我正在使用AutoKeras 1.0，但我无法理解我应该如何保存和重新加载经过训练的模型(加上权重等(。
我可以使用类似于以下内容的代码轻松训练模型：
num_data = 500
train_x = common.generate_structured_data(num_data)
train_y = common.generate_one_hot_labels(num_instances=num_data, num_classes=3)
clf = ak.StructuredDataClassifier(
column_names=common.COLUMN_NAMES_FROM_NUMPY,
max_trials=1,
seed=common.SEED)
clf.fit(train_x, train_y, epochs=4, validation_data=(train_x, train_y))
loss = clf.evaluate(train_x, train_y)
print(loss)
但是，我无法从文档中判断如何保存此模型并在以后在另一个程序中重用它。 我尝试找到"最佳"模型并保存它，如下所示：
preprocess_graph, best_model = clf.tuner.get_best_model()
best_model.save("testmodel.h5")
但是，当我尝试再次加载此模型时，我得到以下结果：
new_model = load_model("testmodel.h5")
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-12-bd01053bfeda> in <module>
----> 1 new_model = load_model("testmodel.h5")
/opt/conda/lib/python3.7/site-packages/keras/engine/saving.py in load_wrapper(*args, **kwargs)
490 os.remove(tmp_filepath)
491 return res
--> 492 return load_function(*args, **kwargs)
493
494 return load_wrapper
/opt/conda/lib/python3.7/site-packages/keras/engine/saving.py in load_model(filepath, custom_objects, compile)
582 if H5Dict.is_supported_type(filepath):
583 with H5Dict(filepath, mode='r') as h5dict:
--> 584 model = _deserialize_model(h5dict, custom_objects, compile)
585 elif hasattr(filepath, 'write') and callable(filepath.write):
586 def load_function(h5file):
/opt/conda/lib/python3.7/site-packages/keras/engine/saving.py in _deserialize_model(h5dict, custom_objects, compile)
272 raise ValueError('No model found in config.')
273 model_config = json.loads(model_config.decode('utf-8'))
--> 274 model = model_from_config(model_config, custom_objects=custom_objects)
275 model_weights_group = h5dict['model_weights']
276
/opt/conda/lib/python3.7/site-packages/keras/engine/saving.py in model_from_config(config, custom_objects)
625 '`Sequential.from_config(config)`?')
626 from ..layers import deserialize
--> 627 return deserialize(config, custom_objects=custom_objects)
628
629
/opt/conda/lib/python3.7/site-packages/keras/layers/__init__.py in deserialize(config, custom_objects)
166 module_objects=globs,
167 custom_objects=custom_objects,
--> 168 printable_module_name='layer')
/opt/conda/lib/python3.7/site-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'])
/opt/conda/lib/python3.7/site-packages/keras/engine/network.py in from_config(cls, config, custom_objects)
1054 # First, we create all layers and enqueue nodes to be processed
1055 for layer_data in config['layers']:
-> 1056 process_layer(layer_data)
1057
1058 # Then we process nodes in order of layer depth.
/opt/conda/lib/python3.7/site-packages/keras/engine/network.py in process_layer(layer_data)
1040
1041 layer = deserialize_layer(layer_data,
-> 1042 custom_objects=custom_objects)
1043 created_layers[layer_name] = layer
1044
/opt/conda/lib/python3.7/site-packages/keras/layers/__init__.py in deserialize(config, custom_objects)
166 module_objects=globs,
167 custom_objects=custom_objects,
--> 168 printable_module_name='layer')
/opt/conda/lib/python3.7/site-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.
/opt/conda/lib/python3.7/site-packages/keras/engine/base_layer.py in from_config(cls, config)
1177 A layer instance.
1178 """
-> 1179 return cls(**config)
1180
1181 def count_params(self):
/opt/conda/lib/python3.7/site-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
TypeError: __init__() got an unexpected keyword argument 'ragged'
我做错了还是有更好的方法？
您可以尝试这样做来加载保存的模型：
import tensorflow as tf
new_model = tf.keras.models.load_model('testmodel.h5')
在 AutoKeras 1.0.2 中，这似乎有效：
best_model = clf.tuner.get_best_model()
best_model.save("testmodel.h5")
model = tf.keras.models.load_model("testmodel.h5")