model-AWS.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## Loading data\n",
    "\n",
    "The dataset of Ryan Moore has been used [https://nd013.s3.amazonaws.com/track1.zip](here)\n",
    "\n",
    "The dataset has two folders 'driving' folder and 'recovery' folder. Currently the driving folder is used only.\n",
    "\n",
    "The driving folder contains csv file for steering measurements and IMG folder. The images are in three colums, snapped from center, right and left cameras. They have a corresponding steering angle – the steer the car had to make in that frame to stay on the track. We load both into images[ ] and measurements[ ] arrays. \n",
    "The csv file named 'driving_log.csv' has 7874 x 7 data entries. We are interested in the first 4 columns. \n",
    "\n",
    "Center image path | Left image path | Right image path | Steering angle range (-1,+1)\n",
    "\n",
    "if steering <0 : steering to left , if steering > 0 : seering to right, steering = 0 : idle \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "import csv\n",
    "import cv2\n",
    "import numpy as np\n",
    "import math\n",
    "\n",
    "lines = []\n",
    "with open('./project3/track1/drive/driving_log.csv') as csvfile:\n",
    "    reader = csv.reader(csvfile)\n",
    "    for line in reader:\n",
    "        lines.append(line)\n",
    "\n",
    "images = []\n",
    "measurements = []\n",
    "correction = 0.2\n",
    "counter = 0\n",
    "\n",
    "#math.ceil(float(len(lines))/2)\n",
    "\n",
    "for line in lines[:6500]:\n",
    "    source_path = line[0]\n",
    "    source_path_left = line[1]\n",
    "    source_path_right = line[2]\n",
    "    tokens = source_path.split('/')\n",
    "    filename = tokens[-1]\n",
    "    local_path = \"./project3/track1/drive/IMG/\" + filename\n",
    "    tokens_left = source_path_left.split('/')\n",
    "    filename_left = tokens_left[-1]\n",
    "    local_path_left = \"./project3/track1/drive/IMG/\" + filename_left\n",
    "    tokens_right = source_path_right.split('/')\n",
    "    filename_right = tokens_right[-1]\n",
    "    local_path_right = \"./project3/track1/drive/IMG/\" + filename_right\n",
    "    image = cv2.imread(local_path)\n",
    "    image_left = cv2.imread(local_path_left)\n",
    "    image_right = cv2.imread(local_path_right)\n",
    "    measurement = line[3]\n",
    "    if float(measurement) == 0:\n",
    "        # if stearing = 0 ignore 5 out of 6 of the images, since they are roughly 6 times more than the others\n",
    "        counter += 1\n",
    "        if counter == 10:\n",
    "            counter == 0\n",
    "            images.append(image)\n",
    "            images.append(image_left)\n",
    "            images.append(image_right)\n",
    "            measurements.append(float(measurement))\n",
    "            measurements.append(float(measurement)+correction) # Add correction for steering for left images\n",
    "            measurements.append(float(measurement)-correction) # Minus correction for steering for right images\n",
    "    else:\n",
    "        images.append(image)\n",
    "        images.append(image_left)\n",
    "        images.append(image_right)\n",
    "        measurements.append(float(measurement))\n",
    "        measurements.append(float(measurement)+correction) # Add correction for steering for left images\n",
    "        measurements.append(float(measurement)-correction) # Minus correction for steering for right images\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "#### Adding recovery set of images"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "lines = []\n",
    "with open('./project3/track1/recovery/driving_log.csv') as csvfile:\n",
    "    reader = csv.reader(csvfile)\n",
    "    for line in reader:\n",
    "        lines.append(line)\n",
    "\n",
    "for line in lines:\n",
    "    source_path = line[0]\n",
    "    source_path_left = line[1]\n",
    "    source_path_right = line[2]\n",
    "    tokens = source_path.split('/')\n",
    "    filename = tokens[-1]\n",
    "    local_path = \"./project3/track1/recovery/IMG/\" + filename\n",
    "    tokens_left = source_path_left.split('/')\n",
    "    filename_left = tokens_left[-1]\n",
    "    local_path_left = \"./project3/track1/recovery/IMG/\" + filename_left\n",
    "    tokens_right = source_path_right.split('/')\n",
    "    filename_right = tokens_right[-1]\n",
    "    local_path_right = \"./project3/track1/recovery/IMG/\" + filename_right\n",
    "    image = cv2.imread(local_path)\n",
    "    image_left = cv2.imread(local_path_left)\n",
    "    image_right = cv2.imread(local_path_right)\n",
    "    measurement = line[3]\n",
    "    if float(measurement) == 0:\n",
    "        # if stearing = 0 ignore 5 out of 6 of the images, since they are roughly 6 times more than the others\n",
    "        counter += 1\n",
    "        if counter == 10:\n",
    "            counter == 0\n",
    "            images.append(image)\n",
    "            images.append(image_left)\n",
    "            images.append(image_right)\n",
    "            measurements.append(float(measurement))\n",
    "            measurements.append(float(measurement)+correction) # Add correction for steering for left images\n",
    "            measurements.append(float(measurement)-correction) # Minus correction for steering for right images\n",
    "    else:\n",
    "        images.append(image)\n",
    "        images.append(image_left)\n",
    "        images.append(image_right)\n",
    "        measurements.append(float(measurement))\n",
    "        measurements.append(float(measurement)+correction) # Add correction for steering for left images\n",
    "        measurements.append(float(measurement)-correction) # Minus correction for steering for right images\n",
    "\n",
    "X_train = np.array(images)\n",
    "y_train = np.array(measurements, dtype = 'float32')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(14688, 160, 320, 3) (14688,)\n"
     ]
    }
   ],
   "source": [
    "print(X_train.shape, y_train.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "#### Showing an example of an image (the last image)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "iii = cv2.imread(local_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7ff22103b710>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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UBxRVghMlnm9RSmAqS5lXGCPRUYFzkGcVDkkctyiNIkkzpHL4nkJpR57Vvu6BDxPtFulw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\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff2e8b49780>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "plt.imshow(cv2.cvtColor(iii, cv2.COLOR_RGB2BGR))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "#### Augmenting the dataset with vertically flipped images"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "images_flipped = []\n",
    "\n",
    "for image in images:\n",
    "    images_flipped.append(cv2.flip(image,1))\n",
    "\n",
    "X_train = np.vstack((X_train,np.array(images_flipped)))\n",
    "y_train_flipped = y_train * -1.\n",
    "y_train = np.concatenate((y_train, y_train_flipped ))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(29376, 160, 320, 3) (29376,)\n"
     ]
    }
   ],
   "source": [
    "print(X_train.shape, y_train.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "#### Plot  dataset histogram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff2e8b49128>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "\n",
    "binwidth = 0.05\n",
    "plt.figure(figsize=(15,7))\n",
    "plt.hist(y_train, bins=np.arange(min(y_train), max(y_train) + binwidth, binwidth))\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 28788 samples, validate on 588 samples\n",
      "Epoch 1/5\n",
      "52s - loss: 0.0506 - val_loss: 0.3422\n",
      "Epoch 2/5\n",
      "50s - loss: 0.0248 - val_loss: 0.3054\n",
      "Epoch 3/5\n",
      "50s - loss: 0.0222 - val_loss: 0.2817\n",
      "Epoch 4/5\n",
      "50s - loss: 0.0213 - val_loss: 0.2956\n",
      "Epoch 5/5\n",
      "50s - loss: 0.0204 - val_loss: 0.2668\n"
     ]
    }
   ],
   "source": [
    "import keras\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Flatten, Dense, Lambda, Cropping2D\n",
    "from keras.layers.convolutional import Convolution2D\n",
    "from keras.layers.pooling import MaxPooling2D\n",
    "\n",
    "\n",
    "model = Sequential()\n",
    "\n",
    "# Normalizing lambda layer\n",
    "model.add(Lambda(lambda x: x / 255.0 - 0.5, input_shape=(160,320,3)))\n",
    "\n",
    "# Cropping out the sky which makes about 70 pixels at the top of the frame, and the dashboard about 25 pixels at the bottom\n",
    "model.add(Cropping2D(cropping=((70,25),(1,1))))\n",
    "\n",
    "# First convolutional layer\n",
    "model.add(Convolution2D(6,5,5,activation='relu'))\n",
    "model.add(MaxPooling2D())\n",
    "\n",
    "# Second convolutional layer\n",
    "model.add(Convolution2D(6,5,5,activation='relu'))\n",
    "model.add(MaxPooling2D())\n",
    "model.add(Flatten())\n",
    "\n",
    "# Fully-connected layers\n",
    "model.add(Dense(120))\n",
    "model.add(Dense(84))\n",
    "model.add(Dense(1))\n",
    "\n",
    "model.compile(optimizer='adam', loss='mse')\n",
    "model.fit(X_train, y_train, validation_split=0.02, shuffle=True , verbose = 2, nb_epoch = 5)\n",
    "model.save('model.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "____________________________________________________________________________________________________\n",
      "Layer (type)                     Output Shape          Param #     Connected to                     \n",
      "====================================================================================================\n",
      "lambda_1 (Lambda)                (None, 160, 320, 3)   0           lambda_input_1[0][0]             \n",
      "____________________________________________________________________________________________________\n",
      "cropping2d_1 (Cropping2D)        (None, 65, 318, 3)    0           lambda_1[0][0]                   \n",
      "____________________________________________________________________________________________________\n",
      "convolution2d_1 (Convolution2D)  (None, 61, 314, 6)    456         cropping2d_1[0][0]               \n",
      "____________________________________________________________________________________________________\n",
      "maxpooling2d_1 (MaxPooling2D)    (None, 30, 157, 6)    0           convolution2d_1[0][0]            \n",
      "____________________________________________________________________________________________________\n",
      "convolution2d_2 (Convolution2D)  (None, 26, 153, 6)    906         maxpooling2d_1[0][0]             \n",
      "____________________________________________________________________________________________________\n",
      "maxpooling2d_2 (MaxPooling2D)    (None, 13, 76, 6)     0           convolution2d_2[0][0]            \n",
      "____________________________________________________________________________________________________\n",
      "flatten_1 (Flatten)              (None, 5928)          0           maxpooling2d_2[0][0]             \n",
      "____________________________________________________________________________________________________\n",
      "dense_1 (Dense)                  (None, 120)           711480      flatten_1[0][0]                  \n",
      "____________________________________________________________________________________________________\n",
      "dense_2 (Dense)                  (None, 84)            10164       dense_1[0][0]                    \n",
      "____________________________________________________________________________________________________\n",
      "dense_3 (Dense)                  (None, 1)             85          dense_2[0][0]                    \n",
      "====================================================================================================\n",
      "Total params: 723,091\n",
      "Trainable params: 723,091\n",
      "Non-trainable params: 0\n",
      "____________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "source": [
    "#### Loading the Keras model and making a visualization of the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Anaconda3\\envs\\carnd-term1\\lib\\site-packages\\keras\\engine\\topology.py:1206: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(kernel_size=(5, 5), kernel_constraint=None, activity_regularizer=None, kernel_regularizer=None, use_bias=True, trainable=True, filters=6, data_format=\"channels_last\", name=\"convolution2d_1\", bias_regularizer=None, padding=\"valid\", kernel_initializer=\"glorot_uniform\", bias_constraint=None, strides=[1, 1], activation=\"relu\")`\n",
      "  return cls(**config)\n",
      "C:\\Anaconda3\\envs\\carnd-term1\\lib\\site-packages\\keras\\engine\\topology.py:1206: UserWarning: Update your `MaxPooling2D` call to the Keras 2 API: `MaxPooling2D(strides=[2, 2], name=\"maxpooling2d_1\", padding=\"valid\", trainable=True, data_format=\"channels_last\", pool_size=[2, 2])`\n",
      "  return cls(**config)\n",
      "C:\\Anaconda3\\envs\\carnd-term1\\lib\\site-packages\\keras\\engine\\topology.py:1206: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(kernel_size=(5, 5), kernel_constraint=None, activity_regularizer=None, kernel_regularizer=None, use_bias=True, trainable=True, filters=6, data_format=\"channels_last\", name=\"convolution2d_2\", bias_regularizer=None, padding=\"valid\", kernel_initializer=\"glorot_uniform\", bias_constraint=None, strides=[1, 1], activation=\"relu\")`\n",
      "  return cls(**config)\n",
      "C:\\Anaconda3\\envs\\carnd-term1\\lib\\site-packages\\keras\\engine\\topology.py:1206: UserWarning: Update your `MaxPooling2D` call to the Keras 2 API: `MaxPooling2D(strides=[2, 2], name=\"maxpooling2d_2\", padding=\"valid\", trainable=True, data_format=\"channels_last\", pool_size=[2, 2])`\n",
      "  return cls(**config)\n",
      "C:\\Anaconda3\\envs\\carnd-term1\\lib\\site-packages\\keras\\engine\\topology.py:1206: UserWarning: Update your `Dense` call to the Keras 2 API: `Dense(input_dim=5928, kernel_constraint=None, activity_regularizer=None, units=120, kernel_initializer=\"glorot_uniform\", name=\"dense_1\", bias_regularizer=None, use_bias=True, trainable=True, kernel_regularizer=None, bias_constraint=None, activation=\"linear\")`\n",
      "  return cls(**config)\n",
      "C:\\Anaconda3\\envs\\carnd-term1\\lib\\site-packages\\keras\\engine\\topology.py:1206: UserWarning: Update your `Dense` call to the Keras 2 API: `Dense(input_dim=120, kernel_constraint=None, activity_regularizer=None, units=84, kernel_initializer=\"glorot_uniform\", name=\"dense_2\", bias_regularizer=None, use_bias=True, trainable=True, kernel_regularizer=None, bias_constraint=None, activation=\"linear\")`\n",
      "  return cls(**config)\n",
      "C:\\Anaconda3\\envs\\carnd-term1\\lib\\site-packages\\keras\\engine\\topology.py:1206: UserWarning: Update your `Dense` call to the Keras 2 API: `Dense(input_dim=84, kernel_constraint=None, activity_regularizer=None, units=1, kernel_initializer=\"glorot_uniform\", name=\"dense_3\", bias_regularizer=None, use_bias=True, trainable=True, kernel_regularizer=None, bias_constraint=None, activation=\"linear\")`\n",
      "  return cls(**config)\n"
     ]
    },
    {
     "ename": "ValueError",
     "evalue": "Optimizer weight shape (6,) not compatible with provided weight shape (5, 5, 3, 6)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-2-bf523318410b>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m      9\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m---> 10\u001b[0;31m \u001b[0mmodel\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mload_model\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'model.h5'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     11\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m     12\u001b[0m \u001b[1;31m# plot_model(model, to_file='model.png')\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mC:\\Anaconda3\\envs\\carnd-term1\\lib\\site-packages\\keras\\models.py\u001b[0m in \u001b[0;36mload_model\u001b[0;34m(filepath, custom_objects)\u001b[0m\n\u001b[1;32m    270\u001b[0m         \u001b[0moptimizer_weight_names\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mn\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdecode\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'utf8'\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mn\u001b[0m \u001b[1;32min\u001b[0m \u001b[0moptimizer_weights_group\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mattrs\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'weight_names'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m    271\u001b[0m         \u001b[0moptimizer_weight_values\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0moptimizer_weights_group\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mn\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mn\u001b[0m \u001b[1;32min\u001b[0m \u001b[0moptimizer_weight_names\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m--> 272\u001b[0;31m         \u001b[0mmodel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0moptimizer\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mset_weights\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0moptimizer_weight_values\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    273\u001b[0m     \u001b[0mf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mclose\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m    274\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0mmodel\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mC:\\Anaconda3\\envs\\carnd-term1\\lib\\site-packages\\keras\\optimizers.py\u001b[0m in \u001b[0;36mset_weights\u001b[0;34m(self, weights)\u001b[0m\n\u001b[1;32m     77\u001b[0m                                  \u001b[0mstr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpv\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m+\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m     78\u001b[0m                                  \u001b[1;34m' not compatible with '\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m---> 79\u001b[0;31m                                  'provided weight shape ' + str(w.shape))\n\u001b[0m\u001b[1;32m     80\u001b[0m             \u001b[0mweight_value_tuples\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mp\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mw\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m     81\u001b[0m         \u001b[0mK\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbatch_set_value\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mweight_value_tuples\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: Optimizer weight shape (6,) not compatible with provided weight shape (5, 5, 3, 6)"
     ]
    }
   ],
   "source": [
    "import keras\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Flatten, Dense, Lambda, Cropping2D\n",
    "from keras.layers.convolutional import Convolution2D\n",
    "from keras.layers.pooling import MaxPooling2D\n",
    "from keras.utils import plot_model\n",
    "from keras.models import load_model\n",
    "\n",
    "\n",
    "model = load_model('model.h5')\n",
    "\n",
    "plot_model(model, to_file='model.png')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'tf' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-6-104c05f1113b>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mversion\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m: name 'tf' is not defined"
     ]
    }
   ],
   "source": [
    "print(tf.version)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'visualize_util' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-17-37bbfbb8e36b>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mhelp\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvisualize_util\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m: name 'visualize_util' is not defined"
     ]
    }
   ],
   "source": [
    "help(visualize_util)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": []
  }
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