I have a dataset of time series that I use as input to an LSTM-RNN for action anticipation. The time series comprises a time of 5 seconds at 30 fps (i.e. 150 data points), and the data represents t...

Tensorflow 1.7 when using dynamic_rnn.It runs fine at first , but at the 32th(it changes when i run the code) step , the error appears. When i used smaller batch , it seems the code can run longer ...

I am trying for multi-class classification and here are the details of my training input and output: train_input.shape= (1, 95000, 360) (95000 length input array with each element being an arra...

I am confused how exactly to encode a sequence of data as an input to an LSTM RNN. In a vanilla DNN, there is an input for every label. What is the "input" in an RNN? Doesnt it have to be a set (o...

Is there a way in Keras to retrieve the cell state (i.e., c vector) of a LSTM layer at every timestep of a given input? It seems the return_state argument returns the last cell state after the com...

Using this implementation I have included attention to my RNN (which classify the input sequences into two classes) as follows. visible = Input(shape=(250,)) embed=Embedding(vocab_size,100)(visib...

I have been doing some research on recurrent neural networks, but I am having trouble understanding if and how they could be used to analyze panel data (meaning cross-sectional data that is capture...

I'm having trouble understanding the documentation for PyTorch's LSTM module (and also RNN and GRU, which are similar). Regarding the outputs, it says: Outputs: output, (h_n, c_n) output (s...

I have seen examples of building an encoder-decoder network using LSTM in Keras but I want to have a ConvLSTM encoder-decoder and since the ConvLSTM2D does not accept any 'initial_state' argument s...

I have a set of tensors that I'm padding with pad_sequence but I need to guarantee a fixed length for them. I can't do it right now as pad_sequence will extend the shorter tensors up to the longest...

I have a question about the following code in pytorch language modeling: print("Training and generating...") for epoch in range(1, config.num_epochs + 1): total_loss = 0.0 model.train() hidd...

I am learning LSTM with PyTorch from someone's code. Here he uses the clip_grad_norm_ function in the training process of a two layer LSTM. I want to know why he uses the clip_grad_norm_ function h...

What is the difference between LSTM and LSTMCell in Pytorch (currently version 1.1)? It seems that LSTMCell is a special case of LSTM (i.e. with only one layer, unidirectional, no dropout). Then,...

I'm working on an NLP sequence labelling problem. My data consists of variable length sequences (w_1, w_2, ..., w_k) with corresponding labels (l_1, l_2, ..., l_k) (in this case the task is named e...

I have read a sequence of images into a numpy array with shape (7338, 225, 1024, 3) where 7338 is the sample size, 225 are the time steps and 1024 (32x32) are flattened image pixels, in 3 channels ...

I am struggling with the concept of attention in the the context of autoencoders. I believe I understand the usage of attention with regards to seq2seq translation - after training the combined enc...

I am trying to solve a time series prediction problem. I tried with ANN and LSTM, played around a lot with the various parameters, but all I could get was 8% better than the persistence prediction....

I am trying to do some vanilla pattern recognition with an LSTM using Keras to predict the next element in a sequence. My data look like this: where the label of the training sequence is the la...

I'm using Keras 1.0. My problem is identical to this one (How to implement a Mean Pooling layer in Keras), but the answer there does not seem to be sufficient for me. I want to implement this netw...

In the code below, I import a saved sparse numpy matrix, created with python, densify it, add a masking, batchnorm and dense ouptput layer to a many to one SimpleRNN. The keras sequential model wor...

In MNIST LSTM examples, I don't understand what "hidden layer" means. Is it the imaginary-layer formed when you represent an unrolled RNN over time? Why is the num_units = 128 in most cases ?

In PyTorch, we can define architectures in multiple ways. Here, I'd like to create a simple LSTM network using the Sequential module. In Lua's torch I would usually go with: model = nn.Sequential...

In a database there are time-series data with records: device - timestamp - temperature - min limit - max limit device - timestamp - temperature - min limit - max limit device - timestamp - temper...

I'm new to the topic of neural networks. I came across the two terms convolutional neural network and recurrent neural network. I'm wondering if these two terms are referring to the same th...

I'm having some difficulty understanding the input-output flow of layers in stacked LSTM networks. Let's say i have created a stacked LSTM network like the one below: # parameters time_steps = 10 f...