The BiRNN class is a fixed length Bidirectional recurrent layer.

fw_cell (TensorFlow cell function for forward direction) -- A RNN cell implemented by tf.keras, e.g. tf.keras.layers.SimpleRNNCell, tf.keras.layers.LSTMCell, tf.keras.layers.GRUCell. Note TF2.0+, TF1.0+ and TF1.0- are different

bw_cell (TensorFlow cell function for backward direction similar with fw_cell) --

return_seq_2d (boolean.) -- If True, return 2D Tensor [batch_size * n_steps, n_hidden], for stacking Dense layer after it. If False, return 3D Tensor [batch_size, n_steps, n_hidden], for stacking multiple RNN after it. In a dynamic model, return_seq_2d can be updated when it is called in customised forward(). By default, False.

return_last_state (boolean) --

If True, the layer will return outputs, the final state of fw_cell and the final state of bw_cell.

If False, the layer will return outputs only.

In a dynamic model, return_last_state can be updated when it is called in customised forward(). By default, False.

in_channels (int) -- Optional, the number of channels of the previous layer which is normally the size of embedding. If given, the layer will be built when init. If None, it will be automatically detected when the layer is forwarded for the first time.

name (str) -- A unique layer name.

实际案例

A simple regression model below. >>> inputs = tl.layers.Input([batch_size, num_steps, embedding_size]) >>> # the fw_cell and bw_cell can be different >>> rnnlayer = tl.layers.BiRNN( >>> fw_cell=tf.keras.layers.SimpleRNNCell(units=hidden_size, dropout=0.1), >>> bw_cell=tf.keras.layers.SimpleRNNCell(units=hidden_size + 1, dropout=0.1), >>> return_seq_2d=True, return_last_state=True >>> ) >>> # if return_last_state=True, the final state of the two cells will be returned together with the outputs >>> # if return_last_state=False, only the outputs will be returned >>> rnn_out, rnn_fw_state, rnn_bw_state = rnnlayer(inputs) >>> # if the BiRNN is followed by a Dense, return_seq_2d should be True. >>> # if the BiRNN is followed by other RNN, return_seq_2d can be False. >>> dense = tl.layers.Dense(n_units=1)(rnn_out) >>> outputs = tl.layers.Reshape([-1, num_steps])(dense) >>> rnn_model = tl.models.Model(inputs=inputs, outputs=[outputs, rnn_out, rnn_fw_state[0], rnn_bw_state[0]])

A stacked BiRNN model. >>> inputs = tl.layers.Input([batch_size, num_steps, embedding_size]) >>> rnn_out1 = tl.layers.BiRNN( >>> fw_cell=tf.keras.layers.SimpleRNNCell(units=hidden_size, dropout=0.1), >>> bw_cell=tf.keras.layers.SimpleRNNCell(units=hidden_size + 1, dropout=0.1), >>> return_seq_2d=False, return_last_state=False >>> )(inputs) >>> rnn_out2 = tl.layers.BiRNN( >>> fw_cell=tf.keras.layers.SimpleRNNCell(units=hidden_size, dropout=0.1), >>> bw_cell=tf.keras.layers.SimpleRNNCell(units=hidden_size + 1, dropout=0.1), >>> return_seq_2d=True, return_last_state=False >>> )(rnn_out1) >>> dense = tl.layers.Dense(n_units=1)(rnn_out2) >>> outputs = tl.layers.Reshape([-1, num_steps])(dense) >>> rnn_model = tl.models.Model(inputs=inputs, outputs=outputs)

提示

Input dimension should be rank 3 : [batch_size, n_steps, n_features]. If not, please see layer Reshape.