target_lstm.py 文件源码

def create_recurrent_unit(self, params):
        # Weights and Bias for input and hidden tensor
        self.Wi = tf.Variable(self.params[1])
        self.Ui = tf.Variable(self.params[2])
        self.bi = tf.Variable(self.params[3])

        self.Wf = tf.Variable(self.params[4])
        self.Uf = tf.Variable(self.params[5])
        self.bf = tf.Variable(self.params[6])

        self.Wog = tf.Variable(self.params[7])
        self.Uog = tf.Variable(self.params[8])
        self.bog = tf.Variable(self.params[9])

        self.Wc = tf.Variable(self.params[10])
        self.Uc = tf.Variable(self.params[11])
        self.bc = tf.Variable(self.params[12])
        params.extend([
            self.Wi, self.Ui, self.bi,
            self.Wf, self.Uf, self.bf,
            self.Wog, self.Uog, self.bog,
            self.Wc, self.Uc, self.bc])

        def unit(x, hidden_memory_tm1):
            previous_hidden_state, c_prev = tf.unpack(hidden_memory_tm1)

            # Input Gate
            i = tf.sigmoid(
                tf.matmul(x, self.Wi) +
                tf.matmul(previous_hidden_state, self.Ui) + self.bi
            )

            # Forget Gate
            f = tf.sigmoid(
                tf.matmul(x, self.Wf) +
                tf.matmul(previous_hidden_state, self.Uf) + self.bf
            )

            # Output Gate
            o = tf.sigmoid(
                tf.matmul(x, self.Wog) +
                tf.matmul(previous_hidden_state, self.Uog) + self.bog
            )

            # New Memory Cell
            c_ = tf.nn.tanh(
                tf.matmul(x, self.Wc) +
                tf.matmul(previous_hidden_state, self.Uc) + self.bc
            )

            # Final Memory cell
            c = f * c_prev + i * c_

            # Current Hidden state
            current_hidden_state = o * tf.nn.tanh(c)

            return tf.pack([current_hidden_state, c])

        return unit