def predict_by_model_one(data): global ready_model if ready_model == False: ready_model = get_trained_model(model_file) image = convert_base64_to_grayscale_array(data["image"]) return ready_model.predict([image])
from model import get_trained_model for key in [('share_count', 100), ('comment_count', 50), ('reaction_count', 50)]: model = get_trained_model(key[0], key[1], is_logistic=False) print(key, model.test())
from bottle import post, run, hook, HTTPError, request, response from textblob import TextBlob, Blobber from textblob_fr import PatternTagger, PatternAnalyzer from model import get_trained_model viral_model = get_trained_model('share_count', 100, is_logistic=True) comment_model = get_trained_model('comment_count', 50) share_model = get_trained_model('share_count', 100) reaction_model = get_trained_model('reaction_count', 50) def assert_in(val, err): if not val: raise HTTPError(status=400, body=err) def extract_sentiment(phrase): tb = Blobber(pos_tagger=PatternTagger(), analyzer=PatternAnalyzer()) return (tb(phrase).sentiment[0] / 2) + 0.5 @hook('after_request') def enable_cors(): """ You need to add some headers to each request. Don't use the wildcard '*' for Access-Control-Allow-Origin in production. """ response.headers['Access-Control-Allow-Origin'] = '*' response.headers['Access-Control-Allow-Methods'] = 'POST'
import cv2 from pyzbar.pyzbar import decode from model import get_trained_model import os model = get_trained_model() if __name__ == '__main__': for image in os.listdir('results/'): image = cv2.imread(f'results/{image}') print(decode(image))
import argparse parser = argparse.ArgumentParser() parser.add_argument('envname', type=str) args = parser.parse_args() envname = args.envname with open(os.path.join('expert_data', envname + '.pkl'), 'rb') as f: expert_data = pickle.load(f) # data for behavioural cloning X = expert_data['observations'] Y = expert_data['actions'] from model import get_trained_model model, metrics = get_trained_model(X, Y) print(metrics.history['loss'][-1], metrics.history['val_loss'][-1]) env = gym.make(envname) num_rollouts = 3 max_steps = 1000 render = True returns = [] for i in range(num_rollouts): obs = env.reset() done = False totalr = 0