624117eaac16a0674201471119d940ccd81b1771,GPy/mappings/mlp.py,MLP,f,#MLP#Any#,22

Before Change

    def f(self, X):
        N, D = X.shape
        activations = np.tanh(np.dot(X,self.W1) + self.b1)
        self.out = np.dot(self.activations,self.W2) + self.b2
        return self.output_fn(self.out)

    def update_gradients(self, dL_dF, X):
        activations = np.tanh(np.dot(X,self.W1) + self.b1)

After Change

    def f(self, X):
        layer1 = np.dot(X, self.W1) + self.b1
        activations = np.tanh(layer1)
        return  np.dot(activations, self.W2) + self.b2

    def update_gradients(self, dL_dF, X):
        layer1 = np.dot(X,self.W1) + self.b1
        activations = np.tanh(layer1)

Instances