keras-visuals

Graphs to help you visualise the training of your Keras models.

Accuracy & loss graph

Graph after 50 epochs

Graph after 150 epochs

The graphs are dynamic and will automatically update and scale: after each epoch during the fit function.

The code

Import AccLossPlotter

from visual_callbacks import AccLossPlotter

Instantiate the plotter

plotter = AccLossPlotter(graphs=['acc', 'loss'], save_graph=True)

• graphs is a list of the different graphs we would like to plot. Available ('acc', 'loss')
• save_graph tells the Plotter to save a screenshot when training is finished.

Register callback with model

model.fit(X, Y, validation_split=0.2, nb_epoch=150, batch_size=10, callbacks=[plotter])

Confusion Matrix

After 50 epochs

After 100 epochs

It is clear from the confusion matrix that your model is confusing iris-versicolor for iris-virginica. Directed insight like this is a valuable tool for finding problem areas and improving your model.

The code

We import the ConfusionMatrixPlotter class from the visual_callbacks package.

from visual_callbacks import ConfusionMatrixPlotter

Instantiate the plotter

plotter = ConfusionMatrixPlotter(X_val=X_test, classes=class_names, Y_val=y_test)

• X_val is a list of input values, this should typically be a seperate test set
• Y_val is the list of output values for your X_val input set
• classes is a list of class names

Register callback with model

model.fit(X_train, y_train, nb_epoch=100, batch_size=16, callbacks=[plotter])

What is next

• Visualising Neural Network Layer Activation
• t-SNE visualisation

Collaboration

Feel free to get in touch or send me a Pull Request

• e: dries.cronje@outlook.com
• t: @dries139