Untitled1.ipynb

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   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "import keras.layers as KL\n",
    "from keras.models import Model\n",
    "import keras.backend as K\n",
    "import tensorflow as tf\n",
    "from dataset import dataset\n",
    "import keras\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def building_block(filters, block,stride=0):\n",
    "    if block !=0:\n",
    "        stride = 1\n",
    "    else:\n",
    "        stride = 2\n",
    "    def f(x):\n",
    "        y = KL.Conv2D(filters,(1,1),strides=stride)(x)\n",
    "        y = KL.BatchNormalization(axis=3)(y)\n",
    "        y = KL.Activation('relu')(y)\n",
    "        \n",
    "        y = KL.Conv2D(filters,(3,3),padding='same')(y)\n",
    "        y = KL.BatchNormalization(axis=3)(y)\n",
    "        y = KL.Activation('relu')(y)        \n",
    "    \n",
    "        y = KL.Conv2D(4*filters,(1,1))(y)\n",
    "        y = KL.BatchNormalization(axis=3)(y)\n",
    "        \n",
    "        if block == 0:\n",
    "            shortcut = KL.Conv2D(4*filters,(1,1),strides=stride)(x)\n",
    "            shortcut = KL.BatchNormalization(axis=3)(shortcut)\n",
    "        else:\n",
    "            shortcut = x\n",
    "        y = KL.Add()([y,shortcut])\n",
    "        y = KL.Activation('relu')(y)\n",
    "        return y\n",
    "    return f"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "inputs = KL.Input(shape=[None,None,3], dtype=tf.float32)\n",
    "y = building_block(64,0)(inputs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = Model([inputs],[y])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.utils.vis_utils import plot_model\n",
    "plot_model(model,to_file='resnet_block.png',show_shapes=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "a=np.array([1,2,3,4,5,6])\n",
    "b=np.array([1,3,5])\n",
    "a[b]=0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 0, 3, 0, 5, 0])"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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