import numpy as np
from tfHelper import tfHelper
import data
tfHelper.log_level_decrease()
# tfHelper.numpy_show_entire_array(28)
# np.set_printoptions(linewidth=200)
print ("Load data ...")
_, X_id, label = data.load_data_predict()
X_pred = tfHelper.get_dataset_with_one_folder('classed/.None', 'L')
X_pred = data.normalize(X_pred)
model = tfHelper.load_model("model_img")
# model = tfHelper.load_model("model")
######################### Predict #########################
predictions = model.predict(X_pred)
# print(predictions)
# exit (0)
# All features
with open("output_img_detailed", "w+") as file:
# Head
for line in label[:-1]:
file.write(line + ",")
else:
from Test import Test
from Train import Train
from tfHelper import tfHelper
import model
import os
te = Test()
tr = Train()
if os.path.exists("model.h5"):
model = tfHelper.load_model("model")
else:
model = model.model()
while True:
te.test(model)
model = tr.train(model)