Session Ready
Exercise

Training with multiple labels

An output of your multi-label model could look like this: [0.76 , 0.99 , 0.66 ]. If we round up probabilities higher than 0.5, this observation will be classified as containing all 3 possible labels [1,1,1]. For this particular problem, this would mean watering all 3 parcels in your farm is the right thing to do, according to the network, given the input sensor measurements.

You will now train and predict with the model you just built. sensors_train, parcels_train, sensors_test and parcels_test are already loaded for you to use.

Let's see how well your intelligent machine performs!

Instructions
100 XP
  • Train the model for 100 epochs using a validation_split of 0.2.
  • Predict with your model using the test data.
  • Round up your preds with np.round().
  • Evaluate your model's accuracy on the test data.
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