Fuel/Custom Datasets

Wrapping Custom Datasets with Fuel

Procedure:

• Define a fuel dataset (basically a stub class)
• Define a fuel downloader (a way of obtaining the data - could be locally available, since you already have it)
• Define a fuel converter (something that will iterate through the data and add it to an HDF5 file, similar to above code snippet)

Once you've defined those, you can run through the procedure of bringing your data set into fuel:

• Run fuel-download <name-of-dataset> to download the data
• Run fuel-convert <name-of-dataset> to convert the data into fuel format

Now you'll be able to do a simple import to get your data into your Python code (may take a while):

from fuel.datasets.billion import OneBillionWord

Examples

LFW

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)