Behavioral-Cloning.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import csv\n",
    "from math import ceil\n",
    "\n",
    "import numpy as np\n",
    "from numpy.random import shuffle\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import cv2\n",
    "import pandas as pd\n",
    "\n",
    "import sklearn\n",
    "from sklearn.model_selection import train_test_split\n",
    "# from sklearn.utils import shuffle\n",
    "from keras.models import Sequential, Model, load_model\n",
    "from keras.layers import Cropping2D, Flatten, Dense, Convolution2D, Dropout, Lambda\n",
    "from keras import optimizers\n",
    "from keras.callbacks import ModelCheckpoint\n",
    "from keras.callbacks import EarlyStopping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "added 4896 dates from: t1-center\n",
      "added 6546 dates from: t3-center-reverse\n",
      "added 3594 dates from: t8-recovery\n",
      "added 1800 dates from: t9-hard-recovery\n",
      "added 1983 dates from: t10-recovery-normal-lines\n",
      "added 24108 dates from: data_udacity\n",
      "total data = 42927\n"
     ]
    }
   ],
   "source": [
    "# Add which test to read. Set whether to use the right and left image in each test.\n",
    "# \"use_sides\": True -> if use left and right cameras for traning (only when recording in center position)\n",
    "# \"turning\" -> direction of circulation\n",
    "records = [\n",
    "    {\"name\": \"t1-center\", \"use_sides\": True} ,\n",
    "#     {\"name\": \"t2-center\", \"use_sides\": True} ,\n",
    "    {\"name\": \"t3-center-reverse\", \"use_sides\": True} ,\n",
    "#     {\"name\": \"t4-center-reverse\", \"use_sides\": True} ,\n",
    "    {\"name\": \"t8-recovery\", \"use_sides\": True} ,\n",
    "    {\"name\": \"t9-hard-recovery\", \"use_sides\": True} ,\n",
    "    {\"name\": \"t10-recovery-normal-lines\", \"use_sides\": True} ,\n",
    "    {\"name\": \"data_udacity\", \"use_sides\": True}\n",
    "]\n",
    "\n",
    "# Read CSVs\n",
    "samples_array = [[],[]]\n",
    "data_len = 0\n",
    "for record in records:\n",
    "    with open('./data/'+record[\"name\"]+'/driving_log.csv') as csvfile:\n",
    "        reader = csv.reader(csvfile)\n",
    "        for line in reader:\n",
    "            center_img = line[0]\n",
    "            steering = float(line[3])\n",
    "            samples_array[0].append(center_img)\n",
    "            samples_array[1].append(steering)\n",
    "            \n",
    "            if record[\"use_sides\"]:\n",
    "                correction = 0.25\n",
    "                steering_left = steering + correction\n",
    "                steering_right = steering - correction\n",
    "                    \n",
    "                left_img = line[1]\n",
    "                samples_array[0].append(left_img)\n",
    "                samples_array[1].append(steering_left)\n",
    "                \n",
    "                right_img = line[2]\n",
    "                samples_array[0].append(right_img)\n",
    "                samples_array[1].append(steering_right)\n",
    "                \n",
    "    new_len = len(samples_array[0])-data_len\n",
    "    data_len += new_len\n",
    "    print(\"added \"+str(new_len)+\" dates from: \"+record[\"name\"])\n",
    "        \n",
    "samples_array = np.array(samples_array).T\n",
    "print(\"total data = \"+str(len(samples_array)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# test_img_route = './data/t5-center-hard-curves/IMG/center_2021_07_20_20_24_41_681.jpg'\n",
    "# test_img = cv2.imread(test_img_route)\n",
    "# crop_img = test_img[50:70, 0:1000]\n",
    "# plt.imshow(crop_img)\n",
    "# plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\envs\\tf1\\lib\\site-packages\\ipykernel_launcher.py:2: DeprecationWarning: `np.float` is a deprecated alias for the builtin `float`. To silence this warning, use `float` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.float64` here.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  \n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "steering = samples_array[:,1].astype(np.float)\n",
    "n, bins, p = plt.hist(steering, bins=100)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\envs\\tf1\\lib\\site-packages\\ipykernel_launcher.py:12: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  if sys.path[0] == '':\n"
     ]
    }
   ],
   "source": [
    "# Crop high peaks of histogram\n",
    "bins_cropped = 3\n",
    "\n",
    "n = np.array(n)\n",
    "crop_index = n.argsort()[-bins_cropped:]\n",
    "crop_index = np.flip(crop_index)\n",
    "\n",
    "remove_indx = []\n",
    "for i in range(bins_cropped):\n",
    "    indx_delete = crop_index[i]\n",
    "    new_bean_values = (n[indx_delete+1]+n[indx_delete-1])/2 # mean of neighbors beans. This will be the new value of cropped bean\n",
    "    nro_delete = np.int(n[crop_index[i]]-new_bean_values) # amount to delete\n",
    "    \n",
    "    indx = np.where((bins[indx_delete]<=steering) & (steering<bins[indx_delete+1]))\n",
    "    indx = np.ravel(indx)\n",
    "    np.random.shuffle(indx)\n",
    "    remove_indx.extend(indx[0:nro_delete])\n",
    "\n",
    "samples_array_balanced = []\n",
    "for i , s in enumerate(samples_array):\n",
    "    if i not in remove_indx:\n",
    "        samples_array_balanced.append(s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\envs\\tf1\\lib\\site-packages\\ipykernel_launcher.py:2: DeprecationWarning: `np.float` is a deprecated alias for the builtin `float`. To silence this warning, use `float` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.float64` here.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  \n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "steering = np.array(samples_array_balanced)[:,1].astype(np.float)\n",
    "sns.displot(steering,bins=100)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Nvidia End to End Learning for Self-Driving Cars\n",
    "Paper: https://images.nvidia.com/content/tegra/automotive/images/2016/solutions/pdf/end-to-end-dl-using-px.pdf\n",
    "<img src=\"nvidia-net.png\" alt=\"NvidiaE2ENet\" style=\"width: 400px;\"/>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_model():\n",
    "    ch, row, col = 3, 80, 320  # Trimmed image format\n",
    "    ch, row, col = 3, 80, 320  # Trimmed image format\n",
    "\n",
    "    # Model: Nvidia End to End Learning for Self-Driving Cars\n",
    "    model = Sequential()\n",
    "    \n",
    "    # Preprocess incoming data, crop interest zone\n",
    "    model.add(Cropping2D(cropping=((60,20), (0,0)), input_shape=(160,320,3)))\n",
    "    # Preprocess incoming data, centered around zero with small standard deviation \n",
    "    model.add(Lambda(lambda x: x/127.5 - 1.))\n",
    "\n",
    "    model.add(Convolution2D(24, (5, 5), strides=(2, 2), activation='relu'))\n",
    "    model.add(Convolution2D(36, (5, 5), strides=(2, 2), activation='relu'))\n",
    "    model.add(Convolution2D(48, (5, 5), strides=(2, 2), activation='relu'))\n",
    "    model.add(Convolution2D(64, (3, 3), activation='relu'))\n",
    "    model.add(Convolution2D(64, (3, 3), activation='relu'))\n",
    "    model.add(Flatten())\n",
    "    model.add(Dense(100))\n",
    "    model.add(Dropout(0.5))\n",
    "    model.add(Dense(50))\n",
    "    model.add(Dropout(0.5))\n",
    "    model.add(Dense(10))\n",
    "    model.add(Dense(1))\n",
    "    \n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# from keras import backend as K\n",
    "# inp = model.input                                           # input placeholder\n",
    "# outputs = [layer.output for layer in model.layers]          # all layer outputs\n",
    "# functors = [K.function([inp], [out]) for out in outputs]    # evaluation functions\n",
    "\n",
    "# # Testing\n",
    "# test_img_route = './data/t5-center-hard-curves/IMG/center_2021_07_20_20_24_41_681.jpg'\n",
    "# test_img = cv2.imread(test_img_route)\n",
    "# test_img = np.expand_dims(test_img, axis=0)\n",
    "# print(test_img.shape)\n",
    "# input_shape=(160,320,3)\n",
    "# layer_outs = [func([test_img, 1.]) for func in functors]\n",
    "\n",
    "# # Display normalized output\n",
    "# a = np.array(layer_outs[1])[0][0]\n",
    "# plt.imshow(a)\n",
    "# plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def change_contrast(img):\n",
    "    hsv = cv2.cvtColor(img, cv2.COLOR_RGB2HSV)\n",
    "    scale = np.random.uniform(.6, 1.2)\n",
    "    hsv[:, :, 2] = hsv[:, :, 2] * scale\n",
    "    return cv2.cvtColor(hsv, cv2.COLOR_HSV2RGB)\n",
    "\n",
    "def generator(samples_array, batch_size=32, traning_mode = False):\n",
    "    num_samples = len(samples_array[0])\n",
    "    while 1: # Loop forever so the generator never terminates\n",
    "        shuffle(samples_array)\n",
    "        for offset in range(0, num_samples, batch_size):\n",
    "            batch_samples = samples_array[offset:offset+batch_size]\n",
    "            images = []\n",
    "            angles = []\n",
    "            for batch_sample in batch_samples:\n",
    "                file_name = batch_sample[0].split(\"\\\\\")[-1]\n",
    "                test_name = batch_sample[0].split(\"\\\\\")[-3]\n",
    "                name = './data/'+test_name+'/IMG/'+batch_sample[0].split(\"\\\\\")[-1]\n",
    "                img = cv2.imread(name)\n",
    "                ang = float(batch_sample[1])\n",
    "                \n",
    "                # Generate random flipped images\n",
    "                # Only for training (we should not augment validation data)\n",
    "                if(traning_mode):\n",
    "                    if np.random.rand() < 0.5:\n",
    "                        img = np.fliplr(img)\n",
    "                        ang = -ang\n",
    "                    # Change contrast randomly\n",
    "                    if np.random.rand() < 0.5:                    \n",
    "                        img = change_contrast(img)\n",
    "                \n",
    "                images.append(img)\n",
    "                angles.append(ang)\n",
    "\n",
    "            # Trim image to only see section with road\n",
    "            X_train = np.array(images)\n",
    "            y_train = np.array(angles)\n",
    "            yield sklearn.utils.shuffle(X_train, y_train)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "77/77 [==============================] - 94s 1s/step - loss: 0.0131 - val_loss: 0.0102\n",
      "\n",
      "Epoch 00001: val_loss improved from inf to 0.01024, saving model to ./weights.best.hdf5\n",
      "Epoch 2/100\n",
      "77/77 [==============================] - 92s 1s/step - loss: 0.0121 - val_loss: 0.0098\n",
      "\n",
      "Epoch 00002: val_loss improved from 0.01024 to 0.00981, saving model to ./weights.best.hdf5\n",
      "Epoch 3/100\n",
      "77/77 [==============================] - 92s 1s/step - loss: 0.0119 - val_loss: 0.0083\n",
      "\n",
      "Epoch 00003: val_loss improved from 0.00981 to 0.00832, saving model to ./weights.best.hdf5\n",
      "Epoch 4/100\n",
      "77/77 [==============================] - 93s 1s/step - loss: 0.0117 - val_loss: 0.0093\n",
      "\n",
      "Epoch 00004: val_loss did not improve from 0.00832\n",
      "Epoch 5/100\n",
      "77/77 [==============================] - 92s 1s/step - loss: 0.0118 - val_loss: 0.0107\n",
      "\n",
      "Epoch 00005: val_loss did not improve from 0.00832\n",
      "Epoch 6/100\n",
      "77/77 [==============================] - 93s 1s/step - loss: 0.0115 - val_loss: 0.0082\n",
      "\n",
      "Epoch 00006: val_loss improved from 0.00832 to 0.00817, saving model to ./weights.best.hdf5\n",
      "Epoch 7/100\n",
      "77/77 [==============================] - 93s 1s/step - loss: 0.0110 - val_loss: 0.0094\n",
      "\n",
      "Epoch 00007: val_loss did not improve from 0.00817\n",
      "Epoch 8/100\n",
      "77/77 [==============================] - 92s 1s/step - loss: 0.0111 - val_loss: 0.0103\n",
      "\n",
      "Epoch 00008: val_loss did not improve from 0.00817\n",
      "Epoch 9/100\n",
      "77/77 [==============================] - 92s 1s/step - loss: 0.0108 - val_loss: 0.0087\n",
      "\n",
      "Epoch 00009: val_loss did not improve from 0.00817\n",
      "Epoch 10/100\n",
      "77/77 [==============================] - 92s 1s/step - loss: 0.0109 - val_loss: 0.0099\n",
      "\n",
      "Epoch 00010: val_loss did not improve from 0.00817\n",
      "Epoch 11/100\n",
      "77/77 [==============================] - 90s 1s/step - loss: 0.0104 - val_loss: 0.0123\n",
      "\n",
      "Epoch 00011: val_loss did not improve from 0.00817\n",
      "Epoch 12/100\n",
      "77/77 [==============================] - 89s 1s/step - loss: 0.0104 - val_loss: 0.0094\n",
      "\n",
      "Epoch 00012: val_loss did not improve from 0.00817\n",
      "Epoch 13/100\n",
      "77/77 [==============================] - 89s 1s/step - loss: 0.0104 - val_loss: 0.0113\n",
      "\n",
      "Epoch 00013: val_loss did not improve from 0.00817\n",
      "Epoch 14/100\n",
      "77/77 [==============================] - 90s 1s/step - loss: 0.0105 - val_loss: 0.0096\n",
      "\n",
      "Epoch 00014: val_loss did not improve from 0.00817\n",
      "Epoch 15/100\n",
      "77/77 [==============================] - 89s 1s/step - loss: 0.0105 - val_loss: 0.0114\n",
      "\n",
      "Epoch 00015: val_loss did not improve from 0.00817\n",
      "Epoch 16/100\n",
      "77/77 [==============================] - 89s 1s/step - loss: 0.0102 - val_loss: 0.0120\n",
      "\n",
      "Epoch 00016: val_loss did not improve from 0.00817\n",
      "Epoch 17/100\n",
      "77/77 [==============================] - 89s 1s/step - loss: 0.0102 - val_loss: 0.0122\n",
      "\n",
      "Epoch 00017: val_loss did not improve from 0.00817\n",
      "Epoch 18/100\n",
      "77/77 [==============================] - 89s 1s/step - loss: 0.0097 - val_loss: 0.0103\n",
      "\n",
      "Epoch 00018: val_loss did not improve from 0.00817\n",
      "Epoch 19/100\n",
      "77/77 [==============================] - 90s 1s/step - loss: 0.0097 - val_loss: 0.0097\n",
      "\n",
      "Epoch 00019: val_loss did not improve from 0.00817\n",
      "Epoch 20/100\n",
      "77/77 [==============================] - 89s 1s/step - loss: 0.0095 - val_loss: 0.0122\n",
      "\n",
      "Epoch 00020: val_loss did not improve from 0.00817\n",
      "Epoch 21/100\n",
      "77/77 [==============================] - 89s 1s/step - loss: 0.0098 - val_loss: 0.0090\n",
      "\n",
      "Epoch 00021: val_loss did not improve from 0.00817\n",
      "Epoch 22/100\n",
      "77/77 [==============================] - 89s 1s/step - loss: 0.0095 - val_loss: 0.0119\n",
      "\n",
      "Epoch 00022: val_loss did not improve from 0.00817\n",
      "Epoch 23/100\n",
      "77/77 [==============================] - 89s 1s/step - loss: 0.0092 - val_loss: 0.0109\n",
      "\n",
      "Epoch 00023: val_loss did not improve from 0.00817\n",
      "Epoch 24/100\n",
      "77/77 [==============================] - 89s 1s/step - loss: 0.0095 - val_loss: 0.0095\n",
      "\n",
      "Epoch 00024: val_loss did not improve from 0.00817\n",
      "Epoch 25/100\n",
      "77/77 [==============================] - 89s 1s/step - loss: 0.0092 - val_loss: 0.0101\n",
      "\n",
      "Epoch 00025: val_loss did not improve from 0.00817\n",
      "Epoch 26/100\n",
      "77/77 [==============================] - 89s 1s/step - loss: 0.0092 - val_loss: 0.0093\n",
      "\n",
      "Epoch 00026: val_loss did not improve from 0.00817\n",
      "Epoch 27/100\n",
      "77/77 [==============================] - 89s 1s/step - loss: 0.0088 - val_loss: 0.0100\n",
      "\n",
      "Epoch 00027: val_loss did not improve from 0.00817\n",
      "Epoch 28/100\n",
      "77/77 [==============================] - 89s 1s/step - loss: 0.0088 - val_loss: 0.0102\n",
      "\n",
      "Epoch 00028: val_loss did not improve from 0.00817\n",
      "Epoch 29/100\n",
      "77/77 [==============================] - 89s 1s/step - loss: 0.0086 - val_loss: 0.0088\n",
      "\n",
      "Epoch 00029: val_loss did not improve from 0.00817\n",
      "Epoch 30/100\n",
      "77/77 [==============================] - 90s 1s/step - loss: 0.0086 - val_loss: 0.0139\n",
      "\n",
      "Epoch 00030: val_loss did not improve from 0.00817\n",
      "Epoch 31/100\n",
      "77/77 [==============================] - 89s 1s/step - loss: 0.0088 - val_loss: 0.0101\n",
      "\n",
      "Epoch 00031: val_loss did not improve from 0.00817\n",
      "Epoch 32/100\n",
      "77/77 [==============================] - 90s 1s/step - loss: 0.0086 - val_loss: 0.0090\n",
      "\n",
      "Epoch 00032: val_loss did not improve from 0.00817\n",
      "Epoch 33/100\n",
      "77/77 [==============================] - 89s 1s/step - loss: 0.0088 - val_loss: 0.0093\n",
      "\n",
      "Epoch 00033: val_loss did not improve from 0.00817\n",
      "Epoch 34/100\n",
      "77/77 [==============================] - 89s 1s/step - loss: 0.0083 - val_loss: 0.0098\n",
      "\n",
      "Epoch 00034: val_loss did not improve from 0.00817\n",
      "Epoch 35/100\n",
      "77/77 [==============================] - 89s 1s/step - loss: 0.0084 - val_loss: 0.0134\n",
      "\n",
      "Epoch 00035: val_loss did not improve from 0.00817\n",
      "Epoch 36/100\n",
      "77/77 [==============================] - 89s 1s/step - loss: 0.0081 - val_loss: 0.0092\n",
      "\n",
      "Epoch 00036: val_loss did not improve from 0.00817\n",
      "Epoch 37/100\n",
      "77/77 [==============================] - 89s 1s/step - loss: 0.0084 - val_loss: 0.0118\n",
      "\n",
      "Epoch 00037: val_loss did not improve from 0.00817\n",
      "Epoch 38/100\n",
      "77/77 [==============================] - 89s 1s/step - loss: 0.0081 - val_loss: 0.0095\n",
      "\n",
      "Epoch 00038: val_loss did not improve from 0.00817\n",
      "Epoch 39/100\n",
      "77/77 [==============================] - 89s 1s/step - loss: 0.0083 - val_loss: 0.0107\n",
      "\n",
      "Epoch 00039: val_loss did not improve from 0.00817\n",
      "Epoch 40/100\n",
      "77/77 [==============================] - 89s 1s/step - loss: 0.0079 - val_loss: 0.0117\n",
      "\n",
      "Epoch 00040: val_loss did not improve from 0.00817\n",
      "Epoch 41/100\n",
      "77/77 [==============================] - 89s 1s/step - loss: 0.0083 - val_loss: 0.0086\n",
      "\n",
      "Epoch 00041: val_loss did not improve from 0.00817\n",
      "Epoch 42/100\n",
      "77/77 [==============================] - 89s 1s/step - loss: 0.0078 - val_loss: 0.0110\n",
      "\n",
      "Epoch 00042: val_loss did not improve from 0.00817\n",
      "Epoch 43/100\n",
      "77/77 [==============================] - 89s 1s/step - loss: 0.0078 - val_loss: 0.0107\n",
      "\n",
      "Epoch 00043: val_loss did not improve from 0.00817\n",
      "Epoch 44/100\n",
      "77/77 [==============================] - 89s 1s/step - loss: 0.0075 - val_loss: 0.0110\n",
      "\n",
      "Epoch 00044: val_loss did not improve from 0.00817\n",
      "Epoch 45/100\n",
      "77/77 [==============================] - 89s 1s/step - loss: 0.0078 - val_loss: 0.0121\n",
      "\n",
      "Epoch 00045: val_loss did not improve from 0.00817\n",
      "Epoch 46/100\n",
      "77/77 [==============================] - 89s 1s/step - loss: 0.0078 - val_loss: 0.0109\n",
      "\n",
      "Epoch 00046: val_loss did not improve from 0.00817\n",
      "Epoch 47/100\n",
      "77/77 [==============================] - 89s 1s/step - loss: 0.0076 - val_loss: 0.0120\n",
      "\n",
      "Epoch 00047: val_loss did not improve from 0.00817\n",
      "Epoch 48/100\n",
      "77/77 [==============================] - 89s 1s/step - loss: 0.0077 - val_loss: 0.0134\n",
      "\n",
      "Epoch 00048: val_loss did not improve from 0.00817\n",
      "Epoch 49/100\n",
      "77/77 [==============================] - 89s 1s/step - loss: 0.0077 - val_loss: 0.0085\n",
      "\n",
      "Epoch 00049: val_loss did not improve from 0.00817\n",
      "Epoch 50/100\n",
      "77/77 [==============================] - 89s 1s/step - loss: 0.0073 - val_loss: 0.0145\n",
      "\n",
      "Epoch 00050: val_loss did not improve from 0.00817\n",
      "Epoch 51/100\n",
      "77/77 [==============================] - 89s 1s/step - loss: 0.0075 - val_loss: 0.0133\n",
      "\n",
      "Epoch 00051: val_loss did not improve from 0.00817\n",
      "Epoch 52/100\n",
      "77/77 [==============================] - 89s 1s/step - loss: 0.0075 - val_loss: 0.0107\n",
      "\n",
      "Epoch 00052: val_loss did not improve from 0.00817\n",
      "Epoch 53/100\n",
      "77/77 [==============================] - 89s 1s/step - loss: 0.0076 - val_loss: 0.0109\n",
      "\n",
      "Epoch 00053: val_loss did not improve from 0.00817\n",
      "Epoch 54/100\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "77/77 [==============================] - 89s 1s/step - loss: 0.0072 - val_loss: 0.0102\n",
      "\n",
      "Epoch 00054: val_loss did not improve from 0.00817\n",
      "Epoch 55/100\n",
      "77/77 [==============================] - 90s 1s/step - loss: 0.0072 - val_loss: 0.0122\n",
      "\n",
      "Epoch 00055: val_loss did not improve from 0.00817\n",
      "Epoch 56/100\n",
      "77/77 [==============================] - 89s 1s/step - loss: 0.0071 - val_loss: 0.0159\n",
      "\n",
      "Epoch 00056: val_loss did not improve from 0.00817\n",
      "Epoch 57/100\n",
      "77/77 [==============================] - 89s 1s/step - loss: 0.0071 - val_loss: 0.0115\n",
      "\n",
      "Epoch 00057: val_loss did not improve from 0.00817\n",
      "Epoch 58/100\n",
      "77/77 [==============================] - 89s 1s/step - loss: 0.0069 - val_loss: 0.0134\n",
      "\n",
      "Epoch 00058: val_loss did not improve from 0.00817\n",
      "Epoch 59/100\n",
      "77/77 [==============================] - 89s 1s/step - loss: 0.0073 - val_loss: 0.0151\n",
      "\n",
      "Epoch 00059: val_loss did not improve from 0.00817\n",
      "Epoch 60/100\n",
      "77/77 [==============================] - 89s 1s/step - loss: 0.0071 - val_loss: 0.0107\n",
      "\n",
      "Epoch 00060: val_loss did not improve from 0.00817\n",
      "Epoch 61/100\n",
      "77/77 [==============================] - 89s 1s/step - loss: 0.0073 - val_loss: 0.0139\n",
      "\n",
      "Epoch 00061: val_loss did not improve from 0.00817\n",
      "Epoch 62/100\n",
      "77/77 [==============================] - 89s 1s/step - loss: 0.0069 - val_loss: 0.0149\n",
      "\n",
      "Epoch 00062: val_loss did not improve from 0.00817\n",
      "Epoch 63/100\n",
      "77/77 [==============================] - 89s 1s/step - loss: 0.0069 - val_loss: 0.0131\n",
      "\n",
      "Epoch 00063: val_loss did not improve from 0.00817\n",
      "Epoch 64/100\n",
      "77/77 [==============================] - 89s 1s/step - loss: 0.0066 - val_loss: 0.0178\n",
      "\n",
      "Epoch 00064: val_loss did not improve from 0.00817\n",
      "Epoch 65/100\n",
      "77/77 [==============================] - 89s 1s/step - loss: 0.0069 - val_loss: 0.0116\n",
      "\n",
      "Epoch 00065: val_loss did not improve from 0.00817\n",
      "Epoch 66/100\n",
      "77/77 [==============================] - 89s 1s/step - loss: 0.0068 - val_loss: 0.0179\n",
      "\n",
      "Epoch 00066: val_loss did not improve from 0.00817\n",
      "Epoch 67/100\n",
      "77/77 [==============================] - 89s 1s/step - loss: 0.0069 - val_loss: 0.0102\n",
      "\n",
      "Epoch 00067: val_loss did not improve from 0.00817\n",
      "Epoch 68/100\n",
      "77/77 [==============================] - 89s 1s/step - loss: 0.0068 - val_loss: 0.0102\n",
      "\n",
      "Epoch 00068: val_loss did not improve from 0.00817\n",
      "Epoch 69/100\n",
      "77/77 [==============================] - 89s 1s/step - loss: 0.0068 - val_loss: 0.0105\n",
      "\n",
      "Epoch 00069: val_loss did not improve from 0.00817\n",
      "Epoch 70/100\n",
      "77/77 [==============================] - 89s 1s/step - loss: 0.0068 - val_loss: 0.0151\n",
      "\n",
      "Epoch 00070: val_loss did not improve from 0.00817\n",
      "Epoch 71/100\n",
      "77/77 [==============================] - 89s 1s/step - loss: 0.0068 - val_loss: 0.0129\n",
      "\n",
      "Epoch 00071: val_loss did not improve from 0.00817\n",
      "Epoch 72/100\n",
      "77/77 [==============================] - 89s 1s/step - loss: 0.0067 - val_loss: 0.0144\n",
      "\n",
      "Epoch 00072: val_loss did not improve from 0.00817\n",
      "Epoch 73/100\n",
      "77/77 [==============================] - 89s 1s/step - loss: 0.0065 - val_loss: 0.0120\n",
      "\n",
      "Epoch 00073: val_loss did not improve from 0.00817\n",
      "Epoch 74/100\n",
      "77/77 [==============================] - 89s 1s/step - loss: 0.0067 - val_loss: 0.0115\n",
      "\n",
      "Epoch 00074: val_loss did not improve from 0.00817\n",
      "Epoch 75/100\n",
      "77/77 [==============================] - 89s 1s/step - loss: 0.0065 - val_loss: 0.0119\n",
      "\n",
      "Epoch 00075: val_loss did not improve from 0.00817\n",
      "Epoch 76/100\n",
      "77/77 [==============================] - 89s 1s/step - loss: 0.0064 - val_loss: 0.0133\n",
      "\n",
      "Epoch 00076: val_loss did not improve from 0.00817\n",
      "Epoch 77/100\n",
      "77/77 [==============================] - 89s 1s/step - loss: 0.0065 - val_loss: 0.0119\n",
      "\n",
      "Epoch 00077: val_loss did not improve from 0.00817\n",
      "Epoch 78/100\n",
      "77/77 [==============================] - 93s 1s/step - loss: 0.0067 - val_loss: 0.0147\n",
      "\n",
      "Epoch 00078: val_loss did not improve from 0.00817\n",
      "Epoch 79/100\n",
      "77/77 [==============================] - 92s 1s/step - loss: 0.0064 - val_loss: 0.0146\n",
      "\n",
      "Epoch 00079: val_loss did not improve from 0.00817\n",
      "Epoch 80/100\n",
      "77/77 [==============================] - 92s 1s/step - loss: 0.0064 - val_loss: 0.0130\n",
      "\n",
      "Epoch 00080: val_loss did not improve from 0.00817\n",
      "Epoch 81/100\n",
      "77/77 [==============================] - 92s 1s/step - loss: 0.0062 - val_loss: 0.0103\n",
      "\n",
      "Epoch 00081: val_loss did not improve from 0.00817\n",
      "Epoch 82/100\n",
      "77/77 [==============================] - 92s 1s/step - loss: 0.0064 - val_loss: 0.0111\n",
      "\n",
      "Epoch 00082: val_loss did not improve from 0.00817\n",
      "Epoch 83/100\n",
      "77/77 [==============================] - 94s 1s/step - loss: 0.0065 - val_loss: 0.0105\n",
      "\n",
      "Epoch 00083: val_loss did not improve from 0.00817\n",
      "Epoch 84/100\n",
      "77/77 [==============================] - 90s 1s/step - loss: 0.0064 - val_loss: 0.0139\n",
      "\n",
      "Epoch 00084: val_loss did not improve from 0.00817\n",
      "Epoch 85/100\n",
      "77/77 [==============================] - 89s 1s/step - loss: 0.0062 - val_loss: 0.0131\n",
      "\n",
      "Epoch 00085: val_loss did not improve from 0.00817\n",
      "Epoch 86/100\n",
      "77/77 [==============================] - 89s 1s/step - loss: 0.0061 - val_loss: 0.0110\n",
      "\n",
      "Epoch 00086: val_loss did not improve from 0.00817\n",
      "Epoch 87/100\n",
      "77/77 [==============================] - 90s 1s/step - loss: 0.0062 - val_loss: 0.0100\n",
      "\n",
      "Epoch 00087: val_loss did not improve from 0.00817\n",
      "Epoch 88/100\n",
      "77/77 [==============================] - 89s 1s/step - loss: 0.0062 - val_loss: 0.0111\n",
      "\n",
      "Epoch 00088: val_loss did not improve from 0.00817\n",
      "Epoch 89/100\n",
      "77/77 [==============================] - 89s 1s/step - loss: 0.0060 - val_loss: 0.0114\n",
      "\n",
      "Epoch 00089: val_loss did not improve from 0.00817\n",
      "Epoch 90/100\n",
      "77/77 [==============================] - 89s 1s/step - loss: 0.0062 - val_loss: 0.0120\n",
      "\n",
      "Epoch 00090: val_loss did not improve from 0.00817\n",
      "Epoch 91/100\n",
      "77/77 [==============================] - 89s 1s/step - loss: 0.0059 - val_loss: 0.0121\n",
      "\n",
      "Epoch 00091: val_loss did not improve from 0.00817\n",
      "Epoch 92/100\n",
      "77/77 [==============================] - 89s 1s/step - loss: 0.0063 - val_loss: 0.0116\n",
      "\n",
      "Epoch 00092: val_loss did not improve from 0.00817\n",
      "Epoch 93/100\n",
      "77/77 [==============================] - 89s 1s/step - loss: 0.0060 - val_loss: 0.0115\n",
      "\n",
      "Epoch 00093: val_loss did not improve from 0.00817\n",
      "Epoch 94/100\n",
      "77/77 [==============================] - 89s 1s/step - loss: 0.0061 - val_loss: 0.0115\n",
      "\n",
      "Epoch 00094: val_loss did not improve from 0.00817\n",
      "Epoch 95/100\n",
      "77/77 [==============================] - 90s 1s/step - loss: 0.0061 - val_loss: 0.0223\n",
      "\n",
      "Epoch 00095: val_loss did not improve from 0.00817\n",
      "Epoch 96/100\n",
      "77/77 [==============================] - 89s 1s/step - loss: 0.0059 - val_loss: 0.0150\n",
      "\n",
      "Epoch 00096: val_loss did not improve from 0.00817\n",
      "Epoch 97/100\n",
      "77/77 [==============================] - 89s 1s/step - loss: 0.0059 - val_loss: 0.0125\n",
      "\n",
      "Epoch 00097: val_loss did not improve from 0.00817\n",
      "Epoch 98/100\n",
      "77/77 [==============================] - 89s 1s/step - loss: 0.0057 - val_loss: 0.0108\n",
      "\n",
      "Epoch 00098: val_loss did not improve from 0.00817\n",
      "Epoch 99/100\n",
      "77/77 [==============================] - 89s 1s/step - loss: 0.0061 - val_loss: 0.0120\n",
      "\n",
      "Epoch 00099: val_loss did not improve from 0.00817\n",
      "Epoch 100/100\n",
      "77/77 [==============================] - 89s 1s/step - loss: 0.0061 - val_loss: 0.0141\n",
      "\n",
      "Epoch 00100: val_loss did not improve from 0.00817\n"
     ]
    }
   ],
   "source": [
    "# Set our batch size\n",
    "batch_size=256\n",
    "\n",
    "# Split in train/validation\n",
    "shuffle(samples_array_balanced)\n",
    "train_samples, validation_samples = train_test_split(samples_array_balanced, test_size=0.3)\n",
    "\n",
    "# compile and train the model using the generator function\n",
    "train_generator = generator(train_samples, batch_size=batch_size, traning_mode  = True)\n",
    "validation_generator = generator(validation_samples, batch_size=batch_size, traning_mode  = False)\n",
    "\n",
    "# model = get_model()\n",
    "model = load_model('./test12-weights.best.hdf5')\n",
    "\n",
    "# optimizer = optimizers.Adam(lr=0.0001)\n",
    "optimizer = optimizers.Adam()\n",
    "model.compile(loss='mse', optimizer=optimizer)\n",
    "\n",
    "# checkpoints_filepath=\"./checkpoints/weights-improvement-{epoch:02d}.hdf5\"\n",
    "checkpoints_filepath=\"./weights.best.hdf5\"\n",
    "checkpoint = ModelCheckpoint(checkpoints_filepath, monitor='val_loss', \n",
    "                             verbose=1, save_best_only=True, mode='min')\n",
    "es = EarlyStopping(monitor='val_loss', mode='min', verbose=1, patience=10, min_delta=0)\n",
    "# callbacks_list = [checkpoint,es]\n",
    "callbacks_list = [checkpoint]\n",
    "\n",
    "history_object = model.fit_generator(train_generator,\n",
    "            steps_per_epoch=ceil(len(train_samples)/batch_size),\n",
    "            validation_data=validation_generator,\n",
    "            validation_steps=ceil(len(validation_samples)/batch_size),\n",
    "            epochs=100, callbacks=callbacks_list, verbose=1)\n",
    "\n",
    "model.save('model.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dict_keys(['val_loss', 'loss'])\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "### print the keys contained in the history object\n",
    "print(history_object.history.keys())\n",
    "\n",
    "### plot the training and validation loss for each epoch\n",
    "plt.plot(history_object.history['loss'])\n",
    "plt.plot(history_object.history['val_loss'])\n",
    "plt.title('model mean squared error loss')\n",
    "plt.ylabel('mean squared error loss')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['training set', 'validation set'], loc='upper right')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# def balance_data(samples, visulization_flag ,N=60, K=1,  bins=100):\n",
    "#     \"\"\" Crop the top part of the steering angle histogram, by removing some images belong to those steering angels\n",
    "\n",
    "#     :param images: images arrays\n",
    "#     :param angles: angles arrays which\n",
    "#     :param n:  The values of the histogram bins\n",
    "#     :param bins: The edges of the bins. Length nbins + 1\n",
    "#     :param K: maximum number of max bins to be cropped\n",
    "#     :param N: the max number of the images which will be used for the bin\n",
    "#     :return: images, angle\n",
    "#     \"\"\"\n",
    "\n",
    "#     angles = samples[:,1].astype(np.float)\n",
    "#     n, bins, patches = plt.hist(angles, bins=bins, color= 'orange', linewidth=0.1)\n",
    "#     angles = np.array(angles)\n",
    "#     n = np.array(n)\n",
    "\n",
    "#     idx = n.argsort()[-K:][::-1]    # find the largest K bins\n",
    "#     del_ind = []                    # collect the index which will be removed from the data\n",
    "#     for i in range(K):\n",
    "#         if n[idx[i]] > N:\n",
    "#             ind = np.where((bins[idx[i]]<=angles) & (angles<bins[idx[i]+1]))\n",
    "#             ind = np.ravel(ind)\n",
    "#             np.random.shuffle(ind)\n",
    "#             del_ind.extend(ind[:len(ind)-N])\n",
    "\n",
    "#     # angles = np.delete(angles,del_ind)\n",
    "#     balanced_samples = [v for i, v in enumerate(samples) if i not in del_ind]\n",
    "#     balanced_angles = np.delete(angles,del_ind)\n",
    "\n",
    "#     plt.subplot(1,2,2)\n",
    "#     plt.hist(balanced_angles, bins=bins, color= 'orange', linewidth=0.1)\n",
    "#     plt.title('modified histogram', fontsize=20)\n",
    "#     plt.xlabel('steering angle', fontsize=20)\n",
    "#     plt.ylabel('counts', fontsize=20)\n",
    "\n",
    "#     if visulization_flag:\n",
    "#         plt.figure\n",
    "#         plt.subplot(1,2,1)\n",
    "#         n, bins, patches = plt.hist(angles, bins=bins, color='orange', linewidth=0.1)\n",
    "#         plt.title('origin histogram', fontsize=20)\n",
    "#         plt.xlabel('steering angle', fontsize=20)\n",
    "#         plt.ylabel('counts', fontsize=20)\n",
    "#         plt.show()\n",
    "\n",
    "#         plt.figure\n",
    "#         aa = np.append(balanced_angles, -balanced_angles)\n",
    "#         bb = np.append(aa, aa)\n",
    "#         plt.hist(bb, bins=bins, color='orange', linewidth=0.1)\n",
    "#         plt.title('final histogram', fontsize=20)\n",
    "#         plt.xlabel('steering angle', fontsize=20)\n",
    "#         plt.ylabel('counts', fontsize=20)\n",
    "#         plt.show()\n",
    "\n",
    "#     return balanced_samples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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