Python 在状态dict:0.0.weight“中遇到缺少的密钥&引用；0.1.重量&引用；0.1.偏差&引用；0.1.运行“你的意思是”；

我正在尝试为我的大学项目开发一个电话分类器模型。我已经训练了我的模型，当我试图通过执行python app/server.py service来部署模型时遇到了一个问题。我读了一篇帖子 我怀疑这个问题是因为我的anaconda和Google Colab之间运行的fast.ai版本不同

因此，我尝试使用pip list fastai、conda list fastai和import fastai在我的计算机中检查fastai的版本；fastai.版本在我的Google colab中（我使用Google colab开发了我的模型），但结果是相同的（fastai版本=1.0.59）。我甚至试着用Google Colab更新我的fastai版本，但都没有成功。以下是异常代码：

Traceback (most recent call last):
  File "app/server.py", line 37, in <module>
    learn = loop.run_until_complete(asyncio.gather(*tasks))[0]
  File "C:\ProgramData\Anaconda3\lib\asyncio\base_events.py", line 584, in run_until_complete
    return future.result()
  File "app/server.py", line 32, in setup_learner
    learn.load(model_file_name)
  File "C:\ProgramData\Anaconda3\lib\site-packages\fastai\basic_train.py", line 279, in load
    get_model(self.model).load_state_dict(state, strict=strict)
  File "C:\ProgramData\Anaconda3\lib\site-packages\torch\nn\modules\module.py", line 845, in load_state_dict
    self.__class__.__name__, "\n\t".join(error_msgs)))
RuntimeError: Error(s) in loading state_dict for Sequential:
        Missing key(s) in state_dict: "0.0.weight", "0.1.weight", "0.1.bias", "0.1.running_mean", "0.1.running_var", "0.4.0.conv1.weight", "0.4.0.bn1.weight", "0.4.0.bn1.bias", "0.4.0.bn1.running_mean", "0.4.0.bn1.running_var", "0.4.0.conv2.weight", "0.4.0.bn2.weight", "0.4.0.bn2.bias", "0.4.0.bn2.running_mean", "0.4.0.bn2.running_var", "0.4.1.conv1.weight", "0.4.1.bn1.weight", "0.4.1.bn1.bias", "0.4.1.bn1.running_mean", "0.4.1.bn1.running_var", "0.4.1.conv2.weight", "0.4.1.bn2.weight", "0.4.1.bn2.bias", "0.4.1.bn2.running_mean", "0.4.1.bn2.running_var", "0.4.2.conv1.weight", "0.4.2.bn1.weight", "0.4.2.bn1.bias", "0.4.2.bn1.running_mean", "0.4.2.bn1.running_var", "0.4.2.conv2.weight", "0.4.2.bn2.weight", "0.4.2.bn2.bias", "0.4.2.bn2.running_mean", "0.4.2.bn2.running_var", "0.5.0.conv1.weight", "0.5.0.bn1.weight", "0.5.0.bn1.bias", "0.5.0.bn1.running_mean", "0.5.0.bn1.running_var", "0.5.0.conv2.weight", "0.5.0.bn2.weight", "0.5.0.bn2.bias", "0.5.0.bn2.running_mean", "0.5.0.bn2.running_var", "0.5.0.downsample.0.weight", "0.5.0.downsample.1.weight", "0.5.0.downsample.1.bias", "0.5.0.downsample.1.running_mean", "0.5.0.downsample.1.running_var", "0.5.1.conv1.weight", "0.5.1.bn1.weight", "0.5.1.bn1.bias", "0.5.1.bn1.running_mean", "0.5.1.bn1.running_var", "0.5.1.conv2.weight", "0.5.1.bn2.weight", "0.5.1.bn2.bias", "0.5.1.bn2.running_mean", "0.5.1.bn2.running_var", "0.5.2.conv1.weight", "0.5.2.bn1.weight", "0.5.2.bn1.bias", "0.5.2.bn1.running_mean", "0.5.2.bn1.running_var", "0.5.2.conv2.weight", "0.5.2.bn2.weight", "0.5.2.bn2.bias", "0.5.2.bn2.running_mean", "0.5.2.bn2.running_var", "0.5.3.conv1.weight", "0.5.3.bn1.weight", "0.5.3.bn1.bias", "0.5.3.bn1.running_mean", "0.5.3.bn1.running_var", "0.5.3.conv2.weight", "0.5.3.bn2.weight", "0.5.3.bn2.bias", "0.5.3.bn2.running_mean", "0.5.3.bn2.running_var", "0.6.0.conv1.weight", "0.6.0.bn1.weight", "0.6.0.bn1.bias", "0.6.0.bn1.running_mean", "0.6.0.bn1.running_var", "0.6.0.conv2.weight", "0.6.0.bn2.weight", "0.6.0.bn2.bias", "0.6.0.bn2.running_mean", "0.6.0.bn2.running_var", "0.6.0.downsample.0.weight", "0.6.0.downsample.1.weight", "0.6.0.downsample.1.bias", "0.6.0.downsample.1.running_mean", "0.6.0.downsample.1.running_var", "0.6.1.conv1.weight", "0.6.1.bn1.weight", "0.6.1.bn1.bias", "0.6.1.bn1.running_mean", "0.6.1.bn1.running_var", "0.6.1.conv2.weight", "0.6.1.bn2.weight", "0.6.1.bn2.bias", "0.6.1.bn2.running_mean", "0.6.1.bn2.running_var", "0.6.2.conv1.weight", "0.6.2.bn1.weight", "0.6.2.bn1.bias", "0.6.2.bn1.running_mean", "0.6.2.bn1.running_var", "0.6.2.conv2.weight", "0.6.2.bn2.weight", "0.6.2.bn2.bias", "0.6.2.bn2.running_mean", "0.6.2.bn2.running_var", "0.6.3.conv1.weight", "0.6.3.bn1.weight", "0.6.3.bn1.bias", "0.6.3.bn1.running_mean", "0.6.3.bn1.running_var", "0.6.3.conv2.weight", "0.6.3.bn2.weight", "0.6.3.bn2.bias", "0.6.3.bn2.running_mean", "0.6.3.bn2.running_var", "0.6.4.conv1.weight", "0.6.4.bn1.weight", "0.6.4.bn1.bias", "0.6.4.bn1.running_mean", "0.6.4.bn1.running_var", "0.6.4.conv2.weight", "0.6.4.bn2.weight", "0.6.4.bn2.bias", "0.6.4.bn2.running_mean", "0.6.4.bn2.running_var", "0.6.5.conv1.weight", "0.6.5.bn1.weight", "0.6.5.bn1.bias", "0.6.5.bn1.running_mean", "0.6.5.bn1.running_var", "0.6.5.conv2.weight", "0.6.5.bn2.weight", "0.6.5.bn2.bias", "0.6.5.bn2.running_mean", "0.6.5.bn2.running_var", "0.7.0.conv1.weight", "0.7.0.bn1.weight", "0.7.0.bn1.bias", "0.7.0.bn1.running_mean", "0.7.0.bn1.running_var", "0.7.0.conv2.weight", "0.7.0.bn2.weight", "0.7.0.bn2.bias", "0.7.0.bn2.running_mean", "0.7.0.bn2.running_var", "0.7.0.downsample.0.weight", "0.7.0.downsample.1.weight", "0.7.0.downsample.1.bias", "0.7.0.downsample.1.running_mean", "0.7.0.downsample.1.running_var", "0.7.1.conv1.weight", "0.7.1.bn1.weight", "0.7.1.bn1.bias", "0.7.1.bn1.running_mean", "0.7.1.bn1.running_var", "0.7.1.conv2.weight", "0.7.1.bn2.weight", "0.7.1.bn2.bias", "0.7.1.bn2.running_mean", "0.7.1.bn2.running_var", "0.7.2.conv1.weight", "0.7.2.bn1.weight", "0.7.2.bn1.bias", "0.7.2.bn1.running_mean", "0.7.2.bn1.running_var", "0.7.2.conv2.weight", "0.7.2.bn2.weight", "0.7.2.bn2.bias", "0.7.2.bn2.running_mean", "0.7.2.bn2.running_var", "1.2.weight", "1.2.bias", "1.2.running_mean", "1.2.running_var", "1.4.weight", "1.4.bias", "1.6.weight", "1.6.bias", "1.6.running_mean", "1.6.running_var", "1.8.weight", "1.8.bias".
        Unexpected key(s) in state_dict: "opt_func", "loss_func", "metrics", "true_wd", "bn_wd", "wd", "train_bn", "model_dir", "callback_fns", "cb_state", "model", "data", "cls".

回溯（最近一次呼叫最后一次）：
文件“app/server.py”，第37行，在
learn=loop.run_直到_完成（asyncio.gather（*tasks））[0]
文件“C:\ProgramData\Anaconda3\lib\asyncio\base\u events.py”，第584行，在运行中直到完成
返回future.result（）
文件“app/server.py”，第32行，在安装程序中
learn.load（模型文件名）
文件“C:\ProgramData\Anaconda3\lib\site packages\fastai\basic\u train.py”，第279行，已加载
获取模型（self.model）。加载状态（state，strict=strict）
文件“C:\ProgramData\Anaconda3\lib\site packages\torch\nn\modules\module.py”，第845行，处于加载状态
self.\uuuuuuu类\uuuuuu.\uuuuuuuu名称，“\n\t”.join（错误\u msgs）））
RuntimeError:加载状态下的错误\u dict用于顺序：
状态命令中缺少键：“0.0.weight”、“0.1.weight”、“0.1.bias”、“0.1.running\u-mean”、“0.1.running\u-var”、“0.4.0.conv1.weight”、“0.4.0.bn1.weight”、“0.4.0.bn1.bias”、“0.4.0.bn1.running\u-mean”、“0.4.0.conv2.weight”、“0.4.0.0.bn2.bias”、“0.4.0.0.bn1.running\bnu.0”，“0.4.1.bn1.weight”、“0.4.1.bn1.weight”、“0.4.1.bn1.bias”、“0.4.1.bn1.running_-mean”、“0.4.1.bn1.var”、“0.4.1.bn2.weight”、“0.4.1.bn2.bias”、“0.4.1.bn2.running_-mean”、“0.4.1.bn2.running_-var”、“0.4.2.conv1.weight”、“0.4.2.bn1.bias”、“running-mean.bn1.var””0.4.2.bn2.weight、0.4.2.bn2.weight、0.4.2.bn2.bias、0.4.2.bn2.running_平均值、0.4.2.bn2.running_var、0.5.0.conv1.weight、0.5.0.bn1.weight、0.5.0.bn1.bias、0.5.0.bn1.running平均值、0.var.bn1.running_var、0.5.0.0“0.5.0.下采样1.权重”、“0.5.0.下采样1.权重”、“0.5.0.下采样1.偏差”、“0.5.0.下采样1.运行平均值”、“0.5.0.下采样1.运行平均值”、“0.5.1.转换1.权重”、“0.5.1.转换1.权重”、“0.5.1.转换1.偏差”、“0.5.1.转换平均值”、“0.5.1.运行平均值”、“0.5.1.转换2.权重”、“0.5.1.转换1.转换1.平均值”、“0.5.1.转换1.转换1.转换1.权重”、“0.1.转换1.转换1.转换1.转换平均值”、“转换1.转换1.转换1.权重”、“转换1.转换1.转换1.转换1.转换1.转换1.转换1.转换为0.5.1.转换为0.5.1.转换为0“，”0.5.1.bn2.running_var“，”0.5.2.conv1.weight“，”0.5.2.bn1.weight“，”0.5.2.bn1.running_mean“，”0.5.2.bn1.running_var“，”0.5.2.conv2.weight“，”0.5.2.bn2.bias“，”0.5.2.bn2.running_mean“，”0.5.2.bn2.running_var“，”0.5.3.conv1.weight“，”0.5.3.bn1.bn1，“0.5.3.bn1.running_var”、“0.5.3.conv2.weight”、“0.5.3.bn2.weight”、“0.5.3.bn2.running_均值”、“0.5.3.bn2.running_var”、“0.6.0.conv1.weight”、“0.6.0.bn1.weight”、“0.6.0.bn1.bias”、“0.6.bn1.running_均值”、“0.6.0.0.bn1.running_均值”、“0.var”、“0.6.0.0.conv2.weight”、“0.0.bn1.0.0.bn2.bias”、“0.0.bnu.0.0”等”“0.6.0.bn2.下样本1.权重”、“0.6.0.下样本1.权重”、“0.6.0.下样本1.偏差”、“0.6.0.下样本1.运行平均值”、“0.6.0.下样本1.运行平均值”、“0.6.0.下样本1.运行平均值”、“0.6.1.下样本1.权重”、“0.6.1.bn1.权重”、“0.6.1.偏差”、“0.6.1.运行平均值”、“0.6.1.下样本1.运行平均值”、“0.6.1.权重”、“0.1.偏差”、“0.6.1.权重”、“0.1.偏差”、“0.6.1.权重”、“0.1.偏差”0.6.1.bn2.跑步平均值、0.6.1.bn2.跑步平均值、0.6.2.bn1.体重、0.6.2.bn1.体重、0.6.2.bn1.偏差、0.6.2.bn1.跑步平均值、0.6.2.bn1.跑步平均值、0.6.2.bn1.跑步平均值、0.6.2.bn2.体重、0.6.2.2.bn2.体重、0.2.bn2.偏差、0.6.2.2.bn2.跑步平均值、0.6.2.2.bn1.体重、0.3.bn1.体重、0.6.1.偏差0.6.3.bn1.跑步平均值、0.6.3.bn1.跑步平均值、0.6.3.bn2.体重、0.6.3.bn2.体重、0.6.3.bn2.偏倚、0.6.3.bn2.跑步平均值、0.6.3.bn2.跑步平均值、0.6.4.bn1.体重、0.6.4.bn1.体重、0.6.4.bn1.偏倚、0.6.4.4.bn1.跑步平均值、0.6.4.bn1.跑步平均值、0.4.bn2.体重、0.4.bn1.体重、0.4.bn2.偏倚0.6.4.bn2.running_mean”、“0.6.4.bn2.running_var”、“0.6.5.conv1.weight”、“0.6.5.bn1.weight”、“0.6.5.bn1.bias”、“0.6.5.bn1.running_mean”、“0.6.5.bn1.running_var”、“0.6.5.conv2.weight”、“0.6.5.bn2.bias”、“0.6.5.bn2.bias”、“0.5.bn2.bias”、“0.5.bn2.running_mean”、“0.6.5.5.bn2.bn2.bn1.running_var”、“0.7.bias”、“0.7.Bn00.7.0.bn1.运行平均值、0.7.0.bn1.运行平均值、0.7.0.bn2.权重、0.7.0.bn2.权重、0.7.0.bn2.偏差、0.7.0.bn2.运行平均值、0.7.0.bn2.运行平均值、0.7.0.降采样0.权重、0.7.0.降采样1.权重、0.7.0.降采样1.偏差、0.7.0.降采样1.运行平均值、0.7.0.0.0.0.降采样1.运行平均值、0.0.0.0.降采样1.权重、0.1.降采样1.重1.1.1.bn1.1.bn1.1.1.1.1.1.1.1.1.1.1.1.bn1.1.1.1.1.1.bn1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.bn1.1.bn1.1.1.bn1.1.1.1.bn1.bn1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.跑步助助助助助跑助助助助助跑的意思表示表示表示表示表示表示表示表示表示表示表示，运行的意思，运行的意思，表示，1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1 2.重量“，”0.7.2.bn2.偏差“，”0.7.2.bn2.运行平均值“，”0.7.2.bn2.运行平均值“，”1.2.权重“，”1.2.运行平均值“，”1.2.运行平均值“，”1.2.运行平均值“，”1.2.运行平均值“，”1.4.偏差“，”1.6.权重“，”1.6.偏差“，”1.6.运行平均值“，”1.6.运行平均值“，”1.8.权重“，”1.8.偏差”。
处于状态的意外键：“opt_func”、“loss_func”、“metrics”、“true_wd”、“bn_wd”、“wd”、“tra”