Pieces.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 222
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 98926,
     "status": "ok",
     "timestamp": 1549193114654,
     "user": {
      "displayName": "Harsh Patel",
      "photoUrl": "https://lh4.googleusercontent.com/-xVEOvhAYCwI/AAAAAAAAAAI/AAAAAAAAAAc/m8NWBboPeYY/s64/photo.jpg",
      "userId": "17018506043059870013"
     },
     "user_tz": -330
    },
    "id": "EwDVm3m-A2zc",
    "outputId": "1551a942-b314-4faa-b2f8-361973c242b0"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "E: Package 'python-software-properties' has no installation candidate\n",
      "Selecting previously unselected package google-drive-ocamlfuse.\n",
      "(Reading database ... 110856 files and directories currently installed.)\n",
      "Preparing to unpack .../google-drive-ocamlfuse_0.7.1-0ubuntu3~ubuntu18.04.1_amd64.deb ...\n",
      "Unpacking google-drive-ocamlfuse (0.7.1-0ubuntu3~ubuntu18.04.1) ...\n",
      "Setting up google-drive-ocamlfuse (0.7.1-0ubuntu3~ubuntu18.04.1) ...\n",
      "Processing triggers for man-db (2.8.3-2ubuntu0.1) ...\n",
      "Please, open the following URL in a web browser: https://accounts.google.com/o/oauth2/auth?client_id=32555940559.apps.googleusercontent.com&redirect_uri=urn%3Aietf%3Awg%3Aoauth%3A2.0%3Aoob&scope=https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdrive&response_type=code&access_type=offline&approval_prompt=force\n",
      "··········\n",
      "Please, open the following URL in a web browser: https://accounts.google.com/o/oauth2/auth?client_id=32555940559.apps.googleusercontent.com&redirect_uri=urn%3Aietf%3Awg%3Aoauth%3A2.0%3Aoob&scope=https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdrive&response_type=code&access_type=offline&approval_prompt=force\n",
      "Please enter the verification code: Access token retrieved correctly.\n"
     ]
    }
   ],
   "source": [
    "!apt-get install -y -qq software-properties-common python-software-properties module-init-tools\n",
    "!add-apt-repository -y ppa:alessandro-strada/ppa 2>&1 > /dev/null\n",
    "!apt-get update -qq 2>&1 > /dev/null\n",
    "!apt-get -y install -qq google-drive-ocamlfuse fuse\n",
    "from google.colab import auth\n",
    "auth.authenticate_user()\n",
    "from oauth2client.client import GoogleCredentials\n",
    "creds = GoogleCredentials.get_application_default()\n",
    "import getpass\n",
    "!google-drive-ocamlfuse -headless -id={creds.client_id} -secret={creds.client_secret} < /dev/null 2>&1 | grep URL\n",
    "vcode = getpass.getpass()\n",
    "!echo {vcode} | google-drive-ocamlfuse -headless -id={creds.client_id} -secret={creds.client_secret}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "9w15dc6-BJhJ"
   },
   "outputs": [],
   "source": [
    "!mkdir -p drive\n",
    "!google-drive-ocamlfuse drive"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 824,
     "status": "ok",
     "timestamp": 1549195084438,
     "user": {
      "displayName": "Harsh Patel",
      "photoUrl": "https://lh4.googleusercontent.com/-xVEOvhAYCwI/AAAAAAAAAAI/AAAAAAAAAAc/m8NWBboPeYY/s64/photo.jpg",
      "userId": "17018506043059870013"
     },
     "user_tz": -330
    },
    "id": "jOHbh_IQBhCZ",
    "outputId": "9f31aa31-2cbb-43aa-ebe4-8f7c7bff6024",
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/home/harsh/harsh/Chess-Aries\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "print(os.getcwd())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 162
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1998,
     "status": "error",
     "timestamp": 1549195074943,
     "user": {
      "displayName": "Harsh Patel",
      "photoUrl": "https://lh4.googleusercontent.com/-xVEOvhAYCwI/AAAAAAAAAAI/AAAAAAAAAAc/m8NWBboPeYY/s64/photo.jpg",
      "userId": "17018506043059870013"
     },
     "user_tz": -330
    },
    "id": "52jddDUmBn9C",
    "outputId": "c28e3ab7-1631-4c2a-983b-09222705ea5e"
   },
   "outputs": [],
   "source": [
    "os.chdir('Dataset')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 861,
     "status": "ok",
     "timestamp": 1549195088664,
     "user": {
      "displayName": "Harsh Patel",
      "photoUrl": "https://lh4.googleusercontent.com/-xVEOvhAYCwI/AAAAAAAAAAI/AAAAAAAAAAc/m8NWBboPeYY/s64/photo.jpg",
      "userId": "17018506043059870013"
     },
     "user_tz": -330
    },
    "id": "PpiCsU-aCb0b",
    "outputId": "63b98d60-50e0-414f-bb99-81021a711c32"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/home/harsh/harsh/Chess-Aries/Dataset\n"
     ]
    }
   ],
   "source": [
    "print(os.getcwd())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 101
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 2931,
     "status": "ok",
     "timestamp": 1549195092554,
     "user": {
      "displayName": "Harsh Patel",
      "photoUrl": "https://lh4.googleusercontent.com/-xVEOvhAYCwI/AAAAAAAAAAI/AAAAAAAAAAc/m8NWBboPeYY/s64/photo.jpg",
      "userId": "17018506043059870013"
     },
     "user_tz": -330
    },
    "id": "84WiYC0FCeHJ",
    "outputId": "6f11a35b-3371-4ba5-fa1e-96451acc43c5"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " Empty\t\t'White Pawn'\t 'Yellow Knight'   chess_train.mat\r\n",
      " New\t\t'White Queen'\t 'Yellow Pawn'\t   chess_train_final.mat\r\n",
      "'White Bishop'\t'White Rook'\t 'Yellow Queen'    saved_models\r\n",
      "'White King'\t'Yellow Bishop'  'Yellow Rook'\t   untitled.m\r\n",
      "'White Knight'\t'Yellow King'\t  _DS_Store\r\n"
     ]
    }
   ],
   "source": [
    "!ls"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "tbWa8HFBCfOD"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "from __future__ import print_function\n",
    "import keras\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense, Dropout, Flatten, Activation\n",
    "from keras.layers.normalization import BatchNormalization\n",
    "from keras.layers import Conv1D, Conv2D, MaxPooling2D, MaxPooling1D\n",
    "from keras import backend as K\n",
    "import h5py\n",
    "import numpy as np\n",
    "import scipy.io as sio\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "jnjgvWdaCxwk"
   },
   "outputs": [],
   "source": [
    "batch_size = 32\n",
    "num_classes = 13\n",
    "epochs = 225"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 50
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 2424,
     "status": "ok",
     "timestamp": 1549195099888,
     "user": {
      "displayName": "Harsh Patel",
      "photoUrl": "https://lh4.googleusercontent.com/-xVEOvhAYCwI/AAAAAAAAAAI/AAAAAAAAAAc/m8NWBboPeYY/s64/photo.jpg",
      "userId": "17018506043059870013"
     },
     "user_tz": -330
    },
    "id": "BnbtkZPvDEUO",
    "outputId": "3497ff1a-1a3e-4d1c-db71-456eb7fb93f0"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "trainset size X: (100, 100, 3, 832)\n",
      "trainset size Y: (832, 1)\n"
     ]
    }
   ],
   "source": [
    "trainset = {}\n",
    "#f = h5py.File('chess_train.mat')\n",
    "#for k, v in f.items():\n",
    "#    trainset[k] = np.array(v)\n",
    "trainset = sio.loadmat('chess_train_final.mat')\n",
    "print(\"trainset size X:\", trainset['X'].shape)\n",
    "print(\"trainset size Y:\", trainset['Y'].shape)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "VVr0j79oDO63"
   },
   "outputs": [],
   "source": [
    "trainset['X'] = np.rollaxis(trainset['X'], axis = 3)\n",
    "#trainset['X'] = np.rollaxis(trainset['X'], axis = 3, start = 1)\n",
    "#trainset['X'] = np.rollaxis(trainset['X'], axis = 3, start = 2)\n",
    "\n",
    "#trainset['Y'] = np.rollaxis(trainset['Y'], axis = 1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 50
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 882,
     "status": "ok",
     "timestamp": 1549195101666,
     "user": {
      "displayName": "Harsh Patel",
      "photoUrl": "https://lh4.googleusercontent.com/-xVEOvhAYCwI/AAAAAAAAAAI/AAAAAAAAAAc/m8NWBboPeYY/s64/photo.jpg",
      "userId": "17018506043059870013"
     },
     "user_tz": -330
    },
    "id": "0ej-9Wy2EF-X",
    "outputId": "43d51655-9527-42fc-d1db-262755387ffa"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Trainset size X: (832, 100, 100, 3)\n",
      "Trainset size Y: (832, 1)\n"
     ]
    }
   ],
   "source": [
    "print('Trainset size X:',trainset['X'].shape)\n",
    "print('Trainset size Y:',trainset['Y'].shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Nd3lje75DJnD"
   },
   "outputs": [],
   "source": [
    "label = trainset['Y']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "2E0mPHTSG8YI"
   },
   "outputs": [],
   "source": [
    "# convert class vectors to binary class matrices\n",
    "trainset['Y'] = keras.utils.to_categorical(trainset['Y'], num_classes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1130,
     "status": "ok",
     "timestamp": 1549195103988,
     "user": {
      "displayName": "Harsh Patel",
      "photoUrl": "https://lh4.googleusercontent.com/-xVEOvhAYCwI/AAAAAAAAAAI/AAAAAAAAAAc/m8NWBboPeYY/s64/photo.jpg",
      "userId": "17018506043059870013"
     },
     "user_tz": -330
    },
    "id": "IixP2uReHCEI",
    "outputId": "39410e01-f5cc-4ed7-b60a-257f0a460551"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(832, 13)\n"
     ]
    }
   ],
   "source": [
    "print(trainset['Y'].shape)\n",
    "#print(trainset['Y'][:,0,:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "QWVcwIrOHGH7"
   },
   "outputs": [],
   "source": [
    "#trainset['Y'] = np.rollaxis(trainset['Y'], axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 50
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 994,
     "status": "ok",
     "timestamp": 1549195104388,
     "user": {
      "displayName": "Harsh Patel",
      "photoUrl": "https://lh4.googleusercontent.com/-xVEOvhAYCwI/AAAAAAAAAAI/AAAAAAAAAAc/m8NWBboPeYY/s64/photo.jpg",
      "userId": "17018506043059870013"
     },
     "user_tz": -330
    },
    "id": "YEUKrgI3HkHh",
    "outputId": "1245ed08-7dda-4719-87d6-e546f792ba82"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(832, 13)\n",
      "[0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1.]\n"
     ]
    }
   ],
   "source": [
    "print(trainset['Y'].shape)\n",
    "print(trainset['Y'][831,:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 839,
     "status": "ok",
     "timestamp": 1549195104390,
     "user": {
      "displayName": "Harsh Patel",
      "photoUrl": "https://lh4.googleusercontent.com/-xVEOvhAYCwI/AAAAAAAAAAI/AAAAAAAAAAc/m8NWBboPeYY/s64/photo.jpg",
      "userId": "17018506043059870013"
     },
     "user_tz": -330
    },
    "id": "2m6EB1waITkj",
    "outputId": "57bbe5fb-7c7a-4129-82b2-8a79b6b5ca3f"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "832 train samples\n"
     ]
    }
   ],
   "source": [
    "print(trainset['X'].shape[0], 'train samples')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "orhtce1TIOzU"
   },
   "outputs": [],
   "source": [
    "# input image dimensions\n",
    "img_rows, img_cols = 100, 100\n",
    "input_shape = (img_rows, img_cols, 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 366
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1018,
     "status": "ok",
     "timestamp": 1549195233216,
     "user": {
      "displayName": "Harsh Patel",
      "photoUrl": "https://lh4.googleusercontent.com/-xVEOvhAYCwI/AAAAAAAAAAI/AAAAAAAAAAc/m8NWBboPeYY/s64/photo.jpg",
      "userId": "17018506043059870013"
     },
     "user_tz": -330
    },
    "id": "s0IY_XEUHlp2",
    "outputId": "7c4e7e57-85d9-4ac6-f493-45dc7dfced86"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Label: B\n"
     ]
    }
   ],
   "source": [
    "# Trainset image\n",
    "#index=831\n",
    "index = np.random.randint(831)\n",
    "M1 = trainset['X'][index,:,:,:]\n",
    "plt.imshow(M1)\n",
    "plt.show()\n",
    "y = label[index,:]\n",
    "\n",
    "if y==0:\n",
    "  classes='E'\n",
    "elif y==1:\n",
    "  classes='B'\n",
    "elif y==2:\n",
    "  classes='K'\n",
    "elif y==3:\n",
    "  classes='N'\n",
    "elif y==4:\n",
    "  classes='P'\n",
    "elif y==5:\n",
    "  classes='Q'\n",
    "elif y==6:\n",
    "  classes='R'\n",
    "elif y==7:\n",
    "  classes='b'\n",
    "elif y==8:\n",
    "  classes='k'\n",
    "elif y==9:\n",
    "  classes='n'\n",
    "elif y==10:\n",
    "  classes='p'\n",
    "elif y==11:\n",
    "  classes='q'\n",
    "else:\n",
    "  classes='r'\n",
    "\n",
    "print('Label:',classes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "UQudeduWJQGg"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "batch_normalization_2 (Batch (None, 100, 100, 3)       12        \n",
      "_________________________________________________________________\n",
      "conv2d_7 (Conv2D)            (None, 98, 98, 32)        896       \n",
      "_________________________________________________________________\n",
      "activation_9 (Activation)    (None, 98, 98, 32)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_8 (Conv2D)            (None, 96, 96, 32)        9248      \n",
      "_________________________________________________________________\n",
      "activation_10 (Activation)   (None, 96, 96, 32)        0         \n",
      "_________________________________________________________________\n",
      "max_pooling2d_4 (MaxPooling2 (None, 48, 48, 32)        0         \n",
      "_________________________________________________________________\n",
      "dropout_5 (Dropout)          (None, 48, 48, 32)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_9 (Conv2D)            (None, 46, 46, 64)        18496     \n",
      "_________________________________________________________________\n",
      "activation_11 (Activation)   (None, 46, 46, 64)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_10 (Conv2D)           (None, 44, 44, 64)        36928     \n",
      "_________________________________________________________________\n",
      "activation_12 (Activation)   (None, 44, 44, 64)        0         \n",
      "_________________________________________________________________\n",
      "max_pooling2d_5 (MaxPooling2 (None, 22, 22, 64)        0         \n",
      "_________________________________________________________________\n",
      "dropout_6 (Dropout)          (None, 22, 22, 64)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_11 (Conv2D)           (None, 20, 20, 128)       73856     \n",
      "_________________________________________________________________\n",
      "activation_13 (Activation)   (None, 20, 20, 128)       0         \n",
      "_________________________________________________________________\n",
      "conv2d_12 (Conv2D)           (None, 18, 18, 128)       147584    \n",
      "_________________________________________________________________\n",
      "activation_14 (Activation)   (None, 18, 18, 128)       0         \n",
      "_________________________________________________________________\n",
      "max_pooling2d_6 (MaxPooling2 (None, 9, 9, 128)         0         \n",
      "_________________________________________________________________\n",
      "dropout_7 (Dropout)          (None, 9, 9, 128)         0         \n",
      "_________________________________________________________________\n",
      "flatten_2 (Flatten)          (None, 10368)             0         \n",
      "_________________________________________________________________\n",
      "dense_3 (Dense)              (None, 128)               1327232   \n",
      "_________________________________________________________________\n",
      "activation_15 (Activation)   (None, 128)               0         \n",
      "_________________________________________________________________\n",
      "dropout_8 (Dropout)          (None, 128)               0         \n",
      "_________________________________________________________________\n",
      "dense_4 (Dense)              (None, 13)                1677      \n",
      "_________________________________________________________________\n",
      "activation_16 (Activation)   (None, 13)                0         \n",
      "=================================================================\n",
      "Total params: 1,615,929\n",
      "Trainable params: 1,615,923\n",
      "Non-trainable params: 6\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model = Sequential()\n",
    "model.add(BatchNormalization(input_shape = input_shape))\n",
    "model.add(Conv2D(32, (3, 3), padding = 'valid'))\n",
    "model.add(Activation('relu'))\n",
    "model.add(Conv2D(32, (3, 3), padding = 'valid'))\n",
    "model.add(Activation('relu'))\n",
    "model.add(MaxPooling2D(pool_size=(2, 2), strides = None, padding = 'valid'))\n",
    "model.add(Dropout(0.25))\n",
    "\n",
    "model.add(Conv2D(64, (3, 3), padding = 'valid'))\n",
    "model.add(Activation('relu'))\n",
    "model.add(Conv2D(64, (3, 3), padding = 'valid'))\n",
    "model.add(Activation('relu'))\n",
    "model.add(MaxPooling2D(pool_size=(2, 2), strides = None, padding = 'valid'))\n",
    "model.add(Dropout(0.25))\n",
    "\n",
    "model.add(Conv2D(128, (3, 3), padding = 'valid'))\n",
    "model.add(Activation('relu'))\n",
    "model.add(Conv2D(128, (3, 3), padding = 'valid'))\n",
    "model.add(Activation('relu'))\n",
    "model.add(MaxPooling2D(pool_size=(2, 2), strides = None, padding = 'valid'))\n",
    "model.add(Dropout(0.25))\n",
    "\n",
    "model.add(Flatten())\n",
    "model.add(Dense(128))\n",
    "model.add(Activation('relu'))\n",
    "model.add(Dropout(0.5))\n",
    "model.add(Dense(num_classes))\n",
    "model.add(Activation('softmax'))\n",
    "\n",
    "# initiate RMSprop optimizer\n",
    "opt = keras.optimizers.Adadelta(lr=1.0, rho=0.95, epsilon=None, decay=0.0)\n",
    "#opt = keras.optimizers.rmsprop(lr=0.0001, decay=1e-6)\n",
    "\n",
    "# Let's train the model using RMSprop\n",
    "model.compile(loss='categorical_crossentropy',\n",
    "              optimizer=opt,\n",
    "              metrics=['accuracy'])\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1042
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1525288,
     "status": "ok",
     "timestamp": 1548506021371,
     "user": {
      "displayName": "Harsh Patel",
      "photoUrl": "https://lh4.googleusercontent.com/-xVEOvhAYCwI/AAAAAAAAAAI/AAAAAAAAAAc/m8NWBboPeYY/s64/photo.jpg",
      "userId": "17018506043059870013"
     },
     "user_tz": -330
    },
    "id": "rGmWYjTkKETK",
    "outputId": "4bae5454-f0f9-4ad7-bfda-530d0d191404"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/225\n",
      "26/26 [==============================] - 1s 46ms/step - loss: 2.5505 - acc: 0.0998\n",
      "Epoch 2/225\n",
      "26/26 [==============================] - 1s 43ms/step - loss: 2.3950 - acc: 0.1502\n",
      "Epoch 3/225\n",
      "26/26 [==============================] - 1s 42ms/step - loss: 2.1865 - acc: 0.1911\n",
      "Epoch 4/225\n",
      "26/26 [==============================] - 1s 43ms/step - loss: 2.0084 - acc: 0.2163\n",
      "Epoch 5/225\n",
      "26/26 [==============================] - 1s 42ms/step - loss: 1.8364 - acc: 0.2356\n",
      "Epoch 6/225\n",
      "26/26 [==============================] - 1s 42ms/step - loss: 1.8087 - acc: 0.2680\n",
      "Epoch 7/225\n",
      "26/26 [==============================] - 1s 43ms/step - loss: 1.7528 - acc: 0.2849\n",
      "Epoch 8/225\n",
      "26/26 [==============================] - 1s 42ms/step - loss: 1.6838 - acc: 0.3161\n",
      "Epoch 9/225\n",
      "26/26 [==============================] - 1s 42ms/step - loss: 1.6789 - acc: 0.2752\n",
      "Epoch 10/225\n",
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    },
    {
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     "text": [
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      "26/26 [==============================] - 1s 42ms/step - loss: 0.4153 - acc: 0.8365\n",
      "Epoch 186/225\n",
      "26/26 [==============================] - 1s 42ms/step - loss: 0.4036 - acc: 0.8450\n",
      "Epoch 187/225\n",
      "26/26 [==============================] - 1s 42ms/step - loss: 0.4242 - acc: 0.8474\n",
      "Epoch 188/225\n",
      "26/26 [==============================] - 1s 43ms/step - loss: 0.3924 - acc: 0.8666\n",
      "Epoch 189/225\n",
      "26/26 [==============================] - 1s 43ms/step - loss: 0.3845 - acc: 0.8474\n",
      "Epoch 190/225\n",
      "26/26 [==============================] - 1s 42ms/step - loss: 0.3736 - acc: 0.8438\n",
      "Epoch 191/225\n",
      "26/26 [==============================] - 1s 42ms/step - loss: 0.3739 - acc: 0.8462\n",
      "Epoch 192/225\n",
      "26/26 [==============================] - 1s 43ms/step - loss: 0.4610 - acc: 0.8293\n",
      "Epoch 193/225\n",
      "26/26 [==============================] - 1s 42ms/step - loss: 0.3893 - acc: 0.8606\n",
      "Epoch 194/225\n",
      "26/26 [==============================] - 1s 42ms/step - loss: 0.3946 - acc: 0.8534\n",
      "Epoch 195/225\n",
      "26/26 [==============================] - 1s 42ms/step - loss: 0.4052 - acc: 0.8486\n",
      "Epoch 196/225\n",
      "26/26 [==============================] - 1s 42ms/step - loss: 0.3125 - acc: 0.8738\n",
      "Epoch 197/225\n",
      "26/26 [==============================] - 1s 43ms/step - loss: 0.3838 - acc: 0.8582\n",
      "Epoch 198/225\n",
      "26/26 [==============================] - 1s 42ms/step - loss: 0.3557 - acc: 0.8834\n",
      "Epoch 199/225\n",
      "26/26 [==============================] - 1s 42ms/step - loss: 0.3851 - acc: 0.8341\n",
      "Epoch 200/225\n",
      "26/26 [==============================] - 1s 42ms/step - loss: 0.3607 - acc: 0.8630\n",
      "Epoch 201/225\n",
      "26/26 [==============================] - 1s 42ms/step - loss: 0.3407 - acc: 0.8798\n",
      "Epoch 202/225\n",
      "26/26 [==============================] - 1s 43ms/step - loss: 0.3788 - acc: 0.8702\n",
      "Epoch 203/225\n",
      "26/26 [==============================] - 1s 42ms/step - loss: 0.4163 - acc: 0.8450\n",
      "Epoch 204/225\n",
      "26/26 [==============================] - 1s 42ms/step - loss: 0.3478 - acc: 0.8666\n",
      "Epoch 205/225\n",
      "26/26 [==============================] - 1s 42ms/step - loss: 0.3580 - acc: 0.8666\n",
      "Epoch 206/225\n",
      "26/26 [==============================] - 1s 43ms/step - loss: 0.3736 - acc: 0.8606\n",
      "Epoch 207/225\n",
      "26/26 [==============================] - 1s 42ms/step - loss: 0.3329 - acc: 0.8762\n",
      "Epoch 208/225\n",
      "26/26 [==============================] - 1s 42ms/step - loss: 0.3815 - acc: 0.8678\n",
      "Epoch 209/225\n",
      "26/26 [==============================] - 1s 42ms/step - loss: 0.2863 - acc: 0.8846\n",
      "Epoch 210/225\n",
      "26/26 [==============================] - 1s 42ms/step - loss: 0.2784 - acc: 0.9014\n",
      "Epoch 211/225\n",
      "26/26 [==============================] - 1s 42ms/step - loss: 0.3284 - acc: 0.8810\n",
      "Epoch 212/225\n",
      "26/26 [==============================] - 1s 42ms/step - loss: 0.3980 - acc: 0.8666\n",
      "Epoch 213/225\n",
      "26/26 [==============================] - 1s 42ms/step - loss: 0.2558 - acc: 0.9075\n",
      "Epoch 214/225\n",
      "26/26 [==============================] - 1s 42ms/step - loss: 0.3729 - acc: 0.8750\n",
      "Epoch 215/225\n",
      "26/26 [==============================] - 1s 43ms/step - loss: 0.2745 - acc: 0.9014\n",
      "Epoch 216/225\n",
      "26/26 [==============================] - 1s 42ms/step - loss: 0.4164 - acc: 0.8618\n",
      "Epoch 217/225\n",
      "26/26 [==============================] - 1s 42ms/step - loss: 0.3015 - acc: 0.8786\n",
      "Epoch 218/225\n",
      "26/26 [==============================] - 1s 43ms/step - loss: 0.2479 - acc: 0.9099\n",
      "Epoch 219/225\n",
      "26/26 [==============================] - 1s 43ms/step - loss: 0.3158 - acc: 0.8870\n",
      "Epoch 220/225\n",
      "26/26 [==============================] - 1s 42ms/step - loss: 0.2146 - acc: 0.9243\n",
      "Epoch 221/225\n",
      "26/26 [==============================] - 1s 43ms/step - loss: 0.3047 - acc: 0.8882\n",
      "Epoch 222/225\n",
      "26/26 [==============================] - 1s 42ms/step - loss: 0.2412 - acc: 0.9159\n",
      "Epoch 223/225\n",
      "26/26 [==============================] - 1s 42ms/step - loss: 0.3642 - acc: 0.8774\n",
      "Epoch 224/225\n",
      "26/26 [==============================] - 1s 42ms/step - loss: 0.3162 - acc: 0.8930\n",
      "Epoch 225/225\n",
      "26/26 [==============================] - 1s 42ms/step - loss: 0.2497 - acc: 0.9099\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x7efc807e9dd8>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from keras.preprocessing.image import ImageDataGenerator\n",
    "\n",
    "datagen = ImageDataGenerator(\n",
    "        rotation_range=40,\n",
    "        width_shift_range=0.2,\n",
    "        height_shift_range=0.2,\n",
    "        shear_range=0.2,\n",
    "        zoom_range=0.2,\n",
    "        horizontal_flip=True,\n",
    "        fill_mode='nearest')\n",
    "\n",
    "#datagen = ImageDataGenerator(\n",
    "#    featurewise_center=True,\n",
    "#    featurewise_std_normalization=True,\n",
    "#    rotation_range=20,\n",
    "#    width_shift_range=0.2,\n",
    "#    height_shift_range=0.2,\n",
    "#    horizontal_flip=True)\n",
    "\n",
    "datagen.fit(trainset['X'])\n",
    "\n",
    "model.fit_generator(datagen.flow(trainset['X'], trainset['Y'], batch_size=32),\n",
    "                    steps_per_epoch=len(trainset['X']) / 32, epochs=epochs)\n",
    "\n",
    "\n",
    "#model.fit(trainset['X'], trainset['Y'],\n",
    "#          batch_size=batch_size,\n",
    "#          epochs=epochs,\n",
    "#          verbose=1,\n",
    "#         )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 6540,
     "status": "ok",
     "timestamp": 1548506035334,
     "user": {
      "displayName": "Harsh Patel",
      "photoUrl": "https://lh4.googleusercontent.com/-xVEOvhAYCwI/AAAAAAAAAAI/AAAAAAAAAAc/m8NWBboPeYY/s64/photo.jpg",
      "userId": "17018506043059870013"
     },
     "user_tz": -330
    },
    "id": "a24yFFmdKLZV",
    "outputId": "69e2709c-cf94-469b-f4ed-7d390e1eef5c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saved trained model at /home/harsh/harsh/Chess-Aries/Dataset/saved_models/trained_model_final_bs32_225_dg.h5 \n"
     ]
    }
   ],
   "source": [
    "save_dir = os.path.join(os.getcwd(), 'saved_models')\n",
    "model_name = 'trained_model_final_bs32_225_dg.h5'\n",
    "\n",
    "if not os.path.isdir(save_dir):\n",
    "    os.makedirs(save_dir)\n",
    "    \n",
    "model_path = os.path.join(save_dir, model_name)\n",
    "model.save(model_path)\n",
    "print('Saved trained model at %s ' % model_path)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "6_PFiznYLa1i"
   },
   "outputs": [],
   "source": [
    "from keras.models import load_model\n",
    "\n",
    "import numpy as np\n",
    "\n",
    "opt = keras.optimizers.Adadelta(lr=1.0, rho=0.95, epsilon=None, decay=0.0)\n",
    "model = load_model('saved_models/trained_model_final_bs32_225_dg.h5')\n",
    "\n",
    "model.compile(loss='binary_crossentropy',\n",
    "              optimizer=opt,\n",
    "              metrics=['accuracy'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "batch_normalization_2 (Batch (None, 100, 100, 3)       12        \n",
      "_________________________________________________________________\n",
      "conv2d_7 (Conv2D)            (None, 98, 98, 32)        896       \n",
      "_________________________________________________________________\n",
      "activation_9 (Activation)    (None, 98, 98, 32)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_8 (Conv2D)            (None, 96, 96, 32)        9248      \n",
      "_________________________________________________________________\n",
      "activation_10 (Activation)   (None, 96, 96, 32)        0         \n",
      "_________________________________________________________________\n",
      "max_pooling2d_4 (MaxPooling2 (None, 48, 48, 32)        0         \n",
      "_________________________________________________________________\n",
      "dropout_5 (Dropout)          (None, 48, 48, 32)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_9 (Conv2D)            (None, 46, 46, 64)        18496     \n",
      "_________________________________________________________________\n",
      "activation_11 (Activation)   (None, 46, 46, 64)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_10 (Conv2D)           (None, 44, 44, 64)        36928     \n",
      "_________________________________________________________________\n",
      "activation_12 (Activation)   (None, 44, 44, 64)        0         \n",
      "_________________________________________________________________\n",
      "max_pooling2d_5 (MaxPooling2 (None, 22, 22, 64)        0         \n",
      "_________________________________________________________________\n",
      "dropout_6 (Dropout)          (None, 22, 22, 64)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_11 (Conv2D)           (None, 20, 20, 128)       73856     \n",
      "_________________________________________________________________\n",
      "activation_13 (Activation)   (None, 20, 20, 128)       0         \n",
      "_________________________________________________________________\n",
      "conv2d_12 (Conv2D)           (None, 18, 18, 128)       147584    \n",
      "_________________________________________________________________\n",
      "activation_14 (Activation)   (None, 18, 18, 128)       0         \n",
      "_________________________________________________________________\n",
      "max_pooling2d_6 (MaxPooling2 (None, 9, 9, 128)         0         \n",
      "_________________________________________________________________\n",
      "dropout_7 (Dropout)          (None, 9, 9, 128)         0         \n",
      "_________________________________________________________________\n",
      "flatten_2 (Flatten)          (None, 10368)             0         \n",
      "_________________________________________________________________\n",
      "dense_3 (Dense)              (None, 128)               1327232   \n",
      "_________________________________________________________________\n",
      "activation_15 (Activation)   (None, 128)               0         \n",
      "_________________________________________________________________\n",
      "dropout_8 (Dropout)          (None, 128)               0         \n",
      "_________________________________________________________________\n",
      "dense_4 (Dense)              (None, 13)                1677      \n",
      "_________________________________________________________________\n",
      "activation_16 (Activation)   (None, 13)                0         \n",
      "=================================================================\n",
      "Total params: 1,615,929\n",
      "Trainable params: 1,615,923\n",
      "Non-trainable params: 6\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 3210
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 3039,
     "status": "ok",
     "timestamp": 1549195783244,
     "user": {
      "displayName": "Harsh Patel",
      "photoUrl": "https://lh4.googleusercontent.com/-xVEOvhAYCwI/AAAAAAAAAAI/AAAAAAAAAAc/m8NWBboPeYY/s64/photo.jpg",
      "userId": "17018506043059870013"
     },
     "user_tz": -330
    },
    "id": "8RDd5T9oOKpZ",
    "outputId": "7a320a50-003d-4e14-c7ca-6a25c00553cf"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(100, 100, 3)\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"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1, 100, 100, 3)\n",
      "[12]\n",
      "r\n",
      "(100, 100, 3)\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"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1, 100, 100, 3)\n",
      "[9]\n",
      "n\n",
      "(100, 100, 3)\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"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1, 100, 100, 3)\n",
      "[7]\n",
      "b\n",
      "(100, 100, 3)\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"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1, 100, 100, 3)\n",
      "[8]\n",
      "k\n",
      "(100, 100, 3)\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"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1, 100, 100, 3)\n",
      "[11]\n",
      "q\n",
      "(100, 100, 3)\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"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1, 100, 100, 3)\n",
      "[7]\n",
      "b\n",
      "(100, 100, 3)\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"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1, 100, 100, 3)\n",
      "[9]\n",
      "n\n",
      "(100, 100, 3)\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"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1, 100, 100, 3)\n",
      "[12]\n",
      "r\n"
     ]
    }
   ],
   "source": [
    "#import sys\n",
    "from PIL import Image\n",
    "#sys.modules['Image'] = Image \n",
    "\n",
    "#index = np.random.randint(63)\n",
    "#from keras.preprocessing import image\n",
    "\n",
    "l = [None] * 64\n",
    "\n",
    "for r in range(1,9):\n",
    "  img = Image.open('New/opencv_frame1_'+str(r)+'.jpg')\n",
    "  img = np.array(img)\n",
    "  img = np.reshape(img,(100,100,3))\n",
    "  #y = image.img_to_array(img)\n",
    "\n",
    "  #print(y.shape)\n",
    "  print(img.shape)\n",
    "\n",
    "  plt.imshow(img)\n",
    "  plt.show()\n",
    "  img = np.reshape(img,(1,100,100,3))\n",
    "  print(img.shape)\n",
    "\n",
    "  y = model.predict_classes(img)\n",
    "  print(y)\n",
    "\n",
    "  if y==0:\n",
    "    classes='E'\n",
    "  elif y==1:\n",
    "    classes='B'\n",
    "  elif y==2:\n",
    "    classes='K'\n",
    "  elif y==3:\n",
    "    classes='N'\n",
    "  elif y==4:\n",
    "    classes='P'\n",
    "  elif y==5:\n",
    "    classes='Q'\n",
    "  elif y==6:\n",
    "    classes='R'\n",
    "  elif y==7:\n",
    "    classes='b'\n",
    "  elif y==8:\n",
    "    classes='k'\n",
    "  elif y==9:\n",
    "    classes='n'\n",
    "  elif y==10:\n",
    "    classes='p'\n",
    "  elif y==11:\n",
    "    classes='q'\n",
    "  else:\n",
    "    classes='r'\n",
    "\n",
    "\n",
    "  print(classes)\n",
    "  \n",
    "  l[r-1]=classes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1092
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 987,
     "status": "ok",
     "timestamp": 1549195562180,
     "user": {
      "displayName": "Harsh Patel",
      "photoUrl": "https://lh4.googleusercontent.com/-xVEOvhAYCwI/AAAAAAAAAAI/AAAAAAAAAAc/m8NWBboPeYY/s64/photo.jpg",
      "userId": "17018506043059870013"
     },
     "user_tz": -330
    },
    "id": "G-1AGdLvKWh_",
    "outputId": "37fc10aa-aaee-45e0-d94d-e3d3aee76de1"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['r',\n",
       " 'n',\n",
       " 'b',\n",
       " 'k',\n",
       " 'q',\n",
       " 'b',\n",
       " 'n',\n",
       " 'r',\n",
       " None,\n",
       " None,\n",
       " None,\n",
       " None,\n",
       " None,\n",
       " None,\n",
       " None,\n",
       " None,\n",
       " None,\n",
       " None,\n",
       " None,\n",
       " None,\n",
       " None,\n",
       " None,\n",
       " None,\n",
       " None,\n",
       " None,\n",
       " None,\n",
       " None,\n",
       " None,\n",
       " None,\n",
       " None,\n",
       " None,\n",
       " None,\n",
       " None,\n",
       " None,\n",
       " None,\n",
       " None,\n",
       " None,\n",
       " None,\n",
       " None,\n",
       " None,\n",
       " None,\n",
       " None,\n",
       " None,\n",
       " None,\n",
       " None,\n",
       " None,\n",
       " None,\n",
       " None,\n",
       " None,\n",
       " None,\n",
       " None,\n",
       " None,\n",
       " None,\n",
       " None,\n",
       " None,\n",
       " None,\n",
       " None,\n",
       " None,\n",
       " None,\n",
       " None,\n",
       " None,\n",
       " None,\n",
       " None,\n",
       " None]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "l"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "ZEkYT15lOoTk"
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "accelerator": "TPU",
  "colab": {
   "collapsed_sections": [],
   "name": "Pieces.ipynb",
   "provenance": [],
   "version": "0.3.2"
  },
  "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": "3.6.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}