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,

After Change

    def __call__(self, inputs, state, scope=None):
        Gated recurrent unit (GRU) with nunits cells.
        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(1, 2, _linear([inputs, state],
                    2 * self._num_units, True, 1.0, self.weights_init,
                    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
            with tf.variable_scope("Linear/Gates", reuse=True):
                self.W.append(tf.get_variable("Matrix"))
                self.b.append(tf.get_variable("Bias"))
            with tf.variable_scope("Linear/Candidate", reuse=True):
                self.W.append(tf.get_variable("Matrix"))
                self.b.append(tf.get_variable("Bias"))

        return new_h, new_h


// --------------------

Instances