continuous_function_2.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from itertools import product\n",
    "\n",
    "from tools import generate_input\n",
    "from models import ContinuousReverseGame, ContinuousGameOfLife"
   ]
  },
  {
   "source": [
    "Defining _f_"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def continuous_game_2(cell, around_cell, almost_zero=0.01, border=0.4):\n",
    "\n",
    "    x1 = tf.math.maximum(((8-around_cell)/(8-3))*almost_zero, (3+border-around_cell)/border )\n",
    "    x2 = tf.math.maximum((around_cell-(3-border))/border, around_cell*almost_zero/3 )\n",
    "\n",
    "    x3 = tf.math.maximum((cell-(1-border))/border, cell*almost_zero)\n",
    "\n",
    "    x4 = tf.math.maximum((around_cell-(2-border))/border, around_cell*almost_zero/2 )\n",
    "\n",
    "    x12 = tf.math.minimum(x1,x2)\n",
    "    x34 = tf.math.minimum(x3,x4)\n",
    "\n",
    "    x5 = tf.math.minimum(x1,x34)\n",
    "    xx = tf.math.maximum(x12,x5)\n",
    "\n",
    "    return xx"
   ]
  },
  {
   "source": [
    "Plotting _f_"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "The colour indicates the state of the cell for the next step\n"
     ]
    },
    {
     "output_type": "display_data",
     "data": {
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "around_cell_possible_values = np.arange(0, 8.00001, 0.02)\n",
    "cell_possible_values = np.arange(1.0000, -0.0000001,-0.01)\n",
    "\n",
    "around_cell = [list(around_cell_possible_values)]*len(cell_possible_values)\n",
    "cell = [list(cell_possible_values)] * len(around_cell_possible_values)\n",
    "\n",
    "around_cell = np.array(around_cell)\n",
    "cell = np.array(cell)\n",
    "cell = np.transpose(cell)\n",
    "\n",
    "around_cell = tf.constant(around_cell)\n",
    "cell = tf.constant(cell)\n",
    "\n",
    "xx = continuous_game_2(cell, around_cell)\n",
    "\n",
    "plt.figure(figsize=(10,4))\n",
    "plt.imshow(xx, vmin=0, vmax=1)\n",
    "\n",
    "plt.xticks([0, 101, 201, 301, 401], [0,2,4,6, 8])\n",
    "plt.yticks([0,100], ['alive: 1', 'dead: 0'])\n",
    "plt.ylabel('State of the cell')\n",
    "plt.xlabel('Sum of the states of the cells around')\n",
    "\n",
    "print('The colour indicates the state of the cell for the next step')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 1512x360 with 14 Axes>",
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   ],
   "source": [
    "# generating some inputs and targets for the general mode\n",
    "\n",
    "x, y = generate_input(trials=8, grid_shape=(25,25), steps_before_generating_output=2)\n",
    "\n",
    "\n",
    "plt.figure(figsize=(3*len(x), 5))\n",
    "for i in range(len(x)):\n",
    "    plt.subplot(2, len(x), i+1)\n",
    "    plt.title('Grid ' + str(i+1))\n",
    "    plt.imshow(x[i])\n",
    "    plt.ylabel('input')\n",
    "    plt.subplot(2, len(x), len(x)+i+1)\n",
    "    plt.imshow(y[i])\n",
    "    plt.ylabel('target')    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# We train the reverse model with the function f defined above\n",
    "\n",
    "game = ContinuousGameOfLife(continuous_game_2)\n",
    "reverse_game = ContinuousReverseGame(continuous_game_2, 0.1, 0.3, 25)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "tags": [
     "outputPrepend"
    ]
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "1/1 [==============================] - 0s 3ms/sample - loss: 0.0395\n",
      "Epoch 258/500\n",
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      "Epoch 259/500\n",
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      "Epoch 260/500\n",
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      "Epoch 261/500\n",
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      "Epoch 262/500\n",
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      "Epoch 263/500\n",
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      "Epoch 264/500\n",
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      "Epoch 265/500\n",
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      "Epoch 266/500\n",
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      "Epoch 267/500\n",
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      "Epoch 268/500\n",
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      "Epoch 269/500\n",
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      "Epoch 270/500\n",
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      "Epoch 271/500\n",
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      "Epoch 275/500\n",
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      "Epoch 276/500\n",
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      "Epoch 279/500\n",
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      "Epoch 284/500\n",
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      "Epoch 285/500\n",
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      "1/1 [==============================] - 0s 9ms/sample - loss: 0.0395\n",
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      "Epoch 289/500\n",
      "1/1 [==============================] - 0s 4ms/sample - loss: 0.0389\n",
      "Epoch 290/500\n",
      "1/1 [==============================] - 0s 8ms/sample - loss: 0.0385\n",
      "Epoch 291/500\n",
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      "Epoch 367/500\n",
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      "Epoch 420/500\n",
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      "Epoch 424/500\n",
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      "Epoch 425/500\n",
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      "Epoch 426/500\n",
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      "Epoch 427/500\n",
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      "Epoch 447/500\n",
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      "Epoch 453/500\n",
      "1/1 [==============================] - 0s 3ms/sample - loss: 0.0300\n",
      "Epoch 454/500\n",
      "1/1 [==============================] - 0s 5ms/sample - loss: 0.0299\n",
      "Epoch 455/500\n",
      "1/1 [==============================] - 0s 3ms/sample - loss: 0.0298\n",
      "Epoch 456/500\n",
      "1/1 [==============================] - 0s 6ms/sample - loss: 0.0297\n",
      "Epoch 457/500\n",
      "1/1 [==============================] - 0s 3ms/sample - loss: 0.0296\n",
      "Epoch 458/500\n",
      "1/1 [==============================] - 0s 3ms/sample - loss: 0.0296\n",
      "Epoch 459/500\n",
      "1/1 [==============================] - 0s 5ms/sample - loss: 0.0295\n",
      "Epoch 460/500\n",
      "1/1 [==============================] - 0s 3ms/sample - loss: 0.0294\n",
      "Epoch 461/500\n",
      "1/1 [==============================] - 0s 4ms/sample - loss: 0.0293\n",
      "Epoch 462/500\n",
      "1/1 [==============================] - 0s 4ms/sample - loss: 0.0292\n",
      "Epoch 463/500\n",
      "1/1 [==============================] - 0s 6ms/sample - loss: 0.0292\n",
      "Epoch 464/500\n",
      "1/1 [==============================] - 0s 3ms/sample - loss: 0.0291\n",
      "Epoch 465/500\n",
      "1/1 [==============================] - 0s 7ms/sample - loss: 0.0291\n",
      "Epoch 466/500\n",
      "1/1 [==============================] - 0s 4ms/sample - loss: 0.0290\n",
      "Epoch 467/500\n",
      "1/1 [==============================] - 0s 9ms/sample - loss: 0.0289\n",
      "Epoch 468/500\n",
      "1/1 [==============================] - 0s 3ms/sample - loss: 0.0289\n",
      "Epoch 469/500\n",
      "1/1 [==============================] - 0s 5ms/sample - loss: 0.0289\n",
      "Epoch 470/500\n",
      "1/1 [==============================] - 0s 4ms/sample - loss: 0.0288\n",
      "Epoch 471/500\n",
      "1/1 [==============================] - 0s 4ms/sample - loss: 0.0288\n",
      "Epoch 472/500\n",
      "1/1 [==============================] - 0s 4ms/sample - loss: 0.0288\n",
      "Epoch 473/500\n",
      "1/1 [==============================] - 0s 5ms/sample - loss: 0.0283\n",
      "Epoch 474/500\n",
      "1/1 [==============================] - 0s 3ms/sample - loss: 0.0279\n",
      "Epoch 475/500\n",
      "1/1 [==============================] - 0s 3ms/sample - loss: 0.0277\n",
      "Epoch 476/500\n",
      "1/1 [==============================] - 0s 4ms/sample - loss: 0.0275\n",
      "Epoch 477/500\n",
      "1/1 [==============================] - 0s 3ms/sample - loss: 0.0274\n",
      "Epoch 478/500\n",
      "1/1 [==============================] - 0s 3ms/sample - loss: 0.0274\n",
      "Epoch 479/500\n",
      "1/1 [==============================] - 0s 5ms/sample - loss: 0.0274\n",
      "Epoch 480/500\n",
      "1/1 [==============================] - 0s 3ms/sample - loss: 0.0274\n",
      "Epoch 481/500\n",
      "1/1 [==============================] - 0s 3ms/sample - loss: 0.0274\n",
      "Epoch 482/500\n",
      "1/1 [==============================] - 0s 4ms/sample - loss: 0.0274\n",
      "Epoch 483/500\n",
      "1/1 [==============================] - 0s 2ms/sample - loss: 0.0274\n",
      "Epoch 484/500\n",
      "1/1 [==============================] - 0s 2ms/sample - loss: 0.0273\n",
      "Epoch 485/500\n",
      "1/1 [==============================] - 0s 5ms/sample - loss: 0.0272\n",
      "Epoch 486/500\n",
      "1/1 [==============================] - 0s 3ms/sample - loss: 0.0272\n",
      "Epoch 487/500\n",
      "1/1 [==============================] - 0s 4ms/sample - loss: 0.0272\n",
      "Epoch 488/500\n",
      "1/1 [==============================] - 0s 4ms/sample - loss: 0.0271\n",
      "Epoch 489/500\n",
      "1/1 [==============================] - 0s 3ms/sample - loss: 0.0271\n",
      "Epoch 490/500\n",
      "1/1 [==============================] - 0s 3ms/sample - loss: 0.0271\n",
      "Epoch 491/500\n",
      "1/1 [==============================] - 0s 3ms/sample - loss: 0.0271\n",
      "Epoch 492/500\n",
      "1/1 [==============================] - 0s 2ms/sample - loss: 0.0271\n",
      "Epoch 493/500\n",
      "1/1 [==============================] - 0s 5ms/sample - loss: 0.0271\n",
      "Epoch 494/500\n",
      "1/1 [==============================] - 0s 4ms/sample - loss: 0.0271\n",
      "Epoch 495/500\n",
      "1/1 [==============================] - 0s 5ms/sample - loss: 0.0271\n",
      "Epoch 496/500\n",
      "1/1 [==============================] - 0s 5ms/sample - loss: 0.0270\n",
      "Epoch 497/500\n",
      "1/1 [==============================] - 0s 5ms/sample - loss: 0.0270\n",
      "Epoch 498/500\n",
      "1/1 [==============================] - 0s 11ms/sample - loss: 0.0270\n",
      "Epoch 499/500\n",
      "1/1 [==============================] - 0s 3ms/sample - loss: 0.0270\n",
      "Epoch 500/500\n",
      "1/1 [==============================] - 0s 37ms/sample - loss: 0.0270\n"
     ]
    },
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x143454d7e08>"
      ]
     },
     "metadata": {},
     "execution_count": 6
    }
   ],
   "source": [
    "reverse_game.compile(loss='mse', optimizer=tf.keras.optimizers.Adam(learning_rate=0.01))\n",
    "\n",
    "reverse_game.fit(y[:1], y[:1], epochs=500)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x143457ecbc8>"
      ]
     },
     "metadata": {},
     "execution_count": 9
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "f = reverse_game.get_weights()[0]\n",
    "\n",
    "# we take away the padding\n",
    "f = f[:,1:-2, 1:-2]\n",
    "\n",
    "plt.imshow(f[0], vmin=0, vmax=1 )\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x143472b8c08>"
      ]
     },
     "metadata": {},
     "execution_count": 10
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
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     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "plt.imshow(game(f)[0], vmin=0, vmax=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "name": "Python 3.7.9 64-bit ('tensorflow_env': conda)",
   "display_name": "Python 3.7.9 64-bit ('tensorflow_env': conda)",
   "metadata": {
    "interpreter": {
     "hash": "d4b6d29c2a1f1286a64751055da364a85240e33ea9e770b13e7d7ff44040646e"
    }
   }
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.9-final"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}