tf.nn.rnn_cell.RNNCell.__call__()

tf.nn.rnn_cell.RNNCell.__call__(inputs, state, scope=None)

Run this RNN cell on inputs, starting from the given state.

Args:

• inputs: 2-D tensor with shape [batch_size x input_size].
• state: if self.state_size is an integer, this should be a 2-D Tensor with shape [batch_size x self.state_size]. Otherwise, if self.state_size is a tuple of integers, this should be a tuple with shapes [batch_size x s] for s in self.state_size.
• scope: VariableScope for the created subgraph; defaults to class name.

Returns:

A pair containing: - Output: A 2-D tensor with shape [batch_size x self.output_size]. - New state: Either a single 2-D tensor, or a tuple of tensors matching the arity and shapes of state.