D05 使用別人訓練好的神經網路.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "from keras.applications.vgg19 import VGG19\n",
    "from keras.preprocessing import image\n",
    "from keras.applications.vgg19 import preprocess_input\n",
    "from keras.models import Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading data from https://github.com/fchollet/deep-learning-models/releases/download/v0.1/vgg19_weights_tf_dim_ordering_tf_kernels.h5\n",
      "574676992/574710816 [============================>.] - ETA: 0s"
     ]
    },
    {
     "ename": "InternalError",
     "evalue": "Failed to create session.",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mInternalError\u001b[0m                             Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-2-46f7d5295cbb>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mbase_model\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mVGG19\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mweights\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'imagenet'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mmodel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mModel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mbase_model\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moutputs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mbase_model\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_layer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'block4_pool'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moutput\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/home/yenlung/anaconda3/lib/python3.6/site-packages/keras/applications/vgg19.py\u001b[0m in \u001b[0;36mVGG19\u001b[0;34m(include_top, weights, input_tensor, input_shape, pooling, classes)\u001b[0m\n\u001b[1;32m    170\u001b[0m                                     \u001b[0mWEIGHTS_PATH_NO_TOP\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    171\u001b[0m                                     cache_subdir='models')\n\u001b[0;32m--> 172\u001b[0;31m         \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[0m\u001b[1;32m    173\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mK\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbackend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'theano'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    174\u001b[0m             \u001b[0mlayer_utils\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconvert_all_kernels_in_model\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/home/yenlung/anaconda3/lib/python3.6/site-packages/keras/engine/topology.py\u001b[0m in \u001b[0;36mload_weights\u001b[0;34m(self, filepath, by_name)\u001b[0m\n\u001b[1;32m   2570\u001b[0m             \u001b[0mload_weights_from_hdf5_group_by_name\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlayers\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2571\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2572\u001b[0;31m             \u001b[0mload_weights_from_hdf5_group\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlayers\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2573\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2574\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mhasattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'close'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/home/yenlung/anaconda3/lib/python3.6/site-packages/keras/engine/topology.py\u001b[0m in \u001b[0;36mload_weights_from_hdf5_group\u001b[0;34m(f, layers)\u001b[0m\n\u001b[1;32m   3006\u001b[0m                              ' elements.')\n\u001b[1;32m   3007\u001b[0m         \u001b[0mweight_value_tuples\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msymbolic_weights\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mweight_values\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3008\u001b[0;31m     \u001b[0mK\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbatch_set_value\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mweight_value_tuples\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3009\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3010\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/home/yenlung/anaconda3/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py\u001b[0m in \u001b[0;36mbatch_set_value\u001b[0;34m(tuples)\u001b[0m\n\u001b[1;32m   2187\u001b[0m             \u001b[0massign_ops\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0massign_op\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2188\u001b[0m             \u001b[0mfeed_dict\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0massign_placeholder\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2189\u001b[0;31m         \u001b[0mget_session\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0massign_ops\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfeed_dict\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfeed_dict\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2190\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2191\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/home/yenlung/anaconda3/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py\u001b[0m in \u001b[0;36mget_session\u001b[0;34m()\u001b[0m\n\u001b[1;32m    161\u001b[0m                 config = tf.ConfigProto(intra_op_parallelism_threads=num_thread,\n\u001b[1;32m    162\u001b[0m                                         allow_soft_placement=True)\n\u001b[0;32m--> 163\u001b[0;31m             \u001b[0m_SESSION\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mSession\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mconfig\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mconfig\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    164\u001b[0m         \u001b[0msession\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_SESSION\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    165\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0m_MANUAL_VAR_INIT\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/home/yenlung/anaconda3/lib/python3.6/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, target, graph, config)\u001b[0m\n\u001b[1;32m   1290\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1291\u001b[0m     \"\"\"\n\u001b[0;32m-> 1292\u001b[0;31m     \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mSession\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__init__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtarget\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgraph\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mconfig\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mconfig\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1293\u001b[0m     \u001b[0;31m# NOTE(mrry): Create these on first `__enter__` to avoid a reference cycle.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1294\u001b[0m     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_default_graph_context_manager\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/home/yenlung/anaconda3/lib/python3.6/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, target, graph, config)\u001b[0m\n\u001b[1;32m    560\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    561\u001b[0m       \u001b[0;32mwith\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mraise_exception_on_not_ok_status\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mstatus\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 562\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_session\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtf_session\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTF_NewDeprecatedSession\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mopts\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstatus\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    563\u001b[0m     \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    564\u001b[0m       \u001b[0mtf_session\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTF_DeleteSessionOptions\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mopts\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/home/yenlung/anaconda3/lib/python3.6/contextlib.py\u001b[0m in \u001b[0;36m__exit__\u001b[0;34m(self, type, value, traceback)\u001b[0m\n\u001b[1;32m     87\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mtype\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     88\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 89\u001b[0;31m                 \u001b[0mnext\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgen\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     90\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mStopIteration\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     91\u001b[0m                 \u001b[0;32mreturn\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/home/yenlung/anaconda3/lib/python3.6/site-packages/tensorflow/python/framework/errors_impl.py\u001b[0m in \u001b[0;36mraise_exception_on_not_ok_status\u001b[0;34m()\u001b[0m\n\u001b[1;32m    464\u001b[0m           \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    465\u001b[0m           \u001b[0mcompat\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mas_text\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpywrap_tensorflow\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTF_Message\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstatus\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--> 466\u001b[0;31m           pywrap_tensorflow.TF_GetCode(status))\n\u001b[0m\u001b[1;32m    467\u001b[0m   \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    468\u001b[0m     \u001b[0mpywrap_tensorflow\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTF_DeleteStatus\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstatus\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mInternalError\u001b[0m: Failed to create session."
     ]
    }
   ],
   "source": [
    "base_model = VGG19(weights='imagenet')\n",
    "model = Model(inputs=base_model.input, outputs=base_model.get_layer('block4_pool').output)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![bird](bird01.jpg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "img_path = 'bird01.jpg'\n",
    "img = image.load_img(img_path, target_size=(224, 224))\n",
    "x = image.img_to_array(img)\n",
    "x = np.expand_dims(x, axis=0)\n",
    "x = preprocess_input(x)\n",
    "block4_pool_features = model.predict(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x10bb93630>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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WKsu1yHKLY30BYwfZi7C8ur3cYqodgNv8EbXnV0F7q83wJS/rjrgi5pTSft0A4Z28Xlnn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\n5sZsGt2Su+8zq4nnlrAsZqUeO/QwFHW+fGNqopw0ap/Ah+4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tJvxmBRxlghjiQ5G32fE2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\nhHfZZZddrij/Pw/drWJARYc0AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10b3d4d30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(block4_pool_features[0,:,:,2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = image.img_to_array(img)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(14, 512)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "block4_pool_features[0][3].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "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": "3.6.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}