Multi-LSTM-bigram-based.py 文件源码

def lstm_cell(i, o, state):
    m_input = tf.pack([i for _ in range(m_rows)])
    m_saved_output = tf.pack([o for _ in range(m_rows)])
    m_all = tf.batch_matmul(m_input, m_input_w) + tf.batch_matmul(m_saved_output, m_middle) + m_biases
    m_all = tf.unpack(m_all)
    input_gate = tf.sigmoid(m_all[m_input_index])
    forget_gate = tf.sigmoid(m_all[m_forget_index])
    update = m_all[m_update_index]
    state = forget_gate * state + input_gate * tf.tanh(update)
    output_gate = tf.sigmoid(m_all[m_output_index])
    return output_gate * tf.tanh(state), state