这是
state_is_tuple=True通过定义状态变量来更新LSTM初始状态的代码。它还支持多层。
我们定义了两个函数-
一个用于获取具有初始零状态的状态变量，另一个用于返回操作的函数，可以传递给该函数以
session.run用LSTM的最后一个隐藏状态更新状态变量。
def get_state_variables(batch_size, cell): # For each layer, get the initial state and make a variable out of it # to enable updating its value. state_variables = [] for state_c, state_h in cell.zero_state(batch_size, tf.float32): state_variables.append(tf.contrib.rnn.LSTMStateTuple( tf.Variable(state_c, trainable=False), tf.Variable(state_h, trainable=False))) # Return as a tuple, so that it can be fed to dynamic_rnn as an initial state return tuple(state_variables)def get_state_update_op(state_variables, new_states): # Add an operation to update the train states with the last state tensors update_ops = [] for state_variable, new_state in zip(state_variables, new_states): # Assign the new state to the state variables on this layer update_ops.extend([state_variable[0].assign(new_state[0]), state_variable[1].assign(new_state[1])]) # Return a tuple in order to combine all update_ops into a single operation. # The tuple's actual value should not be used. return tf.tuple(update_ops)
我们可以用它来更新每批LSTM的状态。请注意，我
tf.nn.dynamic_rnn用于展开：
data = tf.placeholder(tf.float32, (batch_size, max_length, frame_size))cell_layer = tf.contrib.rnn.GRUCell(256)cell = tf.contrib.rnn.MultiRNNCell([cell] * num_layers)# For each layer, get the initial state. states will be a tuple of LSTMStateTuples.states = get_state_variables(batch_size, cell)# Unroll the LSTMoutputs, new_states = tf.nn.dynamic_rnn(cell, data, initial_state=states)# Add an operation to update the train states with the last state tensors.update_op = get_state_update_op(states, new_states)sess = tf.Session()sess.run(tf.global_variables_initializer())sess.run([outputs, update_op], {data: ...})该答案的主要区别在于，
state_is_tuple=True使LSTM的状态成为包含两个变量（单元状态和隐藏状态）而不是单个变量的LSTMStateTuple。然后，使用多层可以使LSTM的状态成为LSTMStateTuples的元组-
每层一个。
重置为零
使用训练有素的模型进行预测/解码时，您可能需要将状态重置为零。然后，您可以使用此功能：
def get_state_reset_op(state_variables, cell, batch_size): # Return an operation to set each variable in a list of LSTMStateTuples to zero zero_states = cell.zero_state(batch_size, tf.float32) return get_state_update_op(state_variables, zero_states)
例如上面的例子：
reset_state_op = get_state_reset_op(state, cell, max_batch_size)# Reset the state to zero before feeding inputsess.run([reset_state_op])sess.run([outputs, update_op], {data: ...})
