continuous_function_1.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_1(cell, around_cell):\n",
    "\n",
    "    x1 = tf.math.maximum(4-around_cell,0)\n",
    "    x2 = tf.math.maximum((around_cell + cell)-2,0)\n",
    "    x3 = tf.math.minimum(x1, x2)\n",
    "    x4 = tf.math.minimum(x3,1)\n",
    "\n",
    "    return x4"
   ]
  },
  {
   "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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     },
     "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",
    "plt.figure(figsize=(10,4))\n",
    "plt.imshow(continuous_game_1(cell, around_cell), 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')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
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     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "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_1)\n",
    "reverse_game = ContinuousReverseGame(continuous_game_1, 0.1, 0.3, 25)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "tags": [
     "outputPrepend"
    ]
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "/500\n",
      "1/1 [==============================] - 0s 3ms/sample - loss: 0.0208\n",
      "Epoch 258/500\n",
      "1/1 [==============================] - 0s 4ms/sample - loss: 0.0208\n",
      "Epoch 259/500\n",
      "1/1 [==============================] - 0s 4ms/sample - loss: 0.0208\n",
      "Epoch 260/500\n",
      "1/1 [==============================] - 0s 2ms/sample - loss: 0.0208\n",
      "Epoch 261/500\n",
      "1/1 [==============================] - 0s 4ms/sample - loss: 0.0208\n",
      "Epoch 262/500\n",
      "1/1 [==============================] - 0s 5ms/sample - loss: 0.0208\n",
      "Epoch 263/500\n",
      "1/1 [==============================] - 0s 4ms/sample - loss: 0.0208\n",
      "Epoch 264/500\n",
      "1/1 [==============================] - 0s 3ms/sample - loss: 0.0208\n",
      "Epoch 265/500\n",
      "1/1 [==============================] - 0s 5ms/sample - loss: 0.0208\n",
      "Epoch 266/500\n",
      "1/1 [==============================] - 0s 2ms/sample - loss: 0.0208\n",
      "Epoch 267/500\n",
      "1/1 [==============================] - 0s 3ms/sample - loss: 0.0208\n",
      "Epoch 268/500\n",
      "1/1 [==============================] - 0s 3ms/sample - loss: 0.0208\n",
      "Epoch 269/500\n",
      "1/1 [==============================] - 0s 3ms/sample - loss: 0.0208\n",
      "Epoch 270/500\n",
      "1/1 [==============================] - 0s 4ms/sample - loss: 0.0208\n",
      "Epoch 271/500\n",
      "1/1 [==============================] - 0s 4ms/sample - loss: 0.0208\n",
      "Epoch 272/500\n",
      "1/1 [==============================] - 0s 3ms/sample - loss: 0.0208\n",
      "Epoch 273/500\n",
      "1/1 [==============================] - 0s 3ms/sample - loss: 0.0208\n",
      "Epoch 274/500\n",
      "1/1 [==============================] - 0s 3ms/sample - loss: 0.0208\n",
      "Epoch 275/500\n",
      "1/1 [==============================] - 0s 3ms/sample - loss: 0.0208\n",
      "Epoch 276/500\n",
      "1/1 [==============================] - 0s 3ms/sample - loss: 0.0208\n",
      "Epoch 277/500\n",
      "1/1 [==============================] - 0s 2ms/sample - loss: 0.0208\n",
      "Epoch 278/500\n",
      "1/1 [==============================] - 0s 3ms/sample - loss: 0.0208\n",
      "Epoch 279/500\n",
      "1/1 [==============================] - 0s 3ms/sample - loss: 0.0208\n",
      "Epoch 280/500\n",
      "1/1 [==============================] - 0s 2ms/sample - loss: 0.0208\n",
      "Epoch 281/500\n",
      "1/1 [==============================] - 0s 4ms/sample - loss: 0.0208\n",
      "Epoch 282/500\n",
      "1/1 [==============================] - 0s 3ms/sample - loss: 0.0208\n",
      "Epoch 283/500\n",
      "1/1 [==============================] - 0s 3ms/sample - loss: 0.0208\n",
      "Epoch 284/500\n",
      "1/1 [==============================] - 0s 3ms/sample - loss: 0.0208\n",
      "Epoch 285/500\n",
      "1/1 [==============================] - 0s 3ms/sample - loss: 0.0208\n",
      "Epoch 286/500\n",
      "1/1 [==============================] - 0s 5ms/sample - loss: 0.0208\n",
      "Epoch 287/500\n",
      "1/1 [==============================] - 0s 3ms/sample - loss: 0.0208\n",
      "Epoch 288/500\n",
      "1/1 [==============================] - 0s 2ms/sample - loss: 0.0208\n",
      "Epoch 289/500\n",
      "1/1 [==============================] - 0s 4ms/sample - loss: 0.0208\n",
      "Epoch 290/500\n",
      "1/1 [==============================] - 0s 3ms/sample - loss: 0.0208\n",
      "Epoch 291/500\n",
      "1/1 [==============================] - 0s 2ms/sample - loss: 0.0208\n",
      "Epoch 292/500\n",
      "1/1 [==============================] - 0s 3ms/sample - loss: 0.0208\n",
      "Epoch 293/500\n",
      "1/1 [==============================] - 0s 3ms/sample - loss: 0.0208\n",
      "Epoch 294/500\n",
      "1/1 [==============================] - 0s 6ms/sample - loss: 0.0208\n",
      "Epoch 295/500\n",
      "1/1 [==============================] - 0s 7ms/sample - loss: 0.0208\n",
      "Epoch 296/500\n",
      "1/1 [==============================] - 0s 4ms/sample - loss: 0.0208\n",
      "Epoch 297/500\n",
      "1/1 [==============================] - 0s 4ms/sample - loss: 0.0208\n",
      "Epoch 298/500\n",
      "1/1 [==============================] - 0s 3ms/sample - loss: 0.0208\n",
      "Epoch 299/500\n",
      "1/1 [==============================] - 0s 3ms/sample - loss: 0.0208\n",
      "Epoch 300/500\n",
      "1/1 [==============================] - 0s 4ms/sample - loss: 0.0208\n",
      "Epoch 301/500\n",
      "1/1 [==============================] - 0s 3ms/sample - loss: 0.0208\n",
      "Epoch 302/500\n",
      "1/1 [==============================] - 0s 3ms/sample - loss: 0.0208\n",
      "Epoch 303/500\n",
      "1/1 [==============================] - 0s 4ms/sample - loss: 0.0208\n",
      "Epoch 304/500\n",
      "1/1 [==============================] - 0s 4ms/sample - loss: 0.0208\n",
      "Epoch 305/500\n",
      "1/1 [==============================] - 0s 3ms/sample - loss: 0.0208\n",
      "Epoch 306/500\n",
      "1/1 [==============================] - 0s 2ms/sample - loss: 0.0208\n",
      "Epoch 307/500\n",
      "1/1 [==============================] - 0s 4ms/sample - loss: 0.0208\n",
      "Epoch 308/500\n",
      "1/1 [==============================] - 0s 3ms/sample - loss: 0.0208\n",
      "Epoch 309/500\n",
      "1/1 [==============================] - 0s 3ms/sample - loss: 0.0208\n",
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      "Epoch 489/500\n",
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      "Epoch 490/500\n",
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      "Epoch 491/500\n",
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      "Epoch 493/500\n",
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      "Epoch 494/500\n",
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      "Epoch 495/500\n",
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      "Epoch 496/500\n",
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      "Epoch 497/500\n",
      "1/1 [==============================] - 0s 3ms/sample - loss: 0.0208\n",
      "Epoch 498/500\n",
      "1/1 [==============================] - 0s 3ms/sample - loss: 0.0208\n",
      "Epoch 499/500\n",
      "1/1 [==============================] - 0s 4ms/sample - loss: 0.0208\n",
      "Epoch 500/500\n",
      "1/1 [==============================] - 0s 4ms/sample - loss: 0.0208\n"
     ]
    },
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x2cce263aac8>"
      ]
     },
     "metadata": {},
     "execution_count": 9
    }
   ],
   "source": [
    "reverse_game.compile(loss='mse', optimizer=tf.keras.optimizers.Adam(learning_rate=0.1))\n",
    "\n",
    "reverse_game.fit(y[:1], y[:1], epochs=500)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x2cce2737e48>"
      ]
     },
     "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": [
    "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": 11,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x2cce2a9d2c8>"
      ]
     },
     "metadata": {},
     "execution_count": 11
    },
    {
     "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": []
  }
 ],
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  "kernelspec": {
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   "display_name": "Python 3.7.9 64-bit ('tensorflow_env': conda)",
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    "interpreter": {
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}