Neural Autoregressive Distribution Estimator for Collaborative Filtering architecture

nade.py

class NADE(Layer):
    def __init__(self, hidden_dim, activation, W_regularizer=None, V_regularizer=None,
                 b_regularizer=None, c_regularizer=None, bias=False, args=None, **kwargs):

        self.init = initializers.get('uniform')

        self.bias = bias
        self.activation = activation
        self.hidden_dim = hidden_dim

        self.W_regularizer = regularizers.get(W_regularizer)
        self.V_regularizer = regularizers.get(V_regularizer)
        self.b_regularizer = regularizers.get(b_regularizer)
        self.c_regularizer = regularizers.get(c_regularizer)

        self.args = args

        super(NADE, self).__init__(**kwargs)

    def build(self, input_shape):
        """
        Build the NADE architecture
        :param input_shape: Shape of the input
        """
        self.input_dim1 = input_shape[1]
        self.input_dim2 = input_shape[2]

        self.W = self.add_weight(shape=(self.input_dim1, self.input_dim2, self.hidden_dim),
                                 initializer=self.init, name='{}_W'.format(self.name), regularizer=self.W_regularizer)

        if self.bias:
            self.c = self.add_weight(shape=(self.hidden_dim,), initializer=self.init,
                                     name='{}_c'.format(self.name), regularizer=self.c_regularizer)

        if self.bias:
            self.b = self.add_weight(shape=(self.input_dim1, self.input_dim2), initializer=self.init,
                                     name='{}_b'.format(self.name), regularizer=self.b_regularizer)

        self.V = self.add_weight(shape=(self.hidden_dim, self.input_dim1, self.input_dim2),
                                 initializer=self.init, name='{}_V'.format(self.name), regularizer=self.V_regularizer)

        super(NADE, self).build(input_shape)