model.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "###READ IN THE LOG FILE\n",
    "import csv\n",
    "import cv2\n",
    "import numpy as np\n",
    "import sklearn\n",
    "lines=[]\n",
    "imagePaths=[]\n",
    "measurements=[]\n",
    "with open('/Volumes/USBDRIVE/Udacity/Term1/CarND-Behavioral-Cloning-P3/recording10/driving_log.csv') as csvfile:\n",
    "#with open('/Volumes/USBDRIVE/Udacity/Term1/CarND-Behavioral-Cloning-P3/recordings/driving_log.csv') as csvfile:\n",
    "    reader=csv.reader(csvfile)\n",
    "    for line in reader:\n",
    "        lines.append(line)\n",
    "        #Append center camera image\n",
    "        imagePaths.append(line[0])\n",
    "        #Append left camera image\n",
    "        imagePaths.append(line[1])\n",
    "        #Append right camera image\n",
    "        imagePaths.append(line[2])\n",
    "        #Append center camera measurement\n",
    "        measurements.append(float(line[3]))\n",
    "        #Append left camera measurement, corrected by a factor\n",
    "        measurements.append(float(line[3])+0.2)\n",
    "        #Append right camera measurement, corrected by a factor\n",
    "        measurements.append(float(line[3])-0.2)\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "###SETUP GENERATOR FUNCTION TO STREAM DATA INSTEAD OF PRE-LOADING INTO MEMORY\n",
    "def generator(samples, batch_size=128):\n",
    "\n",
    "    num_samples = len(samples)\n",
    "    while 1: # Loop forever so the generator never terminates\n",
    "        ###Shuffle the samples so as to remove any sequential data bias\n",
    "        samples = sklearn.utils.shuffle(samples)\n",
    "        for offset in range(0, num_samples, batch_size):\n",
    "            batch_samples = samples[offset:offset+batch_size]\n",
    "\n",
    "            images = []\n",
    "            angles = []\n",
    "            for imagePath, measurement in batch_samples:\n",
    "                ###READ IN IMAGES, CONVERT TO RGB SPACE\n",
    "                originalImage = cv2.imread(imagePath)\n",
    "                image = cv2.cvtColor(originalImage, cv2.COLOR_BGR2RGB)\n",
    "                images.append(image)\n",
    "                angles.append(measurement)\n",
    "                # Augment the original image by Fliping it\n",
    "                images.append(cv2.flip(image,1))\n",
    "                # Since image is flipped, flip the steering direction output as well\n",
    "                angles.append(measurement*-1.0)\n",
    "\n",
    "            \n",
    "            inputs = np.array(images)\n",
    "            outputs = np.array(angles)\n",
    "            ###Yield data via generator\n",
    "            yield sklearn.utils.shuffle(inputs, outputs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train samples: 7996\n",
      "Validation samples: 2000\n"
     ]
    }
   ],
   "source": [
    "# Splitting samples into training & validation, use 20% of data for validation\n",
    "from sklearn.model_selection import train_test_split\n",
    "samples = list(zip(imagePaths, measurements))\n",
    "train_samples, validation_samples = train_test_split(samples, test_size=0.2)\n",
    "\n",
    "print('Train samples: {}'.format(len(train_samples)))\n",
    "print('Validation samples: {}'.format(len(validation_samples)))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "#setup generators, feed data in batches of 32 images to conserve memory\n",
    "train_generator = generator(train_samples, batch_size=32)\n",
    "validation_generator = generator(validation_samples, batch_size=32)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "#DEFINE THE MODEL\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense, Dropout, Flatten, Lambda, ELU,Cropping2D\n",
    "from keras.layers.convolutional import Convolution2D\n",
    "from keras.layers.advanced_activations import LeakyReLU\n",
    "\n",
    "###INITIALIZE Keras sequential model\n",
    "model = Sequential()\n",
    "###Crop the images to remove extraneous information, focus on the immediate road ahead\n",
    "model.add(Cropping2D(cropping=((70,25), (0,0)), input_shape=(160,320,3)))###Pre-process the data, center the data\n",
    "model.add(Lambda(lambda x:x/127.5-1,input_shape=(160,320,3) ))\n",
    "\n",
    "###USE THE MODEL DEFINED IN COMMA.AI Steering model\n",
    "#https://github.com/commaai/research/blob/master/train_steering_model.py\n",
    "model.add(Convolution2D(16, 8, 8, subsample=(4, 4), border_mode=\"same\"))\n",
    "model.add(LeakyReLU(alpha=.001))   # add an advanced activation\n",
    "#model.add(ELU())\n",
    "model.add(Convolution2D(32, 5, 5, subsample=(2, 2), border_mode=\"same\"))\n",
    "model.add(LeakyReLU(alpha=.001))   # add an advanced activation\n",
    "#model.add(ELU())\n",
    "model.add(Convolution2D(64, 5, 5, subsample=(2, 2), border_mode=\"same\"))\n",
    "model.add(Flatten())\n",
    "model.add(Dropout(.2))\n",
    "model.add(LeakyReLU(alpha=.001))   # add an advanced activation\n",
    "#model.add(ELU())\n",
    "model.add(Dense(512))\n",
    "model.add(Dropout(.5))\n",
    "model.add(LeakyReLU(alpha=.001))   # add an advanced activation\n",
    "#model.add(ELU())\n",
    "model.add(Dense(1))\n",
    "###END COMMA.AI MODEL\n",
    "###COMPILE USING ADAM OPTIMIZER, SO THAT LEARNING RATE DOESNT HAVE TO BE SET MANUALLY\n",
    "model.compile(optimizer=\"adam\", loss=\"mse\")\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/3\n",
      "7936/7996 [============================>.] - ETA: 0s - loss: 0.0450"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda3/envs/carnd-term1/lib/python3.5/site-packages/keras/engine/training.py:1569: UserWarning: Epoch comprised more than `samples_per_epoch` samples, which might affect learning results. Set `samples_per_epoch` correctly to avoid this warning.\n",
      "  warnings.warn('Epoch comprised more than '\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "8000/7996 [==============================] - 101s - loss: 0.0450 - val_loss: 0.0356\n",
      "Epoch 2/3\n",
      "8056/7996 [==============================] - 101s - loss: 0.0293 - val_loss: 0.0276\n",
      "Epoch 3/3\n",
      "8000/7996 [==============================] - 100s - loss: 0.0281 - val_loss: 0.0259\n",
      "dict_keys(['val_loss', 'loss'])\n",
      "Loss\n",
      "[0.044971554368734359, 0.029347745666526398, 0.028115500599145891]\n",
      "Validation Loss\n",
      "[0.035609549609944224, 0.027640409649364532, 0.025910924363415688]\n"
     ]
    }
   ],
   "source": [
    "###FEED THE GENERATOR DATA TO THE MODEL, TRAIN\n",
    "### 3 epochs was sufficient, beyond that, seemed to overfit\n",
    "history_object = model.fit_generator(train_generator, samples_per_epoch= \\\n",
    "                 len(train_samples), validation_data=validation_generator, \\\n",
    "                 nb_val_samples=len(validation_samples), nb_epoch=3, verbose=1)\n",
    "###save the trained model\n",
    "model.save('model.h5')\n",
    "print(history_object.history.keys())\n",
    "print('Loss')\n",
    "print(history_object.history['loss'])\n",
    "print('Validation Loss')\n",
    "print(history_object.history['val_loss'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import csv\n",
    "import cv2\n",
    "import numpy as np\n",
    "import sklearn\n",
    "imagePath='flip.jpg'\n",
    "originalImage = cv2.imread(imagePath)\n",
    "#image = cv2.cvtColor(originalImage, cv2.COLOR_BGR2RGB)\n",
    "newImage=cv2.flip(originalImage,1)\n",
    "cv2.imwrite('flipped.jpg',newImage)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.utils.visualize_util import plot  \n",
    "plot(model, to_file='model.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "____________________________________________________________________________________________________\n",
      "Layer (type)                     Output Shape          Param #     Connected to                     \n",
      "====================================================================================================\n",
      "cropping2d_1 (Cropping2D)        (None, 65, 320, 3)    0           cropping2d_input_1[0][0]         \n",
      "____________________________________________________________________________________________________\n",
      "lambda_1 (Lambda)                (None, 65, 320, 3)    0           cropping2d_1[0][0]               \n",
      "____________________________________________________________________________________________________\n",
      "convolution2d_1 (Convolution2D)  (None, 17, 80, 16)    3088        lambda_1[0][0]                   \n",
      "____________________________________________________________________________________________________\n",
      "leakyrelu_1 (LeakyReLU)          (None, 17, 80, 16)    0           convolution2d_1[0][0]            \n",
      "____________________________________________________________________________________________________\n",
      "convolution2d_2 (Convolution2D)  (None, 9, 40, 32)     12832       leakyrelu_1[0][0]                \n",
      "____________________________________________________________________________________________________\n",
      "leakyrelu_2 (LeakyReLU)          (None, 9, 40, 32)     0           convolution2d_2[0][0]            \n",
      "____________________________________________________________________________________________________\n",
      "convolution2d_3 (Convolution2D)  (None, 5, 20, 64)     51264       leakyrelu_2[0][0]                \n",
      "____________________________________________________________________________________________________\n",
      "flatten_1 (Flatten)              (None, 6400)          0           convolution2d_3[0][0]            \n",
      "____________________________________________________________________________________________________\n",
      "dropout_1 (Dropout)              (None, 6400)          0           flatten_1[0][0]                  \n",
      "____________________________________________________________________________________________________\n",
      "leakyrelu_3 (LeakyReLU)          (None, 6400)          0           dropout_1[0][0]                  \n",
      "____________________________________________________________________________________________________\n",
      "dense_1 (Dense)                  (None, 512)           3277312     leakyrelu_3[0][0]                \n",
      "____________________________________________________________________________________________________\n",
      "dropout_2 (Dropout)              (None, 512)           0           dense_1[0][0]                    \n",
      "____________________________________________________________________________________________________\n",
      "leakyrelu_4 (LeakyReLU)          (None, 512)           0           dropout_2[0][0]                  \n",
      "____________________________________________________________________________________________________\n",
      "dense_2 (Dense)                  (None, 1)             513         leakyrelu_4[0][0]                \n",
      "====================================================================================================\n",
      "Total params: 3,345,009\n",
      "Trainable params: 3,345,009\n",
      "Non-trainable params: 0\n",
      "____________________________________________________________________________________________________\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "print(model.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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