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
张量流版本：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'
我也尝试过只保存权重而不是整个模型，但这似乎并不更成功：
Traceback (most recent call last):
File "predict_from_NN.py", line 44, in <module>
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.
虽然我不太确定为什么/如何进行“流式恢复”，并且谷歌在这两种情况下都不是很有用。
如果有帮助，这是我的模型的代码：
from tensorflow.python.keras.layers import Conv2D, MaxPooling2D, ReLU
从tensorflow.keras.models导入顺序 从tensorflow.keras.layers导入扁平化，激活，密集
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
我像这样保存我的模型：
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)
您是否尝试过使用“to_json”函数保存和加载模型，如下所述？
from keras.models import model_from_json
[...]
# serialize model to JSON
model_json = model.to_json()
with open("model.json", "w") as json_file:
json_file.write(model_json)
# serialize weights to HDF5
model.save_weights("model.h5")
print("Saved model to disk")
# later...
# load json and create model
json_file = open('model.json', 'r')
loaded_model_json = json_file.read()
json_file.close()
loaded_model = model_from_json(loaded_model_json)
# load weights into new model
loaded_model.load_weights("model.h5")
print("Loaded model from disk")
P.S：我从这里借用了这个代码。
尝试更新 Keras，因为我也遇到了类似的错误，更新存储库有帮助。 在终端中使用 github 链接更新 keras
git clone https://github.com/fchollet/keras.gitsudo python3 setup.py install我在 Google Colab 上复制了你的代码，效果很好。
Tf version: 2.2.0-rc3
Keras version: 2.3.1
尝试更新您的软件包。