4acd61442955baa2509fdc2961284c9d2a986f34,tflearn/layers/recurrent.py,BasicRNNCell,__call__,#BasicRNNCell#Any#Any#Any#,577

Before Change

    def __call__(self, inputs, state, scope):
        Most basic RNN: output = new_state = tanh(W * input + U * state + B).
        self.W, self.b, concat = _linear([inputs, state], self._num_units,
                                         self.bias, self.W, self.b,
                                         self.W_init, trainable=self.trainable,
                                         restore=self.restore, scope=scope)

After Change

    def __call__(self, inputs, state, scope=None):
        Most basic RNN: output = new_state = activation(W * input + U * state + B).
        with tf.variable_scope(scope or type(self).__name__):
            // "BasicRNNCell"
            output = self._activation(
                _linear([inputs, state], self._num_units, True, 0.,
                        self.weights_init, self.trainable, self.restore,
                        self.reuse))
            // Retrieve RNN Variables
            with tf.variable_scope("Linear", reuse=True):
                self.W = tf.get_variable("Matrix")
                self.b = tf.get_variable("Bias")

        return output, output


class BasicLSTMCell(_rnn_cell.RNNCell):

Instances