Before training, the scripts will first download datasets to a dataset
folder.
For image classification, train with
bash train_mnist.sh
And evaluate the model with best checkpoint with this script:
bash visualize_mnist.sh
This script will generate visualization results in images
folder and corresponding information on test set. mnist_feature_map%d_%s.png
represents the feature map on %d
layer with %s
visualization method. For example, mnist_feature_map3_tsne.png
is the result of feature map of the third layer with t-SNE
visualization method. mnist-train-loss-acc.png
represents the change of loss and accuracy on training set.
Train with the following script:
bash train_fashion_mnish.sh
And evaluate the model with best checkpoint with this script:
bash visualize_vae.sh
This script will generate visualization results in images
folder. vae-image1/2.png
are randomly generated images with two arbitrary Gaussian noise. vae-%.2f-merge.png
is the image generated with interpolation.
Train with the following script:
bash train_sst2.sh
And evaluate the model with best checkpoint with this script:
bash visualize_sst2.sh
This script will generate visualization results in images
folder.
sst2_feature_map%d_%s.png
represents the feature map on %d
layer with %s
visualization method. sst2-%s-loss-acc.png
represents the loss and accuracy change figure either in training set or in validation set.