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), # 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_1187671/3247579378.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_90"
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: 1.121783 [2560/20460 (12%)] Loss: 0.946629 [3840/20460 (19%)] Loss: 0.506714 [5120/20460 (25%)] Loss: 0.533865 [6400/20460 (31%)] Loss: 0.436800 [7680/20460 (38%)] Loss: 0.262740 [8960/20460 (44%)] Loss: 0.302465 [10240/20460 (50%)] Loss: 0.297401 [11520/20460 (56%)] Loss: 0.222155 [12800/20460 (62%)] Loss: 0.380815 [14080/20460 (69%)] Loss: 0.380882 [15360/20460 (75%)] Loss: 0.246122 [16640/20460 (81%)] Loss: 0.206862 [17920/20460 (88%)] Loss: 0.156093 [19200/20460 (94%)] Loss: 0.192158 [14080/20460 (100%)] Loss: 0.278408 Train: Average loss: 0.4585, Accuracy: 0.8359 Validation: Average loss: 1.5108, Accuracy: 0.7027 Train Epoch: 2 [1280/20460 (6%)] Loss: 0.281680 [2560/20460 (12%)] Loss: 0.321837 [3840/20460 (19%)] Loss: 0.238968 [5120/20460 (25%)] Loss: 0.215485 [6400/20460 (31%)] Loss: 0.297618 [7680/20460 (38%)] Loss: 0.161287 [8960/20460 (44%)] Loss: 0.262413 [10240/20460 (50%)] Loss: 0.194598 [11520/20460 (56%)] Loss: 0.066758 [12800/20460 (62%)] Loss: 0.231040 [14080/20460 (69%)] Loss: 0.313682 [15360/20460 (75%)] Loss: 0.149096 [16640/20460 (81%)] Loss: 0.218900 [17920/20460 (88%)] Loss: 0.142537 [19200/20460 (94%)] Loss: 0.157793 [14080/20460 (100%)] Loss: 0.061833 Train: Average loss: 0.2134, Accuracy: 0.9217 Validation: Average loss: 1.2695, Accuracy: 0.8127 Train Epoch: 3 [1280/20460 (6%)] Loss: 0.130062 [2560/20460 (12%)] Loss: 0.238443 [3840/20460 (19%)] Loss: 0.139244 [5120/20460 (25%)] Loss: 0.170909 [6400/20460 (31%)] Loss: 0.097468 [7680/20460 (38%)] Loss: 0.112483 [8960/20460 (44%)] Loss: 0.097386 [10240/20460 (50%)] Loss: 0.049394 [11520/20460 (56%)] Loss: 0.162956 [12800/20460 (62%)] Loss: 0.351354 [14080/20460 (69%)] Loss: 0.102100 [15360/20460 (75%)] Loss: 0.136975 [16640/20460 (81%)] Loss: 0.135995 [17920/20460 (88%)] Loss: 0.308334 [19200/20460 (94%)] Loss: 0.118473 [14080/20460 (100%)] Loss: 0.105289 Train: Average loss: 0.1699, Accuracy: 0.9381 Validation: Average loss: 1.2912, Accuracy: 0.8287 Train Epoch: 4 [1280/20460 (6%)] Loss: 0.146442 [2560/20460 (12%)] Loss: 0.207035 [3840/20460 (19%)] Loss: 0.177183 [5120/20460 (25%)] Loss: 0.081921 [6400/20460 (31%)] Loss: 0.124173 [7680/20460 (38%)] Loss: 0.074887 [8960/20460 (44%)] Loss: 0.074524 [10240/20460 (50%)] Loss: 0.208884 [11520/20460 (56%)] Loss: 0.045999 [12800/20460 (62%)] Loss: 0.102252 [14080/20460 (69%)] Loss: 0.039787 [15360/20460 (75%)] Loss: 0.221526 [16640/20460 (81%)] Loss: 0.087009 [17920/20460 (88%)] Loss: 0.059424 [19200/20460 (94%)] Loss: 0.266214 [14080/20460 (100%)] Loss: 0.224559 Train: Average loss: 0.1398, Accuracy: 0.9497 Validation: Average loss: 1.1637, Accuracy: 0.8879 Train Epoch: 5 [1280/20460 (6%)] Loss: 0.136847 [2560/20460 (12%)] Loss: 0.113813 [3840/20460 (19%)] Loss: 0.094093 [5120/20460 (25%)] Loss: 0.129633 [6400/20460 (31%)] Loss: 0.087359 [7680/20460 (38%)] Loss: 0.123612 [8960/20460 (44%)] Loss: 0.109611 [10240/20460 (50%)] Loss: 0.135459 [11520/20460 (56%)] Loss: 0.069905 [12800/20460 (62%)] Loss: 0.086799 [14080/20460 (69%)] Loss: 0.191349 [15360/20460 (75%)] Loss: 0.206091 [16640/20460 (81%)] Loss: 0.163483 [17920/20460 (88%)] Loss: 0.138722 [19200/20460 (94%)] Loss: 0.206998 [14080/20460 (100%)] Loss: 0.119404 Train: Average loss: 0.1313, Accuracy: 0.9529 Validation: Average loss: 1.0963, Accuracy: 0.8702 Train Epoch: 6 [1280/20460 (6%)] Loss: 0.187999 [2560/20460 (12%)] Loss: 0.086030 [3840/20460 (19%)] Loss: 0.144777 [5120/20460 (25%)] Loss: 0.144760 [6400/20460 (31%)] Loss: 0.124115 [7680/20460 (38%)] Loss: 0.137852 [8960/20460 (44%)] Loss: 0.027978 [10240/20460 (50%)] Loss: 0.109399 [11520/20460 (56%)] Loss: 0.055348 [12800/20460 (62%)] Loss: 0.074755 [14080/20460 (69%)] Loss: 0.131823 [15360/20460 (75%)] Loss: 0.044131 [16640/20460 (81%)] Loss: 0.047118 [17920/20460 (88%)] Loss: 0.137253 [19200/20460 (94%)] Loss: 0.203563 [14080/20460 (100%)] Loss: 0.049812 Train: Average loss: 0.1150, Accuracy: 0.9585 Validation: Average loss: 1.4601, Accuracy: 0.8397 Train Epoch: 7 [1280/20460 (6%)] Loss: 0.217276 [2560/20460 (12%)] Loss: 0.090557 [3840/20460 (19%)] Loss: 0.069154 [5120/20460 (25%)] Loss: 0.047134 [6400/20460 (31%)] Loss: 0.077934 [7680/20460 (38%)] Loss: 0.045398 [8960/20460 (44%)] Loss: 0.098383 [10240/20460 (50%)] Loss: 0.115602 [11520/20460 (56%)] Loss: 0.099869 [12800/20460 (62%)] Loss: 0.135509 [14080/20460 (69%)] Loss: 0.183088 [15360/20460 (75%)] Loss: 0.063021 [16640/20460 (81%)] Loss: 0.109143 [17920/20460 (88%)] Loss: 0.147743 [19200/20460 (94%)] Loss: 0.138264 [14080/20460 (100%)] Loss: 0.061802 Train: Average loss: 0.1046, Accuracy: 0.9606 Validation: Average loss: 1.8557, Accuracy: 0.8429 Train Epoch: 8 [1280/20460 (6%)] Loss: 0.201911 [2560/20460 (12%)] Loss: 0.100179 [3840/20460 (19%)] Loss: 0.068157 [5120/20460 (25%)] Loss: 0.022204 [6400/20460 (31%)] Loss: 0.087012 [7680/20460 (38%)] Loss: 0.065616 [8960/20460 (44%)] Loss: 0.043233 [10240/20460 (50%)] Loss: 0.047198 [11520/20460 (56%)] Loss: 0.044256 [12800/20460 (62%)] Loss: 0.081848 [14080/20460 (69%)] Loss: 0.070314 [15360/20460 (75%)] Loss: 0.068294 [16640/20460 (81%)] Loss: 0.197445 [17920/20460 (88%)] Loss: 0.239804 [19200/20460 (94%)] Loss: 0.038391 [14080/20460 (100%)] Loss: 0.052701 Train: Average loss: 0.0914, Accuracy: 0.9663 Validation: Average loss: 2.1686, Accuracy: 0.6466 Train Epoch: 9 [1280/20460 (6%)] Loss: 0.099265 [2560/20460 (12%)] Loss: 0.108811 [3840/20460 (19%)] Loss: 0.051881 [5120/20460 (25%)] Loss: 0.063936 [6400/20460 (31%)] Loss: 0.039354 [7680/20460 (38%)] Loss: 0.108712 [8960/20460 (44%)] Loss: 0.106972 [10240/20460 (50%)] Loss: 0.059984 [11520/20460 (56%)] Loss: 0.076435 [12800/20460 (62%)] Loss: 0.083024 [14080/20460 (69%)] Loss: 0.129473 [15360/20460 (75%)] Loss: 0.068982 [16640/20460 (81%)] Loss: 0.011408 [17920/20460 (88%)] Loss: 0.122767 [19200/20460 (94%)] Loss: 0.081746 [14080/20460 (100%)] Loss: 0.053206 Train: Average loss: 0.0867, Accuracy: 0.9688 Validation: Average loss: 1.9094, Accuracy: 0.7480 Train Epoch: 10 [1280/20460 (6%)] Loss: 0.050514 [2560/20460 (12%)] Loss: 0.088662 [3840/20460 (19%)] Loss: 0.034884 [5120/20460 (25%)] Loss: 0.058542 [6400/20460 (31%)] Loss: 0.062130 [7680/20460 (38%)] Loss: 0.086473 [8960/20460 (44%)] Loss: 0.038134 [10240/20460 (50%)] Loss: 0.061116 [11520/20460 (56%)] Loss: 0.051446 [12800/20460 (62%)] Loss: 0.140623 [14080/20460 (69%)] Loss: 0.223666 [15360/20460 (75%)] Loss: 0.083736 [16640/20460 (81%)] Loss: 0.025793 [17920/20460 (88%)] Loss: 0.108333 [19200/20460 (94%)] Loss: 0.211818 [14080/20460 (100%)] Loss: 0.240535 Train: Average loss: 0.0854, Accuracy: 0.9692 Validation: Average loss: 1.6490, Accuracy: 0.8474 Train Epoch: 11 [1280/20460 (6%)] Loss: 0.140121 [2560/20460 (12%)] Loss: 0.114145 [3840/20460 (19%)] Loss: 0.068639 [5120/20460 (25%)] Loss: 0.170345 [6400/20460 (31%)] Loss: 0.083817 [7680/20460 (38%)] Loss: 0.035787 [8960/20460 (44%)] Loss: 0.044273 [10240/20460 (50%)] Loss: 0.224257 [11520/20460 (56%)] Loss: 0.032594 [12800/20460 (62%)] Loss: 0.032689 [14080/20460 (69%)] Loss: 0.113383 [15360/20460 (75%)] Loss: 0.070244 [16640/20460 (81%)] Loss: 0.010874 [17920/20460 (88%)] Loss: 0.075980 [19200/20460 (94%)] Loss: 0.071065 [14080/20460 (100%)] Loss: 0.091406 Train: Average loss: 0.0802, Accuracy: 0.9713 Validation: Average loss: 1.7442, Accuracy: 0.8515 Train Epoch: 12 [1280/20460 (6%)] Loss: 0.038020 [2560/20460 (12%)] Loss: 0.156714 [3840/20460 (19%)] Loss: 0.023174 [5120/20460 (25%)] Loss: 0.025744 [6400/20460 (31%)] Loss: 0.048348 [7680/20460 (38%)] Loss: 0.070500 [8960/20460 (44%)] Loss: 0.167784 [10240/20460 (50%)] Loss: 0.022081 [11520/20460 (56%)] Loss: 0.048660 [12800/20460 (62%)] Loss: 0.048582 [14080/20460 (69%)] Loss: 0.108620 [15360/20460 (75%)] Loss: 0.032134 [16640/20460 (81%)] Loss: 0.109318 [17920/20460 (88%)] Loss: 0.093002 [19200/20460 (94%)] Loss: 0.098362 [14080/20460 (100%)] Loss: 0.058102 Train: Average loss: 0.0703, Accuracy: 0.9738 Validation: Average loss: 1.8285, Accuracy: 0.8345 Train Epoch: 13 [1280/20460 (6%)] Loss: 0.087391 [2560/20460 (12%)] Loss: 0.030444 [3840/20460 (19%)] Loss: 0.020562 [5120/20460 (25%)] Loss: 0.173501 [6400/20460 (31%)] Loss: 0.107384 [7680/20460 (38%)] Loss: 0.040159 [8960/20460 (44%)] Loss: 0.132579 [10240/20460 (50%)] Loss: 0.043040 [11520/20460 (56%)] Loss: 0.046314 [12800/20460 (62%)] Loss: 0.034813 [14080/20460 (69%)] Loss: 0.033003 [15360/20460 (75%)] Loss: 0.069163 [16640/20460 (81%)] Loss: 0.047007 [17920/20460 (88%)] Loss: 0.057003 [19200/20460 (94%)] Loss: 0.085094 [14080/20460 (100%)] Loss: 0.162390 Train: Average loss: 0.0650, Accuracy: 0.9763 Validation: Average loss: 1.8287, Accuracy: 0.8924 Train Epoch: 14 [1280/20460 (6%)] Loss: 0.133700 [2560/20460 (12%)] Loss: 0.026024 [3840/20460 (19%)] Loss: 0.024024 [5120/20460 (25%)] Loss: 0.142325 [6400/20460 (31%)] Loss: 0.050719 [7680/20460 (38%)] Loss: 0.025884 [8960/20460 (44%)] Loss: 0.050714 [10240/20460 (50%)] Loss: 0.048575 [11520/20460 (56%)] Loss: 0.008899 [12800/20460 (62%)] Loss: 0.065333 [14080/20460 (69%)] Loss: 0.020567 [15360/20460 (75%)] Loss: 0.106526 [16640/20460 (81%)] Loss: 0.030841 [17920/20460 (88%)] Loss: 0.030987 [19200/20460 (94%)] Loss: 0.073911 [14080/20460 (100%)] Loss: 0.119375 Train: Average loss: 0.0684, Accuracy: 0.9761 Validation: Average loss: 2.0507, Accuracy: 0.8446 Train Epoch: 15 [1280/20460 (6%)] Loss: 0.204143 [2560/20460 (12%)] Loss: 0.113897 [3840/20460 (19%)] Loss: 0.137349 [5120/20460 (25%)] Loss: 0.102300 [6400/20460 (31%)] Loss: 0.082263 [7680/20460 (38%)] Loss: 0.103335 [8960/20460 (44%)] Loss: 0.052692 [10240/20460 (50%)] Loss: 0.043982 [11520/20460 (56%)] Loss: 0.068718 [12800/20460 (62%)] Loss: 0.034984 [14080/20460 (69%)] Loss: 0.051655 [15360/20460 (75%)] Loss: 0.025368 [16640/20460 (81%)] Loss: 0.091609 [17920/20460 (88%)] Loss: 0.033564 [19200/20460 (94%)] Loss: 0.020121 [14080/20460 (100%)] Loss: 0.025609 Train: Average loss: 0.0638, Accuracy: 0.9770 Validation: Average loss: 1.3940, Accuracy: 0.8629 Train Epoch: 16 [1280/20460 (6%)] Loss: 0.021415 [2560/20460 (12%)] Loss: 0.095036 [3840/20460 (19%)] Loss: 0.040700 [5120/20460 (25%)] Loss: 0.095202 [6400/20460 (31%)] Loss: 0.077075 [7680/20460 (38%)] Loss: 0.139535 [8960/20460 (44%)] Loss: 0.109150 [10240/20460 (50%)] Loss: 0.040185 [11520/20460 (56%)] Loss: 0.033069 [12800/20460 (62%)] Loss: 0.091608 [14080/20460 (69%)] Loss: 0.027273 [15360/20460 (75%)] Loss: 0.083298 [16640/20460 (81%)] Loss: 0.037661 [17920/20460 (88%)] Loss: 0.053975 [19200/20460 (94%)] Loss: 0.054019 [14080/20460 (100%)] Loss: 0.046255 Train: Average loss: 0.0634, Accuracy: 0.9770 Validation: Average loss: 2.0374, Accuracy: 0.8543 Train Epoch: 17 [1280/20460 (6%)] Loss: 0.016832 [2560/20460 (12%)] Loss: 0.035048 [3840/20460 (19%)] Loss: 0.057127 [5120/20460 (25%)] Loss: 0.004374 [6400/20460 (31%)] Loss: 0.037183 [7680/20460 (38%)] Loss: 0.102837 [8960/20460 (44%)] Loss: 0.041151 [10240/20460 (50%)] Loss: 0.054957 [11520/20460 (56%)] Loss: 0.035360 [12800/20460 (62%)] Loss: 0.018015 [14080/20460 (69%)] Loss: 0.027054 [15360/20460 (75%)] Loss: 0.016186 [16640/20460 (81%)] Loss: 0.057146 [17920/20460 (88%)] Loss: 0.020438 [19200/20460 (94%)] Loss: 0.006786 [14080/20460 (100%)] Loss: 0.042241 Train: Average loss: 0.0537, Accuracy: 0.9803 Validation: Average loss: 1.8400, Accuracy: 0.8861 Train Epoch: 18 [1280/20460 (6%)] Loss: 0.005049 [2560/20460 (12%)] Loss: 0.024975 [3840/20460 (19%)] Loss: 0.004489 [5120/20460 (25%)] Loss: 0.071913 [6400/20460 (31%)] Loss: 0.058303 [7680/20460 (38%)] Loss: 0.003656 [8960/20460 (44%)] Loss: 0.014360 [10240/20460 (50%)] Loss: 0.036314 [11520/20460 (56%)] Loss: 0.070050 [12800/20460 (62%)] Loss: 0.085967 [14080/20460 (69%)] Loss: 0.016144 [15360/20460 (75%)] Loss: 0.046558 [16640/20460 (81%)] Loss: 0.053486 [17920/20460 (88%)] Loss: 0.018694 [19200/20460 (94%)] Loss: 0.033277 [14080/20460 (100%)] Loss: 0.070492 Train: Average loss: 0.0524, Accuracy: 0.9799 Validation: Average loss: 1.5067, Accuracy: 0.8366 Train Epoch: 19 [1280/20460 (6%)] Loss: 0.024740 [2560/20460 (12%)] Loss: 0.063422 [3840/20460 (19%)] Loss: 0.036730 [5120/20460 (25%)] Loss: 0.076165 [6400/20460 (31%)] Loss: 0.141040 [7680/20460 (38%)] Loss: 0.084085 [8960/20460 (44%)] Loss: 0.076674 [10240/20460 (50%)] Loss: 0.205944 [11520/20460 (56%)] Loss: 0.065567 [12800/20460 (62%)] Loss: 0.025923 [14080/20460 (69%)] Loss: 0.028149 [15360/20460 (75%)] Loss: 0.025548 [16640/20460 (81%)] Loss: 0.088734 [17920/20460 (88%)] Loss: 0.028693 [19200/20460 (94%)] Loss: 0.052938 [14080/20460 (100%)] Loss: 0.017587 Train: Average loss: 0.0557, Accuracy: 0.9798 Validation: Average loss: 2.5894, Accuracy: 0.8117 Train Epoch: 20 [1280/20460 (6%)] Loss: 0.019759 [2560/20460 (12%)] Loss: 0.021306 [3840/20460 (19%)] Loss: 0.005973 [5120/20460 (25%)] Loss: 0.050559 [6400/20460 (31%)] Loss: 0.153467 [7680/20460 (38%)] Loss: 0.092604 [8960/20460 (44%)] Loss: 0.017100 [10240/20460 (50%)] Loss: 0.064931 [11520/20460 (56%)] Loss: 0.041813 [12800/20460 (62%)] Loss: 0.012860 [14080/20460 (69%)] Loss: 0.085731 [15360/20460 (75%)] Loss: 0.120585 [16640/20460 (81%)] Loss: 0.023143 [17920/20460 (88%)] Loss: 0.083957 [19200/20460 (94%)] Loss: 0.022337 [14080/20460 (100%)] Loss: 0.018483 Train: Average loss: 0.0546, Accuracy: 0.9809 Validation: Average loss: 2.5440, Accuracy: 0.8903 Train Epoch: 21 [1280/20460 (6%)] Loss: 0.018003 [2560/20460 (12%)] Loss: 0.046180 [3840/20460 (19%)] Loss: 0.011885 [5120/20460 (25%)] Loss: 0.016037 [6400/20460 (31%)] Loss: 0.050253 [7680/20460 (38%)] Loss: 0.029531 [8960/20460 (44%)] Loss: 0.166838 [10240/20460 (50%)] Loss: 0.021797 [11520/20460 (56%)] Loss: 0.055100 [12800/20460 (62%)] Loss: 0.037381 [14080/20460 (69%)] Loss: 0.029021 [15360/20460 (75%)] Loss: 0.025692 [16640/20460 (81%)] Loss: 0.021525 [17920/20460 (88%)] Loss: 0.054116 [19200/20460 (94%)] Loss: 0.102765 [14080/20460 (100%)] Loss: 0.006914 Train: Average loss: 0.0525, Accuracy: 0.9808 Validation: Average loss: 2.0844, Accuracy: 0.8851 Train Epoch: 22 [1280/20460 (6%)] Loss: 0.013768 [2560/20460 (12%)] Loss: 0.058332 [3840/20460 (19%)] Loss: 0.018160 [5120/20460 (25%)] Loss: 0.069345 [6400/20460 (31%)] Loss: 0.016831 [7680/20460 (38%)] Loss: 0.040255 [8960/20460 (44%)] Loss: 0.023892 [10240/20460 (50%)] Loss: 0.051529 [11520/20460 (56%)] Loss: 0.039424 [12800/20460 (62%)] Loss: 0.015601 [14080/20460 (69%)] Loss: 0.080849 [15360/20460 (75%)] Loss: 0.020875 [16640/20460 (81%)] Loss: 0.018100 [17920/20460 (88%)] Loss: 0.057401 [19200/20460 (94%)] Loss: 0.028669 [14080/20460 (100%)] Loss: 0.015214 Train: Average loss: 0.0454, Accuracy: 0.9839 Validation: Average loss: 2.8083, Accuracy: 0.8809 Train Epoch: 23 [1280/20460 (6%)] Loss: 0.052872 [2560/20460 (12%)] Loss: 0.027792 [3840/20460 (19%)] Loss: 0.017103 [5120/20460 (25%)] Loss: 0.011144 [6400/20460 (31%)] Loss: 0.028261 [7680/20460 (38%)] Loss: 0.233879 [8960/20460 (44%)] Loss: 0.042307 [10240/20460 (50%)] Loss: 0.045837 [11520/20460 (56%)] Loss: 0.067601 [12800/20460 (62%)] Loss: 0.033086 [14080/20460 (69%)] Loss: 0.018354 [15360/20460 (75%)] Loss: 0.046112 [16640/20460 (81%)] Loss: 0.004897 [17920/20460 (88%)] Loss: 0.023041 [19200/20460 (94%)] Loss: 0.011263 [14080/20460 (100%)] Loss: 0.111408 Train: Average loss: 0.0426, Accuracy: 0.9848 Validation: Average loss: 2.2658, Accuracy: 0.7934 Train Epoch: 24 [1280/20460 (6%)] Loss: 0.016250 [2560/20460 (12%)] Loss: 0.061643 [3840/20460 (19%)] Loss: 0.015815 [5120/20460 (25%)] Loss: 0.031169 [6400/20460 (31%)] Loss: 0.026564 [7680/20460 (38%)] Loss: 0.099475 [8960/20460 (44%)] Loss: 0.049851 [10240/20460 (50%)] Loss: 0.025703 [11520/20460 (56%)] Loss: 0.021714 [12800/20460 (62%)] Loss: 0.013830 [14080/20460 (69%)] Loss: 0.152658 [15360/20460 (75%)] Loss: 0.031056 [16640/20460 (81%)] Loss: 0.014765 [17920/20460 (88%)] Loss: 0.010391 [19200/20460 (94%)] Loss: 0.106624 [14080/20460 (100%)] Loss: 0.067084 Train: Average loss: 0.0477, Accuracy: 0.9827 Validation: Average loss: 2.6106, Accuracy: 0.8193 Train Epoch: 25 [1280/20460 (6%)] Loss: 0.109922 [2560/20460 (12%)] Loss: 0.024177 [3840/20460 (19%)] Loss: 0.016365 [5120/20460 (25%)] Loss: 0.020670 [6400/20460 (31%)] Loss: 0.067381 [7680/20460 (38%)] Loss: 0.006828 [8960/20460 (44%)] Loss: 0.009186 [10240/20460 (50%)] Loss: 0.011102 [11520/20460 (56%)] Loss: 0.219380 [12800/20460 (62%)] Loss: 0.060808 [14080/20460 (69%)] Loss: 0.016508 [15360/20460 (75%)] Loss: 0.037232 [16640/20460 (81%)] Loss: 0.032674 [17920/20460 (88%)] Loss: 0.046707 [19200/20460 (94%)] Loss: 0.032258 [14080/20460 (100%)] Loss: 0.029935 Train: Average loss: 0.0499, Accuracy: 0.9820 Validation: Average loss: 2.0060, Accuracy: 0.8872 Train Epoch: 26 [1280/20460 (6%)] Loss: 0.054253 [2560/20460 (12%)] Loss: 0.079039 [3840/20460 (19%)] Loss: 0.184449 [5120/20460 (25%)] Loss: 0.023214 [6400/20460 (31%)] Loss: 0.014753 [7680/20460 (38%)] Loss: 0.005708 [8960/20460 (44%)] Loss: 0.198349 [10240/20460 (50%)] Loss: 0.059076 [11520/20460 (56%)] Loss: 0.006045 [12800/20460 (62%)] Loss: 0.044515 [14080/20460 (69%)] Loss: 0.007924 [15360/20460 (75%)] Loss: 0.032671 [16640/20460 (81%)] Loss: 0.090974 [17920/20460 (88%)] Loss: 0.042169 [19200/20460 (94%)] Loss: 0.036685 [14080/20460 (100%)] Loss: 0.015652 Train: Average loss: 0.0471, Accuracy: 0.9836 Validation: Average loss: 2.7648, Accuracy: 0.8311 Train Epoch: 27 [1280/20460 (6%)] Loss: 0.063548 [2560/20460 (12%)] Loss: 0.035279 [3840/20460 (19%)] Loss: 0.040248 [5120/20460 (25%)] Loss: 0.051909 [6400/20460 (31%)] Loss: 0.034989 [7680/20460 (38%)] Loss: 0.002876 [8960/20460 (44%)] Loss: 0.031894 [10240/20460 (50%)] Loss: 0.079756 [11520/20460 (56%)] Loss: 0.013293 [12800/20460 (62%)] Loss: 0.036099 [14080/20460 (69%)] Loss: 0.013924 [15360/20460 (75%)] Loss: 0.021081 [16640/20460 (81%)] Loss: 0.011268 [17920/20460 (88%)] Loss: 0.021196 [19200/20460 (94%)] Loss: 0.049185 [14080/20460 (100%)] Loss: 0.005441 Train: Average loss: 0.0444, Accuracy: 0.9833 Validation: Average loss: 2.3483, Accuracy: 0.8664 Train Epoch: 28 [1280/20460 (6%)] Loss: 0.051537 [2560/20460 (12%)] Loss: 0.008787 [3840/20460 (19%)] Loss: 0.004649 [5120/20460 (25%)] Loss: 0.012945 [6400/20460 (31%)] Loss: 0.013406 [7680/20460 (38%)] Loss: 0.097199 [8960/20460 (44%)] Loss: 0.013903 [10240/20460 (50%)] Loss: 0.042764 [11520/20460 (56%)] Loss: 0.017919 [12800/20460 (62%)] Loss: 0.063798 [14080/20460 (69%)] Loss: 0.011889 [15360/20460 (75%)] Loss: 0.028720 [16640/20460 (81%)] Loss: 0.028578 [17920/20460 (88%)] Loss: 0.015394 [19200/20460 (94%)] Loss: 0.030783 [14080/20460 (100%)] Loss: 0.051448 Train: Average loss: 0.0383, Accuracy: 0.9861 Validation: Average loss: 2.2801, Accuracy: 0.8525 Train Epoch: 29 [1280/20460 (6%)] Loss: 0.015476 [2560/20460 (12%)] Loss: 0.016706 [3840/20460 (19%)] Loss: 0.012022 [5120/20460 (25%)] Loss: 0.036896 [6400/20460 (31%)] Loss: 0.001162 [7680/20460 (38%)] Loss: 0.039653 [8960/20460 (44%)] Loss: 0.032841 [10240/20460 (50%)] Loss: 0.112310 [11520/20460 (56%)] Loss: 0.040032 [12800/20460 (62%)] Loss: 0.020217 [14080/20460 (69%)] Loss: 0.116125 [15360/20460 (75%)] Loss: 0.048336 [16640/20460 (81%)] Loss: 0.061584 [17920/20460 (88%)] Loss: 0.009391 [19200/20460 (94%)] Loss: 0.035957 [14080/20460 (100%)] Loss: 0.142472 Train: Average loss: 0.0393, Accuracy: 0.9852 Validation: Average loss: 3.4516, Accuracy: 0.8494 Train Epoch: 30 [1280/20460 (6%)] Loss: 0.021184 [2560/20460 (12%)] Loss: 0.059198 [3840/20460 (19%)] Loss: 0.009610 [5120/20460 (25%)] Loss: 0.032773 [6400/20460 (31%)] Loss: 0.020875 [7680/20460 (38%)] Loss: 0.004248 [8960/20460 (44%)] Loss: 0.050532 [10240/20460 (50%)] Loss: 0.012212 [11520/20460 (56%)] Loss: 0.009121 [12800/20460 (62%)] Loss: 0.024016 [14080/20460 (69%)] Loss: 0.036644 [15360/20460 (75%)] Loss: 0.054126 [16640/20460 (81%)] Loss: 0.008078 [17920/20460 (88%)] Loss: 0.014535 [19200/20460 (94%)] Loss: 0.008624 [14080/20460 (100%)] Loss: 0.017956 Train: Average loss: 0.0401, Accuracy: 0.9866 Validation: Average loss: 2.9908, Accuracy: 0.8377
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_1187671/529002640.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 392 (out of 5826) 6.73% Test accuracy 93.27%
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 95.00%