d3bdc5449964423387861c78edd9d890a81a5466,tensorlayer/layers/recurrent.py,RNN,build,#RNN#Any#,223

Before Change

        if self._weights is None:
            self._weights = list()
        self._weights.append(self.cell.weights)

    def forward(self, inputs, **kwargs):
        
        Parameters

After Change

        if len(inputs_shape) != 3:
            raise Exception("RNN : Input dimension should be rank 3 : [batch_size, n_steps, n_features]")

        with tf.name_scope(self.name) as scope:
            self.cell.build(tuple(inputs_shape))

        if self._weights is None:
            self._weights = list()
        for var in self.cell.trainable_variables:
            self._weights.append(var)

Instances