How do I correctly implement a custom activity regularizer in Keras?

Question

I am trying to implement sparse autoencoders according to Andrew Ng's lecture notes as shown here. It requires that a sparsity constraint be applied on an autoencoder layer by introducing a penalty term (K-L divergence). I tried to implement this using the direction provided here, after some minor changes. Here is the K-L divergence and the sparsity penalty term implemented by the SparseActivityRegularizer class as shown below.

def kl_divergence(p, p_hat):
return (p * K.log(p / p_hat)) + ((1-p) * K.log((1-p) / (1-p_hat)))

class SparseActivityRegularizer(Regularizer):
sparsityBeta = None

    def __init__(self, l1=0., l2=0., p=-0.9, sparsityBeta=0.1):
        self.p = p
        self.sparsityBeta = sparsityBeta

    def set_layer(self, layer):
        self.layer = layer

    def __call__(self, loss):
        #p_hat needs to be the average activation of the units in the hidden layer.      
        p_hat = T.sum(T.mean(self.layer.get_output(True) , axis=0))

        loss += self.sparsityBeta * kl_divergence(self.p, p_hat)
        return loss

    def get_config(self):
        return {"name": self.__class__.__name__,
            "p": self.l1}

The model was built like so

X_train = np.load('X_train.npy')
X_test = np.load('X_test.npy')

autoencoder = Sequential()
encoder = containers.Sequential([Dense(250, input_dim=576, init='glorot_uniform', activation='tanh', 
    activity_regularizer=SparseActivityRegularizer(p=-0.9, sparsityBeta=0.1))])

decoder = containers.Sequential([Dense(576, input_dim=250)])
autoencoder.add(AutoEncoder(encoder=encoder, decoder=decoder, output_reconstruction=True))
autoencoder.layers[0].build()
autoencoder.compile(loss='mse', optimizer=SGD(lr=0.001, momentum=0.9, nesterov=True))
loss = autoencoder.fit(X_train_tmp, X_train_tmp, nb_epoch=200, batch_size=800, verbose=True, show_accuracy=True, validation_split = 0.3)
autoencoder.save_weights('SparseAutoEncoder.h5',overwrite = True)
result = autoencoder.predict(X_test)

When I call the fit() function I get negative loss values and the output does not resemble the input at all. I want to know where I am going wrong. What is the correct way to calculate the average activation of a layer and use this custom sparsity regularizer? Any sort of help will be greatly appreciated. Thanks!

I am using Keras 0.3.1 with Python 2.7 as the latest Keras (1.0.1) build does not have the Autoencoder layer.

Answer 1

You have defined self.p = -0.9 instead of the 0.05 value that both the original poster and the lecture notes you referred to are using.