from preprocess import get_test_train_data
from save_load import get_last_file_number
from save_load import load
from model import accuracy, loss
from keras import backend
import sys, os
import tensorflow as tf
from utils import visualize_layers
if __name__ == '__main__':
file = "F:\Projects\python\self_driving_game\data\dataset_mini.pz"
if len(sys.argv)<=1: count = None
else: count = int(sys.argv[1]);
# Load model
exp_folder = 'exp_' + '{0:03d}'.format(get_last_file_number(prefix='exp_', suffix=''))
model = load(count, path=exp_folder)
# Visualize model
x_train, x_test, y_train, y_test = get_test_train_data(file, 10, tanh=True)
visualization_folder = exp_folder + '/visualization'
if not os.path.exists(visualization_folder): os.makedirs(visualization_folder)
visualize_layers(model, x_test, path=visualization_folder)
print('Visualization done...!')
if __name__ == '__main__':
# # Print output to file
# outfolder = 'exp_' + '{0:03d}'.format(get_last_file_number(prefix='exp_', suffix='') + 1); os.makedirs(outfolder)
# outfile = outfolder + '/' + 'train_' + '{0:03d}'.format(get_last_file_number(path=outfolder) + 1) + '.log'
# print('Printing to logfile at', outfile)
# sys.stdout = open(outfile, 'w+')
# Add title to logfile for identification
# if len(sys.argv)>1: print('Title:',sys.argv[1],'\n\n')
# Get test and train data
file = os.environ['DATA_DIR'] + "/dataset_75p_gray.pz"
x_train, x_test, y_train, y_test = get_test_train_data(file,
80000,
tanh=False)
# x_train, x_test, y_train, y_test = get_test_train_data(file, 1000, tanh=False)
learning_rate = 0.0005
models = ['relu_with_scaled_sigmoid']
drop_rates = [0.1]
for i in range(len(models)):
for j in range(len(drop_rates)):
# Print output to file
outfolder = 'exp_' + '{0:03d}'.format(
get_last_file_number(prefix='exp_', suffix='') + 1)
os.makedirs(outfolder)
outfile = outfolder + '/' + 'train_' + '{0:03d}'.format(