25lights part II CNN

25lights2-1.ipynb

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   "source": [
    "from keras.layers import Flatten, Input, Dense, Conv2D, MaxPooling2D, AveragePooling2D, ZeroPadding2D, Dropout, BatchNormalization\n",
    "from keras.layers.core import Flatten, Dense, Dropout, Lambda\n",
    "from keras.models import Model\n",
    "from keras import backend as K\n",
    "\n",
    "# we are using square images 128x128\n",
    "imageSize = 128\n",
    "# 5 x 5 dot array\n",
    "dotsPerSide = 5\n",
    "#our loss function will be the 25bit pattern used to create the image\n",
    "encoding_dim = dotsPerSide * dotsPerSide\n",
    "\n",
    "# this is our input placeholder\n",
    "input_img = Input(shape=(imageSize, imageSize, 1))\n",
    "\n",
    "#a convolution layer with padding and pooling functions\n",
    "def ConvBlock(input, layers, filters):\n",
    "    for i in range(layers):\n",
    "        x = ZeroPadding2D()(input)\n",
    "        x = Conv2D(filters, (3, 3), activation='relu')(x)\n",
    "    \n",
    "    return AveragePooling2D((2, 2), strides=(2, 2))(x)\n",
    "\n",
    "#a fully connected block and our normalization function\n",
    "def FCBlock(input):\n",
    "    x = Dense(4096, activation='relu')(input)\n",
    "    x = BatchNormalization()(x)\n",
    "    return Dropout(0.5)(x)\n",
    "\n",
    "#use a stack of convolutional layers, like VGG16\n",
    "x = ConvBlock(input_img, 2, 64)\n",
    "#x = ConvBlock(x, 2, 64)\n",
    "x = ConvBlock(x, 2, 128)\n",
    "x = ConvBlock(x, 3, 256)\n",
    "x = ConvBlock(x, 3, 512)\n",
    "#flatten out the convolutional layers, then regularize with batch normalization\n",
    "x = Flatten()(x)\n",
    "\n",
    "x = FCBlock(x)\n",
    "#x = FCBlock(x)\n",
    "\n",
    "# \"loss\" is the bit array for each image\n",
    "loss = Dense(encoding_dim, activation='sigmoid')(x)\n",
    "\n",
    "model = Model(input_img, loss)\n",
    "model.compile(optimizer='adadelta', loss='binary_crossentropy')\n",
    "\n"
   ]
  }
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