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!
This exercise is part of the course
Introduction to Deep Learning with Keras
Exercise instructions
- Train the model for 100
epochs
using avalidation_split
of 0.2. - Predict with your
model
using the test data. - Round up your
preds
withnp.round()
. - Evaluate your model's accuracy on the test data.
Hands-on interactive exercise
Have a go at this exercise by completing this sample code.
# Train for 100 epochs using a validation split of 0.2
____.____(____, ____, epochs = ____, validation_split = ____)
# Predict on sensors_test and round up the predictions
preds = ____.____(____)
preds_rounded = np.round(____)
# Print rounded preds
print('Rounded Predictions: \n', preds_rounded)
# Evaluate your model's accuracy on the test data
accuracy = model.evaluate(____, ____)[1]
# Print accuracy
print('Accuracy:', accuracy)