我正在学习使用Pthong 3.6和Jupyter的神经网络。正如每个人（我认为）一样，我开始使用在网上找到的示例，但我不知道为什么我不能保存训练有素的神经网络。我正在使用此代码：
fashion_model.save("fashion_model.h5py")
但是我收到此错误：
TypeError Traceback (most recent call last)
<ipython-input-72-11379a0dd354> in <module>
1 #FALLA NO SE POR QUE
2 from keras.models import save_model
----> 3 fashion_model.save("fashion_model.h5py")
C:\Users\Javi\Anaconda3\lib\site-packages\keras\engine\network.py in save(self, filepath, overwrite, include_optimizer)
1088 raise NotImplementedError
1089 from ..models import save_model
-> 1090 save_model(self, filepath, overwrite, include_optimizer)
1091
1092 def save_weights(self, filepath, overwrite=True):
C:\Users\Javi\Anaconda3\lib\site-packages\keras\engine\saving.py in save_model(model, filepath, overwrite, include_optimizer)
380
381 try:
--> 382 _serialize_model(model, f, include_optimizer)
383 finally:
384 if opened_new_file:
C:\Users\Javi\Anaconda3\lib\site-packages\keras\engine\saving.py in _serialize_model(model, f, include_optimizer)
112 layer_group['weight_names'] = weight_names
113 for name, val in zip(weight_names, weight_values):
--> 114 layer_group[name] = val
115 if include_optimizer and model.optimizer:
116 if isinstance(model.optimizer, optimizers.TFOptimizer):
C:\Users\Javi\Anaconda3\lib\site-packages\keras\utils\io_utils.py in __setitem__(self, attr, val)
216 'Group with name "{}" exists.'.format(attr))
217 if is_np:
--> 218 dataset = self.data.create_dataset(attr, val.shape, dtype=val.dtype)
219 if not val.shape:
220 # scalar
C:\Users\Javi\Anaconda3\lib\site-packages\h5py\_hl\group.py in create_dataset(self, name, shape, dtype, data, **kwds)
114 """
115 with phil:
--> 116 dsid = dataset.make_new_dset(self, shape, dtype, data, **kwds)
117 dset = dataset.Dataset(dsid)
118 if name is not None:
C:\Users\Javi\Anaconda3\lib\site-packages\h5py\_hl\dataset.py in make_new_dset(parent, shape, dtype, data, chunks, compression, shuffle, fletcher32, maxshape, compression_opts, fillvalue, scaleoffset, track_times)
97 dtype = data.dtype
98 else:
---> 99 dtype = numpy.dtype(dtype)
100 tid = h5t.py_create(dtype, logical=1)
101
TypeError: data type not understood
有人知道如何解决吗？我希望能够保存模型以及受过训练的砝码，以便将来无需重新训练即可将其打开。
我也尝试使用此代码，但在第二部分中同样使用，以节省失败的权重。
model_json = fashion_model.to_json()
with open("model.json", "w") as json_file:
json_file.write(model_json)
# serialize weights to HDF5
fashion_model.save_weights("model.h5")
print("Saved model to disk")
我也遇到同样的错误
谢谢。
我无法重现您的问题。
您的代码存在缩进问题。您可以使用JSON序列化模型，如下所示
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")
您可以加载JSON并创建模型
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")
在这里，我运行了一个简单的模型，并使用model.save保存了，并用load_model进行了keras加载。您可以从here
建立并保存模型：
import numpy as np
from numpy import loadtxt
from keras.models import Sequential
from keras.layers import Dense
# load pima indians dataset
dataset = np.loadtxt("/content/pima-indians-diabetes.csv", delimiter=",")
# split into input (X) and output (Y) variables
X = dataset[:,0:8]
Y = dataset[:,8]
# define model
model = Sequential()
model.add(Dense(12, input_dim=8, activation='relu'))
model.add(Dense(8, activation='relu'))
model.add(Dense(1, activation='sigmoid'))
# compile model
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
# Model Summary
model.summary()
# Fit the model
model.fit(X, Y, epochs=150, batch_size=10, verbose=0)
# evaluate the model
scores = model.evaluate(X, Y, verbose=0)
print("%s: %.2f%%" % (model.metrics_names[1], scores[1]*100))
# save model and architecture to single file
model.save("model.h5")
print("Saved model to disk")
输出：
Model: "sequential_2"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
dense_4 (Dense) (None, 12) 108
_________________________________________________________________
dense_5 (Dense) (None, 8) 104
_________________________________________________________________
dense_6 (Dense) (None, 1) 9
=================================================================
Total params: 221
Trainable params: 221
Non-trainable params: 0
_________________________________________________________________
accuracy: 75.52%
Saved model to disk
加载模型并评估以进行验证：
# load and evaluate a saved model
from numpy import loadtxt
from keras.models import load_model
# load model
model = load_model('model.h5')
# summarize model.
model.summary()
# load dataset
dataset = loadtxt("pima-indians-diabetes.csv", delimiter=",")
# split into input (X) and output (Y) variables
X = dataset[:,0:8]
Y = dataset[:,8]
# evaluate the model
score = model.evaluate(X, Y, verbose=0)
print("%s: %.2f%%" % (model.metrics_names[1], score[1]*100))
Output:
Model: "sequential_2"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
dense_4 (Dense) (None, 12) 108
_________________________________________________________________
dense_5 (Dense) (None, 8) 104
_________________________________________________________________
dense_6 (Dense) (None, 1) 9
=================================================================
Total params: 221
Trainable params: 221
Non-trainable params: 0
_________________________________________________________________
accuracy: 75.52%