错误tensorflow 2.3.0：变量未初始化容器本地主机不存在

如何解决错误tensorflow 2.3.0：变量未初始化容器本地主机不存在

我使用Tensorflow 2.3.0和 我有以下错误

FailedPreconditionError：从容器：本地主机读取资源变量rnn / multi_rnn_cell / cell_1 / lstm_cell / kernel时出错。这可能意味着该变量未初始化。找不到：容器lo​​calhost不存在。

defaultPosition={{ x:  '50%',y: '50%' }}

完全错误：

tf.compat.v1.reset_default_graph()
graph = tf.compat.v1.get_default_graph()
with graph.as_default():
    batch_size = 1000
    features = 1
    alpha = 0.50
    time_steps = paths_train.shape[0]
    nodes = [62,46,1]
    name='rnn_050'
    S_t_input = tf.compat.v1.placeholder(tf.float32,[time_steps,batch_size,features])
    K = tf.compat.v1.placeholder(tf.float32,batch_size)
    alpha = tf.compat.v1.placeholder(tf.float32)
    S_T = S_t_input[-1,:,0]
    dS = S_t_input[1:,0] - S_t_input[0:-1,0]
    S_t = tf.unstack(S_t_input[:-1,:],axis=0)

    lstm = tf.compat.v1.nn.rnn_cell.MultiRNNCell([tf.compat.v1.nn.rnn_cell.LSTMCell(n) for n in nodes])
    strategy,state = tf.compat.v1.nn.static_rnn(lstm,S_t,initial_state=lstm.zero_state(batch_size,tf.float32),dtype=tf.float32)
    strategy = tf.reshape(strategy,(time_steps-1,batch_size))
    option = tf.maximum(S_T-K,0)
    Hedging_PnL = - option + tf.reduce_sum(dS*strategy,axis=0)
    Hedging_PnL_Paths = - option + dS*strategy
        # Calculate the CVaR for a given confidence level alpha
        # Take the 1-alpha largest losses (top 1-alpha negative PnLs) and calculate the mean
    CVaR,idx = tf.nn.top_k(-Hedging_PnL,tf.cast((1-alpha)*batch_size,tf.int32))
    CVaR = tf.reduce_mean(CVaR)
    train = tf.compat.v1.train.AdamOptimizer().minimize(CVaR)
    saver = tf.compat.v1.train.Saver()
    modelname = name

    sample_size = paths_train.shape[1]
    batch_size= batch_size
    idx = np.arange(sample_size)
    start = dt.datetime.now()
    epochs = 101
    strikes = np.ones(paths_train.shape[1])*100
    riskaversion = 0.99 # alpha
    for epoch in range(epochs):
            # Save the hedging Pnl for each batch      
        pnls = []
        strategies = []
        np.random.shuffle(idx)
        for i in range(int(sample_size/batch_size)):
            indices = idx[i*batch_size : (i+1)*batch_size]
            batch = paths_train[:,indices,:]
            with tf.compat.v1.Session() as sess:
                _,pnl,strategy = sess.run([train,Hedging_PnL,strategy],{S_t_input: batch,K : strikes[indices],alpha: riskaversion})
                pnls.append(pnl)
                strategies.append(strategy)
        CVaR = np.mean(-np.sort(np.concatenate(pnls))[:int((1-riskaversion)*sample_size)])

有人知道如何解决这个问题吗？