Cifar-10 basic optimization 2 pytorch, test set accuracy rate 88.2 - Code World

Cifar-10 basic optimization 2 pytorch, test set accuracy rate 88.2

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Cifar-10 basic optimization 2 pytorch, test set accuracy rate 88.2

cifar-10 basic optimization 3-pytorch, the accuracy rate of the test set is 90%+ in 3 minutes

CIFAR-10 basic optimization one (add standardization and activation function)

NAN test set accuracy was

CIFAR-10 image data set production

TensorFlow2 выполняет распознавание животных в наборе данных CIFAR-10, сохраняет модель и выполняет внешнее тестирование загружаемых изображений.

Cifar-10 基本最適化 2 pytorch、テストセット精度率 88.2

Cifar-10 기본 최적화 2 pytorch, 테스트 세트 정확도 88.2

Cifar-10 optimización básica 2 pytorch, tasa de precisión del conjunto de prueba 88.2

Pytorch uses resnet50 to achieve cifar10 accuracy of more than 95%

Calculation accuracy, regression rate, F1 score and other indicators in pytorch

Pytorch-Fashion MNIST Dataset Convolutional Neural Network Example Code Program Training Prediction Accuracy Rate 94%

Complete the classification of CIFAR-10 data set and UCI wine data set based on MLP

One minute to remember the accuracy rate and recall rate

The accuracy rate of classification indicators, AUCROC

The second condition GAN deep learning projects using the training data set cifar-10

Use tensorflow training cifar-10 data set (refer to the official documents prepared)

PyTorch real benefits from entry to four of proficiency - convolution neural network CIFAR-10 image classification

Hands-on learning deep learning pytorch study notes-Kaggle image classification 1 (CIFAR-10)

tensorflow(2): neural network optimization (loss, learning_rate)

Actual Kaggle Competition: Image Classification (CIFAR-10) Hands-on Deep Learning v2

np.transpose(img_a,(1,2,0)) during CIFAR-10 parsing

Performance measurement 1 accuracy_score accuracy rate

Linear multi-classification, visual display and test accuracy of iris data set

Linear multi-classification, visual display and test accuracy experiment of iris data set

Continuously reduce the learning rate and improve the training accuracy

PyTorch study notes (18) -- the script to divide the training set and test set

Accuracy, precision, recall, true rate, false positive rate, ROC/AUC

Classification indicators of data mining: recall rate, precision, accuracy rate, false alarm rate and missed alarm rate

Convex optimization [2] common convex set

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