Fuel/Custom Datasets

Wrapping Custom Datasets with Fuel

Repo by github user dribnet illustrates how to wrap a new dataset using Fuel: https://github.com/dribnet/lfw_fuel

Advantages:

• Only takes one command to download the data and import it into fuel
• Then it only takes one command to import the library that wraps the data, and be able to turn it into training/testing X and Y

Disadvantages:

• One-size-fits-all; importing data using load_data() can take a REALLY long time, and must be done every time you run the script (not persistent in memory)
• Complicated to extend
• Removes some of the nicer options of fuel

Here is what the final payoff looks like:

from keras.models import Sequential
from lfw_fuel import lfw

# the data, shuffled and split between train and test sets
(X_train, y_train), (X_test, y_test) = lfw.load_data(format="deepfunneled")

# (build the perfect model here)

model.fit(X_train, Y_train, show_accuracy=True, validation_data=(X_test, Y_test))
score = model.evaluate(X_test, Y_test, show_accuracy=True, verbose=0)