Multiple TS for the same target variable about lstm_tsc HOT 3 OPEN

On 1 June 2017 at 04:37, Pat Cobhan ***@***.***> wrote: Hi there, thank you for this awesome tutorial, it is of great interest for my master research work! I was trying to adapt the code for my problem, as for each user I want to classify I have three time series of the same length. Firstly I edited load_data and sample_batch in order to have: t1_train,t1_val,t1_test,t2_ train,t2_val,t2_test,t3_train,t3_val,t3_test,y_train,y_val,y_test and t1_batch,t2_batch,t3_batch,y_batch In the Model itself is where I'm sure I'm doing something wrong, up to now I've edited your code this way: self.input1 = tf.placeholder(tf.float32, [None, sl], name = 'input_1') self.input2 = tf.placeholder(tf.float32, [None, sl], name = 'input_2') self.input3 = tf.placeholder(tf.float32, [None, sl], name = 'input_3') self.labels = tf.placeholder(tf.int64, [None], name='labels') self.keep_prob = tf.placeholder("float", name = 'Drop_out_keep_prob') with tf.name_scope("LSTM_setup") as scope: def single_cell1(): return tf.contrib.rnn.DropoutWrapper(LSTMCell(hidden_size),output_keep_prob=self.keep_prob) cell1 = tf.contrib.rnn.MultiRNNCell([single_cell1() for _ in range(num_layers)]) initial_state1 = cell1.zero_state(self.batch_size, tf.float32) def single_cell2(): return tf.contrib.rnn.DropoutWrapper(LSTMCell(hidden_size),output_keep_prob=self.keep_prob) cell2 = tf.contrib.rnn.MultiRNNCell([single_cell2() for _ in range(num_layers)]) initial_state2 = cell2.zero_state(self.batch_size, tf.float32) def single_cell3(): return tf.contrib.rnn.DropoutWrapper(LSTMCell(hidden_size),output_keep_prob=self.keep_prob) cell3 = tf.contrib.rnn.MultiRNNCell([single_cell3() for _ in range(num_layers)]) initial_state3 = cell3.zero_state(self.batch_size, tf.float32) input_list1 = tf.unstack(tf.expand_dims(self.input1,axis=2),axis=1) input_list2 = tf.unstack(tf.expand_dims(self.input2,axis=2),axis=1) input_list3 = tf.unstack(tf.expand_dims(self.input3,axis=2),axis=1) outputs1,_ = core_rnn.static_rnn(cell1, input_list1, dtype=tf.float32) outputs2,_ = core_rnn.static_rnn(cell2, input_list2, dtype=tf.float32) outputs3,_ = core_rnn.static_rnn(cell3, input_list3, dtype=tf.float32) output = tf.concat([outputs1[-1], outputs2[-1], outputs3[-1]], 0) When I try to train the network this is the error that I get: ValueError: Attempt to have a second RNNCell use the weights of a variable scope that already has weights: 'rnn/multi_rnn_cell/cell_0/lstm_cell'; and the cell was not constructed as LSTMCell(..., reuse=True). To share the weights of an RNNCell, simply reuse it in your second calculation, or create a new one with the argument reuse=True. Would you have any suggestion or tip for me to get it working? Thank you in advance! — You are receiving this because you are subscribed to this thread. Reply to this email directly, view it on GitHub <#7>, or mute the thread <https://github.com/notifications/unsubscribe-auth/APbLxV4W4nZVIWG-xnV9r3AQQVFxE2Wrks5r_iP2gaJpZM4NsbFi> .