from core.siamese import Siamese model = Siamese('shallow_mnist', input_shape=(28, 28, 3), embedding_size=64, strategy='batch_all') model.load_weights('cache/cache-190213-131256/training/checkpoint-07.h5') model.make_embeddings('data_mnist/train.csv', 'data_mnist/train', batch_size=200) model.predict('data_mnist/train_subset') model.make_csv('cache/cache-190213-131256/idx_to_whales_mapping.npy') # model.load_embeddings('cache/cache-190205-070856/embeddings.pkl') # model.load_predictions('cache/cache-190205-072026/predictions.pkl') # model.make_kaggle_csv('cache/cache-190205-065005/idx_to_whales_mapping.npy') # model.draw_tsne(model.predictions.values[:, 1:])
from core.siamese import Siamese model = Siamese('mobilenet_like', input_shape=(672, 896, 3), embedding_size=128, strategy='batch_hard') model.load_weights('cache/cache-190213-095205/training/checkpoint-07.h5') model.load_embeddings('cache/cache-190213-113426/embeddings.pkl') # model.make_embeddings('data/train.csv', 'data/train', batch_size=32) # model.predict('data/test') model.predict('data/train_subset') model.make_csv('cache/cache-190208-093118/idx_to_whales_mapping.npy')