我正在尝试保存一个模型，该模型使用tf-hub中的USE作为其嵌入层，并在上面堆叠了一些FFN。该模型似乎运行良好，但是我在保存和加载模型时遇到了问题。
disable_eager_execution()
embed = hub.Module(module_url)
def UniversalEmbedding(x):
return embed(tf.squeeze(tf.cast(x, tf.string)))
input_text = Input(shape=[], dtype=tf.string)
response_text = Input(shape=[], dtype=tf.string)
text_embedding = Lambda(UniversalEmbedding, output_shape=(512, ))(input_text)
response_embedding = Lambda(UniversalEmbedding, output_shape=(512, ))(response_text)
response_embedding = Dense(512, activation='relu')(response_embedding)
response_embedding = Dense(512, activation='relu')(response_embedding)
score = Dot(axes=1, normalize=True)([text_embedding, response_embedding])
pred = Dense(2, activation='softmax')(score)
text_encoder = Model(inputs=[input_text], outputs=text_embedding)
response_encoder = Model(inputs=[response_text], outputs=response_embedding)
model = Model(inputs=[input_text, response_text], outputs=pred)
上面的代码是我建立模型的方式（它是使用USE作为编码器的双编码器模型）。
我不得不禁用急切执行，因为USE似乎尚未在急切执行环境中工作。如果没有，并且有解决方法，我也非常感谢您的帮助：）
该模型通过以下代码进行训练和保存：
with tf.compat.v1.Session() as session:
K.set_session(session)
session.run(tf.compat.v1.global_variables_initializer())
session.run(tf.compat.v1.tables_initializer())
history = model.fit_generator(generator=train_neg_sample_generator,
validation_data=val_neg_sample_generator, epochs=20,
callbacks=[checkpointer, earlystopper], verbose=0)
，并且将检查点中的权重（保存在hdf5文件中）加载到上面的代码中定义的模型时，该模型将正确加载。因此，下面的代码可以正常工作，只是因为上面已经定义了架构“模型”。
with tf.compat.v1.Session() as session:
K.set_session(session)
session.run(tf.compat.v1.global_variables_initializer())
session.run(tf.compat.v1.tables_initializer())
model.load_weights('./saved_models/weights.03-0.29.hdf5')
tf.keras.models.save_model(model, 'test_model2.hdf5')
predicts = model.predict([["how are you?", "how are you?", 'hi', 'my two favorites in one pic!'], ["i'm fine", "what the heck", 'hi', 'same!']])
print(predicts)
print(np.argmax(predicts, axis=1))
然后我尝试了2件事。首先，我尝试将架构保存为json格式，加载模型架构，然后加载权重，但是它不起作用。然后，我尝试通过keras.models.save_model保存整个模型，但是它也不起作用。
在两种情况下，他们都返回了 AttributeError：模块'tensorflow'没有属性'placeholder'
如何保存/加载整个模型（如果不是一次加载单独的架构/重量也可以）？
这是整个错误日志
AttributeError Traceback (most recent call last)
<ipython-input-31-47468f2533ad> in <module>()
1 from keras.models import load_model
2
----> 3 model2 = load_model('testest.h5')
13 frames
/usr/local/lib/python3.6/dist-packages/keras/engine/saving.py in load_wrapper(*args, **kwargs)
456 os.remove(tmp_filepath)
457 return res
--> 458 return load_function(*args, **kwargs)
459
460 return load_wrapper
/usr/local/lib/python3.6/dist-packages/keras/engine/saving.py in load_model(filepath, custom_objects, compile)
548 if H5Dict.is_supported_type(filepath):
549 with H5Dict(filepath, mode='r') as h5dict:
--> 550 model = _deserialize_model(h5dict, custom_objects, compile)
551 elif hasattr(filepath, 'write') and callable(filepath.write):
552 def load_function(h5file):
/usr/local/lib/python3.6/dist-packages/keras/engine/saving.py in _deserialize_model(h5dict, custom_objects, compile)
241 raise ValueError('No model found in config.')
242 model_config = json.loads(model_config.decode('utf-8'))
--> 243 model = model_from_config(model_config, custom_objects=custom_objects)
244 model_weights_group = h5dict['model_weights']
245
/usr/local/lib/python3.6/dist-packages/keras/engine/saving.py in model_from_config(config, custom_objects)
591 '`Sequential.from_config(config)`?')
592 from ..layers import deserialize
--> 593 return deserialize(config, custom_objects=custom_objects)
594
595
/usr/local/lib/python3.6/dist-packages/keras/layers/__init__.py in deserialize(config, custom_objects)
166 module_objects=globs,
167 custom_objects=custom_objects,
--> 168 printable_module_name='layer')
/usr/local/lib/python3.6/dist-packages/keras/utils/generic_utils.py in deserialize_keras_object(identifier, module_objects, custom_objects, printable_module_name)
145 config['config'],
146 custom_objects=dict(list(_GLOBAL_CUSTOM_OBJECTS.items()) +
--> 147 list(custom_objects.items())))
148 with CustomObjectScope(custom_objects):
149 return cls.from_config(config['config'])
/usr/local/lib/python3.6/dist-packages/keras/engine/network.py in from_config(cls, config, custom_objects)
1041 # First, we create all layers and enqueue nodes to be processed
1042 for layer_data in config['layers']:
-> 1043 process_layer(layer_data)
1044
1045 # Then we process nodes in order of layer depth.
/usr/local/lib/python3.6/dist-packages/keras/engine/network.py in process_layer(layer_data)
1027
1028 layer = deserialize_layer(layer_data,
-> 1029 custom_objects=custom_objects)
1030 created_layers[layer_name] = layer
1031
/usr/local/lib/python3.6/dist-packages/keras/layers/__init__.py in deserialize(config, custom_objects)
166 module_objects=globs,
167 custom_objects=custom_objects,
--> 168 printable_module_name='layer')
/usr/local/lib/python3.6/dist-packages/keras/utils/generic_utils.py in deserialize_keras_object(identifier, module_objects, custom_objects, printable_module_name)
147 list(custom_objects.items())))
148 with CustomObjectScope(custom_objects):
--> 149 return cls.from_config(config['config'])
150 else:
151 # Then `cls` may be a function returning a class.
/usr/local/lib/python3.6/dist-packages/keras/engine/base_layer.py in from_config(cls, config)
1101 A layer instance.
1102 """
-> 1103 return cls(**config)
1104
1105 def count_params(self):
/usr/local/lib/python3.6/dist-packages/keras/legacy/interfaces.py in wrapper(*args, **kwargs)
89 warnings.warn('Update your `' + object_name + '` call to the ' +
90 'Keras 2 API: ' + signature, stacklevel=2)
---> 91 return func(*args, **kwargs)
92 wrapper._original_function = func
93 return wrapper
/usr/local/lib/python3.6/dist-packages/keras/engine/input_layer.py in __init__(self, input_shape, batch_size, batch_input_shape, dtype, input_tensor, sparse, name)
85 dtype=dtype,
86 sparse=self.sparse,
---> 87 name=self.name)
88 else:
89 self.is_placeholder = False
/usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py in placeholder(shape, ndim, dtype, sparse, name)
539 x = tf.sparse_placeholder(dtype, shape=shape, name=name)
540 else:
--> 541 x = tf.placeholder(dtype, shape=shape, name=name)
542 x._keras_shape = shape
543 x._uses_learning_phase = False
AttributeError: module 'tensorflow' has no attribute 'placeholder'
答案 0 :(得分：0)
提供整个错误日志，而不仅仅是其中的一部分。
如果由于保存而确实出错，那么model.save('model.h5')怎么办？
不使用tf.keras.models中的模块，而是使用Model类本身的方法。
但是为什么要使用这些代码？
with tf.compat.v1.Session() as session:
K.set_session(session)
session.run(tf.compat.v1.global_variables_initializer())
session.run(tf.compat.v1.tables_initializer())
我相信您可以立即拨打电话model.fit，而您的tf是version2吗？为什么打compat.v1？
Tensorflow 2没有Placeholder，所以我认为可能与此有关。
答案 1 :(得分：0)
与tensorflow版本1.15正常工作 期待tf-hub与tensorflow 2.0和keras完全兼容...