deep learning by tensorflow (Classification), Saber.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "2d3a4499",
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf   #For CNN\n",
    "\n",
    "#Loading dataset:\n",
    "mnist= tf.keras.datasets.mnist  #27*28 images of hand written digits 0-9 \n",
    "(x_train, y_train), (x_test, y_test)= mnist.load_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "e9dd731c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "    0   0   0   0   0   0   0   0   0   0]\n",
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      "    0   0   0   0   0   0   0   0   0   0]\n",
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      "    0   0   0   0   0   0   0   0   0   0]\n",
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      "    0   0   0   0   0   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0   0   0   0   0   0   0   3  18  18  18 126 136\n",
      "  175  26 166 255 247 127   0   0   0   0]\n",
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      "  225 172 253 242 195  64   0   0   0   0]\n",
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      "   93  82  82  56  39   0   0   0   0   0]\n",
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      "    0   0   0   0   0   0   0   0   0   0]\n",
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      "    0   0   0   0   0   0   0   0   0   0]\n",
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      "  253 249  64   0   0   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0  46 130 183 253\n",
      "  253 207   2   0   0   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0   0   0   0   0   0   0  39 148 229 253 253 253\n",
      "  250 182   0   0   0   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0   0   0   0   0  24 114 221 253 253 253 253 201\n",
      "   78   0   0   0   0   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0   0   0  23  66 213 253 253 253 253 198  81   2\n",
      "    0   0   0   0   0   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0  18 171 219 253 253 253 253 195  80   9   0   0\n",
      "    0   0   0   0   0   0   0   0   0   0]\n",
      " [  0   0   0   0  55 172 226 253 253 253 253 244 133  11   0   0   0   0\n",
      "    0   0   0   0   0   0   0   0   0   0]\n",
      " [  0   0   0   0 136 253 253 253 212 135 132  16   0   0   0   0   0   0\n",
      "    0   0   0   0   0   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
      "    0   0   0   0   0   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
      "    0   0   0   0   0   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
      "    0   0   0   0   0   0   0   0   0   0]]\n"
     ]
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "print(x_train[0]) #► it is not normal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "3246242b",
   "metadata": {},
   "outputs": [],
   "source": [
    "#normalizing the dataset:\n",
    "x_train= tf.keras.utils.normalize(x_train, axis=1)\n",
    "x_test= tf.keras.utils.normalize(x_test, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "96ba7dd6",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "feb918c4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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gCMIOBEHYgSAIOxAEYQeCIOxAEH8FObYutbv7L+4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(x_train[0], cmap= plt.cm.binary)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "563f79ff",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "  0.15214552 0.17865984 0.25626376 0.1573102  0.12298801 0.\n",
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      "  0.         0.04500225 0.4219755  0.45852825 0.43408872 0.37314701\n",
      "  0.33153488 0.32790981 0.28826244 0.26543758 0.34149427 0.31128482\n",
      "  0.         0.         0.         0.         0.         0.\n",
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      " [0.         0.         0.         0.         0.         0.\n",
      "  0.         0.         0.         0.         0.         0.\n",
      "  0.04586451 0.31235677 0.32757096 0.23335172 0.14931733 0.00129164\n",
      "  0.         0.         0.         0.         0.         0.\n",
      "  0.         0.         0.         0.        ]\n",
      " [0.         0.         0.         0.         0.         0.\n",
      "  0.         0.         0.         0.         0.         0.\n",
      "  0.         0.10498298 0.34940902 0.3689874  0.34978968 0.15370495\n",
      "  0.04089933 0.         0.         0.         0.         0.\n",
      "  0.         0.         0.         0.        ]\n",
      " [0.         0.         0.         0.         0.         0.\n",
      "  0.         0.         0.         0.         0.         0.\n",
      "  0.         0.         0.06551419 0.27127137 0.34978968 0.32678448\n",
      "  0.245396   0.05882702 0.         0.         0.         0.\n",
      "  0.         0.         0.         0.        ]\n",
      " [0.         0.         0.         0.         0.         0.\n",
      "  0.         0.         0.         0.         0.         0.\n",
      "  0.         0.         0.         0.02333517 0.12857881 0.32549285\n",
      "  0.41390126 0.40743158 0.         0.         0.         0.\n",
      "  0.         0.         0.         0.        ]\n",
      " [0.         0.         0.         0.         0.         0.\n",
      "  0.         0.         0.         0.         0.         0.\n",
      "  0.         0.         0.         0.         0.         0.32161793\n",
      "  0.41390126 0.54251585 0.20001074 0.         0.         0.\n",
      "  0.         0.         0.         0.        ]\n",
      " [0.         0.         0.         0.         0.         0.\n",
      "  0.         0.         0.         0.         0.         0.\n",
      "  0.         0.         0.06697006 0.18959827 0.25300993 0.32678448\n",
      "  0.41390126 0.45100715 0.00625034 0.         0.         0.\n",
      "  0.         0.         0.         0.        ]\n",
      " [0.         0.         0.         0.         0.         0.\n",
      "  0.         0.         0.         0.         0.         0.\n",
      "  0.05110617 0.19182076 0.33339444 0.3689874  0.34978968 0.32678448\n",
      "  0.40899334 0.39653769 0.         0.         0.         0.\n",
      "  0.         0.         0.         0.        ]\n",
      " [0.         0.         0.         0.         0.         0.\n",
      "  0.         0.         0.         0.         0.04117838 0.16813739\n",
      "  0.28960162 0.32790981 0.36833534 0.3689874  0.34978968 0.25961929\n",
      "  0.12760592 0.         0.         0.         0.         0.\n",
      "  0.         0.         0.         0.        ]\n",
      " [0.         0.         0.         0.         0.         0.\n",
      "  0.         0.         0.04431706 0.11961607 0.36545809 0.37314701\n",
      "  0.33153488 0.32790981 0.36833534 0.28877275 0.111988   0.00258328\n",
      "  0.         0.         0.         0.         0.         0.\n",
      "  0.         0.         0.         0.        ]\n",
      " [0.         0.         0.         0.         0.         0.\n",
      "  0.05298497 0.42752138 0.4219755  0.45852825 0.43408872 0.37314701\n",
      "  0.33153488 0.25273681 0.11646967 0.01312603 0.         0.\n",
      "  0.         0.         0.         0.         0.         0.\n",
      "  0.         0.         0.         0.        ]\n",
      " [0.         0.         0.         0.         0.37491383 0.56222061\n",
      "  0.66525569 0.63253163 0.48748768 0.45852825 0.43408872 0.359873\n",
      "  0.17428513 0.01425695 0.         0.         0.         0.\n",
      "  0.         0.         0.         0.         0.         0.\n",
      "  0.         0.         0.         0.        ]\n",
      " [0.         0.         0.         0.         0.92705966 0.82698729\n",
      "  0.74473314 0.63253163 0.4084877  0.24466922 0.22648107 0.02359823\n",
      "  0.         0.         0.         0.         0.         0.\n",
      "  0.         0.         0.         0.         0.         0.\n",
      "  0.         0.         0.         0.        ]\n",
      " [0.         0.         0.         0.         0.         0.\n",
      "  0.         0.         0.         0.         0.         0.\n",
      "  0.         0.         0.         0.         0.         0.\n",
      "  0.         0.         0.         0.         0.         0.\n",
      "  0.         0.         0.         0.        ]\n",
      " [0.         0.         0.         0.         0.         0.\n",
      "  0.         0.         0.         0.         0.         0.\n",
      "  0.         0.         0.         0.         0.         0.\n",
      "  0.         0.         0.         0.         0.         0.\n",
      "  0.         0.         0.         0.        ]\n",
      " [0.         0.         0.         0.         0.         0.\n",
      "  0.         0.         0.         0.         0.         0.\n",
      "  0.         0.         0.         0.         0.         0.\n",
      "  0.         0.         0.         0.         0.         0.\n",
      "  0.         0.         0.         0.        ]]\n"
     ]
    }
   ],
   "source": [
    "print(x_train[0]) #► it is not normal"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0c899388",
   "metadata": {},
   "source": [
    "# Building Model:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "a885712f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/3\n",
      "1875/1875 [==============================] - 16s 2ms/step - loss: 0.2572 - accuracy: 0.9240\n",
      "Epoch 2/3\n",
      "1875/1875 [==============================] - 4s 2ms/step - loss: 0.1055 - accuracy: 0.9675\n",
      "Epoch 3/3\n",
      "1875/1875 [==============================] - 5s 3ms/step - loss: 0.0706 - accuracy: 0.9779\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x193e6712d60>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model= tf.keras.models.Sequential() #Input Layer          #sequential is the most common model (CNN)\n",
    "model.add(tf.keras.layers.Flatten()) #Hidden Layer layer\n",
    "model.add(tf.keras.layers.Dense(128, activation= tf.nn.relu)) #Dense layer      #128 units in the layer #default activation layer\n",
    "model.add(tf.keras.layers.Dense(128, activation= tf.nn.relu)) #2nd Dense Layer\n",
    "model.add(tf.keras.layers.Dense(10, activation= tf.nn.softmax)) #Output Layer  #number of classification ► 10 (0-9)\n",
    "\n",
    "#tuning parameters for our model on training dataset:\n",
    "model.compile(optimizer='adam', loss= 'sparse_categorical_crossentropy', metrics=['accuracy'])\n",
    "\n",
    "#fit the model on train data:\n",
    "model.fit(x_train, y_train, epochs=3) #epochs► number of iteration\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "19c3c12b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "313/313 [==============================] - 1s 2ms/step - loss: 0.0923 - accuracy: 0.9708\n"
     ]
    }
   ],
   "source": [
    "val_loss, val_acc= model.evaluate(x_test, y_test)  #iy\\t is close to epoch3 ► not overfitted!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "75d8bee0",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Assets written to: Saber.model\\assets\n"
     ]
    }
   ],
   "source": [
    "model.save('Saber.model')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "74eb15c0",
   "metadata": {},
   "outputs": [],
   "source": [
    "new_model= tf.keras.models.load_model('Saber.model')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "1d16b118",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "313/313 [==============================] - 1s 2ms/step\n"
     ]
    }
   ],
   "source": [
    "Predictions= new_model.predict([x_test])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "eb3f584a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0. 0. 0. ... 1. 0. 0.]\n",
      " [0. 0. 1. ... 0. 0. 0.]\n",
      " [0. 1. 0. ... 0. 0. 0.]\n",
      " ...\n",
      " [0. 0. 0. ... 0. 0. 0.]\n",
      " [0. 0. 0. ... 0. 0. 0.]\n",
      " [0. 0. 0. ... 0. 0. 0.]]\n"
     ]
    }
   ],
   "source": [
    "print (Predictions) #► it's messy!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "b9fa7073",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "print(np.argmax(Predictions[1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "70cc5e61",
   "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.imshow(x_test[1])\n",
    "plt.show()"
   ]
  }
 ],
 "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": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}