feed_dict = {self.X: in_idxs}
for i in range(self.layers):
feed_dict[self.state[i]] = self.predict_state[i]
preds, self.predict_state = self.sess.run(fetches, feed_dict)
preds = np.asarray(preds).T
return pd.DataFrame(data=preds, index=itemidmap.index)
if __name__ == '__main__':
defaults = Defaults()
data = pd.read_csv(PATH_TO_TRAIN, sep='\t', dtype={'ItemId': np.int64})
valid = pd.read_csv(PATH_TO_TEST, sep='\t', dtype={'ItemId': np.int64})
defaults.n_items = len(data['ItemId'].unique())
defaults.dropout_p_hidden = 1.0 if defaults.is_training == 0 else 0.5
if not os.path.exists(defaults.checkpoint_dir):
os.mkdir(defaults.checkpoint_dir)
gpu_config = tf.ConfigProto()
gpu_config.gpu_options.allow_growth = True
with tf.Session(config=gpu_config) as session:
predictor = Session4RecPredictor(defaults, session)
if defaults.is_training:
print('Start session4rec training...')
predictor.train(data)
else:
res = evaluation.evaluate_sessions_batch(predictor, data, valid)
