lstm

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

问题 I am using the LSTM cell in Tensorflow. lstm_cell = tf.contrib.rnn.BasicLSTMCell(lstm_units) I was wondering how the weights and states are initialized or rather what the default initializer is for LSTM cells (states and weights) in Tensorflow? And is there an easy way to manually set an Initializer? Note: For tf.get_variable() the glorot_uniform_initializer is used as far as I could find out from the documentation. 回答1: First of all, there is a difference between the weights of a LSTM (the

问题 I would like to implement LSTM for multivariate input in Pytorch . Following this article https://machinelearningmastery.com/how-to-develop-lstm-models-for-time-series-forecasting/ which uses keras, the input data are in shape of (number of samples, number of timesteps, number of parallel features) in_seq1 = array([10, 20, 30, 40, 50, 60, 70, 80, 90]) in_seq2 = array([15, 25, 35, 45, 55, 65, 75, 85, 95]) out_seq = array([in_seq1[i]+in_seq2[i] for i in range(len(in_seq1))]) . . . Input Output

问题 import torch,ipdb import torch.autograd as autograd import torch.nn as nn import torch.nn.functional as F import torch.optim as optim from torch.autograd import Variable rnn = nn.LSTM(input_size=10, hidden_size=20, num_layers=2) input = Variable(torch.randn(5, 3, 10)) h0 = Variable(torch.randn(2, 3, 20)) c0 = Variable(torch.randn(2, 3, 20)) output, hn = rnn(input, (h0, c0)) This is the LSTM example from the docs. I don't know understand the following things: What is output-size and why is it

问题 Every time I run LSTM network with Keras in jupyter notebook, I got a different result, and I have googled a lot, and I have tried some different solutions, but none of they are work, here are some solutions I tried: set numpy random seed random_seed=2017 from numpy.random import seed seed(random_seed) set tensorflow random seed from tensorflow import set_random_seed set_random_seed(random_seed) set build-in random seed import random random.seed(random_seed) set PYTHONHASHSEED import os os

问题 I am implementing the following architecture in Tensorflow. Dual Encoder LSTM https://i.stack.imgur.com/ZmcsX.png During the first few iterations, the loss remains 0.6915 but after that as you can see in the output below, no matter how many iterations I run, the loss keeps varying between -0.0 and a positive constant depending upon the hyperparams. This is happening because the predictions of my model become very small(close to zero) or very high (close to 1). So the model cannot be trained.

问题 I am implementing the following architecture in Tensorflow. Dual Encoder LSTM https://i.stack.imgur.com/ZmcsX.png During the first few iterations, the loss remains 0.6915 but after that as you can see in the output below, no matter how many iterations I run, the loss keeps varying between -0.0 and a positive constant depending upon the hyperparams. This is happening because the predictions of my model become very small(close to zero) or very high (close to 1). So the model cannot be trained.

问题 I'm trying to make a LSTM model. The data is coming from a csv file that contains values for multiple stocks. I can't use all the rows as they appear in the file to make sequences because each sequence is only relevant in the context of its own stock, so I need to select the rows for each stock and make the sequences based on that. I have something like this: for stock in stocks: stock_df = df.loc[(df['symbol'] == stock)].copy() target = stock_df.pop('price') x = np.array(stock_df.values) y =

问题 What's the default activation function of cudnnlstm in TensorFlow? How can I set an activation function such as relu ? Maybe it's just linear model? I read the document, but I did not find it. For example, the code is below: lstmcell=tf.contrib.cudnn_rnn.CudnnLSTM(1,encoder_size,direction="bidirectional") hq,_ =lstmcell(query) And I read the document of TensorFlow From this link. The function is below __init__( num_layers, num_units, input_mode=CUDNN_INPUT_LINEAR_MODE, direction=CUDNN_RNN