def gen_training_accuracy(args):
# load data and model
params = utils.load_params(args.model_dir)
ckpt_dir = os.path.join(args.model_dir, 'checkpoints')
ckpt_paths = [int(e[11:-3]) for e in os.listdir(ckpt_dir) if e[-3:] == ".pt"]
ckpt_paths = np.sort(ckpt_paths)
# csv
headers = ["epoch", "acc_train", "acc_test"]
csv_path = utils.create_csv(args.model_dir, 'accuracy.csv', headers)
for epoch, ckpt_paths in enumerate(ckpt_paths):
if epoch % 5 != 0:
continue
net, epoch = tf.load_checkpoint(args.model_dir, epoch=epoch, eval_=True)
# load data
train_transforms = tf.load_transforms('test')
trainset = tf.load_trainset(params['data'], train_transforms, train=True)
trainloader = DataLoader(trainset, batch_size=500, num_workers=4)
train_features, train_labels = tf.get_features(net, trainloader, verbose=False)
test_transforms = tf.load_transforms('test')
testset = tf.load_trainset(params['data'], test_transforms, train=False)
testloader = DataLoader(testset, batch_size=500, num_workers=4)
test_features, test_labels = tf.get_features(net, testloader, verbose=False)
acc_train, acc_test = svm(args, train_features, train_labels, test_features, test_labels)
utils.save_state(args.model_dir, epoch, acc_train, acc_test, filename='accuracy.csv')
print("Finished generating accuracy.")
# train/test pass
print("Forward pass - train features")
Z_train = model(X_train, y_train)
utils.save_loss(model.loss_dict, model_dir, "train")
print("Forward pass - test features")
Z_test = model(X_test)
utils.save_loss(model.loss_dict, model_dir, "test")
# save features
utils.save_features(model_dir, "X_train", X_train, y_train)
utils.save_features(model_dir, "X_test", X_test, y_test)
utils.save_features(model_dir, "Z_train", Z_train, y_train)
utils.save_features(model_dir, "Z_test", Z_test, y_test)
# evaluation train
_, acc_svm = evaluate.svm(Z_train, y_train, Z_train, y_train)
acc_knn = evaluate.knn(Z_train, y_train, Z_train, y_train, k=5)
acc_svd = evaluate.nearsub(Z_train, y_train, Z_train, y_train, n_comp=1)
acc = {"svm": acc_svm, "knn": acc_knn, "nearsub-svd": acc_svd}
utils.save_params(model_dir, acc, name="acc_train.json")
# evaluation test
_, acc_svm = evaluate.svm(Z_train, y_train, Z_test, y_test)
acc_knn = evaluate.knn(Z_train, y_train, Z_test, y_test, k=5)
acc_svd = evaluate.nearsub(Z_train, y_train, Z_test, y_test, n_comp=1)
acc = {"svm": acc_svm, "knn": acc_knn, "nearsub-svd": acc_svd}
utils.save_params(model_dir, acc, name="acc_test.json")
# plot
plot.plot_combined_loss(model_dir)
plot.plot_heatmap(X_train, y_train, "X_train", model_dir)