73e563ecaf915d073d8b8fabc4a568ecddb0ea11,keras/layers/recurrent.py,Recurrent,get_initial_states,#Recurrent#Any#,122

Before Change

        initial_state = K.zeros_like(X)  // (samples, timesteps, input_dim)
        initial_state = K.sum(initial_state, axis=1)  // (samples, input_dim)
        reducer = K.zeros((self.input_dim, self.output_dim))
        initial_state = K.dot(initial_state, reducer)  // (samples, output_dim)
        initial_states = [initial_state for _ in range(len(self.states))]
        return initial_states

After Change

    def get_initial_states(self, X):
        // build an all-zero tensor of shape (samples, output_dim)
        initial_state = X[:, 0, 0] * 0  // (samples, )
        initial_state = K.pack([initial_state] * self.output_dim)  // (output_dim, samples)
        initial_state = K.permute_dimensions(initial_state, (1, 0))  // (samples, output_dim)
        initial_states = [initial_state for _ in range(len(self.states))]

Instances