GoogLeNet.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "All modules imported.\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import pickle\n",
    "import math\n",
    "import random\n",
    "import csv\n",
    "from PIL import Image\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import cv2\n",
    "import numpy as np\n",
    "import tensorflow as tf\n",
    "import tensorflow.contrib.layers as layers\n",
    "from sklearn.utils import shuffle\n",
    "\n",
    "print('All modules imported.')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1 Reload the preprocessed data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(34799, 32, 32, 3) (34799,)\n",
      "(4410, 32, 32, 3) (4410,)\n",
      "(12630, 32, 32, 3) (12630,)\n",
      "43\n",
      "Data loaded.\n"
     ]
    }
   ],
   "source": [
    "# 1 Reload the preprocessed data\n",
    "\n",
    "pickle_file = './pre-traffic-signs-data/pre-data.pickle'\n",
    "with open(pickle_file, 'rb') as f:\n",
    "    pickle_data = pickle.load(f)\n",
    "    X_train = pickle_data['train_features']\n",
    "    y_train = pickle_data['train_labels']\n",
    "    X_valid = pickle_data['valid_features']\n",
    "    y_valid = pickle_data['valid_labels']\n",
    "    X_test = pickle_data['test_features']\n",
    "    y_test = pickle_data['test_labels']\n",
    "    signnames = pickle_data['signnames']\n",
    "    del pickle_data  # Free up memory\n",
    "    \n",
    "# Shuffle the data set\n",
    "X_train, y_train = shuffle(X_train, y_train)\n",
    "X_valid, y_valid = shuffle(X_valid, y_valid)\n",
    "X_test, y_test = shuffle(X_test, y_test)\n",
    "\n",
    "print(X_train.shape, y_train.shape)\n",
    "print(X_valid.shape, y_valid.shape)\n",
    "print(X_test.shape, y_test.shape)\n",
    "print(len(signnames))\n",
    "print('Data loaded.')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2 Model Architecture"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.1 Inception model and GoogLeNet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def Inception(inputs, conv11_size, conv33_11_size, conv33_size,conv55_11_size, conv55_size, pool11_size):\n",
    "    conv11 = layers.conv2d(inputs, conv11_size, [1, 1])\n",
    "    conv33_reduce = layers.conv2d(inputs, conv33_11_size, [1, 1])\n",
    "    conv33 = layers.conv2d(conv33_reduce, conv33_size, [3, 3])\n",
    "    conv55_reduce = layers.conv2d(inputs, conv55_11_size, [1, 1])\n",
    "    conv55 = layers.conv2d(conv55_reduce, conv55_size, [5, 5])\n",
    "    pool_proj = layers.max_pool2d(inputs, [3, 3], stride = 1, padding='SAME')\n",
    "    pool11 = layers.conv2d(pool_proj, pool11_size, [1, 1])\n",
    "    return tf.concat([conv11, conv33, conv55, pool11], 3)\n",
    "\n",
    "def GoogLeNet(inputs, dropout_keep_prob): # inputs size:32x32x3\n",
    "    conv1 = layers.conv2d(inputs, 64, [3, 3], stride = 2) # 16x16x64\n",
    "    \n",
    "    inception_2a = Inception(conv1, 64, 96, 128, 16, 32, 32) # 16x16x256\n",
    "    inception_2b = Inception(inception_2a, 128, 128, 192, 32, 96, 64) # 16x16x480\n",
    "    pool2 = layers.max_pool2d(inception_2b, [3, 3]) # 7x7x480 ? why\n",
    "    \n",
    "    inception_3a = Inception(pool2, 192, 96, 208, 16, 48, 64) # 7x7x512\n",
    "    inception_3b = Inception(inception_3a, 160, 112, 224, 24, 64, 64) # 7x7x512\n",
    "    pool3 = layers.max_pool2d(inception_3b, [3, 3]) # 3x3x512\n",
    "    \n",
    "    inception_4a = Inception(pool3, 256, 160, 320, 32, 128, 128) # 3x3x832\n",
    "    inception_4b = Inception(inception_4a, 384, 192, 384, 48, 128, 128) # 3x3x1024\n",
    "    pool4 = layers.avg_pool2d(inception_4b, [3, 3], stride = 1) \n",
    "\n",
    "    reshape = tf.reshape(pool4, [-1, 1024])\n",
    "    dropout = layers.dropout(reshape, dropout_keep_prob)\n",
    "    logits = layers.fully_connected(dropout, 43, activation_fn=None)\n",
    "    \n",
    "    return logits"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3 Training"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.1 Strategy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Placeholder\n",
    "x = tf.placeholder(tf.float32, (None, 32, 32, 3))\n",
    "y = tf.placeholder(tf.int32, (None))\n",
    "one_hot_y = tf.one_hot(y, 43)\n",
    "keep_prob = tf.placeholder_with_default(1.0, shape=())\n",
    "\n",
    "# Hyperparameters\n",
    "LEARNING_RATE = 4e-4\n",
    "EPOCHS = 35\n",
    "BATCH_SIZE = 128\n",
    "\n",
    "# Train method\n",
    "logits = GoogLeNet(x, keep_prob)\n",
    "cross_entropy = tf.nn.softmax_cross_entropy_with_logits(labels=one_hot_y, logits=logits)\n",
    "loss_op = tf.reduce_mean(cross_entropy)\n",
    "optimizer = tf.train.AdamOptimizer(learning_rate = LEARNING_RATE)\n",
    "train_op = optimizer.minimize(loss_op)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.2 Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "correct_prediction = tf.equal(tf.argmax(logits, 1), tf.argmax(one_hot_y, 1))\n",
    "accuracy_op = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))\n",
    "\n",
    "def evaluate(X_data, y_data):\n",
    "    num_examples = len(X_data)\n",
    "    total_accuracy = 0\n",
    "    sess = tf.get_default_session()\n",
    "#     with tf.Session() as sess:\n",
    "#         sess.run(tf.global_variables_initializer())\n",
    "    for offset in range(0, num_examples, BATCH_SIZE):\n",
    "        batch_x, batch_y = X_data[offset:offset + BATCH_SIZE], y_data[offset:offset + BATCH_SIZE]\n",
    "        accuracy = sess.run(accuracy_op, feed_dict={x: batch_x, y: batch_y})\n",
    "        total_accuracy += (accuracy * len(batch_x))\n",
    "    return total_accuracy / num_examples"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.3 Train the Model\n",
    "A validation set can be used to assess how well the model is performing. A low accuracy on the training and validation sets imply underfitting. A high accuracy on the training set but low accuracy on the validation set implies overfitting."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training...\n",
      "EPOCH 1 : Validation Accuracy = 0.460\n",
      "EPOCH 2 : Validation Accuracy = 0.878\n",
      "EPOCH 3 : Validation Accuracy = 0.903\n",
      "EPOCH 4 : Validation Accuracy = 0.948\n",
      "EPOCH 5 : Validation Accuracy = 0.952\n",
      "EPOCH 6 : Validation Accuracy = 0.951\n",
      "EPOCH 7 : Validation Accuracy = 0.951\n",
      "EPOCH 8 : Validation Accuracy = 0.948\n",
      "EPOCH 9 : Validation Accuracy = 0.950\n",
      "EPOCH 10 : Validation Accuracy = 0.968\n",
      "EPOCH 11 : Validation Accuracy = 0.959\n",
      "EPOCH 12 : Validation Accuracy = 0.956\n",
      "EPOCH 13 : Validation Accuracy = 0.965\n",
      "EPOCH 14 : Validation Accuracy = 0.972\n",
      "EPOCH 15 : Validation Accuracy = 0.968\n",
      "EPOCH 16 : Validation Accuracy = 0.952\n",
      "EPOCH 17 : Validation Accuracy = 0.973\n",
      "EPOCH 18 : Validation Accuracy = 0.975\n",
      "EPOCH 19 : Validation Accuracy = 0.959\n",
      "EPOCH 20 : Validation Accuracy = 0.977\n",
      "EPOCH 21 : Validation Accuracy = 0.978\n",
      "EPOCH 22 : Validation Accuracy = 0.973\n",
      "EPOCH 23 : Validation Accuracy = 0.983\n",
      "EPOCH 24 : Validation Accuracy = 0.977\n",
      "EPOCH 25 : Validation Accuracy = 0.979\n",
      "EPOCH 26 : Validation Accuracy = 0.981\n",
      "EPOCH 27 : Validation Accuracy = 0.977\n",
      "EPOCH 28 : Validation Accuracy = 0.984\n",
      "EPOCH 29 : Validation Accuracy = 0.985\n",
      "EPOCH 30 : Validation Accuracy = 0.987\n",
      "EPOCH 31 : Validation Accuracy = 0.987\n",
      "EPOCH 32 : Validation Accuracy = 0.973\n",
      "EPOCH 33 : Validation Accuracy = 0.963\n",
      "EPOCH 34 : Validation Accuracy = 0.968\n",
      "EPOCH 35 : Validation Accuracy = 0.979\n",
      "Model saved\n",
      "CPU times: user 28min 52s, sys: 4min 17s, total: 33min 10s\n",
      "Wall time: 44min 41s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "saver = tf.train.Saver()\n",
    "train_accuracy = []\n",
    "valid_accuracy = []\n",
    "with tf.Session() as sess:\n",
    "    sess.run(tf.global_variables_initializer())\n",
    "    num_examples = len(X_train)\n",
    "    print(\"Training...\")\n",
    "    for i in range(EPOCHS):\n",
    "        X_train, y_train = shuffle(X_train, y_train)\n",
    "        total_train_acc = 0\n",
    "        print(\"EPOCH {} :\".format(i+1), end=' ')\n",
    "        for offset in range(0, num_examples, BATCH_SIZE):\n",
    "            end = offset + BATCH_SIZE\n",
    "            batch_x, batch_y = X_train[offset:end], y_train[offset:end]\n",
    "            _, train_acc = sess.run([train_op, accuracy_op], feed_dict={x: batch_x, y: batch_y, keep_prob: 0.5})\n",
    "            total_train_acc += (train_acc * len(batch_x))\n",
    "        train_accuracy.append(total_train_acc / num_examples)\n",
    "        valid_acc = evaluate(X_valid, y_valid)\n",
    "        valid_accuracy.append(valid_acc)\n",
    "        print(\"Validation Accuracy = {:.3f}\".format(valid_acc))\n",
    "        \n",
    "    saver.save(sess, './model/googlenet.ckpt')\n",
    "    print(\"Model saved\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Plot the accuracy of training and validation operation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f610e83b588>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplot(2, 1, 2)\n",
    "plt.plot(train_accuracy, label='train')\n",
    "plt.plot(valid_accuracy, label='valid')\n",
    "plt.title('Accuracy history')\n",
    "plt.xlabel('Epoch')\n",
    "plt.ylabel('Loss')\n",
    "plt.gcf().set_size_inches(15, 15)\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4 Testing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4.1 Test the model on testing dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Restoring parameters from ./model/googlenet.ckpt\n"
     ]
    }
   ],
   "source": [
    "saver = tf.train.import_meta_graph('./model/googlenet.ckpt.meta')\n",
    "with tf.Session() as sess:\n",
    "    saver.restore(sess, './model/googlenet.ckpt')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Restoring parameters from ./model/googlenet.ckpt\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f60d73d8d68>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "with tf.Session() as sess:\n",
    "    saver.restore(sess, './model/googlenet.ckpt')\n",
    "#     sess.run(tf.global_variables_initializer())\n",
    "    train_accuracy = evaluate(X_train, y_train)\n",
    "    valid_accuracy = evaluate(X_valid, y_valid)\n",
    "    test_accuracy = evaluate(X_test, y_test)\n",
    "    \n",
    "accuracys = [train_accuracy, valid_accuracy, test_accuracy]\n",
    "tick_labels = [\"training set\", \"validation set\", \"testing set\"]\n",
    "plt.bar(range(3), accuracys)\n",
    "plt.xlabel('data set')\n",
    "plt.ylabel('accuracy')\n",
    "plt.xticks(range(3), tick_labels)\n",
    "for x_,y_ in zip(range(3), accuracys):\n",
    "    plt.text(x_ - 0.1, y_, '%.3f'%y_)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4.2 Test the Model on New Images"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6106757c88>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "online_images = []\n",
    "online_labels = [0, 9, 12, 14, 17, 22, 25, 26, 31, 35]\n",
    "\n",
    "for i in range(1, 11):\n",
    "    image = plt.imread('./test_images/' + str(i) +'.jpeg')\n",
    "    image_reshape = cv2.resize(image,(32, 32), interpolation = cv2.INTER_CUBIC)\n",
    "    online_images.append(image_reshape)\n",
    "\n",
    "plt.figure(figsize=(25, 10))\n",
    "plt.subplots_adjust(hspace = .1, wspace=.1)\n",
    "for i in range(len(online_images)):\n",
    "    plt.subplot(1, 10, i + 1)\n",
    "    plt.imshow(online_images[i])\n",
    "    plt.title(signnames[int(online_labels[i])])\n",
    "    plt.xticks([]), plt.yticks([])\n",
    "plt.savefig('./result_images/online image.jpg')\n",
    "\n",
    "online_images = np.array(online_images)\n",
    "online_images = online_images.astype(np.float32) / 128. - 1.\n",
    "online_labels = np.array(online_labels)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Predict the Sign Type for Each Image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Restoring parameters from ./model/googlenet.ckpt\n",
      "Test Accuracy = 1.000\n",
      "Predict the Sign Type for Each Image\n",
      "[ 0  9 12 14 17 22 25 26 31 35]\n"
     ]
    }
   ],
   "source": [
    "with tf.Session() as sess:\n",
    "    saver.restore(sess, './model/googlenet.ckpt')\n",
    "    test_accuracy = evaluate(online_images, online_labels)\n",
    "    print(\"Test Accuracy = {:.3f}\".format(test_accuracy))   \n",
    "    logits_value = sess.run(logits, feed_dict={x: online_images})\n",
    "    probabilities = sess.run(tf.nn.softmax(logits_value))\n",
    "    \n",
    "predict = probabilities.argmax(axis=1)\n",
    "print(\"Predict the Sign Type for Each Image\")\n",
    "print(predict)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Output Top 5 Softmax Probabilities For Each Image Found on the Web"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6106014da0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "### Print out the top five softmax probabilities for the predictions on the German traffic sign images found on the web. \n",
    "with tf.Session() as sess:\n",
    "    top5 = sess.run(tf.nn.top_k(tf.constant(probabilities), k=5))\n",
    "\n",
    "values = top5.values\n",
    "indices = top5.indices\n",
    "fig, axes = plt.subplots(2, 5, figsize=(25, 8))\n",
    "for i in range(2):\n",
    "    for j in range(5):\n",
    "        axes[i][j].bar(range(5), values[i*5+j])\n",
    "        axes[i][j].set_xticklabels(indices[i*5+j])\n",
    "        axes[i][j].set_title(\"answer: \"+str(online_labels[i*5+j]))\n",
    "        for x_,y_ in zip(range(5), values[i*5+j]):\n",
    "            axes[i][j].text(x_ - 0.25, y_, '%.3f'%y_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}