lstm_peep.py

# NOTE: I'm not sure if this is right

from keras.layers.recurrent import LSTM

class LSTMpeephole(LSTM):
    def __init__(self, **kwargs):
        super(LSTMpeephole, self).__init__(**kwargs)

    def build(self):
        super(LSTMpeephole, self).build()
        self.P_i = self.inner_init((self.output_dim, self.output_dim))
        self.P_f = self.inner_init((self.output_dim, self.output_dim))
        self.P_c = self.inner_init((self.output_dim, self.output_dim))
        self.P_o = self.inner_init((self.output_dim, self.output_dim))
        self.trainable_weights += [self.P_i, self.P_f, self.P_o]

    def step(self, x, states):
        assert len(states) == 2
        h_tm1 = states[0]
        c_tm1 = states[1]

        x_i = K.dot(x, self.W_i) + self.b_i
        x_f = K.dot(x, self.W_f) + self.b_f
        x_c = K.dot(x, self.W_c) + self.b_c
        x_o = K.dot(x, self.W_o) + self.b_o

        i = self.inner_activation(x_i + K.dot(h_tm1, self.U_i) + K.dot(c_tm1, self.P_i))
        f = self.inner_activation(x_f + K.dot(h_tm1, self.U_f) + K.dot(c_tm1, self.P_f))
        c = f * c_tm1 + i * self.activation(x_c + K.dot(h_tm1, self.U_c) + K.dot(c_tm1, self.P_c))
        o = self.inner_activation(x_o + K.dot(h_tm1, self.U_o) + K.dot(c_tm1, self.P_o))
        h = o * self.activation(c)
        return h, [h, c]