256_ConnectBot_ResNet_PretrainedModel.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 80631,
     "status": "ok",
     "timestamp": 1587037324012,
     "user": {
      "displayName": "joy kou",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14Ghnr_EC7eeVJnsMQ7qPVlIHwTUhmVUktt9AWoWX=s64",
      "userId": "13603392610369212421"
     },
     "user_tz": -540
    },
    "id": "NYFqpWpv1byy",
    "outputId": "d0e26a09-82fe-4585-9e20-3380f9a48488"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting tensorflow-gpu\n",
      "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/0a/93/c7bca39b23aae45cd2e85ad3871c81eccc63b9c5276e926511e2e5b0879d/tensorflow_gpu-2.1.0-cp36-cp36m-manylinux2010_x86_64.whl (421.8MB)\n",
      "\u001b[K     |████████████████████████████████| 421.8MB 37kB/s \n",
      "\u001b[?25hRequirement already satisfied: wheel>=0.26; python_version >= \"3\" in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu) (0.34.2)\n",
      "Requirement already satisfied: protobuf>=3.8.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu) (3.10.0)\n",
      "Collecting tensorboard<2.2.0,>=2.1.0\n",
      "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/d9/41/bbf49b61370e4f4d245d4c6051dfb6db80cec672605c91b1652ac8cc3d38/tensorboard-2.1.1-py3-none-any.whl (3.8MB)\n",
      "\u001b[K     |████████████████████████████████| 3.9MB 51.7MB/s \n",
      "\u001b[?25hRequirement already satisfied: opt-einsum>=2.3.2 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu) (3.2.0)\n",
      "Collecting tensorflow-estimator<2.2.0,>=2.1.0rc0\n",
      "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/18/90/b77c328a1304437ab1310b463e533fa7689f4bfc41549593056d812fab8e/tensorflow_estimator-2.1.0-py2.py3-none-any.whl (448kB)\n",
      "\u001b[K     |████████████████████████████████| 450kB 59.1MB/s \n",
      "\u001b[?25hRequirement already satisfied: six>=1.12.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu) (1.12.0)\n",
      "Requirement already satisfied: wrapt>=1.11.1 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu) (1.12.1)\n",
      "Requirement already satisfied: grpcio>=1.8.6 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu) (1.28.1)\n",
      "Requirement already satisfied: termcolor>=1.1.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu) (1.1.0)\n",
      "Requirement already satisfied: scipy==1.4.1; python_version >= \"3\" in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu) (1.4.1)\n",
      "Requirement already satisfied: keras-applications>=1.0.8 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu) (1.0.8)\n",
      "Requirement already satisfied: keras-preprocessing>=1.1.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu) (1.1.0)\n",
      "Requirement already satisfied: absl-py>=0.7.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu) (0.9.0)\n",
      "Requirement already satisfied: astor>=0.6.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu) (0.8.1)\n",
      "Requirement already satisfied: google-pasta>=0.1.6 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu) (0.2.0)\n",
      "Collecting gast==0.2.2\n",
      "  Downloading https://files.pythonhosted.org/packages/4e/35/11749bf99b2d4e3cceb4d55ca22590b0d7c2c62b9de38ac4a4a7f4687421/gast-0.2.2.tar.gz\n",
      "Requirement already satisfied: numpy<2.0,>=1.16.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu) (1.18.2)\n",
      "Requirement already satisfied: setuptools in /usr/local/lib/python3.6/dist-packages (from protobuf>=3.8.0->tensorflow-gpu) (46.1.3)\n",
      "Requirement already satisfied: markdown>=2.6.8 in /usr/local/lib/python3.6/dist-packages (from tensorboard<2.2.0,>=2.1.0->tensorflow-gpu) (3.2.1)\n",
      "Requirement already satisfied: google-auth<2,>=1.6.3 in /usr/local/lib/python3.6/dist-packages (from tensorboard<2.2.0,>=2.1.0->tensorflow-gpu) (1.7.2)\n",
      "Requirement already satisfied: requests<3,>=2.21.0 in /usr/local/lib/python3.6/dist-packages (from tensorboard<2.2.0,>=2.1.0->tensorflow-gpu) (2.21.0)\n",
      "Requirement already satisfied: werkzeug>=0.11.15 in /usr/local/lib/python3.6/dist-packages (from tensorboard<2.2.0,>=2.1.0->tensorflow-gpu) (1.0.1)\n",
      "Requirement already satisfied: google-auth-oauthlib<0.5,>=0.4.1 in /usr/local/lib/python3.6/dist-packages (from tensorboard<2.2.0,>=2.1.0->tensorflow-gpu) (0.4.1)\n",
      "Requirement already satisfied: h5py in /usr/local/lib/python3.6/dist-packages (from keras-applications>=1.0.8->tensorflow-gpu) (2.10.0)\n",
      "Requirement already satisfied: rsa<4.1,>=3.1.4 in /usr/local/lib/python3.6/dist-packages (from google-auth<2,>=1.6.3->tensorboard<2.2.0,>=2.1.0->tensorflow-gpu) (4.0)\n",
      "Requirement already satisfied: cachetools<3.2,>=2.0.0 in /usr/local/lib/python3.6/dist-packages (from google-auth<2,>=1.6.3->tensorboard<2.2.0,>=2.1.0->tensorflow-gpu) (3.1.1)\n",
      "Requirement already satisfied: pyasn1-modules>=0.2.1 in /usr/local/lib/python3.6/dist-packages (from google-auth<2,>=1.6.3->tensorboard<2.2.0,>=2.1.0->tensorflow-gpu) (0.2.8)\n",
      "Requirement already satisfied: chardet<3.1.0,>=3.0.2 in /usr/local/lib/python3.6/dist-packages (from requests<3,>=2.21.0->tensorboard<2.2.0,>=2.1.0->tensorflow-gpu) (3.0.4)\n",
      "Requirement already satisfied: urllib3<1.25,>=1.21.1 in /usr/local/lib/python3.6/dist-packages (from requests<3,>=2.21.0->tensorboard<2.2.0,>=2.1.0->tensorflow-gpu) (1.24.3)\n",
      "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.6/dist-packages (from requests<3,>=2.21.0->tensorboard<2.2.0,>=2.1.0->tensorflow-gpu) (2020.4.5.1)\n",
      "Requirement already satisfied: idna<2.9,>=2.5 in /usr/local/lib/python3.6/dist-packages (from requests<3,>=2.21.0->tensorboard<2.2.0,>=2.1.0->tensorflow-gpu) (2.8)\n",
      "Requirement already satisfied: requests-oauthlib>=0.7.0 in /usr/local/lib/python3.6/dist-packages (from google-auth-oauthlib<0.5,>=0.4.1->tensorboard<2.2.0,>=2.1.0->tensorflow-gpu) (1.3.0)\n",
      "Requirement already satisfied: pyasn1>=0.1.3 in /usr/local/lib/python3.6/dist-packages (from rsa<4.1,>=3.1.4->google-auth<2,>=1.6.3->tensorboard<2.2.0,>=2.1.0->tensorflow-gpu) (0.4.8)\n",
      "Requirement already satisfied: oauthlib>=3.0.0 in /usr/local/lib/python3.6/dist-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<0.5,>=0.4.1->tensorboard<2.2.0,>=2.1.0->tensorflow-gpu) (3.1.0)\n",
      "Building wheels for collected packages: gast\n",
      "  Building wheel for gast (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
      "  Created wheel for gast: filename=gast-0.2.2-cp36-none-any.whl size=7540 sha256=9ce607e1e4c8eb10613110946a5b7c6a2c391906a7e003f722d629722aa91cc5\n",
      "  Stored in directory: /root/.cache/pip/wheels/5c/2e/7e/a1d4d4fcebe6c381f378ce7743a3ced3699feb89bcfbdadadd\n",
      "Successfully built gast\n",
      "\u001b[31mERROR: tensorflow 2.2.0rc2 has requirement gast==0.3.3, but you'll have gast 0.2.2 which is incompatible.\u001b[0m\n",
      "\u001b[31mERROR: tensorflow 2.2.0rc2 has requirement tensorboard<2.3.0,>=2.2.0, but you'll have tensorboard 2.1.1 which is incompatible.\u001b[0m\n",
      "\u001b[31mERROR: tensorflow 2.2.0rc2 has requirement tensorflow-estimator<2.3.0,>=2.2.0rc0, but you'll have tensorflow-estimator 2.1.0 which is incompatible.\u001b[0m\n",
      "Installing collected packages: tensorboard, tensorflow-estimator, gast, tensorflow-gpu\n",
      "  Found existing installation: tensorboard 2.2.0\n",
      "    Uninstalling tensorboard-2.2.0:\n",
      "      Successfully uninstalled tensorboard-2.2.0\n",
      "  Found existing installation: tensorflow-estimator 2.2.0rc0\n",
      "    Uninstalling tensorflow-estimator-2.2.0rc0:\n",
      "      Successfully uninstalled tensorflow-estimator-2.2.0rc0\n",
      "  Found existing installation: gast 0.3.3\n",
      "    Uninstalling gast-0.3.3:\n",
      "      Successfully uninstalled gast-0.3.3\n",
      "Successfully installed gast-0.2.2 tensorboard-2.1.1 tensorflow-estimator-2.1.0 tensorflow-gpu-2.1.0\n"
     ]
    }
   ],
   "source": [
    "!pip install tensorflow-gpu"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 2416,
     "status": "ok",
     "timestamp": 1587037336416,
     "user": {
      "displayName": "joy kou",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14Ghnr_EC7eeVJnsMQ7qPVlIHwTUhmVUktt9AWoWX=s64",
      "userId": "13603392610369212421"
     },
     "user_tz": -540
    },
    "id": "s9T8P4S61by2",
    "outputId": "fdc3630e-0eef-4189-e61b-d080110cb5d2"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.1.0\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "print(tf.__version__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1007,
     "status": "ok",
     "timestamp": 1587037339307,
     "user": {
      "displayName": "joy kou",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14Ghnr_EC7eeVJnsMQ7qPVlIHwTUhmVUktt9AWoWX=s64",
      "userId": "13603392610369212421"
     },
     "user_tz": -540
    },
    "id": "UxFdluLu1by5",
    "outputId": "7f9af78d-09ae-41b1-9ae6-54f8d4fffc99"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found GPU at:/device:GPU:0\n"
     ]
    }
   ],
   "source": [
    "device_name = tf.test.gpu_device_name()\n",
    "if device_name!='/device:GPU:0':\n",
    "  raise SystemError('GPU deivce not found')\n",
    "print('Found GPU at:{}'.format(device_name))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 105,
     "resources": {
      "http://localhost:8080/nbextensions/google.colab/files.js": {
       "data": 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",
       "headers": [
        [
         "content-type",
         "application/javascript"
        ]
       ],
       "ok": true,
       "status": 200,
       "status_text": ""
      }
     }
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 364397,
     "status": "ok",
     "timestamp": 1587037706648,
     "user": {
      "displayName": "joy kou",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14Ghnr_EC7eeVJnsMQ7qPVlIHwTUhmVUktt9AWoWX=s64",
      "userId": "13603392610369212421"
     },
     "user_tz": -540
    },
    "id": "OvWeYOki1by7",
    "outputId": "b2d45b49-d2c2-403c-9ab0-a6ba87261290"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "     <input type=\"file\" id=\"files-02b85537-f636-451a-a48e-7e577f044d4c\" name=\"files[]\" multiple disabled />\n",
       "     <output id=\"result-02b85537-f636-451a-a48e-7e577f044d4c\">\n",
       "      Upload widget is only available when the cell has been executed in the\n",
       "      current browser session. Please rerun this cell to enable.\n",
       "      </output>\n",
       "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving x_train.pickle to x_train.pickle\n",
      "Saving y_train.pickle to y_train.pickle\n"
     ]
    }
   ],
   "source": [
    "from google.colab import files\n",
    "file = files.upload()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 184184,
     "status": "ok",
     "timestamp": 1587037893367,
     "user": {
      "displayName": "joy kou",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14Ghnr_EC7eeVJnsMQ7qPVlIHwTUhmVUktt9AWoWX=s64",
      "userId": "13603392610369212421"
     },
     "user_tz": -540
    },
    "id": "NVCvxZj61by9",
    "outputId": "a164e90b-d3b8-455b-998a-6247ffef7fe6"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading data from https://github.com/fchollet/deep-learning-models/releases/download/v0.5/inception_v3_weights_tf_dim_ordering_tf_kernels_notop.h5\n",
      "87916544/87910968 [==============================] - 2s 0us/step\n",
      "Epoch 1/100\n",
      "1/1 [==============================] - 10s 10s/step - loss: 3.9847 - accuracy: 0.0400\n",
      "Epoch 2/100\n",
      "1/1 [==============================] - 0s 248ms/step - loss: 4.2714 - accuracy: 0.0600\n",
      "Epoch 3/100\n",
      "1/1 [==============================] - 1s 747ms/step - loss: 4.8352 - accuracy: 0.0800\n",
      "Epoch 4/100\n",
      "1/1 [==============================] - 1s 786ms/step - loss: 4.9850 - accuracy: 0.1000\n",
      "Epoch 5/100\n",
      "1/1 [==============================] - 1s 797ms/step - loss: 4.5945 - accuracy: 0.1000\n",
      "Epoch 6/100\n",
      "1/1 [==============================] - 1s 739ms/step - loss: 4.1860 - accuracy: 0.2200\n",
      "Epoch 7/100\n",
      "1/1 [==============================] - 1s 764ms/step - loss: 3.7674 - accuracy: 0.1600\n",
      "Epoch 8/100\n",
      "1/1 [==============================] - 1s 788ms/step - loss: 3.5560 - accuracy: 0.3000\n",
      "Epoch 9/100\n",
      "1/1 [==============================] - 1s 795ms/step - loss: 3.3893 - accuracy: 0.2400\n",
      "Epoch 10/100\n",
      "1/1 [==============================] - 1s 794ms/step - loss: 3.1887 - accuracy: 0.2400\n",
      "Epoch 11/100\n",
      "1/1 [==============================] - 1s 787ms/step - loss: 2.9271 - accuracy: 0.3600\n",
      "Epoch 12/100\n",
      "1/1 [==============================] - 1s 781ms/step - loss: 2.8937 - accuracy: 0.3600\n",
      "Epoch 13/100\n",
      "1/1 [==============================] - 1s 757ms/step - loss: 2.7116 - accuracy: 0.3800\n",
      "Epoch 14/100\n",
      "1/1 [==============================] - 1s 765ms/step - loss: 2.7314 - accuracy: 0.3600\n",
      "Epoch 15/100\n",
      "1/1 [==============================] - 1s 770ms/step - loss: 2.6397 - accuracy: 0.3400\n",
      "Epoch 16/100\n",
      "1/1 [==============================] - 1s 730ms/step - loss: 2.5118 - accuracy: 0.3400\n",
      "Epoch 17/100\n",
      "1/1 [==============================] - 1s 788ms/step - loss: 2.4896 - accuracy: 0.3600\n",
      "Epoch 18/100\n",
      "1/1 [==============================] - 1s 761ms/step - loss: 2.4267 - accuracy: 0.4000\n",
      "Epoch 19/100\n",
      "1/1 [==============================] - 1s 750ms/step - loss: 2.3757 - accuracy: 0.4400\n",
      "Epoch 20/100\n",
      "1/1 [==============================] - 1s 786ms/step - loss: 2.4797 - accuracy: 0.4400\n",
      "Epoch 21/100\n",
      "1/1 [==============================] - 1s 795ms/step - loss: 2.3626 - accuracy: 0.3600\n",
      "Epoch 22/100\n",
      "1/1 [==============================] - 1s 793ms/step - loss: 2.4018 - accuracy: 0.4600\n",
      "Epoch 23/100\n",
      "1/1 [==============================] - 1s 775ms/step - loss: 2.4648 - accuracy: 0.4200\n",
      "Epoch 24/100\n",
      "1/1 [==============================] - 1s 774ms/step - loss: 2.2737 - accuracy: 0.4600\n",
      "Epoch 25/100\n",
      "1/1 [==============================] - 1s 766ms/step - loss: 2.4300 - accuracy: 0.4000\n",
      "Epoch 26/100\n",
      "1/1 [==============================] - 1s 769ms/step - loss: 2.3878 - accuracy: 0.4600\n",
      "Epoch 27/100\n",
      "1/1 [==============================] - 1s 758ms/step - loss: 2.1336 - accuracy: 0.5000\n",
      "Epoch 28/100\n",
      "1/1 [==============================] - 1s 797ms/step - loss: 2.3511 - accuracy: 0.4200\n",
      "Epoch 29/100\n",
      "1/1 [==============================] - 1s 793ms/step - loss: 2.0800 - accuracy: 0.4600\n",
      "Epoch 30/100\n",
      "1/1 [==============================] - 1s 798ms/step - loss: 2.2399 - accuracy: 0.4800\n",
      "Epoch 31/100\n",
      "1/1 [==============================] - 1s 784ms/step - loss: 2.0247 - accuracy: 0.4400\n",
      "Epoch 32/100\n",
      "1/1 [==============================] - 1s 796ms/step - loss: 1.9722 - accuracy: 0.5000\n",
      "Epoch 33/100\n",
      "1/1 [==============================] - 1s 781ms/step - loss: 2.0626 - accuracy: 0.4600\n",
      "Epoch 34/100\n",
      "1/1 [==============================] - 1s 727ms/step - loss: 1.8997 - accuracy: 0.5000\n",
      "Epoch 35/100\n",
      "1/1 [==============================] - 1s 790ms/step - loss: 2.0422 - accuracy: 0.4000\n",
      "Epoch 36/100\n",
      "1/1 [==============================] - 1s 775ms/step - loss: 1.9499 - accuracy: 0.4400\n",
      "Epoch 37/100\n",
      "1/1 [==============================] - 1s 756ms/step - loss: 1.9322 - accuracy: 0.4600\n",
      "Epoch 38/100\n",
      "1/1 [==============================] - 1s 770ms/step - loss: 1.8246 - accuracy: 0.5000\n",
      "Epoch 39/100\n",
      "1/1 [==============================] - 1s 797ms/step - loss: 1.8384 - accuracy: 0.4800\n",
      "Epoch 40/100\n",
      "1/1 [==============================] - 1s 771ms/step - loss: 2.0124 - accuracy: 0.4400\n",
      "Epoch 41/100\n",
      "1/1 [==============================] - 1s 793ms/step - loss: 1.9942 - accuracy: 0.4400\n",
      "Epoch 42/100\n",
      "1/1 [==============================] - 1s 795ms/step - loss: 2.0177 - accuracy: 0.5000\n",
      "Epoch 43/100\n",
      "1/1 [==============================] - 1s 799ms/step - loss: 1.7984 - accuracy: 0.5200\n",
      "Epoch 44/100\n",
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      "Epoch 100/100\n",
      "1/1 [==============================] - 1s 761ms/step - loss: 1.2193 - accuracy: 0.5400\n",
      "0 input_1\n",
      "1 conv2d_1\n",
      "2 batch_normalization_1\n",
      "3 activation_1\n",
      "4 conv2d_2\n",
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      "40 mixed0\n",
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      "63 mixed1\n",
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      "86 mixed2\n",
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      "100 mixed3\n",
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      "132 mixed4\n",
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      "164 mixed5\n",
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      "196 mixed6\n",
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      "228 mixed7\n",
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      "247 max_pooling2d_4\n",
      "248 mixed8\n",
      "249 conv2d_81\n",
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      "262 average_pooling2d_8\n",
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      "270 activation_79\n",
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      "276 mixed9_0\n",
      "277 concatenate_1\n",
      "278 activation_85\n",
      "279 mixed9\n",
      "280 conv2d_90\n",
      "281 batch_normalization_90\n",
      "282 activation_90\n",
      "283 conv2d_87\n",
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      "287 activation_87\n",
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      "289 conv2d_88\n",
      "290 conv2d_89\n",
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      "292 conv2d_93\n",
      "293 average_pooling2d_9\n",
      "294 conv2d_86\n",
      "295 batch_normalization_88\n",
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      "298 batch_normalization_93\n",
      "299 conv2d_94\n",
      "300 batch_normalization_86\n",
      "301 activation_88\n",
      "302 activation_89\n",
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      "306 activation_86\n",
      "307 mixed9_1\n",
      "308 concatenate_2\n",
      "309 activation_94\n",
      "310 mixed10\n",
      "Epoch 1/100\n",
      "1/1 [==============================] - 6s 6s/step - loss: 1.2250 - accuracy: 0.5600\n",
      "Epoch 2/100\n",
      "1/1 [==============================] - 0s 300ms/step - loss: 1.3140 - accuracy: 0.5600\n",
      "Epoch 3/100\n",
      "1/1 [==============================] - 1s 762ms/step - loss: 1.2519 - accuracy: 0.5600\n",
      "Epoch 4/100\n",
      "1/1 [==============================] - 1s 798ms/step - loss: 1.2929 - accuracy: 0.5800\n",
      "Epoch 5/100\n",
      "1/1 [==============================] - 1s 792ms/step - loss: 1.2169 - accuracy: 0.6000\n",
      "Epoch 6/100\n",
      "1/1 [==============================] - 1s 795ms/step - loss: 1.4124 - accuracy: 0.5600\n",
      "Epoch 7/100\n",
      "1/1 [==============================] - 1s 800ms/step - loss: 1.2743 - accuracy: 0.6000\n",
      "Epoch 8/100\n",
      "1/1 [==============================] - 1s 718ms/step - loss: 1.2404 - accuracy: 0.5200\n",
      "Epoch 9/100\n",
      "1/1 [==============================] - 1s 782ms/step - loss: 1.3134 - accuracy: 0.5600\n",
      "Epoch 10/100\n",
      "1/1 [==============================] - 1s 807ms/step - loss: 1.1989 - accuracy: 0.5400\n",
      "Epoch 11/100\n",
      "1/1 [==============================] - 1s 770ms/step - loss: 1.3468 - accuracy: 0.6000\n",
      "Epoch 12/100\n",
      "1/1 [==============================] - 1s 787ms/step - loss: 1.3241 - accuracy: 0.5600\n",
      "Epoch 13/100\n",
      "1/1 [==============================] - 1s 742ms/step - loss: 1.2270 - accuracy: 0.5400\n",
      "Epoch 14/100\n",
      "1/1 [==============================] - 1s 788ms/step - loss: 1.3105 - accuracy: 0.5000\n",
      "Epoch 15/100\n",
      "1/1 [==============================] - 1s 789ms/step - loss: 1.2895 - accuracy: 0.5800\n",
      "Epoch 16/100\n",
      "1/1 [==============================] - 1s 777ms/step - loss: 1.3677 - accuracy: 0.5600\n",
      "Epoch 17/100\n",
      "1/1 [==============================] - 1s 792ms/step - loss: 1.3233 - accuracy: 0.5600\n",
      "Epoch 18/100\n",
      "1/1 [==============================] - 1s 736ms/step - loss: 1.2764 - accuracy: 0.6200\n",
      "Epoch 19/100\n",
      "1/1 [==============================] - 1s 808ms/step - loss: 1.2471 - accuracy: 0.5600\n",
      "Epoch 20/100\n",
      "1/1 [==============================] - 1s 782ms/step - loss: 1.2227 - accuracy: 0.6000\n",
      "Epoch 21/100\n",
      "1/1 [==============================] - 1s 775ms/step - loss: 1.4351 - accuracy: 0.5200\n",
      "Epoch 22/100\n",
      "1/1 [==============================] - 1s 795ms/step - loss: 1.2990 - accuracy: 0.5200\n",
      "Epoch 23/100\n",
      "1/1 [==============================] - 1s 805ms/step - loss: 1.2064 - accuracy: 0.6400\n",
      "Epoch 24/100\n",
      "1/1 [==============================] - 1s 797ms/step - loss: 1.2253 - accuracy: 0.5000\n",
      "Epoch 25/100\n",
      "1/1 [==============================] - 1s 781ms/step - loss: 1.2315 - accuracy: 0.5400\n",
      "Epoch 26/100\n",
      "1/1 [==============================] - 1s 739ms/step - loss: 1.2611 - accuracy: 0.5800\n",
      "Epoch 27/100\n",
      "1/1 [==============================] - 1s 802ms/step - loss: 1.2202 - accuracy: 0.6000\n",
      "Epoch 28/100\n",
      "1/1 [==============================] - 1s 788ms/step - loss: 1.1708 - accuracy: 0.5800\n",
      "Epoch 29/100\n",
      "1/1 [==============================] - 1s 784ms/step - loss: 1.2752 - accuracy: 0.5800\n",
      "Epoch 30/100\n",
      "1/1 [==============================] - 1s 786ms/step - loss: 1.3076 - accuracy: 0.5000\n",
      "Epoch 31/100\n",
      "1/1 [==============================] - 1s 782ms/step - loss: 1.2463 - accuracy: 0.5800\n",
      "Epoch 32/100\n",
      "1/1 [==============================] - 1s 752ms/step - loss: 1.2233 - accuracy: 0.5600\n",
      "Epoch 33/100\n",
      "1/1 [==============================] - 1s 785ms/step - loss: 1.2514 - accuracy: 0.5800\n",
      "Epoch 34/100\n",
      "1/1 [==============================] - 1s 760ms/step - loss: 1.2755 - accuracy: 0.5400\n",
      "Epoch 35/100\n",
      "1/1 [==============================] - 1s 790ms/step - loss: 1.1908 - accuracy: 0.5800\n",
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      "1/1 [==============================] - 1s 792ms/step - loss: 1.1612 - accuracy: 0.5800\n",
      "Epoch 37/100\n",
      "1/1 [==============================] - 1s 809ms/step - loss: 1.2282 - accuracy: 0.6200\n",
      "Epoch 38/100\n",
      "1/1 [==============================] - 1s 799ms/step - loss: 1.2841 - accuracy: 0.5800\n",
      "Epoch 39/100\n",
      "1/1 [==============================] - 1s 802ms/step - loss: 1.0220 - accuracy: 0.6000\n",
      "Epoch 40/100\n",
      "1/1 [==============================] - 1s 789ms/step - loss: 1.0864 - accuracy: 0.7200\n",
      "Epoch 41/100\n",
      "1/1 [==============================] - 1s 786ms/step - loss: 1.1451 - accuracy: 0.6000\n",
      "Epoch 42/100\n",
      "1/1 [==============================] - 1s 800ms/step - loss: 1.1808 - accuracy: 0.6400\n",
      "Epoch 43/100\n",
      "1/1 [==============================] - 1s 822ms/step - loss: 1.1251 - accuracy: 0.6400\n",
      "Epoch 44/100\n",
      "1/1 [==============================] - 1s 784ms/step - loss: 1.1504 - accuracy: 0.6400\n",
      "Epoch 45/100\n",
      "1/1 [==============================] - 1s 787ms/step - loss: 1.1601 - accuracy: 0.5800\n",
      "Epoch 46/100\n",
      "1/1 [==============================] - 1s 816ms/step - loss: 1.1536 - accuracy: 0.6200\n",
      "Epoch 47/100\n",
      "1/1 [==============================] - 1s 793ms/step - loss: 1.1244 - accuracy: 0.6000\n",
      "Epoch 48/100\n",
      "1/1 [==============================] - 1s 772ms/step - loss: 1.2590 - accuracy: 0.5600\n",
      "Epoch 49/100\n",
      "1/1 [==============================] - 1s 717ms/step - loss: 1.1943 - accuracy: 0.5400\n",
      "Epoch 50/100\n",
      "1/1 [==============================] - 1s 815ms/step - loss: 1.2469 - accuracy: 0.6200\n",
      "Epoch 51/100\n",
      "1/1 [==============================] - 1s 792ms/step - loss: 1.3308 - accuracy: 0.5200\n",
      "Epoch 52/100\n",
      "1/1 [==============================] - 1s 795ms/step - loss: 1.2203 - accuracy: 0.6000\n",
      "Epoch 53/100\n",
      "1/1 [==============================] - 1s 790ms/step - loss: 1.1134 - accuracy: 0.6400\n",
      "Epoch 54/100\n",
      "1/1 [==============================] - 1s 803ms/step - loss: 1.1437 - accuracy: 0.6400\n",
      "Epoch 55/100\n",
      "1/1 [==============================] - 1s 808ms/step - loss: 1.0681 - accuracy: 0.7000\n",
      "Epoch 56/100\n",
      "1/1 [==============================] - 1s 802ms/step - loss: 1.1781 - accuracy: 0.6200\n",
      "Epoch 57/100\n",
      "1/1 [==============================] - 1s 776ms/step - loss: 1.1261 - accuracy: 0.6400\n",
      "Epoch 58/100\n",
      "1/1 [==============================] - 1s 793ms/step - loss: 1.1019 - accuracy: 0.6000\n",
      "Epoch 59/100\n",
      "1/1 [==============================] - 1s 800ms/step - loss: 1.1339 - accuracy: 0.5600\n",
      "Epoch 60/100\n",
      "1/1 [==============================] - 1s 797ms/step - loss: 1.0720 - accuracy: 0.6200\n",
      "Epoch 61/100\n",
      "1/1 [==============================] - 1s 779ms/step - loss: 1.0675 - accuracy: 0.7000\n",
      "Epoch 62/100\n",
      "1/1 [==============================] - 1s 742ms/step - loss: 1.1160 - accuracy: 0.5800\n",
      "Epoch 63/100\n",
      "1/1 [==============================] - 1s 763ms/step - loss: 1.2300 - accuracy: 0.6000\n",
      "Epoch 64/100\n",
      "1/1 [==============================] - 1s 784ms/step - loss: 1.2324 - accuracy: 0.6000\n",
      "Epoch 65/100\n",
      "1/1 [==============================] - 1s 719ms/step - loss: 1.3007 - accuracy: 0.5200\n",
      "Epoch 66/100\n",
      "1/1 [==============================] - 1s 792ms/step - loss: 1.1530 - accuracy: 0.6200\n",
      "Epoch 67/100\n",
      "1/1 [==============================] - 1s 757ms/step - loss: 1.0726 - accuracy: 0.6400\n",
      "Epoch 68/100\n",
      "1/1 [==============================] - 1s 752ms/step - loss: 1.1385 - accuracy: 0.5800\n",
      "Epoch 69/100\n",
      "1/1 [==============================] - 1s 779ms/step - loss: 1.0446 - accuracy: 0.6200\n",
      "Epoch 70/100\n",
      "1/1 [==============================] - 1s 803ms/step - loss: 1.1781 - accuracy: 0.5600\n",
      "Epoch 71/100\n",
      "1/1 [==============================] - 1s 795ms/step - loss: 1.1124 - accuracy: 0.6200\n",
      "Epoch 72/100\n",
      "1/1 [==============================] - 1s 832ms/step - loss: 1.1122 - accuracy: 0.6800\n",
      "Epoch 73/100\n",
      "1/1 [==============================] - 1s 789ms/step - loss: 1.1396 - accuracy: 0.5400\n",
      "Epoch 74/100\n",
      "1/1 [==============================] - 1s 782ms/step - loss: 1.0941 - accuracy: 0.5800\n",
      "Epoch 75/100\n",
      "1/1 [==============================] - 1s 767ms/step - loss: 0.9906 - accuracy: 0.6600\n",
      "Epoch 76/100\n",
      "1/1 [==============================] - 1s 768ms/step - loss: 1.2318 - accuracy: 0.6000\n",
      "Epoch 77/100\n",
      "1/1 [==============================] - 1s 781ms/step - loss: 1.1861 - accuracy: 0.5600\n",
      "Epoch 78/100\n",
      "1/1 [==============================] - 1s 792ms/step - loss: 1.0244 - accuracy: 0.7000\n",
      "Epoch 79/100\n",
      "1/1 [==============================] - 1s 771ms/step - loss: 1.2440 - accuracy: 0.5800\n",
      "Epoch 80/100\n",
      "1/1 [==============================] - 1s 738ms/step - loss: 1.0664 - accuracy: 0.6200\n",
      "Epoch 81/100\n",
      "1/1 [==============================] - 1s 781ms/step - loss: 1.2292 - accuracy: 0.5800\n",
      "Epoch 82/100\n",
      "1/1 [==============================] - 1s 784ms/step - loss: 1.0302 - accuracy: 0.6200\n",
      "Epoch 83/100\n",
      "1/1 [==============================] - 1s 773ms/step - loss: 1.0419 - accuracy: 0.6200\n",
      "Epoch 84/100\n",
      "1/1 [==============================] - 1s 730ms/step - loss: 1.1626 - accuracy: 0.6200\n",
      "Epoch 85/100\n",
      "1/1 [==============================] - 1s 785ms/step - loss: 1.1497 - accuracy: 0.6000\n",
      "Epoch 86/100\n",
      "1/1 [==============================] - 1s 749ms/step - loss: 1.2172 - accuracy: 0.6000\n",
      "Epoch 87/100\n",
      "1/1 [==============================] - 1s 761ms/step - loss: 1.0613 - accuracy: 0.6800\n",
      "Epoch 88/100\n",
      "1/1 [==============================] - 1s 766ms/step - loss: 1.0643 - accuracy: 0.6400\n",
      "Epoch 89/100\n",
      "1/1 [==============================] - 1s 738ms/step - loss: 1.1149 - accuracy: 0.5600\n",
      "Epoch 90/100\n",
      "1/1 [==============================] - 1s 791ms/step - loss: 0.9598 - accuracy: 0.6800\n",
      "Epoch 91/100\n",
      "1/1 [==============================] - 1s 767ms/step - loss: 1.1993 - accuracy: 0.5400\n",
      "Epoch 92/100\n",
      "1/1 [==============================] - 1s 714ms/step - loss: 1.1391 - accuracy: 0.6800\n",
      "Epoch 93/100\n",
      "1/1 [==============================] - 1s 783ms/step - loss: 1.0714 - accuracy: 0.6200\n",
      "Epoch 94/100\n",
      "1/1 [==============================] - 1s 751ms/step - loss: 1.1727 - accuracy: 0.6000\n",
      "Epoch 95/100\n",
      "1/1 [==============================] - 1s 750ms/step - loss: 1.0432 - accuracy: 0.6600\n",
      "Epoch 96/100\n",
      "1/1 [==============================] - 1s 769ms/step - loss: 1.0161 - accuracy: 0.6800\n",
      "Epoch 97/100\n",
      "1/1 [==============================] - 1s 748ms/step - loss: 1.0545 - accuracy: 0.6400\n",
      "Epoch 98/100\n",
      "1/1 [==============================] - 1s 769ms/step - loss: 1.0482 - accuracy: 0.6400\n",
      "Epoch 99/100\n",
      "1/1 [==============================] - 1s 743ms/step - loss: 1.0664 - accuracy: 0.6200\n",
      "Epoch 100/100\n",
      "1/1 [==============================] - 1s 758ms/step - loss: 1.0727 - accuracy: 0.6000\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.callbacks.History at 0x7f6be7506b70>"
      ]
     },
     "execution_count": 5,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from keras.applications.inception_v3 import InceptionV3\n",
    "from keras.preprocessing import image\n",
    "from keras.models import Model\n",
    "from keras.layers import Dense, GlobalAveragePooling2D\n",
    "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
    "from keras import backend as K\n",
    "import pickle\n",
    "\n",
    "def preprocess_input(x):\n",
    "    # Change BRG => RGB\n",
    "    x = x[:, :, :, ::-1]\n",
    "\n",
    "    # Normalize\n",
    "    x /= 255.\n",
    "    x -= 0.5\n",
    "    x *= 2.\n",
    "    return x\n",
    "\n",
    "x_train = pickle.load(open(\"x_train.pickle\",\"rb\"))\n",
    "y_train = pickle.load(open(\"y_train.pickle\",\"rb\"))\n",
    "\n",
    "BATCH_SIZE = 50\n",
    "EPOCHS = 100\n",
    "preprocess_input(x_train)\n",
    "\n",
    "\n",
    "# create the base pre-trained model\n",
    "base_model = InceptionV3(weights='imagenet', include_top=False)\n",
    "\n",
    "# add a global spatial average pooling layer\n",
    "x = base_model.output\n",
    "x = GlobalAveragePooling2D()(x)\n",
    "# let's add a fully-connected layer\n",
    "x = Dense(1024, activation='relu')(x)\n",
    "# and a logistic layer -- let's say we have 50 classes\n",
    "predictions = Dense(50, activation='softmax')(x)\n",
    "\n",
    "# this is the model we will train\n",
    "model = Model(inputs=base_model.input, outputs=predictions)\n",
    "\n",
    "# first: train only the top layers (which were randomly initialized)\n",
    "# i.e. freeze all convolutional InceptionV3 layers\n",
    "for layer in base_model.layers:\n",
    "    layer.trainable = False\n",
    "\n",
    "# compile the model (should be done *after* setting layers to non-trainable)\n",
    "model.compile(optimizer='rmsprop',metrics=['accuracy'], loss='categorical_crossentropy')\n",
    "\n",
    "# train the model on the new data for a few epochs\n",
    "aug = ImageDataGenerator(rotation_range=20,\n",
    "                         zoom_range=0.15,\n",
    "                         width_shift_range=0.2,\n",
    "                         height_shift_range=0.2,\n",
    "                         shear_range=0.15,\n",
    "                         horizontal_flip=True,\n",
    "                         fill_mode=\"nearest\")\n",
    "model.fit_generator(aug.flow(x_train, y_train, batch_size=BATCH_SIZE),\n",
    "                    steps_per_epoch=len(x_train) // BATCH_SIZE,\n",
    "                    epochs=EPOCHS)\n",
    "\n",
    "# at this point, the top layers are well trained and we can start fine-tuning\n",
    "# convolutional layers from inception V3. We will freeze the bottom N layers\n",
    "# and train the remaining top layers.\n",
    "\n",
    "# let's visualize layer names and layer indices to see how many layers\n",
    "# we should freeze:\n",
    "for i, layer in enumerate(base_model.layers):\n",
    "   print(i, layer.name)\n",
    "\n",
    "# we chose to train the top 2 inception blocks, i.e. we will freeze\n",
    "# the first 249 layers and unfreeze the rest:\n",
    "for layer in model.layers[:249]:\n",
    "   layer.trainable = False\n",
    "for layer in model.layers[249:]:\n",
    "   layer.trainable = True\n",
    "\n",
    "# we need to recompile the model for these modifications to take effect\n",
    "# we use SGD with a low learning rate\n",
    "from keras.optimizers import SGD\n",
    "model.compile(optimizer=SGD(lr=0.0001, momentum=0.9),metrics=['accuracy'], loss='categorical_crossentropy')\n",
    "\n",
    "# we train our model again (this time fine-tuning the top 2 inception blocks\n",
    "# alongside the top Dense layers\n",
    "aug = ImageDataGenerator(rotation_range=20,\n",
    "                         zoom_range=0.15,\n",
    "                         width_shift_range=0.2,\n",
    "                         height_shift_range=0.2,\n",
    "                         shear_range=0.15,\n",
    "                         horizontal_flip=True,\n",
    "                         fill_mode=\"nearest\")\n",
    "model.fit_generator(aug.flow(x_train, y_train, batch_size=BATCH_SIZE),\n",
    "                    steps_per_epoch=len(x_train) // BATCH_SIZE,\n",
    "                    epochs=EPOCHS)"
   ]
  }
 ],
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