deepneural.ipynb

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  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "e2b40e67",
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.datasets import mnist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "6c9ee2fb",
   "metadata": {},
   "outputs": [],
   "source": [
    "(x_train,y_train),(x_test,y_test)=mnist.load_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "b5683a3f",
   "metadata": {},
   "outputs": [
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   "source": [
    "x_train"
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  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "2ff7c05b",
   "metadata": {},
   "outputs": [],
   "source": [
    "from matplotlib import pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "1e2851db",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train=x_train/255\n",
    "x_test=x_test/255"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "8a466e2a",
   "metadata": {},
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     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "3b7caf08",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(60000, 784)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train.shape\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "1975cca9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10000, 784)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "fa257ca2",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train=x_train.reshape((60000,28*28))\n",
    "x_test=x_test.reshape((10000,28*28))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "27e122ff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(50000, 32, 32, 3)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1233b409",
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.layers import Input,Dense,Dropout\n",
    "from tensorflow.keras import Sequential\n",
    "from tensorflow.keras.optimizers import SGD\n",
    "from tensorflow.keras.losses import SparseCategoricalCrossentropy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "5b66efa9",
   "metadata": {},
   "outputs": [],
   "source": [
    "model=Sequential([\n",
    "    Input(784),\n",
    "    Dense(100, activation='sigmoid'),\n",
    "    Dropout(0.2),\n",
    "    Dense(50, activation='sigmoid'),\n",
    "    Dropout(0.2),\n",
    "    Dense(10, activation='softmax')\n",
    "    \n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "a4d4f8e2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " dense (Dense)               (None, 100)               78500     \n",
      "                                                                 \n",
      " dropout (Dropout)           (None, 100)               0         \n",
      "                                                                 \n",
      " dense_1 (Dense)             (None, 50)                5050      \n",
      "                                                                 \n",
      " dropout_1 (Dropout)         (None, 50)                0         \n",
      "                                                                 \n",
      " dense_2 (Dense)             (None, 10)                510       \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 84,060\n",
      "Trainable params: 84,060\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "c27b84b7",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(loss=SparseCategoricalCrossentropy(),optimizer=SGD(),metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "0543657d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20\n",
      "938/938 [==============================] - 5s 4ms/step - loss: 2.3184 - accuracy: 0.1233\n",
      "Epoch 2/20\n",
      "938/938 [==============================] - 4s 5ms/step - loss: 2.2113 - accuracy: 0.2060\n",
      "Epoch 3/20\n",
      "938/938 [==============================] - 4s 5ms/step - loss: 2.0533 - accuracy: 0.3190\n",
      "Epoch 4/20\n",
      "938/938 [==============================] - 4s 5ms/step - loss: 1.8128 - accuracy: 0.4189\n",
      "Epoch 5/20\n",
      "938/938 [==============================] - 4s 5ms/step - loss: 1.5644 - accuracy: 0.4997\n",
      "Epoch 6/20\n",
      "938/938 [==============================] - 4s 4ms/step - loss: 1.3681 - accuracy: 0.5609\n",
      "Epoch 7/20\n",
      "938/938 [==============================] - 4s 5ms/step - loss: 1.2159 - accuracy: 0.6107\n",
      "Epoch 8/20\n",
      "938/938 [==============================] - 4s 5ms/step - loss: 1.0984 - accuracy: 0.6472\n",
      "Epoch 9/20\n",
      "938/938 [==============================] - 4s 5ms/step - loss: 1.0073 - accuracy: 0.6791\n",
      "Epoch 10/20\n",
      "938/938 [==============================] - 4s 5ms/step - loss: 0.9334 - accuracy: 0.7029\n",
      "Epoch 11/20\n",
      "938/938 [==============================] - 4s 5ms/step - loss: 0.8748 - accuracy: 0.7224\n",
      "Epoch 12/20\n",
      "938/938 [==============================] - 4s 5ms/step - loss: 0.8173 - accuracy: 0.7438\n",
      "Epoch 13/20\n",
      "938/938 [==============================] - 4s 5ms/step - loss: 0.7766 - accuracy: 0.7567\n",
      "Epoch 14/20\n",
      "938/938 [==============================] - 4s 5ms/step - loss: 0.7394 - accuracy: 0.7703\n",
      "Epoch 15/20\n",
      "938/938 [==============================] - 4s 5ms/step - loss: 0.7044 - accuracy: 0.7789\n",
      "Epoch 16/20\n",
      "938/938 [==============================] - 4s 5ms/step - loss: 0.6764 - accuracy: 0.7896\n",
      "Epoch 17/20\n",
      "938/938 [==============================] - 4s 5ms/step - loss: 0.6523 - accuracy: 0.7990\n",
      "Epoch 18/20\n",
      "938/938 [==============================] - 4s 5ms/step - loss: 0.6299 - accuracy: 0.8048\n",
      "Epoch 19/20\n",
      "938/938 [==============================] - 4s 5ms/step - loss: 0.6141 - accuracy: 0.8102\n",
      "Epoch 20/20\n",
      "938/938 [==============================] - 4s 5ms/step - loss: 0.5973 - accuracy: 0.8169\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x130297da5b0>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(x_train,y_train, epochs=20,batch_size=64)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d4d053dd",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "91febd40",
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.datasets import cifar10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "1c072d99",
   "metadata": {},
   "outputs": [],
   "source": [
    "(x_train,y_train),(x_test,y_test)=cifar10.load_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "365084d8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[[ 59,  62,  63],\n",
       "         [ 43,  46,  45],\n",
       "         [ 50,  48,  43],\n",
       "         ...,\n",
       "         [158, 132, 108],\n",
       "         [152, 125, 102],\n",
       "         [148, 124, 103]],\n",
       "\n",
       "        [[ 16,  20,  20],\n",
       "         [  0,   0,   0],\n",
       "         [ 18,   8,   0],\n",
       "         ...,\n",
       "         [123,  88,  55],\n",
       "         [119,  83,  50],\n",
       "         [122,  87,  57]],\n",
       "\n",
       "        [[ 25,  24,  21],\n",
       "         [ 16,   7,   0],\n",
       "         [ 49,  27,   8],\n",
       "         ...,\n",
       "         [118,  84,  50],\n",
       "         [120,  84,  50],\n",
       "         [109,  73,  42]],\n",
       "\n",
       "        ...,\n",
       "\n",
       "        [[208, 170,  96],\n",
       "         [201, 153,  34],\n",
       "         [198, 161,  26],\n",
       "         ...,\n",
       "         [160, 133,  70],\n",
       "         [ 56,  31,   7],\n",
       "         [ 53,  34,  20]],\n",
       "\n",
       "        [[180, 139,  96],\n",
       "         [173, 123,  42],\n",
       "         [186, 144,  30],\n",
       "         ...,\n",
       "         [184, 148,  94],\n",
       "         [ 97,  62,  34],\n",
       "         [ 83,  53,  34]],\n",
       "\n",
       "        [[177, 144, 116],\n",
       "         [168, 129,  94],\n",
       "         [179, 142,  87],\n",
       "         ...,\n",
       "         [216, 184, 140],\n",
       "         [151, 118,  84],\n",
       "         [123,  92,  72]]],\n",
       "\n",
       "\n",
       "       [[[154, 177, 187],\n",
       "         [126, 137, 136],\n",
       "         [105, 104,  95],\n",
       "         ...,\n",
       "         [ 91,  95,  71],\n",
       "         [ 87,  90,  71],\n",
       "         [ 79,  81,  70]],\n",
       "\n",
       "        [[140, 160, 169],\n",
       "         [145, 153, 154],\n",
       "         [125, 125, 118],\n",
       "         ...,\n",
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       "         [ 77,  80,  62],\n",
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       "\n",
       "        [[140, 155, 164],\n",
       "         [139, 146, 149],\n",
       "         [115, 115, 112],\n",
       "         ...,\n",
       "         [ 79,  82,  64],\n",
       "         [ 68,  70,  55],\n",
       "         [ 67,  69,  55]],\n",
       "\n",
       "        ...,\n",
       "\n",
       "        [[175, 167, 166],\n",
       "         [156, 154, 160],\n",
       "         [154, 160, 170],\n",
       "         ...,\n",
       "         [ 42,  34,  36],\n",
       "         [ 61,  53,  57],\n",
       "         [ 93,  83,  91]],\n",
       "\n",
       "        [[165, 154, 128],\n",
       "         [156, 152, 130],\n",
       "         [159, 161, 142],\n",
       "         ...,\n",
       "         [103,  93,  96],\n",
       "         [123, 114, 120],\n",
       "         [131, 121, 131]],\n",
       "\n",
       "        [[163, 148, 120],\n",
       "         [158, 148, 122],\n",
       "         [163, 156, 133],\n",
       "         ...,\n",
       "         [143, 133, 139],\n",
       "         [143, 134, 142],\n",
       "         [143, 133, 144]]],\n",
       "\n",
       "\n",
       "       [[[255, 255, 255],\n",
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       "         [253, 253, 253],\n",
       "         ...,\n",
       "         [253, 253, 253],\n",
       "         [253, 253, 253],\n",
       "         [253, 253, 253]],\n",
       "\n",
       "        [[255, 255, 255],\n",
       "         [255, 255, 255],\n",
       "         [255, 255, 255],\n",
       "         ...,\n",
       "         [255, 255, 255],\n",
       "         [255, 255, 255],\n",
       "         [255, 255, 255]],\n",
       "\n",
       "        [[255, 255, 255],\n",
       "         [254, 254, 254],\n",
       "         [254, 254, 254],\n",
       "         ...,\n",
       "         [254, 254, 254],\n",
       "         [254, 254, 254],\n",
       "         [254, 254, 254]],\n",
       "\n",
       "        ...,\n",
       "\n",
       "        [[113, 120, 112],\n",
       "         [111, 118, 111],\n",
       "         [105, 112, 106],\n",
       "         ...,\n",
       "         [ 72,  81,  80],\n",
       "         [ 72,  80,  79],\n",
       "         [ 72,  80,  79]],\n",
       "\n",
       "        [[111, 118, 110],\n",
       "         [104, 111, 104],\n",
       "         [ 99, 106,  98],\n",
       "         ...,\n",
       "         [ 68,  75,  73],\n",
       "         [ 70,  76,  75],\n",
       "         [ 78,  84,  82]],\n",
       "\n",
       "        [[106, 113, 105],\n",
       "         [ 99, 106,  98],\n",
       "         [ 95, 102,  94],\n",
       "         ...,\n",
       "         [ 78,  85,  83],\n",
       "         [ 79,  85,  83],\n",
       "         [ 80,  86,  84]]],\n",
       "\n",
       "\n",
       "       ...,\n",
       "\n",
       "\n",
       "       [[[ 35, 178, 235],\n",
       "         [ 40, 176, 239],\n",
       "         [ 42, 176, 241],\n",
       "         ...,\n",
       "         [ 99, 177, 219],\n",
       "         [ 79, 147, 197],\n",
       "         [ 89, 148, 189]],\n",
       "\n",
       "        [[ 57, 182, 234],\n",
       "         [ 44, 184, 250],\n",
       "         [ 50, 183, 240],\n",
       "         ...,\n",
       "         [156, 182, 200],\n",
       "         [141, 177, 206],\n",
       "         [116, 149, 175]],\n",
       "\n",
       "        [[ 98, 197, 237],\n",
       "         [ 64, 189, 252],\n",
       "         [ 69, 192, 245],\n",
       "         ...,\n",
       "         [188, 195, 206],\n",
       "         [119, 135, 147],\n",
       "         [ 61,  79,  90]],\n",
       "\n",
       "        ...,\n",
       "\n",
       "        [[ 73,  79,  77],\n",
       "         [ 53,  63,  68],\n",
       "         [ 54,  68,  80],\n",
       "         ...,\n",
       "         [ 17,  40,  64],\n",
       "         [ 21,  36,  51],\n",
       "         [ 33,  48,  49]],\n",
       "\n",
       "        [[ 61,  68,  75],\n",
       "         [ 55,  70,  86],\n",
       "         [ 57,  79, 103],\n",
       "         ...,\n",
       "         [ 24,  48,  72],\n",
       "         [ 17,  35,  53],\n",
       "         [  7,  23,  32]],\n",
       "\n",
       "        [[ 44,  56,  73],\n",
       "         [ 46,  66,  88],\n",
       "         [ 49,  77, 105],\n",
       "         ...,\n",
       "         [ 27,  52,  77],\n",
       "         [ 21,  43,  66],\n",
       "         [ 12,  31,  50]]],\n",
       "\n",
       "\n",
       "       [[[189, 211, 240],\n",
       "         [186, 208, 236],\n",
       "         [185, 207, 235],\n",
       "         ...,\n",
       "         [175, 195, 224],\n",
       "         [172, 194, 222],\n",
       "         [169, 194, 220]],\n",
       "\n",
       "        [[194, 210, 239],\n",
       "         [191, 207, 236],\n",
       "         [190, 206, 235],\n",
       "         ...,\n",
       "         [173, 192, 220],\n",
       "         [171, 191, 218],\n",
       "         [167, 190, 216]],\n",
       "\n",
       "        [[208, 219, 244],\n",
       "         [205, 216, 240],\n",
       "         [204, 215, 239],\n",
       "         ...,\n",
       "         [175, 191, 217],\n",
       "         [172, 190, 216],\n",
       "         [169, 191, 215]],\n",
       "\n",
       "        ...,\n",
       "\n",
       "        [[207, 199, 181],\n",
       "         [203, 195, 175],\n",
       "         [203, 196, 173],\n",
       "         ...,\n",
       "         [135, 132, 127],\n",
       "         [162, 158, 150],\n",
       "         [168, 163, 151]],\n",
       "\n",
       "        [[198, 190, 170],\n",
       "         [189, 181, 159],\n",
       "         [180, 172, 147],\n",
       "         ...,\n",
       "         [178, 171, 160],\n",
       "         [175, 169, 156],\n",
       "         [175, 169, 154]],\n",
       "\n",
       "        [[198, 189, 173],\n",
       "         [189, 181, 162],\n",
       "         [178, 170, 149],\n",
       "         ...,\n",
       "         [195, 184, 169],\n",
       "         [196, 189, 171],\n",
       "         [195, 190, 171]]],\n",
       "\n",
       "\n",
       "       [[[229, 229, 239],\n",
       "         [236, 237, 247],\n",
       "         [234, 236, 247],\n",
       "         ...,\n",
       "         [217, 219, 233],\n",
       "         [221, 223, 234],\n",
       "         [222, 223, 233]],\n",
       "\n",
       "        [[222, 221, 229],\n",
       "         [239, 239, 249],\n",
       "         [233, 234, 246],\n",
       "         ...,\n",
       "         [223, 223, 236],\n",
       "         [227, 228, 238],\n",
       "         [210, 211, 220]],\n",
       "\n",
       "        [[213, 206, 211],\n",
       "         [234, 232, 239],\n",
       "         [231, 233, 244],\n",
       "         ...,\n",
       "         [220, 220, 232],\n",
       "         [220, 219, 232],\n",
       "         [202, 203, 215]],\n",
       "\n",
       "        ...,\n",
       "\n",
       "        [[150, 143, 135],\n",
       "         [140, 135, 127],\n",
       "         [132, 127, 120],\n",
       "         ...,\n",
       "         [224, 222, 218],\n",
       "         [230, 228, 225],\n",
       "         [241, 241, 238]],\n",
       "\n",
       "        [[137, 132, 126],\n",
       "         [130, 127, 120],\n",
       "         [125, 121, 115],\n",
       "         ...,\n",
       "         [181, 180, 178],\n",
       "         [202, 201, 198],\n",
       "         [212, 211, 207]],\n",
       "\n",
       "        [[122, 119, 114],\n",
       "         [118, 116, 110],\n",
       "         [120, 116, 111],\n",
       "         ...,\n",
       "         [179, 177, 173],\n",
       "         [164, 164, 162],\n",
       "         [163, 163, 161]]]], dtype=uint8)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "7754a79e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([6, 9, 9, ..., 9, 1, 1], dtype=uint8)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train.flatten()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "ed191c0e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x13045f5abe0>"
      ]
     },
     "execution_count": 25,
     "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": [
    "plt.imshow(x_train[5000])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "835d4ac0",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a64722a6",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d8e36ecb",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "277198db",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
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