{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Tensorflow for poets\n",
    "\n",
    "This notebook steps through the [Tensorflow for poets](https://codelabs.developers.google.com/codelabs/tensorflow-for-poets/) tutorial.\n",
    "\n",
    "First clone the code repository."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!git clone https://github.com/googlecodelabs/tensorflow-for-poets-2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Move into the new directory."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "cd tensorflow-for-poets-2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Download the flowers dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!curl http://download.tensorflow.org/example_images/flower_photos.tgz | tar xz -C tf_files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ls tf_files/flower_photos"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Run this in a terminal, Jupyter doesn't allow background processes...\n",
    "\n",
    "I'm assuming this won't be possible on Binder?\n",
    "\n",
    "```\n",
    "tensorboard --logdir tf_files/training_summaries &\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%bash\n",
    "IMAGE_SIZE=224\n",
    "ARCHITECTURE=\"mobilenet_0.50_${IMAGE_SIZE}\"\n",
    "\n",
    "python -m scripts.retrain \\\n",
    "  --bottleneck_dir=tf_files/bottlenecks \\\n",
    "  --how_many_training_steps=500 \\\n",
    "  --model_dir=tf_files/models/ \\\n",
    "  --summaries_dir=tf_files/training_summaries/\"${ARCHITECTURE}\" \\\n",
    "  --output_graph=tf_files/retrained_graph.pb \\\n",
    "  --output_labels=tf_files/retrained_labels.txt \\\n",
    "  --architecture=\"${ARCHITECTURE}\" \\\n",
    "  --image_dir=tf_files/flower_photos"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Test the trained model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%bash\n",
    "python -m scripts.label_image \\\n",
    "    --graph=tf_files/retrained_graph.pb  \\\n",
    "    --image=tf_files/flower_photos/daisy/21652746_cc379e0eea_m.jpg"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## More testing\n",
    "\n",
    "This bit isn't in the tutorial. I just thought it would be good to do some random testing..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Make a list of all the flower images\n",
    "import os\n",
    "import random\n",
    "from IPython.display import display, HTML\n",
    "flowers = []\n",
    "flower_dir = 'tf_files/flower_photos/'\n",
    "for img_dir in [d for d in os.listdir(flower_dir) if os.path.isdir(os.path.join(flower_dir, d))]:\n",
    "    for img in [i for i in os.listdir(os.path.join(flower_dir, img_dir)) if i[-4:] == '.jpg']:\n",
    "        flowers.append(os.path.join(flower_dir, img_dir, img))    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Choose one flower at random\n",
    "flower = random.sample(flowers, 1)[0]\n",
    "display(HTML('<img src=\"tensorflow-for-poets-2/{0}\"><br>{0}'.format(flower)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!python -m scripts.label_image --graph=tf_files/retrained_graph.pb --image=$flower"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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