLD)
# #print(test_model.predict(my_x))
# test_model.train(my_x, my_y)
## Logistic Regression ###
#print(X_train_b.shape)
#print(Y_train.shape)
model = LogisticRegression(num_features, NUM_CLASSES, BATCH_SIZE,
CONV_THRESHOLD)
#print(model.loss(X_train_b, Y_train))
num_epochs = model.train(X_train_b, Y_train)
acc = model.accuracy(X_test_b, Y_test) * 100
print("Test Accuracy: {:.1f}%".format(acc))
print("Number of Epochs: " + str(num_epochs))
acc = 0
return acc
def test_logreg():
X_train, Y_train, X_test, Y_test = import_mnist(MNIST_TRAIN_INPUTS_PATH,
MNIST_TRAIN_LABELS_PATH,
MNIST_TEST_INPUTS_PATH,
MNIST_TEST_LABELS_PATH)
num_features = X_train.shape[1]
# Add a bias
X_train_b = np.append(X_train, np.ones((len(X_train), 1)), axis=1)
X_test_b = np.append(X_test, np.ones((len(X_test), 1)), axis=1)
### Logistic Regression ###
print('--------- LOGISTIC REGRESSION w/ SGD ---------')
model = LogisticRegression(num_features, MNIST_CLASSES)
model.train(X_train_b, Y_train)
print("Test Accuracy: {:.1f}%".format(
model.accuracy(X_test_b, Y_test) * 100))
def test_logreg():
X_train, Y_train, X_test, Y_test = import_census(CENSUS_FILE_PATH)
num_features = X_train.shape[1]
# Add a bias
X_train_b = np.append(X_train, np.ones((len(X_train), 1)), axis=1)
X_test_b = np.append(X_test, np.ones((len(X_test), 1)), axis=1)
### Logistic Regression ###
model = LogisticRegression(num_features, NUM_CLASSES, BATCH_SIZE,
CONV_THRESHOLD)
num_epochs = model.train(X_train_b, Y_train)
acc = model.accuracy(X_test_b, Y_test) * 100
print("Test Accuracy: {:.1f}%".format(acc))
print("Number of Epochs: " + str(num_epochs))
return acc
# # Plot this dataset
# plt.plot(x_0[np.where(y == 0)], x_1[np.where(y == 0)], 'o', c="b")
# plt.plot(x_0[np.where(y == 1)], x_1[np.where(y == 1)], 'o', c="r")
# plt.xlabel("x_0")
# plt.ylabel("x_1")
# plt.show()
x_0 = scaleFeature(x_0)
x_1 = scaleFeature(x_1)
X = np.transpose(np.vstack((x_0, x_1))) # Transform x into a matrix
lrClassifier.train(X, y, 5, 500) # Train model
predictions = lrClassifier.predict(X) # Train set predictions
evaluateBinaryClassifier(predictions, y) # Evaluate train set predictions
# Plot the decition boundary
plt.plot(x_0[np.where(y == 0)], x_1[np.where(y == 0)], 'o', c="b")
plt.plot(x_0[np.where(y == 1)], x_1[np.where(y == 1)], 'o', c="r")
plt.xlabel("x_0")
plt.ylabel("x_1")
plt.title("Learned Decition Boundary for the Generated Dataset")
# Generate the decition boundary
boundary_x = np.linspace(-0.5,0.5,25)
param = lrClassifier.parameters
boundary_y = (-1 / param[2]) * (param[0] + boundary_x * param[1])
# Plot the decition boundary
def train(batch_size=64, window_size=3, epochs=100):
train_dataset = SupervisedDataset(mode='train',
window_size=window_size,
log_reg=True)
train_loader = DataLoader(train_dataset,
batch_size=batch_size,
shuffle=True)
val_dataset = SupervisedDataset(mode='val',
window_size=window_size,
log_reg=True)
val_loader = DataLoader(val_dataset, batch_size=batch_size, shuffle=True)
base_lr_rate = 1e-3
weight_decay = 1e-6 #0.000016
#model = LSTM(input_size=78, hidden_size=128, num_classes=170, n_layers=2).to(device=torch.device('cuda:0'))
model = LogisticRegression(
num_keypoints=78, num_features=2,
num_classes=170).to(device=torch.device('cuda:0'))
'''
for name, param in model.named_parameters():
if 'bias' in name:
nn.init.constant_(param, 0.0)
elif 'weight' in name:
nn.init.xavier_uniform_(param)
'''
criterion = nn.BCEWithLogitsLoss(
) # Use this for Logistic Regression training
#criterion = nn.BCELoss() # Use this for LSTM training (with Softmax)
optimizer = optim.Adam(
model.parameters(),
lr=base_lr_rate) #, weight_decay=weight_decay, amsgrad=True)
best_epoch_train_accuracy = 0.0
best_epoch_val_accuracy = 0.0
for current_epoch in range(epochs):
current_train_iter = 0
current_val_iter = 0
running_train_loss = 0.0
current_average_train_loss = 0.0
running_val_loss = 0.0
current_average_val_loss = 0.0
num_train_data = 0
num_val_data = 0
running_train_correct_preds = 0
running_train_correct_classwise_preds = [0] * 170
running_val_correct_preds = 0
running_val_correct_classwise_preds = [0] * 170
for phase in ['train', 'val']:
# Train loop
if phase == 'train':
train_epoch_since = time.time()
model.train()
for train_batch_window, train_batch_label in train_loader:
current_train_iter += 1
outs = model(train_batch_window)
#scheduler = poly_lr_scheduler(optimizer = optimizer, init_lr = base_lr_rate, iter = current_iter, lr_decay_iter = 1,
# max_iter = max_iter, power = power) # max_iter = len(train_loader)
optimizer.zero_grad()
loss = criterion(outs, train_batch_label)
gt_confidence, gt_index = torch.max(train_batch_label,
dim=1)
#loss = criterion(outs, gt_index)
running_train_loss += loss.item()
current_average_train_loss = running_train_loss / current_train_iter
loss.backward(retain_graph=False)
optimizer.step()
pred_confidence, pred_index = torch.max(outs, dim=1)
#gt_confidence, gt_index = torch.max(train_batch_label, dim=1)
batch_correct_preds = torch.eq(
pred_index, gt_index).long().sum().item()
batch_accuracy = (batch_correct_preds /
train_batch_window.shape[0]) * 100
num_train_data += train_batch_window.shape[0]
running_train_correct_preds += batch_correct_preds
if current_train_iter % 10 == 0:
#print(outs)
print(
f"\nITER#{current_train_iter} ({current_epoch+1}) BATCH TRAIN ACCURACY: {batch_accuracy:.4f}, RUNNING TRAIN LOSS: {loss.item():.8f}"
)
print(
f"Predicted / GT index:\n{pred_index}\n{gt_index}\n"
)
last_epoch_average_train_loss = current_average_train_loss
epoch_accuracy = (running_train_correct_preds /
num_train_data) * 100
print(
f"\n\nEPOCH#{current_epoch+1} EPOCH TRAIN ACCURACY (BEST): {epoch_accuracy:.4f} ({best_epoch_train_accuracy:.4f}), AVERAGE TRAIN LOSS: {last_epoch_average_train_loss:.8f}\n\n"
)
if epoch_accuracy > best_epoch_train_accuracy:
best_epoch_train_accuracy = epoch_accuracy
torch.save(
model.state_dict(),
f"./model_params/logistic_regression/train/train_logreg_epoch{current_epoch+1}_acc{best_epoch_train_accuracy:.4f}.pth"
)
print("\n\nSAVED BASED ON TRAIN ACCURACY!\n\n")
train_time_elapsed = time.time() - train_epoch_since
# Validation loop
elif phase == 'val':
val_epoch_since = time.time()
model.eval()
with torch.no_grad():
for val_batch_window, val_batch_label in val_loader:
current_val_iter += 1
outs = model(val_batch_window)
gt_confidence, gt_index = torch.max(val_batch_label,
dim=1)
val_loss = criterion(outs, val_batch_label)
#val_loss = criterion(outs, gt_index)
running_val_loss += val_loss.item()
current_average_val_loss = running_val_loss / current_val_iter
pred_confidence, pred_index = torch.max(outs, dim=1)
#gt_confidence, gt_index = torch.max(val_batch_label, dim=1)
batch_correct_preds = torch.eq(
pred_index, gt_index).long().sum().item()
batch_accuracy = (batch_correct_preds /
val_batch_window.shape[0]) * 100
num_val_data += val_batch_window.shape[0]
running_val_correct_preds += batch_correct_preds
if current_val_iter % 10 == 0:
print(
f"\nITER#{current_val_iter} ({current_epoch+1}) BATCH VALIDATION ACCURACY: {batch_accuracy:.4f}, RUNNING VALIDATION LOSS: {val_loss.item():.8f}"
)
print(
f"Predicted / GT index:\n{pred_index}\n{gt_index}\n"
)
last_epoch_average_val_loss = current_average_val_loss
epoch_accuracy = (running_val_correct_preds /
num_val_data) * 100
print(
f"\n\nEPOCH#{current_epoch+1} EPOCH VALIDATION ACCURACY (BEST): {epoch_accuracy:.4f} ({best_epoch_val_accuracy:.4f}), AVERAGE VALIDATION LOSS: {last_epoch_average_val_loss:.8f}\n\n"
)
if epoch_accuracy > best_epoch_val_accuracy:
best_epoch_val_accuracy = epoch_accuracy
torch.save(
model.state_dict(),
f"./model_params/logistic_regression/val/val_logreg_epoch{current_epoch+1}_acc{best_epoch_val_accuracy:.4f}.pth"
)
print("\n\nSAVED BASED ON VAL ACCURACY!\n\n")
val_time_elapsed = time.time() - val_epoch_since
assert x_norm_train.shape[1] + x_priv_train.shape[1] == x.shape[1]
assert x_norm_valid.shape[1] + x_priv_valid.shape[1] == x.shape[1]
# take data to kernel space
k_norm_train = gaussian_kernel(x_norm_train, x_norm_train)
k_norm_valid = gaussian_kernel(x_norm_train, x_norm_valid)
k_priv_train = gaussian_kernel(x_priv_train, x_priv_train)
# loop over svm model parameter space
mdl = LogisticRegression()
params = mdl.hyper_parameters()
for p in params:
# train the model
t_start = time.time()
success = mdl.train(x_norm_train, y_train, gamma=p['gamma'])
# did we succeed?
if success:
# test the model with linear features
y_hat = mdl.predict(x_norm_valid)
# get metrics
recall, precision, f1 = metrics(y_valid, y_hat)
save_result(t, 'lr', 'linear', recall, precision, f1, C=None, gamma=p['gamma'])
# print result
t_elapsed = time.time() - t_start
print('Logistic Regression w/ gamma = {:.2e}'.format(p['gamma'],) +
' | Precision = {:.4f}, Recall = {:.4f}, F1 = {:.4f}, '.format(precision, recall, f1) +
