From cfcc2d538f38a8be3e8f273f63e0474af7dc75bc Mon Sep 17 00:00:00 2001
From: jalezeta <jonanderlezeta@gmail.com>
Date: Thu, 18 Feb 2021 09:34:38 +0000
Subject: [PATCH] [Notebook] Remove option to download and quantize original
dataset
---
.../1-train-mlp-with-brevitas.ipynb | 144 +++++++-----------
1 file changed, 52 insertions(+), 92 deletions(-)
diff --git a/notebooks/end2end_example/cybersecurity/1-train-mlp-with-brevitas.ipynb b/notebooks/end2end_example/cybersecurity/1-train-mlp-with-brevitas.ipynb
index d53a07ae8..d983b0f41 100644
--- a/notebooks/end2end_example/cybersecurity/1-train-mlp-with-brevitas.ipynb
+++ b/notebooks/end2end_example/cybersecurity/1-train-mlp-with-brevitas.ipynb
@@ -85,7 +85,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "## (Option 1, slower) Download original and quantize <a id='dataset_qnt_manual'></a>\n",
+ "## Download original and quantize <a id='dataset_qnt_manual'></a>\n",
"\n",
"We will create a binarized representation for the dataset by following the procedure defined by [Murovic and Trost](https://ev.fe.uni-lj.si/1-2-2019/Murovic.pdf), which we repeat briefly here:\n",
"\n",
@@ -97,24 +97,8 @@
"* All vectors are labeled as bad (0) or normal (1)\n",
"\n",
"Following Murovic and Trost's open-source implementation provided as a Matlab script [here](https://github.com/TadejMurovic/BNN_Deployment/blob/master/cybersecurity_dataset_unswb15.m), we've created a [Python version](dataloader_quantized.py).\n",
- "This `UNSW_NB15_quantized` class implements a PyTorch `DataLoader`, which represents a Python iterable over a dataset. This is useful because enables access to data in batches."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Download the training and test set from the [official website](https://www.unsw.adfa.edu.au/unsw-canberra-cyber/cybersecurity/ADFA-NB15-Datasets/) - uncomment the following lines to download:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "metadata": {},
- "outputs": [],
- "source": [
- "#! wget https://www.unsw.adfa.edu.au/unsw-canberra-cyber/cybersecurity/ADFA-NB15-Datasets/a%20part%20of%20training%20and%20testing%20set/UNSW_NB15_training-set.csv\n",
- "#! wget https://www.unsw.adfa.edu.au/unsw-canberra-cyber/cybersecurity/ADFA-NB15-Datasets/a%20part%20of%20training%20and%20testing%20set/UNSW_NB15_testing-set.csv"
+ "\n",
+ "However, downloading the original dataset and quantizing it can take some time, so we provide a pre-quantized version for your convenience. Uncomment the following line to download:"
]
},
{
@@ -126,46 +110,22 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "Samples in each set: train = 175341, test = 82332\n",
- "Shape of one input sample: torch.Size([593])\n"
+ "--2021-02-18 09:24:29-- https://zenodo.org/record/4519767/files/unsw_nb15_binarized.npz?download=1\n",
+ "Resolving zenodo.org (zenodo.org)... 137.138.76.77\n",
+ "Connecting to zenodo.org (zenodo.org)|137.138.76.77|:443... connected.\n",
+ "HTTP request sent, awaiting response... 200 OK\n",
+ "Length: 13391907 (13M) [application/octet-stream]\n",
+ "Saving to: 'unsw_nb15_binarized.npz'\n",
+ "\n",
+ "unsw_nb15_binarized 100%[===================>] 12.77M 6.12MB/s in 2.1s \n",
+ "\n",
+ "2021-02-18 09:24:32 (6.12 MB/s) - 'unsw_nb15_binarized.npz' saved [13391907/13391907]\n",
+ "\n"
]
}
],
"source": [
- "from torch.utils.data import DataLoader, Dataset\n",
- "from dataloader_quantized import UNSW_NB15_quantized\n",
- "\n",
- "file_path_train = \"UNSW_NB15_training-set.csv\"\n",
- "file_path_test = \"UNSW_NB15_testing-set.csv\"\n",
- "\n",
- "train_quantized_dataset = UNSW_NB15_quantized(file_path_train = file_path_train, \\\n",
- " file_path_test = file_path_test, \\\n",
- " train=True)\n",
- "\n",
- "test_quantized_dataset = UNSW_NB15_quantized(file_path_train = file_path_train, \\\n",
- " file_path_test = file_path_test, \\\n",
- " train=False)\n",
- "\n",
- "print(\"Samples in each set: train = %d, test = %s\" % (len(train_quantized_dataset), len(test_quantized_dataset)))\n",
- "print(\"Shape of one input sample: \" + str(train_quantized_dataset[0][0].shape))"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## (Option 2, faster) Use prequantized version <a id='dataset_qnt_pre'></a>\n",
- "\n",
- "Downloading the original dataset and quantizing it can take some time, so we provide a pre-quantized version for your convenience. Uncomment the following line to download:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "metadata": {},
- "outputs": [],
- "source": [
- "# ! wget -O unsw_nb15_binarized.npz https://zenodo.org/record/4519767/files/unsw_nb15_binarized.npz?download=1"
+ "! wget -O unsw_nb15_binarized.npz https://zenodo.org/record/4519767/files/unsw_nb15_binarized.npz?download=1"
]
},
{
@@ -177,7 +137,7 @@
},
{
"cell_type": "code",
- "execution_count": 5,
+ "execution_count": 4,
"metadata": {},
"outputs": [
{
@@ -223,7 +183,7 @@
},
{
"cell_type": "code",
- "execution_count": 6,
+ "execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
@@ -238,7 +198,7 @@
},
{
"cell_type": "code",
- "execution_count": 7,
+ "execution_count": 6,
"metadata": {},
"outputs": [
{
@@ -276,7 +236,7 @@
},
{
"cell_type": "code",
- "execution_count": 8,
+ "execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
@@ -298,7 +258,7 @@
},
{
"cell_type": "code",
- "execution_count": 9,
+ "execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
@@ -339,7 +299,7 @@
},
{
"cell_type": "code",
- "execution_count": 10,
+ "execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
@@ -368,7 +328,7 @@
},
{
"cell_type": "code",
- "execution_count": 11,
+ "execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
@@ -421,7 +381,7 @@
},
{
"cell_type": "code",
- "execution_count": 12,
+ "execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
@@ -439,7 +399,7 @@
},
{
"cell_type": "code",
- "execution_count": 13,
+ "execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
@@ -450,7 +410,7 @@
},
{
"cell_type": "code",
- "execution_count": 14,
+ "execution_count": 13,
"metadata": {
"scrolled": true
},
@@ -459,7 +419,7 @@
"name": "stderr",
"output_type": "stream",
"text": [
- "Training loss = 0.130799 test accuracy = 0.794551: 100%|██████████| 15/15 [01:25<00:00, 5.66s/it]\n"
+ "Training loss = 0.128492 test accuracy = 0.792705: 100%|██████████| 15/15 [04:28<00:00, 17.97s/it]\n"
]
}
],
@@ -487,14 +447,14 @@
},
{
"cell_type": "code",
- "execution_count": 15,
+ "execution_count": 14,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
- "image/png": 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\n",
+ "image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
@@ -515,12 +475,12 @@
},
{
"cell_type": "code",
- "execution_count": 16,
+ "execution_count": 15,
"metadata": {},
"outputs": [
{
"data": {
- "image/png": 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\n",
+ "image/png": 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HBMgCbvHqMxgTSKu37eP9rHwmjuxOp7jqfxMXEa45sSuDk9tw+7TFTHjpe+44I5U7z0wN+nDPsrxCpi3azOysLRw4UkrPDi34/QXHcemgJNo2jwpqbKb+eHY5bkNil+OahuBXry3ih5zdfHP/6bSO9e9L9uCRUh6etYJ3F+cxrFtbnhk3sMak44WdB44wO2sLMzLzWLl1H9GRYZzfvzPjhyaT3rVNo76EuKkLxuW4xhhXxsbdfL5qB/ed29vvpAHQvFkET449gZN6tOMPs5Zz/rPf8OQVJ3B6nw4eRutMdH++ajvvLc7nq7UFlJUr/RJb8ejo4xmdltioLhk2dWeJwxiPqSpPfLKG9i2accOIlKNq47LBSaQlt+a2qYu54dVF3DyyG/ed24eoiMAtlaGqZGzaw3uL8/hg6Vb2Hy6lU6tobh7ZnUsHJdKro90PYRyWOIzx2JdrC1i4cTd/Gn08sVFH/1+uR3wLZt42gsc+XMU/v8lh4cY9PD9+IF3axh5TfJt2HeS9xfm8vySfzbuLiIkM57x+nbh0UBLDe7QL+ryKaXhsjsMYD5WXKxc8N5+DR0r5/J5TA9ZD+HjZVu5/dykAf7lsAOf371yn8wsPlfDh0q28tziPjE17EIGTerTj0oFJjOrXyTYrMoDNcZgmYu32/SS3jSU6MjzYoQDw76VbWLV1H8+MSwvosNJ5/TvTLzGOO6Yt4dapi5kwLJk/XNi3xs9dUubsP/He4nw+W7Wd4tJyenZowf2jejMmLTEgd3SbpsEShwl5qsq8NTt4Yd56Mjbt4bTe8Uy5bkjQh1iKS8t58tO19OnUkosGJAS8/S5tY5l+y3D+NmcN//h6A5mb9vD8VYPo2eGnGwtVlRVb9vHu4jxmZ21h18Fi2jaP4qqhyVw6KJH+iXF2VZSpM0scJmSVlpXzwdKt/P2r9azetp/E1jFcPjiJGZl5PP35Wu49p3dQ43s7I5fNu4t45fohnt0QFxkexkPnH8eJPdpx7zs/ctFz8/nTmH6c3LM9M7PyeW9xHmu3HyAqPIyz+nbg0oFJnNrb9p8wx8YShwk5h0vKeCcjl8lfbyBvzyF6dWzB/409gYtOSCAiTAgX4bkvsjk+IY5R/ToFJcai4lKenbuOoSltOa13vOfvd3rvDnx810juemsJv5v+IyKgCoO7tuGxS/pxYf8E4mLtEloTGJY4TMgoPFTCm99v4uX5Oew6WMyg5NY8ctHxnNGnw89+o/+f0cezevt+7n0ni54dRgRlWe1XFmykYP8RXpwwqN6Ggjq2imbqr07kje82svdQCaPTEunWvnm9vLdpWuyqKtPgbd93mJfn5zD1h80cOFLK6b3j+c1pPRmSUv1dy1sLD3HRc/NpFR3JzNtH0Cq6/n7b3ltUzMgn5jGsW1teum5Ivb2vMYFmV1WZkJOz8yCTv17Pu5n5lJaXc+GABG45tYdf26x2joth0lWDmPDSD9zzdhaTr0mvt4X3XvxqPQeOlPK7c4M7x2KMVyxxmAZneX4hL365no+WbyUyPIyxQ5KYOLIHye3qdqPbsO7t+P0Fx/HIv1fy7Bfr+O1ZvTyK+CfbCg/z6oKNXJKWSJ9OTWcfcdO0WOIwDYKq8t2GXbz45Xq+WbeTls0i+M2pPbhhRDfiWx79fhTXnZTC0vxCnv58Hf0T4zjzuI4BjPqXnpm7jnJV7j7b+yRlTLBY4jBBVV6ufLpyOy9+tZ4fc/fSvkUzHhjVhwknJgdkXkJE+PMl/Vm7fT+/fSuLWbeP+MUGSoGyoeAA72Tkcs2JXY95GRBjGjJLHCYoikvLmZWVz9+/Ws/6goMkt43lsUv6cdmgpIDf9R0dGc7frx7Mxc8vYOIbmcy8bQQtPFhS48nP1tIsIozbTu8Z8LaNaUjsLiATFL9+I4P7ZiylWUQ4z40fyBf3nsqEYV09WyokqU0sz181kJydB7n3nSzKywN7NeGyvEI+XLqVm04+tqE1Y0KBJQ5T7xbm7GbemgLuObsXH955snPjXj3cyXxSj/Y8dF4f5qxwhsYC6Yk5q2kdG8nNp3QPaLvGNESe/m8VkVEiskZEskXkwSqOPyUiWe5jrYjs9TlW5nNstk95NxH5wW3zbRGx/SpDzDNz19K+RTMmntK93tdJuunkboxOS+Bvn65h3podAWnz2/U7+WbdTm47rWe93i9iTLB4ljhEJByYBJwH9AXGi0hf3zqqereqpqlqGvAc8J7P4UMVx1T1Yp/yvwBPqWpPYA9wk1efwQRexsbdLMjexS2ndg/KCrYiwuOXDqBPp1bcNW0JG3cePKb2KjZp6hwXzTXDuwYoSmMaNi97HEOBbFXdoKrFwFvA6Brqjwem1dSgOL+engHMcIteA8Yce6imvjwzdx3tW0QxYVjwvmRjosKZfM1gwsKEX7+RycEjpUfd1qcrt5OVu5ffnpXaYJZyN8ZrXiaORCDX53WeW/YLItIV6AZ84VMcLSIZIvK9iIxxy9oBe1W14n96TW1OdM/PKCgoOIaPYQIlc9Mevlm3k4mndCcmKrhfsl3axvLc+IGs27Gf+99dytEsvVNWrvx1zhq6xzfnskFJHkRpTMPUUCbHxwEzVLXMp6yru0bKVcDTItKjLg2q6mRVTVfV9Ph471cnNbV7Zu462jWP4uoTG8aQzsjUeO4f1YcPl25l8tcb6nz+e4vzyN5xgPvO6V0vk/vGNBRe/mvPB7r4vE5yy6oyjkrDVKqa7/65AfgSGAjsAlqLSMVF+DW1aRqQJZv38PXaAm4+pfsx7bsdaL8+pTsX9O/MXz5ZzTfr/O+ZHi4p4+nP1zEgKXhLtxsTLF4mjkVAqnsVVBROcphduZKI9AHaAN/5lLURkWbu8/bACGClOuMJ84DL3arXAbM8/AwmQJ6Zu462zaO4poH0NiqICE9cPoDUDi25Y9oScncX+XXe1B82k7/3EA+M6mM76Jkmx7PE4c5D3A7MAVYB76jqChF5VER8r5IaB7ylPx9kPg7IEJEfcRLF46q60j32AHCPiGTjzHlM8eozmMDIyt3Ll2sK+NXIbjT34I7tY9W8WQT/uGYw5eXKxDcyOVRcVmP9/YdLmDQvm5N7tmdEz/b1FKUxDYen/4tV9SPgo0plD1d6/UgV530L9K+mzQ04V2yZEPHs3HW0jo3k2uEpwQ6lWintm/PM+IHc+OoiHnxvKU9fmVZtT+Klb3LYfbCY+2zZdNNE2Yye8dTSvL18sXoHN4/s7sn6UIF0eu8O3Ht2L2ZlbWHK/Jwq6+w8cISXvtnA+f07cUKX1vUboDENhCUO46ln564jLiaSa0Pk5rhbT+vJucd35P99vJpvs3f+4vikedkcLi3n3nOst2GaLkscxjPL8wv5fNUOfnVyN1qGyFIcYWHCk2PT6Na+ObdPW0Lenp8my3N3FzH1+81cMTiJHh4tzW5MKLDEYTzzzNx1tIqO4LoRKcEOpU5auJPlJaXl3PJmJodLnMnypz9fBwJ3nZUa5AiNCS5LHMYTK7YU8tnK7dx0cveQXPivR3wLnroyjeX5+/iv95exZtt+3luSx/UnpdA5LibY4RkTVA17ttKErGfnrqNldATXh1hvw9dZfTvy27NSefrzdXy3fhctopztbI1p6qzHYQJu1dZ9zFmxnRtHdCMuJvR6G77uPCOVs47rwNbCw0w8pTttmtsq/sZYj8ME3LNz19GyWQQ3jugW7FCOWViY8NSVafz7x61cOqjK9TSNaXKsx2ECavW2fXy8fBvXj0ghLja0exsVWkZHctWwZFs23RiXJQ4TUM/NzaZFswhuOjn0exvGmKpVO1QlIpf6cf5hd1kRY1i7fT8fLd/Kraf1oHWszQUY01jVNMfxT5yVZ2ta+vMUKq1FZZqu577IJjYynF+d3D3YoRhjPFRT4vhYVW+s6WQReTPA8ZgQlb1jPx8s3cItp/awK4+MaeSqneNQ1atrO9mfOqZpeO6LbGIiw7l5pPU2jGns/J4cF5GeIvKmiLwrIsO9DMqElvUFB/j3j1u4ZnhX2lpvw5hGr6bJ8WhVPexT9Cfgfvf5v4E0D+MyIeT5L7JpFhHOROttGNMk1NTj+LeIXOvzugRIAboCNW+R5hKRUSKyRkSyReTBKo4/JSJZ7mOtiOx1y9NE5DsRWSEiS0XkSp9zXhWRHJ/z0vyJxXhjQ8EBZmXlc83wrrRr0SzY4Rhj6kFNk+OjgN+IyCfAn4HfAXcCMcCE2hoWkXBgEnA2kAcsEpHZPlvAoqp3+9S/AxjoviwCrlXVdSKSAGSKyBxV3esev09VZ/j5GY2Hnp+XTVREmM1tGNOE1DQ5XqaqzwNXAhcDzwCvqOq9qrraj7aHAtmqukFVi4G3gNE11B8PTHPfe62qrnOfbwF2APH+fCBTfzbuPMisrC1cPawr8S2tt2FMU1Ft4hCRYSIyA3gReBX4PfCYiDwpIq39aDsRyPV5neeWVfVeXYFuwBdVHBsKRAHrfYofc4ewnhKRKr+xRGSiiGSISEZBQYEf4Zq6en5eNhFhwsRTrbdhTFNS0xzHP3CGph4B/qGq61V1HDAbeDvAcYwDZqjqz+ZORKQz8AZwg6qWu8UPAX2AIUBb4IGqGlTVyaqarqrp8fHWWQm0TbsO8v6SfCYM60qHltHBDscYU49qShyl/DQZXlxRqKpfqeq5frSdD3TxeZ3kllVlHO4wVQURaQV8CPy3qn7v8/5b1XEEeAVnSMzUs0lub+MW620Y0+TUlDiuAi4DzgCuraFedRYBqSLSTUSicJLD7MqVRKQP0Ab4zqcsCngfeL3yJLjbC0FEBBgDLD+K2MwxyN1dxHuL8xk/NJkOray3YUxTU+1VVaq6Frj3aBtW1VIRuR2YA4QDL6vqChF5FMhQ1YokMg54S1XV5/SxOOtgtROR692y61U1C5gqIvE4a2hlAbccbYzm6Eyal01YmPCb02w3PGOaoppuAPxAVS+s6eTa6rgr535UqezhSq8fqeK8N4Eq18FS1TNqisl4K3d3ETMy85gwLJmO1tswpkmq6T6Ok0XkF0NLPgToG+B4TAP3wpfrCRPhFuttGNNk1ZQ4arrnokJx7VVMY5G/9xAzMnMZNySZznExwQ7HGBMkNc1xfFWfgZiG74V52QA2t2FME2dbxxq/bNl7iHcychmb3oWE1tbbMKYps8Rh/PLil86N+7ee3jPIkRhjgq3WxCEiF4mIJZgmbGvhId5elMvlg5NItN6GMU2ePwnhSmCdiDzh3qxnmpi/f7meclVuPc16G8YYPxKHuz3sQJxFBl9198mYKCItPY/OBN32fYeZtiiXywYl0aVtbLDDMcY0AH4NQanqPmAGztLonYFLgMXuHhqmEXtlwUbKypXbbG7DGOPyZ47jYhF5H/gSiASGqup5wAkcw5IkpuErLSvn3cV5nN47nuR21tswxjhqugGwwmXAU6r6tW+hqhaJyE3ehGUagq/WFlCw/whXpHepvbIxpsnwJ3E8AmyteCEiMUBHVd2oqnO9CswE3/SMPNo1j+KMPh2CHYoxpgHxZ45jOlDu87rMLTON2K4DR5i7ejuXDEwkMtyuxjbG/MSfb4QId89wANznUd6FZBqCmVlbKClTG6YyxvyCP4mjQEQurnghIqOBnd6FZIJNVZmekcuApDh6d7Krro0xP+dP4rgF+C8R2SwiuTh7fP/a27BMMK3Yso/V2/Zbb8MYU6VaJ8dVdT1wooi0cF8f8DwqE1TvZOQSFRHGxQMSgh2KMaYB8mvWU0QuAG4F7hGRh0Xk4drOcc8bJSJrRCRbRB6s4vhTIpLlPtaKyF6fY9eJyDr3cZ1P+WARWea2+ay797gJkMMlZczK2sK5x3ciLjYy2OEYYxqgWnscIvJ3IBY4HXgJuBxY6Md54cAk4GwgD1gkIrNVdWVFHVW926f+HThLmyAibYE/AumAApnuuXuAF4GbgR9wtqUdBXzsz4c1tfts5XYKD5UwNj0p2KEYYxoof3ocJ6nqtcAeVf0fYDjQy4/zhgLZqrrBvRLrLWreVXA8MM19fi7wmarudpPFZ8AoEekMtFLV71VVgdeBMX7EYvw0PTOPhLhoTurRPtihGGMaKH8Sx2H3zyIRSQBKcNarqk0ikOvzOs8t+wUR6Qp0A76o5dxE97k/bU4UkQwRySgoKPAjXLNl7yG+WVfA5YOTCA+zEUBjTNX8SRz/FpHWwF+BxcBG4F8BjmMcMENVywLVoKpOVtV0VU2Pj48PVLON2nuL81CFywfb1VTGmOrVOMfhbuA0V1X3Au+KyAdAtKoW+tF2PuD7DZTkllVlHHBbpXNPq3Tul255UqXy6to0daCqTM/MY1i3tragoTGmRjX2OFS1HGeCu+L1ET+TBsAiIFVEuolIFE5ymF25krs5VBvgO5/iOcA5ItJGRNoA5wBzVHUrsE9ETnSvproWmOVnPKYGC3N2s2lXEWPt3g1jTC38GaqaKyKX1fWyV1UtBW7HSQKrgHdUdYWIPOp7JzpOQnnLneyuOHc38Cec5LMIeNQtA+ey4JeAbJzNpeyKqgCYnplHi2YRnNe/U7BDMcY0cOLzfV11BZH9QHOgFGeiXABV1VbehxcY6enpmpGREewwGqwDR0oZ+tjnXDQggb9cPiDY4RhjGggRyVTV9Mrl/tw5bosVNXIfLd1KUXEZY4fYvRvGmNr5cwPgKVWVV97YyYSu6Zm5dI9vzqDkNsEOxRgTAvzZyOk+n+fRODf2ZQJneBKRqVcbCg6waOMeHhjVB1u9xRjjD3+Gqi7yfS0iXYCnvQrI1K8ZmXmECVw6qMr7KI0x5heOZmu3POC4QAdi6l9ZufLu4jxO7RVPx1bRwQ7HGBMi/JnjeA5noUFwEk0azh3kJsR9va6A7fuO8MhFdu+GMcZ//sxx+F7HWgpMU9UFHsVj6tGMjDzaxEZy5nEdgx2KMSaE+JM4ZgCHK9aREpFwEYlV1SJvQzNe2nOwmM9WbmfCiclERRzNiKUxpqny685xIMbndQzwuTfhmPoyKyuf4rJyrrAFDY0xdeRP4oj23S7WfW6r4IW46Zl5HJ/Qir4JIbMAgDGmgfAncRwUkUEVL0RkMHDIu5CM11ZsKWTFln22oKEx5qj4M8fxW2C6iGzBWaeqE3Cll0EZb03PyCMqPIzRaQnBDsUYE4L8uQFwkbv0eW+3aI2qlngblvHKkdIyZmXlc3bfjrSOjQp2OMaYEFTrUJWI3AY0V9XlqrocaCEit3ofmvHC3FU72FNUwhXptqChMebo+DPHcbO7AyAAqroHuNmziIynpmfk0qlVNCNTbTtdY8zR8SdxhPtu4iQi4YCNcYSgbYWH+WptAZcNTiQ8zBY0NMYcHX8SxyfA2yJypoicCUxzy2olIqNEZI2IZIvIg9XUGSsiK0VkhYj8yy07XUSyfB6HRWSMe+xVEcnxOZbmTywG3luSR7nC5XbvhjHmGPhzVdUDwETgN+7rz4B/1naS2zOZBJyNszDiIhGZraorfeqkAg8BI1R1j4h0AFDVeThrYiEibXG2if3Up/n7VHWGH7Ebl6oyIyOPISlt6Na+ebDDMcaEsFp7HKparqp/V9XLVfVyYCXwnB9tDwWyVXWDqhYDbwGjK9W5GZjkzpugqjuqaOdy4GNb4uTYZG7aw4adB7nC7t0wxhwjvxYpEpGBIvKEiGwEHgVW+3FaIpDr8zrPLfPVC+glIgtE5HsRGVVFO+Nwhsd8PSYiS0XkKRFpVk3ME0UkQ0QyCgoK/Ai3cZuekUdsVDgX9O8c7FCMMSGu2sQhIr1E5I8ishqnh5ELiKqerqr+9Dj8EQGkAqcB44F/ikhrnxg6A/2BOT7nPAT0AYYAbXGG0n5BVSerarqqpsfHN+0riIqKS/lg6RbO79+Z5s38GZ00xpjq1dTjWI2zPeyFqnqymyzK6tB2PuA7LpLklvnKA2araomq5gBrcRJJhbHA+743HKrqVnUcAV7BGRIzNfho2TYOFpfZEiPGmICoKXFcCmwF5onIP90rqupyDeciIFVEuolIFM6Q0+xKdWbi9DYQkfY4Q1cbfI6Pp9IwldsLwb1EeAywvA4xNUnTM3JJaRfLkJQ2wQ7FGNMIVJs4VHWmqo7DGRaah7NmVQcReVFEzqmtYVUtBW7HGWZaBbyjqitE5FERuditNgfYJSIr3fe4T1V3AYhICk6P5atKTU8VkWXAMqA98L/+ftimaNOug/yQs5sr0rvgczuOMcYcNVHV2mtVVBZpA1wBXKmqZ3oWVYClp6drRkZG7RUboSc/XcOkedksePAMOsfF1H6CMca4RCRTVdMrl9dp6zdV3eNOOodM0mjKysqVdzPzODk13pKGMSZgbM/QRmxB9k62FB5mrC1oaIwJIEscjdj0zDziYiI567iOwQ7FGNOIWOJopAqLSpizYhuj0xKIjgwPdjjGmEbEEkcjNfvHfIpLy+3eDWNMwFniaKSmZ+bRp1NLjk9oFexQjDGNjCWORmj1tn0szStkrN27YYzxgCWORmh6Rh6R4cKYgZXXlDTGmGNniaORKS4t5/0l+ZzZpyNtm9tGjcaYwLPE0ch8sXoHuw8WM3aI3bthjPGGJY5GZkZmLh1aNuOU1Ka9lLwxxjuWOBqRHfsPM29NAZcOSiIi3P5qjTHesG+XRuT9xfmUlStX2BIjxhgPWeJoJFSV6Zl5DEpuTY/4FsEOxxjTiFniaCQyN+0he8cBrrA7xY0xHrPE0Ui8vCCHVtERXHxCQrBDMcY0cpY4GoHc3UV8snwb44cm07xZRLDDMcY0cp4mDhEZJSJrRCRbRB6sps5YEVkpIitE5F8+5WUikuU+ZvuUdxORH9w233b3M2/SXvt2IyLCdSelBDsUY0wT4FniEJFwYBJwHtAXGC8ifSvVSQUeAkao6vE4+5pXOKSqae7jYp/yvwBPqWpPYA9wk1efIRQcOFLK24tyOa9fJxJa2y5/xhjvednjGApkq+oGVS0G3gJGV6pzMzBJVfcAqOqOmhoUZ8W+M4AZbtFrwJhABh1q3lmUy/4jpfxqZPdgh2KMaSK8TByJQK7P6zy3zFcvoJeILBCR70VklM+xaBHJcMvHuGXtgL2qWlpDmwCIyET3/IyCgoJj/jANUVm58sq3OQzu2oa0Lq2DHY4xpokI9kxqBJAKnAYkAV+LSH9V3Qt0VdV8EekOfCEiy4BCfxtW1cnAZID09HQNdOANwWcrt5O7+xAPnXdcsEMxxjQhXvY48gHfmwqS3DJfecBsVS1R1RxgLU4iQVXz3T83AF8CA4FdQGsRiaihzSbj5fk5JLaO4Zy+tqe4Mab+eJk4FgGp7lVQUcA4YHalOjNxehuISHucoasNItJGRJr5lI8AVqqqAvOAy93zrwNmefgZGqxleYUs3LibG0ak2LpUxph65dk3jjsPcTswB1gFvKOqK0TkURGpuEpqDrBLRFbiJIT7VHUXcByQISI/uuWPq+pK95wHgHtEJBtnzmOKV5+hIZsyfwPNo8IZO8TuFDfG1C9P5zhU9SPgo0plD/s8V+Ae9+Fb51ugfzVtbsC5YqvJ2lZ4mA+WbuWa4V1pFR0Z7HCMMU2MjXGEoNe/20iZKjec1C3YoRhjmiBLHCHmUHEZ/1q4mXP6diS5XWywwzHGNEGWOELMu4vz2FtUwk0n2w1/xpjgsMQRQsrLlZcX5NA/MY4hKW2CHY4xpomyxBFCvlpbwIaCg9x0cjec1VeMMab+WeIIIVPm59CxVTPO79852KEYY5owSxwhYvW2fczP3sm1w1OIirC/NmNM8Ng3UIh4eX4O0ZFhTBiWHOxQjDFNnCWOELDzwBFmZm3hskFJtI5t8vtWGWOCzBJHCHjz+00Ul5Zz48l2w58xJvgscTRwh0vKePP7TZzeO54e8S2CHY4xxljiaOhm/7iFnQeK7YY/Y0yDYYmjAVNVXp6fQ59OLRnRs12wwzHGGMASR4227ztM9o79QXv/b9fvYvW2/dw4wm74M8Y0HJY4qqGq3DZ1MRNe+oHc3UVBiWHK/Bzat4ji4rSEoLy/McZUxRJHNUSExy7pz+GScq6Z8gM79h+u1/dfX3CAL1bvYMKwrkRHhtfrextjTE08TRwiMkpE1ohItog8WE2dsSKyUkRWiMi/3LI0EfnOLVsqIlf61H9VRHJEJMt9pHkVf+9OLXnlhiFs33eEa6cspPBQiVdv9QuvLMghKjyMq0/sWm/vaYwx/vAscYhIODAJOA/oC4wXkb6V6qQCDwEjVPV44LfuoSLgWrdsFPC0iLT2OfU+VU1zH1lefQaAQcltmHztYDYUHOTGVxdRVFzq5dsBsLeomHcz8xmdlkB8y2aev58xxtSFlz2OoUC2qm5Q1WLgLWB0pTo3A5NUdQ+Aqu5w/1yrquvc51uAHUC8h7HWaGRqPM+OT2PJ5j3c8uZiikvLPX2/fy3czKGSMm4aaTf8GWMaHi8TRyKQ6/M6zy3z1QvoJSILROR7ERlVuRERGQpEAet9ih9zh7CeEpEqfyUXkYkikiEiGQUFBcf2SYBR/Trz+KUD+HptAXe/k0VZuR5zm1UpKSvn9W83MaJnO/p0auXJexhjzLEI9uR4BJAKnAaMB/7pOyQlIp2BN4AbVLXi1/yHgD7AEKAt8EBVDavqZFVNV9X0+PjAdFbGDunCf59/HB8u3crvZy5HNfDJ46NlW9m27zA32fIixpgGKsLDtvOBLj6vk9wyX3nAD6paAuSIyFqcRLJIRFoBHwL/rarfV5ygqlvdp0dE5BXgd159gKrcfEp39h4qZtK89cTFRPLgeX0C1raqMmV+Dt3jm3Narw4Ba9cYYwLJyx7HIiBVRLqJSBQwDphdqc5MnN4GItIeZ+hqg1v/feB1VZ3he4LbC0GcO+LGAMu9+whV+905vbn6xGT+/tV6Xvxyfe0n+Clj0x6W5hVyw4huhIXZDX/GmIbJsx6HqpaKyO3AHCAceFlVV4jIo0CGqs52j50jIiuBMpyrpXaJyNXAKUA7EbnebfJ69wqqqSISDwiQBdzi1Weojojw6MX92HeolL98sprWsZGMH3rs+2RM+SaHuJhILhtUeSrIGGMaDi+HqlDVj4CPKpU97PNcgXvch2+dN4E3q2nzjMBHWndhYcKTY09g/+ES/uv9ZbSMjuDCAUd/h3fu7iI+XbmNX5/ag9goT/9ajDHmmAR7cjykRYaH8cKEwaR3bcPdb2fx5ZodR93WKws2EibCdcNTAhegMcZ4wBLHMYqJCuel64aQ2qElt7yZScbG3XVuY//hEt7JyOWCAZ3pFBftQZTGGBM4ljgCIC4mktdvGkpCXAw3vLqIlVv21en8txflcuBIqV2Ca4wJCZY4AqR9i2a8ftNQWjSL4NqXF5Kz86Bf55WVK69+u5GhKW0ZkNTa2yCNMSYALHEEUFKbWN64aRjlqlz90g9sK6x9Rd1PV2wjb88h20/cGBMyLHEEWM8OLXjthqEUHirh6ik/sPtgcY31p8zPIbltLGf37VhPERpjzLGxxOGB/klx/PPadDbvLuKGVxZy4EjVK+r+mLuXjE17uP6kFMLthj9jTIiwxOGR4T3a8cJVg1i+ZR83v5bB4ZKyX9SZMj+Hls0iGDukSxUtGGNMw2SJw0Nn9e3I364YwHcbdnHHtCWUlv20HPvWwkN8tGwrVw7pQotmdsOfMSZ0WOLw2CUDk3jkor58tnI7D7y7jHJ3OfbXvt1EuSrXnZQS3ACNMaaO7FfdenD9iG4UHirlqc/X0iomgvvO7c20hZsZ1a8TXdrGBjs8Y4ypE0sc9eTOM3tSeKiElxfkkJW7l8JDJXbDnzEmJFniqCciwu8vOI7CQyW8uziPE7q0ZlBym2CHZYwxdWaJox6FhQl/uaw/yW1jOaNPB5wtRYwxJrRY4qhnEeFh3HVWarDDMMaYo2ZXVRljjKkTSxzGGGPqxNPEISKjRGSNiGSLyIPV1BkrIitFZIWI/Mun/DoRWec+rvMpHywiy9w2nxWbKDDGmHrl2RyHiIQDk4CzgTxgkYjMVtWVPnVSgYeAEaq6R0Q6uOVtgT8C6YACme65e4AXgZuBH3C2pR0FfOzV5zDGGPNzXvY4hgLZqrpBVYuBt4DRlercDExyEwKqWrH36rnAZ6q62z32GTBKRDoDrVT1e3e/8teBMR5+BmOMMZV4mTgSgVyf13luma9eQC8RWSAi34vIqFrOTXSf19QmACIyUUQyRCSjoKDgGD6GMcYYX8GeHI8AUoHTgPHAP0WkdSAaVtXJqpququnx8fGBaNIYYwzeJo58wHe98CS3zFceMFtVS1Q1B1iLk0iqOzfffV5Tm8YYYzwkzlSBBw2LROAkgjNxvtwXAVep6gqfOqOA8ap6nYi0B5YAabgT4sAgt+piYLCq7haRhcCd/DQ5/pyqflRLLAXApqP8KO2BnUd5bjCEUrwWq3dCKd5QihVCK95jjbWrqv5iyMazq6pUtVREbgfmAOHAy6q6QkQeBTJUdbZ77BwRWQmUAfep6i4AEfkTTrIBeFRVd7vPbwVeBWJwrqaq9Yqqqj64v0QkQ1XTj/b8+hZK8Vqs3gmleEMpVgiteL2K1dMlR9yewEeVyh72ea7APe6j8rkvAy9XUZ4B9At4sMYYY/wS7MlxY4wxIcYSR+0mBzuAOgqleC1W74RSvKEUK4RWvJ7E6tnkuDHGmMbJehzGGGPqxBKHMcaYOrHEUQN/VvdtCESki4jM81ll+K5gx1QbEQkXkSUi8kGwY6mNiLQWkRkislpEVonI8GDHVB0Rudv9N7BcRKaJSHSwY/IlIi+LyA4RWe5T1lZEPnNXwv5MRBrMnsrVxPtX99/CUhF5P1CrXRyrqmL1OXaviKh7v9wxs8RRDZ/Vfc8D+gLjRaRvcKOqVilwr6r2BU4EbmvAsVa4C1gV7CD89Azwiar2AU6ggcYtIok4N8emq2o/nPunxgU3ql94FWdFa18PAnNVNRWY675uKF7ll/F+BvRT1QE4Nzk/VN9BVeNVfhkrItIFOAfYHKg3ssRRPX9W920QVHWrqi52n+/H+WKrcvHHhkBEkoALgJeCHUttRCQOOAWYAqCqxaq6N6hB1SwCiHFXbogFtgQ5np9R1a+B3ZWKRwOvuc9fowGteF1VvKr6qaqWui+/5+fLIAVNNT9bgKeA+3FW5AgISxzV82d13wZHRFKAgThLsjRUT+P8Qy4Pchz+6AYUAK+4Q2sviUjzYAdVFVXNB/6G85vlVqBQVT8NblR+6aiqW93n24COwQymjm6kAe8HJCKjgXxV/TGQ7VriaEREpAXwLvBbVd0X7HiqIiIXAjtUNTPYsfgpAmfNtBdVdSBwkIY1lPIf7tzAaJxklwA0F5GrgxtV3birSYTEPQIi8t84w8RTgx1LVUQkFvgv4OHa6taVJY7q+bO6b4MhIpE4SWOqqr4X7HhqMAK4WEQ24gz/nSEibwY3pBrlAXmqWtGDm8FPi282NGcBOapaoKolwHvASUGOyR/b3U3acP/cUUv9oBOR64ELgQnacG+G64HzS8SP7v+3JGCxiHQ61oYtcVRvEZAqIt1EJApnknF2kGOqkrvv+hRglar+X7DjqYmqPqSqSaqagvMz/UJVG+xvxaq6DcgVkd5u0ZnAyhpOCabNwIkiEuv+mziTBjqRX8ls4Dr3+XXArCDGUit3Ve/7gYtVtSjY8VRHVZepagdVTXH/v+UBg9x/08fEEkc13MmvitV9VwHv+C4J38CMAK7B+e09y32cH+ygGpE7gKkishRn2f8/Bzecqrm9ohk42xAsw/n/3aCWxxCRacB3QG8RyRORm4DHgbNFZB1Or+nxYMboq5p4nwdaAp+5/9f+HtQgXdXE6s17NdxeljHGmIbIehzGGGPqxBKHMcaYOrHEYYwxpk4scRhjjKkTSxzGGGPqxBKHMbUQkQPunykiclWA2/6vSq+/DWT7xnjBEocx/ksB6pQ43MUGa/KzxKGqoXCnt2niLHEY47/HgZHuTV93u3uK/FVEFrl7M/waQEROE5FvRGQ27l3mIjJTRDLdvTImumWP46xkmyUiU92yit6NuG0vF5FlInKlT9tf+uwPMtW9SxwReVycPVmWisjf6v2nY5qM2n4bMsb85EHgd6p6IYCbAApVdYiINAMWiEjFarSDcPZsyHFf36iqu0UkBlgkIu+q6oMicruqplXxXpfi3KV+AtDePedr99hA4HicJdMXACNEZBVwCdBHVbWhbC5kGifrcRhz9M4BrhWRLJxl7NsBqe6xhT5JA+BOEfkRZ/+GLj71qnMyME1Vy1R1O/AVMMSn7TxVLQeycIbQCoHDwBQRuRRosGsomdBnicOYoyfAHaqa5j66+ex/cfA/lUROw1mDabiqngAsAY5lS9cjPs/LgAh3bbWhOGtVXQh8cgztG1MjSxzG+G8/zuJ2FeYAv3GXtEdEelWzyVMcsEdVi0SkD872vhVKKs6v5BvgSnceJR5nF8KF1QXm7sUSp6ofAXfjDHEZ4wmb4zDGf0uBMnfI6VWcvchTcPY4EJydAsdUcd4nwC3uPMQanOGqCpOBpSKyWFUn+JS/DwwHfsTZ2Oh+Vd3mJp6qtARmiUg0Tk/onqP6hMb4wVbHNcYYUyc2VGWMMaZOLHEYY4ypE0scxhhj6sQShzHGmDqxxGGMMaZOLHEYY4ypE0scxhhj6uT/A3sRbw9GG1KIAAAAAElFTkSuQmCC\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
@@ -538,16 +498,16 @@
},
{
"cell_type": "code",
- "execution_count": 17,
+ "execution_count": 16,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
- "0.7945513287664577"
+ "0.7927051450225915"
]
},
- "execution_count": 17,
+ "execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
@@ -558,7 +518,7 @@
},
{
"cell_type": "code",
- "execution_count": 18,
+ "execution_count": 17,
"metadata": {},
"outputs": [],
"source": [
@@ -577,7 +537,7 @@
},
{
"cell_type": "code",
- "execution_count": 19,
+ "execution_count": 18,
"metadata": {},
"outputs": [
{
@@ -586,7 +546,7 @@
"IncompatibleKeys(missing_keys=[], unexpected_keys=[])"
]
},
- "execution_count": 19,
+ "execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
@@ -601,7 +561,7 @@
},
{
"cell_type": "code",
- "execution_count": 20,
+ "execution_count": 19,
"metadata": {
"scrolled": true
},
@@ -612,7 +572,7 @@
"0.9188772287810328"
]
},
- "execution_count": 20,
+ "execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
@@ -639,7 +599,7 @@
},
{
"cell_type": "code",
- "execution_count": 21,
+ "execution_count": 20,
"metadata": {},
"outputs": [
{
@@ -648,7 +608,7 @@
"(64, 593)"
]
},
- "execution_count": 21,
+ "execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
@@ -664,7 +624,7 @@
},
{
"cell_type": "code",
- "execution_count": 22,
+ "execution_count": 21,
"metadata": {},
"outputs": [
{
@@ -673,7 +633,7 @@
"(64, 600)"
]
},
- "execution_count": 22,
+ "execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
@@ -688,7 +648,7 @@
},
{
"cell_type": "code",
- "execution_count": 23,
+ "execution_count": 22,
"metadata": {},
"outputs": [
{
@@ -697,7 +657,7 @@
"torch.Size([64, 600])"
]
},
- "execution_count": 23,
+ "execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
@@ -720,7 +680,7 @@
},
{
"cell_type": "code",
- "execution_count": 24,
+ "execution_count": 23,
"metadata": {},
"outputs": [],
"source": [
@@ -748,7 +708,7 @@
},
{
"cell_type": "code",
- "execution_count": 25,
+ "execution_count": 24,
"metadata": {},
"outputs": [],
"source": [
@@ -778,7 +738,7 @@
},
{
"cell_type": "code",
- "execution_count": 26,
+ "execution_count": 25,
"metadata": {},
"outputs": [
{
@@ -787,7 +747,7 @@
"0.9188772287810328"
]
},
- "execution_count": 26,
+ "execution_count": 25,
"metadata": {},
"output_type": "execute_result"
}
@@ -807,7 +767,7 @@
},
{
"cell_type": "code",
- "execution_count": 27,
+ "execution_count": 26,
"metadata": {
"scrolled": true
},
@@ -855,7 +815,7 @@
},
{
"cell_type": "code",
- "execution_count": 28,
+ "execution_count": 27,
"metadata": {},
"outputs": [
{
@@ -879,10 +839,10 @@
" "
],
"text/plain": [
- "<IPython.lib.display.IFrame at 0x7f8a808b3b70>"
+ "<IPython.lib.display.IFrame at 0x7f06a9bcd0f0>"
]
},
- "execution_count": 28,
+ "execution_count": 27,
"metadata": {},
"output_type": "execute_result"
}
--
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