03_ANN_2couches.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.datasets import make_blobs, make_circles\n",
    "from sklearn.metrics import accuracy_score, log_loss\n",
    "from tqdm import tqdm"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Fonctions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def initialisation(n0, n1, n2):\n",
    "\n",
    "    W1 = np.random.randn(n1, n0)\n",
    "    b1 = np.zeros((n1, 1))\n",
    "    W2 = np.random.randn(n2, n1)\n",
    "    b2 = np.zeros((n2, 1))\n",
    "\n",
    "    parametres = {\n",
    "        'W1': W1,\n",
    "        'b1': b1,\n",
    "        'W2': W2,\n",
    "        'b2': b2\n",
    "    }\n",
    "\n",
    "    return parametres"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def forward_propagation(X, parametres):\n",
    "\n",
    "    W1 = parametres['W1']\n",
    "    b1 = parametres['b1']\n",
    "    W2 = parametres['W2']\n",
    "    b2 = parametres['b2']\n",
    "\n",
    "    Z1 = W1.dot(X) + b1\n",
    "    A1 = 1 / (1 + np.exp(-Z1))\n",
    "\n",
    "    Z2 = W2.dot(A1) + b2\n",
    "    A2 = 1 / (1 + np.exp(-Z2))\n",
    "\n",
    "    activations = {\n",
    "        'A1': A1,\n",
    "        'A2': A2\n",
    "    }\n",
    "\n",
    "    return activations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def back_propagation(X, y, parametres, activations):\n",
    "\n",
    "    A1 = activations['A1']\n",
    "    A2 = activations['A2']\n",
    "    W2 = parametres['W2']\n",
    "\n",
    "    m = y.shape[1]\n",
    "\n",
    "    dZ2 = A2 - y\n",
    "    dW2 = 1 / m * dZ2.dot(A1.T)\n",
    "    db2 = 1 / m * np.sum(dZ2, axis=1, keepdims = True)\n",
    "\n",
    "    dZ1 = np.dot(W2.T, dZ2) * A1 * (1 - A1)\n",
    "    dW1 = 1 / m * dZ1.dot(X.T)\n",
    "    db1 = 1 / m * np.sum(dZ1, axis=1, keepdims = True)\n",
    "\n",
    "    gradients = {\n",
    "        'dW1' : dW1,\n",
    "        'db1' : db1,\n",
    "        'dW2' : dW2,\n",
    "        'db2' : db2\n",
    "    }\n",
    "    \n",
    "    return gradients"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def update(gradients, parametres, learning_rate):\n",
    "\n",
    "    W1 = parametres['W1']\n",
    "    b1 = parametres['b1']\n",
    "    W2 = parametres['W2']\n",
    "    b2 = parametres['b2']\n",
    "\n",
    "    dW1 = gradients['dW1']\n",
    "    db1 = gradients['db1']\n",
    "    dW2 = gradients['dW2']\n",
    "    db2 = gradients['db2']\n",
    "\n",
    "    W1 = W1 - learning_rate * dW1\n",
    "    b1 = b1 - learning_rate * db1\n",
    "    W2 = W2 - learning_rate * dW2\n",
    "    b2 = b2 - learning_rate * db2\n",
    "\n",
    "    parametres = {\n",
    "        'W1': W1,\n",
    "        'b1': b1,\n",
    "        'W2': W2,\n",
    "        'b2': b2\n",
    "    }\n",
    "\n",
    "    return parametres"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def predict(X, parametres):\n",
    "  activations = forward_propagation(X, parametres)\n",
    "  A2 = activations['A2']\n",
    "  return A2 >= 0.5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def neural_network(X, y, n1=32, learning_rate = 0.1, n_iter = 1000):\n",
    "\n",
    "    # initialisation parametres\n",
    "    n0 = X.shape[0]\n",
    "    n2 = y.shape[0]\n",
    "    np.random.seed(0)\n",
    "    parametres = initialisation(n0, n1, n2)\n",
    "\n",
    "    train_loss = []\n",
    "    train_acc = []\n",
    "    history = []\n",
    "\n",
    "    # gradient descent\n",
    "    for i in tqdm(range(n_iter)):\n",
    "        activations = forward_propagation(X, parametres)\n",
    "        A2 = activations['A2']\n",
    "\n",
    "        # Plot courbe d'apprentissage\n",
    "        train_loss.append(log_loss(y.flatten(), A2.flatten()))\n",
    "        y_pred = predict(X, parametres)\n",
    "        train_acc.append(accuracy_score(y.flatten(), y_pred.flatten()))\n",
    "        \n",
    "        history.append([parametres.copy(), train_loss, train_acc, i])\n",
    "\n",
    "        # mise a jour\n",
    "        gradients = back_propagation(X, y, parametres, activations)\n",
    "        parametres = update(gradients, parametres, learning_rate)\n",
    "\n",
    "\n",
    "    plt.figure(figsize=(12, 4))\n",
    "    plt.subplot(1, 2, 1)\n",
    "    plt.plot(train_loss, label='train loss')\n",
    "    plt.legend()\n",
    "    plt.subplot(1, 2, 2)\n",
    "    plt.plot(train_acc, label='train acc')\n",
    "    plt.legend()\n",
    "    plt.show()\n",
    "\n",
    "    return parametres"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dimensions de X: (2, 100)\n",
      "dimensions de y: (1, 100)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "X, y = make_circles(n_samples=100, noise=0.1, factor=0.3, random_state=0)\n",
    "X = X.T\n",
    "y = y.reshape((1, y.shape[0]))\n",
    "\n",
    "print('dimensions de X:', X.shape)\n",
    "print('dimensions de y:', y.shape)\n",
    "\n",
    "plt.scatter(X[0, :], X[1, :], c=y, cmap='summer')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 1000/1000 [00:00<00:00, 1652.52it/s]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "{'W1': array([[ 1.80212588,  0.37821374],\n",
       "        [ 1.01147118,  2.72196966],\n",
       "        [ 2.58920092, -1.12112355],\n",
       "        [ 1.0508304 , -0.23999551],\n",
       "        [-0.32911352,  0.61126472],\n",
       "        [ 0.10449739,  1.5211303 ],\n",
       "        [ 0.90568599,  0.00334079],\n",
       "        [ 0.44974817,  0.32479341],\n",
       "        [ 2.19228574, -0.55199865],\n",
       "        [ 0.07362119, -0.72521454],\n",
       "        [-2.55294805,  0.64962759],\n",
       "        [ 0.79621311, -0.67148306],\n",
       "        [ 2.47060832, -1.49806086],\n",
       "        [-0.08319614, -0.07417017],\n",
       "        [ 1.61979578,  1.66597748],\n",
       "        [ 0.1398299 ,  0.38577941],\n",
       "        [-1.5500226 , -2.61344251],\n",
       "        [-0.19459163,  0.0227914 ],\n",
       "        [ 1.3320316 ,  1.1949539 ],\n",
       "        [-0.71462983, -0.02385873],\n",
       "        [-1.23999647, -2.47955126],\n",
       "        [-2.07311228,  2.57822617],\n",
       "        [-0.31829059, -0.65082684],\n",
       "        [-1.46140776,  0.96926358],\n",
       "        [-2.59100928, -0.09863181],\n",
       "        [-0.78361708,  0.24660244],\n",
       "        [-0.63362911, -1.18964448],\n",
       "        [ 0.17256072,  0.22460295],\n",
       "        [ 0.08147971,  0.28453067],\n",
       "        [-0.49084692, -0.52323217],\n",
       "        [-0.63238933, -0.40196504],\n",
       "        [-0.79406174, -2.12548332]]),\n",
       " 'b1': array([[ 0.23078455],\n",
       "        [-1.32264413],\n",
       "        [-1.47764201],\n",
       "        [ 0.16617422],\n",
       "        [-0.09384934],\n",
       "        [-0.28249715],\n",
       "        [ 0.1434703 ],\n",
       "        [-0.00400412],\n",
       "        [ 1.07366016],\n",
       "        [-0.17318453],\n",
       "        [-0.23088063],\n",
       "        [-0.23375636],\n",
       "        [-1.19500203],\n",
       "        [ 0.09860723],\n",
       "        [-0.82273456],\n",
       "        [ 0.00199761],\n",
       "        [-1.53871161],\n",
       "        [-0.08439808],\n",
       "        [ 0.37972245],\n",
       "        [-0.10838977],\n",
       "        [ 1.4616674 ],\n",
       "        [ 1.7604331 ],\n",
       "        [ 0.13016958],\n",
       "        [-0.5653716 ],\n",
       "        [-1.3137538 ],\n",
       "        [ 0.13198297],\n",
       "        [-0.33018176],\n",
       "        [-0.10135498],\n",
       "        [-0.00515508],\n",
       "        [ 0.10008457],\n",
       "        [ 0.01747326],\n",
       "        [ 0.95369839]]),\n",
       " 'W2': array([[ 0.22266084, -1.80462233, -2.42962594,  0.4136683 , -1.32685359,\n",
       "         -0.54372178,  0.59478101, -0.13161512,  1.69377949, -1.37413274,\n",
       "         -0.41048486, -0.74712648, -1.47717838, -0.85803551, -1.07435841,\n",
       "         -0.2700743 , -2.48820231,  0.54650674,  0.41287645, -1.90551401,\n",
       "          2.22419434,  2.47254256,  0.90462615, -1.05235962, -2.3293656 ,\n",
       "          0.56939008, -0.71529265,  0.89493879, -0.11718641,  0.65795803,\n",
       "          0.00674728,  1.13550524]]),\n",
       " 'b2': array([[-0.58428489]])}"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "neural_network(X, y, n1=32)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "038c04557dfd72b4d6039cb7951b93ffe7520921b6515cb88d8784deedfaf89f"
  },
  "kernelspec": {
   "display_name": "Python 3.7.9 ('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": "3.7.9"
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  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}