maple728 / da-rnn Goto Github PK
View Code? Open in Web Editor NEWTensorflow implementation of paper http://arxiv.org/abs/1704.02971
Tensorflow implementation of paper http://arxiv.org/abs/1704.02971
Hi again,
Also wanted to ask about attention layers, in the paper it says
en_w_e = tf.Variable(tf.truncated_normal(shape = [2 * last_hidden_size, config.timestep]), dtype = tf.float32)
en_u_e = tf.Variable(tf.truncated_normal(shape = [config.timestep * config.input_size, config.timestep]), dtype = tf.float32)
en_v_e = tf.Variable(tf.truncated_normal(shape = [config.timestep, 1]))
Are parameters learned through a MLP model, but I dont see any dense layer in your code, you just initialize the weights, can you help me understand how these weights are adjusted through training?
Thanks
Line 159 in a3cdb99
计算最终预测值时,论文中给出的方法是对解码器的隐状态和context_vector进行concatenation。您的代码中是X_encoders,是我哪里理解错了吗?
Hi Great work thank you.
I wanted to ask you the reason why you including last 10 points of train set to valid set also last 10 points of valid set to test set? Is there a specific reason why are you doing like that?
I am referring to here:
return dataset[ :, : train_end_index], dataset[:, train_end_index - config.timestep : valid_end_index], dataset[:, valid_end_index - config.timestep : -1]
Thanks a lot
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