cifar10.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(50000, 32, 32, 3) (50000, 1)\n",
      "(10000, 32, 32, 3) (10000, 1)\n",
      "[[[ 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",
      "[6]\n",
      "Model: \"sequential_1\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " conv2d_6 (Conv2D)           (None, 32, 32, 32)        896       \n",
      "                                                                 \n",
      " conv2d_7 (Conv2D)           (None, 32, 32, 32)        9248      \n",
      "                                                                 \n",
      " max_pooling2d_3 (MaxPoolin  (None, 16, 16, 32)        0         \n",
      " g2D)                                                            \n",
      "                                                                 \n",
      " dropout_3 (Dropout)         (None, 16, 16, 32)        0         \n",
      "                                                                 \n",
      " conv2d_8 (Conv2D)           (None, 16, 16, 64)        18496     \n",
      "                                                                 \n",
      " conv2d_9 (Conv2D)           (None, 16, 16, 64)        36928     \n",
      "                                                                 \n",
      " max_pooling2d_4 (MaxPoolin  (None, 8, 8, 64)          0         \n",
      " g2D)                                                            \n",
      "                                                                 \n",
      " dropout_4 (Dropout)         (None, 8, 8, 64)          0         \n",
      "                                                                 \n",
      " conv2d_10 (Conv2D)          (None, 8, 8, 128)         73856     \n",
      "                                                                 \n",
      " conv2d_11 (Conv2D)          (None, 8, 8, 128)         147584    \n",
      "                                                                 \n",
      " max_pooling2d_5 (MaxPoolin  (None, 4, 4, 128)         0         \n",
      " g2D)                                                            \n",
      "                                                                 \n",
      " dropout_5 (Dropout)         (None, 4, 4, 128)         0         \n",
      "                                                                 \n",
      " flatten_1 (Flatten)         (None, 2048)              0         \n",
      "                                                                 \n",
      " dense_2 (Dense)             (None, 256)               524544    \n",
      "                                                                 \n",
      " dense_3 (Dense)             (None, 10)                2570      \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 814122 (3.11 MB)\n",
      "Trainable params: 814122 (3.11 MB)\n",
      "Non-trainable params: 0 (0.00 Byte)\n",
      "_________________________________________________________________\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-02-29 15:56:41.079971: W external/local_tsl/tsl/framework/cpu_allocator_impl.cc:83] Allocation of 491520000 exceeds 10% of free system memory.\n",
      "2024-02-29 15:56:41.736904: W external/local_tsl/tsl/framework/cpu_allocator_impl.cc:83] Allocation of 491520000 exceeds 10% of free system memory.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/200\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-02-29 15:56:43.087018: E tensorflow/core/grappler/optimizers/meta_optimizer.cc:961] layout failed: INVALID_ARGUMENT: Size of values 0 does not match size of permutation 4 @ fanin shape insequential_1/dropout_3/dropout/SelectV2-2-TransposeNHWCToNCHW-LayoutOptimizer\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "313/313 [==============================] - 11s 28ms/step - loss: 1.7118 - acc: 0.3656 - val_loss: 1.4403 - val_acc: 0.4705\n",
      "Epoch 2/200\n",
      "313/313 [==============================] - 8s 26ms/step - loss: 1.2920 - acc: 0.5308 - val_loss: 1.1539 - val_acc: 0.5936\n",
      "Epoch 3/200\n",
      "313/313 [==============================] - 8s 26ms/step - loss: 1.0587 - acc: 0.6255 - val_loss: 0.9510 - val_acc: 0.6643\n",
      "Epoch 4/200\n",
      "313/313 [==============================] - 8s 26ms/step - loss: 0.9180 - acc: 0.6744 - val_loss: 0.8501 - val_acc: 0.6996\n",
      "Epoch 5/200\n",
      "313/313 [==============================] - 8s 26ms/step - loss: 0.8243 - acc: 0.7086 - val_loss: 0.7634 - val_acc: 0.7319\n",
      "Epoch 6/200\n",
      "313/313 [==============================] - 8s 26ms/step - loss: 0.7313 - acc: 0.7419 - val_loss: 0.7442 - val_acc: 0.7384\n",
      "Epoch 7/200\n",
      "313/313 [==============================] - 8s 26ms/step - loss: 0.6601 - acc: 0.7667 - val_loss: 0.7214 - val_acc: 0.7474\n",
      "Epoch 8/200\n",
      "313/313 [==============================] - 8s 26ms/step - loss: 0.6016 - acc: 0.7881 - val_loss: 0.6895 - val_acc: 0.7630\n",
      "Epoch 9/200\n",
      "313/313 [==============================] - 8s 26ms/step - loss: 0.5499 - acc: 0.8062 - val_loss: 0.6581 - val_acc: 0.7747\n",
      "Epoch 10/200\n",
      "313/313 [==============================] - 8s 26ms/step - loss: 0.5014 - acc: 0.8238 - val_loss: 0.6972 - val_acc: 0.7669\n",
      "Epoch 11/200\n",
      "313/313 [==============================] - 8s 26ms/step - loss: 0.4640 - acc: 0.8360 - val_loss: 0.6431 - val_acc: 0.7866\n",
      "Epoch 12/200\n",
      "313/313 [==============================] - 11s 34ms/step - loss: 0.4192 - acc: 0.8514 - val_loss: 0.6488 - val_acc: 0.7889\n",
      "Epoch 13/200\n",
      "313/313 [==============================] - 11s 36ms/step - loss: 0.3893 - acc: 0.8596 - val_loss: 0.6521 - val_acc: 0.7883\n",
      "Epoch 14/200\n",
      "313/313 [==============================] - 8s 26ms/step - loss: 0.3496 - acc: 0.8743 - val_loss: 0.6913 - val_acc: 0.7877\n",
      "Epoch 15/200\n",
      "313/313 [==============================] - 9s 27ms/step - loss: 0.3294 - acc: 0.8820 - val_loss: 0.7254 - val_acc: 0.7849\n",
      "Epoch 16/200\n",
      "313/313 [==============================] - 8s 26ms/step - loss: 0.3099 - acc: 0.8895 - val_loss: 0.7085 - val_acc: 0.7891\n",
      "Epoch 17/200\n",
      "313/313 [==============================] - 8s 26ms/step - loss: 0.2801 - acc: 0.8998 - val_loss: 0.7199 - val_acc: 0.7953\n",
      "Epoch 18/200\n",
      "313/313 [==============================] - 8s 26ms/step - loss: 0.2679 - acc: 0.9042 - val_loss: 0.7061 - val_acc: 0.7871\n",
      "Epoch 19/200\n",
      "313/313 [==============================] - 8s 26ms/step - loss: 0.2444 - acc: 0.9134 - val_loss: 0.7266 - val_acc: 0.7868\n",
      "Epoch 20/200\n",
      "313/313 [==============================] - 8s 26ms/step - loss: 0.2335 - acc: 0.9169 - val_loss: 0.7677 - val_acc: 0.7842\n",
      "Epoch 21/200\n",
      "313/313 [==============================] - 8s 26ms/step - loss: 0.2280 - acc: 0.9196 - val_loss: 0.7989 - val_acc: 0.7857\n",
      "Epoch 22/200\n",
      "313/313 [==============================] - 8s 26ms/step - loss: 0.2099 - acc: 0.9253 - val_loss: 0.8320 - val_acc: 0.7793\n",
      "Epoch 23/200\n",
      "313/313 [==============================] - 8s 25ms/step - loss: 0.2003 - acc: 0.9297 - val_loss: 0.8025 - val_acc: 0.7931\n",
      "Epoch 24/200\n",
      "313/313 [==============================] - 8s 26ms/step - loss: 0.1894 - acc: 0.9337 - val_loss: 0.7980 - val_acc: 0.7929\n",
      "Epoch 25/200\n",
      "313/313 [==============================] - 8s 26ms/step - loss: 0.1844 - acc: 0.9352 - val_loss: 0.8885 - val_acc: 0.7860\n",
      "Epoch 26/200\n",
      "313/313 [==============================] - 8s 26ms/step - loss: 0.1776 - acc: 0.9380 - val_loss: 0.7893 - val_acc: 0.7984\n",
      "Epoch 27/200\n",
      "313/313 [==============================] - 8s 26ms/step - loss: 0.1655 - acc: 0.9413 - val_loss: 0.9298 - val_acc: 0.7856\n",
      "Epoch 28/200\n",
      "313/313 [==============================] - 8s 26ms/step - loss: 0.1764 - acc: 0.9372 - val_loss: 0.8625 - val_acc: 0.7938\n",
      "Epoch 29/200\n",
      "313/313 [==============================] - 8s 26ms/step - loss: 0.1591 - acc: 0.9444 - val_loss: 0.8548 - val_acc: 0.7871\n",
      "Epoch 30/200\n",
      "313/313 [==============================] - 8s 26ms/step - loss: 0.1556 - acc: 0.9455 - val_loss: 0.8886 - val_acc: 0.7815\n",
      "Epoch 31/200\n",
      "313/313 [==============================] - 8s 26ms/step - loss: 0.1491 - acc: 0.9481 - val_loss: 0.9428 - val_acc: 0.7877\n",
      "Epoch 32/200\n",
      "313/313 [==============================] - 8s 25ms/step - loss: 0.1523 - acc: 0.9464 - val_loss: 0.8984 - val_acc: 0.7874\n",
      "Epoch 33/200\n",
      "313/313 [==============================] - 8s 26ms/step - loss: 0.1430 - acc: 0.9508 - val_loss: 0.9071 - val_acc: 0.7956\n",
      "Epoch 34/200\n",
      "313/313 [==============================] - 8s 25ms/step - loss: 0.1434 - acc: 0.9501 - val_loss: 0.9509 - val_acc: 0.7880\n",
      "Epoch 35/200\n",
      "313/313 [==============================] - 8s 26ms/step - loss: 0.1428 - acc: 0.9501 - val_loss: 0.9635 - val_acc: 0.7827\n",
      "Epoch 36/200\n",
      "313/313 [==============================] - 8s 26ms/step - loss: 0.1407 - acc: 0.9515 - val_loss: 0.9109 - val_acc: 0.7910\n",
      "Epoch 37/200\n",
      "313/313 [==============================] - 8s 26ms/step - loss: 0.1400 - acc: 0.9511 - val_loss: 0.9197 - val_acc: 0.7919\n",
      "Epoch 38/200\n",
      "313/313 [==============================] - 8s 26ms/step - loss: 0.1293 - acc: 0.9561 - val_loss: 0.9356 - val_acc: 0.7952\n",
      "Epoch 39/200\n",
      "313/313 [==============================] - 8s 25ms/step - loss: 0.1374 - acc: 0.9527 - val_loss: 0.9612 - val_acc: 0.7902\n",
      "Epoch 40/200\n",
      "313/313 [==============================] - 8s 25ms/step - loss: 0.1281 - acc: 0.9557 - val_loss: 0.9469 - val_acc: 0.7893\n",
      "Epoch 41/200\n",
      "313/313 [==============================] - 8s 25ms/step - loss: 0.1291 - acc: 0.9556 - val_loss: 0.9240 - val_acc: 0.7899\n",
      "Epoch 42/200\n",
      "313/313 [==============================] - 8s 25ms/step - loss: 0.1179 - acc: 0.9591 - val_loss: 0.9935 - val_acc: 0.7923\n",
      "Epoch 43/200\n",
      "313/313 [==============================] - 8s 25ms/step - loss: 0.1259 - acc: 0.9567 - val_loss: 0.9935 - val_acc: 0.7836\n",
      "Epoch 44/200\n",
      "313/313 [==============================] - 8s 26ms/step - loss: 0.1189 - acc: 0.9581 - val_loss: 0.9770 - val_acc: 0.7952\n",
      "Epoch 45/200\n",
      "313/313 [==============================] - 8s 25ms/step - loss: 0.1259 - acc: 0.9568 - val_loss: 0.9956 - val_acc: 0.7911\n",
      "Epoch 46/200\n",
      "313/313 [==============================] - 8s 26ms/step - loss: 0.1173 - acc: 0.9597 - val_loss: 0.9494 - val_acc: 0.7967\n",
      "Epoch 47/200\n",
      "313/313 [==============================] - 8s 26ms/step - loss: 0.1138 - acc: 0.9608 - val_loss: 1.0063 - val_acc: 0.7952\n",
      "Epoch 48/200\n",
      "313/313 [==============================] - 8s 26ms/step - loss: 0.1197 - acc: 0.9581 - val_loss: 1.0580 - val_acc: 0.7858\n",
      "Epoch 49/200\n",
      "313/313 [==============================] - 8s 26ms/step - loss: 0.1217 - acc: 0.9583 - val_loss: 0.9612 - val_acc: 0.7942\n",
      "Epoch 50/200\n",
      "313/313 [==============================] - 8s 25ms/step - loss: 0.1151 - acc: 0.9611 - val_loss: 1.0349 - val_acc: 0.7902\n",
      "Epoch 51/200\n",
      "313/313 [==============================] - 8s 25ms/step - loss: 0.1195 - acc: 0.9594 - val_loss: 0.9814 - val_acc: 0.7895\n",
      "Epoch 52/200\n",
      "313/313 [==============================] - 8s 26ms/step - loss: 0.1054 - acc: 0.9641 - val_loss: 0.9647 - val_acc: 0.7943\n",
      "Epoch 53/200\n",
      "313/313 [==============================] - 8s 26ms/step - loss: 0.1035 - acc: 0.9646 - val_loss: 1.0330 - val_acc: 0.7939\n",
      "Epoch 54/200\n",
      "313/313 [==============================] - 8s 26ms/step - loss: 0.1063 - acc: 0.9638 - val_loss: 1.0193 - val_acc: 0.7960\n",
      "Epoch 55/200\n",
      "313/313 [==============================] - 8s 25ms/step - loss: 0.1131 - acc: 0.9615 - val_loss: 1.0272 - val_acc: 0.7987\n",
      "Epoch 56/200\n",
      "313/313 [==============================] - 8s 25ms/step - loss: 0.1099 - acc: 0.9629 - val_loss: 1.0556 - val_acc: 0.7958\n",
      "Epoch 57/200\n",
      "313/313 [==============================] - 8s 25ms/step - loss: 0.1079 - acc: 0.9642 - val_loss: 1.0379 - val_acc: 0.7913\n",
      "Epoch 58/200\n",
      "313/313 [==============================] - 8s 26ms/step - loss: 0.1023 - acc: 0.9653 - val_loss: 1.0421 - val_acc: 0.7974\n",
      "Epoch 59/200\n",
      "313/313 [==============================] - 8s 25ms/step - loss: 0.1048 - acc: 0.9647 - val_loss: 1.0488 - val_acc: 0.7965\n",
      "Epoch 60/200\n",
      "313/313 [==============================] - 8s 26ms/step - loss: 0.1064 - acc: 0.9634 - val_loss: 1.0652 - val_acc: 0.7952\n",
      "Epoch 61/200\n",
      "313/313 [==============================] - 8s 25ms/step - loss: 0.1010 - acc: 0.9665 - val_loss: 1.1138 - val_acc: 0.7884\n",
      "Epoch 62/200\n",
      "313/313 [==============================] - 8s 26ms/step - loss: 0.1032 - acc: 0.9650 - val_loss: 1.0939 - val_acc: 0.7846\n",
      "Epoch 63/200\n",
      "313/313 [==============================] - 8s 25ms/step - loss: 0.0980 - acc: 0.9668 - val_loss: 1.0689 - val_acc: 0.7912\n",
      "Epoch 64/200\n",
      "313/313 [==============================] - 8s 25ms/step - loss: 0.1009 - acc: 0.9669 - val_loss: 1.0333 - val_acc: 0.7929\n",
      "Epoch 65/200\n",
      "313/313 [==============================] - 8s 25ms/step - loss: 0.0980 - acc: 0.9679 - val_loss: 1.0670 - val_acc: 0.7927\n",
      "Epoch 66/200\n",
      "313/313 [==============================] - 8s 25ms/step - loss: 0.0965 - acc: 0.9671 - val_loss: 1.0675 - val_acc: 0.7944\n",
      "Epoch 67/200\n",
      "313/313 [==============================] - 8s 26ms/step - loss: 0.1021 - acc: 0.9675 - val_loss: 1.0854 - val_acc: 0.7858\n",
      "Epoch 68/200\n",
      "313/313 [==============================] - 8s 25ms/step - loss: 0.1020 - acc: 0.9662 - val_loss: 1.1162 - val_acc: 0.7886\n",
      "Epoch 69/200\n",
      "313/313 [==============================] - 8s 25ms/step - loss: 0.0985 - acc: 0.9667 - val_loss: 1.0938 - val_acc: 0.7950\n",
      "Epoch 70/200\n",
      "313/313 [==============================] - 8s 25ms/step - loss: 0.0993 - acc: 0.9671 - val_loss: 1.0353 - val_acc: 0.7934\n",
      "Epoch 71/200\n",
      "313/313 [==============================] - 8s 25ms/step - loss: 0.0966 - acc: 0.9676 - val_loss: 1.0690 - val_acc: 0.7931\n",
      "Epoch 72/200\n",
      "313/313 [==============================] - 8s 25ms/step - loss: 0.1014 - acc: 0.9666 - val_loss: 1.0896 - val_acc: 0.7919\n",
      "Epoch 73/200\n",
      "313/313 [==============================] - 8s 25ms/step - loss: 0.0955 - acc: 0.9676 - val_loss: 1.1257 - val_acc: 0.7930\n",
      "Epoch 74/200\n",
      "313/313 [==============================] - 8s 25ms/step - loss: 0.0894 - acc: 0.9699 - val_loss: 1.1191 - val_acc: 0.7909\n",
      "Epoch 75/200\n",
      "313/313 [==============================] - 8s 25ms/step - loss: 0.0887 - acc: 0.9701 - val_loss: 1.1095 - val_acc: 0.7926\n",
      "Epoch 76/200\n",
      "313/313 [==============================] - 8s 25ms/step - loss: 0.0948 - acc: 0.9684 - val_loss: 1.1154 - val_acc: 0.7949\n",
      "Epoch 77/200\n",
      "313/313 [==============================] - 8s 26ms/step - loss: 0.0905 - acc: 0.9694 - val_loss: 1.1234 - val_acc: 0.7952\n",
      "Epoch 78/200\n",
      "313/313 [==============================] - 8s 25ms/step - loss: 0.0966 - acc: 0.9671 - val_loss: 1.1357 - val_acc: 0.7950\n",
      "Epoch 79/200\n",
      "313/313 [==============================] - 8s 26ms/step - loss: 0.0914 - acc: 0.9693 - val_loss: 1.1067 - val_acc: 0.7903\n",
      "Epoch 80/200\n",
      "313/313 [==============================] - 8s 25ms/step - loss: 0.0969 - acc: 0.9675 - val_loss: 1.1142 - val_acc: 0.7874\n",
      "Epoch 81/200\n",
      "313/313 [==============================] - 8s 25ms/step - loss: 0.0886 - acc: 0.9695 - val_loss: 1.1052 - val_acc: 0.7989\n",
      "Epoch 82/200\n",
      "313/313 [==============================] - 8s 26ms/step - loss: 0.0859 - acc: 0.9721 - val_loss: 1.1826 - val_acc: 0.7883\n",
      "Epoch 83/200\n",
      "313/313 [==============================] - 8s 26ms/step - loss: 0.0951 - acc: 0.9682 - val_loss: 1.1488 - val_acc: 0.7953\n",
      "Epoch 84/200\n",
      "313/313 [==============================] - 8s 26ms/step - loss: 0.0897 - acc: 0.9694 - val_loss: 1.1007 - val_acc: 0.7985\n",
      "Epoch 85/200\n",
      "313/313 [==============================] - 8s 26ms/step - loss: 0.0925 - acc: 0.9692 - val_loss: 1.1122 - val_acc: 0.7865\n",
      "Epoch 86/200\n",
      "313/313 [==============================] - 8s 26ms/step - loss: 0.0873 - acc: 0.9715 - val_loss: 1.1389 - val_acc: 0.7921\n",
      "Epoch 87/200\n",
      "313/313 [==============================] - 8s 26ms/step - loss: 0.0857 - acc: 0.9721 - val_loss: 1.1134 - val_acc: 0.7982\n",
      "Epoch 88/200\n",
      "313/313 [==============================] - 9s 28ms/step - loss: 0.0899 - acc: 0.9699 - val_loss: 1.1584 - val_acc: 0.7877\n",
      "Epoch 89/200\n",
      "313/313 [==============================] - 9s 28ms/step - loss: 0.0966 - acc: 0.9685 - val_loss: 1.0670 - val_acc: 0.7893\n",
      "Epoch 90/200\n",
      "313/313 [==============================] - 8s 25ms/step - loss: 0.0852 - acc: 0.9724 - val_loss: 1.1300 - val_acc: 0.7871\n",
      "Epoch 91/200\n",
      "313/313 [==============================] - 8s 25ms/step - loss: 0.0888 - acc: 0.9712 - val_loss: 1.1680 - val_acc: 0.7864\n",
      "Epoch 92/200\n",
      "313/313 [==============================] - 8s 26ms/step - loss: 0.0870 - acc: 0.9714 - val_loss: 1.1541 - val_acc: 0.7896\n",
      "Epoch 93/200\n",
      "313/313 [==============================] - 8s 25ms/step - loss: 0.0904 - acc: 0.9690 - val_loss: 1.1391 - val_acc: 0.7971\n",
      "Epoch 94/200\n",
      "313/313 [==============================] - 8s 26ms/step - loss: 0.0894 - acc: 0.9704 - val_loss: 1.1920 - val_acc: 0.7964\n",
      "Epoch 95/200\n",
      "313/313 [==============================] - 8s 25ms/step - loss: 0.0862 - acc: 0.9725 - val_loss: 1.1233 - val_acc: 0.7920\n",
      "Epoch 96/200\n",
      "313/313 [==============================] - 8s 26ms/step - loss: 0.0888 - acc: 0.9712 - val_loss: 1.1146 - val_acc: 0.7985\n",
      "Epoch 97/200\n",
      "313/313 [==============================] - 8s 26ms/step - loss: 0.0811 - acc: 0.9735 - val_loss: 1.2382 - val_acc: 0.7972\n",
      "Epoch 98/200\n",
      "313/313 [==============================] - 8s 25ms/step - loss: 0.0889 - acc: 0.9712 - val_loss: 1.1286 - val_acc: 0.8003\n",
      "Epoch 99/200\n",
      "313/313 [==============================] - 8s 26ms/step - loss: 0.0809 - acc: 0.9732 - val_loss: 1.1188 - val_acc: 0.7987\n",
      "Epoch 100/200\n",
      "313/313 [==============================] - 8s 25ms/step - loss: 0.0819 - acc: 0.9727 - val_loss: 1.1279 - val_acc: 0.7982\n",
      "Epoch 101/200\n",
      "313/313 [==============================] - 8s 25ms/step - loss: 0.0803 - acc: 0.9734 - val_loss: 1.1600 - val_acc: 0.7955\n",
      "Epoch 102/200\n",
      "313/313 [==============================] - 8s 25ms/step - loss: 0.0902 - acc: 0.9709 - val_loss: 1.1399 - val_acc: 0.7936\n",
      "Epoch 103/200\n",
      "313/313 [==============================] - 8s 25ms/step - loss: 0.0827 - acc: 0.9727 - val_loss: 1.1081 - val_acc: 0.7971\n",
      "Epoch 104/200\n",
      "313/313 [==============================] - 8s 25ms/step - loss: 0.0774 - acc: 0.9746 - val_loss: 1.1711 - val_acc: 0.7971\n",
      "Epoch 105/200\n",
      "313/313 [==============================] - 8s 26ms/step - loss: 0.0862 - acc: 0.9722 - val_loss: 1.1140 - val_acc: 0.7956\n",
      "Epoch 106/200\n",
      "313/313 [==============================] - 9s 28ms/step - loss: 0.0876 - acc: 0.9722 - val_loss: 1.1400 - val_acc: 0.7989\n",
      "Epoch 107/200\n",
      "313/313 [==============================] - 8s 26ms/step - loss: 0.0746 - acc: 0.9750 - val_loss: 1.1815 - val_acc: 0.7902\n",
      "Epoch 108/200\n",
      "313/313 [==============================] - 8s 25ms/step - loss: 0.0788 - acc: 0.9736 - val_loss: 1.1714 - val_acc: 0.7996\n",
      "Epoch 109/200\n",
      "313/313 [==============================] - 8s 26ms/step - loss: 0.0846 - acc: 0.9728 - val_loss: 1.1407 - val_acc: 0.7947\n",
      "Epoch 110/200\n",
      "313/313 [==============================] - 8s 25ms/step - loss: 0.0783 - acc: 0.9745 - val_loss: 1.1595 - val_acc: 0.7987\n",
      "Epoch 111/200\n",
      "313/313 [==============================] - 8s 25ms/step - loss: 0.0825 - acc: 0.9729 - val_loss: 1.1305 - val_acc: 0.7998\n",
      "Epoch 112/200\n",
      "313/313 [==============================] - 8s 26ms/step - loss: 0.0826 - acc: 0.9723 - val_loss: 1.2307 - val_acc: 0.7935\n",
      "Epoch 113/200\n",
      "313/313 [==============================] - 8s 26ms/step - loss: 0.0881 - acc: 0.9713 - val_loss: 1.1548 - val_acc: 0.7985\n",
      "Epoch 114/200\n",
      "313/313 [==============================] - 8s 26ms/step - loss: 0.0796 - acc: 0.9745 - val_loss: 1.1696 - val_acc: 0.7947\n",
      "Epoch 115/200\n",
      "313/313 [==============================] - 8s 25ms/step - loss: 0.0791 - acc: 0.9741 - val_loss: 1.2014 - val_acc: 0.7973\n",
      "Epoch 116/200\n",
      "313/313 [==============================] - 8s 25ms/step - loss: 0.0784 - acc: 0.9756 - val_loss: 1.2290 - val_acc: 0.7880\n",
      "Epoch 117/200\n",
      "313/313 [==============================] - 8s 25ms/step - loss: 0.0825 - acc: 0.9735 - val_loss: 1.1763 - val_acc: 0.7904\n",
      "313/313 [==============================] - 3s 6ms/step - loss: 1.2426 - acc: 0.7816\n",
      "loss: 1.2425628900527954\n",
      "acc: 0.7815999984741211\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1200x1000 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import tensorflow as tf\n",
    "from tensorflow.keras.datasets import cifar10\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Dense, Flatten, Dropout, Conv2D, MaxPooling2D\n",
    "from tensorflow.keras.callbacks import ModelCheckpoint, EarlyStopping\n",
    "\n",
    "# Load the data\n",
    "(x_train, y_train), (x_test, y_test) = cifar10.load_data()\n",
    "\n",
    "# Check the data\n",
    "print(x_train.shape, y_train.shape) # (50000, 32, 32, 3) (50000, 1)\n",
    "print(x_test.shape, y_test.shape)   # (10000, 32, 32, 3) (10000, 1)\n",
    "print(x_train[0])\n",
    "print(y_train[0])\n",
    "\n",
    "# Data preprocessing\n",
    "x_train = x_train.astype('float32') / 255.\n",
    "x_test = x_test.astype('float32') / 255.\n",
    "\n",
    "# Model configuration \n",
    "model = Sequential()\n",
    "model.add(Conv2D(32, (3,3), padding='same', activation='relu', input_shape=(32,32,3)))\n",
    "model.add(Conv2D(32, (3,3), padding='same', activation='relu'))\n",
    "model.add(MaxPooling2D())\n",
    "model.add(Dropout(0.2))\n",
    "model.add(Conv2D(64, (3,3), padding='same', activation='relu'))\n",
    "model.add(Conv2D(64, (3,3), padding='same', activation='relu'))\n",
    "model.add(MaxPooling2D())\n",
    "model.add(Dropout(0.2))\n",
    "model.add(Conv2D(128, (3,3), padding='same', activation='relu'))\n",
    "model.add(Conv2D(128, (3,3), padding='same', activation='relu'))\n",
    "model.add(MaxPooling2D())\n",
    "model.add(Dropout(0.2))\n",
    "model.add(Flatten())\n",
    "model.add(Dense(256, activation='relu'))\n",
    "model.add(Dense(10, activation='softmax'))\n",
    "\n",
    "# Model compilation\n",
    "model.compile(loss='sparse_categorical_crossentropy', optimizer='adam', metrics=['acc'])\n",
    "\n",
    "# Model summary\n",
    "model.summary()\n",
    "\n",
    "# Model fitting\n",
    "es = EarlyStopping(monitor='loss', patience=10, mode='auto')\n",
    "modelpath = '../data/modelcheckpoint/k46_cifar10_{epoch:02d}-{val_loss:.4f}.hdf5'\n",
    "cp = ModelCheckpoint(filepath=modelpath, monitor='val_loss', save_best_only=True, mode='auto')\n",
    "hist = model.fit(x_train, y_train, epochs=200, batch_size=128, validation_split=0.2, callbacks=[es, cp])\n",
    "\n",
    "# Model evaluation\n",
    "loss = model.evaluate(x_test, y_test)\n",
    "print('loss:', loss[0])\n",
    "print('acc:', loss[1])\n",
    "\n",
    "# Visualization\n",
    "plt.figure(figsize=(12, 10))\n",
    "\n",
    "# Training Loss\n",
    "plt.subplot(2, 2, 1)\n",
    "plt.plot(hist.history['loss'], marker='.', c='red', label='Training Loss')\n",
    "plt.grid()\n",
    "plt.title('Training Loss')\n",
    "plt.xlabel('Epoch')\n",
    "plt.legend()\n",
    "\n",
    "# Training Accuracy\n",
    "plt.subplot(2, 2, 3)\n",
    "plt.plot(hist.history['acc'], marker='.', c='blue', label='Training Accuracy')\n",
    "plt.grid()\n",
    "plt.title('Training Accuracy')\n",
    "plt.xlabel('Epoch')\n",
    "plt.legend()\n",
    "\n",
    "# Testing Loss\n",
    "plt.subplot(2, 2, 2)\n",
    "plt.plot(hist.history['val_loss'], marker='.', c='green', label='Testing Loss')\n",
    "plt.grid()\n",
    "plt.title('Testing Loss')\n",
    "plt.xlabel('Epoch')\n",
    "plt.legend()\n",
    "\n",
    "# Testing Accuracy\n",
    "plt.subplot(2, 2, 4)\n",
    "plt.plot(hist.history['val_acc'], marker='.', c='purple', label='Testing Accuracy')\n",
    "plt.grid()\n",
    "plt.title('Testing Accuracy')\n",
    "plt.xlabel('Epoch')\n",
    "plt.legend()\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1200x1000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "import matplotlib.pyplot as plt\n",
    "plt.figure(figsize=(12, 10))\n",
    "\n",
    "# Training Loss\n",
    "plt.subplot(2, 2, 1)\n",
    "plt.plot(hist.history['loss'], marker='.', c='red', label='Training Loss')\n",
    "plt.grid()\n",
    "plt.plot(hist.history['val_loss'], marker='.', c='green', label='Testing Loss')\n",
    "plt.grid()\n",
    "plt.title('Loss')\n",
    "plt.xlabel('Epoch')\n",
    "plt.legend()\n",
    "\n",
    "# Training Accuracy\n",
    "plt.subplot(2, 2, 2)\n",
    "plt.plot(hist.history['acc'], marker='.', c='blue', label='Training Accuracy')\n",
    "plt.grid()\n",
    "plt.plot(hist.history['val_acc'], marker='.', c='purple', label='Testing Accuracy')\n",
    "plt.grid()\n",
    "plt.title('Accuracy')\n",
    "plt.xlabel('Epoch')\n",
    "plt.legend()\n",
    "\n",
    "\n",
    "# Testing Loss\n",
    "#plt.subplot(2, 2, 2)\n",
    "\n",
    "# Testing Accuracy\n",
    "#plt.subplot(2, 2, 4)\n",
    "\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "#save model\n",
    "model.save('cifar10.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "313/313 [==============================] - 2s 5ms/step\n",
      "[[4.2666393e-10 2.0729988e-06 1.9534825e-06 ... 1.3678294e-07\n",
      "  5.8189329e-08 1.8288773e-10]\n",
      " [4.9436867e-06 2.1349913e-05 5.3554415e-21 ... 4.8473135e-19\n",
      "  9.9997365e-01 3.9464054e-11]\n",
      " [5.0197099e-03 2.2317236e-03 1.8804718e-09 ... 5.2081579e-09\n",
      "  9.9264610e-01 1.0251018e-04]\n",
      " ...\n",
      " [3.6732386e-25 4.2634453e-29 2.9281688e-11 ... 2.3220725e-16\n",
      "  2.7936253e-22 6.0710859e-26]\n",
      " [2.8757072e-06 9.9571157e-01 1.5772548e-08 ... 6.1142924e-10\n",
      "  1.1532975e-08 1.3113441e-08]\n",
      " [3.2186905e-25 6.5698637e-28 2.2909576e-19 ... 1.0000000e+00\n",
      "  4.5633268e-21 4.5620645e-25]]\n",
      "(10000, 10)\n",
      "[3 8 8 ... 5 1 7]\n",
      "[[3]\n",
      " [8]\n",
      " [8]\n",
      " ...\n",
      " [5]\n",
      " [1]\n",
      " [7]]\n"
     ]
    }
   ],
   "source": [
    "#import model and predict\n",
    "from tensorflow.keras.models import load_model\n",
    "model = load_model('cifar10.h5')\n",
    "\n",
    "y_pred = model.predict(x_test)\n",
    "print(y_pred)\n",
    "print(y_pred.shape) #(10000, 10)\n",
    "\n",
    "print(np.argmax(y_pred, axis=1))\n",
    "print(y_test)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 500x500 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 1s 761ms/step\n",
      "[[1.37517345e-05 5.42182521e-09 2.91958754e-03 2.55126059e-01\n",
      "  7.41200268e-01 2.99315730e-07 1.14204575e-07 7.39995972e-04\n",
      "  1.30427946e-09 2.61022759e-09]]\n",
      "deer\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "#load a random image\n",
    "from tensorflow.keras.preprocessing.image import load_img\n",
    "from tensorflow.keras.preprocessing.image import img_to_array\n",
    "from tensorflow.keras.models import load_model\n",
    "import numpy as np\n",
    "#load random image from dataset\n",
    "# Load CIFAR-10 dataset\n",
    "(x_train, _), (_, _) = cifar10.load_data()\n",
    "\n",
    "# Choose a random index\n",
    "random_index = np.random.randint(0, len(x_train))\n",
    "\n",
    "# Get the random image\n",
    "random_image = x_train[random_index]\n",
    "\n",
    "# Display the image\n",
    "plt.imshow(random_image)\n",
    "plt.axis('off')\n",
    "plt.figure(figsize=(5, 5))\n",
    "plt.show()\n",
    "\n",
    "img = random_image\n",
    "#img = load_img(r'download (2).jpeg', target_size=(32,32))\n",
    "img = img_to_array(img)\n",
    "img = img.reshape(1,32,32,3)\n",
    "img = img.astype('float32')/255.\n",
    "\n",
    "result = model.predict(img)\n",
    "print(result)\n",
    "\n",
    "#assign labels so we can print the result\n",
    "labels = ['airplane','automobile','bird','cat','deer','dog','frog','horse','ship','truck']\n",
    "print(labels[np.argmax(result)])\n",
    "#shown the image with matplotlib\n",
    "plt.imshow(img.reshape(32,32,3))\n",
    "plt.show()\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "ename": "ModuleNotFoundError",
     "evalue": "No module named 'seaborn'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[7], line 5\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mnumpy\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mnp\u001b[39;00m\n\u001b[1;32m      4\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpyplot\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mplt\u001b[39;00m\n\u001b[0;32m----> 5\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mseaborn\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01msns\u001b[39;00m\n\u001b[1;32m      7\u001b[0m y_pred \u001b[38;5;241m=\u001b[39m model\u001b[38;5;241m.\u001b[39mpredict(x_test)\n\u001b[1;32m      8\u001b[0m y_pred \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39margmax(y_pred, axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m)\n",
      "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'seaborn'"
     ]
    }
   ],
   "source": [
    "#performance metrics\n",
    "from sklearn.metrics import classification_report, confusion_matrix\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "y_pred = model.predict(x_test)\n",
    "y_pred = np.argmax(y_pred, axis=1)\n",
    "y_test = y_test.reshape(-1)\n",
    "\n",
    "cm = confusion_matrix(y_test, y_pred)\n",
    "print(cm)\n",
    "\n",
    "plt.figure(figsize=(10,10))\n",
    "sns.heatmap(cm, annot=True, fmt='d')\n",
    "plt.xlabel('predicted label')\n",
    "plt.ylabel('true label')\n",
    "plt.show()\n",
    "\n",
    "print(classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_1\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " conv2d_6 (Conv2D)           (None, 32, 32, 32)        896       \n",
      "                                                                 \n",
      " conv2d_7 (Conv2D)           (None, 32, 32, 32)        9248      \n",
      "                                                                 \n",
      " max_pooling2d_3 (MaxPoolin  (None, 16, 16, 32)        0         \n",
      " g2D)                                                            \n",
      "                                                                 \n",
      " dropout_3 (Dropout)         (None, 16, 16, 32)        0         \n",
      "                                                                 \n",
      " conv2d_8 (Conv2D)           (None, 16, 16, 64)        18496     \n",
      "                                                                 \n",
      " conv2d_9 (Conv2D)           (None, 16, 16, 64)        36928     \n",
      "                                                                 \n",
      " max_pooling2d_4 (MaxPoolin  (None, 8, 8, 64)          0         \n",
      " g2D)                                                            \n",
      "                                                                 \n",
      " dropout_4 (Dropout)         (None, 8, 8, 64)          0         \n",
      "                                                                 \n",
      " conv2d_10 (Conv2D)          (None, 8, 8, 128)         73856     \n",
      "                                                                 \n",
      " conv2d_11 (Conv2D)          (None, 8, 8, 128)         147584    \n",
      "                                                                 \n",
      " max_pooling2d_5 (MaxPoolin  (None, 4, 4, 128)         0         \n",
      " g2D)                                                            \n",
      "                                                                 \n",
      " dropout_5 (Dropout)         (None, 4, 4, 128)         0         \n",
      "                                                                 \n",
      " flatten_1 (Flatten)         (None, 2048)              0         \n",
      "                                                                 \n",
      " dense_2 (Dense)             (None, 256)               524544    \n",
      "                                                                 \n",
      " dense_3 (Dense)             (None, 10)                2570      \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 814122 (3.11 MB)\n",
      "Trainable params: 814122 (3.11 MB)\n",
      "Non-trainable params: 0 (0.00 Byte)\n",
      "_________________________________________________________________\n"
     ]
    },
    {
     "ename": "",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31mThe Kernel crashed while executing code in the current cell or a previous cell. \n",
      "\u001b[1;31mPlease review the code in the cell(s) to identify a possible cause of the failure. \n",
      "\u001b[1;31mClick <a href='https://aka.ms/vscodeJupyterKernelCrash'>here</a> for more info. \n",
      "\u001b[1;31mView Jupyter <a href='command:jupyter.viewOutput'>log</a> for further details."
     ]
    }
   ],
   "source": [
    "#train vs test accuracy\n",
    "plt.plot(hist.history['acc'], label='train')\n",
    "plt.plot(hist.history['val_acc'], label='test')\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n",
    "#train vs test loss\n",
    "plt.plot(hist.history['loss'], label='train')\n",
    "plt.plot(hist.history['val_loss'], label='test')\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n",
    "#model summary\n",
    "model.summary()"
   ]
  }
 ],
 "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": "3.10.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}