ds_exps.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "from experiment import Experiment\n",
    "import torch\n",
    "import numpy as np\n",
    "import dynamical_systems as ds\n",
    "import matplotlib.pyplot as plt\n",
    "import tasks"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Double well"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "exp = Experiment(task='double_well', model='DSModel', epochs=50, n_examples=2000, timesteps=100, depth=1, lr=1e-3, task_kwargs={'clip_val': 2})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
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     "output_type": "stream",
     "text": [
      "Training...\n",
      "Epoch 1/50\n"
     ]
    },
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     "text": [
      "100%|██████████| 50/50 [00:04<00:00, 12.40it/s]\n"
     ]
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     "text": [
      "Train loss: 1.1694594812393189, Test loss: 0.7504543662071228\n",
      "Epoch 2/50\n"
     ]
    },
    {
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     "text": [
      "100%|██████████| 50/50 [00:03<00:00, 13.60it/s]\n"
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     "text": [
      "Train loss: 0.6504567909240723, Test loss: 0.6394384503364563\n",
      "Epoch 3/50\n"
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      "Train loss: 0.5355527412891388, Test loss: 0.46140605211257935\n",
      "Epoch 4/50\n"
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      "Train loss: 0.4750146669149399, Test loss: 0.4452507793903351\n",
      "Epoch 5/50\n"
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      "Epoch 6/50\n"
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      "Epoch 8/50\n"
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      "Epoch 9/50\n"
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      "Epoch 10/50\n"
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      "Epoch 12/50\n"
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      "Train loss: 0.2582369154691696, Test loss: 0.21957102417945862\n",
      "Epoch 13/50\n"
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      "Epoch 34/50\n"
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      "Epoch 35/50\n"
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      "Epoch 48/50\n"
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      "Epoch 49/50\n"
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      "Epoch 50/50\n"
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train loss: 0.05193918503820896, Test loss: 0.05186331644654274\n",
      "Done.\n"
     ]
    }
   ],
   "source": [
    "exp.train()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='x', ylabel='V(x)'>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "ds.plot_potential_1d(exp.model, (-2,2), ax=ax)\n",
    "ds.plot_potential_1d(ds.double_well, (-2,2), ax=ax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "_,t,x0,_,_,_ = exp.generate_data()\n",
    "t = t[:10]\n",
    "x0 = x0[:10]\n",
    "ze = torch.zeros(10, 100, 1)\n",
    "\n",
    "with torch.no_grad():\n",
    "    y = exp.model(ze, x0)\n",
    "    y = y.detach().numpy()\n",
    "t = t.numpy()\n",
    "x0 = x0.numpy()\n",
    "\n",
    "for i in range(10):\n",
    "    plt.plot(t[i], ls='--', color='black')\n",
    "    plt.plot(y[i], color='red')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Flip-flop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [],
   "source": [
    "exp = Experiment(task='flip_flop1', model='DeepLRUModel', epochs=20, n_examples=2000, timesteps=100, depth=1, lr=1e-3,\n",
    "                 skip_connection=False, task_kwargs={'p': 0.01})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DeepLRUModel(\n",
       "  (layers): ModuleList(\n",
       "    (0): SequenceLayer(\n",
       "      (LRU): LRU(d_in=1, d_hidden=128, d_out=128)\n",
       "      (layers): ModuleList(\n",
       "        (0-1): 2 x Linear(in_features=128, out_features=128, bias=True)\n",
       "      )\n",
       "    )\n",
       "  )\n",
       "  (output_layers): ModuleList(\n",
       "    (0): Linear(in_features=128, out_features=1, bias=True)\n",
       "  )\n",
       ")"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "exp.model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training...\n",
      "Epoch 1/20\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|          | 0/50 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 50/50 [00:02<00:00, 23.06it/s]\n",
      "/Users/avalente/miniconda3/envs/lruexp/lib/python3.11/site-packages/numpy/core/fromnumeric.py:3504: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/Users/avalente/miniconda3/envs/lruexp/lib/python3.11/site-packages/numpy/core/_methods.py:129: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  ret = ret.dtype.type(ret / rcount)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train loss: 0.2736857192218304, Test loss: 0.15225161612033844\n",
      "Epoch 2/20\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 50/50 [00:02<00:00, 24.86it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train loss: 0.10456881411373616, Test loss: 0.07854554802179337\n",
      "Epoch 3/20\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 50/50 [00:01<00:00, 25.02it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train loss: 0.05949413076043129, Test loss: 0.041856732219457626\n",
      "Epoch 4/20\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 50/50 [00:02<00:00, 20.18it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train loss: 0.027185538560152055, Test loss: 0.016536898910999298\n",
      "Epoch 5/20\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 50/50 [00:02<00:00, 19.27it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train loss: 0.01202566197142005, Test loss: 0.008024143986403942\n",
      "Epoch 6/20\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 50/50 [00:02<00:00, 22.71it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train loss: 0.012807613664772361, Test loss: 0.006279214285314083\n",
      "Epoch 7/20\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 50/50 [00:02<00:00, 20.56it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train loss: 0.0074869808799121525, Test loss: 0.009380469098687172\n",
      "Epoch 8/20\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 50/50 [00:02<00:00, 21.64it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train loss: 0.004883791997563094, Test loss: 0.004206412006169558\n",
      "Epoch 9/20\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 50/50 [00:02<00:00, 22.00it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train loss: 0.005881723976053763, Test loss: 0.0032200512941926718\n",
      "Epoch 10/20\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 50/50 [00:02<00:00, 21.16it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train loss: 0.0038840955804334953, Test loss: 0.0032200871501117945\n",
      "Epoch 11/20\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 50/50 [00:02<00:00, 21.62it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train loss: 0.004069053830462508, Test loss: 0.008006634190678596\n",
      "Epoch 12/20\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 50/50 [00:02<00:00, 21.58it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train loss: 0.007226638832362369, Test loss: 0.0033111011143773794\n",
      "Epoch 13/20\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 50/50 [00:02<00:00, 21.52it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train loss: 0.003506876828614622, Test loss: 0.0028838999569416046\n",
      "Epoch 14/20\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 50/50 [00:02<00:00, 21.31it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train loss: 0.0040764277044218035, Test loss: 0.0025645969435572624\n",
      "Epoch 15/20\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 50/50 [00:02<00:00, 18.20it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train loss: 0.0034592691535362975, Test loss: 0.0023692508693784475\n",
      "Epoch 16/20\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 50/50 [00:03<00:00, 14.51it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train loss: 0.010918447711737826, Test loss: 0.0045599061995744705\n",
      "Epoch 17/20\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 50/50 [00:02<00:00, 19.94it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train loss: 0.0027998768814723007, Test loss: 0.0029438072815537453\n",
      "Epoch 18/20\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 50/50 [00:02<00:00, 19.74it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train loss: 0.006634901053039357, Test loss: 0.0025213698390871286\n",
      "Epoch 19/20\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 50/50 [00:03<00:00, 16.37it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train loss: 0.0042202110939979325, Test loss: 0.0022646335419267416\n",
      "Epoch 20/20\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 50/50 [00:02<00:00, 17.31it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train loss: 0.0069006030750460925, Test loss: 0.003603314282372594\n",
      "Done.\n"
     ]
    }
   ],
   "source": [
    "exp.train()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [],
   "source": [
    "x, y = tasks.flip_flop1(n=32)\n",
    "\n",
    "x = torch.zeros(2, 100, 1)\n",
    "x[0, 1, 0] = 1\n",
    "x[0, 50, 0] = -1\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([2, 100, 1])"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [],
   "source": [
    "lru = exp.model.layers[0].LRU"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<torch.utils.hooks.RemovableHandle at 0x147792b10>"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "saved_units = torch.zeros(2, 100, lru.d_hidden)\n",
    "def _hook(m, i, o):\n",
    "    with torch.no_grad():\n",
    "        saved_units.copy_(o)\n",
    "lru.register_forward_hook(_hook)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [],
   "source": [
    "out = exp.model(x).detach().numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x147a5bf90>]"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(out[0, :100, 0])\n",
    "plt.plot(x[0, :100, 0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1200x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Do animation\n",
    "from celluloid import Camera\n",
    "fig, ax = plt.subplots(1, 2, figsize=(12, 4))\n",
    "camera = Camera(fig)\n",
    "\n",
    "colors = plt.rcParams['axes.prop_cycle'].by_key()['color']\n",
    "for t in range(100):\n",
    "    ax[0].plot(out[0, :t, 0], c=colors[0])\n",
    "    ax[0].plot(x[0, :t, 0], c=colors[1])\n",
    "    ax[0].set_xlim(0, 100)\n",
    "    ax[0].set_ylim(-1.1, 1.1)\n",
    "    ax[0].set_ylabel('input/output')\n",
    "    ax[0].set_xlabel('time')\n",
    "    ax[1].axhline(0, c='black', lw=0.5)\n",
    "    ax[1].plot(out[0, :t, 0] / 7, c='gray', ls='--')\n",
    "    for i in range(5):\n",
    "        ax[1].plot(saved_units[0, :t, i], c=colors[i])\n",
    "    ax[1].set_xlim(0, 100)\n",
    "    ax[1].set_ylim(-.5, .5)\n",
    "    ax[1].set_ylabel('hidden units')\n",
    "    ax[1].set_xlabel('time')\n",
    "    camera.snap()\n",
    "\n",
    "anim = camera.animate()\n",
    "# show anim\n",
    "from IPython.display import HTML\n",
    "HTML(anim.to_html5_video())\n",
    "anim.save('flip_flop1.gif', writer='imagemagick')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "base",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.5"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}