深度学习模型喂参数的时候报错In[0] ndims must be >= 2: 1

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import json
import tensorflow as tf
import numpy as np
class TrainingConfig(object):
epoches = 10 #迭代次数参数
evaluateEvery = 100 # 每个评估
checkpointEvery = 100 # 每个检查站
learningRate = 0.001 # 学习率

class ModelConfig(object):
embeddingSize = 100
#词嵌入维度参数：词嵌入维度刻画了模型表达词汇的能力，
hiddenSizes = [128] # LSTM结构的神经元个数
dropoutKeepProb = 0.5 # dropout保持率，防止过拟合
l2RegLambda = 0.0 #L2 正则化项的权重系数，越大模型越保守。
class Config(object):
sequenceLength = 100 #序列长度应当与给出数据的结构相匹配，大约是你希望生成句子的长度，通常设置大一点的值会使模型的能力也跟着强一点。
batchSize = 128 #批处理大小

dataSource = "data/data1.xls"

stopWordSource = "data/stopWordsHIT.dic"

numClasses = 3

rate = 0.8  # 训练集的比例

training = TrainingConfig()

model = ModelConfig()

实例化配置参数对象

config = Config()

x = "这主要是隔膜的工艺所决定的隔膜是要除槽的负压可以防止氯气外逸氯气是含氧是可以" \
" 接受的氢气中含氧是不可以接受的另外在电解过程中氢气出现大正压会便得氯中含氢高危险并且会冲击电槽安全有风险氢气大负压会吸入空气氢中含氧高氯气大正压会产生泄漏事故氯启动"

注：下面两个词典要保证和当前加载的模型对应的词典是一致的

with open("data/wordJson/wordToIndex.json", "r", encoding="utf-8") as f:
word2idx = json.load(f)

with open("data/wordJson/indexToWord.json", "r", encoding="utf-8") as f:
label2idx = json.load(f)
idx2label = {value: key for key, value in label2idx.items()}

xIds = [word2idx.get(item, word2idx["UNK"]) for item in x.split(" ")]
if len(xIds) >= config.sequenceLength:
xIds = xIds[:config.sequenceLength]
else:
xIds = xIds + [word2idx["PAD"]] * (config.sequenceLength - len(xIds))

graph = tf.Graph()
with graph.as_default():
#gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.333)
#session_conf = tf.ConfigProto(allow_soft_placement=True, log_device_placement=False, gpu_options=gpu_options)
checkpoint_file = tf.train.latest_checkpoint("model/Bi-LSTM/model/")
sess = tf.Session()
with sess.as_default():

    saver = tf.train.import_meta_graph("{}.meta".format(checkpoint_file))
    print(saver)
    saver.restore(sess, checkpoint_file)

    # 获得需要喂给模型的参数，输出的结果依赖的输入值
    inputX = graph.get_operation_by_name("inputX").outputs[0]
    dropoutKeepProb = graph.get_operation_by_name("dropoutKeepProb").outputs[0]
    print(inputX)
    xids=np.array(xIds).reshape(-1,100)
    print(xids.shape)
    feed_dict={inputX: xids, dropoutKeepProb: 0.5}
    pred = sess.run("output/predictions:0", feed_dict)
    print(pred)

pred = [idx2label[item] for item in pred]
print(pred)

借鉴了文章https://www.cnblogs.com/jiangxinyang/p/10208227.html的内容，但是测试运行的时候报错
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