lstm

问题 Looking for help on preparing input data for time series classification. The data is from a bunch of users who need to be classified. I want to Use LSTMs(plan to implement via Keras, with Tenserflow backend). I have data in two formats. Which is the right way to feed to RNNs for classification? Any help regrading the input shape would be of great help. Format 1 UserID TimeStamp Duration Label 1 2020:03:01:00:00 10 0 1 2020:03:01:01:00 0 0 1 2020:03:01:02:00 100 0 1 2020:03:01:03:00 15 0 1

问题 Looking for help on preparing input data for time series classification. The data is from a bunch of users who need to be classified. I want to Use LSTMs(plan to implement via Keras, with Tenserflow backend). I have data in two formats. Which is the right way to feed to RNNs for classification? Any help regrading the input shape would be of great help. Format 1 UserID TimeStamp Duration Label 1 2020:03:01:00:00 10 0 1 2020:03:01:01:00 0 0 1 2020:03:01:02:00 100 0 1 2020:03:01:03:00 15 0 1

问题 While running a sentdex tutorial script of a cryptocurrency RNN, link here YouTube Tutorial: Cryptocurrency-predicting RNN Model, but have encountered an error when attempting to train the model. My tensorflow version is 2.0.0 and I'm running python 3.6. When attempting to train the model I receive the following error: File "C:\python36-64\lib\site-packages\tensorflow_core\python\keras\engine\training.py", line 734, in fit use_multiprocessing=use_multiprocessing) File "C:\python36-64\lib\site

问题 When I create a tf.contrib.rnn.LSTMCell, it creates its kernel and bias trainable variables during initialisation. How the code looks now: cell_fw = tf.contrib.rnn.LSTMCell(hidden_size_char, state_is_tuple=True) What I want it to look: kernel = tf.get_variable(...) bias = tf.get_variable(...) cell_fw = tf.contrib.rnn.LSTMCell(kernel, bias, hidden_size, state_is_tuple=True) What I want to do is to create those variables myself, and give it to the LSTMCell class when instantiating it as input

问题 The question is like this one What's the input of each LSTM layer in a stacked LSTM network?, but more into implementing details. For simplicity how about 4 units and 2 units structures like the following model.add(LSTM(4, input_shape=input_shape, return_sequences=True)) model.add(LSTM(2,input_shape=input_shape)) So I know the output of LSTM_1 is 4 length but how do the next 2 units handle these 4 inputs, are they fully connected to the next layer of nodes? I guess they are fully connected

问题 When I want to put the model on the GPU, there is an error! It said the Inputs is on GPU, but the hidden state is on CPU. However, all of them had been put on the GPU. I use for m in model.parameters(): print(m.device) #return cuda:0 to see all of the state on the model is on the GPU device. The error is "RuntimeError: Input and hidden tensors are not at the same device, found input tensor at cuda:0 and hidden tensor at cpu" Windows 10 server Pytorch 1.2.0 + cuda 9.2 cuda 9.2 cudnn 7.6.3 for

问题 When I want to put the model on the GPU, there is an error! It said the Inputs is on GPU, but the hidden state is on CPU. However, all of them had been put on the GPU. I use for m in model.parameters(): print(m.device) #return cuda:0 to see all of the state on the model is on the GPU device. The error is "RuntimeError: Input and hidden tensors are not at the same device, found input tensor at cuda:0 and hidden tensor at cpu" Windows 10 server Pytorch 1.2.0 + cuda 9.2 cuda 9.2 cudnn 7.6.3 for

问题 I'm facing this error when running my code with 3 lstm layers. Not sure how to fix it. Can anyone help. Here MAX_SEQUENCE_LENGTH=250. After running the cost function, i get the error 'ValueError: Shapes (?, 1) and (?,) are incompatible' # Generate a Tensorflow Graph tf.reset_default_graph() batch_size = 25 embedding_size = 50 lstmUnits = 64 max_label = 2 x = tf.placeholder(tf.int32, [None, MAX_SEQUENCE_LENGTH]) y = tf.placeholder(tf.int32, [None]) number_of_layers=3 # Embeddings to represent

问题 I want to build an LSTM based neural network which takes two kinds of inputs and predicts two kinds of outputs. A rough structure can be seen in following figure.. The output 2 is dependent upon output 1 and as described in answer to a similar question here, I have tried to implement this by setting the initial state of LSTM 2 from hidden states of LSTM 1. I have implemented this using tensorflow using following code. import tensorflow as tf from tensorflow.keras.layers import Input from

问题 I want to build an LSTM based neural network which takes two kinds of inputs and predicts two kinds of outputs. A rough structure can be seen in following figure.. The output 2 is dependent upon output 1 and as described in answer to a similar question here, I have tried to implement this by setting the initial state of LSTM 2 from hidden states of LSTM 1. I have implemented this using tensorflow using following code. import tensorflow as tf from tensorflow.keras.layers import Input from