包含EfficientNet子模型的Keras模型不能load_weights；VGG工作良好
包含EfficientNet子模型的Keras模型不能load_weights；VGG工作良好
提问于 2020-04-29 03:13:58
我有一个ubermodel，它使用一个子模型作为一个层进行特征提取。我的代码是模块化的，因此我可以很容易地切换用于执行特征提取的子模型，只需更改我分配的任何子模型：
...
elif FEATURE_EXTRACTOR == "VGG16":
Features = keras.applications.VGG16(
weights = "imagenet",
pooling = FEATURE_POOLING,
include_top = False
)
elif FEATURE_EXTRACTOR == "EfficientNetB0":
Features = keras_applications_master.keras_applications.efficientnet.EfficientNetB0(
# ^ Local copy of official keras repo: https://github.com/keras-team/keras-applications
# because pip install --upgrade keras doesn't install version with efficientnet.
weights = "imagenet",
include_top = False,
pooling = FEATURE_POOLING,
classes = None
)
...我用于保存和加载ubermodel及其权重的例程还知道正在使用哪个子模型进行特征提取：
model.load_weights(submodel_specific_path)使用任何子模型，我都可以执行初始训练运行，并将ubermodel保存到磁盘。如果我尝试继续训练或微调包含VGG16子模型的任何ubermodel，使用load_weights加载权重，一切都很好。但是，当我使用一个效率网子模型(或者，例如，load_weights )对任何一个ubermodel进行load_weights时，我会得到以下错误：
Traceback (most recent call last):
File "image_model.py", line 284, in <module>
model.load_weights(model_path)
File "C:\Users\Username\Anaconda3\envs\tensorflow1\lib\site-packages\keras\engine\saving.py", line 492, in load_wrapper
return load_function(*args, **kwargs)
File "C:\Users\Username\Anaconda3\envs\tensorflow1\lib\site-packages\keras\engine\network.py", line 1227, in load_weights
reshape=reshape)
File "C:\Users\Username\Anaconda3\envs\tensorflow1\lib\site-packages\keras\engine\saving.py", line 1294, in load_weights_from_hdf5_group_by_name
reshape=reshape)
File "C:\Users\Username\Anaconda3\envs\tensorflow1\lib\site-packages\keras\engine\saving.py", line 861, in preprocess_weights_for_loading
weights = convert_nested_model(weights)
File "C:\Users\Username\Anaconda3\envs\tensorflow1\lib\site-packages\keras\engine\saving.py", line 836, in convert_nested_model
original_backend=original_backend))
File "C:\Users\Username\Anaconda3\envs\tensorflow1\lib\site-packages\keras\engine\saving.py", line 980, in preprocess_weights_for_loading
weights[0] = np.transpose(weights[0], (3, 2, 0, 1))
File "<__array_function__ internals>", line 6, in transpose
File "C:\Users\Username\Anaconda3\envs\tensorflow1\lib\site-packages\numpy\core\fromnumeric.py", line 651, in transpose
return _wrapfunc(a, 'transpose', axes)
File "C:\Users\Username\Anaconda3\envs\tensorflow1\lib\site-packages\numpy\core\fromnumeric.py", line 61, in _wrapfunc
return bound(*args, **kwds)
ValueError: axes don't match array我做错了什么？
回答 1
Stack Overflow用户
发布于 2020-05-12 10:01:54
我无法重新创建您的问题，但我找到了这个Github问题，也就是说，这个问题发生在我们使用多GPU模型时。所以诀窍应该是：
multi_gpu_model = multi_gpu_model(model, gpus=G)
model.save_model(...)而不是
multi_gpu_model = multi_gpu_model(model, gpus=G)
multi_gpu_model.save_model(...)尽管如此，我成功地创建了EfficientNetB0模型，评估了模型，保存了模型，并最终加载了模型。
使用 -构建、评估和保存模型的代码
%tensorflow_version 1.x
import keras
from keras.models import Model
from keras.layers import GlobalAveragePooling2D, Dense, Flatten
from keras_efficientnets import EfficientNetB0
model = EfficientNetB0(input_shape=(224, 224, 3), classes=1000, include_top=False, weights='imagenet')
x = model.output
x = GlobalAveragePooling2D()(x)
x = Dense(20, activation='relu')(x)
x = Dense(17, activation='softmax')(x)
model = Model(inputs = model.input, outputs = x)
# summarize model
#model.summary()
# (2) Get Data
import tflearn.datasets.oxflower17 as oxflower17
x, y = oxflower17.load_data(one_hot=True)
# (4) Compile
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
model.fit(x, y, batch_size=64, epochs= 1, verbose=1, validation_split=0.2, shuffle=True)
# 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.h5py")
print("Saved model to disk")输出-
TensorFlow 1.x selected.
Using TensorFlow backend.
WARNING:tensorflow:From /tensorflow-1.15.2/python3.6/tensorflow_core/python/ops/resource_variable_ops.py:1630: calling BaseResourceVariable.__init__ (from tensorflow.python.ops.resource_variable_ops) with constraint is deprecated and will be removed in a future version.
Instructions for updating:
If using Keras pass *_constraint arguments to layers.
WARNING:tensorflow:From /tensorflow-1.15.2/python3.6/tflearn/helpers/summarizer.py:9: The name tf.summary.merge is deprecated. Please use tf.compat.v1.summary.merge instead.
WARNING:tensorflow:From /tensorflow-1.15.2/python3.6/tflearn/helpers/trainer.py:25: The name tf.summary.FileWriter is deprecated. Please use tf.compat.v1.summary.FileWriter instead.
WARNING:tensorflow:From /tensorflow-1.15.2/python3.6/tflearn/collections.py:13: The name tf.GraphKeys is deprecated. Please use tf.compat.v1.GraphKeys instead.
WARNING:tensorflow:From /tensorflow-1.15.2/python3.6/tflearn/config.py:123: The name tf.get_collection is deprecated. Please use tf.compat.v1.get_collection instead.
WARNING:tensorflow:From /tensorflow-1.15.2/python3.6/tflearn/config.py:129: The name tf.add_to_collection is deprecated. Please use tf.compat.v1.add_to_collection instead.
WARNING:tensorflow:From /tensorflow-1.15.2/python3.6/tflearn/config.py:131: The name tf.assign is deprecated. Please use tf.compat.v1.assign instead.
Downloading Oxford 17 category Flower Dataset, Please wait...
100.0% 60276736 / 60270631
('Succesfully downloaded', '17flowers.tgz', 60270631, 'bytes.')
File Extracted
Starting to parse images...
Parsing Done!
WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:422: The name tf.global_variables is deprecated. Please use tf.compat.v1.global_variables instead.
WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:431: The name tf.is_variable_initialized is deprecated. Please use tf.compat.v1.is_variable_initialized instead.
WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:438: The name tf.variables_initializer is deprecated. Please use tf.compat.v1.variables_initializer instead.
Train on 1088 samples, validate on 272 samples
Epoch 1/1
1088/1088 [==============================] - 203s 187ms/step - loss: 1.6433 - accuracy: 0.5561 - val_loss: 1.9315 - val_accuracy: 0.5074
accuracy: 54.85%使用load_model和加载模型
%tensorflow_version 1.x
# load and evaluate a saved model
from numpy import loadtxt
from keras.models import load_model
# load model
model = load_model('model.h5py')
# summarize model
#model.summary()
# (2) Get Data
import tflearn.datasets.oxflower17 as oxflower17
x, y = oxflower17.load_data(one_hot=True)
# evaluate the model
score = model.evaluate(x, y, verbose=0)
print("%s: %.2f%%" % (model.metrics_names[1], score[1]*100))输出-保存前和加载后模型的精度应该与此匹配。
accuracy: 54.85%使用model.save_weights保存模型，并使用model.load_weights -加载模型
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")
# evaluate loaded model on test data
loaded_model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
score = loaded_model.evaluate(x, y, verbose=0)
print("%s: %.2f%%" % (loaded_model.metrics_names[1], score[1]*100))输出-
Saved model to disk
Loaded model from disk
accuracy: 54.85%希望这能回答你的问题。学习愉快。
页面原文内容由Stack Overflow提供。腾讯云小微IT领域专用引擎提供翻译支持
原文链接：
https://stackoverflow.com/questions/61493605
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