import include_sys_path
import numpy as np
import unittest
from keras.layers import Input
from keras.losses import categorical_crossentropy
from keras.models import Model
from qa.keras_custom_layers import Similarity
include_sys_path.void()
class TestKerasCustomLayers(unittest.TestCase):
def setUp(self):
pass
def tearDown(self):
pass
def _do_brute_force(self, x, y, WS):
self.assertEqual(len(x.shape), 3)
self.assertEqual(len(y.shape), 3)
self.assertEqual(x.shape[0], y.shape[0])
self.assertEqual(x.shape[2], y.shape[2])
self.assertEqual(len(WS.shape), 1)
self.assertEqual(WS.shape[0], 3 * x.shape[2])
nr_batches = x.shape[0]
nr_lines = x.shape[1]
import include_sys_path # This must be the first imported module.
from answer.models import Cerebro
from answer.settings import TRAIN_PATH, VAL_PATH, TEST_PATH
from answer.utils import read_dataset
include_sys_path.void() # To remove unused module warning.
def predict_and_save_to_file():
from answer.settings import KAGGLE_CONTEST_PATH
data = read_dataset(KAGGLE_CONTEST_PATH)
model = Cerebro()
rez = model.predict(data)
with open("submission.csv", "w") as g:
g.write("id,correctAnswer\n")
for q_id, ans in rez:
g.write(q_id)
g.write(",")
g.write(chr(65 + int(ans)))
g.write("\n")
g.flush()
def main():
train_data = []
for path in TRAIN_PATH:
train_data += read_dataset(path)