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_all') # model.load_weights('cache/cache-190208-093118/training/checkpoint-03.h5') # TODO mark pretrained mobilenet model.load_weights('cache/cache-190208-093118/training/checkpoint-03.h5') model.train('data/train.csv', 'data/train', epochs=20, batch_size=32, learning_rate=0.0001, margin=2.0)
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', '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')
csv = 'val.csv' #csv = 'train.csv' mode = 'cos_angular' #mode = 'classification' input_shape = (224, 224, 3) img_dir = '../data/train' val = pd.read_csv(csv) true_labels = val["Id"].values if mode == 'classification': model = Siamese(input_shape=(224, 224, 3), train_hidden_layers=True, n_classes=8) model.load_weights('final_weights.h5') img_names = val['Image'].values bboxes = pd.read_pickle('../data/meta/bboxes.pkl').set_index('filename') whales_seq = WhalesSequence(img_dir, bboxes=bboxes, input_shape=input_shape, x_set=img_names, batch_size=1) pred = model.model.predict_generator(whales_seq, verbose=1) pred_labels = np.argmax(pred, axis=1).reshape(-1) for i, whale in enumerate(np.sort(np.unique(true_labels))): true_labels[np.where(true_labels == whale)] = i elif mode == 'cos_angular':
from core.siamese import Siamese model = Siamese('mobilenet_like', input_shape=(672, 896, 3), embedding_size=128) model.load_weights('model/mobilenet_imagenet.h5') #model.load_weights('trained/final_weights.h5') #model.train('data/train', 'data/split_train.csv', meta_dir='D:\\IdeaProjects\\whales\\data\\meta', epochs=20, batch_size=24, learning_rate=0.001) model.train('data/train', 'data/train.csv', meta_dir='data/meta', epochs=500, batch_size=25, learning_rate=0.0005, margin=1.0)
from core.siamese import Siamese model = Siamese('resnet_like_33', input_shape=(384, 512, 3), embedding_size=128, strategy='batch_all') model.load_weights('trained/checkpoint-05.h5') model.load_embeddings('trained/embeddings.pkl') model.load_predictions('trained/predictions.pkl') model.make_kaggle_csv('trained/idx_to_whales_mapping.npy')
from core.siamese import Siamese model = Siamese(input_shape=(224, 224, 3), n_classes=5004) model.load_weights('data/new_whalizer.h5') model.predict_new_whales('data/test', 'data/submission_no_new_whales.csv')