Keras: weighted binary crossentropy

Question

I tried to implement a weighted binary crossentropy with Keras, but I am not sure if the code is correct. The training output seems to be a bit confusing. After a few epochs I j

Answer 1

You can use the sklearn module to automatically calculate the weights for each class like this:

# Import
import numpy as np
from sklearn.utils import class_weight

# Example model
model = Sequential()
model.add(Dense(32, activation='relu', input_dim=100))
model.add(Dense(1, activation='sigmoid'))

# Use binary crossentropy loss
model.compile(optimizer='rmsprop',
              loss='binary_crossentropy',
              metrics=['accuracy'])

# Calculate the weights for each class so that we can balance the data
weights = class_weight.compute_class_weight('balanced',
                                            np.unique(y_train),
                                            y_train)

# Add the class weights to the training                                         
model.fit(x_train, y_train, epochs=10, batch_size=32, class_weight=weights)

Note that the output of the class_weight.compute_class_weight() is an numpy array like this: [2.57569845 0.68250928].