Numpy_MNIST.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "# Download and unpack data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "data_path = 'data/mnist/'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "URL='http://deeplearning.net/data/mnist/'\n",
    "FILENAME='mnist.pkl.gz'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "import os\n",
    "os.makedirs(data_path, exist_ok=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "import requests\n",
    "import shutil\n",
    "\n",
    "def download_file(url_to_file, data_path):\n",
    "    local_filename = url_to_file.split('/')[-1]\n",
    "    if os.path.isfile(data_path + local_filename):\n",
    "        pass\n",
    "    else:\n",
    "        r = requests.get(url_to_file, stream=True)\n",
    "        with open(os.path.join(data_path + local_filename), 'wb') as f:\n",
    "            shutil.copyfileobj(r.raw, f)\n",
    "    return local_filename"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "local_filename = download_file(URL+FILENAME, data_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "import pickle\n",
    "import gzip\n",
    "\n",
    "def depickle(file_path):\n",
    "    return pickle.load(gzip.open(file_path, 'rb'), encoding='latin-1')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "Normalize"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "((x, y), (x_valid, y_valid), _) = depickle(data_path+local_filename)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "mean = x.mean()\n",
    "std = x.std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.13044983, -3.1638146e-07, 0.3072898, 0.99999934)"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = (x-mean)/std\n",
    "mean, x.mean(), std, x.std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-0.005850922, 0.99243325)"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_valid = (x_valid-mean)/std\n",
    "x_valid.mean(), x_valid.std()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "Data is ready. Check it out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "def showim(img, label, index):\n",
    "    x = np.reshape(img[index,:],(-1, 28,28))\n",
    "    plt.title('label = '+str(label[index]), fontsize=19)\n",
    "    plt.imshow(x[0,:,:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.42451727"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.shape\n",
    "x[0,0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff4a3bf1d30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "showim(x,y,300)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Implementation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "## SGD"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "import pickle\n",
    "import gzip\n",
    "\n",
    "def depickle(file_path):\n",
    "    return pickle.load(gzip.open(file_path, 'rb'), encoding='latin-1')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "data_path = 'data/mnist/'\n",
    "local_filename='mnist.pkl.gz'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "Normalize"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "((x, y), (x_valid, y_valid), _) = depickle(data_path+local_filename)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "mean = x.mean()\n",
    "std = x.std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.13044983, -3.1638146e-07, 0.3072898, 0.99999934)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = (x-mean)/std\n",
    "mean, x.mean(), std, x.std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-0.005850922, 0.99243325)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_valid = (x_valid-mean)/std\n",
    "x_valid.mean(), x_valid.std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import math"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "def relu(x):\n",
    "    x[x < 0] = 0\n",
    "    return x\n",
    "    \n",
    "def relu_d(x):\n",
    "     x[x<=0] = 0\n",
    "     x[x>0] = 1\n",
    "     return x\n",
    "    \n",
    "def softmax(x):\n",
    "    '''stable way of computing softmax'''\n",
    "    shiftx = x - np.expand_dims(np.max(x, axis=1),1)\n",
    "    exps = np.exp(shiftx)\n",
    "    return exps / np.expand_dims(np.sum(exps, axis=1), 1)    \n",
    "\n",
    "def sigmoid(x):\n",
    "    return 1 / (1 + np.exp(-x))\n",
    "\n",
    "def sigmoid_d(x):\n",
    "    return x * (1-x)\n",
    "\n",
    "def onehot(y):\n",
    "    size = len(y)\n",
    "    one = np.zeros((size, 10))\n",
    "    one[np.arange(size),y] = 1\n",
    "    return one\n",
    "\n",
    "def onehot_d(x):\n",
    "    return np.argmax(x)\n",
    "    \n",
    "def generator(bs, examples, labels):\n",
    "    n_batches = int(np.floor(len(examples)/bs))\n",
    "    it = 0\n",
    "    while it <= n_batches*bs-1:\n",
    "        yield examples[it:it+bs,:], labels[it:it+bs], n_batches\n",
    "        it += bs\n",
    "    it = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "bs = 32\n",
    "imdim = 28\n",
    "input_layer = imdim**2\n",
    "hidden_layer = 100\n",
    "output_layer = np.max(y)+1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "W1 = np.random.rand(input_layer, hidden_layer)*2-1\n",
    "W2 = np.random.rand(hidden_layer, output_layer)*2-1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "aantal keer per batch:                            0\n",
      "epoch             =  0\n",
      "acc   =  94.01008322663252 %\n",
      "valid   =  92.02724358974359 %\n",
      "aantal keer per batch:                            0\n",
      "epoch             =  1\n",
      "acc   =  94.50624199743918 %\n",
      "valid   =  92.24759615384616 %\n",
      "aantal keer per batch:                            0\n",
      "epoch             =  2\n",
      "acc   =  95.00040012804098 %\n",
      "valid   =  92.4579326923077 %\n",
      "aantal keer per batch:                            0\n",
      "epoch             =  3\n",
      "acc   =  95.47055057618438 %\n",
      "valid   =  92.50801282051282 %\n",
      "aantal keer per batch:                            0\n",
      "epoch             =  4\n",
      "acc   =  95.7766485275288 %\n",
      "valid   =  92.55809294871796 %\n",
      "Done!\n"
     ]
    }
   ],
   "source": [
    "epochs = 5\n",
    "eta = 0.1\n",
    "\n",
    "for i in range(epochs):\n",
    "    trues = 0\n",
    "    for a1, labs, n_batches in generator(bs, x, y):\n",
    "        # forward pass\n",
    "        z2 = np.dot(a1, W1)\n",
    "        a2 = relu(z2)\n",
    "        z3 = np.dot(a2, W2)\n",
    "        a3 = softmax(z3)\n",
    "        \n",
    "        # backward pass\n",
    "        onh = onehot(labs)\n",
    "        d3 = a3 - onh\n",
    "        d2 = np.dot(d3, W2.T) * relu_d(a2)\n",
    "        \n",
    "        # update weights\n",
    "        W2 -= eta * np.dot(a2.T,d3) / bs\n",
    "        W1 -= eta * np.dot(a1.T,d2) / bs\n",
    "        \n",
    "        trues += np.sum(a3.argmax(axis=1)==labs)\n",
    "    print('aantal keer per batch:                           ', test)\n",
    "    \n",
    "    if i%(1) == 0:\n",
    "        print('epoch             = ', i)\n",
    "        print('acc   = ', trues/(n_batches*bs)*100, '%')\n",
    "        trues = 0\n",
    "        for a1_v, labs_v, n_batches in generator(bs, x_valid, y_valid):\n",
    "            z2_v = np.dot(a1_v, W1)\n",
    "            a2_v = relu(z2_v)\n",
    "            z3_v = np.dot(a2_v, W2)\n",
    "            a3_v = softmax(z3_v)\n",
    "            trues += np.sum(a3_v.argmax(axis=1)==labs_v)\n",
    "        print('valid   = ', trues/(n_batches*bs)*100, '%')\n",
    "    test = 0\n",
    "    \n",
    "print('Done!')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "i=0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 431,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f909c1777f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "showim(a1, labs, i)\n",
    "i+=1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "## Plus batchnorm, shuffled generator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "import pickle\n",
    "import gzip\n",
    "\n",
    "def depickle(file_path):\n",
    "    return pickle.load(gzip.open(file_path, 'rb'), encoding='latin-1')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "data_path = 'data/mnist/'\n",
    "local_filename='mnist.pkl.gz'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "Normalize"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "((x, y), (x_valid, y_valid), _) = depickle(data_path+local_filename)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "mean = x.mean()\n",
    "std = x.std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.13044983, -3.1638146e-07, 0.3072898, 0.99999934)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = (x-mean)/std\n",
    "mean, x.mean(), std, x.std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-0.005850922, 0.99243325)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_valid = (x_valid-mean)/std\n",
    "x_valid.mean(), x_valid.std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import math"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "def relu(x):\n",
    "    x[x < 0] = 0\n",
    "    return x\n",
    "    \n",
    "def relu_d(x):\n",
    "     x[x<=0] = 0\n",
    "     x[x>0] = 1\n",
    "     return x\n",
    "    \n",
    "def softmax(x):\n",
    "    '''stable way of computing softmax'''\n",
    "    shiftx = x - np.expand_dims(np.max(x, axis=1),1)\n",
    "    exps = np.exp(shiftx)\n",
    "    return exps / np.expand_dims(np.sum(exps, axis=1), 1)    \n",
    "\n",
    "def sigmoid(x):\n",
    "    return 1 / (1 + np.exp(-x))\n",
    "\n",
    "def sigmoid_d(x):\n",
    "    return x * (1-x)\n",
    "\n",
    "def onehot(y):\n",
    "    size = len(y)\n",
    "    one = np.zeros((size, 10))\n",
    "    one[np.arange(size),y] = 1\n",
    "    return one\n",
    "\n",
    "def onehot_d(x):\n",
    "    return np.argmax(x)\n",
    "    \n",
    "def generator(bs, examples, labels):\n",
    "    n_batches = int(np.floor(len(examples)/bs))\n",
    "    ids = np.random.permutation(len(examples))\n",
    "    it = 0\n",
    "    while it <= n_batches*bs-1:\n",
    "        yield examples[ids[it:it+bs],:], labels[ids[it:it+bs]], n_batches\n",
    "        it += bs\n",
    "    it = 0\n",
    "    ids = np.random.permutation(len(examples))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "bs = 32\n",
    "imdim = 28\n",
    "input_layer = imdim**2\n",
    "hidden_layer = 100\n",
    "output_layer = np.max(y)+1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "W1 = np.random.rand(input_layer, hidden_layer)*2-1\n",
    "W2 = np.random.rand(hidden_layer, output_layer)*2-1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "aantal keer per batch:                            0\n",
      "epoch             =  0\n",
      "acc   =  85.43333866837388 %\n",
      "valid   =  88.84214743589743 %\n",
      "aantal keer per batch:                            0\n",
      "epoch             =  1\n",
      "acc   =  90.22887323943662 %\n",
      "valid   =  90.94551282051282 %\n",
      "aantal keer per batch:                            0\n",
      "epoch             =  2\n",
      "acc   =  92.31153969270166 %\n",
      "valid   =  92.39783653846155 %\n",
      "aantal keer per batch:                            0\n",
      "epoch             =  3\n",
      "acc   =  93.72399167733676 %\n",
      "valid   =  92.74839743589743 %\n",
      "aantal keer per batch:                            0\n",
      "epoch             =  4\n",
      "acc   =  94.58826824583866 %\n",
      "valid   =  93.02884615384616 %\n",
      "Done!\n"
     ]
    }
   ],
   "source": [
    "epochs = 5\n",
    "eta = 0.1\n",
    "\n",
    "for i in range(epochs):\n",
    "    trues = 0\n",
    "    for a1, labs, n_batches in generator(bs, x, y):\n",
    "        # forward pass\n",
    "        z2 = np.dot(a1, W1)\n",
    "        a2 = relu(z2)\n",
    "        z3 = np.dot(a2, W2)\n",
    "        a3 = softmax(z3)\n",
    "        \n",
    "        # backward pass\n",
    "        onh = onehot(labs)\n",
    "        d3 = a3 - onh\n",
    "        d2 = np.dot(d3, W2.T) * relu_d(a2)\n",
    "        \n",
    "        # update weights\n",
    "        W2 -= eta * np.dot(a2.T,d3) / bs\n",
    "        W1 -= eta * np.dot(a1.T,d2) / bs\n",
    "        \n",
    "#         # batch normalization\n",
    "#         temp = (W1.std(), W1.mean())\n",
    "#         W1 = (W1 / temp[0])-temp[1]\n",
    "#         temp = (W2.std(), W2.mean())\n",
    "#         W2 = (W2 / temp[0])-temp[1]\n",
    "        \n",
    "        trues += np.sum(a3.argmax(axis=1)==labs)\n",
    "    print('aantal keer per batch:                           ', test)\n",
    "    \n",
    "    if i%(1) == 0:\n",
    "        print('epoch             = ', i)\n",
    "        print('acc   = ', trues/(n_batches*bs)*100, '%')\n",
    "        trues = 0\n",
    "        for a1_v, labs_v, n_batches in generator(bs, x_valid, y_valid):\n",
    "            z2_v = np.dot(a1_v, W1)\n",
    "            a2_v = relu(z2_v)\n",
    "            z3_v = np.dot(a2_v, W2)\n",
    "            a3_v = softmax(z3_v)\n",
    "            trues += np.sum(a3_v.argmax(axis=1)==labs_v)\n",
    "        print('valid   = ', trues/(n_batches*bs)*100, '%')\n",
    "    test = 0\n",
    "    \n",
    "print('Done!')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "i=0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 431,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f909c1777f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "showim(a1, labs, i)\n",
    "i+=1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "## Formulas"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "We take as a trainings example: (x,y)  \n",
    "where x is the input  \n",
    "and y is the label  \n",
    "  \n",
    "**Forward pass:**  \n",
    "a<sup>1</sup> = x<sup>1</sup>  \n",
    "  \n",
    "z<sup>2</sup> = $\\Theta$<sup>1</sup>a<sup>1</sup>  \n",
    "a<sup>2</sup> = relu(z<sup>2</sup>)  \n",
    "  \n",
    "z<sup>3</sup> = $\\Theta$<sup>2</sup>a<sup>2</sup>  \n",
    "a<sup>3</sup> = softmax(z<sup>3</sup>)  \n",
    "o<sup>3</sup> = a<sup>3</sup>  \n",
    "  \n",
    "**Backward pass:**  \n",
    "$\\delta^3 = \\frac{\\partial err}{\\partial a^3} \\frac{\\partial a^3}{\\partial z^3} = (o^3-y) \\cdot \\sigma(o^3) \\cdot (1 - \\sigma(o^3))$  \n",
    "$\\frac{\\partial err}{\\partial \\Theta^2} = a^2 \\delta^3$  \n",
    "$\\frac{\\partial err}{\\partial b^2} = \\delta^3$  \n",
    "$\\delta^2 = \\delta^3_i \\Theta^2_{ij} \\begin{cases} \n",
    "    \\delta^3_i \\Theta^2_{ij}, & \\text{if } \\delta^3_i \\Theta^2_{ij}\\gt 0 \\\\\n",
    "    0, & \\text{otherwise} \\end{cases}$  \n",
    "$\\frac{\\partial err}{\\partial \\Theta^1} = a^1 \\delta^2$  \n",
    "$\\frac{\\partial err}{\\partial b^1} = \\delta^2$  \n",
    "**Update weights:**  \n",
    "$\\Theta^2_{new} = \\Theta^2 - \\eta \\cdot a^2 \\delta^3$  \n",
    "$b^2_{new} = b^2 - \\eta \\cdot \\delta^3$  \n",
    "$\\Theta^1_{new} = \\Theta^1 - \\eta \\cdot a^1 \\delta^2$  \n",
    "$b^1_{new} = b^1 - \\eta \\cdot \\delta^2$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "**Gradient calculation:**  \n",
    "for the 2nd weight matrix  \n",
    "err = $\\frac{1}{2} (y-o^3)^2$  \n",
    "$\\frac{\\partial err}{\\partial \\Theta^2} = \\frac{\\partial err}{\\partial a^3} \\frac{\\partial a^3}{\\partial z^3} \\frac{\\partial z^3}{\\partial \\Theta^2}$  \n",
    "$\\frac{\\partial err}{\\partial a^3} = o^3-y$  \n",
    "$\\frac{\\partial a^3}{\\partial z^3} = \\sigma(o^3) \\cdot (1 - \\sigma(o^3))$  \n",
    "$\\frac{\\partial z^3}{\\partial \\Theta^2} = a^2 $  \n",
    "  \n",
    "Node $\\delta^3$:  \n",
    "$\\delta^3 = \\frac{\\partial err}{\\partial a^3} \\frac{\\partial a^3}{\\partial z^3} = (o^3-y) \\cdot \\sigma(o^3) \\cdot (1 - \\sigma(o^3))$  \n",
    "$\\frac{\\partial err}{\\partial \\Theta^2} = a^2 \\delta^3$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "for the 1st weight matrix  \n",
    "err = $\\frac{1}{2} (y-o^3)^2$  \n",
    "$\\frac{\\partial err}{\\partial \\Theta^1} = \\frac{\\partial err}{\\partial a^3} \\frac{\\partial a^3}{\\partial z^3} \\frac{\\partial z^3}{\\partial a^2} \\frac{\\partial a^2}{\\partial z^2} \\frac{\\partial z^2}{\\partial \\Theta^1}$  \n",
    "$\\frac{\\partial z^3}{\\partial a^2} = \\Theta^2$  \n",
    "$\\frac{\\partial a^2}{\\partial z^2} = $ relu_d $ = \\begin{cases} 1, & \\text{if } x\\gt 0 \\\\\n",
    "                                                                0, & \\text{otherwise} \\end{cases}$  \n",
    "$\\frac{\\partial z^2}{\\partial \\Theta^1} = a^1$  \n",
    "  \n",
    "Node $\\delta^2$:  \n",
    "$\\delta^2 = \\frac{\\partial err}{\\partial a^3} \\frac{\\partial a^3}{\\partial z^3} \\frac{\\partial z^3}{\\partial a^2} \\frac{\\partial a^2}{\\partial z^2} = \\delta^3 \\frac{\\partial z^3}{\\partial a^2} \\frac{\\partial a^2}{\\partial z^2} = \\delta^3_i \\Theta^2_{ij} \\begin{cases} \n",
    "    \\delta^3_i \\Theta^2_{ij}, & \\text{if } \\delta^3_i \\Theta^2_{ij}\\gt 0 \\\\\n",
    "    0, & \\text{otherwise} \\end{cases}$  \n",
    "$\\frac{\\partial err}{\\partial \\Theta^1} = a^1 \\delta^2$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 762,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "image/jpeg": "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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 762,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import Image\n",
    "Image(\"layers.jpg\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "## Links"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "http://jrusev.github.io/post/hacking-mnist/  \n",
    "https://www.ics.uci.edu/~pjsadows/notes.pdf  \n",
    "http://neuralnetworksanddeeplearning.com/chap3.html   \n",
    "https://eli.thegreenplace.net/2016/the-softmax-function-and-its-derivative/\n",
    "  \n",
    "https://mattmazur.com/2015/03/17/a-step-by-step-backpropagation-example/  \n",
    "https://stackoverflow.com/questions/40575841/numpy-calculate-the-derivative-of-the-softmax-function  \n",
    "https://ayearofai.com/rohan-lenny-1-neural-networks-the-backpropagation-algorithm-explained-abf4609d4f9d  \n",
    "http://florianmuellerklein.github.io/nn/  \n",
    "https://iamtrask.github.io/2015/07/12/basic-python-network/  \n",
    "https://www.coursera.org/learn/machine-learning/lecture/1z9WW/backpropagation-algorithm  \n",
    "en in deze folder zit machine learning ng week5 backprop algo  \n",
    "TD: errors omrekenen naar D en daar Delta's van maken en weights updaten.  \n",
    "http://kawahara.ca/what-is-the-derivative-of-relu/  \n",
    "https://deepnotes.io/softmax-crossentropy  \n"
   ]
  }
 ],
 "metadata": {
  "hide_input": false,
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}