엥.. 예전에 본거 같은데 찾다보니 지원 안하는 모델인가..
회사꺼 T24000M 인데 지원안함.. 포트트렁킹만 보임
| fc san (0) | 2025.03.08 |
|---|---|
| modinfo bnx2x (BCM957810) (0) | 2025.02.26 |
| 랜카드 sr-iov 설정 (0) | 2025.02.26 |
| ubuntu 네트워크 연결되지 않음 40초 간격 (0) | 2025.02.26 |
| BCM957810A1008G 히트싱크 분해 (0) | 2025.02.24 |
tflite 에서 weight만 빼서 거기서 부터 학습 시키면 그게 fine tune / transfer learning(전이학습)이 되는건가?
| The conversion from a TensorFlow SaveModel or tf.keras H5 model to .tflite is an irreversible process. Specifically, the original model topology is optimized during the compilation by the TFLite converter, which leads to some loss of information. Also, the original tf.keras model's loss and optimizer configurations are discarded, because those aren't required for inference. However, the .tflite file still contains some information that can help you restore the original trained model. Most importantly, the weight values are available, although they might be quantized, which could lead to some loss in precision. The code example below shows you how to read weight values from a .tflite file after it's created from a simple trained tf.keras.Model. import numpy as np import tensorflow as tf # First, create and train a dummy model for demonstration purposes. model = tf.keras.Sequential([ tf.keras.layers.Dense(10, input_shape=[5], activation="relu"), tf.keras.layers.Dense(1, activation="sigmoid")]) model.compile(loss="binary_crossentropy", optimizer="sgd") xs = np.ones([8, 5]) ys = np.zeros([8, 1]) model.fit(xs, ys, epochs=1) # Convert it to a TFLite model file. converter = tf.lite.TFLiteConverter.from_keras_model(model) tflite_model = converter.convert() open("converted.tflite", "wb").write(tflite_model) # Use `tf.lite.Interpreter` to load the written .tflite back from the file system. interpreter = tf.lite.Interpreter(model_path="converted.tflite") all_tensor_details = interpreter.get_tensor_details() interpreter.allocate_tensors() for tensor_item in all_tensor_details: print("Weight %s:" % tensor_item["name"]) print(interpreter.tensor(tensor_item["index"])()) |
읽어오든.. tf.keras.application.MobileNetV2 해서 만들던 weight의 유무를 제외하면 동일하게 불러오는건가 보다.
| # Create the base model from the pre-trained model MobileNet V2 IMG_SHAPE = IMG_SIZE + (3,) base_model = tf.keras.applications.MobileNetV2(input_shape=IMG_SHAPE, include_top=False, weights='imagenet') #or load your own #base_model= tf.saved_model.load("./pretrained_models/ssd_mobilenet_v2_320x320_coco17_tpu-8/saved_model") |
[링크 : https://www.aranacorp.com/en/training-a-tensorflow2-model-with-keras/]
다시 찾아보니 keras쪽 표현으로
transfer-learning은 기존의 값을 변화시키지 않고 추가 레이어에 학습시키는 것 같고
| The typical transfer-learning workflow This leads us to how a typical transfer learning workflow can be implemented in Keras: Instantiate a base model and load pre-trained weights into it. Freeze all layers in the base model by setting trainable = False. Create a new model on top of the output of one (or several) layers from the base model. Train your new model on your new dataset. Note that an alternative, more lightweight workflow could also be: Instantiate a base model and load pre-trained weights into it. Run your new dataset through it and record the output of one (or several) layers from the base model. This is called feature extraction. Use that output as input data for a new, smaller model. First, instantiate a base model with pre-trained weights. base_model = keras.applications.Xception( weights='imagenet', # Load weights pre-trained on ImageNet. input_shape=(150, 150, 3), include_top=False) # Do not include the ImageNet classifier at the top. Then, freeze the base model. base_model.trainable = False Create a new model on top. inputs = keras.Input(shape=(150, 150, 3)) # We make sure that the base_model is running in inference mode here, # by passing `training=False`. This is important for fine-tuning, as you will # learn in a few paragraphs. x = base_model(inputs, training=False) # Convert features of shape `base_model.output_shape[1:]` to vectors x = keras.layers.GlobalAveragePooling2D()(x) # A Dense classifier with a single unit (binary classification) outputs = keras.layers.Dense(1)(x) model = keras.Model(inputs, outputs) Train the model on new data. model.compile(optimizer=keras.optimizers.Adam(), loss=keras.losses.BinaryCrossentropy(from_logits=True), metrics=[keras.metrics.BinaryAccuracy()]) model.fit(new_dataset, epochs=20, callbacks=..., validation_data=...) |
fine tuning은 모델 자체의 가중치를 변경시킬수 있도록 해서, 천천히 학습 시키는 것 같다.
| Fine-tuning Once your model has converged on the new data, you can try to unfreeze all or part of the base model and retrain the whole model end-to-end with a very low learning rate. This is an optional last step that can potentially give you incremental improvements. It could also potentially lead to quick overfitting – keep that in mind. It is critical to only do this step after the model with frozen layers has been trained to convergence. If you mix randomly-initialized trainable layers with trainable layers that hold pre-trained features, the randomly-initialized layers will cause very large gradient updates during training, which will destroy your pre-trained features. It's also critical to use a very low learning rate at this stage, because you are training a much larger model than in the first round of training, on a dataset that is typically very small. As a result, you are at risk of overfitting very quickly if you apply large weight updates. Here, you only want to readapt the pretrained weights in an incremental way. This is how to implement fine-tuning of the whole base model: # Unfreeze the base model base_model.trainable = True # It's important to recompile your model after you make any changes # to the `trainable` attribute of any inner layer, so that your changes # are take into account model.compile(optimizer=keras.optimizers.Adam(1e-5), # Very low learning rate loss=keras.losses.BinaryCrossentropy(from_logits=True), metrics=[keras.metrics.BinaryAccuracy()]) # Train end-to-end. Be careful to stop before you overfit! model.fit(new_dataset, epochs=10, callbacks=..., validation_data=...) |
[링크 : https://keras.io/guides/transfer_learning/]
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[링크 : https://89douner.tistory.com/270] vgg16 기준 추가학습
| SavedModel 형식과 비교하여 H5 파일에 포함되지 않은 두 가지가 있습니다. model.add_loss() 및 model.add_metric()을 통해 추가된 외부 손실 및 메트릭은 저장되지 않습니다(SavedModel과 다름). 모델에 이러한 솔실 및 메트릭이 있고 훈련을 다시 시작하려면 모델을 로드한 후 이러한 손실을 다시 추가해야 합니다. 이는 self.add_loss() 및 self.add_metric()을 통해 레이어 내부에서 생성한 손실/메트릭에는 적용되지 않습니다. 이러한 손실 및 메트릭은 레이어가 로드되는 한 레이어의 call 메서드의 일부이기 때문에 계속 유지됩니다. 사용자 정의 레이어와 같은 사용자 정의 객체의 계산 그래프는 저장 파일에 포함되지 않습니다. 로드 시 Keras는 모델을 다시 구성하기 위해 이러한 객체의 Python 클래스/함수에 액세스해야 합니다. 사용자 정의 객체를 참고하세요. |
[링크 : https://www.tensorflow.org/guide/keras/save_and_serialize?hl=ko]
| mobilenet 학습시키기 with keras, tensorflow (0) | 2025.09.15 |
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| ssd mobilenet (0) | 2025.09.11 |
| LLM 시각화 (0) | 2025.09.06 |
| 모델 학습 (0) | 2025.09.05 |
| mean Average Precision(mAP) (0) | 2025.09.05 |
요즘은 복리 보기 힘들은데
이자가 쩝..
아무튼 이자율이 자꾸 떨어지니 3개월 예금으로는 복리처럼 써먹기 힘들 것 같긴하다
2백만원에 2.8% 라고 가정하고 1년 넣으면 세전 56000원 이자
2백만원에 3개월 마자 이자 포함 자동 재예치 하고 2.8% 1년 넣으면 세전 56590원 고작이긴 하지만 590원 더 받는다.
그런데 이자율이 요즘 하락이라
3개월뒤 더 떨어진다고 가정하면
그냥 처음 이자 그나마 높을때 묶어 둔게 유리한 상황
요즘 시대에(?) 이자가 올라갈 확율이 거의 없으니
목돈 있으면 그냥 예금으로 1년 단위로 묶는게 나을것 같긴한데
야곰야곰 늘어나는 재미도 있으니 머 ㅋㅋ
엥....?? 일반권한이랑 root 권한이랑 출력포맷이 다르네?
무슨 dd 한번 썼다고 시스템 크래시 난 줄 -_-
| $ cat nvme.log $ sudo time dd if=/dev/zero of=test bs=1G count=1 conv=fsync 1+0 records in 1+0 records out 1073741824 bytes (1.1 GB, 1.0 GiB) copied, 5.92836 s, 181 MB/s 0.00user 1.06system 0:06.00elapsed 17%CPU (0avgtext+0avgdata 1051008maxresident)k 0inputs+2097152outputs (0major+262242minor)pagefaults 0swaps |
time 자체는 하나인데 권한에 따라서 포맷이나 옵션이 다르게 작동하는 것 같다.
| $ whereis time time: /usr/bin/time /usr/share/man/man7/time.7.gz /usr/share/man/man2/time.2.gz /usr/share/man/man3/time.3am.gz /usr/share/man/man3/time.3avr.gz /usr/share/man/man1/time.1.gz /usr/share/info/time.info.gz |
| $ sudo whereis time time: /usr/bin/time /usr/share/man/man7/time.7.gz /usr/share/man/man2/time.2.gz /usr/share/man/man3/time.3am.gz /usr/share/man/man3/time.3avr.gz /usr/share/man/man1/time.1.gz /usr/share/info/time.info.gz |
| $ ls -al /usr/bin/time -rwxr-xr-x 1 root root 27160 3월 25 2022 /usr/bin/time |
일반 유저는 -v를 쓸 수 없는 것 같고
| $ time ls real 0m0.004s user 0m0.001s sys 0m0.003s |
root 에서 -p를 주면 원래 보면 포맷이긴 한데 시간 정밀도가 떨어진다. 왜지?
| $ sudo time -p ls real 0.00 user 0.00 sys 0.00 |
| $ sudo time ls 0.00user 0.00system 0:00.00elapsed 66%CPU (0avgtext+0avgdata 2432maxresident)k 0inputs+0outputs (0major+106minor)pagefaults 0swaps |
| $ sudo time -v ls Command being timed: "ls" User time (seconds): 0.00 System time (seconds): 0.00 Percent of CPU this job got: 90% Elapsed (wall clock) time (h:mm:ss or m:ss): 0:00.00 Average shared text size (kbytes): 0 Average unshared data size (kbytes): 0 Average stack size (kbytes): 0 Average total size (kbytes): 0 Maximum resident set size (kbytes): 2432 Average resident set size (kbytes): 0 Major (requiring I/O) page faults: 0 Minor (reclaiming a frame) page faults: 107 Voluntary context switches: 1 Involuntary context switches: 0 Swaps: 0 File system inputs: 0 File system outputs: 0 Socket messages sent: 0 Socket messages received: 0 Signals delivered: 0 Page size (bytes): 4096 Exit status: 0 |
도움말에 먼가 보이긴 한데..
언제부터 이렇게 바뀐겨? root로 시간 측정할 일이 없으니.. 당황했네
| $ man time FORMATTING THE OUTPUT The format string FORMAT controls the contents of the time output. The format string can be set using the `-f' or `--format', `-v' or `--verbose', or `-p' or `--portability' options. If they are not given, but the TIME environment variable is set, its value is used as the format string. Otherwise, a built-in default format is used. The default format is: %Uuser %Ssystem %Eelapsed %PCPU (%Xtext+%Ddata %Mmax)k %Iinputs+%Ooutputs (%Fmajor+%Rminor)pagefaults %Wswaps |
| proc fs smp_affinity (0) | 2025.09.19 |
|---|---|
| dd 로 덤프 하면서 바로 압축하기 (0) | 2025.07.24 |
| gpiod - gpiomon (0) | 2025.07.17 |
| scrub (0) | 2025.02.04 |
| Block SCSI generic (bsg) driver (0) | 2024.04.16 |
3월 17일에
3개월 / 200만원 묶는데 2.8%
6월 17일에
3개월 / 200만원 묶는데 2.5%
9월 17일에
3개월 / 200만원 묶는데 2.4%
장기로 묶는게 유리하려나
| 콜래트럴 데미지 (0) | 2025.06.03 |
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| 전장련 시위 시작 (0) | 2025.04.21 |
| 보수(保守)는 보수(報酬)를 먹고 자란다 (0) | 2025.04.18 |
| 윤석열 탄핵 (0) | 2025.04.04 |
| 제주항공 무안공항 대참사 (0) | 2024.12.29 |
역시 잊어야 오는구나 ㅋㅋ
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2025.09.17
nvme 달아서 테스트 해보니 1 lane 이라 181MB/s 정도 밖에 안나온다.
당연하긴 당연한데, 의외로 되니 신기하다.
| $ cat nvme.log $ sudo time dd if=/dev/zero of=test bs=1G count=1 conv=fsync 1+0 records in 1+0 records out 1073741824 bytes (1.1 GB, 1.0 GiB) copied, 5.92836 s, 181 MB/s 0.00user 1.06system 0:06.00elapsed 17%CPU (0avgtext+0avgdata 1051008maxresident)k 0inputs+2097152outputs (0major+262242minor)pagefaults 0swaps |
| abko k1924 적축 키보드 구매 (2) | 2025.09.18 |
|---|---|
| 알리알리 알라성 알라리 알라 망했네? (0) | 2025.09.18 |
| 키보드 수리 2 (0) | 2025.09.14 |
| SK-8845RC USB 포트 (0) | 2025.09.10 |
| fan error .. 3 (0) | 2025.08.10 |
오디오와 비디오로 크게 나뉘고
비디오에서는 classification / obejct detection / segmentation 정도가 현재 관심사
sementic segmentation과 instance-segmentation 차이는 멀까?
| List of domains Audio anomaly detection command recognition speech recognition Vision classification face recognition object detection pose estimation semantic segmentation super resolution instance-segmentation low-light enhancement |
[링크 : https://github.com/NXP/eiq-model-zoo]
텍스트로 넣으니 css 때문에 깨져서 이미지로 복사 -_-
selfie-segmenter는 proprietary dataset이었군..
[링크 : https://github.com/NXP/eiq-model-zoo/tree/main/products]
| eiq 데이터 구조 (0) | 2025.09.05 |
|---|---|
| ubuntu 22.04 + cuda + cudnn 설치 (0) | 2025.09.04 |
| import tensorflow illegal instruction (0) | 2025.09.04 |
| eiq on windows with nvidia (0) | 2025.09.03 |
| vainfo (0) | 2025.09.03 |
조이트론 EX M AIR를 오랫만에 꺼냈더니
아날로그 조이스틱 쪽이 끈끈해져서 떼고, 전에 구매해놨던 아날로그 조이스틱 부품으로 바꾸려고 했으나
구멍 크기가 달라서 실패 -_-
삭아가고 책상에 문지르면 연필처럼 검은 줄이 생겨난다 -_-
나사 풀고 드니 우수수 떨어져서 당황 -_-
배터리도 커넥터 없이 그냥 납땜되어있고 모터쪽도 완전히 잡히면 잡소리 나니까
그냥 공중부양.. 우워.. 굉장하네
내가 가진거 보다 확실히 먼가 좋은 부품이라는 느낌
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2025.09.16
머리는 얘가 맞는거 같은데 가격이..
[링크 : http://itempage3.auction.co.kr/DetailView.aspx?itemno=E854087313] 13,820원/무배
[링크 : http://itempage3.auction.co.kr/DetailView.aspx?itemno=E854088395] 12,600원/무배
내꺼랑 비교하면 구멍이 확실히 작다.
낑낑대면서 겨우겨우 다시 조립 ㅠㅠ 헉헉
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가변저항이 아닌 홀 센서라서 더 비싸다고
물론 비싸다고 무조건 좋은건 아닌데,
데드존이 적어서 장점,
노이즈 제거등의 전처리가 필요해서 응답이 좀 느리기도 한 제품이 있다는게 단점이라고 보면 되려나?
[링크 : https://gall.dcinside.com/mgallery/board/view/?id=gamepad&no=27383]
| Logitech(saitek) X-56 stick 키맵 (0) | 2025.03.03 |
|---|---|
| logitech x-56 플라이트 스틱 (0) | 2025.02.22 |
| 잘가 조이스틱 (0) | 2025.01.10 |
| pxn2119pro 중고구매 (0) | 2025.01.06 |
| ex m air 펌웨어 (안드로이드 갤럭시 S10) (0) | 2023.01.25 |
대충 버전이 맞았는지 돌아는 간다.
Epoch 1/25 52/755 [=>............................] - ETA: 1:04:39 - loss: 0.3203 - accuracy: 0.8534 |
주요 설치 패키지 버전은 아래와 같고
| keras 2.14.0 mobilenet-v3 0.1.2 numpy 1.24.4 tensorflow 2.14.0 |
수정된 소스는 다음과 같다.
그런데 voc 디렉토리를 통채로 넣었는데 어찌 돌아는 가는데.. 어떤 파일로 학습을 하는거냐.. -_-
| from keras.applications import MobileNet from keras.models import Sequential,Model from keras.layers import Dense,Dropout,Activation,Flatten,GlobalAveragePooling2D from keras.layers import Conv2D,MaxPooling2D,ZeroPadding2D from keras.layers.normalization import BatchNormalization from tensorflow.keras.preprocessing.image import ImageDataGenerator # MobileNet is designed to work with images of dim 224,224 img_rows,img_cols = 224,224 MobileNet = MobileNet(weights='imagenet',include_top=False,input_shape=(img_rows,img_cols,3)) # Here we freeze the last 4 layers # Layers are set to trainable as True by default for layer in MobileNet.layers: layer.trainable = True # Let's print our layers for (i,layer) in enumerate(MobileNet.layers): print(str(i),layer.__class__.__name__,layer.trainable) def addTopModelMobileNet(bottom_model, num_classes): """creates the top or head of the model that will be placed ontop of the bottom layers""" top_model = bottom_model.output top_model = GlobalAveragePooling2D()(top_model) top_model = Dense(1024,activation='relu')(top_model) top_model = Dense(1024,activation='relu')(top_model) top_model = Dense(512,activation='relu')(top_model) top_model = Dense(num_classes,activation='softmax')(top_model) return top_model num_classes = 5 # ['Angry','Happy','Neutral','Sad','Surprise'] FC_Head = addTopModelMobileNet(MobileNet, num_classes) model = Model(inputs = MobileNet.input, outputs = FC_Head) print(model.summary()) train_data_dir = 'VOC2012_train_val/VOC2012_train_val' validation_data_dir = 'VOC2012_test/VOC2012_test' train_datagen = ImageDataGenerator( rescale=1./255, rotation_range=30, width_shift_range=0.3, height_shift_range=0.3, horizontal_flip=True, fill_mode='nearest' ) validation_datagen = ImageDataGenerator(rescale=1./255) batch_size = 32 train_generator = train_datagen.flow_from_directory( train_data_dir, target_size = (img_rows,img_cols), batch_size = batch_size, class_mode = 'categorical' ) validation_generator = validation_datagen.flow_from_directory( validation_data_dir, target_size=(img_rows,img_cols), batch_size=batch_size, class_mode='categorical') from keras.optimizers import RMSprop,Adam from keras.callbacks import ModelCheckpoint,EarlyStopping,ReduceLROnPlateau checkpoint = ModelCheckpoint( 'emotion_face_mobilNet.h5', monitor='val_loss', mode='min', save_best_only=True, verbose=1) earlystop = EarlyStopping( monitor='val_loss', min_delta=0, patience=10, verbose=1,restore_best_weights=True) learning_rate_reduction = ReduceLROnPlateau(monitor='val_acc', patience=5, verbose=1, factor=0.2, min_lr=0.0001) callbacks = [earlystop,checkpoint,learning_rate_reduction] model.compile(loss='categorical_crossentropy', optimizer=Adam(learning_rate=0.001), metrics=['accuracy'] ) nb_train_samples = 24176 nb_validation_samples = 3006 epochs = 25 history = model.fit( train_generator, steps_per_epoch=nb_train_samples//batch_size, epochs=epochs, callbacks=callbacks, validation_data=validation_generator, validation_steps=nb_validation_samples//batch_size) |
돌리다가 에러가 나서 멘붕.. 급 귀찮아짐..
먼가 파일 갯수가 안 맞는건가?
| Epoch 1/25 718/755 [===========================>..] - ETA: 3:28 - loss: 0.1569 - accuracy: 0.9301WARNING:tensorflow:Your input ran out of data; interrupting training. Make sure that your dataset or generator can generate at least `steps_per_epoch * epochs` batches (in this case, 18875 batches). You may need to use the repeat() function when building your dataset. Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/home/minimonk/.local/lib/python3.10/site-packages/keras/src/utils/traceback_utils.py", line 70, in error_handler raise e.with_traceback(filtered_tb) from None File "/home/minimonk/.local/lib/python3.10/site-packages/tensorflow/python/eager/execute.py", line 60, in quick_execute tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name, tensorflow.python.framework.errors_impl.InvalidArgumentError: Graph execution error: Detected at node categorical_crossentropy/softmax_cross_entropy_with_logits defined at (most recent call last): File "<stdin>", line 1, in <module> File "/home/minimonk/.local/lib/python3.10/site-packages/keras/src/utils/traceback_utils.py", line 65, in error_handler File "/home/minimonk/.local/lib/python3.10/site-packages/keras/src/engine/training.py", line 1832, in fit File "/home/minimonk/.local/lib/python3.10/site-packages/keras/src/utils/traceback_utils.py", line 65, in error_handler File "/home/minimonk/.local/lib/python3.10/site-packages/keras/src/engine/training.py", line 2272, in evaluate File "/home/minimonk/.local/lib/python3.10/site-packages/keras/src/engine/training.py", line 4079, in run_step File "/home/minimonk/.local/lib/python3.10/site-packages/keras/src/engine/training.py", line 2042, in test_function File "/home/minimonk/.local/lib/python3.10/site-packages/keras/src/engine/training.py", line 2025, in step_function File "/home/minimonk/.local/lib/python3.10/site-packages/keras/src/engine/training.py", line 2013, in run_step File "/home/minimonk/.local/lib/python3.10/site-packages/keras/src/engine/training.py", line 1895, in test_step File "/home/minimonk/.local/lib/python3.10/site-packages/keras/src/engine/training.py", line 1185, in compute_loss File "/home/minimonk/.local/lib/python3.10/site-packages/keras/src/engine/compile_utils.py", line 277, in __call__ File "/home/minimonk/.local/lib/python3.10/site-packages/keras/src/losses.py", line 143, in __call__ File "/home/minimonk/.local/lib/python3.10/site-packages/keras/src/losses.py", line 270, in call File "/home/minimonk/.local/lib/python3.10/site-packages/keras/src/losses.py", line 2221, in categorical_crossentropy File "/home/minimonk/.local/lib/python3.10/site-packages/keras/src/backend.py", line 5581, in categorical_crossentropy logits and labels must be broadcastable: logits_size=[32,5] labels_size=[32,3] [[{{node categorical_crossentropy/softmax_cross_entropy_with_logits}}]] [Op:__inference_test_function_15346] >>> |
전체 pip 패키지들 버전 정보는 아래와 같다.
| $ pip list Package Version ---------------------------- ---------------- absl-py 2.3.1 appdirs 1.4.4 apturl 0.5.2 astunparse 1.6.3 attrs 21.2.0 bcrypt 3.2.0 beautifulsoup4 4.10.0 beniget 0.4.1 blinker 1.4 Brlapi 0.8.3 Brotli 1.0.9 cachetools 5.5.2 certifi 2020.6.20 chardet 4.0.0 click 8.0.3 colorama 0.4.4 command-not-found 0.3 cryptography 3.4.8 cupshelpers 1.0 cycler 0.11.0 dbus-python 1.2.18 decorator 4.4.2 defer 1.0.6 distro 1.7.0 distro-info 1.1+ubuntu0.2 duplicity 0.8.21 fasteners 0.14.1 flatbuffers 25.2.10 fonttools 4.29.1 fs 2.4.12 future 0.18.2 gast 0.6.0 google-auth 2.40.3 google-auth-oauthlib 1.0.0 google-pasta 0.2.0 grpcio 1.74.0 h5py 3.14.0 html5lib 1.1 httplib2 0.20.2 idna 3.3 importlib-metadata 4.6.4 jeepney 0.7.1 keras 2.14.0 keyring 23.5.0 kiwisolver 1.3.2 language-selector 0.1 launchpadlib 1.10.16 lazr.restfulclient 0.14.4 lazr.uri 1.0.6 libclang 18.1.1 lockfile 0.12.2 louis 3.20.0 lxml 4.8.0 lz4 3.1.3+dfsg macaroonbakery 1.3.1 Mako 1.1.3 Markdown 3.9 markdown-it-py 4.0.0 MarkupSafe 3.0.2 matplotlib 3.5.1 mdurl 0.1.2 meld 3.20.4 ml-dtypes 0.2.0 mobilenet-v3 0.1.2 monotonic 1.6 more-itertools 8.10.0 mpmath 0.0.0 namex 0.1.0 netifaces 0.11.0 numpy 1.24.4 oauthlib 3.2.0 olefile 0.46 opt_einsum 3.4.0 optree 0.17.0 packaging 21.3 paramiko 2.9.3 pexpect 4.8.0 Pillow 9.0.1 pip 22.0.2 Pivy 0.6.5 ply 3.11 protobuf 4.25.8 ptyprocess 0.7.0 pyasn1 0.6.1 pyasn1_modules 0.4.2 pycairo 1.20.1 pycups 2.0.1 Pygments 2.19.2 PyGObject 3.42.1 PyJWT 2.3.0 pymacaroons 0.13.0 PyNaCl 1.5.0 pyparsing 2.4.7 pyRFC3339 1.1 python-apt 2.4.0+ubuntu4 python-dateutil 2.8.1 python-debian 0.1.43+ubuntu1.1 pythran 0.10.0 pytz 2022.1 pyxdg 0.27 PyYAML 5.4.1 reportlab 3.6.8 requests 2.25.1 requests-oauthlib 2.0.0 rich 14.1.0 rsa 4.9.1 scipy 1.15.3 scour 0.38.2 SecretStorage 3.3.1 setuptools 59.6.0 six 1.16.0 soupsieve 2.3.1 ssh-import-id 5.11 sympy 1.9 systemd-python 234 tensorboard 2.14.1 tensorboard-data-server 0.7.2 tensorflow 2.14.0 tensorflow-estimator 2.14.0 tensorflow-io-gcs-filesystem 0.37.1 termcolor 3.1.0 typing_extensions 4.15.0 ubuntu-drivers-common 0.0.0 ubuntu-pro-client 8001 ufoLib2 0.13.1 ufw 0.36.1 unattended-upgrades 0.1 unicodedata2 14.0.0 urllib3 1.26.5 usb-creator 0.3.7 wadllib 1.3.6 webencodings 0.5.1 Werkzeug 3.1.3 wheel 0.37.1 wrapt 1.14.2 xdg 5 xkit 0.0.0 zipp 1.0.0 |
-------- 아래는 참고 안하는게 속 편할지도...?
2020년 3월의 문서를 keras와 tensorflow로 2025년에 다시 시도해봄
일단은 아래처럼 설치하니 어찌 되는 느낌
| $ pip install mobilenet-v3 $ pip install tensorflow $ pip install numpy==1.26.4 |
상세로그
| $ pip install mobilenet-v3 Defaulting to user installation because normal site-packages is not writeable Collecting mobilenet-v3 Downloading mobilenet_v3-0.1.4-py3-none-any.whl (18 kB) Installing collected packages: mobilenet-v3 Successfully installed mobilenet-v3-0.1.4 $ pip install tensorflow Defaulting to user installation because normal site-packages is not writeable Collecting tensorflow Downloading tensorflow-2.20.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (620.4 MB) ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 620.4/620.4 MB 2.5 MB/s eta 0:00:00 Collecting google_pasta>=0.1.1 Downloading google_pasta-0.2.0-py3-none-any.whl (57 kB) ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 57.5/57.5 KB 9.6 MB/s eta 0:00:00 Collecting absl-py>=1.0.0 Downloading absl_py-2.3.1-py3-none-any.whl (135 kB) ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 135.8/135.8 KB 12.8 MB/s eta 0:00:00 Collecting libclang>=13.0.0 Downloading libclang-18.1.1-py2.py3-none-manylinux2010_x86_64.whl (24.5 MB) ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 24.5/24.5 MB 9.6 MB/s eta 0:00:00 Collecting gast!=0.5.0,!=0.5.1,!=0.5.2,>=0.2.1 Downloading gast-0.6.0-py3-none-any.whl (21 kB) Collecting protobuf>=5.28.0 Downloading protobuf-6.32.1-cp39-abi3-manylinux2014_x86_64.whl (322 kB) ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 322.0/322.0 KB 11.5 MB/s eta 0:00:00 Collecting ml_dtypes<1.0.0,>=0.5.1 Downloading ml_dtypes-0.5.3-cp310-cp310-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl (4.9 MB) ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 4.9/4.9 MB 9.9 MB/s eta 0:00:00 Collecting numpy>=1.26.0 Downloading numpy-2.2.6-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (16.8 MB) ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 16.8/16.8 MB 9.9 MB/s eta 0:00:00 Requirement already satisfied: packaging in /usr/lib/python3/dist-packages (from tensorflow) (21.3) Requirement already satisfied: six>=1.12.0 in /usr/lib/python3/dist-packages (from tensorflow) (1.16.0) Collecting termcolor>=1.1.0 Downloading termcolor-3.1.0-py3-none-any.whl (7.7 kB) Collecting typing_extensions>=3.6.6 Downloading typing_extensions-4.15.0-py3-none-any.whl (44 kB) ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 44.6/44.6 KB 8.2 MB/s eta 0:00:00 Collecting tensorboard~=2.20.0 Downloading tensorboard-2.20.0-py3-none-any.whl (5.5 MB) ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 5.5/5.5 MB 10.9 MB/s eta 0:00:00 Collecting astunparse>=1.6.0 Downloading astunparse-1.6.3-py2.py3-none-any.whl (12 kB) Collecting opt_einsum>=2.3.2 Downloading opt_einsum-3.4.0-py3-none-any.whl (71 kB) ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 71.9/71.9 KB 8.4 MB/s eta 0:00:00 Collecting h5py>=3.11.0 Downloading h5py-3.14.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (4.6 MB) ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 4.6/4.6 MB 9.4 MB/s eta 0:00:00 Collecting flatbuffers>=24.3.25 Downloading flatbuffers-25.2.10-py2.py3-none-any.whl (30 kB) Collecting keras>=3.10.0 Downloading keras-3.11.3-py3-none-any.whl (1.4 MB) ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 1.4/1.4 MB 8.0 MB/s eta 0:00:00 Requirement already satisfied: setuptools in /usr/lib/python3/dist-packages (from tensorflow) (59.6.0) Requirement already satisfied: requests<3,>=2.21.0 in /usr/lib/python3/dist-packages (from tensorflow) (2.25.1) Collecting wrapt>=1.11.0 Downloading wrapt-1.17.3-cp310-cp310-manylinux1_x86_64.manylinux_2_28_x86_64.manylinux_2_5_x86_64.whl (81 kB) ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 82.0/82.0 KB 7.3 MB/s eta 0:00:00 Collecting grpcio<2.0,>=1.24.3 Downloading grpcio-1.74.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (6.2 MB) ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 6.2/6.2 MB 12.3 MB/s eta 0:00:00 Requirement already satisfied: wheel<1.0,>=0.23.0 in /usr/lib/python3/dist-packages (from astunparse>=1.6.0->tensorflow) (0.37.1) Collecting optree Downloading optree-0.17.0-cp310-cp310-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl (387 kB) ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 388.0/388.0 KB 11.8 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Consider adding this directory to PATH or, if you prefer to suppress this warning, use --no-warn-script-location. WARNING: The scripts f2py and numpy-config are installed in '/home/minimonk/.local/bin' which is not on PATH. Consider adding this directory to PATH or, if you prefer to suppress this warning, use --no-warn-script-location. WARNING: The script markdown_py is installed in '/home/minimonk/.local/bin' which is not on PATH. Consider adding this directory to PATH or, if you prefer to suppress this warning, use --no-warn-script-location. WARNING: The script markdown-it is installed in '/home/minimonk/.local/bin' which is not on PATH. Consider adding this directory to PATH or, if you prefer to suppress this warning, use --no-warn-script-location. WARNING: The script tensorboard is installed in '/home/minimonk/.local/bin' which is not on PATH. Consider adding this directory to PATH or, if you prefer to suppress this warning, use --no-warn-script-location. WARNING: The scripts import_pb_to_tensorboard, saved_model_cli, tensorboard, tf_upgrade_v2, tflite_convert and toco are installed in '/home/minimonk/.local/bin' which is not on PATH. Consider adding this directory to PATH or, if you prefer to suppress this warning, use --no-warn-script-location. Successfully installed MarkupSafe-3.0.2 absl-py-2.3.1 astunparse-1.6.3 flatbuffers-25.2.10 gast-0.6.0 google_pasta-0.2.0 grpcio-1.74.0 h5py-3.14.0 keras-3.11.3 libclang-18.1.1 markdown-3.9 markdown-it-py-4.0.0 mdurl-0.1.2 ml_dtypes-0.5.3 namex-0.1.0 numpy-2.2.6 opt_einsum-3.4.0 optree-0.17.0 protobuf-6.32.1 pygments-2.19.2 rich-14.1.0 tensorboard-2.20.0 tensorboard-data-server-0.7.2 tensorflow-2.20.0 termcolor-3.1.0 typing_extensions-4.15.0 werkzeug-3.1.3 wrapt-1.17.3 minimonk@minimonk-HP-EliteBook-2760p:~$ pip install keras Defaulting to user installation because normal site-packages is not writeable Requirement already satisfied: keras in ./.local/lib/python3.10/site-packages (3.11.3) Requirement already satisfied: absl-py in ./.local/lib/python3.10/site-packages (from keras) (2.3.1) Requirement already satisfied: numpy in ./.local/lib/python3.10/site-packages (from keras) (2.2.6) Requirement already satisfied: rich in ./.local/lib/python3.10/site-packages (from keras) (14.1.0) Requirement already satisfied: ml-dtypes in ./.local/lib/python3.10/site-packages (from keras) (0.5.3) Requirement already satisfied: namex in ./.local/lib/python3.10/site-packages (from keras) (0.1.0) Requirement already satisfied: optree in ./.local/lib/python3.10/site-packages (from keras) (0.17.0) Requirement already satisfied: h5py in ./.local/lib/python3.10/site-packages (from keras) (3.14.0) Requirement already satisfied: packaging in /usr/lib/python3/dist-packages (from keras) (21.3) Requirement already satisfied: typing-extensions>=4.6.0 in ./.local/lib/python3.10/site-packages (from optree->keras) (4.15.0) Requirement already satisfied: markdown-it-py>=2.2.0 in ./.local/lib/python3.10/site-packages (from rich->keras) (4.0.0) Requirement already satisfied: pygments<3.0.0,>=2.13.0 in ./.local/lib/python3.10/site-packages (from rich->keras) (2.19.2) Requirement already satisfied: mdurl~=0.1 in ./.local/lib/python3.10/site-packages (from markdown-it-py>=2.2.0->rich->keras) (0.1.2) $ pip install numpy==1.26.4 |
numpy 1.26.4를 깔게 된 에러메시지
| $ python3 Python 3.10.12 (main, Aug 15 2025, 14:32:43) [GCC 11.4.0] on linux Type "help", "copyright", "credits" or "license" for more information. >>> import tensorflow as tf 2025-09-15 15:28:06.544207: I external/local_xla/xla/tsl/cuda/cudart_stub.cc:31] Could not find cuda drivers on your machine, GPU will not be used. /usr/lib/python3/dist-packages/scipy/__init__.py:146: UserWarning: A NumPy version >=1.17.3 and <1.25.0 is required for this version of SciPy (detected version 2.2.6 warnings.warn(f"A NumPy version >={np_minversion} and <{np_maxversion}" A module that was compiled using NumPy 1.x cannot be run in NumPy 2.2.6 as it may crash. To support both 1.x and 2.x versions of NumPy, modules must be compiled with NumPy 2.0. Some module may need to rebuild instead e.g. with 'pybind11>=2.12'. If you are a user of the module, the easiest solution will be to downgrade to 'numpy<2' or try to upgrade the affected module. We expect that some modules will need time to support NumPy 2. Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/home/minimonk/.local/lib/python3.10/site-packages/tensorflow/__init__.py", line 49, in <module> from tensorflow._api.v2 import __internal__ File "/home/minimonk/.local/lib/python3.10/site-packages/tensorflow/_api/v2/__internal__/__init__.py", line 13, in <module> from tensorflow._api.v2.__internal__ import feature_column File "/home/minimonk/.local/lib/python3.10/site-packages/tensorflow/_api/v2/__internal__/feature_column/__init__.py", line 8, in <module> from tensorflow.python.feature_column.feature_column_v2 import DenseColumn # line: 1777 File "/home/minimonk/.local/lib/python3.10/site-packages/tensorflow/python/feature_column/feature_column_v2.py", line 38, in <module> from tensorflow.python.feature_column import feature_column as fc_old File "/home/minimonk/.local/lib/python3.10/site-packages/tensorflow/python/feature_column/feature_column.py", line 41, in <module> from tensorflow.python.layers import base File "/home/minimonk/.local/lib/python3.10/site-packages/tensorflow/python/layers/base.py", line 16, in <module> from tensorflow.python.keras.legacy_tf_layers import base File "/home/minimonk/.local/lib/python3.10/site-packages/tensorflow/python/keras/__init__.py", line 25, in <module> from tensorflow.python.keras import models File "/home/minimonk/.local/lib/python3.10/site-packages/tensorflow/python/keras/models.py", line 25, in <module> from tensorflow.python.keras.engine import training_v1 File "/home/minimonk/.local/lib/python3.10/site-packages/tensorflow/python/keras/engine/training_v1.py", line 46, in <module> from tensorflow.python.keras.engine import training_arrays_v1 File "/home/minimonk/.local/lib/python3.10/site-packages/tensorflow/python/keras/engine/training_arrays_v1.py", line 37, in <module> from scipy.sparse import issparse # pylint: disable=g-import-not-at-top File "/usr/lib/python3/dist-packages/scipy/sparse/__init__.py", line 267, in <module> from ._csr import * File "/usr/lib/python3/dist-packages/scipy/sparse/_csr.py", line 10, in <module> from ._sparsetools import (csr_tocsc, csr_tobsr, csr_count_blocks, AttributeError: _ARRAY_API not found |
[링크 : https://mhui123.tistory.com/143]
그런데 mobilenet이 ssd가 없으면 classification만 되는 놈이었나?
| from keras.applications import MobileNet from keras.layers import Dense,Dropout,Activation, Flatten, GlobalAveragePooling2D from keras.layers import Conv2D, MaxPooling2D, ZeroPadding2D from tensorflow.keras.preprocessing.image import ImageDataGenerator from keras.optimizers import RMSprop, Adam from keras.callbacks import ModelCheckpoint, EarlyStopping, ReduceLROnPlateau img_rows,img_cols = 224,224 MobileNet = MobileNet(weights='imagenet', include_top=False, input_shape=(img_rows, img_cols, 3)) 2025-09-15 16:00:19.852870: E external/local_xla/xla/stream_executor/cuda/cuda_platform.cc:51] failed call to cuInit: INTERNAL: CUDA error: Failed call to cuInit: UNKNOWN ERROR (303) Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/mobilenet/mobilenet_1_0_224_tf_no_top.h5 17225924/17225924 ━━━━━━━━━━━━━━━━━━━━ 1s 0us/step >>> for layer in MobileNet.layers: ... layer.trainable = True ... >>> for (i, layer) in enumerate(MobileNet.layers): ... print(str(i), layer.__class__.__name__, layer.trainable) ... 0 InputLayer True 1 Conv2D True 2 BatchNormalization True 3 ReLU True 4 DepthwiseConv2D True 5 BatchNormalization True 6 ReLU True 7 Conv2D True 8 BatchNormalization True 9 ReLU True 10 ZeroPadding2D True 11 DepthwiseConv2D True 12 BatchNormalization True 13 ReLU True 14 Conv2D True 15 BatchNormalization True 16 ReLU True 17 DepthwiseConv2D True 18 BatchNormalization True 19 ReLU True 20 Conv2D True 21 BatchNormalization True 22 ReLU True 23 ZeroPadding2D True 24 DepthwiseConv2D True 25 BatchNormalization True 26 ReLU True 27 Conv2D True 28 BatchNormalization True 29 ReLU True 30 DepthwiseConv2D True 31 BatchNormalization True 32 ReLU True 33 Conv2D True 34 BatchNormalization True 35 ReLU True 36 ZeroPadding2D True 37 DepthwiseConv2D True 38 BatchNormalization True 39 ReLU True 40 Conv2D True 41 BatchNormalization True 42 ReLU True 43 DepthwiseConv2D True 44 BatchNormalization True 45 ReLU True 46 Conv2D True 47 BatchNormalization True 48 ReLU True 49 DepthwiseConv2D True 50 BatchNormalization True 51 ReLU True 52 Conv2D True 53 BatchNormalization True 54 ReLU True 55 DepthwiseConv2D True 56 BatchNormalization True 57 ReLU True 58 Conv2D True 59 BatchNormalization True 60 ReLU True 61 DepthwiseConv2D True 62 BatchNormalization True 63 ReLU True 64 Conv2D True 65 BatchNormalization True 66 ReLU True 67 DepthwiseConv2D True 68 BatchNormalization True 69 ReLU True 70 Conv2D True 71 BatchNormalization True 72 ReLU True 73 ZeroPadding2D True 74 DepthwiseConv2D True 75 BatchNormalization True 76 ReLU True 77 Conv2D True 78 BatchNormalization True 79 ReLU True 80 DepthwiseConv2D True 81 BatchNormalization True 82 ReLU True 83 Conv2D True 84 BatchNormalization True 85 ReLU True >>> MobileNet.output <KerasTensor shape=(None, 7, 7, 1024), dtype=float32, sparse=False, ragged=False, name=keras_tensor_85> >>> MobileNet.input <KerasTensor shape=(None, 224, 224, 3), dtype=float32, sparse=False, ragged=False, name=keras_tensor> >>> MobileNet.summary() Model: "mobilenet_1.00_224" ┏--------------------------------------┳-----------------------------┳-----------------┓ ┃ Layer (type) ┃ Output Shape ┃ Param # ┃ ┡--------------------------------------╇-----------------------------╇-----------------┩ │ input_layer (InputLayer) │ (None, 224, 224, 3) │ 0 │ ├--------------------------------------┼-----------------------------┼-----------------┤ │ conv1 (Conv2D) │ (None, 112, 112, 32) │ 864 │ ├--------------------------------------┼-----------------------------┼-----------------┤ │ conv1_bn (BatchNormalization) │ (None, 112, 112, 32) │ 128 │ ├--------------------------------------┼-----------------------------┼-----------------┤ │ conv1_relu (ReLU) │ (None, 112, 112, 32) │ 0 │ ├--------------------------------------┼-----------------------------┼-----------------┤ │ conv_dw_1 (DepthwiseConv2D) │ (None, 112, 112, 32) │ 288 │ ├--------------------------------------┼-----------------------------┼-----------------┤ │ conv_dw_1_bn (BatchNormalization) │ (None, 112, 112, 32) │ 128 │ ├--------------------------------------┼-----------------------------┼-----------------┤ │ conv_dw_1_relu (ReLU) │ (None, 112, 112, 32) │ 0 │ ├--------------------------------------┼-----------------------------┼-----------------┤ │ conv_pw_1 (Conv2D) │ (None, 112, 112, 64) │ 2,048 │ ├--------------------------------------┼-----------------------------┼-----------------┤ │ conv_pw_1_bn (BatchNormalization) │ (None, 112, 112, 64) │ 256 │ ├--------------------------------------┼-----------------------------┼-----------------┤ │ conv_pw_1_relu (ReLU) │ (None, 112, 112, 64) │ 0 │ ├--------------------------------------┼-----------------------------┼-----------------┤ │ conv_pad_2 (ZeroPadding2D) │ (None, 113, 113, 64) │ 0 │ ├--------------------------------------┼-----------------------------┼-----------------┤ │ conv_dw_2 (DepthwiseConv2D) │ (None, 56, 56, 64) │ 576 │ ├--------------------------------------┼-----------------------------┼-----------------┤ │ conv_dw_2_bn (BatchNormalization) │ (None, 56, 56, 64) │ 256 │ ├--------------------------------------┼-----------------------------┼-----------------┤ │ conv_dw_2_relu (ReLU) │ (None, 56, 56, 64) │ 0 │ ├--------------------------------------┼-----------------------------┼-----------------┤ │ conv_pw_2 (Conv2D) │ (None, 56, 56, 128) │ 8,192 │ ├--------------------------------------┼-----------------------------┼-----------------┤ │ conv_pw_2_bn (BatchNormalization) │ (None, 56, 56, 128) │ 512 │ ├--------------------------------------┼-----------------------------┼-----------------┤ │ conv_pw_2_relu (ReLU) │ (None, 56, 56, 128) │ 0 │ ├--------------------------------------┼-----------------------------┼-----------------┤ │ conv_dw_3 (DepthwiseConv2D) │ (None, 56, 56, 128) │ 1,152 │ ├--------------------------------------┼-----------------------------┼-----------------┤ │ conv_dw_3_bn (BatchNormalization) │ (None, 56, 56, 128) │ 512 │ ├--------------------------------------┼-----------------------------┼-----------------┤ │ conv_dw_3_relu (ReLU) │ (None, 56, 56, 128) │ 0 │ ├--------------------------------------┼-----------------------------┼-----------------┤ │ conv_pw_3 (Conv2D) │ (None, 56, 56, 128) │ 16,384 │ ├--------------------------------------┼-----------------------------┼-----------------┤ │ conv_pw_3_bn (BatchNormalization) │ (None, 56, 56, 128) │ 512 │ ├--------------------------------------┼-----------------------------┼-----------------┤ │ conv_pw_3_relu (ReLU) │ (None, 56, 56, 128) │ 0 │ ├--------------------------------------┼-----------------------------┼-----------------┤ │ conv_pad_4 (ZeroPadding2D) │ (None, 57, 57, 128) │ 0 │ ├--------------------------------------┼-----------------------------┼-----------------┤ │ conv_dw_4 (DepthwiseConv2D) │ (None, 28, 28, 128) │ 1,152 │ ├--------------------------------------┼-----------------------------┼-----------------┤ │ conv_dw_4_bn (BatchNormalization) │ (None, 28, 28, 128) │ 512 │ ├--------------------------------------┼-----------------------------┼-----------------┤ │ conv_dw_4_relu (ReLU) │ (None, 28, 28, 128) │ 0 │ ├--------------------------------------┼-----------------------------┼-----------------┤ │ conv_pw_4 (Conv2D) │ (None, 28, 28, 256) │ 32,768 │ ├--------------------------------------┼-----------------------------┼-----------------┤ │ conv_pw_4_bn (BatchNormalization) │ (None, 28, 28, 256) │ 1,024 │ ├--------------------------------------┼-----------------------------┼-----------------┤ │ conv_pw_4_relu (ReLU) │ (None, 28, 28, 256) │ 0 │ ├--------------------------------------┼-----------------------------┼-----------------┤ │ conv_dw_5 (DepthwiseConv2D) │ (None, 28, 28, 256) │ 2,304 │ ├--------------------------------------┼-----------------------------┼-----------------┤ │ conv_dw_5_bn (BatchNormalization) │ (None, 28, 28, 256) │ 1,024 │ ├--------------------------------------┼-----------------------------┼-----------------┤ │ conv_dw_5_relu (ReLU) │ (None, 28, 28, 256) │ 0 │ ├--------------------------------------┼-----------------------------┼-----------------┤ │ conv_pw_5 (Conv2D) │ (None, 28, 28, 256) │ 65,536 │ ├--------------------------------------┼-----------------------------┼-----------------┤ │ conv_pw_5_bn (BatchNormalization) │ (None, 28, 28, 256) │ 1,024 │ ├--------------------------------------┼-----------------------------┼-----------------┤ │ conv_pw_5_relu (ReLU) │ (None, 28, 28, 256) │ 0 │ ├--------------------------------------┼-----------------------------┼-----------------┤ │ conv_pad_6 (ZeroPadding2D) │ (None, 29, 29, 256) │ 0 │ ├--------------------------------------┼-----------------------------┼-----------------┤ │ conv_dw_6 (DepthwiseConv2D) │ (None, 14, 14, 256) │ 2,304 │ ├--------------------------------------┼-----------------------------┼-----------------┤ │ conv_dw_6_bn (BatchNormalization) │ (None, 14, 14, 256) │ 1,024 │ ├--------------------------------------┼-----------------------------┼-----------------┤ │ conv_dw_6_relu (ReLU) │ (None, 14, 14, 256) │ 0 │ ├--------------------------------------┼-----------------------------┼-----------------┤ │ conv_pw_6 (Conv2D) │ (None, 14, 14, 512) │ 131,072 │ ├--------------------------------------┼-----------------------------┼-----------------┤ │ conv_pw_6_bn (BatchNormalization) │ (None, 14, 14, 512) │ 2,048 │ ├--------------------------------------┼-----------------------------┼-----------------┤ │ conv_pw_6_relu (ReLU) │ (None, 14, 14, 512) │ 0 │ ├--------------------------------------┼-----------------------------┼-----------------┤ │ conv_dw_7 (DepthwiseConv2D) │ (None, 14, 14, 512) │ 4,608 │ ├--------------------------------------┼-----------------------------┼-----------------┤ │ conv_dw_7_bn (BatchNormalization) │ (None, 14, 14, 512) │ 2,048 │ ├--------------------------------------┼-----------------------------┼-----------------┤ │ conv_dw_7_relu (ReLU) │ (None, 14, 14, 512) │ 0 │ ├--------------------------------------┼-----------------------------┼-----------------┤ │ conv_pw_7 (Conv2D) │ (None, 14, 14, 512) │ 262,144 │ ├--------------------------------------┼-----------------------------┼-----------------┤ │ conv_pw_7_bn (BatchNormalization) │ (None, 14, 14, 512) │ 2,048 │ ├--------------------------------------┼-----------------------------┼-----------------┤ │ conv_pw_7_relu (ReLU) │ (None, 14, 14, 512) │ 0 │ ├--------------------------------------┼-----------------------------┼-----------------┤ │ conv_dw_8 (DepthwiseConv2D) │ (None, 14, 14, 512) │ 4,608 │ ├--------------------------------------┼-----------------------------┼-----------------┤ │ conv_dw_8_bn (BatchNormalization) │ (None, 14, 14, 512) │ 2,048 │ ├--------------------------------------┼-----------------------------┼-----------------┤ │ conv_dw_8_relu (ReLU) │ (None, 14, 14, 512) │ 0 │ ├--------------------------------------┼-----------------------------┼-----------------┤ │ conv_pw_8 (Conv2D) │ (None, 14, 14, 512) │ 262,144 │ ├--------------------------------------┼-----------------------------┼-----------------┤ │ conv_pw_8_bn (BatchNormalization) │ (None, 14, 14, 512) │ 2,048 │ ├--------------------------------------┼-----------------------------┼-----------------┤ │ conv_pw_8_relu (ReLU) │ (None, 14, 14, 512) │ 0 │ ├--------------------------------------┼-----------------------------┼-----------------┤ │ conv_dw_9 (DepthwiseConv2D) │ (None, 14, 14, 512) │ 4,608 │ ├--------------------------------------┼-----------------------------┼-----------------┤ │ conv_dw_9_bn (BatchNormalization) │ (None, 14, 14, 512) │ 2,048 │ ├--------------------------------------┼-----------------------------┼-----------------┤ │ conv_dw_9_relu (ReLU) │ (None, 14, 14, 512) │ 0 │ ├--------------------------------------┼-----------------------------┼-----------------┤ │ conv_pw_9 (Conv2D) │ (None, 14, 14, 512) │ 262,144 │ ├--------------------------------------┼-----------------------------┼-----------------┤ │ conv_pw_9_bn (BatchNormalization) │ (None, 14, 14, 512) │ 2,048 │ ├--------------------------------------┼-----------------------------┼-----------------┤ │ conv_pw_9_relu (ReLU) │ (None, 14, 14, 512) │ 0 │ ├--------------------------------------┼-----------------------------┼-----------------┤ │ conv_dw_10 (DepthwiseConv2D) │ (None, 14, 14, 512) │ 4,608 │ ├--------------------------------------┼-----------------------------┼-----------------┤ │ conv_dw_10_bn (BatchNormalization) │ (None, 14, 14, 512) │ 2,048 │ ├--------------------------------------┼-----------------------------┼-----------------┤ │ conv_dw_10_relu (ReLU) │ (None, 14, 14, 512) │ 0 │ ├--------------------------------------┼-----------------------------┼-----------------┤ │ conv_pw_10 (Conv2D) │ (None, 14, 14, 512) │ 262,144 │ ├--------------------------------------┼-----------------------------┼-----------------┤ │ conv_pw_10_bn (BatchNormalization) │ (None, 14, 14, 512) │ 2,048 │ ├--------------------------------------┼-----------------------------┼-----------------┤ │ conv_pw_10_relu (ReLU) │ (None, 14, 14, 512) │ 0 │ ├--------------------------------------┼-----------------------------┼-----------------┤ │ conv_dw_11 (DepthwiseConv2D) │ (None, 14, 14, 512) │ 4,608 │ ├--------------------------------------┼-----------------------------┼-----------------┤ │ conv_dw_11_bn (BatchNormalization) │ (None, 14, 14, 512) │ 2,048 │ ├--------------------------------------┼-----------------------------┼-----------------┤ │ conv_dw_11_relu (ReLU) │ (None, 14, 14, 512) │ 0 │ ├--------------------------------------┼-----------------------------┼-----------------┤ │ conv_pw_11 (Conv2D) │ (None, 14, 14, 512) │ 262,144 │ ├--------------------------------------┼-----------------------------┼-----------------┤ │ conv_pw_11_bn (BatchNormalization) │ (None, 14, 14, 512) │ 2,048 │ ├--------------------------------------┼-----------------------------┼-----------------┤ │ conv_pw_11_relu (ReLU) │ (None, 14, 14, 512) │ 0 │ ├--------------------------------------┼-----------------------------┼-----------------┤ │ conv_pad_12 (ZeroPadding2D) │ (None, 15, 15, 512) │ 0 │ ├--------------------------------------┼-----------------------------┼-----------------┤ │ conv_dw_12 (DepthwiseConv2D) │ (None, 7, 7, 512) │ 4,608 │ ├--------------------------------------┼-----------------------------┼-----------------┤ │ conv_dw_12_bn (BatchNormalization) │ (None, 7, 7, 512) │ 2,048 │ ├--------------------------------------┼-----------------------------┼-----------------┤ │ conv_dw_12_relu (ReLU) │ (None, 7, 7, 512) │ 0 │ ├--------------------------------------┼-----------------------------┼-----------------┤ │ conv_pw_12 (Conv2D) │ (None, 7, 7, 1024) │ 524,288 │ ├--------------------------------------┼-----------------------------┼-----------------┤ │ conv_pw_12_bn (BatchNormalization) │ (None, 7, 7, 1024) │ 4,096 │ ├--------------------------------------┼-----------------------------┼-----------------┤ │ conv_pw_12_relu (ReLU) │ (None, 7, 7, 1024) │ 0 │ ├--------------------------------------┼-----------------------------┼-----------------┤ │ conv_dw_13 (DepthwiseConv2D) │ (None, 7, 7, 1024) │ 9,216 │ ├--------------------------------------┼-----------------------------┼-----------------┤ │ conv_dw_13_bn (BatchNormalization) │ (None, 7, 7, 1024) │ 4,096 │ ├--------------------------------------┼-----------------------------┼-----------------┤ │ conv_dw_13_relu (ReLU) │ (None, 7, 7, 1024) │ 0 │ ├--------------------------------------┼-----------------------------┼-----------------┤ │ conv_pw_13 (Conv2D) │ (None, 7, 7, 1024) │ 1,048,576 │ ├--------------------------------------┼-----------------------------┼-----------------┤ │ conv_pw_13_bn (BatchNormalization) │ (None, 7, 7, 1024) │ 4,096 │ ├--------------------------------------┼-----------------------------┼-----------------┤ │ conv_pw_13_relu (ReLU) │ (None, 7, 7, 1024) │ 0 │ └--------------------------------------┴-----------------------------┴-----------------┘ Total params: 3,228,864 (12.32 MB) Trainable params: 3,206,976 (12.23 MB) Non-trainable params: 21,888 (85.50 KB) |
[링크 : https://kau-deeperent.tistory.com/m/59]
| # from keras.preprocessing.image import ImageDataGenerator # 에러났음 from tensorflow.keras.preprocessing.image import ImageDataGenerator |
[링크 : https://sugyeong0425.tistory.com/151]
voc2012 데이터셋설명
[링크 : https://bo-10000.tistory.com/38]
[링크 : https://velog.io/@kyungmin1029/CV-OpenCV]
2024.8 월 이니 한번 시도해볼 만할 듯?
[링크 : https://velog.io/@choonsik_mom/MobileNet-SSD-object-detector-커스텀-데이터-학습하기-m3j5d0xh]
| keras - transfer learning / fine tuning (0) | 2025.09.17 |
|---|---|
| ssd mobilenet (0) | 2025.09.11 |
| LLM 시각화 (0) | 2025.09.06 |
| 모델 학습 (0) | 2025.09.05 |
| mean Average Precision(mAP) (0) | 2025.09.05 |
초음파를 쏴서 전 대역에 재밍거는 방법과 백색 소음을 전대역에 재밍거는 방법이 있다는데
일단 초음파를 쓰면 line of sight에서만 작동하는 듯.
[링크 : https://www.isecus.com/audio-recording-jammer-comparison/]
| pwm 화음 출력 (0) | 2025.08.06 |
|---|---|
| 힐베르트 변환포락선 인벨로프 (0) | 2025.06.27 |
| ntc r/t-tol (0) | 2025.04.29 |
| 전자로드 사용법 (0) | 2025.04.28 |
| 합성저항, 목표저항 계산 (0) | 2025.01.07 |
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