import tensorflow as tf dims, layers = 32, 2 # Creating the forward and backwards cells lstm_fw_cell = tf.nn.rnn_cell.BasicLSTMCell(dims, forget_bias=1.0) lstm_bw_cell = tf.nn.rnn_cell.BasicLSTMCell(dims, forget_bias=1.0) # Pass lstm_fw_cell / lstm_bw_cell directly to tf.nn.bidrectional_rnn # if only a single layer is needed lstm_fw_multicell = tf.nn.rnn_cell.MultiRNNCell([lstm_fw_cell]*layers) lstm_bw_multicell = tf.nn.rnn_cell.MultiRNNCell([lstm_bw_cell]*layers) # tf.nn.bidirectional_rnn takes a list of tensors with shape # [batch_size x cell_fw.state_size], so separate the input into discrete # timesteps. _X = tf.unpack(state_below, axis=1) # state_fw and state_bw are the final states of the forwards/backwards LSTM, respectively outputs, state_fw, state_bw = tf.nn.bidirectional_rnn(lstm_fw_multicell, lstm_bw_multicell, _X, dtype='float32')
state_belowis a 3D tensor of with the following dimensions: [
batch_size, maximum sequence index,
dims]. This comes from a previous operation, such as looking up a word embedding.
dimsis the number of hidden units.
layerscan be adjusted above 1 to create a stacked LSTM network.
tf.unpackmay not be able to determine the size of a given axis (use the
numsargument if this is the case).
tf.matmul(state_below, U) + b.