converion to tflite.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.13.1\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "print(tf.__version__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "tf.contrib.saved_model.save_keras_model(model,model_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "saved_model_dir='D:/tmp/results/output/1581435417'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From C:\\Users\\DELL\\Miniconda3\\lib\\site-packages\\tensorflow\\python\\training\\saver.py:1266: checkpoint_exists (from tensorflow.python.training.checkpoint_management) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use standard file APIs to check for files with this prefix.\n",
      "INFO:tensorflow:Restoring parameters from D:/tmp/results/output/1581435417\\variables\\variables\n",
      "INFO:tensorflow:The given SavedModel MetaGraphDef contains SignatureDefs with the following keys: {'serving_default'}\n",
      "INFO:tensorflow:input tensors info: \n",
      "INFO:tensorflow:Tensor's key in saved_model's tensor_map: input_1\n",
      "INFO:tensorflow: tensor name: input_1:0, shape: (-1, 224, 224, 3), type: DT_FLOAT\n",
      "INFO:tensorflow:output tensors info: \n",
      "INFO:tensorflow:Tensor's key in saved_model's tensor_map: dense\n",
      "INFO:tensorflow: tensor name: dense/Softmax:0, shape: (-1, 7), type: DT_FLOAT\n",
      "INFO:tensorflow:Restoring parameters from D:/tmp/results/output/1581435417\\variables\\variables\n",
      "WARNING:tensorflow:From C:\\Users\\DELL\\Miniconda3\\lib\\site-packages\\tensorflow\\lite\\python\\convert_saved_model.py:275: convert_variables_to_constants (from tensorflow.python.framework.graph_util_impl) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use tf.compat.v1.graph_util.convert_variables_to_constants\n",
      "WARNING:tensorflow:From C:\\Users\\DELL\\Miniconda3\\lib\\site-packages\\tensorflow\\python\\framework\\graph_util_impl.py:245: extract_sub_graph (from tensorflow.python.framework.graph_util_impl) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use tf.compat.v1.graph_util.extract_sub_graph\n",
      "INFO:tensorflow:Froze 137 variables.\n",
      "INFO:tensorflow:Converted 137 variables to const ops.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "12824492"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "converter = tf.lite.TFLiteConverter.from_saved_model(saved_model_dir)\n",
    "tflite_model = converter.convert()\n",
    "open(\"/tmp/skin_model.tflite\", \"wb\").write(tflite_model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "interpreter = tf.lite.Interpreter(model_path=\"/tmp/skin_model.tflite\")\n",
    "interpreter.allocate_tensors()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "== Input details ==\n",
      "name: input_1\n",
      "shape: [  1 224 224   3]\n",
      "type: <class 'numpy.float32'>\n",
      "\n",
      "== Output details ==\n",
      "name: dense/Softmax\n",
      "shape: [1 7]\n",
      "type: <class 'numpy.float32'>\n"
     ]
    }
   ],
   "source": [
    "print(\"== Input details ==\")\n",
    "print(\"name:\", interpreter.get_input_details()[0]['name'])\n",
    "print(\"shape:\", interpreter.get_input_details()[0]['shape'])\n",
    "print(\"type:\", interpreter.get_input_details()[0]['dtype'])\n",
    "\n",
    "print(\"\\n== Output details ==\")\n",
    "print(\"name:\", interpreter.get_output_details()[0]['name'])\n",
    "print(\"shape:\", interpreter.get_output_details()[0]['shape'])\n",
    "print(\"type:\", interpreter.get_output_details()[0]['dtype'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "== Input details ==\n",
      "name: conv2d_95_input\n",
      "shape: [  1  75 100   3]\n",
      "type: <class 'numpy.float32'>\n",
      "\n",
      "== Output details ==\n",
      "name: dense_2/Softmax\n",
      "shape: [1 8]\n",
      "type: <class 'numpy.float32'>\n"
     ]
    }
   ],
   "source": [
    "interpreter = tf.lite.Interpreter(model_path=\"/tmp/test_model_3.tflite\")\n",
    "interpreter.allocate_tensors()\n",
    "\n",
    "print(\"== Input details ==\")\n",
    "print(\"name:\", interpreter.get_input_details()[0]['name'])\n",
    "print(\"shape:\", interpreter.get_input_details()[0]['shape'])\n",
    "print(\"type:\", interpreter.get_input_details()[0]['dtype'])\n",
    "\n",
    "print(\"\\n== Output details ==\")\n",
    "print(\"name:\", interpreter.get_output_details()[0]['name'])\n",
    "print(\"shape:\", interpreter.get_output_details()[0]['shape'])\n",
    "print(\"type:\", interpreter.get_output_details()[0]['dtype'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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