model_training.ipynb

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     "text": [
      "/home/simon/anaconda3/lib/python3.6/site-packages/h5py/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
      "  from ._conv import register_converters as _register_converters\n",
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import keras\n",
    "import tensorflow as tf\n",
    "config = tf.ConfigProto()\n",
    "config.gpu_options.allow_growth = True\n",
    "#config.gpu_options.per_process_gpu_memory_fraction = 0.95\n",
    "session = tf.Session(config=config)\n",
    "\n",
    "from keras.models import Model, Sequential\n",
    "from keras.layers import *\n",
    "from keras.optimizers import Adam\n",
    "import matplotlib.pyplot as plt\n",
    "from keras.models import load_model\n",
    "from sklearn.model_selection import train_test_split\n",
    "from keras.callbacks import ModelCheckpoint, TensorBoard"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## config"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.seed(777)\n",
    "width = 320\n",
    "height = 240\n",
    "tb = TensorBoard('logs/unet_small')\n",
    "cp = ModelCheckpoint('unet_small.cp')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = np.load('X.npy')\n",
    "y = np.load('y.npy')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## split training and testing set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "idx = np.arange(X.shape[0])\n",
    "np.random.shuffle(idx)\n",
    "treshold = np.int(idx.shape[0]*0.8)\n",
    "X_train = X[:treshold]\n",
    "X_valid = X[treshold:]\n",
    "y_train = y[:treshold]\n",
    "y_valid = y[treshold:]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# for model_testing notebook\n",
    "np.save('X_valid.npy', X_valid)\n",
    "np.save('y_valid.npy', y_valid)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## model creation : small unet\n",
    "We decrease drastically the number of filters and the level of the pyramid since we want :\n",
    "1. Mobile implementation\n",
    "2. Fast training for fast experimentation\n",
    "3. Save electricity bill :)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_unet():\n",
    "\n",
    "    inputs = Input((height, width, 3))\n",
    "    conv1 = Conv2D(32, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(inputs)\n",
    "    conv1 = Conv2D(32, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv1)\n",
    "    pool1 = MaxPooling2D(pool_size=(2, 2))(conv1)\n",
    "\n",
    "    conv2 = Conv2D(64, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(pool1)\n",
    "    conv2 = Conv2D(64, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv2)\n",
    "    pool2 = MaxPooling2D(pool_size=(2, 2))(conv2)\n",
    "\n",
    "    conv4 = Conv2D(64, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(pool2)\n",
    "    conv4 = Conv2D(64, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv4)\n",
    "    drop4 = Dropout(0.5)(conv4)\n",
    "    pool4 = MaxPooling2D(pool_size=(2, 2))(drop4)\n",
    "\n",
    "    conv5 = Conv2D(256, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(pool4)\n",
    "    conv5 = Conv2D(256, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv5)\n",
    "    drop5 = Dropout(0.5)(conv5)\n",
    "\n",
    "    up6 = Conv2D(256, 2, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(UpSampling2D(size = (2,2))(drop5))\n",
    "    merge6 = concatenate([drop4,up6], axis = 3)\n",
    "    conv6 = Conv2D(128, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(merge6)\n",
    "    conv6 = Conv2D(128, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv6)\n",
    "\n",
    "    up8 = Conv2D(64, 2, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(UpSampling2D(size = (2,2))(conv6))\n",
    "    merge8 = concatenate([conv2,up8], axis = 3)\n",
    "    conv8 = Conv2D(64, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(merge8)\n",
    "    conv8 = Conv2D(64, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv8)\n",
    "\n",
    "    up9 = Conv2D(64, 2, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(UpSampling2D(size = (2,2))(conv8))\n",
    "    merge9 = concatenate([conv1,up9], axis = 3)\n",
    "    conv9 = Conv2D(64, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(merge9)\n",
    "    conv9 = Conv2D(32, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv9)\n",
    "\n",
    "    conv9 = Conv2D(32, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv9)\n",
    "    conv10 = Conv2D(7, 1, activation = 'sigmoid')(conv9)\n",
    "\n",
    "    \n",
    "    model = Model(inputs = inputs, outputs = conv10)\n",
    "\n",
    "    model.compile(optimizer = Adam(lr = 1e-4), loss = 'binary_crossentropy', metrics = ['accuracy'])\n",
    "    model.summary()\n",
    "    return model\n",
    "try:\n",
    "    model = load_model('unet_small.cp')\n",
    "except:\n",
    "    print(\"Not pre-existing model, creating a new one\")\n",
    "    model = get_unet()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.fit(X_train, y_train, validation_data=[X_valid, y_valid], batch_size = 1, epochs = 3000, callbacks = [tb,cp], verbose = 0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## To do \n",
    "1. remove concatenation issues for tflite\n",
    "2. more data\n",
    "3. data augmentation\n",
    "4. Predict 8x8x7 board game directly using mask r-cnn with ROI annotation added"
   ]
  }
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