6b083c769d953662125be0840fa2f1507fe49494,tflearn/layers/recurrent.py,GRUCell,__call__,#GRUCell#Any#Any#Any#,594

Before Change

        with tf.variable_scope(scope or type(self).__name__):  // "GRUCell"
            with tf.variable_scope("Gates"):  // Reset gate and update gate.
                // We start with bias of 1.0 to not reset and not update.
                r, u = array_ops.split(value=_linear([inputs, state],
                    2 * self._num_units, True, 1.0, self.weights_init,
                    self.trainable, self.restore, self.reuse),
                    num_or_size_splits=2, axis=1)
                r, u = self._inner_activation(r), self._inner_activation(u)
            with tf.variable_scope("Candidate"):

After Change

        with tf.variable_scope(scope or type(self).__name__):  // "GRUCell"
            with tf.variable_scope("Gates"):  // Reset gate and update gate.
                // We start with bias of 1.0 to not reset and not update.
                _w = _linear([inputs, state],
                    2 * self._num_units, True, 1.0, self.weights_init,
                    self.trainable, self.restore, self.reuse)
                r, u = array_ops.split(value=_w, num_or_size_splits=2, axis=1)
                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.,

Instances