minor fixes to RL and BNN codes

bayesian-code.ipynb

@@ -38,7 +38,8 @@
         "## Overview\n",
         "\n",
         "We are now considering the same setup as in the notebook {doc}`supervised-airfoils`: A turbulent airflow around wing profiles, for which we'd like to know the average motion\n",
-        "and pressure distribution around this airfoil for different Reynolds numbers and angles of attack. In the earlier notebook, we tackled this by completely bypassing any physical solver and instead training a neural network that learns the quantities of interest. Now, we want to extend this approach to the variational Bayesian Neural Networks (BNNs) of the previous section. In contrast to traditional networks, that learn a single point estimate for each weight value, BNNs aim at learning a _distribution_ over each weight parameter (e.g. a Gaussian with mean $\\mu$ and variance $\\sigma^{2}$). During a forward-pass, each parameter in the network is then sampled from its corresponding approximate posterior distribution $q_{\\phi}(\\theta)$. In that sense, the network parameters themselves are _random variables_ and each forward pass becomes _stochastic_, because for a given input the predictions will vary with every forward-pass. This allows to assess how _uncertain_ the network is: If the predictions vary a lot, we think that the network is uncertain about its output. "
+        "and pressure distribution around this airfoil for different Reynolds numbers and angles of attack. In the earlier notebook, we tackled this by completely bypassing any physical solver and instead training a neural network that learns the quantities of interest. Now, we want to extend this approach to the variational Bayesian Neural Networks (BNNs) of the previous section. In contrast to traditional networks, that learn a single point estimate for each weight value, BNNs aim at learning a _distribution_ over each weight parameter (e.g. a Gaussian with mean $\\mu$ and variance $\\sigma^{2}$). During a forward-pass, each parameter in the network is then sampled from its corresponding approximate posterior distribution $q_{\\phi}(\\theta)$. In that sense, the network parameters themselves are _random variables_ and each forward pass becomes _stochastic_, because for a given input the predictions will vary with every forward-pass. This allows to assess how _uncertain_ the network is: If the predictions vary a lot, we think that the network is uncertain about its output. ",
+		"[[run in colab]](https://colab.research.google.com/github/tum-pbs/pbdl-book/blob/main/bayesian-code.ipynb)"
       ]
     },
     {
@@ -1152,4 +1153,4 @@
       ]
     }
   ]
-}
+}

reinflearn-code.ipynb

@@ -99,7 +99,7 @@
         "colab": {
           "base_uri": "https://localhost:8080/"
         },
-        "outputId": "23bfdf80-3d9d-4195-dbd8-d03cd4061144"
+        "outputId": "73046435-964d-4d69-adae-ec7e519ede10"
       },
       "source": [
         "import sys; sys.path.append('PDE-Control/src'); sys.path.append('PDE-Control-RL/src')\n",
@@ -170,7 +170,7 @@
         "colab": {
           "base_uri": "https://localhost:8080/"
         },
-        "outputId": "ed4f054a-2fdf-479e-e00b-847f63edf474"
+        "outputId": "bf634970-0b1d-4202-ef7b-6b6043af3f84"
       },
       "source": [
         "for batch_index in range(SCENE_COUNT // BATCH_SIZE):\n",
@@ -304,7 +304,7 @@
         "colab": {
           "base_uri": "https://localhost:8080/"
         },
-        "outputId": "42f94dd3-5d45-4911-88a5-75c873d0d090"
+        "outputId": "31a11968-97d5-4acc-bde9-57d209c41174"
       },
       "source": [
         "rl_trainer = BurgersTraining(\n",
@@ -348,6 +348,18 @@
         "The graph titled \"forces\" shows how the overall amount of forces generated by the network evolves during training. \"rew_unnormalized\" shows the reward values without the normalization step described above. The corresponding values with normalization are shown under \"rollout/ep_rew_mean\". \"val_set_forces\" outlines the performance of the agent on the validation set."
       ]
     },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "ARgXZocR_stV"
+      },
+      "source": [
+        "%load_ext tensorboard\n",
+        "%tensorboard --logdir PDE-Control-RL/networks/rl-models/bench/tensorboard-log"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
     {
       "cell_type": "markdown",
       "metadata": {
@@ -364,12 +376,12 @@
         "colab": {
           "base_uri": "https://localhost:8080/"
         },
-        "outputId": "a6483d80-9658-4c1f-fc5b-07c5cd9c30bf"
+        "outputId": "937e7a7f-ab45-42fa-802e-461a81463cc4"
       },
       "source": [
         "rl_trainer.train(n_rollouts=RL_ROLLOUTS, save_freq=50)"
       ],
-      "execution_count": 8,
+      "execution_count": 7,
       "outputs": [
         {
           "output_type": "stream",
@@ -426,7 +438,7 @@
           "base_uri": "https://localhost:8080/",
           "height": 545
         },
-        "outputId": "d23253e4-1fa7-4df2-c5bb-03ec1a958830"
+        "outputId": "00a91082-04c5-48ab-f7be-4debf0f5586a"
       },
       "source": [
         "TEST_SAMPLE = 0    # Change this to display a reconstruction of another scene\n",
@@ -443,7 +455,7 @@
         "    axs[1].plot(gt_frames[frame][TEST_SAMPLE,:], color=frame_color, linewidth=0.8)\n",
         "    axs[2].plot(unc_frames[frame][TEST_SAMPLE,:], color=frame_color, linewidth=0.8)"
       ],
-      "execution_count": 9,
+      "execution_count": 8,
       "outputs": [
         {
           "output_type": "stream",
@@ -456,7 +468,7 @@
         {
           "output_type": "display_data",
           "data": {
-            "image/png": 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truncated
+            "image/png": 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truncated
             "text/plain": [
               "<Figure size 1360.8x691.2 with 3 Axes>"
             ]
@@ -497,14 +509,14 @@
         "colab": {
           "base_uri": "https://localhost:8080/"
         },
-        "outputId": "4f7dda1c-5457-4bc5-816c-ee4938eaff94"
+        "outputId": "f2540cb8-aaec-401b-c954-49c2e2324f7a"
       },
       "source": [
         "from control.pde.burgers import BurgersPDE\n",
         "from control.control_training import ControlTraining\n",
         "from control.sequences import StaggeredSequence"
       ],
-      "execution_count": 10,
+      "execution_count": 9,
       "outputs": [
         {
           "output_type": "stream",
@@ -544,7 +556,7 @@
         "colab": {
           "base_uri": "https://localhost:8080/"
         },
-        "outputId": "07f31dc7-9c20-45d8-d81d-05c92a783c3c"
+        "outputId": "e4051cf9-c3c9-49bc-e1aa-8969b771c54b"
       },
       "source": [
         "dp_app = ControlTraining(\n",
@@ -564,14 +576,14 @@
         "    dt=DT\n",
         ").prepare()"
       ],
-      "execution_count": 11,
+      "execution_count": 10,
       "outputs": [
         {
           "output_type": "stream",
           "text": [
-            "App created. Scene directory is /root/phi/model/control-training/sim_000000 (INFO), 2021-08-03 17:13:32,681n\n",
+            "App created. Scene directory is /root/phi/model/control-training/sim_000000 (INFO), 2021-08-04 10:11:58,466n\n",
             "\n",
-            "Sequence class: <class 'control.sequences.StaggeredSequence'> (INFO), 2021-08-03 17:13:35,832n\n",
+            "Sequence class: <class 'control.sequences.StaggeredSequence'> (INFO), 2021-08-04 10:12:01,449n\n",
             "\n",
             "Partition length 32 sequence (from 0 to 32) at frame 16\n"
           ],
@@ -654,20 +666,20 @@
             "Partition length 2 sequence (from 30 to 32) at frame 31\n",
             "Execute -> 31\n",
             "Execute -> 32\n",
-            "Target loss: Tensor(\"truediv_1:0\", shape=(), dtype=float32) (INFO), 2021-08-03 17:14:46,313n\n",
+            "Target loss: Tensor(\"truediv_1:0\", shape=(), dtype=float32) (INFO), 2021-08-04 10:13:10,701n\n",
             "\n",
-            "Force loss: Tensor(\"Sum_97:0\", shape=(), dtype=float32) (INFO), 2021-08-03 17:14:49,832n\n",
+            "Force loss: Tensor(\"Sum_97:0\", shape=(), dtype=float32) (INFO), 2021-08-04 10:13:14,221n\n",
             "\n",
-            "Setting up loss (INFO), 2021-08-03 17:14:49,834n\n",
+            "Setting up loss (INFO), 2021-08-04 10:13:14,223n\n",
             "\n",
-            "Preparing data (INFO), 2021-08-03 17:15:26,279n\n",
+            "Preparing data (INFO), 2021-08-04 10:13:51,128n\n",
             "\n",
             "INFO:tensorflow:Summary name Total Force is illegal; using Total_Force instead.\n",
-            "Initializing variables (INFO), 2021-08-03 17:15:26,310n\n",
+            "Initializing variables (INFO), 2021-08-04 10:13:51,156n\n",
             "\n",
-            "Model variables contain 0 total parameters. (INFO), 2021-08-03 17:15:31,021n\n",
+            "Model variables contain 0 total parameters. (INFO), 2021-08-04 10:13:55,961n\n",
             "\n",
-            "Validation (000000): Learning_Rate: 0.001, Loss_reg_unscaled: 78.66845, Loss_reg_scale: 1.0, Loss: 0.0, Total Force: 218.16597 (INFO), 2021-08-03 17:16:07,346n\n",
+            "Validation (000000): Learning_Rate: 0.001, Loss_reg_unscaled: 205.98526, Loss_reg_scale: 1.0, Loss: 0.0, Total Force: 393.8109 (INFO), 2021-08-04 10:14:32,455n\n",
             "\n"
           ],
           "name": "stdout"
@@ -718,7 +730,12 @@
     {
       "cell_type": "code",
       "metadata": {
-        "id": "kzfckDMc8O__"
+        "id": "kzfckDMc8O__",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 58
+        },
+        "outputId": "4c88893b-e366-46aa-ecae-f1f12becca22"
       },
       "source": [
         "DP_STORE_PATH = 'networks/dp-models/bench'\n",
@@ -735,8 +752,24 @@
         "dp_checkpoint = dp_app.save_model()\n",
         "shutil.move(dp_checkpoint, DP_STORE_PATH)"
       ],
-      "execution_count": 32,
-      "outputs": []
+      "execution_count": 12,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "string"
+            },
+            "text/plain": [
+              "'networks/dp-models/bench/checkpoint_00010000'"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 12
+        }
+      ]
     },
     {
       "cell_type": "markdown",
@@ -758,7 +791,7 @@
         "\n",
         "# dp_app.load_checkpoints({net: dp_path for net in networks_to_load})"
       ],
-      "execution_count": 14,
+      "execution_count": 13,
       "outputs": []
     },
     {
@@ -778,7 +811,7 @@
           "base_uri": "https://localhost:8080/",
           "height": 545
         },
-        "outputId": "8c7342ce-dbd3-417c-9802-2f6ef631a012"
+        "outputId": "2f22a1da-669e-44b2-cfd7-8d3962bfe208"
       },
       "source": [
         "dp_frames = dp_app.infer_all_frames(TEST_RANGE)\n",
@@ -803,7 +836,7 @@
         "    axs[1].plot(gt_frames[frame][TEST_SAMPLE,:], color=frame_color, linewidth=0.8)\n",
         "    axs[2].plot(unc_frames[frame][TEST_SAMPLE,:], color=frame_color, linewidth=0.8)"
       ],
-      "execution_count": 15,
+      "execution_count": 14,
       "outputs": [
         {
           "output_type": "stream",
@@ -816,7 +849,7 @@
         {
           "output_type": "display_data",
           "data": {
-            "image/png": 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truncated
+            "image/png": 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truncated
             "text/plain": [
               "<Figure size 1360.8x691.2 with 3 Axes>"
             ]
@@ -863,7 +896,7 @@
         "import utils\n",
         "import pandas as pd"
       ],
-      "execution_count": null,
+      "execution_count": 15,
       "outputs": []
     },
     {
@@ -885,7 +918,7 @@
         "colab": {
           "base_uri": "https://localhost:8080/"
         },
-        "outputId": "4814446b-7e23-493e-c929-43f68c15af72"
+        "outputId": "2d447412-4b6d-4043-ad47-7d5c437fe03e"
       },
       "source": [
         "rl_frames, gt_frames, unc_frames = rl_trainer.infer_test_set_frames()\n",
@@ -900,7 +933,7 @@
         "    (1, 1): ('Differentiable Physics', dp_frames),\n",
         "}"
       ],
-      "execution_count": 17,
+      "execution_count": 16,
       "outputs": [
         {
           "output_type": "stream",
@@ -920,37 +953,32 @@
           "base_uri": "https://localhost:8080/",
           "height": 620
         },
-        "outputId": "b4a039c9-f8f8-410d-8f81-2600d193dd64"
+        "outputId": "8a718c7a-2d31-4176-8b0c-b06b38633e98"
       },
       "source": [
         "TEST_SAMPLE = 0  # Specifies which sample of the test set should be displayed\n",
         "\n",
         "def plot(axs, xy, title, field):\n",
-        "    axs[xy].set_ylim(-2, 2)\n",
-        "    axs[xy].set_xlabel('x')\n",
-        "    axs[xy].set_ylabel('u(x)')\n",
-        "    axs[xy].set_title(title)\n",
-        "\n",
-        "    label = 'Initial state in dark red, final state in dark blue'\n",
+        "    axs[xy].set_ylim(-2, 2); axs[xy].set_title(title)\n",
+        "    axs[xy].set_xlabel('x'); axs[xy].set_ylabel('u(x)')\n",
+        "    label = 'Initial state (red), final state (blue)'\n",
         "    for step_idx in range(0, STEP_COUNT + 1):\n",
         "        color = bplt.gradient_color(step_idx, STEP_COUNT+1)\n",
         "        axs[xy].plot(\n",
         "            field[step_idx][TEST_SAMPLE].squeeze(), color=color, linewidth=0.8, label=label)\n",
         "        label = None\n",
-        "\n",
         "    axs[xy].legend()\n",
         "\n",
         "fig, axs = plt.subplots(2, 2, figsize=(12.8, 9.6))\n",
         "for xy in frames:\n",
-        "    plot(axs, xy, *frames[xy])\n",
-        "    "
+        "    plot(axs, xy, *frames[xy])"
       ],
-      "execution_count": 18,
+      "execution_count": 24,
       "outputs": [
         {
           "output_type": "display_data",
           "data": {
-            "image/png": "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 truncated
+            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAwYAAAJHCAYAAAAqtUGpAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAgAElEQVR4nOzdeZhU1Zn48e+5t9au3jd6oRuatdmbRRFwQXFB0ZgYTaIYNYlxkplMJjNZnV8mmcxqJskkY9ZxJhkn0WTiGo1b3EABRQVERPadppveu/aqu53fH9WQFgGBAMXyfp6nnq5777nnnrpdfd9+z7mL0lojhBBCCCGEOLsZ+W6AEEIIIYQQIv8kMRBCCCGEEEJIYiCEEEIIIYSQxEAIIYQQQgiBJAZCCCGEEEIIJDEQQgghhBBCIImBECecUmq4UkorpXx52PYOpdSlJ3u7Qgghjj+l1N8rpe4beH/MsSWfcUmc2iQxEGcEpdTHlFKvKaWSSqnOgfd/rpRS+W7b4SilEoNenlIqPWh64VHWda9S6p9OVFuFEOJ0MfBP76gD5u3/pzpPbZqrlGrN1/aFOBKSGIjTnlLqi8B/AN8BaoAhwGeAOUDgEOuYJ62Bh6G1Ltz3AnYB1wyad/++ctKrI4QQZz451ot8k8RAnNaUUiXAPwB/rrV+SGsd1zlvaq0Xaq2zA+XuVUr9VCn1lFIqCVyslBqnlFqslOpXSr2jlPrAoHoXK6VuHzR9m1Jq6aBprZT6jFJq88D6P943OqGUMpVS31VKdSultgELjuFzzVVKtSqlvqqU2gv8z4FtGNSOUUqpO4CFwFcGRht+P6hYi1JqjVIqqpT6rVIqdLTtEUKIM8mgY+wXB0aZ25VSnxi0PKyU+p5SaufAsXOpUio8sOwDAzGjfyBWjBu03g6l1JcOPOYqpSLA00DdoFHhuoFRjIeUUvcppWLAbQPzH1dK9SqltiilPn2En6lEKfXzgc+yRyn1T/s6wY5HXBJnB0kMxOluFhAEHjuCsjcB/wwUAa8BvweeBaqBvwTuV0qNPYptXw2cA0wGPgJcMTD/0wPLpgIzgOuPos7BaoByYBhwx+EKaq3vAe4H/m1gtOGaQYs/AswHmgbaetsxtkcIIc4kNUAJUA98CvixUqpsYNl3genAbHLH4a8AnlJqDPAb4AtAFfAU8Hul1ODR6fccc7XWSeBKoG3QqHDbQPlrgYeAUnLH8f8DWoE6cvHjX5RSlxzB57kXcIBR5OLP5cC+Dq7jFZfEGU4SA3G6qwS6tdbOvhlKqVcGenLSSqkLB5V9TGu9TGvtAS1AIXCX1trSWr8IPAHceBTbvktr3a+13gUsGqgTckHhB1rr3VrrXuBfj/GzecA3tdZZrXX6GOsAuFtr3TbQlt8PaqcQQpzNbOAftNa21vopIAGMVUoZwCeBv9Ja79Fau1rrVwZGoD8KPKm1fk5rbZNLIMLkEoh9jvaY+6rW+ncDsamS3GmwX9VaZ7TWq4H/Bm45XAVKqSHAVcAXtNZJrXUn8H3gYwNFjldcEmc4OZdNnO56gEqllG9fcqC1ng0wcJHX4OR396D3dcDugQPxPjvJ9Rwdqb2D3qfIJRr76z6g3mPRpbXOHOO6gx3YzrrjUKcQQpzKXMB/wDw/uWRgn57BnUr88TheCYSArQept45Bx3SttaeU2s27Y8fRHnMPjE29Wuv4oHk7yfXyH84wcp+vfdA9N4xBdR+vuCTOcDJiIE53rwJZckOx70cPet8GNAz0DO3TCOwZeJ8ECgYtqzmKNrUDDQfUeyz0AdPvapNS6sA2HVheCCHOVruA4QfMa+LI/iHuBjLAyIMsayP3TzgAA9eWNfDH2HE4hzpGHxibypVSRYPmDY5Nh7KbXCys1FqXDryKtdYTBpYfr7gkznCSGIjTmta6H/gW8BOl1PVKqSKllKGUagEih1n1NXI9OV9RSvmVUnOBa8id2wmwGrhOKVUwcMu7Tx1Fsx4APq+UGjpwvurXjvJjHcpbwASlVMvABcR/f8DyDmDEcdqWEEKczn4LfH3gOGyo3PNcriF3Lv9hDYwk/wL494ELgU2l1CylVJDc8X2BUmqeUsoPfJHcP+SvHEGbOoAKlbtpxqG2vXugrn8duGh5Mrn4c9jbrGqt28ldM/c9pVTxwGceqZS6aKDIiYpL4gwjiYE47Wmt/w34G3IXh3UMvP4T+CqHOFhrrS1yQeJKcr1DPwFu0VpvGCjyfcAaqOt/yV0QdqT+C/gDuX/kVwGPHN0nOjit9SZyd2B6HtgMLD2gyM+B8QPXV/zueGxTCCFOU/9A7vi/FOgD/g1YqLVee4Trfwl4G3gD6AW+DRha643AzcAPycWOa8jdZtp6vwoH4stvgG0Dx+lDnWJ0I7nRjjbgUXLXmj1/BG2+hdwtuteR+8wPAbUDy05IXBJnHqW1nH0ghBBCCCHE2U5GDIQQQgghhBD5SwyUUg1KqUVKqXUDDwr5q4OUUUqpuwce8LFGKTUtH20VQghx4klcEEKI/Mrn7Uod4Ita61UDV9+vVEo9p7VeN6jMlcDogddM4KcDP4UQQpx5JC4IIUQe5W3EQGvdrrVeNfA+DqznvfeQvxb4pc5ZDpQqpWoRQghxxpG4IIQQ+XVKXGOglBpO7jHdrx2wqJ53P5CjlaN7AJUQQojTkMQFIYQ4+fL+5GOlVCHwMLnHeMf+hHruAO4AiEQi05ubm49TC4UQ4vS1cuXKbq11Vb7bcTSOR1yQmCCEEAd3uLiQ18Rg4OEgDwP3a60Pdk/dPbz7SX1DOcTT/7TW9wD3AMyYMUOvWLHiOLdWCCFOP0qpI3nS6ynjeMUFiQlCCHFwh4sL+bwrkSL3QKb1Wut/P0Sxx4FbBu5CcR4QHXi6nxBCiDOMxAUhhMivfI4YzAE+DrytlFo9MO9vgUYArfXPgKeAq4AtQAr4RB7aKYQQ4uSQuCCEEHmUt8RAa70UUO9TRgN/cXJaJIQQIp8kLgghRH7l/eJjIQazbZvW1lYymUy+myLEaSUUCjF06FD8fn++myLEcSMxQYhjdyxxQRIDcUppbW2lqKiI4cOHkzvdWAjxfrTW9PT00NraSlNTU76bI8RxIzFBiGNzrHHhlHiOgRD7ZDIZKioqJAAIcRSUUlRUVEivqjjjSEwQ4tgca1yQxECcciQACHH05O9GnKnkuy3EsTmWvx1JDIQQQgghhBCSGAhxoMLCwvctc/vtt7Nu3ToA/uVf/uVdy2bPnn1ctrHPD37wA1Kp1HErd6AvfOELvPzyy0e93j6LFy/m6quvBuCJJ57gG9/4xhGtd+ONNzJ58mS+//3v841vfIPnn3/+T97+++nv7+cnP/nJcSt3IK01l1xyCbFYjB07djBx4sSDlps7dy7H+sCto9nHQog/ncSEoyMx4Y9Oy5igtT7jXtOnT9fi9LRu3bp8N0FHIpETWv5o1xk2bJju6uo6buUG6+7u1jNnzjzoMsdxjqiORYsW6QULFmittfY8T7e0tOhkMnnYddrb2/XIkSOPqq1Hsv33s337dj1hwoTjVu5ATzzxhP7CF77wvnVcdNFF+o033jjq+rU+/D4+2N8PsEKfAsflfL4kJpy+JCa8l8SEI9/++znTY4LWRx8XZMRAiENYvHgxc+fO5frrr6e5uZmFCxeS+3v6Y3b/ta99jXQ6TUtLCwsXLgT+2POTSCSYN28e06ZNY9KkSTz22GOH3V4ymWTBggVMmTKFiRMn8tvf/pa7776btrY2Lr74Yi6++GIAPvvZzzJjxgwmTJjAN7/5TYCDlnv22WeZNWsW06ZN44YbbiCRSLxnmw8//DDz58/fPz18+HC++tWvMm3aNB588MFD1vHMM8/Q3NzMtGnTeOSRR/avr5Ri7ty5PPHEE4f9rJdffjl79uyhpaWFJUuWcNttt/HQQw/tb8M3v/nN/fttw4YNALz++uvMmjWLqVOnMnv2bDZu3HjYbbzzzjuce+65tLS0MHnyZDZv3szXvvY1tm7dSktLC1/+8pcP+Ts6sBzAd77zHc455xwmT568f78f6P777+faa6/dP+04DgsXLmTcuHFcf/31B+29G9xT+NBDD3HbbbcB0NXVxYc//GHOOecczjnnHJYtW3ZU+1gIcXxJTJCYcFbEhENlDKfzS3qHTl8HZrb/ccXH9T9Pv/q4vf7jio+/bxv29dwsWrRIFxcX6927d2vXdfV5552nlyxZorV+d3Z/YE/PvmnbtnU0GtVaa93V1aVHjhypPc876Dpaa/3QQw/p22+/ff90f3+/1vq9vT49PT1a61zvzUUXXaTfeuut95Tr6urSF1xwgU4kElprre+66y79rW996z3bvOWWW/Tjjz++f3rYsGH629/+9mHrSKfTeujQoXrTpk3a8zx9ww03vKt35r777tOf+9znDr5zBxzYc3LrrbfqBx98cH8b7r77bq211j/+8Y/1pz71Ka211tFoVNu2rbXW+rnnntPXXXed1vrQvUOf+9zn9H333ae11jqbzepUKvWe7R7qd3RguT/84Q/605/+tPY8T7uuqxcsWKBfeuml92yzsbFRx2Kx/Z8R0EuXLtVaa/2JT3xCf+c739FaH/r78+CDD+pbb71Va631jTfeuP/7tnPnTt3c3Ly/3KH2sYwYSEw400hMkJggMeFWrfWxxQStjz4uyHMMxCnt88/8Mq/bP/fccxk6dCgALS0t7Nixg/PPP/+I1tVa87d/+7e8/PLLGIbBnj176OjooKam5qDlJ02axBe/+EW++tWvcvXVV3PBBRcctNwDDzzAPffcg+M4tLe3s27dOiZPnvyuMsuXL2fdunXMmTMHAMuymDVr1nvqam9vp6qq6l3zPvrRjx62jg0bNtDU1MTo0aMBuPnmm7nnnnv2r19dXU1bW9uR7KJDuu666wCYPn36/t6naDTKrbfeyubNm1FKYdv2YeuYNWsW//zP/0xraLine truncated
             "text/plain": [
               "<Figure size 921.6x691.2 with 4 Axes>"
             ]
@@ -985,7 +1013,11 @@
     {
       "cell_type": "code",
       "metadata": {
-        "id": "me4rb-_qCXgC"
+        "id": "me4rb-_qCXgC",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "107ce3dc-2022-45a0-a547-79459df0b458"
       },
       "source": [
         "gt_forces = utils.infer_forces_sum_from_frames(\n",
@@ -996,8 +1028,25 @@
         ")\n",
         "rl_forces = rl_trainer.infer_test_set_forces()\n"
       ],
-      "execution_count": null,
-      "outputs": []
+      "execution_count": 18,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Sanity check - maximum deviation from target state: 0.000000\n",
+            "Sanity check - maximum deviation from target state: 0.000011\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "stream",
+          "text": [
+            "/usr/local/lib/python3.7/dist-packages/torch/nn/functional.py:3613: UserWarning: Default upsampling behavior when mode=linear is changed to align_corners=False since 0.4.0. Please specify align_corners=True if the old behavior is desired. See the documentation of nn.Upsample for details.\n",
+            "  \"See the documentation of nn.Upsample for details.\".format(mode)\n"
+          ],
+          "name": "stderr"
+        }
+      ]
     },
     {
       "cell_type": "markdown",
@@ -1016,7 +1065,7 @@
           "base_uri": "https://localhost:8080/",
           "height": 411
         },
-        "outputId": "739eea96-3135-4c98-c520-c5b07ab26291"
+        "outputId": "887b4c42-8f05-4a0f-d0aa-a6d91d4f03c1"
       },
       "source": [
         "plt.figure(figsize=(8, 6))\n",
@@ -1026,7 +1075,7 @@
         "    color = [\"#0065bd\", \"#e37222\", \"#a2ad00\"],\n",
         "    align='center', label='Absolute forces comparison' )"
       ],
-      "execution_count": 20,
+      "execution_count": 19,
       "outputs": [
         {
           "output_type": "execute_result",
@@ -1038,12 +1087,12 @@
           "metadata": {
             "tags": []
           },
-          "execution_count": 20
+          "execution_count": 19
         },
         {
           "output_type": "display_data",
           "data": {
-            "image/png": "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 truncated
+            "image/png": "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 truncated
             "text/plain": [
               "<Figure size 576x432 with 1 Axes>"
             ]
@@ -1075,7 +1124,7 @@
           "base_uri": "https://localhost:8080/",
           "height": 592
         },
-        "outputId": "95958fea-4d90-4a0c-e64c-6cf224e8de03"
+        "outputId": "7da3f87f-2337-49d1-b949-6f9a7eff4629"
       },
       "source": [
         "plt.figure(figsize=(12, 9))\n",
@@ -1085,24 +1134,24 @@
         "plt.xlabel('ground truth'); plt.ylabel('reconstruction')\n",
         "plt.xlim(0, 1200); plt.ylim(0, 1200); plt.grid(); plt.legend()"
       ],
-      "execution_count": 21,
+      "execution_count": 20,
       "outputs": [
         {
           "output_type": "execute_result",
           "data": {
             "text/plain": [
-              "<matplotlib.legend.Legend at 0x7fec4b9652d0>"
+              "<matplotlib.legend.Legend at 0x7f4cbc6d5090>"
             ]
           },
           "metadata": {
             "tags": []
           },
-          "execution_count": 21
+          "execution_count": 20
         },
         {
           "output_type": "display_data",
           "data": {
-            "image/png": 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truncated
+            "image/png": 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truncated
             "text/plain": [
               "<Figure size 864x648 with 1 Axes>"
             ]
@@ -1140,7 +1189,7 @@
           "base_uri": "https://localhost:8080/",
           "height": 605
         },
-        "outputId": "5328fba7-2046-4968-87f0-4fa9f037cf2d"
+        "outputId": "938b703a-2af2-4d40-ea64-9d8a6800033e"
       },
       "source": [
         "w=0.25; plot_count=20   # How many scenes to show\n",
@@ -1151,12 +1200,12 @@
         "plt.xlabel('Scenes'); plt.xticks(range(plot_count))\n",
         "plt.ylabel('Forces'); plt.legend(); plt.show()"
       ],
-      "execution_count": 22,
+      "execution_count": 21,
       "outputs": [
         {
           "output_type": "display_data",
           "data": {
-            "image/png": 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truncated
+            "image/png": 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truncated
             "text/plain": [
               "<Figure size 921.6x691.2 with 1 Axes>"
             ]
@@ -1190,7 +1239,7 @@
           "height": 371
         },
         "id": "JCQBzhNub16h",
-        "outputId": "8233d8f0-0585-40f2-d85e-4429ccd092a2"
+        "outputId": "58f375b7-a636-4358-85c3-46b38f71b04e"
       },
       "source": [
         "def get_dp_val_set_forces(experiment_path):\n",
@@ -1207,24 +1256,24 @@
         "plt.xlabel('Wall time (hours)'); plt.ylabel('Forces')\n",
         "plt.ylim(0, 1500); plt.grid(); plt.legend()"
       ],
-      "execution_count": 33,
+      "execution_count": 22,
       "outputs": [
         {
           "output_type": "execute_result",
           "data": {
             "text/plain": [
-              "<matplotlib.legend.Legend at 0x7fecba4b0f90>"
+              "<matplotlib.legend.Legend at 0x7f4cbc595c50>"
             ]
           },
           "metadata": {
             "tags": []
           },
-          "execution_count": 33
+          "execution_count": 22
         },
         {
           "output_type": "display_data",
           "data": {
-            "image/png": 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truncated
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truncated
             "text/plain": [
               "<Figure size 864x360 with 1 Axes>"
             ]