lstm

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 is in continue to ( LSTM - Making predictions on partial sequence ). As described in the previous question I've trained a stateful LSTM model for binary classification with batches of 100 samples/labels like so: [Feature 1,Feature 2, .... ,Feature 3][Label 1] [Feature 1,Feature 2, .... ,Feature 3][Label 2] ... [Feature 1,Feature 2, .... ,Feature 3][Label 100] Model Code: def build_model(num_samples, num_features, is_training): model = Sequential() opt = optimizers.Adam(lr=0.0005, beta_1=0.9, beta_2=0.999, epsilon=1e-08, decay=0.0001) batch_size = None if is_training else 1

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 data with a shape of (10000, 20, 15, 4) where num samples = 10000 , num series in time = 20 , height = 15 , weight = 4 . So I have table 15x4 which is distributed over time. Here is the model I want to train it over this data: ... model.add((LSTM(nums-1,return_sequences=True,input_shape=(20,15,4), activation='relu'))) model.add((LSTM(nums-1,return_sequences=False,input_shape=(20,15,4), activation='tanh'))) model.add(Dense(15,activation='relu')) ... However, I get the following error: ValueError: Input 0 is incompatible with layer lstm_1: expected ndim=3, found ndim=4 How do I define a

I want to predict one output variable from nine input variables. This data is also a time series and the goal is to predict the output variable 2 timesteps ahead. I normalised all data using mean normalisation and added some features so now the data look like this: weekday (weekend vs weekday) hour (f_real - 50)*70 ACE [Mwh] \ 0 -1.579094 -1.341627 0.032171 2.017604 1 -1.579094 -0.447209 0.032171 -0.543702 2 -1.579094 0.447209 0.037651 0.204731 3 -1.579094 1.341627 0.043130 -0.601538 4 -1.579094 -1.341627 0.021211 11.759046 IGCC [Mwh] SRE [Mwh] TertCalls [Mwh] Imbalance [Mwh] Time 0 0.257560 5

How does the input dimensions get converted to the output dimensions for the LSTM Layer in Keras? From reading Colah's blog post , it seems as though the number of "timesteps" (AKA the input_dim or the first value in the input_shape ) should equal the number of neurons, which should equal the number of outputs from this LSTM layer (delineated by the units argument for the LSTM layer). From reading this post , I understand the input shapes. What I am baffled by is how Keras plugs the inputs into each of the LSTM "smart neurons". Keras LSTM reference Example code that baffles me: model =