keras_model_practice.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "import keras\n",
    "from keras.models import Model\n",
    "\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense, Activation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense_1 (Dense)              (None, 2)                 8         \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 3)                 9         \n",
      "=================================================================\n",
      "Total params: 17\n",
      "Trainable params: 17\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "[array([[ 0.5467241 , -0.57646537],\n",
      "       [-0.88949263,  0.03872693],\n",
      "       [ 0.56926394, -0.27453983]], dtype=float32), array([0., 0.], dtype=float32)]\n",
      "[array([[ 0.7123369 ,  1.0482664 ,  0.25098026],\n",
      "       [-0.6575545 ,  0.29635358,  0.00701773]], dtype=float32), array([0., 0., 0.], dtype=float32)]\n"
     ]
    }
   ],
   "source": [
    "model = Sequential()\n",
    "model.add(Dense(2, input_dim=3, name='dense_1'))\n",
    "model.add(Dense(3, name='dense_2'))\n",
    "fname ='./test_weight.h5'\n",
    "model.save_weights(fname)\n",
    "\n",
    "# model summary\n",
    "model.summary()\n",
    "\n",
    "for layer in model.layers:\n",
    "#     print( model.get_layer(layer_name))\n",
    "    print(layer.get_weights())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[array([[ 0.3634112 , -0.03811157],\n",
      "       [ 0.97057843, -0.46573573],\n",
      "       [-0.5910582 ,  0.11941993]], dtype=float32), array([0., 0.], dtype=float32)]\n",
      "[array([[-0.63099915,  0.02219301,  0.49612063, -0.3468259 ,  0.01588768,\n",
      "         0.26914436, -0.10066926,  0.20100826, -0.50348556, -0.03977805],\n",
      "       [-0.5218549 , -0.60437787, -0.35720903,  0.56756467, -0.20298344,\n",
      "        -0.00881898, -0.20957452, -0.01430649,  0.5764455 , -0.32512668]],\n",
      "      dtype=float32), array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0.], dtype=float32)]\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense_1 (Dense)              (None, 2)                 8         \n",
      "_________________________________________________________________\n",
      "new_dense (Dense)            (None, 10)                30        \n",
      "=================================================================\n",
      "Total params: 38\n",
      "Trainable params: 38\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "[array([[ 0.5467241 , -0.57646537],\n",
      "       [-0.88949263,  0.03872693],\n",
      "       [ 0.56926394, -0.27453983]], dtype=float32), array([0., 0.], dtype=float32)]\n"
     ]
    }
   ],
   "source": [
    "# new model\n",
    "model = Sequential()\n",
    "model.add(Dense(2, input_dim=3, name='dense_1'))  # will be loaded\n",
    "model.add(Dense(10, name='new_dense', activation='softmax'))  # will not be loaded\n",
    "\n",
    "for layer in model.layers:\n",
    "#     print( model.get_layer(layer_name))\n",
    "    print(layer.get_weights())\n",
    "\n",
    "# load weights from first model; will only affect the first layer, dense_1.\n",
    "model.load_weights(fname, by_name=True)\n",
    "\n",
    "# model summary\n",
    "model.summary()\n",
    "\n",
    "for layer in model.layers[:-1]:\n",
    "#     print( model.get_layer(layer_name))\n",
    "    print(layer.get_weights())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[array([[ 0.2649634 , -0.21551806],\n",
      "       [ 1.0733602 , -0.10063785],\n",
      "       [ 0.28879964,  0.7637768 ]], dtype=float32), array([0., 0.], dtype=float32)]\n",
      "[array([[ 0.34625965,  0.3822468 , -0.43370456, -0.15196651, -0.465474  ,\n",
      "         0.3951307 , -0.42655745,  0.5202661 ,  0.00820518,  0.55745953],\n",
      "       [ 0.60681754, -0.6765404 , -0.20293522,  0.35511118,  0.2578935 ,\n",
      "         0.35924274,  0.46151727,  0.70193976, -0.08491611,  0.02645051]],\n",
      "      dtype=float32), array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0.], dtype=float32)]\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense_1 (Dense)              (None, 2)                 8         \n",
      "_________________________________________________________________\n",
      "new_dense (Dense)            (None, 10)                30        \n",
      "=================================================================\n",
      "Total params: 38\n",
      "Trainable params: 38\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "[array([[ 0.5467241 , -0.57646537],\n",
      "       [-0.88949263,  0.03872693],\n",
      "       [ 0.56926394, -0.27453983]], dtype=float32), array([0., 0.], dtype=float32)]\n"
     ]
    }
   ],
   "source": [
    "# new model\n",
    "model = Sequential()\n",
    "model.add(Dense(2, input_dim=3, name='dense_1'))  # will be loaded\n",
    "model.add(Dense(10, name='new_dense'))  # will not be loaded\n",
    "\n",
    "for layer in model.layers:\n",
    "#     print( model.get_layer(layer_name))\n",
    "    print(layer.get_weights())\n",
    "\n",
    "# load weights from first model; will only affect the first layer, dense_1.\n",
    "model.load_weights(fname, by_name=True)\n",
    "\n",
    "# model summary\n",
    "model.summary()\n",
    "\n",
    "for layer in model.layers[:-1]:\n",
    "#     print( model.get_layer(layer_name))\n",
    "    print(layer.get_weights())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "new_dense\n",
      "Tensor(\"dense_1_2/BiasAdd:0\", shape=(?, 2), dtype=float32)\n",
      "(None, 2)\n",
      "Tensor(\"new_dense_1/Softmax:0\", shape=(?, 10), dtype=float32)\n",
      "(None, 10)\n",
      "Tensor(\"new_dense_1/Softmax:0\", shape=(?, 10), dtype=float32)\n"
     ]
    }
   ],
   "source": [
    "print(model.layers[-1].name) # no matrix multiple?\n",
    "print(model.layers[-1].input)\n",
    "print(model.layers[-1].input_shape)\n",
    "print(model.layers[-1].output)\n",
    "print(model.layers[-1].output_shape)\n",
    "print(model.output) # the same as the output of the last layer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 0.5467241  -0.57646537]\n",
      " [-0.88949263  0.03872693]\n",
      " [ 0.56926394 -0.27453983]]\n",
      "[0. 0.]\n"
     ]
    }
   ],
   "source": [
    "weights = model.layers[0].get_weights()[0]\n",
    "print(weights)\n",
    "biases = model.layers[0].get_weights()[1]\n",
    "print(biases)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tensor(\"dense_1_2/BiasAdd:0\", shape=(?, 2), dtype=float32)\n"
     ]
    }
   ],
   "source": [
    "model.pop()\n",
    "print(model.output)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tensor(\"dense_1_2/BiasAdd:0\", shape=(?, 2), dtype=float32)\n",
      "dense_1\n"
     ]
    }
   ],
   "source": [
    "print(model.layers[-1].output)\n",
    "print(model.layers[-1].name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading data from https://github.com/fchollet/deep-learning-models/releases/download/v0.4/xception_weights_tf_dim_ordering_tf_kernels_notop.h5\n"
     ]
    },
    {
     "ename": "Exception",
     "evalue": "URL fetch failure on https://github.com/fchollet/deep-learning-models/releases/download/v0.4/xception_weights_tf_dim_ordering_tf_kernels_notop.h5: None -- [Errno -2] Name or service not known",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mgaierror\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m/usr/lib/python3.5/urllib/request.py\u001b[0m in \u001b[0;36mdo_open\u001b[0;34m(self, http_class, req, **http_conn_args)\u001b[0m\n\u001b[1;32m   1253\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1254\u001b[0;31m                 \u001b[0mh\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrequest\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mreq\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_method\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreq\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mselector\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreq\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mheaders\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1255\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mOSError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;31m# timeout error\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python3.5/http/client.py\u001b[0m in \u001b[0;36mrequest\u001b[0;34m(self, method, url, body, headers)\u001b[0m\n\u001b[1;32m   1105\u001b[0m         \u001b[0;34m\"\"\"Send a complete request to the server.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1106\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_send_request\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmethod\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0murl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbody\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mheaders\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1107\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python3.5/http/client.py\u001b[0m in \u001b[0;36m_send_request\u001b[0;34m(self, method, url, body, headers)\u001b[0m\n\u001b[1;32m   1150\u001b[0m             \u001b[0mbody\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_encode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbody\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'body'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1151\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mendheaders\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbody\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1152\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python3.5/http/client.py\u001b[0m in \u001b[0;36mendheaders\u001b[0;34m(self, message_body)\u001b[0m\n\u001b[1;32m   1101\u001b[0m             \u001b[0;32mraise\u001b[0m \u001b[0mCannotSendHeader\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1102\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_send_output\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmessage_body\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1103\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python3.5/http/client.py\u001b[0m in \u001b[0;36m_send_output\u001b[0;34m(self, message_body)\u001b[0m\n\u001b[1;32m    933\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 934\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmsg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    935\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mmessage_body\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python3.5/http/client.py\u001b[0m in \u001b[0;36msend\u001b[0;34m(self, data)\u001b[0m\n\u001b[1;32m    876\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mauto_open\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 877\u001b[0;31m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconnect\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    878\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python3.5/http/client.py\u001b[0m in \u001b[0;36mconnect\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1251\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1252\u001b[0;31m             \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconnect\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1253\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python3.5/http/client.py\u001b[0m in \u001b[0;36mconnect\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    848\u001b[0m         self.sock = self._create_connection(\n\u001b[0;32m--> 849\u001b[0;31m             (self.host,self.port), self.timeout, self.source_address)\n\u001b[0m\u001b[1;32m    850\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msock\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msetsockopt\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msocket\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mIPPROTO_TCP\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msocket\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTCP_NODELAY\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python3.5/socket.py\u001b[0m in \u001b[0;36mcreate_connection\u001b[0;34m(address, timeout, source_address)\u001b[0m\n\u001b[1;32m    692\u001b[0m     \u001b[0merr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 693\u001b[0;31m     \u001b[0;32mfor\u001b[0m \u001b[0mres\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mgetaddrinfo\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhost\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mport\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mSOCK_STREAM\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    694\u001b[0m         \u001b[0maf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msocktype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mproto\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcanonname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msa\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mres\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python3.5/socket.py\u001b[0m in \u001b[0;36mgetaddrinfo\u001b[0;34m(host, port, family, type, proto, flags)\u001b[0m\n\u001b[1;32m    731\u001b[0m     \u001b[0maddrlist\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 732\u001b[0;31m     \u001b[0;32mfor\u001b[0m \u001b[0mres\u001b[0m \u001b[0;32min\u001b[0m \u001b[0m_socket\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgetaddrinfo\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhost\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mport\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfamily\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mproto\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mflags\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    733\u001b[0m         \u001b[0maf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msocktype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mproto\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcanonname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msa\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mres\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mgaierror\u001b[0m: [Errno -2] Name or service not known",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[0;31mURLError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m~/tensorflow_virtualenv/lib/python3.5/site-packages/keras/utils/data_utils.py\u001b[0m in \u001b[0;36mget_file\u001b[0;34m(fname, origin, untar, md5_hash, file_hash, cache_subdir, hash_algorithm, extract, archive_format, cache_dir)\u001b[0m\n\u001b[1;32m    221\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 222\u001b[0;31m                 \u001b[0murlretrieve\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0morigin\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfpath\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdl_progress\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    223\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mHTTPError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python3.5/urllib/request.py\u001b[0m in \u001b[0;36murlretrieve\u001b[0;34m(url, filename, reporthook, data)\u001b[0m\n\u001b[1;32m    187\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 188\u001b[0;31m     \u001b[0;32mwith\u001b[0m \u001b[0mcontextlib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclosing\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0murlopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0murl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mfp\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    189\u001b[0m         \u001b[0mheaders\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minfo\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python3.5/urllib/request.py\u001b[0m in \u001b[0;36murlopen\u001b[0;34m(url, data, timeout, cafile, capath, cadefault, context)\u001b[0m\n\u001b[1;32m    162\u001b[0m         \u001b[0mopener\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_opener\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 163\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mopener\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0murl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtimeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    164\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python3.5/urllib/request.py\u001b[0m in \u001b[0;36mopen\u001b[0;34m(self, fullurl, data, timeout)\u001b[0m\n\u001b[1;32m    465\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 466\u001b[0;31m         \u001b[0mresponse\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_open\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mreq\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    467\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python3.5/urllib/request.py\u001b[0m in \u001b[0;36m_open\u001b[0;34m(self, req, data)\u001b[0m\n\u001b[1;32m    483\u001b[0m         result = self._call_chain(self.handle_open, protocol, protocol +\n\u001b[0;32m--> 484\u001b[0;31m                                   '_open', req)\n\u001b[0m\u001b[1;32m    485\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python3.5/urllib/request.py\u001b[0m in \u001b[0;36m_call_chain\u001b[0;34m(self, chain, kind, meth_name, *args)\u001b[0m\n\u001b[1;32m    443\u001b[0m             \u001b[0mfunc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhandler\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmeth_name\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 444\u001b[0;31m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    445\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mresult\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python3.5/urllib/request.py\u001b[0m in \u001b[0;36mhttps_open\u001b[0;34m(self, req)\u001b[0m\n\u001b[1;32m   1296\u001b[0m             return self.do_open(http.client.HTTPSConnection, req,\n\u001b[0;32m-> 1297\u001b[0;31m                 context=self._context, check_hostname=self._check_hostname)\n\u001b[0m\u001b[1;32m   1298\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python3.5/urllib/request.py\u001b[0m in \u001b[0;36mdo_open\u001b[0;34m(self, http_class, req, **http_conn_args)\u001b[0m\n\u001b[1;32m   1255\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mOSError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;31m# timeout error\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1256\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mURLError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0merr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1257\u001b[0m             \u001b[0mr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mh\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgetresponse\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mURLError\u001b[0m: <urlopen error [Errno -2] Name or service not known>",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[0;31mException\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-11-e1ae9759299f>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mxception\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mXception\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpreprocess_input\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mmodel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mXception\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msummary\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/study/machine_learning/distillation/Knowledge-Distillation/xception.py\u001b[0m in \u001b[0;36mXception\u001b[0;34m(weight_decay, classes, input_shape)\u001b[0m\n\u001b[1;32m    114\u001b[0m     weights_path = get_file(\n\u001b[1;32m    115\u001b[0m         \u001b[0;34m'xception_weights_tf_dim_ordering_tf_kernels_notop.h5'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 116\u001b[0;31m         \u001b[0mTF_WEIGHTS_PATH_NO_TOP\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcache_subdir\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'models'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    117\u001b[0m     )\n\u001b[1;32m    118\u001b[0m     \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload_weights\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mweights_path\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/tensorflow_virtualenv/lib/python3.5/site-packages/keras/utils/data_utils.py\u001b[0m in \u001b[0;36mget_file\u001b[0;34m(fname, origin, untar, md5_hash, file_hash, cache_subdir, hash_algorithm, extract, archive_format, cache_dir)\u001b[0m\n\u001b[1;32m    224\u001b[0m                 \u001b[0;32mraise\u001b[0m \u001b[0mException\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0merror_msg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0morigin\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcode\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmsg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    225\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mURLError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 226\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mException\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0merror_msg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0morigin\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0merrno\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreason\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    227\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mException\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mKeyboardInterrupt\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    228\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexists\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfpath\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mException\u001b[0m: URL fetch failure on https://github.com/fchollet/deep-learning-models/releases/download/v0.4/xception_weights_tf_dim_ordering_tf_kernels_notop.h5: None -- [Errno -2] Name or service not known"
     ]
    }
   ],
   "source": [
    "from xception import Xception, preprocess_input\n",
    "model = Xception(weights='imagenet')\n",
    "\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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