/home/yanni/Documents/src/adversarial-ai-book/ch3/
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Jun 28, 2024 · 7:37 AM

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PII Found

Path: notebooks/.ipynb_checkpoints/simple-cnn-cifar10-checkpoint.ipynb

Description:

A total of 1 PII found in cell number 3 with the following tag(s):

{
    "PERSON": 1
}

Location: cell input

Cell #3

#load dependencies and initialisations from tensorflow import keras from tensorflow.keras.datasets import cifar10 import matplotlib.pyplot as plt import warnings import numpy as np import tensorflow as tf from sklearn.model_selection import train_test_split warnings.filterwarnings('ignore') gpus = tf.config.list_physical_devices('GPU') # prevent memory error messages in GPU environments by setting memory growth equal to all GPUs if gpus: try: # Currently, memory growth needs to be the same across GPUs for [PII] in [PII]s: tf.config.experimental.set_memory_growth(gpu, True) logical_gpus = tf.config.list_logical_devices('GPU') print(len(gpus), "Physical GPUs,", len(logical_gpus), "Logical GPUs") except RuntimeError as e: # Memory growth must be set before GPUs have been initialized print(e) model_filename = 'simple-cifar10.h5'

PII Found

Path: notebooks/.ipynb_checkpoints/simple-cnn-cifar10-checkpoint.ipynb

Description:

A total of 1 PII found in cell number 22 with the following tag(s):

{
    "PERSON": 1
}

Location: cell input

Cell #22

### set the optimisation and loss function suitable to the job, metrics to use during training, and compile the model model.compile(optimizer='[PII]', loss=keras.losses.categorical_crossentropy, metrics=['accuracy'])

PII Found

Path: notebooks/.ipynb_checkpoints/simple-cnn-cifar10-checkpoint.ipynb

Description:

A total of 2 PII found in cell number 26 with the following tag(s):

{
    "PERSON": 2
}

Location: cell output

Cell #26

625/625 [==============================] - 5s 8ms/step - loss: 0.2970 - accuracy: 0.8961 - val_loss: 0.4040 - val_accuracy: 0.8730 Epoch 59/100 625/625 [==============================] - 5s 8ms/step - loss: 0.2959 - accuracy: 0.8957 - val_loss: 0.4241 - val_accuracy: 0.8705 Epoch 60/100 625/625 [==============================] - 5s 8ms/step - loss: 0.2961 - accuracy: 0.8963 - val_loss: 0.4091 - val_accuracy: 0.8723 Epoch 61/100 625/625 [==============================] - 5s 8ms/step - loss: 0.2919 - accuracy: 0.8974 - val_loss: 0.5588 - val_accuracy: 0.8304 Epoch 62/100 625/625 [==============================] - 5s 8ms/step - loss: 0.2928 - accuracy: 0.8974 - val_loss: 0.4725 - val_accuracy: 0.8555 Epoch 63/100 625/625 [==============================] - 4s 7ms/step - loss: 0.2901 - accuracy: 0.8969 - val_loss: 0.4424 - val_accuracy: 0.8613 Epoch 64/100 625/625 [==============================] - 5s 7ms/step - loss: 0.2889 - accuracy: 0.8984 - val_loss: 0.4420 - val_accuracy: 0.8628 [PII] 625/625 [==============================] - 5s 7ms/step - loss: 0.2854 - accuracy: 0.8989 - val_loss: 0.4545 - val_accuracy: 0.8607 Epoch 66/100 625/625 [==============================] - 5s 8ms/step - loss: 0.2807 - accuracy: 0.9028 - val_loss: 0.4170 - val_accuracy: 0.8690 Epoch 67/100 625/625 [==============================] - 5s 7ms/step - loss: 0.2767 - accuracy: 0.9029 - val_loss: 0.4377 - val_accuracy: 0.8673 [PII] 625/625 [==============================] - 5s 8ms/step - loss: 0.2791 - accuracy: 0.9035 - val_loss: 0.3982 - val_accuracy: 0.8774 Epoch 69/100 625/625 [==============================] - 4s 6ms/step - loss: 0.2754 - accuracy: 0.9043 - val_loss: 0.4301 - val_accuracy: 0.8679 Epoch 70/100 625/625 [==============================] - 4s 7ms/step - loss: 0.2818 - accuracy: 0.9014 - val_loss: 0.4299 - val_accuracy: 0.8649 Epoch 71/100 625/625 [==============================] - 5s 8ms/step - loss: 0.2783 - accuracy: 0.9033 - val_loss: 0.4184 - val_accuracy: 0.8730 Epoch 72/100 625/625 [==============================] - 5s 8ms/step - loss: 0.2741 - accuracy: 0.9046 - val_loss: 0.4515 - val_accuracy: 0.8600 Epoch 73/100 625/625 [==============================] - 5s 8ms/step - loss: 0.2730 - accuracy: 0.9058 - val_loss: 0.4553 - val_accuracy: 0.8601 Epoch 74/100 625/625 [==============================] - 5s 8ms/step - loss: 0.2746 - accuracy: 0.9046 - val_loss: 0.4316 - val_accuracy: 0.8667 Epoch 75/100 625/625 [==============================] - 5s 8ms/step - loss: 0.2684 - accuracy: 0.9061 - val_loss: 0.4165 - val_accuracy: 0.8696 Epoch 76/100 625/625 [==============================] - 5s 8ms/step - loss: 0.2656 - accuracy: 0.9057 - val_loss: 0.4259 - val_accuracy: 0.8651 Epoch 77/100 625/625 [==============================] - 5s 8ms/step - loss: 0.2616 - accuracy: 0.9100 - val_loss: 0.4208 - val_accuracy: 0.8713 Epoch 78/100 625/625 [==============================] - 5s 8ms/step - loss: 0.2677 - accuracy: 0.9071 - val_loss: 0.3952 - val_accuracy: 0.8775 Epoch 79/100 625/625 [==============================] - 5s 8ms/step - loss: 0.2590 - accuracy: 0.9113 - val_loss: 0.4317 - val_accuracy: 0.8669 Epoch 80/100 625/625 [==============================] - 5s 8ms/step - loss: 0.2608 - accuracy: 0.9090 - val_loss: 0.4219 - val_accuracy: 0.8700 Epoch 81/100 625/625 [==============================] - 5s 7ms/step - loss: 0.2642 - accuracy: 0.9086 - val_loss: 0.5142 - val_accuracy: 0.8457 Epoch 82/100 625/625 [==============================] - 5s 8ms/step - loss: 0.2548 - accuracy: 0.9122 - val_loss: 0.4744 - val_accuracy: 0.8600 Epoch 83/100 625/625 [==============================] - 5s 8ms/step - loss: 0.2549 - accuracy: 0.9117 - val_loss: 0.4337 - val_accuracy: 0.8680 Epoch 84/100 625/625 [==============================] - 2s 4ms/step - loss: 0.2555 - accuracy: 0.9125 - val_loss: 0.4545 - val_accuracy: 0.8616 Epoch 85/100 625/625 [==============================] - 5s 8ms/step - loss: 0.2546 - accuracy: 0.9098 - val_loss: 0.4220 - val_accuracy: 0.8717 Epoch 86/100 625/625 [==============================] - 4s 7ms/step - loss: 0.2523 - accuracy: 0.9121 - val_loss: 0.4462 - val_accuracy: 0.8676 Epoch 87/100 625/625 [==============================] - 4s 7ms/step - loss: 0.2525 - accuracy: 0.9105 - val_loss: 0.3951 - val_accuracy: 0.8812 Epoch 88/100 625/625 [==============================] - 5s 7ms/step - loss: 0.2463 - accuracy: 0.9135 - val_loss: 0.4216 - val_accuracy: 0.8736 Epoch 89/100 625/625 [==============================] - 4s 7ms/step - loss: 0.2537 - accuracy: 0.9117 - val_loss: 0.4092 - val_accuracy: 0.8764 Epoch 90/100 625/625 [==============================] - 5s 8ms/step - loss: 0.2437 - accuracy: 0.9143 - val_loss: 0.4009 - val_accuracy: 0.8792 Epoch 91/100 625/625 [==============================] - 4s 7ms/step - loss: 0.2499 - accuracy: 0.9122 - val_loss: 0.4654 - val_accuracy: 0.8596 Epoch 92/100 625/625 [==============================] - 5s 8ms/step - loss: 0.2421 - accuracy: 0.9152 - val_loss: 0.4214 - val_accuracy: 0.8746 Epoch 93/100 625/625 [==============================] - 4s 7ms/step - loss: 0.2402 - accuracy: 0.9164 - val_loss: 0.4299 - val_accuracy: 0.8709 Epoch 94/100 625/625 [==============================] - 4s 6ms/step - loss: 0.2408 - accuracy: 0.9160 - val_loss: 0.4424 - val_accuracy: 0.8696 Epoch 95/100 625/625 [==============================] - 4s 6ms/step - loss: 0.2427 - accuracy: 0.9135 - val_loss: 0.4669 - val_accuracy: 0.8649 Epoch 96/100 625/625 [==============================] - 4s 6ms/step - loss: 0.2404 - accuracy: 0.9166 - val_loss: 0.4375 - val_accuracy: 0.8693 Epoch 97/100 625/625 [==============================] - 4s 6ms/step - loss: 0.2412 - accuracy: 0.9166 - val_loss: 0.4061 - val_accuracy: 0.8770 Epoch 98/100 625/625 [==============================] - 4s 7ms/step - loss: 0.2358 - accuracy: 0.9173 - val_loss: 0.3998 - val_accuracy: 0.8793 Epoch 99/100 625/625 [==============================] - 3s 5ms/step - loss: 0.2372 - accuracy: 0.9175 - val_loss: 0.4052 - val_accuracy: 0.8809 Epoch 100/100 625/625 [==============================] - 4s 7ms/step - loss: 0.2326 - accuracy: 0.9194 - val_loss: 0.4069 - val_accuracy: 0.8790

PII Found

Path: notebooks/.ipynb_checkpoints/simple-cnn-cifar10-wsl2-checkpoint.ipynb

Description:

A total of 1 PII found in cell number 3 with the following tag(s):

{
    "PERSON": 1
}

Location: cell input

Cell #3

#load dependencies and initialisations from tensorflow import keras from tensorflow.keras.datasets import cifar10 import matplotlib.pyplot as plt import warnings import numpy as np import tensorflow as tf from sklearn.model_selection import train_test_split warnings.filterwarnings('ignore') gpus = tf.config.list_physical_devices('GPU') # prevent memory error messages in GPU environments by setting memory growth equal to all GPUs if gpus: try: # Currently, memory growth needs to be the same across GPUs for [PII] in [PII]s: tf.config.experimental.set_memory_growth(gpu, True) logical_gpus = tf.config.list_logical_devices('GPU') print(len(gpus), "Physical GPUs,", len(logical_gpus), "Logical GPUs") except RuntimeError as e: # Memory growth must be set before GPUs have been initialized print(e) model_filename = 'simple-cifar10.h5'

PII Found

Path: notebooks/.ipynb_checkpoints/simple-cnn-cifar10-wsl2-checkpoint.ipynb

Description:

A total of 1 PII found in cell number 22 with the following tag(s):

{
    "PERSON": 1
}

Location: cell input

Cell #22

### set the optimisation and loss function suitable to the job, metrics to use during training, and compile the model model.compile(optimizer='[PII]', loss=keras.losses.categorical_crossentropy, metrics=['accuracy'])
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Medium

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Low

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Files scanned

  • Notebooks
    • /home/yanni/Documents/src/adversarial-ai-book/ch3/notebooks/deploy-simple-cnn-cifar10.ipynb
    • /home/yanni/Documents/src/adversarial-ai-book/ch3/notebooks/verify-environment.ipynb
    • /home/yanni/Documents/src/adversarial-ai-book/ch3/notebooks/.ipynb_checkpoints/DeployResNet50v2-checkpoint.ipynb
    • /home/yanni/Documents/src/adversarial-ai-book/ch3/notebooks/.ipynb_checkpoints/simple-cnn-cifar10-checkpoint.ipynb
    • /home/yanni/Documents/src/adversarial-ai-book/ch3/notebooks/.ipynb_checkpoints/simple-cnn-cifar10-wsl2-checkpoint.ipynb
    • /home/yanni/Documents/src/adversarial-ai-book/ch3/notebooks/.ipynb_checkpoints/deploy-simple-cnn-cifar10-checkpoint.ipynb
  • Requirements