import os
import torch
import torchvision.transforms as transforms
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
from torchvision.datasets import ImageFolder
from torch.utils.data import DataLoader, Subset
from torchvision.transforms import v2
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = False
import numpy as np
import matplotlib.pyplot as plt
import sklearn.metrics as metrics
import random
random.seed(42)
torch.manual_seed(42)
np.random.seed(42)
import timm
from pprint import pprint
from collections import Counter
device = 'cuda'
DATA_PATH = '/net/travail/bformanek/MRI_dataset'
TRAIN_FOLDER = DATA_PATH + '/train'
VAL_FOLDER = DATA_PATH + '/val'
TEST_FOLDER = DATA_PATH + '/test'
train_categories = os.listdir(TRAIN_FOLDER)
val_categories = os.listdir(VAL_FOLDER)
test_categories = os.listdir(TEST_FOLDER)
print("Train image distribution: ")
class_num_in_train = []
for i in range(0, len(train_categories)):
CLASS_FOLDER = TRAIN_FOLDER + '/' + train_categories[i]
class_elements = os.listdir(CLASS_FOLDER)
class_num_in_train.append(len(class_elements))
print(f' {train_categories[i]}: {class_num_in_train[i]}')
print("Validation image distribution: ")
class_num_in_val = []
for i in range(0, len(val_categories)):
CLASS_FOLDER = VAL_FOLDER + '/' + val_categories[i]
class_elements = os.listdir(CLASS_FOLDER)
class_num_in_val.append(len(class_elements))
print(f' {val_categories[i]}: {class_num_in_val[i]}')
print("Test image distribution: ")
class_num_in_test = []
for i in range(0, len(test_categories)):
CLASS_FOLDER = TEST_FOLDER + '/' + test_categories[i]
class_elements = os.listdir(CLASS_FOLDER)
class_num_in_test.append(len(class_elements))
print(f' {test_categories[i]}: {class_num_in_test[i]}')
num_classes = len(class_num_in_train)
Train image distribution: T2star: 25 T2w: 1156 FLAIRCE: 1126 FLAIR: 5950 T1w: 5881 OTHER: 382 T1wCE: 5947 Validation image distribution: T2w: 160 FLAIRCE: 157 FLAIR: 844 T1w: 838 OTHER: 49 T1wCE: 847 Test image distribution: T2star: 4 T2w: 325 FLAIRCE: 319 FLAIR: 1693 T1w: 1678 OTHER: 118 T1wCE: 1696
def train_for_epoch_with_scaler(model, train_loader, optimizer, criterion, scaler, device):
# set model to train
model.train()
train_losses = []
train_accuracies = []
counter = 0
for batch, target in train_loader:
# data to GPU
batch = batch.to(device)
target = target.to(device)
# reset optimizer
optimizer.zero_grad()
# forward pass
predictions = model(batch)
# calculate accuracy
accuracy = (torch.argmax(predictions, dim=1) == target).sum().item() / target.size(0)
# calculate loss
loss = criterion(predictions, target)
# backward pass
scaler.scale(loss).backward()
# parameter update
scaler.step(optimizer)
scaler.update()
# track loss
train_losses.append(float(loss.item()))
train_accuracies.append(accuracy)
counter += 1
if counter % 20 == 0:
print('[{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format(
int(counter * len(batch)), len(train_loader.dataset),
100. * counter / len(train_loader), loss.item()))
train_loss = np.mean(np.array(train_losses))
train_accuracy = np.mean(np.array(train_accuracies))
print('\nTrain: Average loss: {:.4f}, Accuracy: {:.4f}\n'.format(
train_loss, train_accuracy))
return train_loss, train_accuracy
def validate(model, val_loader, criterion, device):
model.eval()
val_losses = []
y_true, y_pred = [], []
with torch.no_grad():
for batch, target in val_loader:
# move data to the device
batch = batch.to(device)
target = target.to(device)
with torch.autocast(device_type=device, dtype=torch.float16):
# make predictions
predictions = model(batch)
# calculate loss
loss = criterion(predictions, target)
# track losses and predictions
val_losses.append(float(loss.item()))
y_true.extend(target.cpu().numpy())
y_pred.extend(predictions.argmax(dim=1).cpu().numpy())
y_true = np.array(y_true)
y_pred = np.array(y_pred)
val_losses = np.array(val_losses)
# calculate validation accuracy from y_true and y_pred
val_accuracy = np.mean(y_true == y_pred)
# calculate the mean validation loss
val_loss = np.mean(val_losses)
print('Validation: Average loss: {:.4f}, Accuracy: {:.4f}\n'.format(
val_loss, val_accuracy))
return val_loss, val_accuracy
def train_with_scaler(model, train_loader, val_loader, optimizer, criterion, epochs, scaler, device, checkpoints_foler = None, first_epoch=1):
train_losses, val_losses = [], []
train_accuracies, val_accuracies = [], []
max_val_acc = 0
best_epoch = 0
for epoch in range(first_epoch, epochs+first_epoch):
print('Train Epoch: {}'.format(epoch))
# train
train_loss, train_acc = train_for_epoch_with_scaler(model, train_loader, optimizer, criterion, scaler, device)
# validation
valid_loss, valid_acc = validate(model, val_loader, criterion, device)
train_losses.append(train_loss)
val_losses.append(valid_loss)
train_accuracies.append(train_acc)
val_accuracies.append(valid_acc)
# save checkpoint
if checkpoints_foler != None and max_val_acc < valid_acc:
max_val_acc = valid_acc
best_epoch = epoch
torch.save(model, checkpoints_foler+f'/avp_{epoch:03d}.pkl')
return train_losses, val_losses, train_accuracies, val_accuracies, best_epoch
# define custom resample class to change image resolution without rescaling
class RandomResample:
def __init__(self, scale_factor):
self.scale_factor = random.uniform(0,scale_factor)
def __call__(self, img):
# Downsample
width, height = img.size
downscaled_size = (int(width / self.scale_factor), int(height / self.scale_factor))
# Downsample the image
img_downsampled = img.resize(downscaled_size)
# Upsample back to the original size
img_upsampled = img_downsampled.resize((width, height))
return img_upsampled
train_transform = transforms.Compose([
transforms.v2.Resize(224),
# augmentations
transforms.v2.RandomHorizontalFlip(p=0.5),
transforms.v2.RandomVerticalFlip(p=0.5),
#transforms.v2.RandomRotation(degrees=90, expand=True),
#transforms.v2.ColorJitter(contrast=0.1),
#transforms.v2.GaussianBlur(7, sigma=2),
#RandomResample(scale_factor=2),
transforms.ToTensor()
])
valid_transform = transforms.Compose([
transforms.Resize(224),
transforms.ToTensor()
])
train_set = ImageFolder(TRAIN_FOLDER, transform = train_transform)
val_set = ImageFolder(VAL_FOLDER, transform = valid_transform)
test_set = ImageFolder(TEST_FOLDER, transform = valid_transform)
BATCH_SIZE = 64
WORKERS = 8
train_loader = DataLoader(train_set, batch_size = BATCH_SIZE, shuffle = True, num_workers=WORKERS)
val_loader = DataLoader(val_set, batch_size = BATCH_SIZE, shuffle = False, num_workers=WORKERS)
test_loader = DataLoader(test_set, batch_size = BATCH_SIZE, shuffle = False, num_workers=WORKERS)
# print(f'train samples: {len(train_set)} validation samples: {len(val_set)} test samples: {len(test_set)}')
#for image_batch, labels_batch in train_loader:
# print("Batch sizes:", image_batch.shape, "(batch, channels, height, width)")
# print("Label vector size:", labels_batch.shape)
# break
num_in_class_dict = dict(Counter(train_set.targets[i] for i in range(len(train_set))))
num_in_class = np.zeros([1,len(num_in_class_dict)])
for i in range(0, len(num_in_class_dict)):
num_in_class[0, i] = num_in_class_dict[i]
class_weights = 1-(num_in_class/num_in_class.sum()).squeeze()
class_weights_tensor = torch.Tensor(class_weights).to(device)
# print(num_in_class_dict)
# print(num_in_class)
MODEL_NAME = 'resnet18' ##resnet18, resnet50, efficientnet_b0
model = timm.create_model(MODEL_NAME, pretrained=True, num_classes=num_classes)
model.to(device)
model.safetensors: 0%| | 0.00/46.8M [00:00<?, ?B/s]
ResNet( (conv1): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False) (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act1): ReLU(inplace=True) (maxpool): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False) (layer1): Sequential( (0): BasicBlock( (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (drop_block): Identity() (act1): ReLU(inplace=True) (aa): Identity() (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act2): ReLU(inplace=True) ) (1): BasicBlock( (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (drop_block): Identity() (act1): ReLU(inplace=True) (aa): Identity() (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act2): ReLU(inplace=True) ) ) (layer2): Sequential( (0): BasicBlock( (conv1): Conv2d(64, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False) (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (drop_block): Identity() (act1): ReLU(inplace=True) (aa): Identity() (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act2): ReLU(inplace=True) (downsample): Sequential( (0): Conv2d(64, 128, kernel_size=(1, 1), stride=(2, 2), bias=False) (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) ) ) (1): BasicBlock( (conv1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (drop_block): Identity() (act1): ReLU(inplace=True) (aa): Identity() (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act2): ReLU(inplace=True) ) ) (layer3): Sequential( (0): BasicBlock( (conv1): Conv2d(128, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False) (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (drop_block): Identity() (act1): ReLU(inplace=True) (aa): Identity() (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act2): ReLU(inplace=True) (downsample): Sequential( (0): Conv2d(128, 256, kernel_size=(1, 1), stride=(2, 2), bias=False) (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) ) ) (1): BasicBlock( (conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (drop_block): Identity() (act1): ReLU(inplace=True) (aa): Identity() (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act2): ReLU(inplace=True) ) ) (layer4): Sequential( (0): BasicBlock( (conv1): Conv2d(256, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False) (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (drop_block): Identity() (act1): ReLU(inplace=True) (aa): Identity() (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act2): ReLU(inplace=True) (downsample): Sequential( (0): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False) (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) ) ) (1): BasicBlock( (conv1): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (drop_block): Identity() (act1): ReLU(inplace=True) (aa): Identity() (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (act2): ReLU(inplace=True) ) ) (global_pool): SelectAdaptivePool2d(pool_type=avg, flatten=Flatten(start_dim=1, end_dim=-1)) (fc): Linear(in_features=512, out_features=7, bias=True) )
criterion_balanced = nn.CrossEntropyLoss(weight = class_weights_tensor)
optimizer_Adam = optim.Adam(model.parameters(), 1e-3)
scaler = torch.cuda.amp.GradScaler()
/tmp/cache-bformanek/ipykernel_798439/3247579378.py:3: FutureWarning: `torch.cuda.amp.GradScaler(args...)` is deprecated. Please use `torch.amp.GradScaler('cuda', args...)` instead. scaler = torch.cuda.amp.GradScaler()
RESULT_FOLDER_NAME = MODEL_NAME+"_flips"
checkpoints_foler = '/net/travail/bformanek/checkpoints/transfer_checkpoints_'+RESULT_FOLDER_NAME
if not os.path.exists(checkpoints_foler):
os.mkdir(checkpoints_foler)
epochs = 30
train_losses, val_losses, train_accuracies, val_accuracies, best_epoch = train_with_scaler(model, train_loader, val_loader, optimizer_Adam, criterion_balanced,
epochs, scaler, device, checkpoints_foler=checkpoints_foler)
Train Epoch: 1 [1280/20460 (6%)] Loss: 0.843502 [2560/20460 (12%)] Loss: 0.636460 [3840/20460 (19%)] Loss: 0.265568 [5120/20460 (25%)] Loss: 0.319542 [6400/20460 (31%)] Loss: 0.280687 [7680/20460 (38%)] Loss: 0.138691 [8960/20460 (44%)] Loss: 0.234106 [10240/20460 (50%)] Loss: 0.151387 [11520/20460 (56%)] Loss: 0.057821 [12800/20460 (62%)] Loss: 0.083600 [14080/20460 (69%)] Loss: 0.237462 [15360/20460 (75%)] Loss: 0.181196 [16640/20460 (81%)] Loss: 0.165535 [17920/20460 (88%)] Loss: 0.099655 [19200/20460 (94%)] Loss: 0.194050 [14080/20460 (100%)] Loss: 0.130613 Train: Average loss: 0.3007, Accuracy: 0.9019 Validation: Average loss: 0.8051, Accuracy: 0.8986 Train Epoch: 2 [1280/20460 (6%)] Loss: 0.037925 [2560/20460 (12%)] Loss: 0.331594 [3840/20460 (19%)] Loss: 0.103097 [5120/20460 (25%)] Loss: 0.126842 [6400/20460 (31%)] Loss: 0.142411 [7680/20460 (38%)] Loss: 0.236230 [8960/20460 (44%)] Loss: 0.129673 [10240/20460 (50%)] Loss: 0.042502 [11520/20460 (56%)] Loss: 0.032729 [12800/20460 (62%)] Loss: 0.135224 [14080/20460 (69%)] Loss: 0.218228 [15360/20460 (75%)] Loss: 0.043241 [16640/20460 (81%)] Loss: 0.036705 [17920/20460 (88%)] Loss: 0.061121 [19200/20460 (94%)] Loss: 0.049615 [14080/20460 (100%)] Loss: 0.032041 Train: Average loss: 0.1071, Accuracy: 0.9642 Validation: Average loss: 0.8064, Accuracy: 0.8681 Train Epoch: 3 [1280/20460 (6%)] Loss: 0.064735 [2560/20460 (12%)] Loss: 0.121229 [3840/20460 (19%)] Loss: 0.038687 [5120/20460 (25%)] Loss: 0.029820 [6400/20460 (31%)] Loss: 0.032307 [7680/20460 (38%)] Loss: 0.066418 [8960/20460 (44%)] Loss: 0.052793 [10240/20460 (50%)] Loss: 0.008758 [11520/20460 (56%)] Loss: 0.057714 [12800/20460 (62%)] Loss: 0.213928 [14080/20460 (69%)] Loss: 0.021839 [15360/20460 (75%)] Loss: 0.010417 [16640/20460 (81%)] Loss: 0.089229 [17920/20460 (88%)] Loss: 0.228579 [19200/20460 (94%)] Loss: 0.172073 [14080/20460 (100%)] Loss: 0.027754 Train: Average loss: 0.0821, Accuracy: 0.9712 Validation: Average loss: 1.2141, Accuracy: 0.8875 Train Epoch: 4 [1280/20460 (6%)] Loss: 0.051589 [2560/20460 (12%)] Loss: 0.024568 [3840/20460 (19%)] Loss: 0.108657 [5120/20460 (25%)] Loss: 0.063263 [6400/20460 (31%)] Loss: 0.085084 [7680/20460 (38%)] Loss: 0.032412 [8960/20460 (44%)] Loss: 0.041494 [10240/20460 (50%)] Loss: 0.028417 [11520/20460 (56%)] Loss: 0.008242 [12800/20460 (62%)] Loss: 0.068644 [14080/20460 (69%)] Loss: 0.012760 [15360/20460 (75%)] Loss: 0.089875 [16640/20460 (81%)] Loss: 0.011166 [17920/20460 (88%)] Loss: 0.008395 [19200/20460 (94%)] Loss: 0.194889 [14080/20460 (100%)] Loss: 0.222704 Train: Average loss: 0.0631, Accuracy: 0.9788 Validation: Average loss: 1.3527, Accuracy: 0.8522 Train Epoch: 5 [1280/20460 (6%)] Loss: 0.075228 [2560/20460 (12%)] Loss: 0.183242 [3840/20460 (19%)] Loss: 0.052669 [5120/20460 (25%)] Loss: 0.070792 [6400/20460 (31%)] Loss: 0.033730 [7680/20460 (38%)] Loss: 0.036751 [8960/20460 (44%)] Loss: 0.018535 [10240/20460 (50%)] Loss: 0.112883 [11520/20460 (56%)] Loss: 0.011364 [12800/20460 (62%)] Loss: 0.009888 [14080/20460 (69%)] Loss: 0.008560 [15360/20460 (75%)] Loss: 0.176944 [16640/20460 (81%)] Loss: 0.045591 [17920/20460 (88%)] Loss: 0.091157 [19200/20460 (94%)] Loss: 0.047302 [14080/20460 (100%)] Loss: 0.055603 Train: Average loss: 0.0530, Accuracy: 0.9810 Validation: Average loss: 1.0392, Accuracy: 0.8958 Train Epoch: 6 [1280/20460 (6%)] Loss: 0.194056 [2560/20460 (12%)] Loss: 0.054524 [3840/20460 (19%)] Loss: 0.096819 [5120/20460 (25%)] Loss: 0.027385 [6400/20460 (31%)] Loss: 0.040478 [7680/20460 (38%)] Loss: 0.066533 [8960/20460 (44%)] Loss: 0.001637 [10240/20460 (50%)] Loss: 0.016554 [11520/20460 (56%)] Loss: 0.104257 [12800/20460 (62%)] Loss: 0.034533 [14080/20460 (69%)] Loss: 0.058659 [15360/20460 (75%)] Loss: 0.007250 [16640/20460 (81%)] Loss: 0.038391 [17920/20460 (88%)] Loss: 0.146151 [19200/20460 (94%)] Loss: 0.008795 [14080/20460 (100%)] Loss: 0.009480 Train: Average loss: 0.0525, Accuracy: 0.9822 Validation: Average loss: 1.3084, Accuracy: 0.8532 Train Epoch: 7 [1280/20460 (6%)] Loss: 0.066683 [2560/20460 (12%)] Loss: 0.029595 [3840/20460 (19%)] Loss: 0.035559 [5120/20460 (25%)] Loss: 0.016948 [6400/20460 (31%)] Loss: 0.041707 [7680/20460 (38%)] Loss: 0.009982 [8960/20460 (44%)] Loss: 0.044010 [10240/20460 (50%)] Loss: 0.006969 [11520/20460 (56%)] Loss: 0.120111 [12800/20460 (62%)] Loss: 0.025863 [14080/20460 (69%)] Loss: 0.060326 [15360/20460 (75%)] Loss: 0.002928 [16640/20460 (81%)] Loss: 0.028577 [17920/20460 (88%)] Loss: 0.028448 [19200/20460 (94%)] Loss: 0.074866 [14080/20460 (100%)] Loss: 0.032483 Train: Average loss: 0.0395, Accuracy: 0.9866 Validation: Average loss: 1.1550, Accuracy: 0.9176 Train Epoch: 8 [1280/20460 (6%)] Loss: 0.085236 [2560/20460 (12%)] Loss: 0.027678 [3840/20460 (19%)] Loss: 0.158847 [5120/20460 (25%)] Loss: 0.021061 [6400/20460 (31%)] Loss: 0.051230 [7680/20460 (38%)] Loss: 0.009708 [8960/20460 (44%)] Loss: 0.007543 [10240/20460 (50%)] Loss: 0.112185 [11520/20460 (56%)] Loss: 0.052509 [12800/20460 (62%)] Loss: 0.015509 [14080/20460 (69%)] Loss: 0.028660 [15360/20460 (75%)] Loss: 0.040962 [16640/20460 (81%)] Loss: 0.069964 [17920/20460 (88%)] Loss: 0.050259 [19200/20460 (94%)] Loss: 0.016087 [14080/20460 (100%)] Loss: 0.004772 Train: Average loss: 0.0363, Accuracy: 0.9874 Validation: Average loss: 1.9441, Accuracy: 0.8889 Train Epoch: 9 [1280/20460 (6%)] Loss: 0.039214 [2560/20460 (12%)] Loss: 0.019671 [3840/20460 (19%)] Loss: 0.017750 [5120/20460 (25%)] Loss: 0.014158 [6400/20460 (31%)] Loss: 0.025787 [7680/20460 (38%)] Loss: 0.048959 [8960/20460 (44%)] Loss: 0.040279 [10240/20460 (50%)] Loss: 0.008633 [11520/20460 (56%)] Loss: 0.033648 [12800/20460 (62%)] Loss: 0.015922 [14080/20460 (69%)] Loss: 0.035533 [15360/20460 (75%)] Loss: 0.020027 [16640/20460 (81%)] Loss: 0.001845 [17920/20460 (88%)] Loss: 0.057735 [19200/20460 (94%)] Loss: 0.006176 [14080/20460 (100%)] Loss: 0.037356 Train: Average loss: 0.0284, Accuracy: 0.9898 Validation: Average loss: 1.1217, Accuracy: 0.9169 Train Epoch: 10 [1280/20460 (6%)] Loss: 0.099287 [2560/20460 (12%)] Loss: 0.031279 [3840/20460 (19%)] Loss: 0.016670 [5120/20460 (25%)] Loss: 0.021939 [6400/20460 (31%)] Loss: 0.029313 [7680/20460 (38%)] Loss: 0.066098 [8960/20460 (44%)] Loss: 0.002704 [10240/20460 (50%)] Loss: 0.063547 [11520/20460 (56%)] Loss: 0.043373 [12800/20460 (62%)] Loss: 0.073674 [14080/20460 (69%)] Loss: 0.028117 [15360/20460 (75%)] Loss: 0.003981 [16640/20460 (81%)] Loss: 0.004771 [17920/20460 (88%)] Loss: 0.016762 [19200/20460 (94%)] Loss: 0.029757 [14080/20460 (100%)] Loss: 0.101116 Train: Average loss: 0.0334, Accuracy: 0.9883 Validation: Average loss: 1.7975, Accuracy: 0.9121 Train Epoch: 11 [1280/20460 (6%)] Loss: 0.041546 [2560/20460 (12%)] Loss: 0.051478 [3840/20460 (19%)] Loss: 0.022118 [5120/20460 (25%)] Loss: 0.030113 [6400/20460 (31%)] Loss: 0.046417 [7680/20460 (38%)] Loss: 0.007106 [8960/20460 (44%)] Loss: 0.020893 [10240/20460 (50%)] Loss: 0.061642 [11520/20460 (56%)] Loss: 0.023493 [12800/20460 (62%)] Loss: 0.012820 [14080/20460 (69%)] Loss: 0.011199 [15360/20460 (75%)] Loss: 0.017625 [16640/20460 (81%)] Loss: 0.001538 [17920/20460 (88%)] Loss: 0.104249 [19200/20460 (94%)] Loss: 0.018723 [14080/20460 (100%)] Loss: 0.001469 Train: Average loss: 0.0345, Accuracy: 0.9880 Validation: Average loss: 1.3650, Accuracy: 0.9100 Train Epoch: 12 [1280/20460 (6%)] Loss: 0.021998 [2560/20460 (12%)] Loss: 0.040917 [3840/20460 (19%)] Loss: 0.024007 [5120/20460 (25%)] Loss: 0.082473 [6400/20460 (31%)] Loss: 0.012331 [7680/20460 (38%)] Loss: 0.018738 [8960/20460 (44%)] Loss: 0.004542 [10240/20460 (50%)] Loss: 0.011837 [11520/20460 (56%)] Loss: 0.007478 [12800/20460 (62%)] Loss: 0.001182 [14080/20460 (69%)] Loss: 0.014484 [15360/20460 (75%)] Loss: 0.010896 [16640/20460 (81%)] Loss: 0.016414 [17920/20460 (88%)] Loss: 0.026211 [19200/20460 (94%)] Loss: 0.002081 [14080/20460 (100%)] Loss: 0.036857 Train: Average loss: 0.0217, Accuracy: 0.9927 Validation: Average loss: 2.2220, Accuracy: 0.9055 Train Epoch: 13 [1280/20460 (6%)] Loss: 0.005104 [2560/20460 (12%)] Loss: 0.140588 [3840/20460 (19%)] Loss: 0.011966 [5120/20460 (25%)] Loss: 0.071950 [6400/20460 (31%)] Loss: 0.190116 [7680/20460 (38%)] Loss: 0.002321 [8960/20460 (44%)] Loss: 0.008502 [10240/20460 (50%)] Loss: 0.132841 [11520/20460 (56%)] Loss: 0.029625 [12800/20460 (62%)] Loss: 0.042037 [14080/20460 (69%)] Loss: 0.010593 [15360/20460 (75%)] Loss: 0.053600 [16640/20460 (81%)] Loss: 0.011899 [17920/20460 (88%)] Loss: 0.022766 [19200/20460 (94%)] Loss: 0.003575 [14080/20460 (100%)] Loss: 0.088787 Train: Average loss: 0.0329, Accuracy: 0.9892 Validation: Average loss: 1.5505, Accuracy: 0.9166 Train Epoch: 14 [1280/20460 (6%)] Loss: 0.002508 [2560/20460 (12%)] Loss: 0.003015 [3840/20460 (19%)] Loss: 0.048746 [5120/20460 (25%)] Loss: 0.010032 [6400/20460 (31%)] Loss: 0.001074 [7680/20460 (38%)] Loss: 0.004669 [8960/20460 (44%)] Loss: 0.003444 [10240/20460 (50%)] Loss: 0.002633 [11520/20460 (56%)] Loss: 0.000428 [12800/20460 (62%)] Loss: 0.021712 [14080/20460 (69%)] Loss: 0.003693 [15360/20460 (75%)] Loss: 0.002779 [16640/20460 (81%)] Loss: 0.013868 [17920/20460 (88%)] Loss: 0.004539 [19200/20460 (94%)] Loss: 0.004694 [14080/20460 (100%)] Loss: 0.005490 Train: Average loss: 0.0203, Accuracy: 0.9937 Validation: Average loss: 1.7363, Accuracy: 0.9114 Train Epoch: 15 [1280/20460 (6%)] Loss: 0.022853 [2560/20460 (12%)] Loss: 0.058304 [3840/20460 (19%)] Loss: 0.023011 [5120/20460 (25%)] Loss: 0.028808 [6400/20460 (31%)] Loss: 0.001351 [7680/20460 (38%)] Loss: 0.004174 [8960/20460 (44%)] Loss: 0.021585 [10240/20460 (50%)] Loss: 0.168816 [11520/20460 (56%)] Loss: 0.020091 [12800/20460 (62%)] Loss: 0.003948 [14080/20460 (69%)] Loss: 0.011772 [15360/20460 (75%)] Loss: 0.043764 [16640/20460 (81%)] Loss: 0.032338 [17920/20460 (88%)] Loss: 0.006927 [19200/20460 (94%)] Loss: 0.000218 [14080/20460 (100%)] Loss: 0.001732 Train: Average loss: 0.0182, Accuracy: 0.9926 Validation: Average loss: 1.7640, Accuracy: 0.9218 Train Epoch: 16 [1280/20460 (6%)] Loss: 0.001045 [2560/20460 (12%)] Loss: 0.007786 [3840/20460 (19%)] Loss: 0.030352 [5120/20460 (25%)] Loss: 0.011805 [6400/20460 (31%)] Loss: 0.002842 [7680/20460 (38%)] Loss: 0.032796 [8960/20460 (44%)] Loss: 0.000502 [10240/20460 (50%)] Loss: 0.003590 [11520/20460 (56%)] Loss: 0.043356 [12800/20460 (62%)] Loss: 0.004678 [14080/20460 (69%)] Loss: 0.067378 [15360/20460 (75%)] Loss: 0.000902 [16640/20460 (81%)] Loss: 0.002897 [17920/20460 (88%)] Loss: 0.042462 [19200/20460 (94%)] Loss: 0.000499 [14080/20460 (100%)] Loss: 0.001359 Train: Average loss: 0.0232, Accuracy: 0.9917 Validation: Average loss: 1.6588, Accuracy: 0.8834 Train Epoch: 17 [1280/20460 (6%)] Loss: 0.001905 [2560/20460 (12%)] Loss: 0.000994 [3840/20460 (19%)] Loss: 0.081658 [5120/20460 (25%)] Loss: 0.000705 [6400/20460 (31%)] Loss: 0.001401 [7680/20460 (38%)] Loss: 0.015049 [8960/20460 (44%)] Loss: 0.009079 [10240/20460 (50%)] Loss: 0.024857 [11520/20460 (56%)] Loss: 0.030782 [12800/20460 (62%)] Loss: 0.019273 [14080/20460 (69%)] Loss: 0.001851 [15360/20460 (75%)] Loss: 0.002028 [16640/20460 (81%)] Loss: 0.002474 [17920/20460 (88%)] Loss: 0.012784 [19200/20460 (94%)] Loss: 0.036569 [14080/20460 (100%)] Loss: 0.082116 Train: Average loss: 0.0232, Accuracy: 0.9925 Validation: Average loss: 1.7074, Accuracy: 0.9013 Train Epoch: 18 [1280/20460 (6%)] Loss: 0.003554 [2560/20460 (12%)] Loss: 0.002146 [3840/20460 (19%)] Loss: 0.001330 [5120/20460 (25%)] Loss: 0.002112 [6400/20460 (31%)] Loss: 0.002504 [7680/20460 (38%)] Loss: 0.005189 [8960/20460 (44%)] Loss: 0.006362 [10240/20460 (50%)] Loss: 0.005909 [11520/20460 (56%)] Loss: 0.003749 [12800/20460 (62%)] Loss: 0.120280 [14080/20460 (69%)] Loss: 0.026630 [15360/20460 (75%)] Loss: 0.020769 [16640/20460 (81%)] Loss: 0.003625 [17920/20460 (88%)] Loss: 0.017444 [19200/20460 (94%)] Loss: 0.001120 [14080/20460 (100%)] Loss: 0.030654 Train: Average loss: 0.0193, Accuracy: 0.9932 Validation: Average loss: 1.9890, Accuracy: 0.9211 Train Epoch: 19 [1280/20460 (6%)] Loss: 0.001809 [2560/20460 (12%)] Loss: 0.002560 [3840/20460 (19%)] Loss: 0.006533 [5120/20460 (25%)] Loss: 0.005828 [6400/20460 (31%)] Loss: 0.015907 [7680/20460 (38%)] Loss: 0.019339 [8960/20460 (44%)] Loss: 0.081810 [10240/20460 (50%)] Loss: 0.005694 [11520/20460 (56%)] Loss: 0.019671 [12800/20460 (62%)] Loss: 0.013080 [14080/20460 (69%)] Loss: 0.153794 [15360/20460 (75%)] Loss: 0.010798 [16640/20460 (81%)] Loss: 0.137059 [17920/20460 (88%)] Loss: 0.008022 [19200/20460 (94%)] Loss: 0.015594 [14080/20460 (100%)] Loss: 0.001842 Train: Average loss: 0.0230, Accuracy: 0.9927 Validation: Average loss: 2.2278, Accuracy: 0.9103 Train Epoch: 20 [1280/20460 (6%)] Loss: 0.006314 [2560/20460 (12%)] Loss: 0.057334 [3840/20460 (19%)] Loss: 0.000594 [5120/20460 (25%)] Loss: 0.001636 [6400/20460 (31%)] Loss: 0.044090 [7680/20460 (38%)] Loss: 0.049883 [8960/20460 (44%)] Loss: 0.001009 [10240/20460 (50%)] Loss: 0.036804 [11520/20460 (56%)] Loss: 0.002672 [12800/20460 (62%)] Loss: 0.000661 [14080/20460 (69%)] Loss: 0.024494 [15360/20460 (75%)] Loss: 0.005727 [16640/20460 (81%)] Loss: 0.027821 [17920/20460 (88%)] Loss: 0.034874 [19200/20460 (94%)] Loss: 0.017702 [14080/20460 (100%)] Loss: 0.001258 Train: Average loss: 0.0208, Accuracy: 0.9926 Validation: Average loss: 1.6647, Accuracy: 0.9214 Train Epoch: 21 [1280/20460 (6%)] Loss: 0.006564 [2560/20460 (12%)] Loss: 0.015446 [3840/20460 (19%)] Loss: 0.000575 [5120/20460 (25%)] Loss: 0.004266 [6400/20460 (31%)] Loss: 0.004085 [7680/20460 (38%)] Loss: 0.000880 [8960/20460 (44%)] Loss: 0.010062 [10240/20460 (50%)] Loss: 0.021801 [11520/20460 (56%)] Loss: 0.021008 [12800/20460 (62%)] Loss: 0.011042 [14080/20460 (69%)] Loss: 0.001105 [15360/20460 (75%)] Loss: 0.039772 [16640/20460 (81%)] Loss: 0.015889 [17920/20460 (88%)] Loss: 0.098535 [19200/20460 (94%)] Loss: 0.036734 [14080/20460 (100%)] Loss: 0.000547 Train: Average loss: 0.0210, Accuracy: 0.9934 Validation: Average loss: 1.8642, Accuracy: 0.9259 Train Epoch: 22 [1280/20460 (6%)] Loss: 0.014994 [2560/20460 (12%)] Loss: 0.003497 [3840/20460 (19%)] Loss: 0.017347 [5120/20460 (25%)] Loss: 0.017197 [6400/20460 (31%)] Loss: 0.012267 [7680/20460 (38%)] Loss: 0.004205 [8960/20460 (44%)] Loss: 0.055639 [10240/20460 (50%)] Loss: 0.039475 [11520/20460 (56%)] Loss: 0.001111 [12800/20460 (62%)] Loss: 0.052459 [14080/20460 (69%)] Loss: 0.010310 [15360/20460 (75%)] Loss: 0.000916 [16640/20460 (81%)] Loss: 0.019943 [17920/20460 (88%)] Loss: 0.006757 [19200/20460 (94%)] Loss: 0.000739 [14080/20460 (100%)] Loss: 0.119508 Train: Average loss: 0.0155, Accuracy: 0.9941 Validation: Average loss: 1.9463, Accuracy: 0.9294 Train Epoch: 23 [1280/20460 (6%)] Loss: 0.011790 [2560/20460 (12%)] Loss: 0.006092 [3840/20460 (19%)] Loss: 0.008761 [5120/20460 (25%)] Loss: 0.000485 [6400/20460 (31%)] Loss: 0.000202 [7680/20460 (38%)] Loss: 0.010171 [8960/20460 (44%)] Loss: 0.001184 [10240/20460 (50%)] Loss: 0.004038 [11520/20460 (56%)] Loss: 0.037764 [12800/20460 (62%)] Loss: 0.021872 [14080/20460 (69%)] Loss: 0.003651 [15360/20460 (75%)] Loss: 0.018937 [16640/20460 (81%)] Loss: 0.010850 [17920/20460 (88%)] Loss: 0.118696 [19200/20460 (94%)] Loss: 0.007832 [14080/20460 (100%)] Loss: 0.000214 Train: Average loss: 0.0116, Accuracy: 0.9959 Validation: Average loss: 2.1865, Accuracy: 0.9083 Train Epoch: 24 [1280/20460 (6%)] Loss: 0.014718 [2560/20460 (12%)] Loss: 0.015482 [3840/20460 (19%)] Loss: 0.001641 [5120/20460 (25%)] Loss: 0.004247 [6400/20460 (31%)] Loss: 0.117957 [7680/20460 (38%)] Loss: 0.002905 [8960/20460 (44%)] Loss: 0.112692 [10240/20460 (50%)] Loss: 0.012402 [11520/20460 (56%)] Loss: 0.000886 [12800/20460 (62%)] Loss: 0.004219 [14080/20460 (69%)] Loss: 0.049096 [15360/20460 (75%)] Loss: 0.002734 [16640/20460 (81%)] Loss: 0.005479 [17920/20460 (88%)] Loss: 0.120161 [19200/20460 (94%)] Loss: 0.032658 [14080/20460 (100%)] Loss: 0.001505 Train: Average loss: 0.0197, Accuracy: 0.9936 Validation: Average loss: 2.4084, Accuracy: 0.9183 Train Epoch: 25 [1280/20460 (6%)] Loss: 0.004264 [2560/20460 (12%)] Loss: 0.007109 [3840/20460 (19%)] Loss: 0.085215 [5120/20460 (25%)] Loss: 0.026944 [6400/20460 (31%)] Loss: 0.000698 [7680/20460 (38%)] Loss: 0.015090 [8960/20460 (44%)] Loss: 0.001711 [10240/20460 (50%)] Loss: 0.007621 [11520/20460 (56%)] Loss: 0.002430 [12800/20460 (62%)] Loss: 0.001329 [14080/20460 (69%)] Loss: 0.010306 [15360/20460 (75%)] Loss: 0.041298 [16640/20460 (81%)] Loss: 0.002016 [17920/20460 (88%)] Loss: 0.025135 [19200/20460 (94%)] Loss: 0.021565 [14080/20460 (100%)] Loss: 0.000667 Train: Average loss: 0.0237, Accuracy: 0.9923 Validation: Average loss: 2.1785, Accuracy: 0.9256 Train Epoch: 26 [1280/20460 (6%)] Loss: 0.010072 [2560/20460 (12%)] Loss: 0.011213 [3840/20460 (19%)] Loss: 0.102337 [5120/20460 (25%)] Loss: 0.004811 [6400/20460 (31%)] Loss: 0.077677 [7680/20460 (38%)] Loss: 0.023315 [8960/20460 (44%)] Loss: 0.018149 [10240/20460 (50%)] Loss: 0.005450 [11520/20460 (56%)] Loss: 0.112735 [12800/20460 (62%)] Loss: 0.019306 [14080/20460 (69%)] Loss: 0.005151 [15360/20460 (75%)] Loss: 0.004922 [16640/20460 (81%)] Loss: 0.000809 [17920/20460 (88%)] Loss: 0.000919 [19200/20460 (94%)] Loss: 0.000461 [14080/20460 (100%)] Loss: 0.004289 Train: Average loss: 0.0223, Accuracy: 0.9927 Validation: Average loss: 2.2146, Accuracy: 0.9183 Train Epoch: 27 [1280/20460 (6%)] Loss: 0.016471 [2560/20460 (12%)] Loss: 0.001076 [3840/20460 (19%)] Loss: 0.019334 [5120/20460 (25%)] Loss: 0.004151 [6400/20460 (31%)] Loss: 0.006527 [7680/20460 (38%)] Loss: 0.007019 [8960/20460 (44%)] Loss: 0.005090 [10240/20460 (50%)] Loss: 0.117043 [11520/20460 (56%)] Loss: 0.001335 [12800/20460 (62%)] Loss: 0.008948 [14080/20460 (69%)] Loss: 0.001296 [15360/20460 (75%)] Loss: 0.001040 [16640/20460 (81%)] Loss: 0.001769 [17920/20460 (88%)] Loss: 0.001021 [19200/20460 (94%)] Loss: 0.030267 [14080/20460 (100%)] Loss: 0.000470 Train: Average loss: 0.0096, Accuracy: 0.9972 Validation: Average loss: 2.2071, Accuracy: 0.9114 Train Epoch: 28 [1280/20460 (6%)] Loss: 0.098673 [2560/20460 (12%)] Loss: 0.001463 [3840/20460 (19%)] Loss: 0.004082 [5120/20460 (25%)] Loss: 0.001982 [6400/20460 (31%)] Loss: 0.002641 [7680/20460 (38%)] Loss: 0.008651 [8960/20460 (44%)] Loss: 0.000318 [10240/20460 (50%)] Loss: 0.037235 [11520/20460 (56%)] Loss: 0.029735 [12800/20460 (62%)] Loss: 0.010024 [14080/20460 (69%)] Loss: 0.001574 [15360/20460 (75%)] Loss: 0.014452 [16640/20460 (81%)] Loss: 0.017526 [17920/20460 (88%)] Loss: 0.003730 [19200/20460 (94%)] Loss: 0.003598 [14080/20460 (100%)] Loss: 0.075045 Train: Average loss: 0.0201, Accuracy: 0.9947 Validation: Average loss: 1.6083, Accuracy: 0.9277 Train Epoch: 29 [1280/20460 (6%)] Loss: 0.000327 [2560/20460 (12%)] Loss: 0.003786 [3840/20460 (19%)] Loss: 0.001012 [5120/20460 (25%)] Loss: 0.003225 [6400/20460 (31%)] Loss: 0.000164 [7680/20460 (38%)] Loss: 0.028374 [8960/20460 (44%)] Loss: 0.002373 [10240/20460 (50%)] Loss: 0.039698 [11520/20460 (56%)] Loss: 0.000525 [12800/20460 (62%)] Loss: 0.000442 [14080/20460 (69%)] Loss: 0.004335 [15360/20460 (75%)] Loss: 0.001106 [16640/20460 (81%)] Loss: 0.016350 [17920/20460 (88%)] Loss: 0.000121 [19200/20460 (94%)] Loss: 0.005580 [14080/20460 (100%)] Loss: 0.002199 Train: Average loss: 0.0084, Accuracy: 0.9972 Validation: Average loss: 1.6909, Accuracy: 0.9256 Train Epoch: 30 [1280/20460 (6%)] Loss: 0.002233 [2560/20460 (12%)] Loss: 0.000188 [3840/20460 (19%)] Loss: 0.000091 [5120/20460 (25%)] Loss: 0.026006 [6400/20460 (31%)] Loss: 0.003943 [7680/20460 (38%)] Loss: 0.000050 [8960/20460 (44%)] Loss: 0.000116 [10240/20460 (50%)] Loss: 0.002004 [11520/20460 (56%)] Loss: 0.000191 [12800/20460 (62%)] Loss: 0.043413 [14080/20460 (69%)] Loss: 0.005176 [15360/20460 (75%)] Loss: 0.019791 [16640/20460 (81%)] Loss: 0.001810 [17920/20460 (88%)] Loss: 0.002378 [19200/20460 (94%)] Loss: 0.000498 [14080/20460 (100%)] Loss: 0.016982 Train: Average loss: 0.0097, Accuracy: 0.9970 Validation: Average loss: 2.0909, Accuracy: 0.9135
epochs = range(1, len(train_losses) + 1)
plt.figure(figsize=(15,6))
plt.subplot(1,2,1)
plt.plot(epochs, train_losses, '-o', label='Training loss')
plt.plot(epochs, val_losses, '-o', label='Validation loss')
plt.legend()
plt.title('Learning curves')
plt.xlabel('Epoch')
plt.ylabel('Loss')
plt.xticks(epochs)
plt.subplot(1,2,2)
plt.plot(epochs, train_accuracies, '-o', label='Training accuracy')
plt.plot(epochs, val_accuracies, '-o', label='Validation accuracy')
plt.legend()
plt.title('Learning curves')
plt.xlabel('Epoch')
plt.ylabel('accuracy')
plt.xticks(epochs)
plt.show()
# best_epoch = 32
model = torch.load(checkpoints_foler+f'/avp_{best_epoch:03d}.pkl')
/tmp/cache-bformanek/ipykernel_798439/529002640.py:2: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature. model = torch.load(checkpoints_foler+f'/avp_{best_epoch:03d}.pkl')
def predict(model, data_loader):
model.eval()
# save the predictions in this list
y_pred = []
# no gradient needed
with torch.no_grad():
# go over each batch in the loader. We can ignore the targets here
for batch, _ in data_loader:
# Move batch to the GPU
batch = batch.to(device)
# predict probabilities of each class
predictions = model(batch)
# apply a softmax to the predictions
predictions = F.softmax(predictions, dim=1)
# move to the cpu and convert to numpy
predictions = predictions.cpu().numpy()
# save
y_pred.append(predictions)
# stack predictions into a (num_samples, 10) array
y_pred = np.vstack(y_pred)
return y_pred
# compute predictions on the test set
y_pred = predict(model, test_loader)
# find the argmax of each of the predictions
y_pred = y_pred.argmax(axis=1)
# get the true labels and convert to numpy
y_true = np.array(test_set.targets)
num_errors = np.sum(y_true != y_pred)
print(f'Test errors {num_errors} (out of {len(test_set)}) {num_errors/len(test_set)*100:0.2f}%')
print(f'Test accuracy {100-num_errors/len(test_set)*100:0.2f}%')
Test errors 75 (out of 5826) 1.29% Test accuracy 98.71%
from sklearn.metrics import confusion_matrix
import seaborn as sns
conf_matrix = confusion_matrix(y_true, y_pred)
plt.figure(figsize=(8, 6))
sns.heatmap(conf_matrix, annot=True, fmt='d', cmap='Blues',
xticklabels=train_categories,
yticklabels=train_categories)
plt.xlabel('Predicted Labels')
plt.ylabel('True Labels')
plt.title('Confusion Matrix')
plt.show()
/usr/lib/python3/dist-packages/statsmodels/__init__.py:6: UserWarning: This appears to be an armel system, on which statsmodels is buggy (crashes and possibly wrong answers) - https://bugs.debian.org/968210 warnings.warn("This appears to be an armel system, on which statsmodels is buggy (crashes and possibly wrong answers) - https://bugs.debian.org/968210")
TP = conf_matrix.diagonal()
P = conf_matrix.sum(axis=1)
# Calculate balanced accuracy
balanced_accuracy = sum(TP / P) / len(P)
print(f'Balanced accuracy {balanced_accuracy*100:0.2f}%')
Balanced accuracy 98.73%