Multi-LSTM-bigram-based.py 文件源码

def lstm_cell1(i, o, state):
    """Create a LSTM cell. See e.g.: http://arxiv.org/pdf/1402.1128v1.pdf
    Note that in this formulation, we omit the various connections between the
    previous state and the gates."""    
    m_input2 = tf.pack([i for _ in range(m_rows)])
    m_saved_output2 = tf.pack([o for _ in range(m_rows)])

   # m_input2 = tf.nn.dropout(m_input2, keep_prob)
    m_all = tf.batch_matmul(m_input2, m_input_w2) + tf.batch_matmul(m_saved_output2, m_middle_w2) + 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

  # Input data.