Untitled2.ipynb

{
 "cells": [
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "%%time\n",
    "from tensorflow.keras.preprocessing import image  \n",
    "from tensorflow.keras.models import load_model  \n",
    "import numpy as np  \n",
    "model = load_model(\"./refactor_finetune.h5\")  \n",
    "model.compile(loss='categorical_crossentropy',\n",
    "          optimizer='sgd',\n",
    "          metrics=['accuracy'])\n",
    "\n",
    "img = np.zeros((20, 224,224,3), dtype=np.float32)  \n",
    "classes = model(img)  \n"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "%%time\n",
    "classes = model(img)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "load graph\n"
     ]
    },
    {
     "data": {
      "text/plain": [
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       "        1.51136584e-04, 4.50227031e-04, 8.51852121e-04, 1.00809790e-03,\n",
       "        1.27811613e-03, 6.42212166e-04, 3.50155140e-04, 1.33196660e-03,\n",
       "        1.68631581e-04, 4.44526377e-05, 5.63298468e-04, 5.37047628e-04,\n",
       "        3.07764247e-04, 3.21121141e-03, 6.58682780e-04, 9.49305104e-05,\n",
       "        6.65862404e-04, 4.04095161e-04, 1.93350323e-04, 1.16737858e-04,\n",
       "        2.04868818e-04, 2.85406422e-04, 1.69753635e-04, 3.01372958e-04,\n",
       "        7.54212728e-04, 3.80535203e-04, 1.05556479e-04, 8.23375667e-05,\n",
       "        1.58674913e-04, 2.41050857e-05, 1.08099623e-04, 1.23682563e-04,\n",
       "        6.96892093e-05, 1.18628355e-04, 2.65993178e-04, 1.31288059e-02]],\n",
       "      dtype=float32)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "from tensorflow.python.platform import gfile\n",
    "import numpy as np\n",
    "GRAPH_PB_PATH = './freeze_graph_base.pb'\n",
    "sess = tf.compat.v1.Session() \n",
    "print(\"load graph\")\n",
    "with gfile.FastGFile(GRAPH_PB_PATH,'rb') as f:\n",
    "   graph_def = tf.compat.v1.GraphDef()\n",
    "graph_def.ParseFromString(f.read())\n",
    "sess.graph.as_default()\n",
    "tf.import_graph_def(graph_def, name='')\n",
    "graph_nodes=[n for n in graph_def.node]\n",
    "names = []\n",
    "synth_data = np.zeros((20, 224,224,3), np.float32)\n",
    "for t in graph_nodes:\n",
    "  names.append(t.name)\n",
    "#print(names)\n",
    "sess.run('predictions/Softmax:0', feed_dict={'input_1:0': np.zeros((1, 224, 224, 3), np.float32)})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "78.7 ms ± 426 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n"
     ]
    }
   ],
   "source": [
    "%%timeit\n",
    "sess.run('predictions/Softmax:0', feed_dict={'input_1:0': synth_data})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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