{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false,
    "execution": {
     "iopub.execute_input": "2020-09-26T10:40:55.780387Z",
     "iopub.status.busy": "2020-09-26T10:40:55.778867Z",
     "iopub.status.idle": "2020-09-26T10:41:00.602183Z",
     "shell.execute_reply": "2020-09-26T10:41:00.603561Z"
    },
    "papermill": {
     "duration": 4.851623,
     "end_time": "2020-09-26T10:41:00.604247",
     "exception": false,
     "start_time": "2020-09-26T10:40:55.752624",
     "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 os\n",
    "# os.environ['CUDA_VISIBLE_DEVICES'] = '2,3'\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": "2020-09-26T10:41:00.640033Z",
     "iopub.status.busy": "2020-09-26T10:41:00.638219Z",
     "iopub.status.idle": "2020-09-26T10:41:00.642801Z",
     "shell.execute_reply": "2020-09-26T10:41:00.644474Z"
    },
    "papermill": {
     "duration": 0.030097,
     "end_time": "2020-09-26T10:41:00.644941",
     "exception": false,
     "start_time": "2020-09-26T10:41:00.614844",
     "status": "completed"
    },
    "pycharm": {
     "name": "#%%\n"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "model_tags = ('CNN-MNIST+LUCID', 'CNN-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": "2020-09-26T10:41:00.683172Z",
     "iopub.status.busy": "2020-09-26T10:41:00.681789Z",
     "iopub.status.idle": "2020-09-26T13:51:10.274507Z",
     "shell.execute_reply": "2020-09-26T13:51:10.273515Z"
    },
    "papermill": {
     "duration": 11409.621134,
     "end_time": "2020-09-26T13:51:10.274840",
     "exception": false,
     "start_time": "2020-09-26T10:41:00.653706",
     "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: 0.5659999847412109\n"
     ]
    },
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      "random losses: [0.73, 0.566, 0.528, 0.754, 0.493, 0.537, 0.497, 0.356, 0.333]\n"
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      "random losses: [1.284, 1.735, 0.602, 0.917, 0.815, 0.687, 0.719, 1.288, 0.556]\n"
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      "random losses: [0.768, 1.037, 0.891, 0.668, 1.96, 0.415, 0.549, 1.483, 1.134]\n"
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      "loss: 0.6819999814033508\n"
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     "text": [
      "random losses: [1.042, 1.448, 1.68, 1.819, 1.324, 2.075, 1.808, 1.598, 1.115]\n"
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      "loss: 3.6530001163482666\n"
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     "text": [
      "random losses: [4.189, 2.381, 3.723, 2.429, 3.755, 3.772, 2.651, 2.931, 2.935]\n"
     ]
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      "loss: 1.9040000438690186\n"
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     "text": [
      "random losses: [1.671, 2.266, 2.166, 4.877, 2.708, 2.049, 2.822, 1.38, 2.084]\n"
     ]
    },
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      "random losses: [0.714, 0.773, 0.975, 0.859, 1.005, 1.118, 0.904, 0.934, 0.974]\n"
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      "random losses: [1.788, 0.655, 2.976, 1.316, 1.156, 1.156, 2.983, 1.2, 2.524]\n"
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      "random losses: [0.916, 0.338, 2.044, 0.972, 1.187, 2.092, 0.776, 1.113, 1.114]\n"
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      "loss: 1.190000057220459\n"
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      "random losses: [2.17, 1.532, 2.852, 2.365, 1.431, 3.721, 2.236, 1.627, 2.668]\n"
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    },
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      "loss: 2.430000066757202\n"
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      "random losses: [2.785, 2.117, 1.604, 2.281, 1.899, 3.468, 2.676, 2.954, 2.327]\n"
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      "loss: 2.069000005722046\n"
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      "loss: 3.5320000648498535\n"
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     "text": [
      "random losses: [2.779, 3.631, 2.727, 1.721, 2.895, 2.434, 2.607, 3.529, 3.759]\n"
     ]
    },
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      "loss: 3.759999990463257\n"
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      "random losses: [2.699, 2.614, 3.279, 2.414, 1.826, 2.233, 1.625, 2.64, 4.394]\n"
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      "loss: 0.24300000071525574\n"
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     "text": [
      "random losses: [1.54, 1.253, 1.1, 1.554, 1.186, 1.631, 1.315, 2.055, 1.084]\n"
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      "loss: 2.509999990463257\n"
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     "text": [
      "random losses: [2.038, 2.032, 2.78, 2.625, 3.177, 2.574, 1.792, 2.249, 3.526]\n"
     ]
    },
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     "data": {
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      "loss: 1.6649999618530273\n"
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      "random losses: [1.455, 2.041, 1.072, 1.14, 2.165, 1.378, 2.558, 1.753, 2.309]\n"
     ]
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      "loss: 1.6660000085830688\n"
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loss: 1.0700000524520874\n"
     ]
    },
    {
     "data": {
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      ],
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "random losses: [0.792, 0.69, 0.618, 1.062, 1.074, 0.817, 0.887, 0.573, 0.585]\n"
     ]
    },
    {
     "data": {
      "text/html": [
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      ],
      "text/plain": [
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loss: 0.5540000200271606\n"
     ]
    },
    {
     "data": {
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       "            </div></div>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "random losses: [0.561, 0.283, 0.913, 0.717, 0.647, 0.854, 0.431, 0.794, 1.285]\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<img src=\"data:image/PNG;base64,[encoded data, 25692 characters]\" style=\"image-rendering: pixelated;\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loss: 0.8930000066757202\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div style=\"display: flex; flex-direction: row;\"><div style=\"margin-right:10px; margin-top: 4px;\">\n",
       "              0 <br/>\n",
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       "            </div></div>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "random losses: [0.758, 0.301, 0.274, 0.466, 0.579, 0.469, 0.287, 0.697, 0.499]\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<img src=\"data:image/PNG;base64,[encoded data, 25624 characters]\" style=\"image-rendering: pixelated;\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "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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