arch_gdashboard_med_rnn_silent.py 文件源码

def BiRNN(x, n_hidden):
    # Prepare data shape to match `bidirectional_rnn` function requirements
    # Current data input shape: (batch_size, n_steps, n_input)
    # Required shape: 'n_steps' tensors list of shape (batch_size, n_input)
    # Permuting batch_size and n_steps
    x = tf.transpose(x, [1, 0, 2])
    # Reshape to (n_steps*batch_size, n_input)
    x = tf.reshape(x, [-1, n_input])
    # Split to get a list of 'n_steps' tensors of shape (batch_size, n_input)
    x = tf.split(0, n_steps, x)
    # Define lstm cells with tensorflow
    # Forward direction cell
    lstm_fw_cell = rnn_cell.BasicLSTMCell(n_hidden, forget_bias=1.0)
    # Backward direction cell
    lstm_bw_cell = rnn_cell.BasicLSTMCell(n_hidden, forget_bias=1.0)
    # Get lstm cell output
    outputs, _, _ = rnn.bidirectional_rnn(lstm_fw_cell, lstm_bw_cell, x, dtype=tf.float32)
    return outputs