I am unsure how to interpret the default behavior of Keras in the following situation:
My Y (ground truth) was set up using scikit-learn's MultilabelBinarizer
().
Therefore, to give a random example, one row of my y
column is one-hot encoded as such:
[0,0,0,1,0,1,0,0,0,0,1]
.
So I have 11 classes that could be predicted, and more than one can be true; hence the multilabel nature of the problem. There are three labels for this particular sample.
I train the model as I would for a non multilabel problem (business as usual) and I get no errors.
from keras.models import Sequential
from keras.layers import Dense, Dropout, Activation
from keras.optimizers import SGD
model = Sequential()
model.add(Dense(5000, activation='relu', input_dim=X_train.shape[1]))
model.add(Dropout(0.1))
model.add(Dense(600, activation='relu'))
model.add(Dropout(0.1))
model.add(Dense(y_train.shape[1], activation='softmax'))
sgd = SGD(lr=0.01, decay=1e-6, momentum=0.9, nesterov=True)
model.compile(loss='categorical_crossentropy',
optimizer=sgd,
metrics=['accuracy',])
model.fit(X_train, y_train,epochs=5,batch_size=2000)
score = model.evaluate(X_test, y_test, batch_size=2000)
score
What does Keras do when it encounters my y_train
and sees that it is "multi" one-hot encoded, meaning there is more than one 'one' present in each row of y_train
? Basically, does Keras automatically perform multilabel classification? Any differences in the interpretation of the scoring metrics?
Don't use softmax
.
Use sigmoid
for activation of your output layer.
Use binary_crossentropy
for loss function.
Use predict
for evaluation.
In softmax
when increasing score for one label, all others are lowered (it's a probability distribution). You don't want that when you have multiple labels.
from keras.models import Sequential
from keras.layers import Dense, Dropout, Activation
from keras.optimizers import SGD
model = Sequential()
model.add(Dense(5000, activation='relu', input_dim=X_train.shape[1]))
model.add(Dropout(0.1))
model.add(Dense(600, activation='relu'))
model.add(Dropout(0.1))
model.add(Dense(y_train.shape[1], activation='sigmoid'))
sgd = SGD(lr=0.01, decay=1e-6, momentum=0.9, nesterov=True)
model.compile(loss='binary_crossentropy',
optimizer=sgd)
model.fit(X_train, y_train, epochs=5, batch_size=2000)
preds = model.predict(X_test)
preds[preds>=0.5] = 1
preds[preds<0.5] = 0
# score = compare preds and y_test