json - JSONDecodeError：加载 tf.Keras 模型时的期望值

问题描述

当我尝试加载经过训练tf.keras的模型时，出现错误：JSONDecodeError: Expecting value. 该问题不可重现，即，当我尝试加载已在集群上训练并下载到计算机的模型时出现错误。当我尝试编译、保存和加载相同的模型时，错误不会出现。

任何想法可能是什么问题？

以下代码有效（与加载训练模型相反）：

import tensorflow as tf
from tensorflow import keras as ks
import numpy as np
import os
def UNet(n_classes, input_shape = (256, 256, 3), dropout = 0.05,
         ops = {"activation" : "relu",
                "padding" : "same",
                "kernel_initializer" : "he_normal"
        }):
    # input layer
    inputz = ks.layers.Input(shape = input_shape)
    
    # encoder part
    ## 1st convolution
    c1 = ks.layers.Conv2D(64, (3, 3), **ops)(inputz)
    c1 = ks.layers.Conv2D(64, (3, 3), **ops)(c1)
    ## 1st max pooling
    p1 = ks.layers.MaxPooling2D(pool_size = (2, 2))(c1)
    
    ## 2nd convolution
    c2 = ks.layers.Conv2D(128, (3, 3), **ops)(p1)
    c2 = ks.layers.Conv2D(128, (3, 3), **ops)(c2)
    ## 2nd max pooling
    p2 = ks.layers.MaxPooling2D(pool_size = (2, 2))(c2)
    
    ## 3rd convolution
    c3 = ks.layers.Conv2D(256, (3, 3), **ops)(p2)
    c3 = ks.layers.Conv2D(256, (3, 3), **ops)(c3)
    ## 3rd max pooling
    p3 = ks.layers.MaxPooling2D(pool_size = (2, 2))(c3)
    
    ## 4th convolution
    c4 = ks.layers.Conv2D(512, (3, 3), **ops)(p3)
    c4 = ks.layers.Conv2D(512, (3, 3), **ops)(c4)
    ## Drop
    d4 = ks.layers.Dropout(dropout)(c4)
    ## 4th max pooling
    p4 = ks.layers.MaxPooling2D(pool_size = (2, 2))(d4)
    
    ## 5th convolution
    c5 = ks.layers.Conv2D(1024, (3, 3), **ops)(p4)
    c5 = ks.layers.Conv2D(1024, (3, 3), **ops)(c5)
    ## Drop
    d5 = ks.layers.Dropout(dropout)(c5)
    
    # decoder part
    ## 1st up convolution
    us6 = ks.layers.UpSampling2D(size = (2, 2))(d5)
    up6 = ks.layers.Conv2D(512, (2, 2), **ops)(us6)
    ## merge
    ct6 = ks.layers.concatenate([d4, up6], axis = 3)
    uc6 = ks.layers.Conv2D(512, (3, 3), **ops)(ct6)
    uc6 = ks.layers.Conv2D(512, (3, 3), **ops)(uc6)
    
    ## 2nd up convolution
    us7 = ks.layers.UpSampling2D(size = (2, 2))(uc6)
    up7 = ks.layers.Conv2D(256, (2, 2), **ops)(us7)
    ## merge
    ct7 = ks.layers.concatenate([c3, up7], axis = 3)
    uc7 = ks.layers.Conv2D(256, (3, 3), **ops)(ct7)
    uc7 = ks.layers.Conv2D(256, (2, 2), **ops)(uc7)
     
    ## 3rd up convolution
    us8 = ks.layers.UpSampling2D(size = (2, 2))(uc7)
    up8 = ks.layers.Conv2D(128, (2, 2), **ops)(us8)
    ## merge
    ct8 = ks.layers.concatenate([c2, up8], axis = 3)
    uc8 = ks.layers.Conv2D(128, (3, 3), **ops)(ct8)
    uc8 = ks.layers.Conv2D(128, (3, 3), **ops)(uc8)
     
    ## 4th up convolution
    us9 = ks.layers.UpSampling2D(size = (2, 2))(uc8)
    up9 = ks.layers.Conv2D(64, (2, 2), **ops)(us9)
    ## merge
    ct9 = ks.layers.concatenate([c1, up9], axis = 3)
    uc9 = ks.layers.Conv2D(64, (3, 3), **ops)(ct9)
    uc9 = ks.layers.Conv2D(64, (3, 3), **ops)(uc9)
    uc9 = ks.layers.Conv2D(2, (3, 3), **ops)(uc9)
    
    # output layer
    if n_classes > 2:
        activ = "softmax"
    else:
        activ = "sigmoid"
    outputz = ks.layers.Conv2D(n_classes, 1, activation = activ)(uc9)
    
    model = ks.Model(inputs = [inputz], outputs = [outputz])
    print(model.summary())
    print(f'Total number of layers: {len(model.layers)}')
    return model

# get model
model = UNet(n_classes = 5)

class UpdatedMeanIoU(tf.keras.metrics.MeanIoU):
  def __init__(self,
               y_true = None,
               y_pred = None,
               num_classes = None,
               name = None,
               dtype = None):
    super(UpdatedMeanIoU, self).__init__(num_classes = num_classes,
                                         name = name, dtype = dtype)

  def update_state(self, y_true, y_pred, sample_weight = None):
    y_pred = tf.math.argmax(y_pred, axis = -1)
    return super().update_state(y_true, y_pred, sample_weight)
mIoU = UpdatedMeanIoU(num_classes = 5)



lr_sched = ks.optimizers.schedules.ExponentialDecay(
    initial_learning_rate = 1e-3,
    decay_steps = np.floor(50),
    decay_rate = 0.995)

optimizer = ks.optimizers.RMSprop(learning_rate = lr_sched, clipnorm = 1)

lozz = ks.losses.SparseCategoricalCrossentropy()

model.compile(optimizer = optimizer, loss = lozz,
              metrics = [mIoU])

model.save("G:\\mot", save_format = "tf")
os.chdir("G:\\mot")
trained_model = ks.models.load_model("G:\\mot", custom_objects = {"UpdatedMeanIoU": mIoU})

不起作用的部分代码只包括一个model.fitbetweenmodel.compile和model.save。

腐败模型可以在这里找到。

这是完整的错误消息：

trained_model = ks.models.load_model(moddir,\
                                     custom_objects = {"UpdatedMeanIoU": mIoU})
Traceback (most recent call last):

  File "<ipython-input-140-4d44f44a3739>", line 1, in <module>
    trained_model = ks.models.load_model(moddir,\

  File "c:\users\manuel\python\lib\site-packages\tensorflow\python\keras\saving\save.py", line 206, in load_model
    return saved_model_load.load(filepath, compile, options)

  File "c:\users\manuel\python\lib\site-packages\tensorflow\python\keras\saving\saved_model\load.py", line 155, in load
    keras_loader.finalize_objects()

  File "c:\users\manuel\python\lib\site-packages\tensorflow\python\keras\saving\saved_model\load.py", line 626, in finalize_objects
    self._reconstruct_all_models()

  File "c:\users\manuel\python\lib\site-packages\tensorflow\python\keras\saving\saved_model\load.py", line 645, in _reconstruct_all_models
    self._reconstruct_model(model_id, model, layers)

  File "c:\users\manuel\python\lib\site-packages\tensorflow\python\keras\saving\saved_model\load.py", line 661, in _reconstruct_model
    config = json_utils.decode(

  File "c:\users\manuel\python\lib\site-packages\tensorflow\python\keras\saving\saved_model\json_utils.py", line 62, in decode
    return json.loads(json_string, object_hook=_decode_helper)

  File "c:\users\manuel\python\lib\json\__init__.py", line 359, in loads
    return cls(**kw).decode(s)

  File "c:\users\manuel\python\lib\json\decoder.py", line 337, in decode
    obj, end = self.raw_decode(s, idx=_w(s, 0).end())

  File "c:\users\manuel\python\lib\json\decoder.py", line 355, in raw_decode
    raise JSONDecodeError("Expecting value", s, err.value) from None

JSONDecodeError: Expecting value

编辑：

我可以将模型加载到我训练它的服务器上（在单独的 Python 会话中）。但是，下载文件后，我无法在计算机上执行相同操作。该模型是否存在一些隐藏文件？或者不同的操作系统或不同的模块版本会不会有问题？

标签： jsonpython-3.xtensorflowkeras

json - JSONDecodeError：加载 tf.Keras 模型时的期望值

问题描述

解决方案

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