Fruits.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "0bb58933",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "train_dir = \"/mnt/c/Users/akrc2/Downloads/fruits/train\"\n",
    "valid_dir = \"/mnt/c/Users/akrc2/Downloads/fruits/validation\"\n",
    "test_dir = \"/mnt/c/Users/akrc2/Downloads/fruits/test\"\n",
    "num_classes = 36"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "78d08072",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-07-02 13:49:32.358328: I tensorflow/tsl/cuda/cudart_stub.cc:28] Could not find cuda drivers on your machine, GPU will not be used.\n",
      "2023-07-02 13:49:32.711022: I tensorflow/tsl/cuda/cudart_stub.cc:28] Could not find cuda drivers on your machine, GPU will not be used.\n",
      "2023-07-02 13:49:32.713156: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n",
      "To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
      "2023-07-02 13:49:34.020416: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n"
     ]
    }
   ],
   "source": [
    "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
    "\n",
    "train_datagen = ImageDataGenerator(\n",
    "    rescale=1./255,\n",
    "    shear_range=0.2,\n",
    "    zoom_range=0.2,\n",
    "    horizontal_flip=True\n",
    ")\n",
    "\n",
    "test_datagen = ImageDataGenerator(\n",
    "    rescale=1./255\n",
    ")\n",
    "\n",
    "validation_datagen = ImageDataGenerator(\n",
    "    rescale=1./255\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "cf2d9820",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 3115 images belonging to 36 classes.\n",
      "Found 359 images belonging to 36 classes.\n",
      "Found 351 images belonging to 36 classes.\n"
     ]
    }
   ],
   "source": [
    "train_generator = train_datagen.flow_from_directory(\n",
    "    train_dir,\n",
    "    target_size=(224, 224),\n",
    "    batch_size=32,\n",
    "    class_mode=\"categorical\"\n",
    ")\n",
    "\n",
    "test_generator = test_datagen.flow_from_directory(\n",
    "    test_dir,\n",
    "    target_size=(224, 224),\n",
    "    batch_size=32,\n",
    "    class_mode=\"categorical\",\n",
    "    shuffle=False\n",
    ")\n",
    "\n",
    "validation_generator = validation_datagen.flow_from_directory(\n",
    "    valid_dir,\n",
    "    target_size=(224, 224),\n",
    "    batch_size=32,\n",
    "    class_mode=\"categorical\"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "f9c26522",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-07-02 13:49:46.560753: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:982] could not open file to read NUMA node: /sys/bus/pci/devices/0000:08:00.0/numa_node\n",
      "Your kernel may have been built without NUMA support.\n",
      "2023-07-02 13:49:46.561111: W tensorflow/core/common_runtime/gpu/gpu_device.cc:1956] Cannot dlopen some GPU libraries. Please make sure the missing libraries mentioned above are installed properly if you would like to use GPU. Follow the guide at https://www.tensorflow.org/install/gpu for how to download and setup the required libraries for your platform.\n",
      "Skipping registering GPU devices...\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/mobilenet_v2/mobilenet_v2_weights_tf_dim_ordering_tf_kernels_1.0_224_no_top.h5\n",
      "9406464/9406464 [==============================] - 1s 0us/step\n"
     ]
    }
   ],
   "source": [
    "from tensorflow.keras.applications import MobileNetV2\n",
    "\n",
    "base_model = MobileNetV2(weights=\"imagenet\", include_top=False, input_shape=(224, 224, 3))\n",
    "\n",
    "for layer in base_model.layers:\n",
    "    layer.trainable = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "e69e82b2",
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.layers import GlobalAveragePooling2D, Dense\n",
    "from tensorflow.keras.models import Model\n",
    "\n",
    "x = base_model.output\n",
    "x = GlobalAveragePooling2D()(x)\n",
    "x = Dense(1024, activation=\"relu\")(x)\n",
    "predictions = Dense(num_classes, activation=\"softmax\")(x)\n",
    "\n",
    "model = Model(inputs=base_model.input, outputs=predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "82a80107",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(optimizer=\"adam\", loss=\"categorical_crossentropy\", metrics=[\"accuracy\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "537dcf20",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-07-02 13:50:02.237676: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype int32\n",
      "\t [[{{node Placeholder/_0}}]]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "16/97 [===>..........................] - ETA: 2:23 - loss: 2.6344 - accuracy: 0.3555"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/alper/.local/lib/python3.10/site-packages/PIL/Image.py:996: UserWarning: Palette images with Transparency expressed in bytes should be converted to RGBA images\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "97/97 [==============================] - ETA: 0s - loss: 1.2743 - accuracy: 0.6387"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-07-02 13:53:15.505589: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype int32\n",
      "\t [[{{node Placeholder/_0}}]]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "97/97 [==============================] - 209s 2s/step - loss: 1.2743 - accuracy: 0.6387 - val_loss: 0.4213 - val_accuracy: 0.8594\n",
      "Epoch 2/10\n",
      "97/97 [==============================] - 177s 2s/step - loss: 0.5127 - accuracy: 0.8388 - val_loss: 0.3241 - val_accuracy: 0.8938\n",
      "Epoch 3/10\n",
      "97/97 [==============================] - 154s 2s/step - loss: 0.3296 - accuracy: 0.8894 - val_loss: 0.2306 - val_accuracy: 0.9312\n",
      "Epoch 4/10\n",
      "97/97 [==============================] - 145s 2s/step - loss: 0.2540 - accuracy: 0.9147 - val_loss: 0.2129 - val_accuracy: 0.9187\n",
      "Epoch 5/10\n",
      "97/97 [==============================] - 147s 2s/step - loss: 0.2154 - accuracy: 0.9283 - val_loss: 0.2050 - val_accuracy: 0.9406\n",
      "Epoch 6/10\n",
      "97/97 [==============================] - 151s 2s/step - loss: 0.1627 - accuracy: 0.9465 - val_loss: 0.1801 - val_accuracy: 0.9312\n",
      "Epoch 7/10\n",
      "97/97 [==============================] - 152s 2s/step - loss: 0.1274 - accuracy: 0.9591 - val_loss: 0.1988 - val_accuracy: 0.9438\n",
      "Epoch 8/10\n",
      "97/97 [==============================] - 155s 2s/step - loss: 0.1090 - accuracy: 0.9624 - val_loss: 0.1442 - val_accuracy: 0.9594\n",
      "Epoch 9/10\n",
      "97/97 [==============================] - 164s 2s/step - loss: 0.0956 - accuracy: 0.9685 - val_loss: 0.1717 - val_accuracy: 0.9688\n",
      "Epoch 10/10\n",
      "97/97 [==============================] - 155s 2s/step - loss: 0.0907 - accuracy: 0.9705 - val_loss: 0.1692 - val_accuracy: 0.9750\n"
     ]
    }
   ],
   "source": [
    "history = model.fit(\n",
    "    train_generator,\n",
    "    steps_per_epoch=train_generator.samples // train_generator.batch_size,\n",
    "    validation_data=validation_generator,\n",
    "    validation_steps=validation_generator.samples // validation_generator.batch_size,\n",
    "    epochs=10\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "6b99a07e",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>loss</th>\n",
       "      <th>accuracy</th>\n",
       "      <th>val_loss</th>\n",
       "      <th>val_accuracy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.274315</td>\n",
       "      <td>0.638664</td>\n",
       "      <td>0.421291</td>\n",
       "      <td>0.859375</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.512737</td>\n",
       "      <td>0.838793</td>\n",
       "      <td>0.324073</td>\n",
       "      <td>0.893750</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.329622</td>\n",
       "      <td>0.889393</td>\n",
       "      <td>0.230639</td>\n",
       "      <td>0.931250</td>\n",
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       "      <th>3</th>\n",
       "      <td>0.253990</td>\n",
       "      <td>0.914693</td>\n",
       "      <td>0.212915</td>\n",
       "      <td>0.918750</td>\n",
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       "      <th>4</th>\n",
       "      <td>0.215369</td>\n",
       "      <td>0.928317</td>\n",
       "      <td>0.205019</td>\n",
       "      <td>0.940625</td>\n",
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      "text/plain": [
       "       loss  accuracy  val_loss  val_accuracy\n",
       "0  1.274315  0.638664  0.421291      0.859375\n",
       "1  0.512737  0.838793  0.324073      0.893750\n",
       "2  0.329622  0.889393  0.230639      0.931250\n",
       "3  0.253990  0.914693  0.212915      0.918750\n",
       "4  0.215369  0.928317  0.205019      0.940625"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "history_df = pd.DataFrame(history.history)\n",
    "history_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "8fc51164",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.figure()\n",
    "plt.plot(history.history[\"loss\"])\n",
    "plt.plot(history.history[\"val_loss\"])\n",
    "plt.xlabel(\"Epoch\")\n",
    "plt.ylabel(\"Loss\")\n",
    "plt.legend([\"train\", \"valid\"])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "c92f7745",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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oUNauXVvlZ4qLi11+wQL4+/uzevVql3179uwhJiYGPz8/Bg4cyNy5c885jVNcXExxccWDsvLyzvKU0LPxDnCMxtQ374BqHT5q1CjsdjvLly+nX79+fP/99zz//PMAPPjgg6xcuZJnn32Wtm3b4u/vzx//+EdKSn5/wUARuTB5RaX8dDDHOaX1S1outt8EnjYRgQxs08w5yhPekAIPQOEJ2LPSEXr2roKSSn1KvsGO5uWOI6HtUD2IUOqVYQEoKysLq9VKVFSUy/6oqKiz9usMGzaMefPmcfHFF9OmTRuSk5NZunQpVmvFcryJiYn861//okOHDhw9epRHH32UIUOGsH37doKCgqo879y5c8/oG6oWk+m8p6KM5Ofnx7XXXss777zD3r176dChA7179wZgzZo1TJw4kTFjxgCOnp6DBw+e97k7derEW2+9RVFRkTOkrlu3rtavQaQhyy8+zY8HHbekr9uXzbbUMwNP6/BABjgDTxiRQQ2wyTc3taKJ+eD3YKu0REZQ87Im5pEQP0RraolhDG+Cro4XXniByZMn07FjR0wmE23atGHSpEksWrTIeczw4cOdX3fv3p3ExERatWrFBx98wG233VbleadPn05SUpJzOy8vj7i4uLq7EAONGzeOq6++ml9++YVbbrnFub9du3YsXbqUUaNGYTKZmDlzJjab7bzPe/PNN/Pwww8zefJkpk+fzsGDB3n22Wfr4hJEGoyC4tP8dOi4s4dnW2ou1t8knvhmAY4prTbNSExoRnRIAww8djtk7qzo50nb7Pp+RMeKO7dieoHZgCdFi/yGYQEoPDwci8VCRkaGy/6MjIyz9u9ERETw8ccfU1RURHZ2NjExMUybNo3WrVuf9fs0bdqU9u3bs3fv3rMe4+vri69vAxtWvkCXXXYZYWFh7Nq1i5tvvtm5f968edx6661cdNFFhIeH87e//a1aU4FNmjThv//9L3fddRe9evWic+fOPPXUU1x33XV1cRkibisl+xQfb0nl292Z/Hz4BKd/E3jiwvzL7tJyvGKa+htUaQ3ZrHB4Q0XoOX6g0psmiOtf1s8zEsLbGlamyNkYFoB8fHzo06cPycnJzufI2Gw2kpOTf/euIz8/P2JjYyktLeU///kPN9xww1mPzc/PZ9++ffzpT3+qzfIbLLPZTFramf1K8fHxfPXVVy77pkyZ4rL92ykx+2+6MwcMGMCWLVvOeYxIY3TiVAnLtx1l2aZUfjp03OW92Kb+lXp4wmgRWr3ePbdSWgj7vylrYl4Bp7Iq3rP4QutLykLPcGhy5p28Iu7E0CmwpKQkJkyYQN++fenfvz/z58+noKDAeVfY+PHjiY2NZe7cuQCsX7+e1NRUevbsSWpqKrNnz8Zms/HXv/7Vec4HH3yQUaNG0apVK9LS0njkkUewWCyMHTvWkGsUkcap+LSVr3dmsmzzEb7emUmJ1TFlbDLBoDbhXN29OYPahhMX1oADD8CpHNj9peMZPXuTXZ955hfiWGS040hoczn4NjGuTpFqMjQA3XjjjWRmZjJr1izS09Pp2bMnK1ascDZGp6SkuDy1uKioiBkzZrB//36aNGnCiBEjeOutt2jatKnzmCNHjjB27Fiys7OJiIhg8ODBrFu3joiIiPq+PBFpZOx2OxsPHWfZ5lQ+23qU3MJS53sdo4O4tncsf+gR2zD7eMqVFDhWUz+4Bg6udjyU0F5xownBLRyBp+MIaDVIq6pLg2Wya47iDHl5eYSEhJCbm0twcLDLe0VFRRw4cICEhIQzbsmX6tHfpTQUB7IKWLY5lY83p5KSUzECEhXsy+iesYzuFUun5sHnOIMbcwae1Y5X6kbXu7YAIruUhZ6R0LyHHkoobutcv79/q0HdBSYiUl9yCkr4bGsaSzelsuXwCef+AB8LV3WN5tpeLRjYphkWcwMLA8X5roEnbdOZgSe4hWNl9fjBkDAEQuMNKVWkLikAXSANnNWc/g7FyW53/FL+aREU5UJUV4ju5niFJtTbbdNFpVaSdxxj2eYjfLMr03kHl9kEQ9pFcG3vWK7oHEWATwP6T2dxPhxeVynwbD4z8ITEVQSe+MHQtJVGeaTRa0D/FrsHb2/HfPepU6fw92+gt6+6ifKnTFssWtjQY50ugV+WwfrXXJ8ds3tFxdc+TSoForI/IzuDd+38+2ez2dlwMIePN6eyfNtRThZVhIOuscGM6dWCUT2aN5wHEhafhJT1cKh8SmuTaw8PlAWeIRWBJ7SVMbWKGEgBqJosFgtNmzbl2LFjAAQEBGDS/ylVm81mIzMzk4CAgLpfrFbcT34mbFwMP/6zYj0oLz/ofgNEdYOMbZC+HY796lg64fA6x6ucyexYILN8lCiqK0R3hybnf7PD3mMny/p60kg9UejcHxPix+hesYzpFUu7qKqfHu9WygPPwe8rRnh+G3iatnQEnlaDFHhEyug3zwUof1BjeQiSC2M2m2nZsqUCpCdJ3+4Y7dn6IVjL1t8Lag79boc+kyCwmevx1tOQvRfSt0H61rI/tzmeP5O50/Ha9mHF8U2iK0JRdDdHKApLALNjlDErv5hPt6SxbHMq21JznR8L8vVieLdoxvRqQWJCGGZ37uspyivr4SkPPFuqCDytKkZ3Wg1S4BGpgu4Cq8L5dpFbrVZKS0vP+r6cm4+Pj8tjDqSRslkdz5FZ96rjl3a5mN4wcAp0vqZ6t1Lb7XAyHTK2u4ai7H3Amf85s3sHcrxJW7aUxvHViSi2W1ux0x7HabMf/9c+gjG9YxnaKQo/bzedii3Kg5R1FYHn6M9nCTzlU1qDHCM+Ih6oOneBKQBVoTp/gSJyFkV5sOUdWL8Ajh907DNZHIFnwN3Qol/tNtoW5zumzNK3YkvfTsGhzfhm78DHXnzGoTbM2MLa4BXTvWL6LLobBEVVceJ6VpRbReD5zbp8ofFlYadsWqtp41y7UKS6dBu8iBgnZz+sfx02vw0lJx37/JpCn4nQfzKEtKib7+vbhJ3eHVmWFcQn2xJIz7sCMzYSTEcZEnSUERFZdLWkEJCzA3N+BuacPZCzB7b/p+IcgZG/mULrBs3aOqfQ6kRRLhxaW9G0XGXgSXCd0lLgEakxBSARqTm73TFisW4B7Poc51RUeAcYcBd0vxF8AuvkWx/LK+KTLWks3ZzKjqMVC/gG+3kxsnsM1/YeRN9Woa69ZiczyhqtK72y9kDBMdiX7HiV8/KHqM6ufUWRnS982YfCE64jPOlbzww8Ya3LGpaHOKa06io0ingwBSARuXClRbD9I1j3mqMnp1zbKxzTXG0uq5PnyRQUn+bLX9JZtjmVNXuzKF9w3dti4tIOkVzbO5ZLO0bi63WWkZugKMer7dCKfSUFcGyHayjK2O5Y+yp1o+PlZHKElMqhKLobBEWfeb2FJyBlbdlzeL6Ho1s5o1cprLXrlFZIbA3/hkTk96gHqArqAfIQNhvs/9pxS3Vc/zoboWiUTqbDjwsdDy4sXxHcOwB63gz974SI9rX+LU9bbazZl83Hm1NZsT2dwtKKRuA+rUIZ0yuWq7s3p2mAT+19U5sVcg64NltnbIeTR6s+PiC84llFNptjWqvKwNOmIvDED4LgmNqrWcSDqQm6hhSAPEBBFiy7E/aucmybvSC2T0WfRVyiAlFV0jY7Rnu2LwVb2R2QIXGO3p7e48E/tFa/nd1u59ejeSzblMonP6eRebKioTm+WQBjerVgdK8YWjWr559VfmYVU2i7z5zKKtesbVn/zmAFHpE6pABUQwpAjdzBNfCf2xz/F+/l5/i/9rwjrseYvRy3aVcORBfa89HQWU/Dzs8cwafywwhbDoTEu6Dj1WCp3dn0o7mFfLw5jWWbj7A7I9+5PzTAm6u7xzCmdyy94pq61zOkSgtdp9DsNmh1kWNKK7i50dWJeAQFoBpSAGqkbDZY/Rx8Pcfxyym8PVz/JkR2ghOHKtZKOrgacg+7ftYlEA2CuAGNPxAVHodN/4YNb1T8fZi9oeu1juAT27tWv92R46f4bncWn21NY+3+bMr/y+TjZWZop0jG9GrB/7WPwMdLz44SkaopANWQAlAjlJ8JSyc7en4AeoyFEc+ePcQc/20gSnF93+wFMb0qjRA1okCUtcfx7J4t7zoagAECmkHfW6HvbbU2mnGyqJR1+3P4fk8mq/dksT+rwOX9/glhXNsrluHdmhPiX40HJYqIx1IAqiEFoEbmwPfwn9shP91xS/PI56DXuOqd4/ghOLSm4k6eE78JRCZLpUA0BFomgm8DWEeqnN0O+75yTHPtXVmxP6qrY7Sn2/XgXbPFQE9bbWxNzeX73Vms3pvJppQTWG0V//mxmE30jGvKZR0j+UOPGOLCAmr0/UTE8ygA1ZACUCNhs8J3z8K3f3dMeUV0hOv/5ZjyqilnIFpTFogOub5vskBMz0qBaIB7BqKSU7D1fVj/D8e6WgCYoMNwx23s8UNqdBt7SvYpvisb4VmzL8tlpXVwNDIPaRfB4HbhDGzTjGA/jfSIyIVTAKohBaBGIP+YY9TnwLeO7Z63wIin6+7OrhMpZWFotePW5/KlH8pVDkStBjsCkZ+B/2zlpsKPb8DGfzl6fQB8gqDXLZB4h+O5NBdy2sJS1u7L5vs9mXy/J4uUnFMu7wf7eTG4XTiD20YwpF24RnlEpFYpANWQAlADt/9bR79Pfobj2TQj50HPsfVbw4nDZSNEZU/7PSMQmaF5T9cRovoIRId/dCxK+usnFQtqhsY7prl6jqt2DaVWGz8fPsF3e7JYvSeTLYdPUGlWCy+zid6tQhnSNpwh7SPoFhuCxZ1XWheRBk0BqIYUgBoomxW+fRq+fQqwQ0QnuOFNiOhgdGWQe6Riuuzgajh+wPV9kxma9/hNIAqpne9tLXUEnnWvuj7NOH6IY5qr/VXnvdaV3W7nYPYp5wjP2n3Z5Be7Tmu1jgjk4nYRDG4bzoA2zWjiqwfOi0j9UACqIQWgBuhkhuPZPge/d2z3+hMMfxp83HSKpTwQlS+AmbPf9f3yQFS+HlSrgdUPRAXZsHEx/PjPiicXW3wdDc0D7nI8rfg8nDhVwpq92azem8l3u7NIPVHo8n5ogDeD2oYzpF04g9tFENvUv3p1iojUEgWgGlIAamD2fe2Y8irIBO9AuPp56HGj0VVVT25qpSmzNZCzz/V9k9mx3lT5bfctB4J/06rPlfErrH8Ntn4Ap4sc+5pEQb/boc8kaBJxzlJKTtvYnHKc7/dk8f2eTLam5lL5vxLeFhN9W4UxuF04F7eLoEtMMGZNa4mIG1AAqiEFoAbCZoVv/g7fPQPYIbKL4y6vOliHqt7lpVVMmR1aA9l7f3OACZp3L1tLquxJ1Yc3OILP/m8qDmveAwZMgS5jwKvqNbLsdjv7Mgucz+NZuz+bUyVWl2PaRzVxNC63DycxIYwAH01riYj7UQCqIQWgBiDvqOMur0OrHdu9J8Dwp8C7kU6/5B11bao+IxBVYjI7lqcY8P8cvURV3MaeU1DC6r2OxuXv92RxNLfI5f3wJj5l01qOXp7okJo9A0hEpD5U5/e3/jdOGp69ybD0Dscq5D5N4Or50P16o6uqW8HNodsfHS9wrMZe+UnV2XvANwT6jIf+d0DTli4fLz5tZePB43y/1zGt9Utansu0lo+Xmf7xYWV9POF0ita0log0bgpA0nBYT8M3c+H75wA7RHVzTHmFtzW6svoXFO0aiE7lOG75L3tas91uZ8+xfL7b7Rjh2XAgh8JS12mtjtFBDGnnGOXpnxCGn/f53QkmItIYKABJw5CXBh/dBik/OLb73grD5jTeKa/qCggj82Qxa7an8v0ex1ITGXnFLodEBPmWPY8nnEFtw4kM0rSWiHguBSBxf3tWwbI74FS242nFf3gBul5ndFVuwW63s/LXDF79Zh9bDp9wec/P20z/hGZcXDat1SEqCFMNlrUQEWlMFIDEfVlPw9dPwOrnHdvR3eD6N6FZG2PrcgN2u53kHceYn7yb7al5zv1dYoIZ0s6xzESfVqGa1hIROQsFIHFPuamOBxumrHVs97sdrnyyxiuSN3R2u52vdx1j/qo9bD2SC0Cgj4UJF8UzcVC8prVERM6TApC4n93/g2V3QmGOY8rrmpccz7HxYHa7nW92ZzJ/1R5+LpvqCvCxMH5gPHdc3JqwwKqf8SMiIlVTABL3YS2Frx6HNS84tpv3gD8u9ugpL7vdzvd7snh+1W42p5wAwN/bwviBrbjj4tY0a+JrbIEiIg2UApC4hxOHHVNeh9c7tvvfAVc+AV6e+QvebrezZm82z6/azcZDxwFHU/Mtia248//aEBHkmX8vIiK1RQFIjLdrBXx8FxQeB99guOZl6HyN0VUZ5od9WTy/cjc/HnQEH18vM+MSW3HXJa3V4yMiUksUgMQ41lJYNRvWvuzYjunlmPIKSzC0LKOs25/N8yt3s/5ADuB4OvPN/Vvy/y5pQ2Swgo+ISG1SABJjnEiBj26FIz86thPvgise88gprw0Hcnh+5W7W7s8GwMdiZmz/OO6+pK3W4BIRqSMKQFL/dn4OH98NRScc61eNfgU6jTK6qnr308Ecnl+1mzV7HcHH22Lixn5x/L9L2hLTVE+4FhGpSwpAUn9OlzimvNa94tiO6Q3XL4bQeCOrqnebUo7z/MrdfL8nC3AEn+v7xjHl0rbEKviIiNQLBSCpH8cPwUeTIHWjY3vAFBg6G7w85/k1Ww6f4PmVu/l2dyYAXmYT1/dtwZRL29IiNMDg6kREPIvZ6AJeeeUV4uPj8fPzIzExkQ0bNpz12NLSUh577DHatGmDn58fPXr0YMWKFTU6p9SDHZ/BP4Y4wo9fCNz0Llw1x2PCz9YjJ5i0eAOjX1nDt7szsZhN3NC3BV8/eAlzr+2u8CMiYgBDR4CWLFlCUlISCxYsIDExkfnz5zNs2DB27dpFZGTkGcfPmDGDt99+mzfeeIOOHTvy5ZdfMmbMGH744Qd69ep1QeeUOnS6BFbOgvWvObZj+zqmvJq2NLauerI9NZfnV+4meecxACxmE2N6xXLvZW1p1SzQ4OpERDybyW6324365omJifTr14+XX3bcBm2z2YiLi+Pee+9l2rRpZxwfExPDww8/zJQpU5z7rrvuOvz9/Xn77bcv6JxVycvLIyQkhNzcXIKDg2t6mZ4p54Bjyitts2N74D1w+SMeMerzS1ou81ftYeWvGQCYTTC6Vyz3XdaO+HAFHxGRulKd39+GjQCVlJSwceNGpk+f7txnNpsZOnQoa9eurfIzxcXF+Pm53hbs7+/P6tWrL/ic5ectLi52bufl5Z31WDkPv34Kn9wDxbng1xTGLIAOw42uqs7tOJrH/FW7+fKXiuBzTU/HiE/riCYGVyciIpUZFoCysrKwWq1ERUW57I+KimLnzp1VfmbYsGHMmzePiy++mDZt2pCcnMzSpUuxWq0XfE6AuXPn8uijj9bwioTTxfC/GbDhdcd2i/7wx0XQNM7YuurYzvQ8Xli1hy+2pwNgMsGo7jHcd3k72kYq+IiIuKMGdRfYCy+8wOTJk+nYsSMmk4k2bdowadIkFi1aVKPzTp8+naSkJOd2Xl4ecXGN+5d2rcvZDx9OgqNbHNuDpsJlM8HibWhZdWl3xkleWLWH5duOAo7gM7Jbc6Ze3o52UUEGVyciIudiWAAKDw/HYrGQkZHhsj8jI4Po6OgqPxMREcHHH39MUVER2dnZxMTEMG3aNFq3bn3B5wTw9fXF19fznkBca35ZBp/eB8V54B8KY/4B7YcZXVWd2XvsJC8k7+WzrWmUd9CN6BbN1Mvb0yFawUdEpCEw7DZ4Hx8f+vTpQ3JysnOfzWYjOTmZgQMHnvOzfn5+xMbGcvr0af7zn/9wzTXX1PiccgFKi2D5n+HDiY7wEzcA7lrdaMPPvsx8pr6/mSue/47//uwIP1d1ieaLqUN4dVwfhR8RkQbE0CmwpKQkJkyYQN++fenfvz/z58+noKCASZMmATB+/HhiY2OZO3cuAOvXryc1NZWePXuSmprK7Nmzsdls/PWvfz3vc0otyd7nCD7pWx3bgx+ASx9ulFNeB7IKeDF5D59sScVWNuJzZecopg5tR5eYEGOLExGRC2JoALrxxhvJzMxk1qxZpKen07NnT1asWOFsYk5JScFsrhikKioqYsaMGezfv58mTZowYsQI3nrrLZo2bXre55Qastthy7vwxd+g5CQENHNMebW7wujKat2h7AJeTN7Lss1HnMFnaKco7h/ajq6xCj4iIg2Zoc8Bcld6DtBZ5GfCf6fCruWO7ZYD4bqFEBJrbF21LCX7FC99tYelm1OxliWfyzpGcv/QdnRv0dTY4kRE5KwaxHOApIHZ8V9H+DmVDWZvuPQhx51eZovRldWawzmnePmrvfxn0xFOlwWfSzpEcP/Q9vSMa2pscSIiUqsUgOTcCk84pru2vu/YjuwC1/4DorsZWlZtOnL8FK98vZcPf6oIPhe3j+D+oe3o3TLU4OpERKQuKADJ2e37Gj6ZAnmpYDI7RnwumQ5ejeORAUWlVuZ8voP3NqRQanUEn8Ftw3nginb0aRVmcHUiIlKXFIDkTCWnYNUjFU90Dk1wNDq3TDS2rlpUVGpl8r9/4vs9WQBc1KYZD1zRnn7xCj4iIp5AAUhcHf4Rlt0JOfsc2/1uhyseA5/Gs4hnUamVO9/ayPd7sgjwsfDKuN5c2iHS6LJERKQeKQCJw+kS+PbvsPp5sNsgKAaueRnaXm50ZbWq+LSVu9/eyLe7M/H3trBoYj8GtG5mdFkiIlLPFIAEMn6BpXdCxjbHdrcbYMTTjmUtGhFH+NnE17sy8fM2K/yIiHgwBSBPZrPCDy/C13PAWgL+YXD189BltNGV1bqS0zamvLOJr3Yew9fLzKIJ/RjYRuFHRMRTKQB5qpz9sOxuOLzOsd1+OIx6AYIa3xOzS07bmPLuJlbtcISfhRP6cVHbcKPLEhERAykAeRq7HX5aBP+bCaUF4BMEV82FXreAyWR0dbWu1Grj3vc2sfLXDHy8zPxzQl8Gt1P4ERHxdApAniQvDT65B/YlO7ZbDYbRr0JoK2PrqiOlVhv3vbeZL39xhJ83xvdlSLsIo8sSERE3oADkCex22PYRfP5nKMoFLz+4/BFIvAsqLTbbmJRabUx9fzNfbE/Hx2LmH3/qw/+1V/gREREHBaDGriAblifBrx87tmN6OR5qGNHB0LLq0mmrjfuXbOHzbRXhR8/5ERGRyhSAGrNdK+DTe6HgGJi94OK/wJA/g8Xb6MrqzGmrjQc++JnlW4/ibTHx2i29ubSjwo+IiLhSAGqMivLgy4dg81uO7fAOjgVMY3oZW1cds9rs/PnDn/nvz2l4W0y8Oq4Pl3dqfHe1iYhIzSkANTYHV8PHd8OJFMAEA6fAZTPA29/oyuqU1WbnwQ9/5pMtaXiZTbx8c2+u6KzwIyIiVVMAaixKi+Crx2HtK4AdmraE0a9B/GCjK6tzVpudv3z0M8s2pzrDz7Au0UaXJSIibkwBqDFI3QTL7oKsXY7t3uNh2BzwDTK2rnpgs9n523+2snRTKhaziZfG9uKqrgo/IiJybgpADZm1FL5/Dr59GuxWCIx0LGDafpjRldULm83OtKVb+WjjESxmEy/e1Ivh3ZobXZaIiDQACkANVeYuWHYnpG12bHe+BkY+D4Gesb6VzWbnoWXb+OCnI5hNMP/GnozsrvAjIiLnRwGoobHZYP1rsOpRsBaDXwiMeA66/bFRLmVRFZvNzsMfb+f9Hw9jNsHzN/ZkVI8Yo8sSEZEGRAGoITl+CD6ZAge/d2y3udwx5RXsOb/87XY7Mz/ZznsbUjCbYN4NPbmmZ6zRZYmISAOjANQQ2O2w+W1YMR1KToJ3AFz5BPS91WNGfcARfmZ98gvvrE/BZIJnr+/B6F4KPyIiUn0KQO7uZAb8dyrs/sKxHZfouL29WRtj66pndrud2Z/+wlvrDmEywTN/7MG1vVsYXZaIiDRQCkDu7JeP4bMHoDAHLD5w6cNw0b1gthhdWb2y2+089tmvvLnWEX6evq47f+yj8CMiIhdOAcgdFR6Hz/8K2z5wbEd1cyxlEdXF2LoMYLfbefyzHSxecxCAp67tzvV944wtSkREGjwFIHezNxk+uQdOpoHJDIMfgP+bBl4+RldW7+x2O08u38GiNQcAmHttN27op/AjIiI1pwDkLkoK4H8z4aeFju2wNjDmHxDXz9i6DGK32/n7Fzv552pH+Jkzphtj+7c0uCoREWksFIDcQcp6+PguyNnv2O5/BwydDT6BhpZlFLvdzlMrdvGP7xx/H4+P7srNiQo/IiJSexSAjHS6GL6ZC2teALsNgmPhmlegzaVGV2YYu93OM1/uYsG3+wB47Jou/GlAK4OrEhGRxkYByCjp2xwLmGZsd2z3GAtX/R38mxpalpHsdjvP/W83r37jCD+zR3Vm/MB4Y4sSEZFGSQGovllPww8vwNdzwVYKAc1g1AvQaZTRlRnu+VV7ePnrvQDMurozEwclGFyRiIg0VgpA9Sl7n2PU58gGx3aHkTBqPjSJNLQsdzB/1W5eTN4DwIyRnbh1sMKPiIjUHQWg+vT9PEf48Q2G4U85pr08aCmLs3kxeQ/zVznCz8MjOnH7kNYGVyQiIo2dAlB9GvaEYwX3yx+BpnqeDcDLX+1h3srdAEwf3pHJFyv8iIhI3VMAqk/+oXDdP42uwm28+s1env2fI/z87aqO3Pl/nrW+mYiIGMdsdAHimRZ8u4+nV+wC4C/DOnD3JQo/IiJSfxSApN69/t0+/v7FTgD+fEV7plza1uCKRETE0ygASb365/f7mfO5I/w8MLQ9917ezuCKRETEExkegF555RXi4+Px8/MjMTGRDRs2nPP4+fPn06FDB/z9/YmLi+OBBx6gqKjI+f7s2bMxmUwur44dO9b1Zch5WLj6AE8s3wHAfZe3Y+pQhR8RETGGoU3QS5YsISkpiQULFpCYmMj8+fMZNmwYu3btIjLyzGfjvPvuu0ybNo1FixZx0UUXsXv3biZOnIjJZGLevHnO47p06cKqVauc215e6vU22r/WHODxz34F4N7L2vKAwo+IiBjI0BGgefPmMXnyZCZNmkTnzp1ZsGABAQEBLFq0qMrjf/jhBwYNGsTNN99MfHw8V155JWPHjj1j1MjLy4vo6GjnKzw8vD4uR87i32sPMvu/jvAz5dI2JF3RHpOefyQiIgYyLACVlJSwceNGhg4dWlGM2czQoUNZu3ZtlZ+56KKL2LhxozPw7N+/n88//5wRI0a4HLdnzx5iYmJo3bo148aNIyUl5Zy1FBcXk5eX5/KS2vHW2oPM+uQXAO6+pA0PXtlB4UdERAxn2NxQVlYWVquVqKgol/1RUVHs3Lmzys/cfPPNZGVlMXjwYOx2O6dPn+auu+7ioYcech6TmJjIv/71Lzp06MDRo0d59NFHGTJkCNu3bycoKKjK886dO5dHH3209i5OAHhn/SFmloWfOy9uzV+HKfyIiIh7MLwJujq++eYb5syZw6uvvsqmTZtYunQpy5cv5/HHH3ceM3z4cK6//nq6d+/OsGHD+Pzzzzlx4gQffPDBWc87ffp0cnNzna/Dhw/Xx+U0au9tSOHhZY6V7icPSWDa8I4KPyIi4jYMGwEKDw/HYrGQkZHhsj8jI4Po6OgqPzNz5kz+9Kc/cfvttwPQrVs3CgoKuOOOO3j44Ycxm8/Mc02bNqV9+/bs3bv3rLX4+vri6+tbg6uRypb8mML0pdsAuG1wAg+N6KTwIyIibsWwESAfHx/69OlDcnKyc5/NZiM5OZmBAwdW+ZlTp06dEXIsFgsAdru9ys/k5+ezb98+mjdvXkuVy7l88ONhppWFn0mD4pkxUuFHRETcj6H3hyclJTFhwgT69u1L//79mT9/PgUFBUyaNAmA8ePHExsby9y5cwEYNWoU8+bNo1evXiQmJrJ3715mzpzJqFGjnEHowQcfZNSoUbRq1Yq0tDQeeeQRLBYLY8eONew6PcVHG4/wt6Vbsdth4kXxzLq6s8KPiIi4JUMD0I033khmZiazZs0iPT2dnj17smLFCmdjdEpKisuIz4wZMzCZTMyYMYPU1FQiIiIYNWoUTz75pPOYI0eOMHbsWLKzs4mIiGDw4MGsW7eOiIiIer8+T7J00xH+8tHP2O3wpwGteGSUwo+IiLgvk/1sc0ceLC8vj5CQEHJzcwkODja6HLe3bPMRkj5whJ9bBrTk8Wu6KvyIiEi9q87v7wZ1F5i4n0+2pPLnsvAztn9LHvuDwo+IiLg/BSC5YJ/+nMYDS7Zgs8NN/eJ4cnRXzGaFHxERcX8KQHJBPttaEX5u6NuCOWO6KfyIiEiDoQAk1bbtSC5T39+C1Wbnj31a8Pdruyv8iIhIg6IAJNX2+fajWG12LukQwVPXKfyIiEjDowAk1bZ2XzYAo7rHYFH4ERGRBkgBSKolv/g021JzAUhsHWZwNSIiIhdGAUiq5aeDOVhtduLC/GkRGmB0OSIiIhek2gEoPj6exx57jJSUlLqoR9zcuv05AAxs3czgSkRERC5ctQPQ/fffz9KlS2ndujVXXHEF77//PsXFxXVRm7ihdfsd/T8DFIBERKQBu6AAtGXLFjZs2ECnTp249957ad68Offccw+bNm2qixrFTbj2/ygAiYhIw3XBPUC9e/fmxRdfdK64/s9//pN+/frRs2dPFi1ahJYYa3x+LOv/aRkWQGxTf6PLERERuWAXvBp8aWkpy5YtY/HixaxcuZIBAwZw2223ceTIER566CFWrVrFu+++W5u1isEqpr9095eIiDRs1Q5AmzZtYvHixbz33nuYzWbGjx/P888/T8eOHZ3HjBkzhn79+tVqoWK88gZo9f+IiEhDV+0A1K9fP6644gpee+01Ro8ejbe39xnHJCQkcNNNN9VKgeIeThaVsr2s/0cBSEREGrpqB6D9+/fTqlWrcx4TGBjI4sWLL7gocT8/HTqO1WanVbMAYtT/IyIiDVy1m6CPHTvG+vXrz9i/fv16fvrpp1opStyPs/8nQaM/IiLS8FU7AE2ZMoXDhw+fsT81NZUpU6bUSlHifpz9P23UAC0iIg1ftQPQr7/+Su/evc/Y36tXL3799ddaKUrcS+X+n0SNAImISCNQ7QDk6+tLRkbGGfuPHj2Kl9cF31Uvbuyng+r/ERGRxqXaAejKK69k+vTp5ObmOvedOHGChx56iCuuuKJWixP3oP4fERFpbKo9ZPPss89y8cUX06pVK3r16gXAli1biIqK4q233qr1AsV4zgCk/h8REWkkqh2AYmNj2bp1K++88w4///wz/v7+TJo0ibFjx1b5TCBp2E4WlTrX/9Lzf0REpLG4oKadwMBA7rjjjtquRdzQTwePY7NDfLMAmoeo/0dERBqHC+5a/vXXX0lJSaGkpMRl/x/+8IcaFyXuY61z/S+N/oiISONxQU+CHjNmDNu2bcNkMjlXfTeZTABYrdbarVAMtU4BSEREGqFq3wU2depUEhISOHbsGAEBAfzyyy9899139O3bl2+++aYOShSj5FV+/o9WgBcRkUak2iNAa9eu5auvviI8PByz2YzZbGbw4MHMnTuX++67j82bN9dFnWKAnw7mqP9HREQapWqPAFmtVoKCggAIDw8nLS0NgFatWrFr167arU4M5Vz+QtNfIiLSyFR7BKhr1678/PPPJCQkkJiYyNNPP42Pjw+vv/46rVu3rosaxSDl/T8D2ygAiYhI41LtADRjxgwKCgoAeOyxx7j66qsZMmQIzZo1Y8mSJbVeoBgjT+t/iYhII1btADRs2DDn123btmXnzp3k5OQQGhrqvBNMGr4fDzj6fxLCA4kO8TO6HBERkVpVrR6g0tJSvLy82L59u8v+sLAwhZ9GpuL2d939JSIijU+1ApC3tzctW7bUs348gBqgRUSkMav2XWAPP/wwDz30EDk5OXVRj7iB3MJSfklT/4+IiDRe1e4Bevnll9m7dy8xMTG0atWKwMBAl/c3bdpUa8WJMcqf/6P+HxERaayqHYBGjx5dB2WIO9HyFyIi0thVOwA98sgjdVGHuJGK/h81QIuISONU7R4gadwq9/9oBEhERBqragcgs9mMxWI566u6XnnlFeLj4/Hz8yMxMZENGzac8/j58+fToUMH/P39iYuL44EHHqCoqKhG55QK5c//aR0eSFSw+n9ERKRxqvYU2LJly1y2S0tL2bx5M2+++SaPPvpotc61ZMkSkpKSWLBgAYmJicyfP59hw4axa9cuIiMjzzj+3XffZdq0aSxatIiLLrqI3bt3M3HiREwmE/Pmzbugc4qr8v6fRI3+iIhII2ay2+322jjRu+++y5IlS/jkk0/O+zOJiYn069ePl19+GQCbzUZcXBz33nsv06ZNO+P4e+65hx07dpCcnOzc9+c//5n169ezevXqCzpnVfLy8ggJCSE3N5fg4ODzvp7G4OqXvmd7ah4v3NSTa3rGGl2OiIjIeavO7+9a6wEaMGCASzD5PSUlJWzcuJGhQ4dWFGM2M3ToUNauXVvlZy666CI2btzonNLav38/n3/+OSNGjLjgcwIUFxeTl5fn8vJEjv4fx7UP1AiQiIg0YtWeAqtKYWEhL774IrGx5z9ikJWVhdVqJSoqymV/VFQUO3furPIzN998M1lZWQwePBi73c7p06e56667eOihhy74nABz586t9vRdY/TjgRzsdmgdEUik+n9ERKQRq/YIUGhoKGFhYc5XaGgoQUFBLFq0iGeeeaYuanT65ptvmDNnDq+++iqbNm1i6dKlLF++nMcff7xG550+fTq5ubnO1+HDh2up4oZlrZ7/IyIiHqLaI0DPP/+8y8KnZrOZiIgIEhMTCQ0NPe/zhIeHY7FYyMjIcNmfkZFBdHR0lZ+ZOXMmf/rTn7j99tsB6NatGwUFBdxxxx08/PDDF3ROAF9fX3x9fc+79sZKD0AUERFPUe0ANHHixFr5xj4+PvTp04fk5GTn06VtNhvJycncc889VX7m1KlTmM2ug1blt97b7fYLOqc45J4q5dejjv6fAQl6AKKIiDRu1Q5AixcvpkmTJlx//fUu+z/88ENOnTrFhAkTzvtcSUlJTJgwgb59+9K/f3/mz59PQUEBkyZNAmD8+PHExsYyd+5cAEaNGsW8efPo1asXiYmJ7N27l5kzZzJq1ChnEPq9c0rVNhxU/4+IiHiOageguXPn8o9//OOM/ZGRkdxxxx3VCkA33ngjmZmZzJo1i/T0dHr27MmKFSucTcwpKSkuIz4zZszAZDIxY8YMUlNTiYiIYNSoUTz55JPnfU6pmqa/RETEk1T7OUB+fn7s3LmT+Ph4l/0HDx6kU6dOFBYW1mZ9hvDE5wCNfPF7fknL46WxvRjVI8bockRERKqtTp8DFBkZydatW8/Y//PPP9OsmUYPGqLK/T+JWgBVREQ8QLUD0NixY7nvvvv4+uuvsVqtWK1WvvrqK6ZOncpNN91UFzVKHVt/IBu7HdpEBBIZpP4fERFp/KrdA/T4449z8OBBLr/8cry8HB+32WyMHz+eOXPm1HqBUvfW7c8B1P8jIiKeo9oByMfHhyVLlvDEE0+wZcsW/P396datG61ataqL+qQeqAFaREQ8zQUvhdGuXTvatWtXm7WIAU6cKmFHuvp/RETEs1S7B+i6667jqaeeOmP/008/fcazgcT9bShb/0v9PyIi4kmqHYC+++475+rrlQ0fPpzvvvuuVoqS+lPe/zOwjaa/RETEc1Q7AOXn5+Pj43PGfm9vb/Ly8mqlKKk/WgBVREQ8UbUDULdu3ViyZMkZ+99//306d+5cK0VJ/ThxqoSd5f0/CQpAIiLiOardBD1z5kyuvfZa9u3bx2WXXQZAcnIy7777Lh999FGtFyh1Z31Z/0/byCZEBPkaXY6IiEi9qXYAGjVqFB9//DFz5szho48+wt/fnx49evDVV18RFqa7iBqSitvf9XMTERHPckG3wY8cOZKRI0cCjnU33nvvPR588EE2btyI1Wqt1QKl7ugBiCIi4qmq3QNU7rvvvmPChAnExMTw3HPPcdlll7Fu3brarE3qkPp/RETEk1VrBCg9PZ1//etfLFy4kLy8PG644QaKi4v5+OOP1QDdwJT3/7RT/4+IiHig8x4BGjVqFB06dGDr1q3Mnz+ftLQ0XnrppbqsTerQ2n26/V1ERDzXeY8AffHFF9x3333cfffdWgKjEdD6XyIi4snOewRo9erVnDx5kj59+pCYmMjLL79MVlZWXdYmdeR4QQk7008CWv9LREQ803kHoAEDBvDGG29w9OhR7rzzTt5//31iYmKw2WysXLmSkydP1mWdUovWH3Dc/dUusgnhTdT/IyIinqfad4EFBgZy6623snr1arZt28af//xn/v73vxMZGckf/vCHuqhRapmmv0RExNNd8G3wAB06dODpp5/myJEjvPfee7VVk9QxBSAREfF0NQpA5SwWC6NHj+bTTz+tjdNJHcpR/4+IiEjtBCBpODYccIz+tI9S/4+IiHguBSAPo+UvREREFIA8jvp/REREFIA8SuX+n/4J6v8RERHPpQDkQdT/IyIi4qAA5EHK+38GavpLREQ8nAKQB9ECqCIiIg4KQB4iO7+YXRnq/xEREQEFII+xoWz9rw5RQTRT/4+IiHg4BSAPUXH7u0Z/REREFIA8hB6AKCIiUkEByANU7v9JVAASERFRAPIE68v6fzpGBxEW6GNwNSIiIsZTAPIAWv5CRETElQKQB1ADtIiIiCsFoEYuK7+Y3Rn5APRP0AiQiIgIKAA1ehvU/yMiInIGBaBGTv0/IiIiZ3KLAPTKK68QHx+Pn58fiYmJbNiw4azHXnLJJZhMpjNeI0eOdB4zceLEM96/6qqr6uNS3I76f0RERM7kZXQBS5YsISkpiQULFpCYmMj8+fMZNmwYu3btIjIy8ozjly5dSklJiXM7OzubHj16cP3117scd9VVV7F48WLntq+v5y3/ULn/J1H9PyIiIk6GjwDNmzePyZMnM2nSJDp37syCBQsICAhg0aJFVR4fFhZGdHS087Vy5UoCAgLOCEC+vr4ux4WGhtbH5biV9fsr+n9C1f8jIiLiZGgAKikpYePGjQwdOtS5z2w2M3ToUNauXXte51i4cCE33XQTgYGBLvu/+eYbIiMj6dChA3fffTfZ2dlnPUdxcTF5eXkur8ZA/T8iIiJVMzQAZWVlYbVaiYqKctkfFRVFenr6735+w4YNbN++ndtvv91l/1VXXcW///1vkpOTeeqpp/j2228ZPnw4Vqu1yvPMnTuXkJAQ5ysuLu7CL8qNKACJiIhUzfAeoJpYuHAh3bp1o3///i77b7rpJufX3bp1o3v37rRp04ZvvvmGyy+//IzzTJ8+naSkJOd2Xl5egw9BWfnF7DlW3v+jBmgREZHKDB0BCg8Px2KxkJGR4bI/IyOD6Ojoc362oKCA999/n9tuu+13v0/r1q0JDw9n7969Vb7v6+tLcHCwy6uhU/+PiIjI2RkagHx8fOjTpw/JycnOfTabjeTkZAYOHHjOz3744YcUFxdzyy23/O73OXLkCNnZ2TRv3rzGNTcUa/dnAZr+EhERqYrhd4ElJSXxxhtv8Oabb7Jjxw7uvvtuCgoKmDRpEgDjx49n+vTpZ3xu4cKFjB49mmbNXH/B5+fn85e//IV169Zx8OBBkpOTueaaa2jbti3Dhg2rl2tyB+vKRoAGtlEAEhER+S3De4BuvPFGMjMzmTVrFunp6fTs2ZMVK1Y4G6NTUlIwm11z2q5du1i9ejX/+9//zjifxWJh69atvPnmm5w4cYKYmBiuvPJKHn/8cY95FlDmyWL2HsvHZFL/j4iISFVMdrvdbnQR7iYvL4+QkBByc3MbZD/QZ1vTuOfdzXRqHswXU4cYXY6IiEi9qM7vb8OnwKT2afkLERGRc1MAaoTK+3/UAC0iIlI1BaBGRv0/IiIiv08BqJEpn/7qGB1M0wA9/0dERKQqCkCNTHkAGqjpLxERkbNSAGpk1AAtIiLy+xSAGpFjJ4vYl1mAyQT91f8jIiJyVgpAjUj5+l+d1P8jIiJyTgpAjUjF9Jf6f0RERM5FAagRWav+HxERkfOiANRIHMsrYn9Z/09igkaAREREzkUBqJFYd8DR/9O5eTAhAd4GVyMiIuLeFIAaCfX/iIiInD8FoEZCAUhEROT8KQA1ApX7f/rHqwFaRETk9ygANQLq/xEREakeBaBGYO0+TX+JiIhUhwJQI7BeC6CKiIhUiwJQA5eRV8T+LEf/Tz+t/yUiInJeFIAauPK7v7rEBBPir/4fERGR86EA1MCtK1sAdYCe/iwiInLeFIAauPV6/o+IiEi1KQA1YOr/ERERuTAKQA2Y+n9EREQujAJQA7ZOt7+LiIhcEAWgBszZAK0AJCIiUi0KQA1Uem4RB7IKMJugr9b/EhERqRYFoAZq/YHy/p8Q9f+IiIhUkwJQA7XOefu7Rn9ERESqSwGogdICqCIiIhdOAagBOppbyMHsU5j1/B8REZELogDUAK0vu/ura2wIwX7q/xEREakuBaAGaJ2WvxAREakRBaAGSA3QIiIiNaMA1MBU7v/R839EREQujAJQA6P+HxERkZpTAGpgdPu7iIhIzSkANTDrDqj/R0REpKYUgBqQtBOFHCp//o/6f0RERC6YWwSgV155hfj4ePz8/EhMTGTDhg1nPfaSSy7BZDKd8Ro5cqTzGLvdzqxZs2jevDn+/v4MHTqUPXv21Mel1Kny9b+6xYYQpP4fERGRC2Z4AFqyZAlJSUk88sgjbNq0iR49ejBs2DCOHTtW5fFLly7l6NGjztf27duxWCxcf/31zmOefvppXnzxRRYsWMD69esJDAxk2LBhFBUV1ddl1Yl1+xwN0Or/ERERqRnDA9C8efOYPHkykyZNonPnzixYsICAgAAWLVpU5fFhYWFER0c7XytXriQgIMAZgOx2O/Pnz2fGjBlcc801dO/enX//+9+kpaXx8ccf1+OV1b6K/h8FIBERkZowNACVlJSwceNGhg4d6txnNpsZOnQoa9euPa9zLFy4kJtuuonAwEAADhw4QHp6uss5Q0JCSExMPOs5i4uLycvLc3m5m8r9P33jQ40uR0REpEEzNABlZWVhtVqJiopy2R8VFUV6evrvfn7Dhg1s376d22+/3bmv/HPVOefcuXMJCQlxvuLi4qp7KXWu/OnP6v8RERGpOcOnwGpi4cKFdOvWjf79+9foPNOnTyc3N9f5Onz4cC1VWHucy1+00fSXiIhITRkagMLDw7FYLGRkZLjsz8jIIDo6+pyfLSgo4P333+e2225z2V/+ueqc09fXl+DgYJeXu1m3Xw3QIiIitcXQAOTj40OfPn1ITk527rPZbCQnJzNw4MBzfvbDDz+kuLiYW265xWV/QkIC0dHRLufMy8tj/fr1v3tOd5V6opCUnFNYzCb6tlL/j4iISE15GV1AUlISEyZMoG/fvvTv35/58+dTUFDApEmTABg/fjyxsbHMnTvX5XMLFy5k9OjRNGvmOiJiMpm4//77eeKJJ2jXrh0JCQnMnDmTmJgYRo8eXV+XVavWl01/dVX/j4iISK0wPADdeOONZGZmMmvWLNLT0+nZsycrVqxwNjGnpKRgNrsOVO3atYvVq1fzv//9r8pz/vWvf6WgoIA77riDEydOMHjwYFasWIGfn1+dX09dcPb/aPkLERGRWmGy2+12o4twN3l5eYSEhJCbm+sW/UBDnv6KwzmFLJ7Uj0s7RBpdjoiIiFuqzu/vBn0XmCc4cvwUh3MKsZhNWv9LRESkligAubn1ZXd/dYsNoYmv4TOWIiIijYICkJur6P/R7e8iIiK1RQHIzVWs/6XpLxERkdqiAOTGKvf/9FX/j4iISK1RAHJj69T/IyIiUicUgNyY+n9ERETqhgKQG9MDEEVEROqGApCbOpxziiPH9fwfERGRuqAA5KbWH3D0/3RvEUKg+n9ERERqlQKQm1L/j4iISN1RAHJTCkAiIiJ1RwHIDVXu/+nbKtTockRERBodBSA3VD76o/4fERGRuqEA5IbKH4Co6S8REZG6oQDkhspHgAYqAImIiNQJBSA3czjnFKknCvEym+ij/h8REZE6oQDkZtT/IyIiUvcUgNzMWt3+LiIiUucUgNyI3W5nvRqgRURE6pwCkBs5crxQ/T8iIiL1QAHIjaxV/4+IiEi9UAByI87b39to+ktERKQuKQC5CfX/iIiI1B8FIDeh/h8REZH6owDkJtbuc0x/9YhrSoCP+n9ERETqkgKQm1jnfP5PmMGViIiINH4KQG7AbrdXCkDq/xEREalrCkBu4HBOIWm5RXhb1P8jIiJSHxSA3ED56E+PFur/ERERqQ8KQG5A018iIiL1SwHIYHa7XQugioiI1DMFIIOl5JziaFn/T+9WTY0uR0RExCMoABlM/T8iIiL1TwHIYOu0/IWIiEi9UwAyUOXn/2gBVBERkfqjAGQgl/6flnr+j4iISH1RADJQ+ehPz7im+PtYDK5GRETEcygAGah8AVT1/4iIiNQvwwPQK6+8Qnx8PH5+fiQmJrJhw4ZzHn/ixAmmTJlC8+bN8fX1pX379nz++efO92fPno3JZHJ5dezYsa4vo9oc/T9qgBYRETGCofddL1myhKSkJBYsWEBiYiLz589n2LBh7Nq1i8jIyDOOLykp4YorriAyMpKPPvqI2NhYDh06RNOmTV2O69KlC6tWrXJue3m53+3lh7JPkZ6n/h8REREjGJoM5s2bx+TJk5k0aRIACxYsYPny5SxatIhp06adcfyiRYvIycnhhx9+wNvbG4D4+PgzjvPy8iI6OrpOa68p9f+IiIgYx7ApsJKSEjZu3MjQoUMrijGbGTp0KGvXrq3yM59++ikDBw5kypQpREVF0bVrV+bMmYPVanU5bs+ePcTExNC6dWvGjRtHSkrKOWspLi4mLy/P5VXXnLe/a/pLRESk3hkWgLKysrBarURFRbnsj4qKIj09vcrP7N+/n48++gir1crnn3/OzJkzee6553jiiSecxyQmJvKvf/2LFStW8Nprr3HgwAGGDBnCyZMnz1rL3LlzCQkJcb7i4uJq5yLPQv0/IiIixnK/5phzsNlsREZG8vrrr2OxWOjTpw+pqak888wzPPLIIwAMHz7ceXz37t1JTEykVatWfPDBB9x2221Vnnf69OkkJSU5t/Py8uo0BB0s6//xsZjppf4fERGRemdYAAoPD8disZCRkeGyPyMj46z9O82bN8fb2xuLpaJnplOnTqSnp1NSUoKPj88Zn2natCnt27dn7969Z63F19cXX1/fC7yS6lP/j4iIiLEMmwLz8fGhT58+JCcnO/fZbDaSk5MZOHBglZ8ZNGgQe/fuxWazOfft3r2b5s2bVxl+APLz89m3bx/Nmzev3QuogfIANKB1mMGViIiIeCZDnwOUlJTEG2+8wZtvvsmOHTu4++67KSgocN4VNn78eKZPn+48/u677yYnJ4epU6eye/duli9fzpw5c5gyZYrzmAcffJBvv/2WgwcP8sMPPzBmzBgsFgtjx46t9+urSuX1v9T/IyIiYgxDe4BuvPFGMjMzmTVrFunp6fTs2ZMVK1Y4G6NTUlIwmysyWlxcHF9++SUPPPAA3bt3JzY2lqlTp/K3v/3NecyRI0cYO3Ys2dnZREREMHjwYNatW0dERES9X19VDmafIiOvGB+Lmd6t1P8jIiJiBJPdbrcbXYS7ycvLIyQkhNzcXIKDg2v13O9tSGH60m30TwjjgzurnuoTERGR6qvO72/Dl8LwNJr+EhERMZ4CUD2y2+2VFkBVA7SIiIhRFIDq0YGsAo6dLOv/0fN/REREDKMAVI/Kn/7cs2VT/Lz1/B8RERGjKADVo+OnSvD3tqj/R0RExGC6C6wKdXkXWMlpG8WnrQT5edfqeUVERDxddX5/N6i1wBoDHy8zPl4aeBMRETGSfhOLiIiIx1EAEhEREY+jACQiIiIeRwFIREREPI4CkIiIiHgcBSARERHxOApAIiIi4nEUgERERMTjKACJiIiIx1EAEhEREY+jACQiIiIeRwFIREREPI4CkIiIiHgcrQZfBbvdDkBeXp7BlYiIiMj5Kv+9Xf57/FwUgKpw8uRJAOLi4gyuRERERKrr5MmThISEnPMYk/18YpKHsdlspKWlERQUhMlkqtVz5+XlERcXx+HDhwkODq7Vc0v16efhXvTzcC/6ebgX/Tx+n91u5+TJk8TExGA2n7vLRyNAVTCbzbRo0aJOv0dwcLD+AXYj+nm4F/083It+Hu5FP49z+72Rn3JqghYRERGPowAkIiIiHkcBqJ75+vryyCOP4Ovra3Qpgn4e7kY/D/ein4d70c+jdqkJWkRERDyORoBERETE4ygAiYiIiMdRABIRERGPowAkIiIiHkcBqB698sorxMfH4+fnR2JiIhs2bDC6JI80d+5c+vXrR1BQEJGRkYwePZpdu3YZXZaU+fvf/47JZOL+++83uhSPlpqayi233EKzZs3w9/enW7du/PTTT0aX5ZGsViszZ84kISEBf39/2rRpw+OPP35e613J2SkA1ZMlS5aQlJTEI488wqZNm+jRowfDhg3j2LFjRpfmcb799lumTJnCunXrWLlyJaWlpVx55ZUUFBQYXZrH+/HHH/nHP/5B9+7djS7Fox0/fpxBgwbh7e3NF198wa+//spzzz1HaGio0aV5pKeeeorXXnuNl19+mR07dvDUU0/x9NNP89JLLxldWoOm2+DrSWJiIv369ePll18GHOuNxcXFce+99zJt2jSDq/NsmZmZREZG8u2333LxxRcbXY7Hys/Pp3fv3rz66qs88cQT9OzZk/nz5xtdlkeaNm0aa9as4fvvvze6FAGuvvpqoqKiWLhwoXPfddddh7+/P2+//baBlTVsGgGqByUlJWzcuJGhQ4c695nNZoYOHcratWsNrEwAcnNzAQgLCzO4Es82ZcoURo4c6fLviRjj008/pW/fvlx//fVERkbSq1cv3njjDaPL8lgXXXQRycnJ7N69G4Cff/6Z1atXM3z4cIMra9i0GGo9yMrKwmq1EhUV5bI/KiqKnTt3GlSVgGMk7v7772fQoEF07drV6HI81vvvv8+mTZv48ccfjS5FgP379/Paa6+RlJTEQw89xI8//sh9992Hj48PEyZMMLo8jzNt2jTy8vLo2LEjFosFq9XKk08+ybhx44wurUFTABKPNmXKFLZv387q1auNLsVjHT58mKlTp7Jy5Ur8/PyMLkdw/I9B3759mTNnDgC9evVi+/btLFiwQAHIAB988AHvvPMO7777Ll26dGHLli3cf//9xMTE6OdRAwpA9SA8PByLxUJGRobL/oyMDKKjow2qSu655x4+++wzvvvuO1q0aGF0OR5r48aNHDt2jN69ezv3Wa1WvvvuO15++WWKi4uxWCwGVuh5mjdvTufOnV32derUif/85z8GVeTZ/vKXvzBt2jRuuukmALp168ahQ4eYO3euAlANqAeoHvj4+NCnTx+Sk5Od+2w2G8nJyQwcONDAyjyT3W7nnnvuYdmyZXz11VckJCQYXZJHu/zyy9m2bRtbtmxxvvr27cu4cePYsmWLwo8BBg0adMajIXbv3k2rVq0MqsiznTp1CrPZ9de1xWLBZrMZVFHjoBGgepKUlMSECRPo27cv/fv3Z/78+RQUFDBp0iSjS/M4U6ZM4d133+WTTz4hKCiI9PR0AEJCQvD39ze4Os8TFBR0Rv9VYGAgzZo1U1+WQR544AEuuugi5syZww033MCGDRt4/fXXef31140uzSONGjWKJ598kpYtW9KlSxc2b97MvHnzuPXWW40urUHTbfD16OWXX+aZZ54hPT2dnj178uKLL5KYmGh0WR7HZDJVuX/x4sVMnDixfouRKl1yySW6Dd5gn332GdOnT2fPnj0kJCSQlJTE5MmTjS7LI508eZKZM2eybNkyjh07RkxMDGPHjmXWrFn4+PgYXV6DpQAkIiIiHkc9QCIiIuJxFIBERETE4ygAiYiIiMdRABIRERGPowAkIiIiHkcBSERERDyOApCIiIh4HAUgERER8TgKQCIi58FkMvHxxx8bXYaI1BIFIBFxexMnTsRkMp3xuuqqq4wuTUQaKC2GKiINwlVXXcXixYtd9vn6+hpUjYg0dBoBEpEGwdfXl+joaJdXaGgo4Jieeu211xg+fDj+/v60bt2ajz76yOXz27Zt47LLLsPf359mzZpxxx13kJ+f73LMokWL6NKlC76+vjRv3px77rnH5f2srCzGjBlDQEAA7dq149NPP63bixaROqMAJCKNwsyZM7nuuuv4+eefGTduHDfddBM7duwAoKCggGHDhhEaGsqPP/7Ihx9+yKpVq1wCzmuvvcaUKVO444472LZtG59++ilt27Z1+R6PPvooN9xwA1u3bmXEiBGMGzeOnJycer1OEakldhERNzdhwgS7xWKxBwYGuryefPJJu91utwP2u+66y+UziYmJ9rvvvttut9vtr7/+uj00NNSen5/vfH/58uV2s9lsT09Pt9vtdntMTIz94YcfPmsNgH3GjBnO7fz8fDtg/+KLL2rtOkWk/qgHSEQahEsvvZTXXnvNZV9YWJjz64EDB7q8N3DgQLZs2QLAjh076NGjB4GBgc73Bw0ahM1mY9euXZhMJtLS0rj88svPWUP37t2dXwcGBhIcHMyxY8cu9JJExEAKQCLSIAQGBp4xJVVb/P39z+s4b29vl22TyYTNZquLkkSkjqkHSEQahXXr1p2x3alTJwA6derEzz//TEFBgfP9NWvWYDab6dChA0FBQcTHx5OcnFyvNYuIcTQCJCINQnFxMenp6S77vLy8CA8PB+DDDz+kb9++DB48mHfeeYcNGzawcOFCAMaNG8cjjzzChAkTmD17NpmZmdx777386U9/IioqCoDZs2dz1113ERkZyfDhwzl58iRr1qzh3nvvrd8LFZF6oQAkIg3CihUraN68ucu+Dh06sHPnTsBxh9b777/P//t//4/mzZvz3nvv0blzZwACAgL48ssvmTp1Kv369SMgIIDrrruOefPmOc81YcIEioqKeP7553nwwQcJDw/nj3/8Y/1doIjUK5PdbrcbXYSISE2YTCaWLVvG6NGjjS5FRBoI9QCJiIiIx1EAEhEREY+jHiARafA0ky8i1aURIBEREfE4CkAiIiLicRSARERExOMoAImIiIjHUQASERERj6MAJCIiIh5HAUhEREQ8jgKQiIiIeJz/DxZISTrCgrJTAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "plt.plot(history.history[\"accuracy\"])\n",
    "plt.plot(history.history[\"val_accuracy\"])\n",
    "plt.xlabel(\"Epoch\")\n",
    "plt.ylabel(\"Accuracy\")\n",
    "plt.legend([\"train\", \"valid\"])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "08e58cc8",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-07-02 14:18:20.034017: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype int32\n",
      "\t [[{{node Placeholder/_0}}]]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "12/12 [==============================] - 16s 1s/step\n"
     ]
    }
   ],
   "source": [
    "preds = model.predict(test_generator)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "0f9b588a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['apple',\n",
       " 'banana',\n",
       " 'beetroot',\n",
       " 'bell pepper',\n",
       " 'cabbage',\n",
       " 'capsicum',\n",
       " 'carrot',\n",
       " 'cauliflower',\n",
       " 'chilli pepper',\n",
       " 'corn',\n",
       " 'cucumber',\n",
       " 'eggplant',\n",
       " 'garlic',\n",
       " 'ginger',\n",
       " 'grapes',\n",
       " 'jalepeno',\n",
       " 'kiwi',\n",
       " 'lemon',\n",
       " 'lettuce',\n",
       " 'mango',\n",
       " 'onion',\n",
       " 'orange',\n",
       " 'paprika',\n",
       " 'pear',\n",
       " 'peas',\n",
       " 'pineapple',\n",
       " 'pomegranate',\n",
       " 'potato',\n",
       " 'raddish',\n",
       " 'soy beans',\n",
       " 'spinach',\n",
       " 'sweetcorn',\n",
       " 'sweetpotato',\n",
       " 'tomato',\n",
       " 'turnip',\n",
       " 'watermelon']"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "os.listdir(train_dir)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "d1dcfa2c",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = np.argmax(preds, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "698458b0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 : apple\n",
      "1 : banana\n",
      "2 : beetroot\n",
      "3 : bell pepper\n",
      "4 : cabbage\n",
      "5 : capsicum\n",
      "6 : carrot\n",
      "7 : cauliflower\n",
      "8 : chilli pepper\n",
      "9 : corn\n",
      "10 : cucumber\n",
      "11 : eggplant\n",
      "12 : garlic\n",
      "13 : ginger\n",
      "14 : grapes\n",
      "15 : jalepeno\n",
      "16 : kiwi\n",
      "17 : lemon\n",
      "18 : lettuce\n",
      "19 : mango\n",
      "20 : onion\n",
      "21 : orange\n",
      "22 : paprika\n",
      "23 : pear\n",
      "24 : peas\n",
      "25 : pineapple\n",
      "26 : pomegranate\n",
      "27 : potato\n",
      "28 : raddish\n",
      "29 : soy beans\n",
      "30 : spinach\n",
      "31 : sweetcorn\n",
      "32 : sweetpotato\n",
      "33 : tomato\n",
      "34 : turnip\n",
      "35 : watermelon\n"
     ]
    }
   ],
   "source": [
    "for k, v in test_generator.class_indices.items():\n",
    "    print(v, \":\", k)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "d7d33cf5",
   "metadata": {},
   "outputs": [],
   "source": [
    "pred_classes = {\n",
    "    0 : \"apple\",\n",
    "    1 : \"banana\",\n",
    "    2 : \"beetroot\",\n",
    "    3 : \"bell pepper\",\n",
    "    4 : \"cabbage\",\n",
    "    5 : \"capsicum\",\n",
    "    6 : \"carrot\",\n",
    "    7 : \"cauliflower\",\n",
    "    8 : \"chilli pepper\",\n",
    "    9 : \"corn\",\n",
    "    10 : \"cucumber\",\n",
    "    11 : \"eggplant\",\n",
    "    12 : \"garlic\",\n",
    "    13 : \"ginger\",\n",
    "    14 : \"grapes\",\n",
    "    15 : \"jalepeno\",\n",
    "    16 : \"kiwi\",\n",
    "    17 : \"lemon\",\n",
    "    18 : \"lettuce\",\n",
    "    19 : \"mango\",\n",
    "    20 : \"onion\",\n",
    "    21 : \"orange\",\n",
    "    22 : \"paprika\",\n",
    "    23 : \"pear\",\n",
    "    24 : \"peas\",\n",
    "    25 : \"pineapple\",\n",
    "    26 : \"pomegranate\",\n",
    "    27 : \"potato\",\n",
    "    28 : \"raddish\",\n",
    "    29 : \"soy beans\",\n",
    "    30 : \"spinach\",\n",
    "    31 : \"sweetcorn\",\n",
    "    32 : \"sweetpotato\",\n",
    "    33 : \"tomato\",\n",
    "    34 : \"turnip\",\n",
    "    35 : \"watermelon\"\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "664c0f7a",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = list(map(lambda x: pred_classes[x], y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "d30c758f",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_test = list(map(lambda x: pred_classes[x], test_generator.classes))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "9651c77e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "               precision    recall  f1-score   support\n",
      "\n",
      "        apple       0.90      0.75      0.82        12\n",
      "       banana       0.78      1.00      0.88         7\n",
      "     beetroot       1.00      1.00      1.00        10\n",
      "  bell pepper       1.00      0.77      0.87        13\n",
      "      cabbage       1.00      1.00      1.00        10\n",
      "     capsicum       0.70      1.00      0.82         7\n",
      "       carrot       1.00      1.00      1.00        10\n",
      "  cauliflower       1.00      1.00      1.00        10\n",
      "chilli pepper       1.00      0.91      0.95        11\n",
      "         corn       1.00      0.83      0.91        12\n",
      "     cucumber       1.00      1.00      1.00        10\n",
      "     eggplant       1.00      1.00      1.00        10\n",
      "       garlic       1.00      1.00      1.00        10\n",
      "       ginger       1.00      1.00      1.00        10\n",
      "       grapes       1.00      1.00      1.00        10\n",
      "     jalepeno       1.00      0.91      0.95        11\n",
      "         kiwi       1.00      1.00      1.00        10\n",
      "        lemon       1.00      1.00      1.00        10\n",
      "      lettuce       1.00      1.00      1.00        10\n",
      "        mango       1.00      1.00      1.00        10\n",
      "        onion       1.00      1.00      1.00        10\n",
      "       orange       1.00      1.00      1.00        10\n",
      "      paprika       1.00      1.00      1.00        10\n",
      "         pear       1.00      1.00      1.00        10\n",
      "         peas       1.00      1.00      1.00        10\n",
      "    pineapple       1.00      1.00      1.00        10\n",
      "  pomegranate       1.00      1.00      1.00        10\n",
      "       potato       0.80      1.00      0.89         8\n",
      "      raddish       1.00      1.00      1.00        10\n",
      "    soy beans       1.00      1.00      1.00        10\n",
      "      spinach       1.00      1.00      1.00        10\n",
      "    sweetcorn       0.80      1.00      0.89         8\n",
      "  sweetpotato       1.00      1.00      1.00        10\n",
      "       tomato       1.00      1.00      1.00        10\n",
      "       turnip       1.00      1.00      1.00        10\n",
      "   watermelon       1.00      1.00      1.00        10\n",
      "\n",
      "     accuracy                           0.97       359\n",
      "    macro avg       0.97      0.98      0.97       359\n",
      " weighted avg       0.98      0.97      0.97       359\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "\n",
    "print(classification_report(y_pred, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "38c1f80b",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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