def test_embeddings():
model = Siamese('dummy', input_shape=(6, 8, 3), embedding_size=3)
model.make_embeddings('data_tiny/train',
'data_tiny/train.csv',
batch_size=1)
emb = pd.read_pickle(os.path.join(model.cache_dir, 'embeddings.pkl'))
print(emb)
model.predict('data_tiny/train')
pred = pd.read_pickle(os.path.join(model.cache_dir, 'predictions.pkl'))
print(pred)
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)
model.load_weights('trained/final_weights.h5')
#model.make_embeddings('data/train', 'data/train.csv', batch_size=5, meta_dir='data/meta')
model.load_embeddings('trained/embeddings.pkl')
model.predict('data/test', meta_dir='data/meta')
#model.load_predictions('trained/predictions.pkl')
model.make_kaggle_csv('data/meta/idx_to_whales_mapping.npy')
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')
import sys
sys.path.insert(0, '../')
from core.siamese import Siamese
model = Siamese('mobilenet_like', input_shape=(672, 896, 3), embedding_size=128)
model.load_weights('trained/final_weights.h5')
model.make_embeddings('../data/train', 'train.csv', mappings_filename='../data/meta/whales_to_idx_mapping.npy', batch_size=25)
#model.load_embeddings('trained/embeddings.pkl')
model.predict('../data/train', 'val.csv')
#model.predict('../data/train', 'train.csv')