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research/denoised_smoothing/notebooks/Certification_Test.ipynb

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@@ -41,7 +41,7 @@
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"colab_type": "text"
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},
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"source": [
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"<a href=\"https://colab.research.google.com/github/sayakpaul/Denoised-Smoothing-TF/blob/main/Certification_Test.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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"<a href=\"https://colab.research.google.com/github/sayakpaul/neural-structured-learning/blob/master/research/denoised_smoothing/notebooks/Certification_Test.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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]
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},
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{
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"id": "FDlxwScPHSW_"
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},
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"source": [
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"!git clone https://github.com/sayakpaul/Denoised-Smoothing-TF\n",
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"!cd Denoised-Smoothing-TF && tar xf models.tar.gz"
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"!git clone https://github.com/sayakpaul/neural-structured-learning\n",
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"!cd neural-structured-learning/research/denoised_smoothing && tar xf models.tar.gz"
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],
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"execution_count": null,
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"outputs": []
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},
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"source": [
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"import sys\n",
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"sys.path.append(\"Denoised-Smoothing-TF\")\n",
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"sys.path.append(\"neural-structured-learning/research/denoised_smoothing\")\n",
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"\n",
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"from scripts import certify, analyze\n",
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"import matplotlib.pyplot as plt\n",
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{
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"cell_type": "code",
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"id": "Dzhq0lXNHssc",
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"outputId": "2b184ca9-4c2b-4df0-8a2f-b00a407b75d4"
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"id": "Dzhq0lXNHssc"
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},
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"source": [
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"print(\"Loading models.\")\n",
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"print(\"Models loaded.\")"
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],
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"execution_count": null,
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"outputs": [
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{
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"output_type": "stream",
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"text": [
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"Loading models.\n",
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"WARNING:tensorflow:No training configuration found in save file, so the model was *not* compiled. Compile it manually.\n",
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"WARNING:tensorflow:No training configuration found in save file, so the model was *not* compiled. Compile it manually.\n",
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"Models loaded.\n"
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],
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"name": "stdout"
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}
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]
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"outputs": []
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},
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{
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"cell_type": "markdown",
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{
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"cell_type": "code",
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"id": "flIat9vpJqpl",
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"outputId": "ba351f95-acfd-4b0f-e757-08881c1a7890"
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"id": "flIat9vpJqpl"
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},
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"source": [
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"(x_train, _), (x_test, y_test) = tf.keras.datasets.cifar10.load_data()\n",
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"norm_layer.adapt(x_train/255.0) # Notice the scaling step"
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],
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"execution_count": null,
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"outputs": [
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{
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"output_type": "stream",
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"text": [
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"Total test examples: 10000\n"
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],
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"name": "stdout"
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}
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]
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"outputs": []
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},
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{
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"cell_type": "markdown",

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