{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false,
    "execution": {
     "iopub.execute_input": "2020-09-26T10:36:07.426217Z",
     "iopub.status.busy": "2020-09-26T10:36:07.424614Z",
     "iopub.status.idle": "2020-09-26T10:36:12.998566Z",
     "shell.execute_reply": "2020-09-26T10:36:12.999566Z"
    },
    "papermill": {
     "duration": 5.598587,
     "end_time": "2020-09-26T10:36:13.000025",
     "exception": false,
     "start_time": "2020-09-26T10:36:07.401438",
     "status": "completed"
    },
    "pycharm": {
     "name": "#%%\n"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "import sys\n",
    "sys.path.append('..')\n",
    "import tensorflow as tf\n",
    "from src.lucid import make_lucid_dataset, lucid_model_factory\n",
    "import lucid.optvis.transform as transform\n",
    "from src.experiment_tagging import get_model_path\n",
    "from src.utils import get_model_paths, load_model2, suppress, all_logging_disabled\n",
    "from src.visualization import run_spectral_cluster"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false,
    "execution": {
     "iopub.execute_input": "2020-09-26T10:36:13.025536Z",
     "iopub.status.busy": "2020-09-26T10:36:13.024654Z",
     "iopub.status.idle": "2020-09-26T10:36:13.029749Z",
     "shell.execute_reply": "2020-09-26T10:36:13.033089Z"
    },
    "papermill": {
     "duration": 0.026152,
     "end_time": "2020-09-26T10:36:13.033296",
     "exception": false,
     "start_time": "2020-09-26T10:36:13.007144",
     "status": "completed"
    },
    "pycharm": {
     "name": "#%%\n"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "model_tags = ('MNIST+LUCID', 'MNIST+DROPOUT+LUCID')\n",
    "transforms = []\n",
    "side_len = 28\n",
    "n_clust = 12\n",
    "n_reps = 5\n",
    "\n",
    "transform_jitter = 2\n",
    "transform_scale = 1.1\n",
    "transform_rotate = 0\n",
    "if transform_jitter != 0:\n",
    "    transforms.append(transform.jitter(transform_jitter))\n",
    "if transform_scale != 1:\n",
    "    transforms.append(transform.random_scale([transform_scale ** (n/10.) for n in range(-10, 11)]))\n",
    "if transform_rotate != 0:\n",
    "    transforms.append(transform.random_rotate(range(-transform_rotate, transform_rotate+1)))\n",
    "transforms.append(transform.crop_or_pad_to(side_len, side_len))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false,
    "execution": {
     "iopub.execute_input": "2020-09-26T10:36:13.081368Z",
     "iopub.status.busy": "2020-09-26T10:36:13.080768Z",
     "iopub.status.idle": "2020-09-26T19:47:19.421810Z",
     "shell.execute_reply": "2020-09-26T19:47:19.420833Z"
    },
    "papermill": {
     "duration": 33066.383343,
     "end_time": "2020-09-26T19:47:19.422125",
     "exception": false,
     "start_time": "2020-09-26T10:36:13.038782",
     "status": "completed"
    },
    "pycharm": {
     "name": "#%%\n"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /root/.local/share/virtualenvs/nn_clustering-Lo7V74L4/src/lucid/lucid/optvis/param/spatial.py:82: The name tf.spectral.irfft2d is deprecated. Please use tf.signal.irfft2d instead.\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /root/.local/share/virtualenvs/nn_clustering-Lo7V74L4/src/lucid/lucid/optvis/param/spatial.py:82: The name tf.spectral.irfft2d is deprecated. Please use tf.signal.irfft2d instead.\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /root/.local/share/virtualenvs/nn_clustering-Lo7V74L4/src/lucid/lucid/optvis/render.py:242: The name tf.train.AdamOptimizer is deprecated. Please use tf.compat.v1.train.AdamOptimizer instead.\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /root/.local/share/virtualenvs/nn_clustering-Lo7V74L4/src/lucid/lucid/optvis/render.py:242: The name tf.train.AdamOptimizer is deprecated. Please use tf.compat.v1.train.AdamOptimizer instead.\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /root/.local/share/virtualenvs/nn_clustering-Lo7V74L4/src/lucid/lucid/optvis/render.py:170: The name tf.train.get_or_create_global_step is deprecated. Please use tf.compat.v1.train.get_or_create_global_step instead.\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /root/.local/share/virtualenvs/nn_clustering-Lo7V74L4/src/lucid/lucid/optvis/render.py:170: The name tf.train.get_or_create_global_step is deprecated. Please use tf.compat.v1.train.get_or_create_global_step instead.\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /root/.local/share/virtualenvs/nn_clustering-Lo7V74L4/src/lucid/lucid/optvis/render.py:171: The name tf.variables_initializer is deprecated. Please use tf.compat.v1.variables_initializer instead.\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /root/.local/share/virtualenvs/nn_clustering-Lo7V74L4/src/lucid/lucid/optvis/render.py:171: The name tf.variables_initializer is deprecated. Please use tf.compat.v1.variables_initializer instead.\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /root/.local/share/virtualenvs/nn_clustering-Lo7V74L4/src/lucid/lucid/optvis/transform.py:38: The name tf.random_crop is deprecated. Please use tf.image.random_crop instead.\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /root/.local/share/virtualenvs/nn_clustering-Lo7V74L4/src/lucid/lucid/optvis/transform.py:38: The name tf.random_crop is deprecated. Please use tf.image.random_crop instead.\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /root/.local/share/virtualenvs/nn_clustering-Lo7V74L4/src/lucid/lucid/optvis/transform.py:141: The name tf.random_uniform is deprecated. Please use tf.random.uniform instead.\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /root/.local/share/virtualenvs/nn_clustering-Lo7V74L4/src/lucid/lucid/optvis/transform.py:141: The name tf.random_uniform is deprecated. Please use tf.random.uniform instead.\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /root/.local/share/virtualenvs/nn_clustering-Lo7V74L4/src/lucid/lucid/optvis/transform.py:83: The name tf.image.resize_bilinear is deprecated. Please use tf.compat.v1.image.resize_bilinear instead.\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /root/.local/share/virtualenvs/nn_clustering-Lo7V74L4/src/lucid/lucid/optvis/transform.py:83: The name tf.image.resize_bilinear is deprecated. Please use tf.compat.v1.image.resize_bilinear instead.\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /root/.local/share/virtualenvs/nn_clustering-Lo7V74L4/src/lucid/lucid/optvis/transform.py:160: The name tf.image.resize_image_with_crop_or_pad is deprecated. Please use tf.image.resize_with_crop_or_pad instead.\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /root/.local/share/virtualenvs/nn_clustering-Lo7V74L4/src/lucid/lucid/optvis/transform.py:160: The name tf.image.resize_image_with_crop_or_pad is deprecated. Please use tf.image.resize_with_crop_or_pad instead.\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /root/.local/share/virtualenvs/nn_clustering-Lo7V74L4/src/lucid/lucid/modelzoo/util.py:58: The name tf.placeholder_with_default is deprecated. Please use tf.compat.v1.placeholder_with_default instead.\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /root/.local/share/virtualenvs/nn_clustering-Lo7V74L4/src/lucid/lucid/modelzoo/util.py:58: The name tf.placeholder_with_default is deprecated. Please use tf.compat.v1.placeholder_with_default instead.\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /root/.local/share/virtualenvs/nn_clustering-Lo7V74L4/src/lucid/lucid/misc/io/loading.py:129: The name tf.GraphDef is deprecated. Please use tf.compat.v1.GraphDef instead.\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /root/.local/share/virtualenvs/nn_clustering-Lo7V74L4/src/lucid/lucid/misc/io/loading.py:129: The name tf.GraphDef is deprecated. Please use tf.compat.v1.GraphDef instead.\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /root/.local/share/virtualenvs/nn_clustering-Lo7V74L4/src/lucid/lucid/misc/redirected_relu_grad.py:95: where (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use tf.where in 2.0, which has the same broadcast rule as np.where\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /root/.local/share/virtualenvs/nn_clustering-Lo7V74L4/src/lucid/lucid/misc/redirected_relu_grad.py:95: where (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use tf.where in 2.0, which has the same broadcast rule as np.where\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From ../src/lucid.py:93: The name tf.global_variables_initializer is deprecated. Please use tf.compat.v1.global_variables_initializer instead.\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From ../src/lucid.py:93: The name tf.global_variables_initializer is deprecated. Please use tf.compat.v1.global_variables_initializer instead.\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loss: 137.0189971923828\n"
     ]
    },
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      "random losses: [264.693, 399.153, 226.351, 364.024, 302.777, 90.165, 220.312, 93.073, 301.542]\n"
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      "random losses: [41.652, 136.297, 56.653, 98.623, 99.032, 101.324, 76.441, 24.165, 76.344]\n"
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      "loss: 60.047000885009766\n"
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     "text": [
      "random losses: [41.801, 47.93, 139.768, 71.377, 61.29, 49.506, 25.751, 156.116, 72.804]\n"
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     "text": [
      "random losses: [110.514, 66.761, 120.689, 127.943, 0.998, 62.669, 95.82, 14.249, 101.986]\n"
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      "random losses: [53.253, 38.58, 52.934, 63.798, 28.643, 100.072, 76.962, 76.96, 75.089]\n"
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      "loss: 29.253999710083008\n"
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      "random losses: [9.733, 54.135, 35.952, 31.332, 38.991, 20.41, 40.086, 30.242, 17.842]\n"
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      "random losses: [327.253, 186.473, 193.341, 282.307, 380.63, 389.615, 183.596, 314.506, 170.844]\n"
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      "random losses: [74.319, 71.371, 99.642, 152.769, 103.496, 31.439, 43.374, 33.878, 110.046]\n"
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      "loss: 376.58099365234375\n"
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      "random losses: [352.763, 214.106, 401.941, 152.79, 576.93, 329.106, 295.631, 342.285, 439.832]\n"
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      "random losses: [65.02, 51.61, 31.833, 37.123, 28.774, 54.77, 27.466, 5.332, 58.153]\n"
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      "random losses: [39.635, 57.242, 43.321, 25.925, 33.047, 43.729, 64.941, 15.13, 17.385]\n"
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      "loss: 285.06201171875\n"
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     "text": [
      "random losses: [116.87, 83.169, 55.35, 92.246, 142.978, 102.267, 287.121, 168.89, 132.425]\n"
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      "random losses: [139.987, 53.996, 62.345, 109.858, 76.909, 198.651, 47.112, 92.779, 81.902]\n"
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      "random losses: [43.852, 142.295, 99.202, 138.199, 153.347, 103.714, 146.862, 35.955, 238.024]\n"
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      "loss: 197.21499633789062\n"
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      "random losses: [286.299, 286.273, 316.714, 414.564, 264.945, 284.807, 325.647, 434.251, 342.851]\n"
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      "random losses: [195.062, 284.036, 231.831, 230.753, 290.332, 251.438, 292.585, 241.627, 268.551]\n"
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      "random losses: [49.691, 48.737, 51.521, 34.89, 102.716, 67.735, 49.796, 66.658, 37.605]\n"
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      "loss: 28.5\n"
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      "random losses: [28.727, 41.501, 40.133, 34.47, 20.112, 33.955, 32.64, 22.331, 101.317]\n"
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      "loss: 128.1840057373047\n"
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      "random losses: [414.769, 322.579, 104.219, 167.97, 160.599, 103.544, 246.494, 415.554, 294.59]\n"
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      "random losses: [45.538, 13.856, 35.056, 58.886, 52.959, 43.121, 7.959, 24.077, 0.44]\n"
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     "text": [
      "random losses: [48.131, 28.076, 102.998, 34.327, 14.269, 58.881, 25.578, 108.76, 183.083]\n"
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      "random losses: [46.371, 53.877, 15.495, 25.944, 67.478, 55.041, 18.706, 49.038, 31.086]\n"
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      "loss: 31.54599952697754\n"
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      "random losses: [71.293, 63.657, 44.86, 52.681, 67.201, 59.854, 94.246, 100.599, 126.068]\n"
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      "loss: 214.375\n"
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      "random losses: [161.565, 199.418, 193.81, 199.241, 259.33, 197.934, 235.706, 203.858, 304.847]\n"
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      "random losses: [222.457, 313.848, 324.53, 364.513, 201.882, 388.183, 370.382, 283.57, 327.911]\n"
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      "random losses: [44.351, 162.822, 76.517, 67.385, 99.089, 87.85, 104.164, 71.605, 108.004]\n"
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      "random losses: [410.84, 386.812, 380.618, 469.29, 401.458, 411.453, 283.44, 232.272, 328.194]\n"
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      "random losses: [30.671, 40.42, 69.69, 14.673, 43.13, 38.276, 37.134, 81.85, 64.466]\n"
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     "text": [
      "random losses: [193.419, 112.659, 218.246, 204.781, 189.644, 171.118, 69.62, 135.113, 183.064]\n"
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     "text": [
      "random losses: [58.457, 59.964, 32.329, 30.596, 22.089, 59.938, 50.667, 46.717, 28.119]\n"
     ]
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      "loss: 366.12701416015625\n"
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     "text": [
      "random losses: [236.176, 258.03, 180.646, 375.485, 314.67, 276.393, 282.689, 244.074, 401.724]\n"
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      "loss: 164.10299682617188\n"
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      "random losses: [120.922, 262.027, 68.899, 82.057, 115.841, 77.08, 176.558, 124.563, 127.171]\n"
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      "random losses: [108.073, 234.921, 318.084, 204.925, 264.588, 170.569, 231.808, 125.808, 229.945]\n"
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     "text": [
      "random losses: [347.087, 294.057, 132.766, 164.463, 179.606, 214.688, 110.977, 348.892, 246.814]\n"
     ]
    },
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      "loss: 248.8470001220703\n"
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     "text": [
      "random losses: [143.039, 201.868, 241.058, 275.267, 94.377, 276.448, 292.406, 275.109, 407.15]\n"
     ]
    },
    {
     "data": {
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      "loss: 68.06800079345703\n"
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     "text": [
      "random losses: [35.985, 170.848, 36.71, 116.925, 166.362, 62.978, 130.496, 259.583, 211.796]\n"
     ]
    },
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      "loss: 59.70399856567383\n"
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      "random losses: [157.241, 229.149, 320.7, 317.88, 199.818, 164.256, 278.897, 239.477, 249.71]\n"
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      "random losses: [94.832, 20.682, 98.188, 347.692, 194.681, 202.968, 185.042, 126.071, 177.167]\n"
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      "random losses: [18.166, 116.184, 46.189, 28.506, 23.428, 132.96, 73.316, 59.929, 92.175]\n"
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      "random losses: [82.838, 146.069, 50.541, 137.125, 25.737, 114.394, 51.522, 139.766, 26.383]\n"
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      "random losses: [414.151, 320.906, 270.748, 155.846, 313.266, 97.208, 241.353, 265.651, 216.483]\n"
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      "random losses: [30.325, 64.084, 32.542, 33.085, 29.765, 25.442, 31.682, 23.363, 62.767]\n"
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      "loss: 70.9000015258789\n"
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      "random losses: [39.457, 90.879, 63.725, 53.029, 63.74, 78.348, 48.44, 21.165, 42.717]\n"
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      "random losses: [110.826, 111.854, 112.315, 89.232, 57.558, 124.871, 110.409, 55.539, 91.427]\n"
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      "random losses: [269.12, 278.57, 345.067, 295.447, 216.526, 223.087, 183.245, 394.879, 274.05]\n"
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     "text": [
      "random losses: [118.569, 107.758, 109.042, 83.479, 79.026, 97.44, 94.331, 97.981, 140.946]\n"
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      "random losses: [158.105, 249.929, 173.135, 292.746, 99.263, 149.753, 117.76, 129.531, 159.24]\n"
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     "text": [
      "random losses: [461.26, 638.417, 638.992, 681.146, 531.041, 474.286, 655.179, 777.178, 657.442]\n"
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      "loss: 856.6060180664062\n"
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      "random losses: [48.104, 49.368, 48.884, 63.268, 80.569, 46.284, 69.241, 57.965, 50.756]\n"
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     "text": [
      "random losses: [77.351, 81.782, 107.696, 60.441, 120.185, 99.625, 146.691, 85.337, 99.415]\n"
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      "loss: 113.88800048828125\n"
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     "text": [
      "random losses: [190.293, 89.237, 84.548, 261.752, 194.36, 117.08, 101.867, 187.264, 116.416]\n"
     ]
    },
    {
     "data": {
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      "loss: 51.41600036621094\n"
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       "              0 <br/>\n",
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     "text": [
      "random losses: [30.721, 87.425, 27.491, 83.839, 70.602, 78.486, 92.862, 127.179, 51.346]\n"
     ]
    },
    {
     "data": {
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     "text": [
      "loss: 124.23500061035156\n"
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     "text": [
      "random losses: [231.529, 91.505, 47.141, 96.891, 38.839, 154.5, 27.892, 110.048, 65.94]\n"
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    },
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      "loss: 290.35198974609375\n"
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     "text": [
      "random losses: [91.176, 171.388, 98.243, 104.054, 234.955, 73.295, 155.263, 177.064, 149.51]\n"
     ]
    },
    {
     "data": {
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      "loss: 88.1259994506836\n"
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     "text": [
      "random losses: [129.155, 69.075, 44.197, 147.251, 190.517, 66.783, 84.715, 304.436, 224.026]\n"
     ]
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      "loss: 88.66699981689453\n"
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      "random losses: [131.365, 117.598, 23.115, 32.794, 42.461, 129.538, 28.085, 88.153, 22.224]\n"
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     "text": [
      "random losses: [192.886, 216.285, 108.398, 214.529, 270.336, 280.209, 253.228, 279.621, 197.05]\n"
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     "text": [
      "random losses: [27.329, 84.021, 65.941, 106.575, 57.018, 138.378, 77.18, 142.046, 109.379]\n"
     ]
    },
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      "loss: 263.4209899902344\n"
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     "text": [
      "random losses: [190.091, 204.937, 351.263, 41.154, 211.773, 65.747, 86.052, 127.872, 28.085]\n"
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      "loss: 17.22800064086914\n"
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     "text": [
      "random losses: [56.839, 151.955, 45.403, 165.737, 112.509, 27.134, 62.072, 71.485, 87.363]\n"
     ]
    },
    {
     "data": {
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     "text": [
      "loss: 5.76200008392334\n"
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     "text": [
      "random losses: [38.522, 27.064, 12.757, 42.29, 22.848, 36.319, 18.099, 32.834, 33.125]\n"
     ]
    },
    {
     "data": {
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      "loss: 46.433998107910156\n"
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     "text": [
      "random losses: [92.031, 76.014, 69.472, 44.115, 88.322, 51.739, 64.568, 82.166, 65.89]\n"
     ]
    },
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     "data": {
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      "loss: 92.04399871826172\n"
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      "random losses: [24.993, 23.082, 60.8, 11.659, 59.021, 46.253, 42.47, 6.348, 1.042]\n"
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      "random losses: [53.257, 114.401, 117.73, 63.889, 98.969, 10.573, 137.041, 56.433, 107.688]\n"
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      "loss: 22.57200050354004\n"
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      "random losses: [38.517, 26.66, 85.515, 25.391, 33.629, 13.932, 37.317, 39.702, 16.352]\n"
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      "random losses: [286.189, 216.818, 349.089, 308.284, 289.294, 258.336, 281.951, 325.826, 153.111]\n"
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      "random losses: [149.708, 48.138, 80.306, 113.295, 240.396, 161.857, 218.367, 83.766, 77.597]\n"
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      "loss: 28.315000534057617\n"
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     "text": [
      "random losses: [25.768, 47.037, 15.405, 1.374, 17.683, 0.957, 43.205, 25.729, 24.128]\n"
     ]
    },
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      "loss: 53.893001556396484\n"
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     "text": [
      "random losses: [97.217, 103.265, 2.557, 149.383, 119.831, 85.098, 133.659, 224.004, 152.617]\n"
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    },
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      "loss: 36.55699920654297\n"
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     "text": [
      "random losses: [38.459, 62.955, 27.0, 78.568, 69.633, 185.409, 71.741, 58.856, 3.235]\n"
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      "loss: 167.6649932861328\n"
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      "random losses: [105.103, 104.322, 39.417, 85.342, 47.875, 64.676, 55.25, 54.182, 91.35]\n"
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      "random losses: [1309.362, 794.804, 982.633, 701.065, 802.905, 563.645, 1458.873, 662.003, 1057.558]\n"
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      "random losses: [438.091, 741.32, 359.697, 21.242, 731.953, 487.945, 386.825, 561.55, 314.51]\n"
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     "text": [
      "random losses: [30.184, 31.431, 43.675, 0.463, 0.0, 9.317, 0.457, 0.747, 22.118]\n"
     ]
    },
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      "loss: 101.63300323486328\n"
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     "text": [
      "random losses: [30.563, 3.758, 147.421, 66.102, 3.11, 143.843, 217.578, 3.628, 2.057]\n"
     ]
    },
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      "loss: 2.9749999046325684\n"
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     "text": [
      "random losses: [13.437, 0.773, 1.297, 39.905, 0.0, 18.598, 0.385, 1.059, 26.72]\n"
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      "loss: 159.73500061035156\n"
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     "text": [
      "random losses: [7.038, 5.917, 71.625, 5.076, 7.02, 6.052, 22.689, 97.971, 103.47]\n"
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      "loss: 321.7250061035156\n"
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     "text": [
      "random losses: [13.887, 202.617, 14.314, 96.128, 12.823, 14.142, 16.719, 61.157, 124.853]\n"
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      "loss: 14.956999778747559\n"
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     "text": [
      "random losses: [109.799, 12.652, 210.784, 8.722, 87.627, 14.443, 97.104, 13.032, 14.834]\n"
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      "loss: 4.085000038146973\n"
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      "random losses: [64.592, 25.684, 43.064, 246.328, 141.743, 48.538, 43.467, 75.693, 158.029]\n"
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      "random losses: [112.582, 82.855, 108.563, 113.589, 66.326, 98.016, 131.483, 104.129, 111.848]\n"
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      "random losses: [100.653, 96.611, 108.04, 50.088, 58.057, 75.373, 71.51, 44.107, 125.715]\n"
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      "random losses: [121.111, 110.909, 169.379, 125.464, 129.784, 112.941, 114.305, 208.995, 101.852]\n"
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      "random losses: [133.588, 395.535, 237.134, 376.557, 193.808, 457.911, 162.476, 328.801, 29.962]\n"
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      "random losses: [82.312, 0.925, 162.415, 32.139, 114.707, 57.417, 53.522, 95.249, 77.338]\n"
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      "random losses: [38.549, 145.51, 0.355, 82.754, 65.066, 83.345, 96.848, 88.545, 27.327]\n"
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      "random losses: [0.044, 22.193, 18.337, 7.871, 25.334, 8.987, 22.717, 27.096, 12.153]\n"
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      "random losses: [17.561, 10.43, 16.588, 18.085, 227.886, 19.657, 18.548, 9.617, 20.623]\n"
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      "random losses: [4.772, 84.909, 189.968, 41.31, 32.619, 6.676, 4.204, 7.558, 203.543]\n"
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      "loss: 175.68800354003906\n"
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      "random losses: [83.391, 50.321, 77.283, 72.425, 81.24, 38.199, 69.976, 76.096, 35.909]\n"
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      "random losses: [140.963, 82.762, 52.379, 93.742, 58.939, 159.982, 32.597, 56.861, 114.415]\n"
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      "random losses: [264.506, 246.383, 346.176, 230.178, 117.854, 180.526, 144.498, 78.815, 201.399]\n"
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      "random losses: [40.345, 86.599, 78.917, 27.301, 29.304, 96.847, 68.637, 55.089, 31.448]\n"
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      "random losses: [89.354, 78.073, 110.544, 33.734, 50.007, 74.442, 135.866, 69.441, 101.585]\n"
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      "random losses: [108.164, 1.239, 38.496, 34.895, 26.128, 52.223, 59.557, 48.408, 53.118]\n"
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      "random losses: [80.448, 144.403, 62.448, 180.933, 102.675, 87.515, 71.029, 3.396, 144.701]\n"
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     "text": [
      "random losses: [82.812, 90.444, 38.132, 96.355, 97.005, 85.872, 72.681, 62.995, 96.022]\n"
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     "text": [
      "random losses: [139.485, 157.066, 92.795, 43.239, 116.591, 148.283, 120.424, 132.334, 101.121]\n"
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      "random losses: [29.746, 57.411, 11.857, 41.135, 58.993, 0.875, 6.225, 74.998, 56.012]\n"
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      "loss: 160.47799682617188\n"
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      "random losses: [117.962, 68.678, 41.49, 169.941, 68.016, 106.08, 134.679, 99.101, 140.299]\n"
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      "random losses: [174.671, 139.359, 126.295, 246.113, 108.784, 138.785, 184.375, 159.173, 167.05]\n"
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     "text": [
      "random losses: [72.107, 89.023, 70.143, 48.763, 54.546, 78.369, 38.779, 64.465, 69.491]\n"
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      "loss: 93.90899658203125\n"
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     "text": [
      "random losses: [118.61, 152.895, 54.552, 258.078, 148.179, 124.588, 99.511, 147.149, 194.957]\n"
     ]
    },
    {
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      "loss: 11.064000129699707\n"
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     "text": [
      "random losses: [114.397, 43.352, 24.177, 70.528, 66.133, 73.04, 15.409, 13.313, 62.212]\n"
     ]
    },
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      "loss: 1.8730000257492065\n"
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      "random losses: [70.971, 122.87, 58.799, 73.327, 26.722, 134.689, 29.323, 77.252, 70.467]\n"
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      "random losses: [129.196, 99.247, 3.625, 5.538, 266.003, 9.216, 145.445, 74.898, 130.274]\n"
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      "loss: 115.12999725341797\n"
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      "random losses: [0.175, 5.001, 6.961, 63.583, 6.675, 3.565, 1.433, 53.208, 75.311]\n"
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      "random losses: [174.313, 19.54, 149.239, 17.309, 12.249, 194.988, 18.314, 18.106, 357.791]\n"
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      "random losses: [40.95, 57.98, 87.106, 30.022, 611.436, 33.292, 1011.036, 43.182, 41.33]\n"
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      "random losses: [14.207, 12.185, 17.785, 16.272, 11.283, 20.434, 26.213, 167.075, 307.239]\n"
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     "text": [
      "random losses: [40.352, 37.12, 34.659, 47.455, 36.264, 28.478, 31.574, 27.163, 21.932]\n"
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    },
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      "loss: 7.394000053405762\n"
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     "text": [
      "random losses: [188.61, 7.322, 3.661, 5.475, 11.395, 7.541, 7.828, 7.389, 7.699]\n"
     ]
    },
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     "data": {
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      "loss: 12.49899959564209\n"
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     "text": [
      "random losses: [229.541, 64.99, 204.597, 18.79, 16.383, 145.158, 6.027, 4.944, 262.66]\n"
     ]
    },
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      "loss: 181.9459991455078\n"
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      "random losses: [425.18, 461.374, 411.831, 397.554, 400.182, 500.959, 151.373, 410.236, 406.489]\n"
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      "random losses: [140.064, 76.489, 91.779, 47.62, 116.807, 138.065, 160.3, 148.064, 126.09]\n"
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      "loss: 84.875\n"
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      "random losses: [86.904, 46.974, 110.556, 41.191, 37.91, 58.617, 50.562, 6.445, 15.057]\n"
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      "loss: 133.51499938964844\n"
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      "random losses: [66.37, 254.442, 191.213, 148.788, 202.881, 113.978, 70.868, 210.842, 74.534]\n"
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      "random losses: [122.965, 20.412, 13.625, 99.717, 110.461, 110.574, 54.863, 38.628, 109.975]\n"
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      "random losses: [33.451, 57.401, 23.368, 66.085, 41.873, 52.627, 10.179, 68.405, 18.72]\n"
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      "loss: 78.65699768066406\n"
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      "random losses: [131.858, 28.154, 49.856, 4.666, 136.971, 65.885, 95.282, 2.872, 127.617]\n"
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      "random losses: [329.732, 103.334, 30.162, 425.28, 162.091, 198.465, 53.026, 466.557, 221.576]\n"
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      "random losses: [137.951, 2.89, 100.634, 32.261, 115.39, 106.098, 81.061, 109.925, 40.096]\n"
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      "random losses: [228.286, 135.146, 43.604, 73.079, 96.38, 10.58, 81.505, 70.475, 57.608]\n"
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      "random losses: [2.12, 4.312, 17.6, 29.656, 50.745, 20.412, 19.918, 53.49, 35.102]\n"
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      "loss: 6.692999839782715\n"
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      "random losses: [71.575, 92.498, 96.232, 2.0, 150.313, 11.726, 46.954, 43.197, 74.89]\n"
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      "random losses: [431.679, 214.391, 445.824, 426.924, 173.87, 168.949, 268.79, 360.548, 358.387]\n"
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      "loss: 6.581999778747559\n"
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      "random losses: [437.952, 568.954, 282.508, 363.332, 219.154, 238.548, 431.492, 448.419, 396.12]\n"
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      "random losses: [68.495, 154.037, 136.448, 119.771, 65.386, 105.21, 134.452, 181.86, 207.649]\n"
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      "random losses: [0.454, 1.742, 0.644, 0.485, 0.895, 1.587, 5.231, 9.884, 1.12]\n"
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      "random losses: [11.539, 2.995, 9.238, 11.749, 12.471, 8.269, 14.237, 3.022, 17.46]\n"
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      "random losses: [16.786, 6.498, 7.08, 29.146, 16.942, 9.514, 11.308, 27.794, 9.255]\n"
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     "text": [
      "random losses: [4.828, 143.896, 3.018, 105.027, 194.03, 2.875, 9.354, 7.287, 9.597]\n"
     ]
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      "loss: 49.79999923706055\n"
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      "loss: 12.130999565124512\n"
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     "text": [
      "random losses: [10.255, 13.688, 20.078, 188.873, 3.424, 13.833, 7.375, 10.787, 12.703]\n"
     ]
    },
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     "data": {
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     "text": [
      "loss: 14.998000144958496\n"
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       "              0 <br/>\n",
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     "text": [
      "random losses: [113.461, 16.39, 12.367, 13.659, 5.484, 15.901, 6.501, 16.604, 10.616]\n"
     ]
    },
    {
     "data": {
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     "text": [
      "loss: 9.979999542236328\n"
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       "              0 <br/>\n",
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     "text": [
      "random losses: [11.072, 13.956, 6.598, 3.355, 4.784, 59.686, 4.332, 4.334, 5.628]\n"
     ]
    },
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     "text": [
      "loss: 10.946999549865723\n"
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     "text": [
      "random losses: [8.226, 3.52, 4.681, 5.182, 10.388, 14.814, 7.744, 6.899, 12.751]\n"
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     "text": [
      "random losses: [430.717, 32.521, 31.507, 29.432, 55.373, 50.572, 32.903, 35.03, 46.091]\n"
     ]
    },
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      "loss: 37.018001556396484\n"
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     "text": [
      "random losses: [10.827, 8.106, 93.263, 158.307, 7.031, 11.098, 26.102, 24.212, 238.684]\n"
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      "loss: 55.861000061035156\n"
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      "random losses: [41.557, 53.468, 42.254, 48.849, 53.473, 63.752, 53.002, 38.366, 56.369]\n"
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      "random losses: [193.237, 263.262, 284.152, 288.027, 183.205, 304.024, 139.755, 233.484, 186.356]\n"
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      "random losses: [55.99, 32.346, 48.185, 46.228, 0.029, 19.406, 19.152, 39.183, 47.319]\n"
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      "random losses: [63.431, 13.784, 43.093, 21.309, 55.178, 26.303, 20.481, 48.374, 30.141]\n"
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      "loss: 145.2270050048828\n"
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      "random losses: [120.798, 56.135, 99.934, 178.994, 67.935, 79.306, 188.147, 59.485, 70.269]\n"
     ]
    },
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      "loss: 45.54100036621094\n"
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      "random losses: [57.554, 31.846, 17.415, 10.177, 143.357, 55.496, 43.583, 91.793, 58.168]\n"
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      "loss: 87.10399627685547\n"
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     "text": [
      "random losses: [71.512, 19.189, 38.02, 92.044, 17.554, 45.128, 76.598, 68.689, 47.734]\n"
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     "text": [
      "random losses: [108.616, 115.81, 103.738, 93.545, 61.073, 50.919, 41.881, 32.714, 103.008]\n"
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      "loss: 165.92999267578125\n"
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      "random losses: [140.783, 136.027, 145.491, 148.124, 101.982, 85.287, 270.031, 204.997, 225.809]\n"
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      "loss: 43.38800048828125\n"
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      "random losses: [19.251, 13.157, 40.52, 2.231, 62.747, 62.03, 31.126, 2.084, 28.267]\n"
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      "random losses: [43.331, 32.144, 73.547, 22.102, 28.293, 18.494, 38.176, 20.006, 32.124]\n"
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      "random losses: [52.438, 97.64, 58.836, 18.652, 60.819, 41.664, 40.169, 1.449, 62.329]\n"
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      "loss: 82.82599639892578\n"
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      "random losses: [68.508, 194.825, 166.082, 75.198, 127.104, 72.49, 121.548, 48.119, 124.636]\n"
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      "random losses: [121.928, 157.112, 232.676, 88.726, 209.54, 93.909, 84.111, 146.712, 131.72]\n"
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     "metadata": {},
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "random losses: [286.284, 423.88, 248.651, 593.169, 197.473, 156.013, 354.27, 228.08, 91.228]\n"
     ]
    },
    {
     "data": {
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     "text": [
      "loss: 56.41699981689453\n"
     ]
    },
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "random losses: [144.999, 11.668, 126.232, 300.173, 120.629, 96.398, 67.346, 59.373, 75.659]\n"
     ]
    },
    {
     "data": {
      "text/html": [
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   ],
   "source": [
    "for tag in model_tags:\n",
    "\n",
    "    model_dirs = get_model_path(tag, filter_='all')[-n_reps:]\n",
    "    paths = [get_model_paths(d) for d in model_dirs]\n",
    "\n",
    "    for i, path in enumerate(paths):\n",
    "\n",
    "        for is_unpruned in [True, False]:\n",
    "            model_path = path[is_unpruned]\n",
    "\n",
    "            if 'mlp' in str(model_path).lower():\n",
    "                inpt = 'flatten_input'\n",
    "                outpt = 'dense_4/Softmax'\n",
    "            else:\n",
    "                inpt = 'conv2d_input'\n",
    "                outpt = 'dense_1/Softmax'\n",
    "            if not is_unpruned:\n",
    "                inpt = 'prune_low_magnitude_' + inpt\n",
    "                outpt = 'prune_low_magnitude_' + outpt\n",
    "                weight_path = str(next(model_dirs[i].glob('*-pruned-weights.pckl')))\n",
    "            else:\n",
    "                weight_path = str(next(model_dirs[i].glob('*-unpruned-weights.pckl')))\n",
    "\n",
    "            with tf.Graph().as_default() as graph, tf.Session() as sess, suppress(), all_logging_disabled():\n",
    "                keras_model = load_model2(model_path)\n",
    "                lucid_model = lucid_model_factory(model_input_name=inpt, model_output_name=outpt)\n",
    "\n",
    "            labels, _ = run_spectral_cluster(weight_path, n_clusters=n_clust, with_shuffle=False)\n",
    "\n",
    "            make_lucid_dataset(tag+str(i), lucid_model, labels, is_unpruned, transforms=transforms)\n"
   ]
  }
 ],
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   "display_name": "Python 3",
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    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.5"
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  "papermill": {
   "duration": 33078.754479,
   "end_time": "2020-09-26T19:47:25.158753",
   "environment_variables": {},
   "exception": null,
   "input_path": "./notebooks/lucid_make_dataset_mlp.ipynb",
   "output_path": "./notebooks/lucid_make_dataset_mlp.ipynb",
   "parameters": {},
   "start_time": "2020-09-26T10:36:06.404274",
   "version": "2.1.2"
  }
 },
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 "nbformat_minor": 0
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