The simplest way to build a training model is to start with the [Sequential] model, our tool follows the Keras functional API, with some changes explained in the paper:
Here is the Sequential model:
model = Sequential()Add all layers as easily as by using .add() function.
Dense layer takes three arguments: num_inputs(number of input unit), num_outputs(number of output unit), lr_rate(leaning rate), and name(name of layer).
The activation function is added as a new layer.
lr = 0.01
model.add(Dense(num_inputs=100, num_outputs=64, lr_rate=lr, name='FC1'))
model.add(ReLu())
model.add(Dense(num_inputs=64, num_outputs=10, lr_rate=lr, name='FC2'))
model.add(softmax())Once you finished building your model, you can use .compile() to start the learning process after determing the loss function, optimizer and the metrics:
model.compile(loss='categorical_crossentropy',
optimizer='sgd',
metrics=['accuracy'])The core principle of our tool is to make the training model simple, while inserting instrumentation
in the .fit() function to observe the model variables, then make the user to be fully in control when they need the variables.
model.fit(x_train, y_train, epochs=5, batch_size=32)You can start training a new model using the following commands in Terminal (macOS/Linux) or cmd (Windows) as following:
python main.py
To use our callback, you need to add our callback as subclass in your keras.callbacks.py file.
The core principle of our callback to get a view on internal states and statistics of the model during training.
Then you can pass our callback DeepLocalize() to the .fit() method of a model as following:
callback = keras.callbacks.DeepLocalize(inputs, outputs, layer_number, batch_size, startTime)
model = keras.models.Sequential()
model.add(keras.layers.Dense(64))
model.add(keras.layers.Activation(activations.relu))
model.compile(keras.optimizers.SGD(), loss='mse')
model.fit(np.arange(100).reshape(5, 20), np.zeros(5), epochs=10, batch_size=1,
... callbacks=[callback], verbose=0)Version numbers below are of confirmed working releases for this project.
python 3.6.5
Keras 2.2.0
Keras-Applications 1.0.2
Keras-Preprocessing 1.0.1
numpy 1.19.2
pandas 1.1.5
scikit-learn 0.21.2
scipy 1.6.0
tensorflow 1.14.0
If you find this paper useful in your research, please consider citing:
@inproceedings{wardat21DeepLocalize,
Author = {Mohammad Wardat and Wei Le and Hridesh Rajan},
Title = {DeepLocalize: Fault Localization for Deep Neural Networks},
Booktitle = {ICSE'21: The 43nd International Conference on Software Engineering},
Year = {2021},
entrysubtype = {conference}
}