import datetime as dt
#
from sklearn.metrics import log_loss
#
import utils
import models
import params
###############################################################################
if __name__ == '__main__':
np.random.seed(1017)
target = 'is_iceberg'
#Load data
test, test_bands = utils.read_jason(file='test.json', loc='../input/')
test_X_dup = utils.rescale(test_bands)
test_meta = test['inc_angle'].values
tmp = dt.datetime.now().strftime("%Y-%m-%d-%H-%M")
file_weights = '../weights/weights_current.hdf5'
if os.path.isfile(file_weights):
#define and load model
nb_filters = params.nb_filters
nb_dense = params.nb_dense
weights_file = params.weights_file
model = models.get_model(img_shape=(75, 75, 2),
f=nb_filters,
h=nb_dense)
out = Lambda(lambda img: ktf.image.resize_images(img, 224, 224))(inp)
model = Model(input=inp, output=out)
out = model.predict(images)
return out
###############################################################################
if __name__ == '__main__':
np.random.seed(1017)
target = 'is_iceberg'
#Load data
train, train_bands = utils.read_jason(file='train.json')
test, test_bands = utils.read_jason(file='test.json')
#target
train_y = train[target].values
split_indices = train_y.copy()
#data set
train_X = utils.rescale(train_bands)
train_meta = train['inc_angle'].values
test_X_dup = utils.rescale(test_bands)
test_meta = test['inc_angle'].values
opt_augments = {
'Flip': False,
'Rotate': False,