german_traffic_signs_cnn.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### #5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "import time\n",
    "import os\n",
    "import numpy as np\n",
    "import cv2\n",
    "from keras.utils import np_utils\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = []\n",
    "img_vectors = []\n",
    "folder_path = 'GTSRB'\n",
    "width = 32\n",
    "height = 32\n",
    "for idx, folder_name in enumerate(os.listdir(folder_path)):\n",
    "    for filename in (os.listdir(os.path.join(folder_path, folder_name))):\n",
    "        img = cv2.imread(os.path.join(folder_path, folder_name, filename))\n",
    "        img = cv2.resize(img, (width, height))\n",
    "        img_vectors.append(img)\n",
    "        y.append(idx)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8910"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = np.asarray(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(8910,)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8910"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(img_vectors)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = np.asarray(img_vectors)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(8910, 32, 32, 3)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(7128, 32, 32, 3)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(7128,)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1782, 32, 32, 3)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1782,)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_train = np_utils.to_categorical(y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(7128, 9)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "layers = [tf.keras.layers.Conv2D(filters=32, kernel_size=3, activation='relu', padding='same', input_shape=(32, 32, 3)), \n",
    "         tf.keras.layers.MaxPool2D(pool_size=2, strides=2, padding='same'), \n",
    "         tf.keras.layers.Conv2D(filters=64, kernel_size=3, activation='relu', padding='same'), \n",
    "         tf.keras.layers.Conv2D(filters=64, kernel_size=3, activation='relu', padding='same'), \n",
    "         tf.keras.layers.MaxPool2D(pool_size=2, strides=2, padding='same'), \n",
    "         tf.keras.layers.Flatten(), \n",
    "         tf.keras.layers.Dense(units=128, activation='relu'), \n",
    "         tf.keras.layers.Dropout(0.5), \n",
    "         tf.keras.layers.Dense(units=9, activation='softmax')]\n",
    "model = tf.keras.Sequential(layers)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "223/223 [==============================] - 16s 73ms/step - loss: 2.5208 - accuracy: 0.3541\n",
      "Epoch 2/10\n",
      "223/223 [==============================] - 16s 72ms/step - loss: 0.7712 - accuracy: 0.7567\n",
      "Epoch 3/10\n",
      "223/223 [==============================] - 16s 73ms/step - loss: 0.3891 - accuracy: 0.8844\n",
      "Epoch 4/10\n",
      "223/223 [==============================] - 16s 72ms/step - loss: 0.2758 - accuracy: 0.9157\n",
      "Epoch 5/10\n",
      "223/223 [==============================] - 16s 73ms/step - loss: 0.2079 - accuracy: 0.9376\n",
      "Epoch 6/10\n",
      "223/223 [==============================] - 16s 73ms/step - loss: 0.1639 - accuracy: 0.9501\n",
      "Epoch 7/10\n",
      "223/223 [==============================] - 16s 72ms/step - loss: 0.1330 - accuracy: 0.9603\n",
      "Epoch 8/10\n",
      "223/223 [==============================] - 16s 72ms/step - loss: 0.1496 - accuracy: 0.9582\n",
      "Epoch 9/10\n",
      "223/223 [==============================] - 16s 74ms/step - loss: 0.0979 - accuracy: 0.9711\n",
      "Epoch 10/10\n",
      "223/223 [==============================] - 16s 73ms/step - loss: 0.0672 - accuracy: 0.9787\n",
      "Train time is: 163.97725629806519\n"
     ]
    }
   ],
   "source": [
    "train_start_time = time.time()\n",
    "model.fit(x=X_train, y=y_train, epochs=10)\n",
    "train_elapsed_time = time.time() - train_start_time\n",
    "\n",
    "print('Train time is:', train_elapsed_time)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Prediction time is: 1.1458101272583008\n"
     ]
    }
   ],
   "source": [
    "prediction_start_time = time.time()\n",
    "y_pred = model.predict(X_test)\n",
    "prediction_elapsed_time = time.time() - prediction_start_time\n",
    "\n",
    "print('Prediction time is:', prediction_elapsed_time)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1782, 9)"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = np.argmax(y_pred, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([5, 8, 1, ..., 6, 2, 1], dtype=int64)"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([5, 8, 1, ..., 6, 2, 1])"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Prediction accuracy: 97.87 %\n"
     ]
    }
   ],
   "source": [
    "print('Prediction accuracy: {:.2f} %'.format(accuracy_score(y_test, y_pred) * 100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_1\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d_3 (Conv2D)            (None, 32, 32, 32)        896       \n",
      "_________________________________________________________________\n",
      "max_pooling2d_2 (MaxPooling2 (None, 16, 16, 32)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_4 (Conv2D)            (None, 16, 16, 64)        18496     \n",
      "_________________________________________________________________\n",
      "conv2d_5 (Conv2D)            (None, 16, 16, 64)        36928     \n",
      "_________________________________________________________________\n",
      "max_pooling2d_3 (MaxPooling2 (None, 8, 8, 64)          0         \n",
      "_________________________________________________________________\n",
      "flatten_1 (Flatten)          (None, 4096)              0         \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 128)               524416    \n",
      "_________________________________________________________________\n",
      "dropout_1 (Dropout)          (None, 128)               0         \n",
      "_________________________________________________________________\n",
      "dense_3 (Dense)              (None, 9)                 1161      \n",
      "=================================================================\n",
      "Total params: 581,897\n",
      "Trainable params: 581,897\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "print(model.summary())"
   ]
  }
 ],
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