我正在尝试按照本教程创建一个暹罗网络https://pyimagesearch.com/2020/11/30/siamese-networks-with-keras-tensorflow-and-deep-learning/
我唯一修改的是
config.py(tf-23) D:\Code\burgle\ml>python train_siamese_network.py
2.3.0
[]
[INFO] loading MNIST dataset...
[INFO] preparing positive and negative pairs...
[INFO] building siamese network...
2023-04-23 04:58:24.093663: I tensorflow/core/platform/cpu_feature_guard.cc:142] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN)to use the following CPU instructions in performance-critical operations: AVX AVX2
To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
[INFO] compiling model...
[INFO] training model...
Epoch 1/10
1875/1875 [==============================] - 78s 41ms/step - loss: 0.6933 - accuracy: 0.4952 - val_loss: 0.6933 - val_accuracy: 0.4991
Epoch 2/10
1875/1875 [==============================] - 76s 41ms/step - loss: 0.6932 - accuracy: 0.4999 - val_loss: 0.6932 - val_accuracy: 0.4990
Epoch 3/10
1875/1875 [==============================] - 77s 41ms/step - loss: 0.6932 - accuracy: 0.4995 - val_loss: 0.6933 - val_accuracy: 0.4816
Epoch 4/10
1875/1875 [==============================] - 75s 40ms/step - loss: 0.6932 - accuracy: 0.4982 - val_loss: 0.6933 - val_accuracy: 0.4845
Epoch 5/10
1875/1875 [==============================] - 75s 40ms/step - loss: 0.6932 - accuracy: 0.4990 - val_loss: 0.6932 - val_accuracy: 0.4978
Epoch 6/10
1875/1875 [==============================] - 76s 40ms/step - loss: 0.6932 - accuracy: 0.4999 - val_loss: 0.6933 - val_accuracy: 0.5000
Epoch 7/10
1875/1875 [==============================] - 77s 41ms/step - loss: 0.6932 - accuracy: 0.4996 - val_loss: 0.6932 - val_accuracy: 0.5000
Epoch 8/10
1875/1875 [==============================] - 77s 41ms/step - loss: 0.6932 - accuracy: 0.4983 - val_loss: 0.6933 - val_accuracy: 0.4997
Epoch 9/10
1875/1875 [==============================] - 77s 41ms/step - loss: 0.6932 - accuracy: 0.4976 - val_loss: 0.6933 - val_accuracy: 0.5000
Epoch 10/10
1875/1875 [==============================] - 77s 41ms/step - loss: 0.6932 - accuracy: 0.4983 - val_loss: 0.6932 - val_accuracy: 0.5000
[INFO] saving siamese model...
WARNING:tensorflow:From C:\Users\heihachi\anaconda3\envs\tf-23\lib\site-packages\tensorflow\python\training\tracking\tracking.py:111: Model.state_updates (from tensorflow.python.keras.engine.training) is deprecated and will be removed in a future version.
Instructions for updating:
This property should not be used in TensorFlow 2.0, as updates are applied automatically.
2023-04-23 05:11:11.403233: W tensorflow/python/util/util.cc:348] Sets are not currently considered sequences, but this may change in the future, so consider avoiding using them.
WARNING:tensorflow:From C:\Users\heihachi\anaconda3\envs\tf-23\lib\site-packages\tensorflow\python\training\tracking\tracking.py:111: Layer.updates (from tensorflow.python.keras.engine.base_layer) is deprecated and will be removed in a future version.
Instructions for updating:
This property should not be used in TensorFlow 2.0, as updates are applied automatically.
[INFO] plotting training history...
(tf-23) D:\Code\burgle\ml>python test_siamese_network.py --input examples
2.3.0
[INFO] loading test dataset...
[INFO] loading siamese model...
2023-04-23 15:27:34.627626: I tensorflow/core/platform/cpu_feature_guard.cc:142] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN)to use the following CPU instructions in performance-critical operations: AVX AVX2
To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
XXX lineno: 43, opcode: 47
Traceback (most recent call last):
File "test_siamese_network.py", line 27, in <module>
model = load_model(config.MODEL_PATH)
File "C:\Users\heihachi\anaconda3\envs\tf-23\lib\site-packages\tensorflow\python\keras\saving\save.py", line 187, in load_model
return saved_model_load.load(filepath, compile, options)
File "C:\Users\heihachi\anaconda3\envs\tf-23\lib\site-packages\tensorflow\python\keras\saving\saved_model\load.py", line 120, in load
model = tf_load.load_internal(
File "C:\Users\heihachi\anaconda3\envs\tf-23\lib\site-packages\tensorflow\python\saved_model\load.py", line 632, in load_internal
loader = loader_cls(object_graph_proto, saved_model_proto, export_dir,
File "C:\Users\heihachi\anaconda3\envs\tf-23\lib\site-packages\tensorflow\python\keras\saving\saved_model\load.py", line 194, in __init__
super(KerasObjectLoader, self).__init__(*args, **kwargs)
File "C:\Users\heihachi\anaconda3\envs\tf-23\lib\site-packages\tensorflow\python\saved_model\load.py", line 130, in __init__
self._load_all()
File "C:\Users\heihachi\anaconda3\envs\tf-23\lib\site-packages\tensorflow\python\keras\saving\saved_model\load.py", line 221, in _load_all
self._finalize_objects()
File "C:\Users\heihachi\anaconda3\envs\tf-23\lib\site-packages\tensorflow\python\keras\saving\saved_model\load.py", line 530, in _finalize_objects
self._reconstruct_all_models()
File "C:\Users\heihachi\anaconda3\envs\tf-23\lib\site-packages\tensorflow\python\keras\saving\saved_model\load.py", line 548, in _reconstruct_all_models
self._reconstruct_model(model_id, model, layers)
File "C:\Users\heihachi\anaconda3\envs\tf-23\lib\site-packages\tensorflow\python\keras\saving\saved_model\load.py", line 588, in _reconstruct_model
created_layers) = functional_lib.reconstruct_from_config(
File "C:\Users\heihachi\anaconda3\envs\tf-23\lib\site-packages\tensorflow\python\keras\engine\functional.py", line 1214, in reconstruct_from_config
process_node(layer, node_data)
File "C:\Users\heihachi\anaconda3\envs\tf-23\lib\site-packages\tensorflow\python\keras\engine\functional.py", line 1162, in process_node
output_tensors = layer(input_tensors, **kwargs)
File "C:\Users\heihachi\anaconda3\envs\tf-23\lib\site-packages\tensorflow\python\keras\engine\base_layer.py", line 925, in __call__
return self._functional_construction_call(inputs, args, kwargs,
File "C:\Users\heihachi\anaconda3\envs\tf-23\lib\site-packages\tensorflow\python\keras\engine\base_layer.py", line 1117, in _functional_construction_call
outputs = call_fn(cast_inputs, *args, **kwargs)
File "C:\Users\heihachi\anaconda3\envs\tf-23\lib\site-packages\tensorflow\python\keras\layers\core.py", line 903, in call
result = self.function(inputs, **kwargs)
File "D:/Code/burgle/ml/pyimagesearch/utils.py", line 43, in euclidean_distance
(featsA, featsB) = vectors
SystemError: unknown opcode
我按照教程作者的建议使用 TF 2.3 和 Python3.8。这两个脚本都在同一环境中运行。以下是我的输出
conda list(tf-23) D:\Code\burgle\ml>conda list
# packages in environment at C:\Users\heihachi\anaconda3\envs\tf-23:
#
# Name Version Build Channel
_tflow_select 2.3.0 eigen
abseil-cpp 20211102.0 hd77b12b_0
absl-py 1.3.0 py38haa95532_0
aiohttp 3.8.3 py38h2bbff1b_0
aiosignal 1.2.0 pyhd3eb1b0_0
appdirs 1.4.4 pyhd3eb1b0_0
astor 0.8.1 py38haa95532_0
astunparse 1.6.3 py_0
async-timeout 4.0.2 py38haa95532_0
attrs 22.1.0 py38haa95532_0
blas 1.0 mkl
blinker 1.4 py38haa95532_0
brotli 1.0.9 h2bbff1b_7
brotli-bin 1.0.9 h2bbff1b_7
brotlipy 0.7.0 py38h2bbff1b_1003
c-ares 1.19.0 h2bbff1b_0
ca-certificates 2022.12.7 h5b45459_0 conda-forge
cachetools 4.2.2 pyhd3eb1b0_0
certifi 2022.12.7 pyhd8ed1ab_0 conda-forge
cffi 1.15.1 py38h2bbff1b_3
charset-normalizer 2.0.4 pyhd3eb1b0_0
click 8.0.4 py38haa95532_0
colorama 0.4.6 py38haa95532_0
contourpy 1.0.5 py38h59b6b97_0
cryptography 39.0.1 py38h21b164f_0
cycler 0.11.0 pyhd3eb1b0_0
eigen 3.4.0 h2d74725_0 conda-forge
fonttools 4.25.0 pyhd3eb1b0_0
freetype 2.12.1 ha860e81_0
frozenlist 1.3.3 py38h2bbff1b_0
gast 0.4.0 pyhd3eb1b0_0
giflib 5.2.1 h8cc25b3_3
glib 2.69.1 h5dc1a3c_2
google-auth 2.6.0 pyhd3eb1b0_0
google-auth-oauthlib 0.4.4 pyhd3eb1b0_0
google-pasta 0.2.0 pyhd3eb1b0_0
grpc-cpp 1.48.2 hf108199_0
grpcio 1.48.2 py38hf108199_0
gst-plugins-base 1.18.5 h9e645db_0
gstreamer 1.18.5 hd78058f_0
h5py 2.10.0 py38h5e291fa_0
hdf5 1.10.4 h7ebc959_0
icc_rt 2022.1.0 h6049295_2
icu 58.2 ha925a31_3
idna 3.4 py38haa95532_0
importlib-metadata 6.0.0 py38haa95532_0
importlib_resources 5.2.0 pyhd3eb1b0_1
imutils 0.5.4 py38haa244fe_3 conda-forge
intel-openmp 2021.4.0 haa95532_3556
jpeg 9e h2bbff1b_1
keras-applications 1.0.8 py_1
keras-preprocessing 1.1.2 pyhd3eb1b0_0
kiwisolver 1.4.4 py38hd77b12b_0
krb5 1.19.4 h5b6d351_0
lerc 3.0 hd77b12b_0
libbrotlicommon 1.0.9 h2bbff1b_7
libbrotlidec 1.0.9 h2bbff1b_7
libbrotlienc 1.0.9 h2bbff1b_7
libclang 14.0.6 default_hb5a9fac_1
libclang13 14.0.6 default_h8e68704_1
libdeflate 1.17 h2bbff1b_0
libffi 3.4.2 hd77b12b_6
libiconv 1.16 h2bbff1b_2
libogg 1.3.5 h2bbff1b_1
libpng 1.6.39 h8cc25b3_0
libprotobuf 3.20.3 h23ce68f_0
libtiff 4.5.0 h6c2663c_2
libvorbis 1.3.7 he774522_0
libwebp 1.2.4 hbc33d0d_1
libwebp-base 1.2.4 h2bbff1b_1
libxml2 2.10.3 h0ad7f3c_0
libxslt 1.1.37 h2bbff1b_0
lz4-c 1.9.4 h2bbff1b_0
markdown 3.4.1 py38haa95532_0
markupsafe 2.1.1 py38h2bbff1b_0
matplotlib 3.7.1 py38haa244fe_0 conda-forge
matplotlib-base 3.7.1 py38hf11a4ad_1
mkl 2021.4.0 haa95532_640
mkl-service 2.4.0 py38h2bbff1b_0
mkl_fft 1.3.1 py38h277e83a_0
mkl_random 1.2.2 py38hf11a4ad_0
multidict 6.0.2 py38h2bbff1b_0
munkres 1.1.4 py_0
numpy 1.23.5 py38h3b20f71_0
numpy-base 1.23.5 py38h4da318b_0
oauthlib 3.2.2 py38haa95532_0
opencv 4.6.0 py38h104de81_2
openssl 1.1.1t h2bbff1b_0
opt_einsum 3.3.0 pyhd3eb1b0_1
packaging 23.0 py38haa95532_0
pcre 8.45 hd77b12b_0
pillow 9.5.0 pypi_0 pypi
pip 23.0.1 py38haa95532_0
ply 3.11 py38_0
pooch 1.4.0 pyhd3eb1b0_0
protobuf 3.20.3 py38hd77b12b_0
pyasn1 0.4.8 pyhd3eb1b0_0
pyasn1-modules 0.2.8 py_0
pycparser 2.21 pyhd3eb1b0_0
pyjwt 2.4.0 py38haa95532_0
pyopenssl 23.0.0 py38haa95532_0
pyparsing 3.0.9 py38haa95532_0
pyqt 5.15.7 py38hd77b12b_0
pyqt5-sip 12.11.0 py38hd77b12b_0
pyreadline 2.1 py38_1
pysocks 1.7.1 py38haa95532_0
python 3.8.16 h6244533_3
python-dateutil 2.8.2 pyhd3eb1b0_0
python_abi 3.8 2_cp38 conda-forge
qt-main 5.15.2 he8e5bd7_8
qt-webengine 5.15.9 hb9a9bb5_5
qtwebkit 5.212 h2bbfb41_5
re2 2022.04.01 hd77b12b_0
requests 2.28.1 py38haa95532_1
requests-oauthlib 1.3.0 py_0
rsa 4.7.2 pyhd3eb1b0_1
scipy 1.10.1 py38h321e85e_0
setuptools 66.0.0 py38haa95532_0
sip 6.6.2 py38hd77b12b_0
six 1.16.0 pyhd3eb1b0_1
sqlite 3.41.2 h2bbff1b_0
tensorboard 2.10.0 py38haa95532_0
tensorboard-data-server 0.6.1 py38haa95532_0
tensorboard-plugin-wit 1.8.1 py38haa95532_0
tensorflow 2.3.0 mkl_py38h8c0d9a2_0
tensorflow-base 2.3.0 eigen_py38h75a453f_0
tensorflow-estimator 2.6.0 pyh7b7c402_0
termcolor 2.1.0 py38haa95532_0
tk 8.6.12 h2bbff1b_0
toml 0.10.2 pyhd3eb1b0_0
tornado 6.2 py38h2bbff1b_0
urllib3 1.26.15 py38haa95532_0
vc 14.2 h21ff451_1
vs2015_runtime 14.27.29016 h5e58377_2
werkzeug 2.2.3 py38haa95532_0
wheel 0.38.4 py38haa95532_0
win_inet_pton 1.1.0 py38haa95532_0
wrapt 1.14.1 py38h2bbff1b_0
xz 5.2.10 h8cc25b3_1
yarl 1.8.1 py38h2bbff1b_0
zipp 3.11.0 py38haa95532_0
zlib 1.2.13 h8cc25b3_0
zstd 1.5.5 hd43e919_0
问题出在哪里？我发现的有关此错误的所有内容都与 python 版本中的问题有关。我也尝试过使用最新的 TF 版本，但是，附加信息也会出现相同的错误，如下所示：
XXX lineno: 43, opcode: 47
Traceback (most recent call last):
File "D:\Code\burgle\ml\test_siamese_network.py", line 27, in <module>
model = load_model(config.MODEL_PATH)
File "C:\Users\heihachi\anaconda3\envs\tf\lib\site-packages\keras\utils\traceback_utils.py", line 70, in error_handler
raise e.with_traceback(filtered_tb) from None
File "D:/Code/burgle/ml/pyimagesearch/utils.py", line 43, in euclidean_distance
(featsA, featsB) = vectors
SystemError: Exception encountered when calling layer "lambda" (type Lambda).
unknown opcode
Call arguments received by layer "lambda" (type Lambda):
• inputs=['tf.Tensor(shape=(None, 48), dtype=float32)', 'tf.Tensor(shape=(None, 48), dtype=float32)']
• mask=None
• training=False
这个 github 问题（https://github.com/tensorflow/tensorflow/issues/18660）可能相关吗？
我在使用
tensorflow 2.4.0Lambda层出了问题，文档甚至在使用它们时警告（反）序列化“限制”，似乎子类化和创建自己的层可以解决问题。 对我有用的是在训练后保存网络的权重。然后，加载回（实例化）网络架构并加载原始权重，然后进行预测，如下所示：
# Creating the DNN:
def createModel():
model = tf.keras.Sequential([
keras.layers.Dense(512, activation='relu', input_shape=(784,)),
keras.layers.Dropout(0.2),
keras.layers.Dense(10)
])
model.compile(optimizer='adam',
loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
metrics=[tf.keras.metrics.SparseCategoricalAccuracy()])
return model
# Create the model:
model = createModel()
# Get summary:
model.summary()
# After training...
# Save the network's weights:
model.save_weights("modelWeights.h5")
# Later, possibly in another script:
model2 = createModel()
# Load the original weights:
model2.load_weights("modelWeights.h5")
# Get summary:
model2.summary()
# Predict:
predictions = model2.predict(testX)
总之，只保存权重，创建一个新的模型对象实例化网络的原始架构，不进行训练，只需将保存的权重加载到其中，然后进行预测。更新：2023年9月24日
我设法通过对图层类进行子分类来解决最初的问题，本质上是编写一个
EuclideanDistance层来计算两个张量之间的距离。实现很简单，这是一个名为
EuclideanDistance.py的新文件
# Imports:
import tensorflow as tf
from tensorflow.keras.layers import Layer
class EuclideanDistance(Layer):
def __init__(self, **kwargs):
super().__init__(**kwargs)
def call(self, inputs):
# Unpack the vectors into separate lists
featsA, featsB = inputs
# Compute the sum of squared distances between the vectors
sumSquared = tf.keras.backend.sum(tf.keras.backend.square(featsA - featsB), axis=1, keepdims=True)
# Return the euclidean distance between the vectors
return tf.keras.backend.sqrt(tf.keras.backend.maximum(sumSquared, tf.keras.backend.epsilon()))
def get_config(self):
config = super().get_config()
return config
您可以像这样实例化图层。只需传递两个张量的列表即可：
from EuclideanDistance import EuclideanDistance
distance = EuclideanDistance()([image1Embeddings, image2Embeddings])
然后，要保存模型，您需要包含
dict自定义对象，以便可以正确引用和加载它们：
# Load the model. Possibly in another file:
from tensorflow.keras.models import load_model
from EuclideanDistance import EuclideanDistance
# Include the custom objects dict:
model = load_model(modelPath, custom_objects={"EuclideanDistance": EuclideanDistance})