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:
