MNIST_DIGIT.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from tensorflow.keras.datasets import mnist"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Loading MNIST DATA and Checking"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [],
   "source": [
    "(train_images, train_labels), (test_images, test_labels) = mnist.load_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(60000, 28, 28)"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_images.shape #60000 images(depth),28(rows),28(columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x1d842f01d48>"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(train_images[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10000, 28, 28)"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_images.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(60000,)"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_labels.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_images.ndim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "60000"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(train_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([5, 0, 4, ..., 5, 6, 8], dtype=uint8)"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_images[0] #28*28"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "digit = train_images[0]\n",
    "import matplotlib.pyplot as plt\n",
    "plt.imshow(digit,cmap=plt.cm.binary)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# show the first 2 images in the # training set \n",
    "for i in range(5):\n",
    "    plt.figure() \n",
    "    plt.imshow(train_images[i]) \n",
    "    plt.colorbar() \n",
    "    plt.grid(True) \n",
    "    plt.xlabel(train_labels[i]) \n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# MODEL CREATION and TRAINING"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras import models\n",
    "from tensorflow.keras import layers\n",
    "network = models.Sequential()\n",
    "network.add(layers.Dense(512, activation='relu', input_shape=(28 * 28,))) #512 neurons .. 28*28=784 ,,, 784*512 \n",
    "network.add(layers.Dense(10, activation='softmax')) #probability scores\n",
    "#here we made 3 layers , first input layer (784 neurons) , second (512 neurons) and last layer with 10 neurons\n",
    "#conventionally -> we don't count input layer in layers counting\n",
    "#densely connected -> previous neurons are connected with after coming neurons\n",
    "#dense layer accepts input as a vector (1D) here 28*28 dim 1D\n",
    "#first layer is input , last layer is output , middle ones are hidden layers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [],
   "source": [
    "network.compile(optimizer='rmsprop', #optimizer->to adjust weight\n",
    "loss='categorical_crossentropy',  #multiclass\n",
    "metrics=['accuracy']) #can be -> accuracy,precision,recall\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Normalizing the Data\n",
    "You also need to convert the values of the images from a range of 0 to 255 to a new range of 0 to 1. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_images = train_images.reshape((60000, 28 * 28))\n",
    "train_images = train_images.astype('float32') / 255\n",
    "test_images = test_images.reshape((10000, 28 * 28))\n",
    "test_images = test_images.astype('float32') / 255"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.utils import to_categorical\n",
    "train_labels = to_categorical(train_labels)\n",
    "test_labels = to_categorical(test_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0., 0., 0., 0., 0., 1., 0., 0., 0., 0.], dtype=float32)"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_labels[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 60000 samples\n",
      "Epoch 1/6\n",
      "60000/60000 [==============================] - 7s 123us/sample - loss: 0.2570 - acc: 0.9257\n",
      "Epoch 2/6\n",
      "60000/60000 [==============================] - 7s 122us/sample - loss: 0.1042 - acc: 0.9697\n",
      "Epoch 3/6\n",
      "60000/60000 [==============================] - 7s 111us/sample - loss: 0.0687 - acc: 0.9799s - l\n",
      "Epoch 4/6\n",
      "60000/60000 [==============================] - 8s 126us/sample - loss: 0.0500 - acc: 0.9851s - lo\n",
      "Epoch 5/6\n",
      "60000/60000 [==============================] - 7s 117us/sample - loss: 0.0374 - acc: 0.9888s - loss: 0.0380 -  - ETA: 1s - loss: 0.0 - ETA: 0s - loss: 0.0377 - \n",
      "Epoch 6/6\n",
      "60000/60000 [==============================] - 7s 111us/sample - loss: 0.0287 - acc: 0.9915\n"
     ]
    }
   ],
   "source": [
    "history = network.fit(train_images, train_labels, epochs=6, batch_size=128)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10000/10000 [==============================] - 1s 112us/sample - loss: 0.0613 - acc: 0.9830\n",
      "test_acc:  0.983\n"
     ]
    }
   ],
   "source": [
    "test_loss, test_acc = network.evaluate(test_images, test_labels)\n",
    "print('test_acc: ',test_acc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dict_keys(['loss', 'acc'])\n"
     ]
    },
    {
     "data": {
      "image/png": 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lvbnnmjP5wBn9dGJZ5ASEGRDVwOC49VJgS3wDd98CXAdgZnnAFHevC3oGC9y9Idj3N+ACYiEiklBzSyvPLN3Cj15cw8Zd+zlzUC8e+WwFHxylYBA5GWEGxEJghJkNJdYzuB64Mb6BmRUBte7eCtxN7IomgE3AbWZ2H7GeyGTggRBrlS6spdWZsWwzP5y9lvU79zF6YC+m3zyOy0f3VzCInILQAsLdm83sTuB5Ype5Puruy83sHmCRu88ALgXuMzMn1jv4cnD408BlwFvEhqWec/c/h1WrdE0trc5f3tzCg7PXULljHyMH5POTm8bx4dH9dUmqSDuwVLm1oKKiwhctWhR1GdIBWlqdv75Vww9nr2Ht9gbO6J/PVz40givOHKBgEDlBZrbY3SsS7dOd1NJltLY6z75dw4Oz1rBmewMj+uXx0I1j+ehZAxUMIiFQQEin19rqPLd8Kw/OWsOqbfUML+7JD28Yy5VnD9TNbCIhUkBIp9Xa6rywYisPzFrDO1vrGVbckwevP5ePjRmkYBDpAAoI6XTcnZkrtvHArDWsqNnL0KKe/ODT53D1OSUKBpEOpICQTsPdmb1yOw/MXs3bm/cypDCX733yHK45d5CmwRCJgAJCIufuvLRqOw/MWsOb1XWUFeTy358Yw7VjSxQMIhFSQEhk3J2XV+/ggVlrWFa1h9K+PfivKWO49rwSshQMIpFTQEiHc3fmrtnJA7NWs2TTHkr69OA7153NlHGlCgaRTkQBIR3G3Zm3dicPzFrD4o27GdQ7h3uvPYtPjhusmVVFOiEFhITO3Zm/bhc/mLWahRt2M7B3Dv/58bP4VEUp3TMzoi5PRI5BASGhOhwMr6+vpX+v7txzzZl8+vzBCgaRLkABIaF4rTIWDAsqa+mX351vXzWa68eXkZOlYBDpKhQQ0q4WbqjlBzNX8+q6XRTnd+ebHxvNjRMUDCJdkQJC2sXijbt5YNZqXlmzk6K8bP7jylF8ZsIQemQrGES6KgWEnJIlm3bzg1lrmLt6B4U9s/n3j47ipgsUDCKpQAEhJ2VZ1R5+MGs1L6/aQUHPbO7+yEhunjiE3Gz9lRJJFfrXLCfkreo6fjBrNS++s50+uVn86xVn8LmJ5fTsrr9KIqlG/6olKW9vruOBWauZtXI7vXtk8S//4ww+d2E5eQoGkZSlf93yvpZvqeOBWWuYuWIbvXIy+frlp3PLpHLyc7KiLk1EQqaAkIRW1uzlwVlreG75VvJzMvnah07n1ovK6aVgEEkbCgg5yqqt9Tw4ezXPvrWV/O6ZfOWDI/j8RUPp3UPBIJJuQg0IM7sCeBDIAB5x9++02T8EeBQoBmqBm9y92sw+APwgrulI4Hp3fybMetPZmm31PDB7Dc++VUPP7Ez+8bLT+OJFw+idq2AQSVehBYSZZQAPA5cD1cBCM5vh7ivimt0PPO7uj5nZZcB9wM3u/hJwbvA6BcBa4IWwak1na7fX8+DstfzlzS3kZmXwpUuHc9vFw+iTmx11aSISsTB7EOOBte5eCWBmTwLXAPEBMRr4WrD8EpCoh/AJ4G/uvj/EWtPOuh0N/HD2GmYs20KPrAzumBwLhoKeCgYRiQkzIEqAqrj1amBCmzbLgCnEhqGuBfLNrNDdd8W1uR74fqI3MLOpwFSAsrKydio79b29uY5r/9/fyezWjamXDGPqxcMozOsedVki0smEGRCWYJu3Wb8LeMjMbgHmApuB5iMvYDYQOBt4PtEbuPt0YDpARUVF29eWY/jxy+vIycpg9j9Ppl+vnKjLEZFOKsyAqAYGx62XAlviG7j7FuA6ADPLA6a4e11ck08Bf3T3phDrTCsbd+3jb2/XcPvk4QoHEXlfYT7ncSEwwsyGmlk2saGiGfENzKzIzA7XcDexK5ri3QA8EWKNaeeRV9aT2a0bt15YHnUpItLJhRYQ7t4M3ElseGgl8JS7Lzeze8zs6qDZpcAqM1sN9AfuPXy8mZUT64HMCavGdLOr4SBPLari2rEl6j2IyHGFeh+Euz8LPNtm2zfjlp8Gnj7GsRuIneiWdvLY/I0cbG7ltkuGRV2KiHQBYQ4xSSey/1Azj8/fwOWj+3Nav7yoyxGRLkABkSaeWljFnv1N3DFZvQcRSY4CIg00t7TyyLz1VAzpy7ghBVGXIyJdhAIiDTz79laqdx/g9snDoy5FRLoQBUSKc3emzVnH8OKefHBkv6jLEZEuRAGR4v6+dhfLt+xl6iXD6NYt0c3tIiKJKSBS3LS56yjO787Hx+qKYRE5MQqIFPb25jpeWbOTz08aSvfMjKjLEZEuJqmAMLPfm9mVcdNiSBcwfW4led0zuXGCZroVkROX7Bf+j4EbgTVm9h0zGxliTdIOqmr389e3arhxQpkeFyoiJyWpgHD3We7+GeA8YAMw08xeNbNbzUzfPp3Qz+atx4BbJ5VHXYqIdFFJDxmZWSFwC/BFYAmxh/ycB8wMpTI5abv3HeK3C6u45twSBvbuEXU5ItJFJTVZn5n9ARgJ/BK4yt1rgl2/NbNFYRUnJ+eXCzZyoKmFqZqUT0ROQbKzuT7k7i8m2uHuFe1Yj5yixqYWfvHqBi4b2Y8zBuRHXY6IdGHJDjGNMrM+h1fMrK+ZfSmkmuQU/G5xNbX7DnG7eg8icoqSDYjb3H3P4RV33w3cFk5JcrJaWp1HXqnk3MF9GD9Uk/KJyKlJNiC6mdmReRrMLAPIDqckOVnPL9/Kxl37uWPyMOL+d4mInJRkz0E8DzxlZj8BHLgDeC60quSEHZ6Ur7wwl8tHD4i6HBFJAckGxL8BtwP/ABjwAvBIWEXJiVtQWcuy6jruvfYsMjQpn4i0g6QCwt1bid1N/eNwy5GTNW3uOorysplyXmnUpYhIikh2LqYRZva0ma0ws8rDP2EXJ8lZWbOXl1ft4JYLy8nJ0qR8ItI+kj1J/XNivYdm4APA48RumntfZnaFma0ys7Vm9o0E+4eY2Wwze9PMXjaz0rh9ZWb2gpmtDIKpPMla085P51aSm53BTRcMiboUEUkhyQZED3efDZi7b3T3bwOXvd8BwZVODwMfAUYDN5jZ6DbN7gced/cxwD3AfXH7Hgf+291HAeOB7UnWmla27DnAjGVbuP78Mvrk6sIyEWk/yQZEYzDV9xozu9PMrgWO9/zK8cBad69090PAk8A1bdqMBmYHyy8d3h8ESaa7zwRw9wZ3359krWnl0XnrceALFw+NuhQRSTHJBsRXgVzgn4BxwE3A545zTAlQFbdeHWyLtwyYEixfC+QHkwKeDuwxsz+Y2RIz+++gR3IUM5tqZovMbNGOHTuS/Cipo25/E0+8vomrxgykpI8m5ROR9nXcgAi+mD8V/BZf7e63uvsUd19wvEMTbPM263cBk81sCTAZ2EzsPEcmcHGw/3xgGLGZZI9+Mffp7l7h7hXFxcXH+ygp51evbWTfoRamXjI86lJEJAUdNyDcvQUYF38ndZKqgcFx66XAljavvcXdr3P3scC/B9vqgmOXBMNTzcAzxKYWl0BjUws///sGLjm9mNGDekVdjoikoGRvlFsC/MnMfgfsO7zR3f/wPscsBEaY2VBiPYPriT2V7ggzKwJqg/ss7gYejTu2r5kVu/sOYifENa14nD8u2czOhoPcoUn5RCQkyQZEAbCLo69ccuCYAeHuzWZ2J7FpOjKAR919uZndAyxy9xnApcB9ZubAXODLwbEtZnYXMDvouSwGfnpCnyyFtbQ6P51bydklvZk4vDDqckQkRSV7J/WtJ/Pi7v4s8Gybbd+MW34aePoYx84ExpzM+6a6mSu2UblzHw/dOFaT8olIaJJ9otzPee8JZtz98+1ekbwvd2fa3HWUFeRyxZmalE9EwpPsENNf4pZziF2SuuUYbSVEizbuZsmmPdxzzZlkZiT9SHERkROW7BDT7+PXzewJYFYoFcn7mjZnHX1zs/jkuMHHbywicgpO9lfQEUBZexYix7dmWz2zVm7ncxeW0yNbk/KJSLiSPQdRz9HnILYSe0aEdKDpcyvJyerGZyeWR12KiKSBZIeY8sMuRN7f1rpGnlm6mRvHl1HQU5PyiUj4kn0exLVm1jtuvY+ZfTy8sqStn7+6npZW54sX68Y4EekYyZ6D+FYwBQYA7r4H+FY4JUlbexub+M2CTXz07IEMLsiNuhwRSRPJBkSidsleIiun6InXNlF/sJnbNSmfiHSgZANikZl938yGm9kwM/sBsekvJGQHm1t49O/rmXRaIWeX9j7+ASIi7STZgPhH4BDwW+Ap4ADBvEkSrj8t3cK2vQfVexCRDpfsVUz7gPc8U1rC1drqTJ9byaiBvbh4RFHU5YhImkn2KqaZZtYnbr2vmT0fXlkC8OI721m7vYE7Jg/TpHwi0uGSHWIqCq5cAsDdd3P8Z1LLKZo+t5KSPj346NkDoy5FRNJQsgHRamZHptYws3ISzO4q7Wfxxt28vqGWL1w0lCxNyiciEUj2UtV/B+aZ2Zxg/RJgajglCcD0uevo3SOLT5+vSflEJBpJ/Wrq7s8BFcAqYlcyfZ3YlUwSgnU7GnhhxTY+O3EIPbvrdhMRiUayk/V9EfgKUAosBS4A5nP0I0ilnTzySiVZGd343IXlUZciImks2cHtrwDnAxvd/QPAWGBHaFWlse31jfx+8WY+Oa6UorzuUZcjImks2YBodPdGADPr7u7vAGeEV1b6+sXfN9DU2sptmpRPRCKW7AB3dXAfxDPATDPbjR452u4aDjbzqwUb+chZAygv6hl1OSKS5pK9k/raYPHbZvYS0Bt47njHmdkVwINABvCIu3+nzf4hwKNAMVAL3OTu1cG+FuCtoOkmd786mVq7sidf38TexmamaloNEekETvgSGXefc/xWYGYZwMPA5UA1sNDMZrj7irhm9wOPu/tjZnYZcB9wc7DvgLufe6L1dVVNLa38bN56Jgwt4NzBfY5/gIhIyMK8A2s8sNbdK939EPAkcE2bNqOB2cHySwn2p40/L9tCTV0jd0xW70FEOocwA6IEqIpbrw62xVsGTAmWrwXyzawwWM8xs0VmtuBYT68zs6lBm0U7dnTdi6rcnWlzKjmjfz6XnlEcdTkiIkC4AZFodrm203PcBUw2syXAZGAz0BzsK3P3CuBG4AEze8+v1u4+3d0r3L2iuLjrfrG+vHoHq7bVM/USTconIp1HmLfpVgPx80SU0ubKJ3ffAlwHYGZ5wJTDjzYN9uHulWb2MrF7L9aFWG9kps1Zx8DeOVx1zqCoSxEROSLMHsRCYISZDTWzbOB6YEZ8AzMrMrPDNdxN7Iqmw9OJdz/cBpgExJ/cThnLqvawoDI2KV92piblE5HOI7RvJHdvBu4EngdWAk+5+3Izu8fMDl+yeimwysxWA/2Be4Pto4g95nQZsZPX32lz9VPKmD63kvycTK4fX3b8xiIiHSjUmeDc/Vng2Tbbvhm3/DTwdILjXgXODrO2zmDDzn387e0abp88nDxNyicinYzGNCL0yLxKMrt141ZNyicinZACIiI7Gw7yu0XVXHdeCf165URdjojIeyggIvL4qxs41NLKbZdoUj4R6ZwUEBHYf6iZxxds5PJR/RlenBd1OSIiCSkgIvDUwir27G/i9snqPYhI56WA6GDNLa389JX1VAzpy7ghBVGXIyJyTAqIDvbXt2rYvOcAt2tSPhHp5BQQHejwpHzDi3vywZH9oi5HROR9KSA60Ly1O1lRs5fbLxlOt26alE9EOjcFRAeaNqeSfvnduWasJuUTkc5PAdFB3t5cx7y1O/n8RUPpnpkRdTkiIselgOgg0+dWktc9kxsnaFI+EekaFBAdoKp2P399q4YbJ5TRKycr6nJERJKigOgAP5u3nm4Gt04qj7oUEZGkKSBCVrvvEE8u3MQ155YwsHePqMsREUmaAiJkv5y/kcamVqZqUj4R6WIUECE6cKiFx+Zv4IMj+3F6//yoyxEROSEKiBA9/UY1tfsOaVoNEemSFBAhaWl1fjq3knMH9+H88r5RlyMicsIUECF57u2tbKrdzx2Th2GmaTVEpOtRQITA3fnJnHUMLerJ5aMHRF2OiMhJCTUgzOwKM1tlZmvN7BsJ9g8xs9lm9qaZvWxmpW329zKzzWb2UJh1trf5lfGGJTUAAAsiSURBVLt4a3Mdt108jAxNyiciXVRoAWFmGcDDwEeA0cANZja6TbP7gcfdfQxwD3Bfm/3/CcwJq8awTJtTSVFeNtedVxJ1KSIiJy3MHsR4YK27V7r7IeBJ4Jo2bUYDs4Pll+L3m9k4oD/wQog1truVNXuZs3oHt04aSk6WJuUTka4rzIAoAari1quDbfGWAVOC5WuBfDMrNLNuwPeAf3m/NzCzqWa2yMwW7dixo53KPjU/nVtJbnYGN00YEnUpIiKnJMyASDT47m3W7wImm9kSYDKwGWgGvgQ86+5VvA93n+7uFe5eUVxc3B41n5LNew4wY9kWrj+/jN65mpRPRLq2zBBfuxoYHLdeCmyJb+DuW4DrAMwsD5ji7nVmNhG42My+BOQB2WbW4O7vOdHdmTw6bz0OfOHioVGXIiJyysIMiIXACDMbSqxncD1wY3wDMysCat29FbgbeBTA3T8T1+YWoKKzh0Pd/iaeeH0TV58ziJI+mpRPRLq+0IaY3L0ZuBN4HlgJPOXuy83sHjO7Omh2KbDKzFYTOyF9b1j1hO1Xr21k/6EWTconIinD3NueFuiaKioqfNGiRZG8d2NTCxd99yXOHNSLxz4/PpIaREROhpktdveKRPt0J3U7+OOSzexsOMjtk9V7EJHUoYA4RYcn5Tu7pDcThxVGXY6ISLtRQJyimSu2UblzH7drUj4RSTEKiFNweFK+soJcrjhTk/KJSGpRQJyChRt2s7RqD7ddPJTMDP1Rikhq0bfaKZg2Zx0FPbP5xLjBx28sItLFKCBO0ppt9cx+Zzufm1hOj2xNyiciqUcBcZKmz62kR1YGn52oSflEJDUpIE7C1rpGnlm6mU9VlNK3Z3bU5YiIhEIBcRJ+/vf1tLQ6X7xYN8aJSOpSQJygvY1N/Pq1TVw5ZhCDC3KjLkdEJDQKiBP0m9c20XCwmds1KZ+IpDgFxAk42NzCo/PWc9FpRZxV0jvqckREQqWAOAF/WrqF7fWalE9E0oMCIkmtrc70uZWMHtiLi04rirocEZHQKSCS9OI721m7vUGT8olI2lBAJGna3HWU9OnBR88eGHUpIiIdQgGRhMUba1m4YTdfvHgoWZqUT0TShL7tkjBtTiV9crP49PmalE9E0ocC4jjW7Whg5sptfPaCIeRmZ0ZdjohIh1FAHMcjr1SSndGNz15YHnUpIiIdKtSAMLMrzGyVma01s28k2D/EzGab2Ztm9rKZlcZtX2xmS81suZndEWadx7K9vpHfL97MJ8aVUpTXPYoSREQiE1pAmFkG8DDwEWA0cIOZjW7T7H7gcXcfA9wD3BdsrwEudPdzgQnAN8xsUFi1Hssv/r6BptZWbtOkfCKShsLsQYwH1rp7pbsfAp4ErmnTZjQwO1h+6fB+dz/k7geD7d1DrjOhhoPN/HLBRj5y1gDKi3p29NuLiEQuzC/eEqAqbr062BZvGTAlWL4WyDezQgAzG2xmbwav8V1339L2DcxsqpktMrNFO3bsaNfin3x9E/WNzdx+yfB2fV0Rka4izIBIdLuxt1m/C5hsZkuAycBmoBnA3auCoafTgM+ZWf/3vJj7dHevcPeK4uLidiv8UHMrP5u3nguGFXDO4D7t9roiIl1JmAFRDcTfOFAKHNULcPct7n6du48F/j3YVte2DbAcuDjEWo/y52VbqKlr5PbJ6j2ISPoKMyAWAiPMbKiZZQPXAzPiG5hZkZkdruFu4NFge6mZ9QiW+wKTgFUh1nqEe2xSvjP653Pp6e3XKxER6WpCCwh3bwbuBJ4HVgJPuftyM7vHzK4Oml0KrDKz1UB/4N5g+yjgNTNbBswB7nf3t8KqNd7Lq3ewals9Uy/RpHwikt5CvTXY3Z8Fnm2z7Ztxy08DTyc4biYwJszajmXanHUM7J3DVed0+FW1IiKdiu6kjrO0ag8LKmv5wkVDyc7UH42IpDd9C8aZPncd+TmZXD++LOpSREQip4AIbNi5j7+9vZWbLxhCXndNyiciooAIPDKvkqxu3bhlUnnUpYiIdAoKCGBnw0F+t6iaKeNK6JefE3U5IiKdggICePzVDRxqaeWLmpRPROSItA+IfQebeWz+Ri4f1Z/hxXlRlyMi0mmk/dnYhoPNXHRaEZ+/aGjUpYiIdCppHxD9e+Xw8GfOi7oMEZFOJ+2HmEREJDEFhIiIJKSAEBGRhBQQIiKSkAJCREQSUkCIiEhCCggREUlIASEiIgmZu0ddQ7swsx3AxlN4iSJgZzuV01Wk22dOt88L+szp4lQ+8xB3L060I2UC4lSZ2SJ3r4i6jo6Ubp853T4v6DOni7A+s4aYREQkIQWEiIgkpIB41/SoC4hAun3mdPu8oM+cLkL5zDoHISIiCakHISIiCSkgREQkobQPCDO7wsxWmdlaM/tG1PWEzcweNbPtZvZ21LV0FDMbbGYvmdlKM1tuZl+JuqawmVmOmb1uZsuCz/y/o66pI5hZhpktMbO/RF1LRzGzDWb2lpktNbNF7fra6XwOwswygNXA5UA1sBC4wd1XRFpYiMzsEqABeNzdz4q6no5gZgOBge7+hpnlA4uBj6f4/2cDerp7g5llAfOAr7j7gohLC5WZ/TNQAfRy949FXU9HMLMNQIW7t/vNgenegxgPrHX3Snc/BDwJXBNxTaFy97lAbdR1dCR3r3H3N4LlemAlUBJtVeHymIZgNSv4SenfBs2sFLgSeCTqWlJFugdECVAVt15Nin9xpDszKwfGAq9FW0n4guGWpcB2YKa7p/pnfgD4V6A16kI6mAMvmNliM5vani+c7gFhCbal9G9Z6czM8oDfA191971R1xM2d29x93OBUmC8maXskKKZfQzY7u6Lo64lApPc/TzgI8CXg2HkdpHuAVENDI5bLwW2RFSLhCgYh/898Gt3/0PU9XQkd98DvAxcEXEpYZoEXB2Mxz8JXGZmv4q2pI7h7luC/24H/khs6LxdpHtALARGmNlQM8sGrgdmRFyTtLPghO3PgJXu/v2o6+kIZlZsZn2C5R7Ah4B3oq0qPO5+t7uXuns5sX/HL7r7TRGXFToz6xlceIGZ9QQ+DLTbFYppHRDu3gzcCTxP7MTlU+6+PNqqwmVmTwDzgTPMrNrMvhB1TR1gEnAzsd8qlwY/H426qJANBF4yszeJ/SI0093T5tLPNNIfmGdmy4DXgb+6+3Pt9eJpfZmriIgcW1r3IERE5NgUECIikpACQkREElJAiIhIQgoIERFJSAEh0gmY2aXpNAOpdA0KCBERSUgBIXICzOym4DkLS81sWjAhXoOZfc/M3jCz2WZWHLQ918wWmNmbZvZHM+sbbD/NzGYFz2p4w8yGBy+fZ2ZPm9k7Zvbr4A5wkcgoIESSZGajgE8TmxztXKAF+AzQE3gjmDBtDvCt4JDHgX9z9zHAW3Hbfw087O7nABcCNcH2scBXgdHAMGJ3gItEJjPqAkS6kA8C44CFwS/3PYhNpd0K/DZo8yvgD2bWG+jj7nOC7Y8BvwvmzSlx9z8CuHsjQPB6r7t7dbC+FCgn9qAfkUgoIESSZ8Bj7n73URvN/lebdu83f837DRsdjFtuQf8+JWIaYhJJ3mzgE2bWD8DMCsxsCLF/R58I2twIzHP3OmC3mV0cbL8ZmBM8h6LazD4evEZ3M8vt0E8hkiT9hiKSJHdfYWb/QezpXd2AJuDLwD7gTDNbDNQRO08B8DngJ0EAVAK3BttvBqaZ2T3Ba3yyAz+GSNI0m6vIKTKzBnfPi7oOkfamISYREUlIPQgREUlIPQgREUlIASEiIgkpIEREJCEFhIiIJKSAEBGRhP4/ynaVNDJgXFcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# list all data in history\n",
    "print(history.history.keys())\n",
    "plt.plot(history.history['acc'])\n",
    "# plt.plot(history.history['val_accuracy'])\n",
    "plt.title('model accuracy')\n",
    "plt.ylabel('accuracy')\n",
    "plt.xlabel('epoch')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(history.history['loss'])\n",
    "plt.title('model loss')\n",
    "plt.ylabel('loss')\n",
    "plt.xlabel('epoch')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_1\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense_2 (Dense)              (None, 512)               401920    \n",
      "_________________________________________________________________\n",
      "dense_3 (Dense)              (None, 10)                5130      \n",
      "=================================================================\n",
      "Total params: 407,050\n",
      "Trainable params: 407,050\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "network.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(10000, 784)\n",
      "(784,)\n",
      "(1, 784)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x1d843400dc8>"
      ]
     },
     "execution_count": 153,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "index = 90\n",
    "# shape of X_test\n",
    "print(test_images.shape) # (10000, 784)\n",
    "# shape of the first item in X_test\n",
    "print(test_images[index].shape)# (784,)\n",
    "# in order to do prediction, you need # to send in a 2-d array of shape (,784)\n",
    "x = test_images[index].reshape(-1,784)\n",
    "print(x.shape) # (1,784)\n",
    "# show the number\n",
    "plt.title(\"Digit: {}\".format(test_labels[index].argmax()))\n",
    "plt.imshow(test_images[index].reshape(28,28), interpolation='none')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[4.6057446e-11 5.3708293e-10 3.4817713e-08 9.9998057e-01 4.0039678e-14\n",
      "  7.7372630e-07 5.4155323e-15 7.4172248e-12 1.5085243e-05 3.5651742e-06]]\n"
     ]
    }
   ],
   "source": [
    "print(network.predict(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[3]\n"
     ]
    }
   ],
   "source": [
    "print(network.predict_classes(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0. 0. 0. 1. 0. 0. 0. 0. 0. 0.]\n"
     ]
    }
   ],
   "source": [
    "print(test_labels[index])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Saving the Trained Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [],
   "source": [
    "# creates a HDF5 file 'trained_model.h5'\n",
    "network.save('trained_model.h5')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Loading the Saved Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From C:\\Users\\Toshiba\\Anaconda3\\lib\\site-packages\\tensorflow_core\\python\\ops\\init_ops.py:97: calling GlorotUniform.__init__ (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Call initializer instance with the dtype argument instead of passing it to the constructor\n",
      "WARNING:tensorflow:From C:\\Users\\Toshiba\\Anaconda3\\lib\\site-packages\\tensorflow_core\\python\\ops\\init_ops.py:97: calling Zeros.__init__ (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Call initializer instance with the dtype argument instead of passing it to the constructor\n"
     ]
    }
   ],
   "source": [
    "from tensorflow.keras.models import load_model\n",
    "# returns a compiled model identical to # the previous one\n",
    "model = load_model('trained_model.h5')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## TESTING OWN IMAGE OF NUMBER 6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(28, 28)\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from skimage import io\n",
    "from skimage.color import rgb2gray\n",
    "original= io.imread(\"6.png\")\n",
    "plt.imshow(original)\n",
    "grayscale = rgb2gray(original)\n",
    "print(grayscale.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1.18159574e-35 0.00000000e+00 1.00000000e+00 8.38181721e-32\n",
      "  0.00000000e+00 1.79936469e-19 1.35106657e-15 6.73455184e-17\n",
      "  0.00000000e+00 0.00000000e+00]]\n",
      "2\n"
     ]
    }
   ],
   "source": [
    "final = network.predict(grayscale.reshape(-1,784))\n",
    "print(final)\n",
    "print(final.argmax())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2]\n"
     ]
    }
   ],
   "source": [
    "print(model.predict_classes(grayscale.reshape(-1,784)))"
   ]
  }
 ],
 "metadata": {
  "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": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}