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=360, expand=True), # expand=True: esnure that the whole image is represented on the rotated image
#transforms.v2.ColorJitter(contrast=0.1),
#transforms.v2.GaussianBlur(7, sigma=2),
#RandomResample(scale_factor=2),
transforms.v2.Resize(224),
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)
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_1725551/3247579378.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_360"
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: 0.999237 [2560/20460 (12%)] Loss: 0.840347 [3840/20460 (19%)] Loss: 0.486756 [5120/20460 (25%)] Loss: 0.559186 [6400/20460 (31%)] Loss: 0.458453 [7680/20460 (38%)] Loss: 0.272512 [8960/20460 (44%)] Loss: 0.324681 [10240/20460 (50%)] Loss: 0.250867 [11520/20460 (56%)] Loss: 0.180001 [12800/20460 (62%)] Loss: 0.255491 [14080/20460 (69%)] Loss: 0.443328 [15360/20460 (75%)] Loss: 0.181067 [16640/20460 (81%)] Loss: 0.198571 [17920/20460 (88%)] Loss: 0.151553 [19200/20460 (94%)] Loss: 0.178173 [14080/20460 (100%)] Loss: 0.257576 Train: Average loss: 0.4285, Accuracy: 0.8485 Validation: Average loss: 1.2914, Accuracy: 0.6632 Train Epoch: 2 [1280/20460 (6%)] Loss: 0.277554 [2560/20460 (12%)] Loss: 0.357009 [3840/20460 (19%)] Loss: 0.283155 [5120/20460 (25%)] Loss: 0.209387 [6400/20460 (31%)] Loss: 0.173001 [7680/20460 (38%)] Loss: 0.174767 [8960/20460 (44%)] Loss: 0.298894 [10240/20460 (50%)] Loss: 0.210401 [11520/20460 (56%)] Loss: 0.121789 [12800/20460 (62%)] Loss: 0.149770 [14080/20460 (69%)] Loss: 0.275387 [15360/20460 (75%)] Loss: 0.094935 [16640/20460 (81%)] Loss: 0.083764 [17920/20460 (88%)] Loss: 0.129739 [19200/20460 (94%)] Loss: 0.171136 [14080/20460 (100%)] Loss: 0.029456 Train: Average loss: 0.2039, Accuracy: 0.9264 Validation: Average loss: 1.3303, Accuracy: 0.7283 Train Epoch: 3 [1280/20460 (6%)] Loss: 0.116036 [2560/20460 (12%)] Loss: 0.181494 [3840/20460 (19%)] Loss: 0.157793 [5120/20460 (25%)] Loss: 0.123821 [6400/20460 (31%)] Loss: 0.152550 [7680/20460 (38%)] Loss: 0.141733 [8960/20460 (44%)] Loss: 0.122220 [10240/20460 (50%)] Loss: 0.054822 [11520/20460 (56%)] Loss: 0.069806 [12800/20460 (62%)] Loss: 0.253814 [14080/20460 (69%)] Loss: 0.062691 [15360/20460 (75%)] Loss: 0.122664 [16640/20460 (81%)] Loss: 0.105224 [17920/20460 (88%)] Loss: 0.349258 [19200/20460 (94%)] Loss: 0.246001 [14080/20460 (100%)] Loss: 0.112209 Train: Average loss: 0.1521, Accuracy: 0.9467 Validation: Average loss: 1.1219, Accuracy: 0.8522 Train Epoch: 4 [1280/20460 (6%)] Loss: 0.145345 [2560/20460 (12%)] Loss: 0.208852 [3840/20460 (19%)] Loss: 0.147174 [5120/20460 (25%)] Loss: 0.046925 [6400/20460 (31%)] Loss: 0.117533 [7680/20460 (38%)] Loss: 0.093898 [8960/20460 (44%)] Loss: 0.068285 [10240/20460 (50%)] Loss: 0.174730 [11520/20460 (56%)] Loss: 0.091854 [12800/20460 (62%)] Loss: 0.261202 [14080/20460 (69%)] Loss: 0.063772 [15360/20460 (75%)] Loss: 0.183514 [16640/20460 (81%)] Loss: 0.035941 [17920/20460 (88%)] Loss: 0.081779 [19200/20460 (94%)] Loss: 0.452818 [14080/20460 (100%)] Loss: 0.279215 Train: Average loss: 0.1423, Accuracy: 0.9497 Validation: Average loss: 1.2911, Accuracy: 0.8858 Train Epoch: 5 [1280/20460 (6%)] Loss: 0.162708 [2560/20460 (12%)] Loss: 0.138465 [3840/20460 (19%)] Loss: 0.150480 [5120/20460 (25%)] Loss: 0.179126 [6400/20460 (31%)] Loss: 0.074554 [7680/20460 (38%)] Loss: 0.129655 [8960/20460 (44%)] Loss: 0.028121 [10240/20460 (50%)] Loss: 0.205477 [11520/20460 (56%)] Loss: 0.053683 [12800/20460 (62%)] Loss: 0.104494 [14080/20460 (69%)] Loss: 0.042177 [15360/20460 (75%)] Loss: 0.206843 [16640/20460 (81%)] Loss: 0.180544 [17920/20460 (88%)] Loss: 0.067690 [19200/20460 (94%)] Loss: 0.131991 [14080/20460 (100%)] Loss: 0.087955 Train: Average loss: 0.1191, Accuracy: 0.9574 Validation: Average loss: 1.4581, Accuracy: 0.8702 Train Epoch: 6 [1280/20460 (6%)] Loss: 0.174448 [2560/20460 (12%)] Loss: 0.124685 [3840/20460 (19%)] Loss: 0.136886 [5120/20460 (25%)] Loss: 0.115015 [6400/20460 (31%)] Loss: 0.064571 [7680/20460 (38%)] Loss: 0.083053 [8960/20460 (44%)] Loss: 0.073907 [10240/20460 (50%)] Loss: 0.079761 [11520/20460 (56%)] Loss: 0.151325 [12800/20460 (62%)] Loss: 0.070430 [14080/20460 (69%)] Loss: 0.099623 [15360/20460 (75%)] Loss: 0.086542 [16640/20460 (81%)] Loss: 0.037445 [17920/20460 (88%)] Loss: 0.145197 [19200/20460 (94%)] Loss: 0.029267 [14080/20460 (100%)] Loss: 0.132985 Train: Average loss: 0.1029, Accuracy: 0.9617 Validation: Average loss: 1.5689, Accuracy: 0.7698 Train Epoch: 7 [1280/20460 (6%)] Loss: 0.092860 [2560/20460 (12%)] Loss: 0.068341 [3840/20460 (19%)] Loss: 0.078423 [5120/20460 (25%)] Loss: 0.050065 [6400/20460 (31%)] Loss: 0.059183 [7680/20460 (38%)] Loss: 0.095126 [8960/20460 (44%)] Loss: 0.080075 [10240/20460 (50%)] Loss: 0.079809 [11520/20460 (56%)] Loss: 0.100286 [12800/20460 (62%)] Loss: 0.205798 [14080/20460 (69%)] Loss: 0.157868 [15360/20460 (75%)] Loss: 0.051074 [16640/20460 (81%)] Loss: 0.093577 [17920/20460 (88%)] Loss: 0.076363 [19200/20460 (94%)] Loss: 0.152964 [14080/20460 (100%)] Loss: 0.058098 Train: Average loss: 0.0981, Accuracy: 0.9644 Validation: Average loss: 1.8628, Accuracy: 0.8501 Train Epoch: 8 [1280/20460 (6%)] Loss: 0.266822 [2560/20460 (12%)] Loss: 0.054511 [3840/20460 (19%)] Loss: 0.064749 [5120/20460 (25%)] Loss: 0.067840 [6400/20460 (31%)] Loss: 0.101704 [7680/20460 (38%)] Loss: 0.041162 [8960/20460 (44%)] Loss: 0.061517 [10240/20460 (50%)] Loss: 0.093574 [11520/20460 (56%)] Loss: 0.088456 [12800/20460 (62%)] Loss: 0.124745 [14080/20460 (69%)] Loss: 0.097899 [15360/20460 (75%)] Loss: 0.124717 [16640/20460 (81%)] Loss: 0.223142 [17920/20460 (88%)] Loss: 0.163567 [19200/20460 (94%)] Loss: 0.028378 [14080/20460 (100%)] Loss: 0.009691 Train: Average loss: 0.0871, Accuracy: 0.9687 Validation: Average loss: 1.4028, Accuracy: 0.8352 Train Epoch: 9 [1280/20460 (6%)] Loss: 0.103873 [2560/20460 (12%)] Loss: 0.061105 [3840/20460 (19%)] Loss: 0.050217 [5120/20460 (25%)] Loss: 0.064988 [6400/20460 (31%)] Loss: 0.054004 [7680/20460 (38%)] Loss: 0.187985 [8960/20460 (44%)] Loss: 0.100011 [10240/20460 (50%)] Loss: 0.032275 [11520/20460 (56%)] Loss: 0.095999 [12800/20460 (62%)] Loss: 0.046272 [14080/20460 (69%)] Loss: 0.108111 [15360/20460 (75%)] Loss: 0.117144 [16640/20460 (81%)] Loss: 0.039111 [17920/20460 (88%)] Loss: 0.048137 [19200/20460 (94%)] Loss: 0.116113 [14080/20460 (100%)] Loss: 0.040787 Train: Average loss: 0.0807, Accuracy: 0.9713 Validation: Average loss: 1.6492, Accuracy: 0.7127 Train Epoch: 10 [1280/20460 (6%)] Loss: 0.129111 [2560/20460 (12%)] Loss: 0.160861 [3840/20460 (19%)] Loss: 0.142680 [5120/20460 (25%)] Loss: 0.043355 [6400/20460 (31%)] Loss: 0.095755 [7680/20460 (38%)] Loss: 0.093439 [8960/20460 (44%)] Loss: 0.085926 [10240/20460 (50%)] Loss: 0.108597 [11520/20460 (56%)] Loss: 0.145176 [12800/20460 (62%)] Loss: 0.058684 [14080/20460 (69%)] Loss: 0.120203 [15360/20460 (75%)] Loss: 0.053747 [16640/20460 (81%)] Loss: 0.031906 [17920/20460 (88%)] Loss: 0.092290 [19200/20460 (94%)] Loss: 0.196654 [14080/20460 (100%)] Loss: 0.110593 Train: Average loss: 0.0808, Accuracy: 0.9706 Validation: Average loss: 2.1011, Accuracy: 0.8214 Train Epoch: 11 [1280/20460 (6%)] Loss: 0.043188 [2560/20460 (12%)] Loss: 0.149980 [3840/20460 (19%)] Loss: 0.063318 [5120/20460 (25%)] Loss: 0.103241 [6400/20460 (31%)] Loss: 0.077571 [7680/20460 (38%)] Loss: 0.049932 [8960/20460 (44%)] Loss: 0.037021 [10240/20460 (50%)] Loss: 0.122454 [11520/20460 (56%)] Loss: 0.037386 [12800/20460 (62%)] Loss: 0.094418 [14080/20460 (69%)] Loss: 0.150054 [15360/20460 (75%)] Loss: 0.086483 [16640/20460 (81%)] Loss: 0.010899 [17920/20460 (88%)] Loss: 0.065263 [19200/20460 (94%)] Loss: 0.060945 [14080/20460 (100%)] Loss: 0.187086 Train: Average loss: 0.0749, Accuracy: 0.9733 Validation: Average loss: 2.1704, Accuracy: 0.8103 Train Epoch: 12 [1280/20460 (6%)] Loss: 0.036053 [2560/20460 (12%)] Loss: 0.078786 [3840/20460 (19%)] Loss: 0.031719 [5120/20460 (25%)] Loss: 0.023891 [6400/20460 (31%)] Loss: 0.060088 [7680/20460 (38%)] Loss: 0.073726 [8960/20460 (44%)] Loss: 0.211413 [10240/20460 (50%)] Loss: 0.031129 [11520/20460 (56%)] Loss: 0.045819 [12800/20460 (62%)] Loss: 0.008133 [14080/20460 (69%)] Loss: 0.075346 [15360/20460 (75%)] Loss: 0.032074 [16640/20460 (81%)] Loss: 0.099488 [17920/20460 (88%)] Loss: 0.128713 [19200/20460 (94%)] Loss: 0.113352 [14080/20460 (100%)] Loss: 0.078803 Train: Average loss: 0.0693, Accuracy: 0.9746 Validation: Average loss: 2.3092, Accuracy: 0.8280 Train Epoch: 13 [1280/20460 (6%)] Loss: 0.021361 [2560/20460 (12%)] Loss: 0.064092 [3840/20460 (19%)] Loss: 0.007784 [5120/20460 (25%)] Loss: 0.113468 [6400/20460 (31%)] Loss: 0.023264 [7680/20460 (38%)] Loss: 0.014301 [8960/20460 (44%)] Loss: 0.042146 [10240/20460 (50%)] Loss: 0.097107 [11520/20460 (56%)] Loss: 0.150676 [12800/20460 (62%)] Loss: 0.051004 [14080/20460 (69%)] Loss: 0.057212 [15360/20460 (75%)] Loss: 0.160044 [16640/20460 (81%)] Loss: 0.047489 [17920/20460 (88%)] Loss: 0.095587 [19200/20460 (94%)] Loss: 0.060530 [14080/20460 (100%)] Loss: 0.217007 Train: Average loss: 0.0684, Accuracy: 0.9756 Validation: Average loss: 2.4277, Accuracy: 0.8615 Train Epoch: 14 [1280/20460 (6%)] Loss: 0.089016 [2560/20460 (12%)] Loss: 0.032950 [3840/20460 (19%)] Loss: 0.025786 [5120/20460 (25%)] Loss: 0.109084 [6400/20460 (31%)] Loss: 0.037234 [7680/20460 (38%)] Loss: 0.018847 [8960/20460 (44%)] Loss: 0.041286 [10240/20460 (50%)] Loss: 0.035738 [11520/20460 (56%)] Loss: 0.041484 [12800/20460 (62%)] Loss: 0.067456 [14080/20460 (69%)] Loss: 0.083951 [15360/20460 (75%)] Loss: 0.167649 [16640/20460 (81%)] Loss: 0.034117 [17920/20460 (88%)] Loss: 0.022157 [19200/20460 (94%)] Loss: 0.037026 [14080/20460 (100%)] Loss: 0.083594 Train: Average loss: 0.0658, Accuracy: 0.9762 Validation: Average loss: 2.3616, Accuracy: 0.8577 Train Epoch: 15 [1280/20460 (6%)] Loss: 0.065958 [2560/20460 (12%)] Loss: 0.174801 [3840/20460 (19%)] Loss: 0.098779 [5120/20460 (25%)] Loss: 0.063341 [6400/20460 (31%)] Loss: 0.098103 [7680/20460 (38%)] Loss: 0.046305 [8960/20460 (44%)] Loss: 0.073324 [10240/20460 (50%)] Loss: 0.040650 [11520/20460 (56%)] Loss: 0.065830 [12800/20460 (62%)] Loss: 0.035708 [14080/20460 (69%)] Loss: 0.120711 [15360/20460 (75%)] Loss: 0.056538 [16640/20460 (81%)] Loss: 0.036377 [17920/20460 (88%)] Loss: 0.091699 [19200/20460 (94%)] Loss: 0.012143 [14080/20460 (100%)] Loss: 0.008556 Train: Average loss: 0.0574, Accuracy: 0.9800 Validation: Average loss: 3.1694, Accuracy: 0.7975 Train Epoch: 16 [1280/20460 (6%)] Loss: 0.021537 [2560/20460 (12%)] Loss: 0.074768 [3840/20460 (19%)] Loss: 0.025322 [5120/20460 (25%)] Loss: 0.030596 [6400/20460 (31%)] Loss: 0.026692 [7680/20460 (38%)] Loss: 0.117345 [8960/20460 (44%)] Loss: 0.011465 [10240/20460 (50%)] Loss: 0.059155 [11520/20460 (56%)] Loss: 0.030028 [12800/20460 (62%)] Loss: 0.036679 [14080/20460 (69%)] Loss: 0.017885 [15360/20460 (75%)] Loss: 0.021472 [16640/20460 (81%)] Loss: 0.007105 [17920/20460 (88%)] Loss: 0.169662 [19200/20460 (94%)] Loss: 0.176929 [14080/20460 (100%)] Loss: 0.127220 Train: Average loss: 0.0638, Accuracy: 0.9769 Validation: Average loss: 2.7536, Accuracy: 0.8477 Train Epoch: 17 [1280/20460 (6%)] Loss: 0.045164 [2560/20460 (12%)] Loss: 0.068306 [3840/20460 (19%)] Loss: 0.114620 [5120/20460 (25%)] Loss: 0.008024 [6400/20460 (31%)] Loss: 0.081870 [7680/20460 (38%)] Loss: 0.124366 [8960/20460 (44%)] Loss: 0.004715 [10240/20460 (50%)] Loss: 0.022715 [11520/20460 (56%)] Loss: 0.064733 [12800/20460 (62%)] Loss: 0.010231 [14080/20460 (69%)] Loss: 0.080186 [15360/20460 (75%)] Loss: 0.008757 [16640/20460 (81%)] Loss: 0.095369 [17920/20460 (88%)] Loss: 0.011833 [19200/20460 (94%)] Loss: 0.027831 [14080/20460 (100%)] Loss: 0.037789 Train: Average loss: 0.0557, Accuracy: 0.9805 Validation: Average loss: 2.5960, Accuracy: 0.7106 Train Epoch: 18 [1280/20460 (6%)] Loss: 0.049802 [2560/20460 (12%)] Loss: 0.011394 [3840/20460 (19%)] Loss: 0.032899 [5120/20460 (25%)] Loss: 0.086893 [6400/20460 (31%)] Loss: 0.044579 [7680/20460 (38%)] Loss: 0.040786 [8960/20460 (44%)] Loss: 0.059407 [10240/20460 (50%)] Loss: 0.042121 [11520/20460 (56%)] Loss: 0.021799 [12800/20460 (62%)] Loss: 0.106940 [14080/20460 (69%)] Loss: 0.025000 [15360/20460 (75%)] Loss: 0.167877 [16640/20460 (81%)] Loss: 0.022861 [17920/20460 (88%)] Loss: 0.021975 [19200/20460 (94%)] Loss: 0.074256 [14080/20460 (100%)] Loss: 0.138714 Train: Average loss: 0.0554, Accuracy: 0.9801 Validation: Average loss: 2.6120, Accuracy: 0.8342 Train Epoch: 19 [1280/20460 (6%)] Loss: 0.004554 [2560/20460 (12%)] Loss: 0.042295 [3840/20460 (19%)] Loss: 0.028682 [5120/20460 (25%)] Loss: 0.053749 [6400/20460 (31%)] Loss: 0.079738 [7680/20460 (38%)] Loss: 0.050336 [8960/20460 (44%)] Loss: 0.073925 [10240/20460 (50%)] Loss: 0.133525 [11520/20460 (56%)] Loss: 0.042605 [12800/20460 (62%)] Loss: 0.003682 [14080/20460 (69%)] Loss: 0.018496 [15360/20460 (75%)] Loss: 0.071872 [16640/20460 (81%)] Loss: 0.193883 [17920/20460 (88%)] Loss: 0.046756 [19200/20460 (94%)] Loss: 0.037845 [14080/20460 (100%)] Loss: 0.024763 Train: Average loss: 0.0518, Accuracy: 0.9816 Validation: Average loss: 3.2742, Accuracy: 0.8034 Train Epoch: 20 [1280/20460 (6%)] Loss: 0.010771 [2560/20460 (12%)] Loss: 0.125281 [3840/20460 (19%)] Loss: 0.007049 [5120/20460 (25%)] Loss: 0.016924 [6400/20460 (31%)] Loss: 0.100763 [7680/20460 (38%)] Loss: 0.085736 [8960/20460 (44%)] Loss: 0.045819 [10240/20460 (50%)] Loss: 0.065345 [11520/20460 (56%)] Loss: 0.093369 [12800/20460 (62%)] Loss: 0.017157 [14080/20460 (69%)] Loss: 0.072407 [15360/20460 (75%)] Loss: 0.108482 [16640/20460 (81%)] Loss: 0.026168 [17920/20460 (88%)] Loss: 0.060181 [19200/20460 (94%)] Loss: 0.026590 [14080/20460 (100%)] Loss: 0.092837 Train: Average loss: 0.0549, Accuracy: 0.9803 Validation: Average loss: 2.3883, Accuracy: 0.8591 Train Epoch: 21 [1280/20460 (6%)] Loss: 0.021656 [2560/20460 (12%)] Loss: 0.057478 [3840/20460 (19%)] Loss: 0.005611 [5120/20460 (25%)] Loss: 0.082956 [6400/20460 (31%)] Loss: 0.036128 [7680/20460 (38%)] Loss: 0.023281 [8960/20460 (44%)] Loss: 0.022550 [10240/20460 (50%)] Loss: 0.019933 [11520/20460 (56%)] Loss: 0.053786 [12800/20460 (62%)] Loss: 0.062733 [14080/20460 (69%)] Loss: 0.020300 [15360/20460 (75%)] Loss: 0.066334 [16640/20460 (81%)] Loss: 0.005735 [17920/20460 (88%)] Loss: 0.094226 [19200/20460 (94%)] Loss: 0.074330 [14080/20460 (100%)] Loss: 0.007086 Train: Average loss: 0.0461, Accuracy: 0.9840 Validation: Average loss: 1.7902, Accuracy: 0.8602 Train Epoch: 22 [1280/20460 (6%)] Loss: 0.023283 [2560/20460 (12%)] Loss: 0.022632 [3840/20460 (19%)] Loss: 0.039984 [5120/20460 (25%)] Loss: 0.097088 [6400/20460 (31%)] Loss: 0.066772 [7680/20460 (38%)] Loss: 0.045607 [8960/20460 (44%)] Loss: 0.105642 [10240/20460 (50%)] Loss: 0.073934 [11520/20460 (56%)] Loss: 0.079578 [12800/20460 (62%)] Loss: 0.119628 [14080/20460 (69%)] Loss: 0.029313 [15360/20460 (75%)] Loss: 0.048237 [16640/20460 (81%)] Loss: 0.035918 [17920/20460 (88%)] Loss: 0.156017 [19200/20460 (94%)] Loss: 0.139338 [14080/20460 (100%)] Loss: 0.103584 Train: Average loss: 0.0456, Accuracy: 0.9836 Validation: Average loss: 1.8767, Accuracy: 0.9058 Train Epoch: 23 [1280/20460 (6%)] Loss: 0.104166 [2560/20460 (12%)] Loss: 0.017932 [3840/20460 (19%)] Loss: 0.053743 [5120/20460 (25%)] Loss: 0.001651 [6400/20460 (31%)] Loss: 0.056572 [7680/20460 (38%)] Loss: 0.145369 [8960/20460 (44%)] Loss: 0.072902 [10240/20460 (50%)] Loss: 0.058727 [11520/20460 (56%)] Loss: 0.022644 [12800/20460 (62%)] Loss: 0.056106 [14080/20460 (69%)] Loss: 0.036463 [15360/20460 (75%)] Loss: 0.131182 [16640/20460 (81%)] Loss: 0.029406 [17920/20460 (88%)] Loss: 0.018448 [19200/20460 (94%)] Loss: 0.056077 [14080/20460 (100%)] Loss: 0.021559 Train: Average loss: 0.0463, Accuracy: 0.9829 Validation: Average loss: 2.4885, Accuracy: 0.7930 Train Epoch: 24 [1280/20460 (6%)] Loss: 0.029782 [2560/20460 (12%)] Loss: 0.024480 [3840/20460 (19%)] Loss: 0.033176 [5120/20460 (25%)] Loss: 0.014249 [6400/20460 (31%)] Loss: 0.037911 [7680/20460 (38%)] Loss: 0.023197 [8960/20460 (44%)] Loss: 0.105220 [10240/20460 (50%)] Loss: 0.003205 [11520/20460 (56%)] Loss: 0.031969 [12800/20460 (62%)] Loss: 0.032188 [14080/20460 (69%)] Loss: 0.101091 [15360/20460 (75%)] Loss: 0.007326 [16640/20460 (81%)] Loss: 0.107772 [17920/20460 (88%)] Loss: 0.076094 [19200/20460 (94%)] Loss: 0.060503 [14080/20460 (100%)] Loss: 0.026952 Train: Average loss: 0.0433, Accuracy: 0.9847 Validation: Average loss: 2.1807, Accuracy: 0.8872 Train Epoch: 25 [1280/20460 (6%)] Loss: 0.001547 [2560/20460 (12%)] Loss: 0.023376 [3840/20460 (19%)] Loss: 0.046724 [5120/20460 (25%)] Loss: 0.092519 [6400/20460 (31%)] Loss: 0.027603 [7680/20460 (38%)] Loss: 0.037492 [8960/20460 (44%)] Loss: 0.013154 [10240/20460 (50%)] Loss: 0.016246 [11520/20460 (56%)] Loss: 0.081681 [12800/20460 (62%)] Loss: 0.021114 [14080/20460 (69%)] Loss: 0.012098 [15360/20460 (75%)] Loss: 0.018630 [16640/20460 (81%)] Loss: 0.053905 [17920/20460 (88%)] Loss: 0.046273 [19200/20460 (94%)] Loss: 0.030135 [14080/20460 (100%)] Loss: 0.008063 Train: Average loss: 0.0443, Accuracy: 0.9842 Validation: Average loss: 1.5726, Accuracy: 0.8986 Train Epoch: 26 [1280/20460 (6%)] Loss: 0.044958 [2560/20460 (12%)] Loss: 0.005513 [3840/20460 (19%)] Loss: 0.026505 [5120/20460 (25%)] Loss: 0.051464 [6400/20460 (31%)] Loss: 0.054007 [7680/20460 (38%)] Loss: 0.047236 [8960/20460 (44%)] Loss: 0.180607 [10240/20460 (50%)] Loss: 0.015927 [11520/20460 (56%)] Loss: 0.063853 [12800/20460 (62%)] Loss: 0.056948 [14080/20460 (69%)] Loss: 0.095953 [15360/20460 (75%)] Loss: 0.087786 [16640/20460 (81%)] Loss: 0.042396 [17920/20460 (88%)] Loss: 0.056485 [19200/20460 (94%)] Loss: 0.039375 [14080/20460 (100%)] Loss: 0.013858 Train: Average loss: 0.0443, Accuracy: 0.9845 Validation: Average loss: 1.9030, Accuracy: 0.8854 Train Epoch: 27 [1280/20460 (6%)] Loss: 0.014182 [2560/20460 (12%)] Loss: 0.004076 [3840/20460 (19%)] Loss: 0.057770 [5120/20460 (25%)] Loss: 0.012988 [6400/20460 (31%)] Loss: 0.080241 [7680/20460 (38%)] Loss: 0.032667 [8960/20460 (44%)] Loss: 0.020630 [10240/20460 (50%)] Loss: 0.204567 [11520/20460 (56%)] Loss: 0.025905 [12800/20460 (62%)] Loss: 0.035793 [14080/20460 (69%)] Loss: 0.003653 [15360/20460 (75%)] Loss: 0.086452 [16640/20460 (81%)] Loss: 0.006440 [17920/20460 (88%)] Loss: 0.006412 [19200/20460 (94%)] Loss: 0.053606 [14080/20460 (100%)] Loss: 0.006571 Train: Average loss: 0.0395, Accuracy: 0.9860 Validation: Average loss: 2.2398, Accuracy: 0.9048 Train Epoch: 28 [1280/20460 (6%)] Loss: 0.143015 [2560/20460 (12%)] Loss: 0.002720 [3840/20460 (19%)] Loss: 0.101957 [5120/20460 (25%)] Loss: 0.076931 [6400/20460 (31%)] Loss: 0.040592 [7680/20460 (38%)] Loss: 0.012416 [8960/20460 (44%)] Loss: 0.062311 [10240/20460 (50%)] Loss: 0.039100 [11520/20460 (56%)] Loss: 0.123086 [12800/20460 (62%)] Loss: 0.012272 [14080/20460 (69%)] Loss: 0.077870 [15360/20460 (75%)] Loss: 0.097095 [16640/20460 (81%)] Loss: 0.009405 [17920/20460 (88%)] Loss: 0.018047 [19200/20460 (94%)] Loss: 0.011251 [14080/20460 (100%)] Loss: 0.089883 Train: Average loss: 0.0451, Accuracy: 0.9833 Validation: Average loss: 2.0306, Accuracy: 0.9003 Train Epoch: 29 [1280/20460 (6%)] Loss: 0.049717 [2560/20460 (12%)] Loss: 0.020472 [3840/20460 (19%)] Loss: 0.038108 [5120/20460 (25%)] Loss: 0.126613 [6400/20460 (31%)] Loss: 0.026770 [7680/20460 (38%)] Loss: 0.017383 [8960/20460 (44%)] Loss: 0.014881 [10240/20460 (50%)] Loss: 0.097292 [11520/20460 (56%)] Loss: 0.060368 [12800/20460 (62%)] Loss: 0.017599 [14080/20460 (69%)] Loss: 0.011290 [15360/20460 (75%)] Loss: 0.052744 [16640/20460 (81%)] Loss: 0.011328 [17920/20460 (88%)] Loss: 0.014417 [19200/20460 (94%)] Loss: 0.009475 [14080/20460 (100%)] Loss: 0.026258 Train: Average loss: 0.0350, Accuracy: 0.9879 Validation: Average loss: 1.7274, Accuracy: 0.8339 Train Epoch: 30 [1280/20460 (6%)] Loss: 0.055909 [2560/20460 (12%)] Loss: 0.012958 [3840/20460 (19%)] Loss: 0.009414 [5120/20460 (25%)] Loss: 0.028990 [6400/20460 (31%)] Loss: 0.026297 [7680/20460 (38%)] Loss: 0.014383 [8960/20460 (44%)] Loss: 0.045923 [10240/20460 (50%)] Loss: 0.003171 [11520/20460 (56%)] Loss: 0.074503 [12800/20460 (62%)] Loss: 0.034564 [14080/20460 (69%)] Loss: 0.267748 [15360/20460 (75%)] Loss: 0.021972 [16640/20460 (81%)] Loss: 0.005628 [17920/20460 (88%)] Loss: 0.024788 [19200/20460 (94%)] Loss: 0.026827 [14080/20460 (100%)] Loss: 0.074816 Train: Average loss: 0.0402, Accuracy: 0.9847 Validation: Average loss: 2.2622, Accuracy: 0.7979
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_1725551/529002640.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 284 (out of 5826) 4.87% Test accuracy 95.13%
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 95.65%