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=180, 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_1444885/3247579378.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_180"
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: 1.154608 [2560/20460 (12%)] Loss: 0.838899 [3840/20460 (19%)] Loss: 0.485876 [5120/20460 (25%)] Loss: 0.579889 [6400/20460 (31%)] Loss: 0.437122 [7680/20460 (38%)] Loss: 0.203284 [8960/20460 (44%)] Loss: 0.307418 [10240/20460 (50%)] Loss: 0.271076 [11520/20460 (56%)] Loss: 0.123049 [12800/20460 (62%)] Loss: 0.285132 [14080/20460 (69%)] Loss: 0.528378 [15360/20460 (75%)] Loss: 0.182749 [16640/20460 (81%)] Loss: 0.245985 [17920/20460 (88%)] Loss: 0.191815 [19200/20460 (94%)] Loss: 0.233691 [14080/20460 (100%)] Loss: 0.164780 Train: Average loss: 0.4413, Accuracy: 0.8430 Validation: Average loss: 1.0846, Accuracy: 0.7587 Train Epoch: 2 [1280/20460 (6%)] Loss: 0.328148 [2560/20460 (12%)] Loss: 0.399888 [3840/20460 (19%)] Loss: 0.296892 [5120/20460 (25%)] Loss: 0.236196 [6400/20460 (31%)] Loss: 0.224795 [7680/20460 (38%)] Loss: 0.237054 [8960/20460 (44%)] Loss: 0.245093 [10240/20460 (50%)] Loss: 0.234150 [11520/20460 (56%)] Loss: 0.184884 [12800/20460 (62%)] Loss: 0.172342 [14080/20460 (69%)] Loss: 0.336267 [15360/20460 (75%)] Loss: 0.117645 [16640/20460 (81%)] Loss: 0.118147 [17920/20460 (88%)] Loss: 0.104366 [19200/20460 (94%)] Loss: 0.239917 [14080/20460 (100%)] Loss: 0.081711 Train: Average loss: 0.2037, Accuracy: 0.9266 Validation: Average loss: 1.5448, Accuracy: 0.6954 Train Epoch: 3 [1280/20460 (6%)] Loss: 0.079502 [2560/20460 (12%)] Loss: 0.329850 [3840/20460 (19%)] Loss: 0.119493 [5120/20460 (25%)] Loss: 0.205317 [6400/20460 (31%)] Loss: 0.075518 [7680/20460 (38%)] Loss: 0.141399 [8960/20460 (44%)] Loss: 0.067538 [10240/20460 (50%)] Loss: 0.052563 [11520/20460 (56%)] Loss: 0.168270 [12800/20460 (62%)] Loss: 0.281087 [14080/20460 (69%)] Loss: 0.102163 [15360/20460 (75%)] Loss: 0.122213 [16640/20460 (81%)] Loss: 0.101528 [17920/20460 (88%)] Loss: 0.381102 [19200/20460 (94%)] Loss: 0.156751 [14080/20460 (100%)] Loss: 0.072861 Train: Average loss: 0.1577, Accuracy: 0.9413 Validation: Average loss: 1.2452, Accuracy: 0.8650 Train Epoch: 4 [1280/20460 (6%)] Loss: 0.137309 [2560/20460 (12%)] Loss: 0.233674 [3840/20460 (19%)] Loss: 0.200537 [5120/20460 (25%)] Loss: 0.044271 [6400/20460 (31%)] Loss: 0.064060 [7680/20460 (38%)] Loss: 0.081689 [8960/20460 (44%)] Loss: 0.111413 [10240/20460 (50%)] Loss: 0.122126 [11520/20460 (56%)] Loss: 0.039462 [12800/20460 (62%)] Loss: 0.157551 [14080/20460 (69%)] Loss: 0.085240 [15360/20460 (75%)] Loss: 0.282377 [16640/20460 (81%)] Loss: 0.051879 [17920/20460 (88%)] Loss: 0.137520 [19200/20460 (94%)] Loss: 0.490313 [14080/20460 (100%)] Loss: 0.123210 Train: Average loss: 0.1368, Accuracy: 0.9499 Validation: Average loss: 1.2225, Accuracy: 0.8830 Train Epoch: 5 [1280/20460 (6%)] Loss: 0.073242 [2560/20460 (12%)] Loss: 0.110220 [3840/20460 (19%)] Loss: 0.197832 [5120/20460 (25%)] Loss: 0.208767 [6400/20460 (31%)] Loss: 0.085845 [7680/20460 (38%)] Loss: 0.083905 [8960/20460 (44%)] Loss: 0.034090 [10240/20460 (50%)] Loss: 0.084934 [11520/20460 (56%)] Loss: 0.061121 [12800/20460 (62%)] Loss: 0.052989 [14080/20460 (69%)] Loss: 0.133051 [15360/20460 (75%)] Loss: 0.233461 [16640/20460 (81%)] Loss: 0.186377 [17920/20460 (88%)] Loss: 0.070991 [19200/20460 (94%)] Loss: 0.119511 [14080/20460 (100%)] Loss: 0.110713 Train: Average loss: 0.1166, Accuracy: 0.9576 Validation: Average loss: 1.1017, Accuracy: 0.8892 Train Epoch: 6 [1280/20460 (6%)] Loss: 0.165688 [2560/20460 (12%)] Loss: 0.085802 [3840/20460 (19%)] Loss: 0.174651 [5120/20460 (25%)] Loss: 0.102024 [6400/20460 (31%)] Loss: 0.103382 [7680/20460 (38%)] Loss: 0.232773 [8960/20460 (44%)] Loss: 0.071463 [10240/20460 (50%)] Loss: 0.069022 [11520/20460 (56%)] Loss: 0.104528 [12800/20460 (62%)] Loss: 0.056172 [14080/20460 (69%)] Loss: 0.080539 [15360/20460 (75%)] Loss: 0.034545 [16640/20460 (81%)] Loss: 0.077291 [17920/20460 (88%)] Loss: 0.120810 [19200/20460 (94%)] Loss: 0.104904 [14080/20460 (100%)] Loss: 0.047831 Train: Average loss: 0.1061, Accuracy: 0.9625 Validation: Average loss: 1.7595, Accuracy: 0.7792 Train Epoch: 7 [1280/20460 (6%)] Loss: 0.083496 [2560/20460 (12%)] Loss: 0.096981 [3840/20460 (19%)] Loss: 0.118549 [5120/20460 (25%)] Loss: 0.048066 [6400/20460 (31%)] Loss: 0.105432 [7680/20460 (38%)] Loss: 0.089801 [8960/20460 (44%)] Loss: 0.169708 [10240/20460 (50%)] Loss: 0.074856 [11520/20460 (56%)] Loss: 0.107643 [12800/20460 (62%)] Loss: 0.074542 [14080/20460 (69%)] Loss: 0.128725 [15360/20460 (75%)] Loss: 0.047379 [16640/20460 (81%)] Loss: 0.081939 [17920/20460 (88%)] Loss: 0.160932 [19200/20460 (94%)] Loss: 0.173909 [14080/20460 (100%)] Loss: 0.083087 Train: Average loss: 0.0967, Accuracy: 0.9655 Validation: Average loss: 1.6147, Accuracy: 0.8300 Train Epoch: 8 [1280/20460 (6%)] Loss: 0.176052 [2560/20460 (12%)] Loss: 0.069961 [3840/20460 (19%)] Loss: 0.074433 [5120/20460 (25%)] Loss: 0.015392 [6400/20460 (31%)] Loss: 0.047652 [7680/20460 (38%)] Loss: 0.050061 [8960/20460 (44%)] Loss: 0.079029 [10240/20460 (50%)] Loss: 0.071507 [11520/20460 (56%)] Loss: 0.060875 [12800/20460 (62%)] Loss: 0.078049 [14080/20460 (69%)] Loss: 0.044981 [15360/20460 (75%)] Loss: 0.153947 [16640/20460 (81%)] Loss: 0.189049 [17920/20460 (88%)] Loss: 0.308131 [19200/20460 (94%)] Loss: 0.058292 [14080/20460 (100%)] Loss: 0.024300 Train: Average loss: 0.0868, Accuracy: 0.9671 Validation: Average loss: 1.4454, Accuracy: 0.8190 Train Epoch: 9 [1280/20460 (6%)] Loss: 0.034268 [2560/20460 (12%)] Loss: 0.034598 [3840/20460 (19%)] Loss: 0.050957 [5120/20460 (25%)] Loss: 0.151066 [6400/20460 (31%)] Loss: 0.027893 [7680/20460 (38%)] Loss: 0.121117 [8960/20460 (44%)] Loss: 0.096919 [10240/20460 (50%)] Loss: 0.026340 [11520/20460 (56%)] Loss: 0.129731 [12800/20460 (62%)] Loss: 0.103561 [14080/20460 (69%)] Loss: 0.112842 [15360/20460 (75%)] Loss: 0.046268 [16640/20460 (81%)] Loss: 0.038462 [17920/20460 (88%)] Loss: 0.089703 [19200/20460 (94%)] Loss: 0.123095 [14080/20460 (100%)] Loss: 0.033711 Train: Average loss: 0.0839, Accuracy: 0.9692 Validation: Average loss: 1.4662, Accuracy: 0.8837 Train Epoch: 10 [1280/20460 (6%)] Loss: 0.096313 [2560/20460 (12%)] Loss: 0.116083 [3840/20460 (19%)] Loss: 0.019745 [5120/20460 (25%)] Loss: 0.037900 [6400/20460 (31%)] Loss: 0.056467 [7680/20460 (38%)] Loss: 0.121352 [8960/20460 (44%)] Loss: 0.027698 [10240/20460 (50%)] Loss: 0.037800 [11520/20460 (56%)] Loss: 0.039997 [12800/20460 (62%)] Loss: 0.041773 [14080/20460 (69%)] Loss: 0.100383 [15360/20460 (75%)] Loss: 0.065768 [16640/20460 (81%)] Loss: 0.037660 [17920/20460 (88%)] Loss: 0.146939 [19200/20460 (94%)] Loss: 0.083759 [14080/20460 (100%)] Loss: 0.107660 Train: Average loss: 0.0765, Accuracy: 0.9726 Validation: Average loss: 1.6286, Accuracy: 0.8834 Train Epoch: 11 [1280/20460 (6%)] Loss: 0.101994 [2560/20460 (12%)] Loss: 0.232339 [3840/20460 (19%)] Loss: 0.082329 [5120/20460 (25%)] Loss: 0.089559 [6400/20460 (31%)] Loss: 0.075837 [7680/20460 (38%)] Loss: 0.034033 [8960/20460 (44%)] Loss: 0.106017 [10240/20460 (50%)] Loss: 0.143096 [11520/20460 (56%)] Loss: 0.017976 [12800/20460 (62%)] Loss: 0.006701 [14080/20460 (69%)] Loss: 0.160220 [15360/20460 (75%)] Loss: 0.056265 [16640/20460 (81%)] Loss: 0.002834 [17920/20460 (88%)] Loss: 0.070355 [19200/20460 (94%)] Loss: 0.110825 [14080/20460 (100%)] Loss: 0.081801 Train: Average loss: 0.0703, Accuracy: 0.9739 Validation: Average loss: 1.7393, Accuracy: 0.8242 Train Epoch: 12 [1280/20460 (6%)] Loss: 0.038984 [2560/20460 (12%)] Loss: 0.076067 [3840/20460 (19%)] Loss: 0.040983 [5120/20460 (25%)] Loss: 0.036458 [6400/20460 (31%)] Loss: 0.026381 [7680/20460 (38%)] Loss: 0.090406 [8960/20460 (44%)] Loss: 0.216269 [10240/20460 (50%)] Loss: 0.031442 [11520/20460 (56%)] Loss: 0.034816 [12800/20460 (62%)] Loss: 0.008865 [14080/20460 (69%)] Loss: 0.056212 [15360/20460 (75%)] Loss: 0.044774 [16640/20460 (81%)] Loss: 0.165942 [17920/20460 (88%)] Loss: 0.096666 [19200/20460 (94%)] Loss: 0.087762 [14080/20460 (100%)] Loss: 0.041471 Train: Average loss: 0.0709, Accuracy: 0.9730 Validation: Average loss: 1.8173, Accuracy: 0.8809 Train Epoch: 13 [1280/20460 (6%)] Loss: 0.027849 [2560/20460 (12%)] Loss: 0.041348 [3840/20460 (19%)] Loss: 0.009213 [5120/20460 (25%)] Loss: 0.062436 [6400/20460 (31%)] Loss: 0.020248 [7680/20460 (38%)] Loss: 0.032236 [8960/20460 (44%)] Loss: 0.027384 [10240/20460 (50%)] Loss: 0.051286 [11520/20460 (56%)] Loss: 0.061135 [12800/20460 (62%)] Loss: 0.092660 [14080/20460 (69%)] Loss: 0.101285 [15360/20460 (75%)] Loss: 0.072427 [16640/20460 (81%)] Loss: 0.102357 [17920/20460 (88%)] Loss: 0.037063 [19200/20460 (94%)] Loss: 0.025966 [14080/20460 (100%)] Loss: 0.235572 Train: Average loss: 0.0634, Accuracy: 0.9766 Validation: Average loss: 1.5884, Accuracy: 0.8435 Train Epoch: 14 [1280/20460 (6%)] Loss: 0.099768 [2560/20460 (12%)] Loss: 0.008558 [3840/20460 (19%)] Loss: 0.009383 [5120/20460 (25%)] Loss: 0.076057 [6400/20460 (31%)] Loss: 0.014432 [7680/20460 (38%)] Loss: 0.019608 [8960/20460 (44%)] Loss: 0.013267 [10240/20460 (50%)] Loss: 0.019755 [11520/20460 (56%)] Loss: 0.010128 [12800/20460 (62%)] Loss: 0.040537 [14080/20460 (69%)] Loss: 0.065339 [15360/20460 (75%)] Loss: 0.176545 [16640/20460 (81%)] Loss: 0.016213 [17920/20460 (88%)] Loss: 0.026585 [19200/20460 (94%)] Loss: 0.084198 [14080/20460 (100%)] Loss: 0.125569 Train: Average loss: 0.0589, Accuracy: 0.9790 Validation: Average loss: 1.8937, Accuracy: 0.8674 Train Epoch: 15 [1280/20460 (6%)] Loss: 0.123536 [2560/20460 (12%)] Loss: 0.177383 [3840/20460 (19%)] Loss: 0.106905 [5120/20460 (25%)] Loss: 0.101723 [6400/20460 (31%)] Loss: 0.096286 [7680/20460 (38%)] Loss: 0.034977 [8960/20460 (44%)] Loss: 0.048631 [10240/20460 (50%)] Loss: 0.022317 [11520/20460 (56%)] Loss: 0.061900 [12800/20460 (62%)] Loss: 0.046843 [14080/20460 (69%)] Loss: 0.029109 [15360/20460 (75%)] Loss: 0.057164 [16640/20460 (81%)] Loss: 0.020887 [17920/20460 (88%)] Loss: 0.029551 [19200/20460 (94%)] Loss: 0.009166 [14080/20460 (100%)] Loss: 0.022743 Train: Average loss: 0.0594, Accuracy: 0.9792 Validation: Average loss: 1.4142, Accuracy: 0.8872 Train Epoch: 16 [1280/20460 (6%)] Loss: 0.038064 [2560/20460 (12%)] Loss: 0.109800 [3840/20460 (19%)] Loss: 0.167969 [5120/20460 (25%)] Loss: 0.102499 [6400/20460 (31%)] Loss: 0.078386 [7680/20460 (38%)] Loss: 0.015721 [8960/20460 (44%)] Loss: 0.093845 [10240/20460 (50%)] Loss: 0.064943 [11520/20460 (56%)] Loss: 0.037591 [12800/20460 (62%)] Loss: 0.037282 [14080/20460 (69%)] Loss: 0.016141 [15360/20460 (75%)] Loss: 0.097310 [16640/20460 (81%)] Loss: 0.113463 [17920/20460 (88%)] Loss: 0.069364 [19200/20460 (94%)] Loss: 0.117187 [14080/20460 (100%)] Loss: 0.154718 Train: Average loss: 0.0575, Accuracy: 0.9786 Validation: Average loss: 1.6305, Accuracy: 0.8380 Train Epoch: 17 [1280/20460 (6%)] Loss: 0.017140 [2560/20460 (12%)] Loss: 0.145840 [3840/20460 (19%)] Loss: 0.031945 [5120/20460 (25%)] Loss: 0.016463 [6400/20460 (31%)] Loss: 0.086557 [7680/20460 (38%)] Loss: 0.060283 [8960/20460 (44%)] Loss: 0.043195 [10240/20460 (50%)] Loss: 0.021971 [11520/20460 (56%)] Loss: 0.069196 [12800/20460 (62%)] Loss: 0.033842 [14080/20460 (69%)] Loss: 0.093399 [15360/20460 (75%)] Loss: 0.066984 [16640/20460 (81%)] Loss: 0.085902 [17920/20460 (88%)] Loss: 0.033242 [19200/20460 (94%)] Loss: 0.053666 [14080/20460 (100%)] Loss: 0.124028 Train: Average loss: 0.0628, Accuracy: 0.9789 Validation: Average loss: 2.1148, Accuracy: 0.8366 Train Epoch: 18 [1280/20460 (6%)] Loss: 0.010938 [2560/20460 (12%)] Loss: 0.135357 [3840/20460 (19%)] Loss: 0.033848 [5120/20460 (25%)] Loss: 0.032860 [6400/20460 (31%)] Loss: 0.066463 [7680/20460 (38%)] Loss: 0.008554 [8960/20460 (44%)] Loss: 0.104291 [10240/20460 (50%)] Loss: 0.030621 [11520/20460 (56%)] Loss: 0.020883 [12800/20460 (62%)] Loss: 0.178456 [14080/20460 (69%)] Loss: 0.059583 [15360/20460 (75%)] Loss: 0.149433 [16640/20460 (81%)] Loss: 0.052170 [17920/20460 (88%)] Loss: 0.026881 [19200/20460 (94%)] Loss: 0.056583 [14080/20460 (100%)] Loss: 0.056181 Train: Average loss: 0.0525, Accuracy: 0.9802 Validation: Average loss: 1.6125, Accuracy: 0.8612 Train Epoch: 19 [1280/20460 (6%)] Loss: 0.007964 [2560/20460 (12%)] Loss: 0.031899 [3840/20460 (19%)] Loss: 0.024459 [5120/20460 (25%)] Loss: 0.053967 [6400/20460 (31%)] Loss: 0.251782 [7680/20460 (38%)] Loss: 0.047591 [8960/20460 (44%)] Loss: 0.080097 [10240/20460 (50%)] Loss: 0.136145 [11520/20460 (56%)] Loss: 0.061841 [12800/20460 (62%)] Loss: 0.010178 [14080/20460 (69%)] Loss: 0.019113 [15360/20460 (75%)] Loss: 0.010566 [16640/20460 (81%)] Loss: 0.156852 [17920/20460 (88%)] Loss: 0.018930 [19200/20460 (94%)] Loss: 0.012789 [14080/20460 (100%)] Loss: 0.036572 Train: Average loss: 0.0552, Accuracy: 0.9804 Validation: Average loss: 2.5698, Accuracy: 0.8730 Train Epoch: 20 [1280/20460 (6%)] Loss: 0.026344 [2560/20460 (12%)] Loss: 0.117985 [3840/20460 (19%)] Loss: 0.010154 [5120/20460 (25%)] Loss: 0.016642 [6400/20460 (31%)] Loss: 0.092467 [7680/20460 (38%)] Loss: 0.045687 [8960/20460 (44%)] Loss: 0.015743 [10240/20460 (50%)] Loss: 0.039033 [11520/20460 (56%)] Loss: 0.059726 [12800/20460 (62%)] Loss: 0.021537 [14080/20460 (69%)] Loss: 0.036110 [15360/20460 (75%)] Loss: 0.014566 [16640/20460 (81%)] Loss: 0.080196 [17920/20460 (88%)] Loss: 0.113845 [19200/20460 (94%)] Loss: 0.030796 [14080/20460 (100%)] Loss: 0.026460 Train: Average loss: 0.0475, Accuracy: 0.9824 Validation: Average loss: 1.9205, Accuracy: 0.8678 Train Epoch: 21 [1280/20460 (6%)] Loss: 0.018865 [2560/20460 (12%)] Loss: 0.030405 [3840/20460 (19%)] Loss: 0.007892 [5120/20460 (25%)] Loss: 0.050218 [6400/20460 (31%)] Loss: 0.022357 [7680/20460 (38%)] Loss: 0.018565 [8960/20460 (44%)] Loss: 0.012994 [10240/20460 (50%)] Loss: 0.008311 [11520/20460 (56%)] Loss: 0.018364 [12800/20460 (62%)] Loss: 0.028620 [14080/20460 (69%)] Loss: 0.014857 [15360/20460 (75%)] Loss: 0.016304 [16640/20460 (81%)] Loss: 0.062942 [17920/20460 (88%)] Loss: 0.098809 [19200/20460 (94%)] Loss: 0.042336 [14080/20460 (100%)] Loss: 0.019796 Train: Average loss: 0.0488, Accuracy: 0.9823 Validation: Average loss: 1.9240, Accuracy: 0.8702 Train Epoch: 22 [1280/20460 (6%)] Loss: 0.024643 [2560/20460 (12%)] Loss: 0.040994 [3840/20460 (19%)] Loss: 0.033839 [5120/20460 (25%)] Loss: 0.072899 [6400/20460 (31%)] Loss: 0.046923 [7680/20460 (38%)] Loss: 0.064991 [8960/20460 (44%)] Loss: 0.135172 [10240/20460 (50%)] Loss: 0.044573 [11520/20460 (56%)] Loss: 0.027280 [12800/20460 (62%)] Loss: 0.036682 [14080/20460 (69%)] Loss: 0.069262 [15360/20460 (75%)] Loss: 0.080054 [16640/20460 (81%)] Loss: 0.048438 [17920/20460 (88%)] Loss: 0.039535 [19200/20460 (94%)] Loss: 0.020977 [14080/20460 (100%)] Loss: 0.069100 Train: Average loss: 0.0498, Accuracy: 0.9809 Validation: Average loss: 1.7595, Accuracy: 0.9017 Train Epoch: 23 [1280/20460 (6%)] Loss: 0.020308 [2560/20460 (12%)] Loss: 0.014758 [3840/20460 (19%)] Loss: 0.023325 [5120/20460 (25%)] Loss: 0.007724 [6400/20460 (31%)] Loss: 0.015425 [7680/20460 (38%)] Loss: 0.084390 [8960/20460 (44%)] Loss: 0.032476 [10240/20460 (50%)] Loss: 0.027792 [11520/20460 (56%)] Loss: 0.071208 [12800/20460 (62%)] Loss: 0.030492 [14080/20460 (69%)] Loss: 0.048210 [15360/20460 (75%)] Loss: 0.071811 [16640/20460 (81%)] Loss: 0.037236 [17920/20460 (88%)] Loss: 0.026675 [19200/20460 (94%)] Loss: 0.022739 [14080/20460 (100%)] Loss: 0.041387 Train: Average loss: 0.0468, Accuracy: 0.9829 Validation: Average loss: 2.2490, Accuracy: 0.8889 Train Epoch: 24 [1280/20460 (6%)] Loss: 0.042783 [2560/20460 (12%)] Loss: 0.032059 [3840/20460 (19%)] Loss: 0.015904 [5120/20460 (25%)] Loss: 0.027754 [6400/20460 (31%)] Loss: 0.028237 [7680/20460 (38%)] Loss: 0.042711 [8960/20460 (44%)] Loss: 0.044785 [10240/20460 (50%)] Loss: 0.006280 [11520/20460 (56%)] Loss: 0.080609 [12800/20460 (62%)] Loss: 0.012572 [14080/20460 (69%)] Loss: 0.074517 [15360/20460 (75%)] Loss: 0.033603 [16640/20460 (81%)] Loss: 0.088048 [17920/20460 (88%)] Loss: 0.017385 [19200/20460 (94%)] Loss: 0.022218 [14080/20460 (100%)] Loss: 0.081137 Train: Average loss: 0.0438, Accuracy: 0.9837 Validation: Average loss: 2.7997, Accuracy: 0.8300 Train Epoch: 25 [1280/20460 (6%)] Loss: 0.013926 [2560/20460 (12%)] Loss: 0.084272 [3840/20460 (19%)] Loss: 0.030428 [5120/20460 (25%)] Loss: 0.066782 [6400/20460 (31%)] Loss: 0.022440 [7680/20460 (38%)] Loss: 0.002669 [8960/20460 (44%)] Loss: 0.042892 [10240/20460 (50%)] Loss: 0.007015 [11520/20460 (56%)] Loss: 0.120389 [12800/20460 (62%)] Loss: 0.029422 [14080/20460 (69%)] Loss: 0.023923 [15360/20460 (75%)] Loss: 0.013669 [16640/20460 (81%)] Loss: 0.030782 [17920/20460 (88%)] Loss: 0.009424 [19200/20460 (94%)] Loss: 0.020586 [14080/20460 (100%)] Loss: 0.011779 Train: Average loss: 0.0402, Accuracy: 0.9849 Validation: Average loss: 2.3393, Accuracy: 0.8418 Train Epoch: 26 [1280/20460 (6%)] Loss: 0.111363 [2560/20460 (12%)] Loss: 0.060291 [3840/20460 (19%)] Loss: 0.078664 [5120/20460 (25%)] Loss: 0.033395 [6400/20460 (31%)] Loss: 0.023525 [7680/20460 (38%)] Loss: 0.030828 [8960/20460 (44%)] Loss: 0.095568 [10240/20460 (50%)] Loss: 0.045007 [11520/20460 (56%)] Loss: 0.014352 [12800/20460 (62%)] Loss: 0.089438 [14080/20460 (69%)] Loss: 0.058843 [15360/20460 (75%)] Loss: 0.065361 [16640/20460 (81%)] Loss: 0.010174 [17920/20460 (88%)] Loss: 0.004271 [19200/20460 (94%)] Loss: 0.025217 [14080/20460 (100%)] Loss: 0.236916 Train: Average loss: 0.0435, Accuracy: 0.9840 Validation: Average loss: 2.8346, Accuracy: 0.8913 Train Epoch: 27 [1280/20460 (6%)] Loss: 0.005194 [2560/20460 (12%)] Loss: 0.010395 [3840/20460 (19%)] Loss: 0.043335 [5120/20460 (25%)] Loss: 0.033927 [6400/20460 (31%)] Loss: 0.031609 [7680/20460 (38%)] Loss: 0.008762 [8960/20460 (44%)] Loss: 0.083460 [10240/20460 (50%)] Loss: 0.031592 [11520/20460 (56%)] Loss: 0.012969 [12800/20460 (62%)] Loss: 0.052551 [14080/20460 (69%)] Loss: 0.033673 [15360/20460 (75%)] Loss: 0.009864 [16640/20460 (81%)] Loss: 0.019074 [17920/20460 (88%)] Loss: 0.062375 [19200/20460 (94%)] Loss: 0.004191 [14080/20460 (100%)] Loss: 0.008334 Train: Average loss: 0.0457, Accuracy: 0.9838 Validation: Average loss: 2.9753, Accuracy: 0.8948 Train Epoch: 28 [1280/20460 (6%)] Loss: 0.057985 [2560/20460 (12%)] Loss: 0.018710 [3840/20460 (19%)] Loss: 0.005535 [5120/20460 (25%)] Loss: 0.012951 [6400/20460 (31%)] Loss: 0.027756 [7680/20460 (38%)] Loss: 0.075175 [8960/20460 (44%)] Loss: 0.005305 [10240/20460 (50%)] Loss: 0.029375 [11520/20460 (56%)] Loss: 0.014121 [12800/20460 (62%)] Loss: 0.076132 [14080/20460 (69%)] Loss: 0.014897 [15360/20460 (75%)] Loss: 0.037047 [16640/20460 (81%)] Loss: 0.042828 [17920/20460 (88%)] Loss: 0.033081 [19200/20460 (94%)] Loss: 0.009244 [14080/20460 (100%)] Loss: 0.058014 Train: Average loss: 0.0342, Accuracy: 0.9874 Validation: Average loss: 3.7855, Accuracy: 0.8892 Train Epoch: 29 [1280/20460 (6%)] Loss: 0.017775 [2560/20460 (12%)] Loss: 0.021924 [3840/20460 (19%)] Loss: 0.020371 [5120/20460 (25%)] Loss: 0.087809 [6400/20460 (31%)] Loss: 0.026205 [7680/20460 (38%)] Loss: 0.008068 [8960/20460 (44%)] Loss: 0.094903 [10240/20460 (50%)] Loss: 0.074380 [11520/20460 (56%)] Loss: 0.041095 [12800/20460 (62%)] Loss: 0.089401 [14080/20460 (69%)] Loss: 0.012103 [15360/20460 (75%)] Loss: 0.056116 [16640/20460 (81%)] Loss: 0.102136 [17920/20460 (88%)] Loss: 0.006600 [19200/20460 (94%)] Loss: 0.045323 [14080/20460 (100%)] Loss: 0.005971 Train: Average loss: 0.0392, Accuracy: 0.9864 Validation: Average loss: 3.1971, Accuracy: 0.8114 Train Epoch: 30 [1280/20460 (6%)] Loss: 0.007091 [2560/20460 (12%)] Loss: 0.010009 [3840/20460 (19%)] Loss: 0.009125 [5120/20460 (25%)] Loss: 0.049172 [6400/20460 (31%)] Loss: 0.060266 [7680/20460 (38%)] Loss: 0.039820 [8960/20460 (44%)] Loss: 0.011549 [10240/20460 (50%)] Loss: 0.004018 [11520/20460 (56%)] Loss: 0.019896 [12800/20460 (62%)] Loss: 0.042440 [14080/20460 (69%)] Loss: 0.057332 [15360/20460 (75%)] Loss: 0.042854 [16640/20460 (81%)] Loss: 0.006532 [17920/20460 (88%)] Loss: 0.013735 [19200/20460 (94%)] Loss: 0.016025 [14080/20460 (100%)] Loss: 0.014934 Train: Average loss: 0.0390, Accuracy: 0.9850 Validation: Average loss: 3.4696, Accuracy: 0.8764
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_1444885/529002640.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 236 (out of 5826) 4.05% Test accuracy 95.95%
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 95.60%