ValueError:加载先前保存的模型时，轴与数组不匹配 [英] ValueError: axes don't match array when loading previously saved model
本文介绍了ValueError:加载先前保存的模型时，轴与数组不匹配的处理方法，对大家解决问题具有一定的参考价值，需要的朋友们下面随着小编来一起学习吧！
问题描述
系统信息
- 我是否编写了自定义代码(与使用示例目录相对):是
- OS平台和发行版(例如Linux Ubuntu 16.04):Linux Ubuntu 16.04
- TensorFlow后端(是/否):是
- TensorFlow版本:1.14.0
- Keras版本:2.2.5
- Python版本:3.6.8
- CUDA/cuDNN版本:N/A
- GPU模型和内存:N/A
描述当前行为:
- 使用
model = load_model(file.h5)加载模型时出错
- Error when loading model with
model = load_model(file.h5)
ValueError: axes don't match array
描述预期的行为
- 使用
model.save(file.h5)保存后，模型不会再次加载
- Model is not loading again after saving with
model.save(file.h5)
这就是我想要做的:
- 我有15个模型作为单个
merged_model，基本上只是具有15个输入和15个输出的分类模型. - 我正在尝试将这15个模型的输入合并为一个输入模型.因此，我不必提供15个输入！
- 这就是我的操作方式: (这没有任何问题)
- I have 15 models as a single
merged_modelwhich are basically just the classification models with 15 inputs and 15 outputs. - I'm trying to combine the these 15 model's input into a single input model. So I don't have to provide 15 inputs!
- Here is how I'm doing that: (this is working without any issue)
>> model_single_input = layers.Input((15,), dtype='int32', name='single.input')
>> model_multiple_inputs = layers.Lambda(lambda x: [x] * 15, name='single.input.multiplier')(model_single_input)
>> single_input_model = Model(inputs=model_single_input, outputs=model_multiple_inputs)
>> single_input_model.input, single_input_model.output
(<tf.Tensor 'single.input:0' shape=(?, 15) dtype=int32>,
[<tf.Tensor 'single.input.multiplier/Identity:0' shape=(?, 15) dtype=int32>,
<tf.Tensor 'single.input.multiplier/Identity_1:0' shape=(?, 15) dtype=int32>,
<tf.Tensor 'single.input.multiplier/Identity_2:0' shape=(?, 15) dtype=int32>,
<tf.Tensor 'single.input.multiplier/Identity_3:0' shape=(?, 15) dtype=int32>,
<tf.Tensor 'single.input.multiplier/Identity_4:0' shape=(?, 15) dtype=int32>,
<tf.Tensor 'single.input.multiplier/Identity_5:0' shape=(?, 15) dtype=int32>,
<tf.Tensor 'single.input.multiplier/Identity_6:0' shape=(?, 15) dtype=int32>,
<tf.Tensor 'single.input.multiplier/Identity_7:0' shape=(?, 15) dtype=int32>,
<tf.Tensor 'single.input.multiplier/Identity_8:0' shape=(?, 15) dtype=int32>,
<tf.Tensor 'single.input.multiplier/Identity_9:0' shape=(?, 15) dtype=int32>,
<tf.Tensor 'single.input.multiplier/Identity_10:0' shape=(?, 15) dtype=int32>,
<tf.Tensor 'single.input.multiplier/Identity_11:0' shape=(?, 15) dtype=int32>,
<tf.Tensor 'single.input.multiplier/Identity_12:0' shape=(?, 15) dtype=int32>,
<tf.Tensor 'single.input.multiplier/Identity_13:0' shape=(?, 15) dtype=int32>,
<tf.Tensor 'single.input.multiplier/Identity_14:0' shape=(?, 15) dtype=int32>])
- 现在这是我将单个输入模型与15个模型组合在一起的方法. (这没有任何问题)
>> single_input_merged_output_model = Model(inputs = single_input_model.input, outputs = merged_model(single_input_model.output))
>> encoded_data = np.array([
[12073, 14512, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[336, 0, 744, 481, 13043, 118, 2563, 0, 0, 0, 0, 0, 0, 0, 0]
])
>> predictions = single_input_merged_output_model.predict(encoded_data)
>> predictions
[array([[ 0. , 18. , 0.23679169],
[ 0. , 13. , 0.5127094 ]], dtype=float32),
array([[1.0000000e+00, 2.0700000e+02, 4.9950428e-02],
[1.0000000e+00, 9.2000000e+01, 3.4491304e-01]], dtype=float32),
array([[ 2. , 229. , 0.9984485],
[ 4. , 60. , 0.9372796]], dtype=float32),
array([[2.000000e+00, 1.194000e+03, 9.985555e-01],
[3.000000e+00, 1.030000e+02, 9.584518e-01]], dtype=float32),
array([[2.000000e+00, 1.558000e+03, 9.996946e-01],
[3.000000e+00, 8.800000e+01, 9.738545e-01]], dtype=float32),
array([[2.000000e+00, 1.997000e+03, 9.998343e-01],
[7.000000e+00, 7.020000e+02, 9.954461e-01]], dtype=float32),
array([[2.0000000e+00, 1.7690000e+03, 9.9997449e-01],
[3.0000000e+00, 1.7900000e+02, 9.9776447e-01]], dtype=float32),
array([[2.000000e+00, 1.448000e+03, 9.999393e-01],
[3.000000e+00, 2.430000e+02, 9.982481e-01]], dtype=float32),
array([[2.0000000e+00, 1.0770000e+03, 9.9984264e-01],
[3.0000000e+00, 2.0700000e+02, 9.9882430e-01]], dtype=float32),
array([[ 2. , 754. , 0.9998847 ],
[ 3. , 493. , 0.99971205]], dtype=float32),
array([[ 2. , 536. , 0.9996455],
[ 3. , 239. , 0.9998828]], dtype=float32),
array([[ 2. , 444. , 0.99973446],
[ 3. , 98. , 0.99974567]], dtype=float32),
array([[8.0000000e+00, 1.0400000e+02, 1.3962857e-01],
[2.0000000e+00, 2.3600000e+02, 7.3362941e-01]], dtype=float32),
array([[ 2. , 34. , 0.06541887],
[ 2. , 46. , 0.3399737 ]], dtype=float32),
array([[ 2. , 52. , 0.24562976],
[ 2. , 7. , 0.5339988 ]], dtype=float32)]
- 这是我保存最终模型的方式. (这没有任何问题)
>> single_input_merged_output_model.save('file.h5', include_optimizer=False)
- ...这是我尝试加载刚刚保存的上述模型时的问题！
>> single_input_merged_output_model = load_model('file.h5', compile=False)
- 错误:
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<timed exec> in <module>
~/anaconda3/lib/python3.6/site-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
~/anaconda3/lib/python3.6/site-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):
~/anaconda3/lib/python3.6/site-packages/keras/engine/saving.py in _deserialize_model(h5dict, custom_objects, compile)
290 original_keras_version,
291 original_backend,
--> 292 reshape=False)
293 if len(weight_values) != len(symbolic_weights):
294 raise ValueError('Layer #' + str(k) +
~/anaconda3/lib/python3.6/site-packages/keras/engine/saving.py in preprocess_weights_for_loading(layer, weights, original_keras_version, original_backend, reshape)
821 weights = convert_nested_time_distributed(weights)
822 elif layer.__class__.__name__ in ['Model', 'Sequential']:
--> 823 weights = convert_nested_model(weights)
824
825 if original_keras_version == '1':
~/anaconda3/lib/python3.6/site-packages/keras/engine/saving.py in convert_nested_model(weights)
809 weights=weights[:num_weights],
810 original_keras_version=original_keras_version,
--> 811 original_backend=original_backend))
812 weights = weights[num_weights:]
813 return new_weights
~/anaconda3/lib/python3.6/site-packages/keras/engine/saving.py in preprocess_weights_for_loading(layer, weights, original_keras_version, original_backend, reshape)
821 weights = convert_nested_time_distributed(weights)
822 elif layer.__class__.__name__ in ['Model', 'Sequential']:
--> 823 weights = convert_nested_model(weights)
824
825 if original_keras_version == '1':
~/anaconda3/lib/python3.6/site-packages/keras/engine/saving.py in convert_nested_model(weights)
797 weights=weights[:num_weights],
798 original_keras_version=original_keras_version,
--> 799 original_backend=original_backend))
800 weights = weights[num_weights:]
801
~/anaconda3/lib/python3.6/site-packages/keras/engine/saving.py in preprocess_weights_for_loading(layer, weights, original_keras_version, original_backend, reshape)
940 weights[0] = np.reshape(weights[0], layer_weights_shape)
941 elif layer_weights_shape != weights[0].shape:
--> 942 weights[0] = np.transpose(weights[0], (3, 2, 0, 1))
943 if layer.__class__.__name__ == 'ConvLSTM2D':
944 weights[1] = np.transpose(weights[1], (3, 2, 0, 1))
~/anaconda3/lib/python3.6/site-packages/numpy/core/fromnumeric.py in transpose(a, axes)
637
638 """
--> 639 return _wrapfunc(a, 'transpose', axes)
640
641
~/anaconda3/lib/python3.6/site-packages/numpy/core/fromnumeric.py in _wrapfunc(obj, method, *args, **kwds)
54 def _wrapfunc(obj, method, *args, **kwds):
55 try:
---> 56 return getattr(obj, method)(*args, **kwds)
57
58 # An AttributeError occurs if the object does not have
ValueError: axes don't match array
我已经尝试过的事情:
- 分别保存模型架构和权重并加载(不起作用)
关于加载模型有什么建议吗?
Any suggestions on loading model?
推荐答案
解决了！如果在 model.save()之前冻结模型层的权重，然后保存模型； load_model()可以正常工作！仅当您不想进一步重新训练模型时，此方法才有效.
Solved it! If you freeze weights of the model's layers before model.save(), and then save the model; load_model() works without any issue! This only works when you don't want to re-train your model further.
from keras.models import Model
def freeze_layers(model):
for i in model.layers:
i.trainable = False
if isinstance(i, Model):
freeze_layers(i)
return model
>> model_freezed = freeze_layers(model)
>> model_freezed.save('file.tf')
# refresh the notebook
from keras.models import load_model
>> model = load_model('file.tf', compile=False)
这篇关于ValueError:加载先前保存的模型时，轴与数组不匹配的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！
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