trash-CNN-attempt1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "3d299c66",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import os\n",
    "from PIL import Image\n",
    "\n",
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "from tensorflow.keras import Sequential\n",
    "from tensorflow.keras.layers import Dense, Conv2D, Dropout, Flatten, MaxPooling2D"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "653a118f",
   "metadata": {},
   "outputs": [],
   "source": [
    "def toy_CNN(X_train, y_train, **kwargs):\n",
    "        \n",
    "    model = Sequential([\n",
    "        Conv2D(filters=8, kernel_size=(8,8), strides=(4,4), activation='relu', input_shape=(384, 512, 3)),\n",
    "        MaxPooling2D(pool_size=(8,8)),\n",
    "        Conv2D(filters=8, kernel_size=(8,8), strides=(4,4), activation='relu'),\n",
    "        Dropout(0.1),\n",
    "        Flatten(),\n",
    "        Dense(1, activation='sigmoid')\n",
    "    ])\n",
    "    \n",
    "    model.compile(loss='binary_crossentropy', optimizer='adam', metrics='accuracy')\n",
    "    history = model.fit(X_train, y_train, **kwargs)\n",
    "    return model, history"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "3aab9c04",
   "metadata": {},
   "outputs": [],
   "source": [
    "def small_CNN(X_train, y_train, **kwargs):\n",
    "        \n",
    "    model = Sequential([\n",
    "        Conv2D(filters=8, kernel_size=(8,8), strides=(4,4), activation='relu', input_shape=(384, 512, 3)),\n",
    "        MaxPooling2D(pool_size=(8,8)),\n",
    "        Conv2D(filters=8, kernel_size=(4,4), strides=(2,2), activation='relu'),\n",
    "        MaxPooling2D(pool_size=(4,4)),\n",
    "        Conv2D(filters=4, kernel_size=(4,4), strides=(2,2), activation='relu'),\n",
    "        MaxPooling2D(pool_size=(2,2)),\n",
    "        Conv2D(filters=4, kernel_size=(2,2), strides=(1,1), activation='relu'),\n",
    "        MaxPooling2D(pool_size=(2,2)),\n",
    "        Flatten(),\n",
    "        Dense(1, activation='sigmoid')\n",
    "    ])\n",
    "    \n",
    "    model.compile(loss='binary_crossentropy', optimizer='adam', metrics='accuracy')\n",
    "    history = model.fit(X_train, y_train, **kwargs)\n",
    "    return model, history"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "3c834abd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total datapoints: 2527\n",
      "Kept datapoints: 2527\n",
      "Trash datapoints: 137\n",
      "done\n"
     ]
    }
   ],
   "source": [
    "X, y = [], []\n",
    "folder = 'dataset-resized'\n",
    "folder_list = os.listdir(folder)\n",
    "nTotal = len(folder_list)\n",
    "print('Total datapoints:', nTotal)\n",
    "nTrash = 0\n",
    "for file in folder_list:\n",
    "    trash = int(file.split('_')[0] == 'trash')\n",
    "    if not trash:\n",
    "        if np.random.rand() > 1: #0.2:\n",
    "            continue\n",
    "    img = Image.open(f'{folder}/{file}')\n",
    "    arr = np.array(img)\n",
    "    X.append(arr)\n",
    "    y.append(trash)\n",
    "    nTrash += trash\n",
    "print('Kept datapoints:', len(y))\n",
    "print('Trash datapoints:', nTrash)\n",
    "X, y = np.array(X), np.array(y)\n",
    "print('done')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "d8a6f920",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " -- Performing NEW train-test split -- \n",
      "\n"
     ]
    }
   ],
   "source": [
    "def train_test_split(X, y, test_idx=None, test_size=0.2):\n",
    "    ''' Split input data X and labels y into training and testing arrays '''\n",
    "    n_list = list(range(len(y)))\n",
    "    if test_idx is None:\n",
    "        print('\\n -- Performing NEW train-test split -- \\n')\n",
    "        test_idx = np.random.choice(n_list, replace=False, size=int(test_size*len(y)))\n",
    "    else:\n",
    "        test_idx = np.array(test_idx)\n",
    "    train_idx = np.array([i for i in n_list if i not in test_idx])\n",
    "    X_train = X[train_idx,:]\n",
    "    X_test = X[test_idx,:]\n",
    "    y_train = y[train_idx]\n",
    "    y_test = y[test_idx]\n",
    "    return X_train, y_train, X_test, y_test, test_idx\n",
    "\n",
    "X_train, y_train, X_test, y_test, test_idx = train_test_split(X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "381aadb8",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{0: 1, 1: 100}\n",
      "Epoch 1/10\n",
      "51/51 [==============================] - 14s 279ms/step - loss: 0.0516 - accuracy: 0.9981 - val_loss: 97.7999 - val_accuracy: 0.7185\n",
      "Epoch 2/10\n",
      "51/51 [==============================] - 15s 289ms/step - loss: 7.7281e-28 - accuracy: 1.0000 - val_loss: 98.3668 - val_accuracy: 0.7185\n",
      "Epoch 3/10\n",
      "51/51 [==============================] - 15s 295ms/step - loss: 1.0941e-28 - accuracy: 1.0000 - val_loss: 98.3701 - val_accuracy: 0.7185\n",
      "Epoch 4/10\n",
      "51/51 [==============================] - 15s 302ms/step - loss: 2.9936e-17 - accuracy: 1.0000 - val_loss: 98.3701 - val_accuracy: 0.7185\n",
      "Epoch 5/10\n",
      "51/51 [==============================] - 16s 307ms/step - loss: 1.4014e-32 - accuracy: 1.0000 - val_loss: 98.3701 - val_accuracy: 0.7185\n",
      "Epoch 6/10\n",
      "51/51 [==============================] - 14s 272ms/step - loss: 4.2409e-29 - accuracy: 1.0000 - val_loss: 98.3701 - val_accuracy: 0.7185\n",
      "Epoch 7/10\n",
      "51/51 [==============================] - 13s 262ms/step - loss: 2.3036e-22 - accuracy: 1.0000 - val_loss: 98.3701 - val_accuracy: 0.7185\n",
      "Epoch 8/10\n",
      "51/51 [==============================] - 13s 258ms/step - loss: 2.4975e-28 - accuracy: 1.0000 - val_loss: 98.3701 - val_accuracy: 0.7185\n",
      "Epoch 9/10\n",
      "51/51 [==============================] - 14s 271ms/step - loss: 3.3354e-28 - accuracy: 1.0000 - val_loss: 98.3701 - val_accuracy: 0.7185\n",
      "Epoch 10/10\n",
      "51/51 [==============================] - 14s 269ms/step - loss: 3.3827e-35 - accuracy: 1.0000 - val_loss: 98.3701 - val_accuracy: 0.7185\n"
     ]
    }
   ],
   "source": [
    "class_weight = {0:1, 1:100}\n",
    "print(class_weight)\n",
    "toy_model, toy_hist = toy_CNN(X_train, y_train, verbose=True, epochs=10, validation_split=0.2,\n",
    "                                class_weight = class_weight)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "3cec5c0b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_loss(hist):\n",
    "    plt.figure()\n",
    "    plt.plot(hist.history['loss'], label='loss')\n",
    "    plt.plot(hist.history['val_loss'], label='val_loss')\n",
    "    plt.xlabel('Epoch')\n",
    "    plt.ylabel('Loss (Binary Cross Entropy)')\n",
    "    plt.legend()\n",
    "    plt.grid(True)\n",
    "    \n",
    "plot_loss(toy_hist)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "140f33f6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_pred = np.round(toy_model.predict(X_test), 4)\n",
    "plt.figure(figsize=(6,6))\n",
    "plt.plot(y_test, y_pred, 'o', label='Test Data')\n",
    "plt.axvline(x=0.5, ls='--', c='k')\n",
    "plt.axvline(x=0, ls='-', c='grey', zorder=0, lw=0.5)\n",
    "plt.axvline(x=1, ls='-', c='grey', zorder=0, lw=0.5)\n",
    "plt.axhline(y=0.5, ls='--', c='k')\n",
    "plt.axhline(y=0, ls='-', c='grey', zorder=0, lw=0.5)\n",
    "plt.axhline(y=1, ls='-', c='grey', zorder=0, lw=0.5)\n",
    "plt.xlim(-0.5,1.5)\n",
    "plt.xticks([0,1],['Not Trash', 'Trash'], size=20)\n",
    "plt.ylim(-0.1,1.1)\n",
    "plt.yticks(np.arange(0,1.01,0.1))\n",
    "plt.legend(bbox_to_anchor=(1,1), loc='upper left', edgecolor='k', fontsize=16)\n",
    "plt.title('Prelim CNN test', size=20)\n",
    "None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6f0ffdfa",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.8.13 64-bit ('py38')",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.13"
  },
  "vscode": {
   "interpreter": {
    "hash": "7eb2f8f0579378df5cf922f452277afe4ba82fcad8503bc4d9fa6375a689b0ff"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}