# Keras LSTM predicts every signal in the same category

I'm working on a project that involves signal classification. I'm trying different models of ANN using keras to see which one is better, for now focusing in simple networks but I'm struggling with the LSTM one, following this example: https://machinelearningmastery.com/how-to-develop-rnn-models-for-human-activity-recognition-time-series-classification/.

My inputs are 1D signals that I get from an electronic sensor that will be divided in 3 different categories. See here one signal of two different categories so you see they are quite different over time. https://www.dropbox.com/s/9ctdegtuyjamp48/example_signals.png?dl=0

To start with a simple model, we are trying the following simple model. Since signals are of different length, a masking process has been performed on them, enlarge each one to the longest one with the masked value of -1000 (impossible value to happen in our signal). Data is correctly reshaped from 2D to 3D (since is needed in 3D for the LSTM layer) using the following command since I only have a feature:

Inputs = Inputs.reshape((Inputs.shape[0],Inputs.shape[1],1))


Then, data is divided in training and validation one and feed into the following model:

model = Sequential()