android_generator.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## .tflite conversion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.12.0\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "print(tf.VERSION)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "\n",
    "def convert_hdf5_to_tflite(keras_file, filename):\n",
    "    converter = tf.contrib.lite.TFLiteConverter.from_keras_model_file(keras_file)\n",
    "    tflite_model = converter.convert()\n",
    "    open(filename, \"wb\").write(tflite_model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Error in loading the saved optimizer state. As a result, your model is starting with a freshly initialized optimizer.\n",
      "INFO:tensorflow:Froze 10 variables.\n",
      "INFO:tensorflow:Converted 10 variables to const ops.\n"
     ]
    }
   ],
   "source": [
    "convert_hdf5_to_tflite('./keras_models/weights.best.from_scratch.hdf5', './tflite/model_from_scratch.tflite')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Error in loading the saved optimizer state. As a result, your model is starting with a freshly initialized optimizer.\n",
      "INFO:tensorflow:Froze 4 variables.\n",
      "INFO:tensorflow:Converted 4 variables to const ops.\n"
     ]
    }
   ],
   "source": [
    "convert_hdf5_to_tflite('./keras_models/weights.extra_layers.hdf5', './tflite/extra_layers.tflite')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Froze 324 variables.\n",
      "INFO:tensorflow:Converted 324 variables to const ops.\n"
     ]
    }
   ],
   "source": [
    "convert_hdf5_to_tflite('./keras_models/weights.best_single_model.hdf5', './tflite/best_single_resnet_model.tflite')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Froze 143 variables.\n",
      "INFO:tensorflow:Converted 143 variables to const ops.\n"
     ]
    }
   ],
   "source": [
    "convert_hdf5_to_tflite('./keras_models/weights.single_model_mobilenet.hdf5', './tflite/single_model_mobilenet.tflite')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Froze 143 variables.\n",
      "INFO:tensorflow:Converted 143 variables to const ops.\n"
     ]
    }
   ],
   "source": [
    "convert_hdf5_to_tflite('keras_models/weights.dog_six_classifier_mobilenet.hdf5', './tflite/dog_six_classifier_mobilenet.tflite')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'input_3': [None, 224, 244, 3]}\n",
      "WARNING:tensorflow:No training configuration found in save file: the model was *not* compiled. Compile it manually.\n",
      "INFO:tensorflow:Froze 318 variables.\n",
      "INFO:tensorflow:Converted 318 variables to const ops.\n"
     ]
    },
    {
     "ename": "ValueError",
     "evalue": "None is only supported in the 1st dimension. Tensor 'input_3' has invalid shape '[None, None, None, 3]'.",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-15-9bb03295cff8>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0minput_shapes\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m{\u001b[0m \u001b[0;34m'input_3'\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m224\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m244\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m3\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mconvert_hdf5_to_tflite\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'./keras_models/base_resnet.hdf5'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'./tflite/base_resnet.tflite'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput_shapes\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m<ipython-input-14-0f96b16a91a5>\u001b[0m in \u001b[0;36mconvert_hdf5_to_tflite\u001b[0;34m(keras_file, filename, input_shapes)\u001b[0m\n\u001b[1;32m      4\u001b[0m     \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput_shapes\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m     \u001b[0mconverter\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlite\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTFLiteConverter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfrom_keras_model_file\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkeras_file\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0minput_shapes\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m     \u001b[0mtflite_model\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mconverter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconvert\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      7\u001b[0m     \u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilename\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"wb\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwrite\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtflite_model\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.6/site-packages/tensorflow/lite/python/lite.py\u001b[0m in \u001b[0;36mconvert\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    409\u001b[0m           raise ValueError(\n\u001b[1;32m    410\u001b[0m               \u001b[0;34m\"None is only supported in the 1st dimension. Tensor '{0}' has \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 411\u001b[0;31m               \"invalid shape '{1}'.\".format(_tensor_name(tensor), shape_list))\n\u001b[0m\u001b[1;32m    412\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0mshape_list\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\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    413\u001b[0m           \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_set_batch_size\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbatch_size\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;31mValueError\u001b[0m: None is only supported in the 1st dimension. Tensor 'input_3' has invalid shape '[None, None, None, 3]'."
     ]
    }
   ],
   "source": [
    "input_shapes = { 'input_3': [None, 224, 244, 3] }\n",
    "convert_hdf5_to_tflite('./keras_models/base_resnet.hdf5', './tflite/base_resnet.tflite', input_shapes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/akshata/.local/lib/python3.6/site-packages/keras/engine/saving.py:292: UserWarning: No training configuration found in save file: the model was *not* compiled. Compile it manually.\n",
      "  warnings.warn('No training configuration found in save file: '\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tf.Tensor 'input_3_4:0' shape=(?, ?, ?, 3) dtype=float32>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from keras.models import load_model\n",
    "\n",
    "base_resnet = load_model('./keras_models/base_resnet.hdf5')\n",
    "base_resnet.inputs[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "## update base_resnet input shape to be compatible with tflite\n",
    "\n",
    "from keras.models import Input, Model\n",
    "\n",
    "def change_input_shape(model, input_shape = (None, 224, 224, 3)):\n",
    "    model.layers.pop(0)\n",
    "    newInput = Input(batch_shape=input_shape)    \n",
    "    newOutputs = base_resnet(newInput)\n",
    "    newModel = Model(newInput, newOutputs)\n",
    "    newModel.summary()\n",
    "    return newModel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "ename": "AssertionError",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAssertionError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-58-a41ff4200052>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     16\u001b[0m               \u001b[0mloss\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'categorical_crossentropy'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     17\u001b[0m               metrics=['accuracy'])\n\u001b[0;32m---> 18\u001b[0;31m \u001b[0mtest_model\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# starts training\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     19\u001b[0m \u001b[0mtest_model\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~/.local/lib/python3.6/site-packages/keras/engine/training.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, **kwargs)\u001b[0m\n\u001b[1;32m    950\u001b[0m             \u001b[0msample_weight\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msample_weight\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    951\u001b[0m             \u001b[0mclass_weight\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mclass_weight\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 952\u001b[0;31m             batch_size=batch_size)\n\u001b[0m\u001b[1;32m    953\u001b[0m         \u001b[0;31m# Prepare validation data.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    954\u001b[0m         \u001b[0mdo_validation\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.6/site-packages/keras/engine/training.py\u001b[0m in \u001b[0;36m_standardize_user_data\u001b[0;34m(self, x, y, sample_weight, class_weight, check_array_lengths, batch_size)\u001b[0m\n\u001b[1;32m    675\u001b[0m             \u001b[0;31m# to match the value shapes.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    676\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 677\u001b[0;31m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_set_inputs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    678\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    679\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0my\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~/.local/lib/python3.6/site-packages/keras/engine/training.py\u001b[0m in \u001b[0;36m_set_inputs\u001b[0;34m(self, inputs, outputs, training)\u001b[0m\n\u001b[1;32m    585\u001b[0m             \u001b[0;31m# since `Sequential` depends on `Model`.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    586\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 587\u001b[0;31m                 \u001b[0;32massert\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    588\u001b[0m                 \u001b[0minputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0minputs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    589\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbuild\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput_shape\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0minputs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\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;31mAssertionError\u001b[0m: "
     ]
    }
   ],
   "source": [
    "## create a new model\n",
    "from keras.models import Sequential, InputLayer\n",
    "\n",
    "# Start construction of the Keras Sequential model.\n",
    "test_model = Sequential()\n",
    "\n",
    "# Add an input layer which is similar to a feed_dict in TensorFlow.\n",
    "# Note that the input-shape must be a tuple containing the image-size.\n",
    "# test_model.add(InputLayer(input_shape=(224, 244, 3)))\n",
    "\n",
    "for layer in base_resnet.layers:\n",
    "    layer.trainable = False\n",
    "    test_model.add(layer)\n",
    "\n",
    "test_model.compile(optimizer='rmsprop',\n",
    "              loss='categorical_crossentropy',\n",
    "              metrics=['accuracy'])\n",
    "test_model.fit([], [])  # starts training\n",
    "test_model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## .pb Conversion\n",
    "\n",
    "Deploying to android requires a .pb format. Use the following function, to convert your model "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Method 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "## not compatible with old versions of tensorflow\n",
    "import os\n",
    "from tensorflow.python.framework import graph_util, graph_io\n",
    "from tensorflow.python.tools import import_pb_to_tensorboard\n",
    "\n",
    "def keras_to_tensorflow(keras_model, \n",
    "        output_dir, \n",
    "        model_name,\n",
    "        out_prefix=\"output_\", \n",
    "        log_tensorboard=True):\n",
    "\n",
    "    if os.path.exists(output_dir) == False:\n",
    "        os.mkdir(output_dir)\n",
    "\n",
    "    out_nodes = []\n",
    "\n",
    "    for i in range(len(keras_model.outputs)):\n",
    "        out_nodes.append(out_prefix + str(i + 1))\n",
    "        tf.identity(keras_model.output[i], \n",
    "            out_prefix + str(i + 1))\n",
    "\n",
    "    sess = K.get_session()\n",
    "\n",
    "\n",
    "    init_graph = sess.graph.as_graph_def()\n",
    "\n",
    "    main_graph = graph_util.convert_variables_to_constants(\n",
    "            sess, init_graph, out_nodes)\n",
    "\n",
    "    graph_io.write_graph(main_graph, output_dir, \n",
    "        name=model_name, as_text=False)\n",
    "\n",
    "    if log_tensorboard:\n",
    "        import_pb_to_tensorboard.import_to_tensorboard(\n",
    "            os.path.join(output_dir, model_name),\n",
    "            output_dir)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Method 2: Freeze Session"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "## not compatible with old versions of tensorflow\n",
    "import os\n",
    "from tensorflow.python.framework import graph_util, graph_io\n",
    "from tensorflow.python.tools import import_pb_to_tensorboard\n",
    "\n",
    "def freeze_session(keras_model, \n",
    "        output_dir, \n",
    "        model_name,\n",
    "        out_prefix=\"output_\", \n",
    "        log_tensorboard=True):\n",
    "\n",
    "    # todo\n",
    "    pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def save_frozen_graph(model, name, filename):\n",
    "    frozen_graph = freeze_session(K.get_session(),\n",
    "                              output_names=[out.op.name for out in model.outputs])\n",
    "    tf.train.write_graph(frozen_graph, name, filename, as_text=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "## save both models\n",
    "import os\n",
    "import tensorflow as tf\n",
    "from keras import backend as K\n",
    "\n",
    "save_frozen_graph(base_resnet, 'android', 'base_resnet_model.pb')\n",
    "save_frozen_graph(last_layers_model, 'last_layers', 'last_layers_model.pb')\n",
    "\n",
    "# keras_to_tensorflow(base_resnet, 'android', 'base_resnet_model.pb')\n",
    "# keras_to_tensorflow(last_layers_model, 'android', 'last_layers_model.pb')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Method 3 (from Siraj Raval Video)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from tensorflow.python.tools import freeze_graph\n",
    "from tensorflow.python.tools import optimize_for_inference_lib\n",
    "\n",
    "def export_model(saver, model, input_node_names, output_node_name, model_name):\n",
    "    tf.train.write_graph(K.get_session().graph_def, 'out', \\\n",
    "        model_name + '_graph.pbtxt')\n",
    "\n",
    "    saver.save(K.get_session(), 'out/' + model_name + '.chkp')\n",
    "\n",
    "    freeze_graph.freeze_graph('out/' + model_name + '_graph.pbtxt', None, \\\n",
    "        False, 'out/' + model_name + '.chkp', output_node_name, \\\n",
    "        \"save/restore_all\", \"save/Const:0\", \\\n",
    "        'out/frozen_' + model_name + '.pb', True, \"\")\n",
    "\n",
    "    input_graph_def = tf.GraphDef()\n",
    "    with tf.gfile.Open('out/frozen_' + model_name + '.pb', \"rb\") as f:\n",
    "        input_graph_def.ParseFromString(f.read())\n",
    "\n",
    "    output_graph_def = optimize_for_inference_lib.optimize_for_inference(\n",
    "            input_graph_def, input_node_names, [output_node_name],\n",
    "            tf.float32.as_datatype_enum)\n",
    "\n",
    "    with tf.gfile.FastGFile('out/opt_' + model_name + '.pb', \"wb\") as f:\n",
    "        f.write(output_graph_def.SerializeToString())\n",
    "\n",
    "    print(\"graph saved!\")\n",
    "    \n",
    "export_model(tf.train.Saver(), last_layers_model, [\"flatten_7_input\"], 'dense_16/Softmax', 'last_layers')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Getting input / output names of model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "[node.op.name for node in last_layers_model.inputs]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Testing if the probuf models were saved corrected"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import tensorflow as tf \n",
    "\n",
    "def test_protobuf(filepath):\n",
    "    g = tf.GraphDef()\n",
    "    g.ParseFromString(open(filepath, 'rb').read())\n",
    "\n",
    "    # same for output or any other node you want to make sure is ok\n",
    "    print()\n",
    "    print(filepath)\n",
    "    print(\"=======================INPUT===================\")\n",
    "    print([n for n in g.node if n.name.find('input') != -1])\n",
    "    print(\"=======================OUTPUT==================\")\n",
    "    print([n for n in g.node if n.name.find('output') != -1])\n",
    "    print(\"===================KERAS_LEARNING==============\")\n",
    "    print([n for n in g.node if n.name.find('keras_learning_phase') != -1])\n",
    "    print(\"===============================================\")\n",
    "    print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "resnet_pb = 'android/base_resnet_model.pb'\n",
    "ex_layers_pb = 'android/last_layers_model.pb'\n",
    "\n",
    "test_protobuf(resnet_pb)\n",
    "test_protobuf(ex_layers_pb)"
   ]
  }
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