Human-Activity-Recognition | Simple 1D CNN approach to | Machine Learning library

You are trying to reshape X_train to (51135, 4,32,32). It is impossible because X_train shape is (51135, 128, 12). 51135 x 128 x 12 != 51135 x 4 x 32 x 32

please runing the code: model.predict([trainX[0]]), and the model outputs the predicted results

The output_node_names is the name of the node you want to return. You can give a name to your tensorflow operation and that name will serve as output_name.

Clarifying range of prediction values

as I see it, your values are 0.007 and 0.993 or something in that Magnitude - so what exactly do you mean by higher, since the sum is always 1. So if one gets higher the other should get smaller - if I understood your Problem correctly.

Clarifying prediction correspondance

Since you labeled your data with the classes "catch" and "nothing" that's what your predictions correspond to. So an Output of (0, 1) would mean that your Network predicts the Input to be of the second class.

Clarifying window size

To solve this I first Need you to explain, what you mean by window size. The amount of recorded seconds that you use as an input? If so, I would say, that if you can recognize a "catch" within this time Frame - then your Network should also be able to do so. Other than that, this would be an appropriate case for a line search.

first install gradle this linke then you can build the project on Command Line './gradlew assembleDebug' This creates an APK named module_name-debug.apk in project_name/module_name/build/outputs/apk/. The file is already signed with the debug key and aligned with zipalign, so you can immediately install it on a device.

How have you tried to cbind the column, could you please show the code? I think you simply stepped into the difficulties produced by StringsAsFactors = TRUE. Does the following line solve your problem:

(size / 2) in:

Actually, this is because that you are using an older version of tf.split. Modify your code as follows and should be working.

I think for most LSTM's you're going to want to think of your data in this way (as it will be easy to use as input for the networks).

You'll have 3 dimension measurements:

feature_size = the number of different features (torque, velocity, etc.)

number_of_time_steps = the number of time steps collected for a single car

number_of_cars = the number of cars

It will most likely be easiest to read your data in as a set of matrices, where each matrix corresponds to one full sample (all the time steps for a single car).

You can arrange these matrices so that each row is an observation and each column is a different parameter (or the opposite, you may have to transpose the matrices, look at how your network input is formatted).

So each matrix is of size: number_of_time_steps x feature_size (#rows x #columns). You will have number_of_cars different matrices. Each matrix is a sample.

To convert your array to this format, you can use this block of code (note, you can already access a single sample in your array with A[n], but this makes it so the shape of the accessed elements are what you expect):