with tf.name_scope('Evaluation'): # MAPE : if it not y+1, will be inf self.MAPE = tf.reduce_mean( tf.divide(tf.abs(tf.subtract(self.pred, self.y)), tf.add(self.y, 1.0))) # RMSE : if it not y+1, will be inf self.RMSE = tf.sqrt( tf.reduce_mean(tf.square(tf.subtract(self.pred, self.y)))) self.merged = tf.summary.merge_all() if __name__ == '__main__': pdata = pdata() training_X, training_Y, testing_X, testing_Y = pdata.get_taxi_data_24() training_X_batch = np.shape(np.reshape( training_X, (-1, 24, 3600)))[0] # (B, T, N) 255*24*3600 g = Graph() config = tf.ConfigProto() # config.gpu_options.allow_growth = True with tf.Session(graph=g.graph, config=config) as sess: # tensorboard writer writer = tf.summary.FileWriter('logs/', sess.graph) tStart = time.time() sess.run(tf.global_variables_initializer()) step = 0 while step < training_iters: