4acd61442955baa2509fdc2961284c9d2a986f34,tflearn/layers/recurrent.py,GRUCell,__call__,#GRUCell#Any#Any#Any#,697

Before Change

        r, u = array_ops.split(1, 2, r_u)
        r, u = self.activation(r), self.activation(u)

        self.W[1], self.b[1], c = _linear([inputs, r * state], self._num_units,
                                          self.bias, self.W[1],
                                          self.b[1], self.W_init,
                                          trainable=self.trainable,
                                          restore=self.restore,
                                          scope=scope + "/Candidate")
        c = self.inner_activation(c)
        new_h = u * state + (1 - u) * c
        return new_h, new_h

After Change

                    self.trainable, self.restore, self.reuse))
                r, u = self._inner_activation(r), self._inner_activation(u)
            with tf.variable_scope("Candidate"):
                c = self._activation(
                    _linear([inputs, r * state], self._num_units, True, 0.,
                            self.weights_init, self.trainable, self.restore,
                            self.reuse))
            new_h = u * state + (1 - u) * c

            self.W, self.b = list(), list()
            // Retrieve RNN Variables

Instances