加载由tensorflow训练的Keras模型时出错

我正在尝试加载我使用Tensorflow和Keras训练和保存的模型，但它给了我一个错误 .

Python版本：3.6.6

Tensorflow版本：1.11.0

输出：

Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/packages/tensorflow/1.11.0/Python-3.6.6/tensorflow/python/keras/engine/saving.py", line 230, in load_model
    model = model_from_config(model_config, custom_objects=custom_objects)
  File "/packages/tensorflow/1.11.0/Python-3.6.6/tensorflow/python/keras/engine/saving.py", line 310, in model_from_config
    return deserialize(config, custom_objects=custom_objects)
  File "/packages/tensorflow/1.11.0/Python-3.6.6/tensorflow/python/keras/layers/serialization.py", line 64, in deserialize
    printable_module_name='layer')
  File "/packages/tensorflow/1.11.0/Python-3.6.6/tensorflow/python/keras/utils/generic_utils.py", line 173, in deserialize_keras_object
    list(custom_objects.items())))
  File "/packages/tensorflow/1.11.0/Python-3.6.6/tensorflow/python/keras/engine/sequential.py", line 339, in from_config
    custom_objects=custom_objects)
  File "/packages/tensorflow/1.11.0/Python-3.6.6/tensorflow/python/keras/layers/serialization.py", line 64, in deserialize
    printable_module_name='layer')
  File "/packages/tensorflow/1.11.0/Python-3.6.6/tensorflow/python/keras/utils/generic_utils.py", line 175, in deserialize_keras_object
    return cls.from_config(config['config'])
  File "/packages/tensorflow/1.11.0/Python-3.6.6/tensorflow/python/keras/engine/base_layer.py", line 1617, in from_config
    return cls(**config)
  File "/packages/tensorflow/1.11.0/Python-3.6.6/tensorflow/python/keras/layers/advanced_activations.py", line 310, in __init__
    if max_value is not None and max_value < 0.:
TypeError: '<' not supported between instances of 'dict' and 'float'

我也试过保存权重而不是整个模型，但这似乎不太成功：

回溯（最近一次调用最后一次）：文件“predict_from_NN.py”，第44行，in

model.load_weights('/home/me/Data/Out/finished_model_2_weights.hdf5.index')
  File "/packages/tensorflow/1.11.0/Python-3.6.6/tensorflow/python/keras/engine/network.py", line 1526, in load_weights
    checkpointable_utils.streaming_restore(status=status, session=session)
  File "/packages/tensorflow/1.11.0/Python-3.6.6/tensorflow/python/training/checkpointable/util.py", line 880, in streaming_restore
    "Streaming restore not supported from name-based checkpoints. File a "
NotImplementedError: Streaming restore not supported from name-based checkpoints. File a feature request if this limitation bothers you.

虽然我不确定为什么/如何进行“流式恢复”，但谷歌在这两种情况下都不是很有用 .

如果它有帮助，这是我的模型的代码：

from tensorflow.python.keras.layers import Conv2D, MaxPooling2D, ReLU

来自tensorflow.keras.models从tensorflow.keras.layers导入顺序导入Flatten，Activation，Dense

def cnn_model(img_rows, img_cols, img_channels):
    model = Sequential()
    model.add(Conv2D(64, (3, 3),activation='linear',kernel_initializer='he_uniform',
                     input_shape=(img_rows, img_cols, img_channels)))
    model.add(ReLU())   # add an advanced activation
    model.add(MaxPooling2D(pool_size=(5, 5)))
    model.add(Conv2D(32, (3, 3),activation='linear',kernel_initializer='he_uniform'))
    model.add(ReLU())   # add an advanced activation
    model.add(MaxPooling2D(pool_size=(3, 3)))
    model.add(Conv2D(16, (3, 3),activation='linear',kernel_initializer='he_uniform'))
    model.add(ReLU())   # add an advanced activation
    model.add(MaxPooling2D(pool_size=(3, 3)))
    model.add(Flatten())
    model.add(Dense(1024))
    model.add(Dense(1024))
    model.add(ReLU())   # add an advanced activation
    model.add(Dense(4))
    model.add(Activation('softmax'))

    return model

我保存我的模型：

model.save(os.path.join(output_folder, model_name + '_GPU.hdf5'))

并尝试像这样加载它：

from tensorflow.python.keras.models import load_model
model = load_model(model_file)