Train model on labelled 2D measurement data

TensorFlow General Discussion
,
1

I have a dataset of several hundreds measurements each represented by an array of 2x60 datapoints. The measurements are categorized by labels into 8 different categories.

The data is structured as follows:
DATA = [
{‘labelA’: np.array([[0.0, 0.0], [1.1, 3.1], [2.5, 6.5], …, [0.1, 0.0]])},
{‘labelB’: np.array([[0.0, 0.0], [2.1, 1.1], [3.2, 4.5], …, [0.2, 0.0]])},
{‘labelA’: np.array([[0.0, 0.0], [1.1, 3.2], [2.5, 6.6], …, [0.1, 0.0]])},
…
{‘labelH’: np.array([[0.0, 0.0], [3.1, 1.1], [4.2, 4.2], …, [0.1, 0.0]])},
]

A single measurement can be represented in a graph:

image
image419×316 13.3 KB

I have two questions:

  1. What is the best way to load this data into a Tensorflow dataset?
  2. What Tensorflow model is best to use for this type of data? Since expanding my dataset is very time consuming I would like to make the most out of it.

Any reference to similar problems is appreciated!
Many thanks in advance.

2

Sure! Here are a few options:

  • Thanks for sharing! I’m also curious about the best approach for this type of data.
  • Interesting problem! I’ll look into similar use cases and get back to you.
  • This looks challenging. I’m not familiar with this, but I’m willing to learn.
4

Hi @rvm, you can create a Tensorflow dataset in a way as shown in the below code. considering this as the raw data.

Initially you can get the labels and values from the data

label=[]
values=[]
for i in range(len(data)):
    label.append(list(data[i].keys())[0])
    values.append(list(data[i].values())[0])

and can create a tensorflow dataset using this

dataset = tf.data.Dataset.from_tensors((label, values))

please refer to this gist for working code example.

There are multiple options when it comes to building classification problem. you can use a fully connected neural network(FCNN), cnn, rnn’s etc. Thank You.