recurrent-neural-network

I am trying to solve how I can feed data to my LSTM model for training. (I will simplify the problem in my example below.) I have the following data format in csv files in my dataset. Timestep Feature1 Feature2 Feature3 Feature4 Output 1 1 2 3 4 a 2 5 6 7 8 b 3 9 10 11 12 c 4 13 14 15 16 d 5 17 18 19 20 e 6 21 22 23 24 f 7 25 26 27 28 g 8 29 30 31 32 h 9 33 34 35 36 i 10 37 38 39 40 j The task is to estimate the Output of any future timestep based on the data from last 3 timesteps. Some input-output exapmles are as following: Example 1: Input: Timestep Feature1 Feature2 Feature3 Feature4 1 1 2

I looked into the saved weights for a LSTMCell in Tensorflow. It has one big kernel and bias weights. The dimensions of the kernel are (input_size + hidden_size)*(hidden_size*4) Now from what I understand this is encapsulating 4 input to hidden layer affine transforms as well as 4 hidden to hidden layer transforms. So there should be 4 matrices of size input_size*hidden_size and 4 of size hidden_size*hidden_size Can someone tell me or point me to the code where TF saves these, so I can break the kernel matrix into smaller matrices. c2huc2hu The weights are combined as mentioned in the other

This question already has answers here : Many to one and many to many LSTM examples in Keras (2 answers) Closed last year . I am reading this article (The Unreasonable Effectiveness of Recurrent Neural Networks) and want to understand how to express one-to-one, one-to-many, many-to-one, and many-to-many LSTM neural networks in Keras. I have read a lot about RNN and understand how LSTM NNs work, in particular vanishing gradient, LSTM cells, their outputs and states, sequence output and etc. However, I have trouble expressing all these concepts in Keras. To start with I have created the

I have access to a dataframe of 100 persons and how they performed on a certain motion test. This frame contains about 25,000 rows per person since the performance of this person is kept track of (approximately) each centisecond (10^-2). We want to use this data to predict a binary y-label, that is to say, if someone has a motor problem or not. The columns and some values of the dataset are follows: 'Person_ID', 'time_in_game', 'python_time', 'permutation_game, 'round', 'level', 'times_level_played_before', 'speed', 'costheta', 'y_label', 'gender', 'age_precise', 'ax_f', 'ay_f', 'az_f', 'acc',

It seems that the tf.nn.dynamic_rnn has been deprecated: Warning: THIS FUNCTION IS DEPRECATED. It will be removed in a future version. Instructions for updating: Please use keras.layers.RNN(cell), which is equivalent to this API I have checked out keras.layers.RNN(cell) and it says that it can use masking which I assume can act as a replacement for dynamic_rnn 's sequence_length parameter? This layer supports masking for input data with a variable number of timesteps. To introduce masks to your data, use an Embedding layer with the mask_zero parameter set to True. But there is no further

Problem Summary In the following example, my NMT model has high loss because it correctly predicts target_input instead of target_output . Targetin : 1 3 3 3 3 6 6 6 9 7 7 7 4 4 4 4 4 9 9 10 10 10 3 3 10 10 3 10 3 3 10 10 3 9 9 4 4 4 4 4 3 10 3 3 9 9 3 6 6 6 6 6 6 10 9 9 10 10 4 4 4 4 4 4 4 4 4 4 4 4 9 9 9 9 3 3 3 6 6 6 6 6 9 9 10 3 4 4 4 4 4 4 4 4 4 4 4 4 9 9 10 3 10 9 9 3 4 4 4 4 4 4 4 4 4 10 10 4 4 4 4 4 4 4 4 4 4 9 9 10 3 6 6 6 6 3 3 3 10 3 3 3 4 4 4 4 4 4 4 4 4 4 4 4 4 9 9 3 3 10 6 6 6 6 6 3 9 9 3 3 3 3 3 3 3 10 10 3 9 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 9 3 6 6 6 6 6 6 3 5 3 3 3 3 10 10