Code for experiments regarding importance sampling for training neural networks
This python package provides a library that accelerates the training of arbitrary neural networks created with
Keras__ using importance sampling.
.. code:: python
# Keras imports
from importance_sampling.training import ImportanceTraining
x_train, y_train, x_val, y_val = load_data() model = create_keras_model() model.compile( optimizer="adam", loss="categorical_crossentropy", metrics=["accuracy"] )
ImportanceTraining(model).fit( x_train, y_train, batch_size=32, epochs=10, verbose=1, validation_data=(x_val, y_val) )
Importance sampling for Deep Learning is an active research field and this library is undergoing development so your mileage may vary.
Normally if you already have a functional Keras installation you just need to
pip install keras-importance-sampling.
scikit-learnare optional (used by the plot scripts)
The module has a dedicated
documentation site__ but you can also read the
source code__ and the
examples__ to get an idea of how the library should be used and extended.
examplesfolder you can find some Keras examples that have been edited to use importance sampling.
In this section we will showcase part of the API that can be used to train neural networks with importance sampling.
.. code:: python
# Import what is needed to build the Keras model from keras import backend as K from keras.layers import Dense, Activation, Flatten from keras.models import Sequential
Import a toy dataset and the importance training
from importance_sampling.datasets import MNIST from importance_sampling.training import ImportanceTraining
def create_nn(): """Build a simple fully connected NN""" model = Sequential([ Flatten(input_shape=(28, 28, 1)), Dense(40, activation="tanh"), Dense(40, activation="tanh"), Dense(10), Activation("softmax") # Needs to be separate to automatically # get the preactivation outputs ])
model.compile( optimizer="adam", loss="categorical_crossentropy", metrics=["accuracy"] ) return model
if name == "main": # Load the data dataset = MNIST() x_train, y_train = dataset.train_data[:] x_test, y_test = dataset.test_data[:]
# Create the NN and keep the initial weights model = create_nn() weights = model.get_weights() # Train with uniform sampling K.set_value(model.optimizer.lr, 0.01) model.fit( x_train, y_train, batch_size=64, epochs=10, validation_data=(x_test, y_test) ) # Train with importance sampling model.set_weights(weights) K.set_value(model.optimizer.lr, 0.01) ImportanceTraining(model).fit( x_train, y_train, batch_size=64, epochs=2, validation_data=(x_test, y_test) )
Using the script
The following terminal commands train a small VGG-like network to ~0.65% error on MNIST (the numbers are from a CPU). .. code::
$ # Train a small cnn with mnist for 500 mini-batches using importance $ # sampling with bias to achieve ~ 0.65% error (on the CPU). $ time ./importance_sampling.py \ > small_cnn \ > oracle-gnorm \ > model \ > predicted \ > mnist \ > /tmp/is \ > --hyperparams 'batch_size=i128;lr=f0.003;lr_reductions=I10000' \ > --train_for 500 --validate_every 500 real 1m41.985s user 8m14.400s sys 0m35.900s $ $ # And with uniform sampling to achieve ~ 0.9% error. $ time ./importance_sampling.py \ > small_cnn \ > oracle-loss \ > uniform \ > unweighted \ > mnist \ > /tmp/uniform \ > --hyperparams 'batch_size=i128;lr=f0.003;lr_reductions=I10000' \ > --train_for 3000 --validate_every 3000 real 9m23.971s user 47m32.600s sys 3m4.188s