\n",
"The previous Tutorial #08 showed how to use the pre-trained Inception model on the CIFAR-10 data-set in so-called Transfer Learning. This tutorial shows how to use your own images.\n",
"
\n",
"For demonstration, we use a new data-set called [Knifey-Spoony](https://github.com/Hvass-Labs/knifey-spoony) that contains thousands of images of cutlery knives, spoons and forks on a few different backgrounds. The training-set contains 4170 images and the test-set contains 530 images. The classes are named knifey, spoony and forky as a reference to The Simpsons.\n",
"
\n",
"The images in the knifey-spoony data-set were created from video-files using a small Python [script](https://github.com/Hvass-Labs/TensorFlow-Tutorials/blob/master/convert.py) that is run on Linux (it requires the `avconv`-program for conversion from videos to images). This allows you to easily create very large data-sets with thousands of images from just a few minutes of video recordings.\n",
"
\n",
"This tutorial builds on the previous tutorials so you should be familiar with Tutorial #08 on Transfer Learning, as well as earlier tutorials on how to build and train Neural Networks in TensorFlow.\n",
"
\n",
"\n",
" \n",
"\n",
"
[Click here to follow along with the video on YouTube](https://www.youtube.com/watch?v=9sYBC2_AVfA&list=PL9Hr9sNUjfsmEu1ZniY0XpHSzl5uihcXZ&t=0s&index=13)
\n",
"\n",
"The following chart shows how the data flows when using the Inception model for Transfer Learning. First we input and process an image with the Inception model. Just prior to the final classification layer of the Inception model, we save the so-called Transfer Values to a cache-file.\n",
"\n",
"This is very similar to how it was done in Tutorial #08, except that we now use the Knifey-Spoony data-set instead of CIFAR-10, which means that we are now feeding jpeg-images into the Inception model instead of numpy-arrays with image-data.\n",
"\n",
"When all the images in the new data-set have been processed through the Inception model and the resulting transfer-values saved to a cache file, then we can use those transfer-values as the input to another neural network. We will then train the second neural network using the classes from the new data-set, so the network learns how to classify images based on the transfer-values from the Inception model.\n",
"\n",
"In this way, the Inception model is used to extract useful information from the images and another neural network is then used for the actual classification.\n",
"\n",
""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"
\n",
"PREPARATION AND PRE-PROCESSING\n",
"
\n",
"\n",
"
Imports
\n",
"\n",
"Now that we are a few lessons in, we have removed a portion of redundant code in order to minimize the time it takes to read each lesson.\n",
"\n",
"If you need to review it or make changes, it is in the data folder provided with these lessons."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\Reasonable\\Anaconda3\\lib\\site-packages\\h5py\\__init__.py:34: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
" from ._conv import register_converters as _register_converters\n"
]
}
],
"source": [
"%matplotlib inline\n",
"import matplotlib.pyplot as plt\n",
"import tensorflow as tf\n",
"import numpy as np\n",
"import time\n",
"from datetime import timedelta\n",
"import os\n",
"\n",
"# Functions and classes for loading and using the Inception model.\n",
"from data import inception\n",
"\n",
"# We use Pretty Tensor to define the new classifier.\n",
"import prettytensor as pt"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This was developed using Python 3.5.2 (Anaconda) and TensorFlow version:"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"'1.6.0'"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"tf.__version__"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"PrettyTensor version:"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'0.7.4'"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pt.__version__"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"
Load Data
"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"from data import knifey"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The data dimensions have already been defined in the `knifey` module, so we just need to import the ones we need."
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"from data.knifey import num_classes"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Set the directory for storing the data-set on your computer."
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"knifey.data_dir = \"data/knifey-spoony/\""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Set the directory that will be used for cache-files in this tutorial."
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"data_dir = knifey.data_dir"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The Knifey-Spoony data-set is about 22 MB and will be downloaded automatically if it is not located in the given path."
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"- Download progress: 100.0%\n",
"Download finished. Extracting files.\n",
"Done.\n"
]
}
],
"source": [
"knifey.maybe_download_and_extract()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now load the data-set. This scans the sub-directories for all `*.jpg` images and puts the filenames into two lists for the training-set and test-set. This does not actually load the images, which will be done when the transfer-values are being calculated further below.\n",
"\n",
"The lists of filenames are cached to harddisk so we can be sure that they are ordered in the same way when reloading the data-set later. This is important so we know which image-file corresponds to which transfer-values."
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Creating dataset from the files in: data/knifey-spoony/\n",
"- Data saved to cache-file: data/knifey-spoony/knifey-spoony.pkl\n"
]
}
],
"source": [
"dataset = knifey.load()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"
Your Data
\n",
"\n",
"You can use your own images instead of loading the knifey-spoony data-set. You have to create a `DataSet`-object from the `dataset.py` module. The best way is to use the `load_cached()`-wrapper-function which automatically saves a cache-file with the lists of image-files, so you make sure that the ordering is consistent with the transfer-values created below.\n",
"\n",
"The images must be organized in sub-directories for each of the classes. See the documentation in the `dataset.py` module for more details."
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [],
"source": [
"# This is the code you would run to load your own image-files.\n",
"# It has been commented out so it won't run now.\n",
"\n",
"# from dataset import load_cached\n",
"#dataset = load_cached(cache_path='my_dataset_cache.pkl', in_dir='my_images/')\n",
"num_classes = dataset.num_classes"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"
\n",
"TRAINING AND TEST SET\n",
"
\n",
"\n",
"Get the class-names."
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"['forky', 'knifey', 'spoony']"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"class_names = dataset.class_names\n",
"class_names"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Get the training-set. This returns the file-paths for the images, the class-numbers as integers, and the class-numbers as One-Hot encoded arrays called labels."
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"image_paths_train, cls_train, labels_train = dataset.get_training_set()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Print the first image-path to see if it looks OK."
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'D:\\\\OneDrive\\\\Projects\\\\GitRepositories\\\\cooking-dough-with-tensorflow\\\\data\\\\knifey-spoony\\\\forky\\\\forky-01-0001.jpg'"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"image_paths_train[0]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Get the test-set."
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"image_paths_test, cls_test, labels_test = dataset.get_test_set()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Print the first image-path to see if it looks OK."
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'D:\\\\OneDrive\\\\Projects\\\\GitRepositories\\\\cooking-dough-with-tensorflow\\\\data\\\\knifey-spoony\\\\forky\\\\test/forky-test-01-0001.jpg'"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"image_paths_test[0]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The Knifey-Spoony data-set has now been loaded and consists of 4700 images and associated labels (i.e. classifications of the images). The data-set is split into 2 mutually exclusive sub-sets, the training-set and the test-set."
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Size of:\n",
"- Training-set:\t\t4170\n",
"- Test-set:\t\t530\n"
]
}
],
"source": [
"print(\"Size of:\")\n",
"print(\"- Training-set:\\t\\t{}\".format(len(image_paths_train)))\n",
"print(\"- Test-set:\\t\\t{}\".format(len(image_paths_test)))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"
Helper-Function for Plotting Images
\n",
"\n",
"Function used to plot at most 9 images in a 3x3 grid, and writing the true and predicted classes below each image."
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def plot_images(images, cls_true, cls_pred=None, smooth=True):\n",
"\n",
" assert len(images) == len(cls_true)\n",
"\n",
" # Create figure with sub-plots.\n",
" fig, axes = plt.subplots(3, 3)\n",
"\n",
" # Adjust vertical spacing.\n",
" if cls_pred is None:\n",
" hspace = 0.3\n",
" else:\n",
" hspace = 0.6\n",
" fig.subplots_adjust(hspace=hspace, wspace=0.3)\n",
"\n",
" # Interpolation type.\n",
" if smooth:\n",
" interpolation = 'spline16'\n",
" else:\n",
" interpolation = 'nearest'\n",
"\n",
" for i, ax in enumerate(axes.flat):\n",
" # There may be less than 9 images, ensure it doesn't crash.\n",
" if i < len(images):\n",
" # Plot image.\n",
" ax.imshow(images[i],\n",
" interpolation=interpolation)\n",
"\n",
" # Name of the true class.\n",
" cls_true_name = class_names[cls_true[i]]\n",
"\n",
" # Show true and predicted classes.\n",
" if cls_pred is None:\n",
" xlabel = \"True: {0}\".format(cls_true_name)\n",
" else:\n",
" # Name of the predicted class.\n",
" cls_pred_name = class_names[cls_pred[i]]\n",
"\n",
" xlabel = \"True: {0}\\nPred: {1}\".format(cls_true_name, cls_pred_name)\n",
"\n",
" # Show the classes as the label on the x-axis.\n",
" ax.set_xlabel(xlabel)\n",
" \n",
" # Remove ticks from the plot.\n",
" ax.set_xticks([])\n",
" ax.set_yticks([])\n",
" \n",
" # Ensure the plot is shown correctly with multiple plots\n",
" # in a single Notebook cell.\n",
" plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"
Helper-Function for Loading Images
"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This data-set does not load the actual images, instead it has a list of the images in the training-set and another list for the images in the test-set. This helper-function loads some image-files."
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from matplotlib.image import imread\n",
"\n",
"def load_images(image_paths):\n",
" # Load the images from disk.\n",
" images = [imread(path) for path in image_paths]\n",
"\n",
" # Convert to a numpy array and return it.\n",
" return np.asarray(images)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"
Plot a few Images to See if Data is Correct
"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAUMAAAD5CAYAAAC9FVegAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Load the first images from the test-set.\n",
"images = load_images(image_paths=image_paths_test[0:9])\n",
"\n",
"# Get the true classes for those images.\n",
"cls_true = cls_test[0:9]\n",
"\n",
"# Plot the images and labels using our helper-function above.\n",
"plot_images(images=images, cls_true=cls_true, smooth=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"
Download the Inception Model
"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The Inception model is downloaded from the internet. This is the default directory where you want to save the data-files. The directory will be created if it does not exist."
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"inception.data_dir = 'data/inception/'"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Download the data for the Inception model if it doesn't already exist in the directory. It is 85 MB.\n",
"\n",
"See Tutorial #07 for more details."
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Downloading Inception v3 Model ...\n",
"Data has apparently already been downloaded and unpacked.\n"
]
}
],
"source": [
"inception.maybe_download()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"
Load the Inception Model
"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Load the Inception model so it is ready for classifying images.\n",
"\n",
"Note the deprecation warning, which might cause the program to fail in the future."
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {
"collapsed": true,
"scrolled": false
},
"outputs": [],
"source": [
"model = inception.Inception()"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"
Calculate Transfer Values
"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Import a helper-function for caching the transfer-values of the Inception model."
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from data.inception import transfer_values_cache"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Set the file-paths for the caches of the training-set and test-set."
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"file_path_cache_train = os.path.join(data_dir, 'inception-knifey-train.pkl')\n",
"file_path_cache_test = os.path.join(data_dir, 'inception-knifey-test.pkl')"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Processing Inception transfer-values for training-images ...\n",
"- Processing image: 4170 / 4170\n",
"- Data saved to cache-file: data/knifey-spoony/inception-knifey-train.pkl\n"
]
}
],
"source": [
"print(\"Processing Inception transfer-values for training-images ...\")\n",
"\n",
"# If transfer-values have already been calculated then reload them,\n",
"# otherwise calculate them and save them to a cache-file.\n",
"transfer_values_train = transfer_values_cache(cache_path=file_path_cache_train,\n",
" image_paths=image_paths_train,\n",
" model=model)"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Processing Inception transfer-values for test-images ...\n",
"- Processing image: 530 / 530\n",
"- Data saved to cache-file: data/knifey-spoony/inception-knifey-test.pkl\n"
]
}
],
"source": [
"print(\"Processing Inception transfer-values for test-images ...\")\n",
"\n",
"# If transfer-values have already been calculated then reload them,\n",
"# otherwise calculate them and save them to a cache-file.\n",
"transfer_values_test = transfer_values_cache(cache_path=file_path_cache_test,\n",
" image_paths=image_paths_test,\n",
" model=model)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Check the shape of the array with the transfer-values. There are 4170 images in the training-set and for each image there are 2048 transfer-values."
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(4170, 2048)"
]
},
"execution_count": 34,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"transfer_values_train.shape"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Similarly, there are 530 images in the test-set with 2048 transfer-values for each image."
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(530, 2048)"
]
},
"execution_count": 35,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"transfer_values_test.shape"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"
"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Use Principal Component Analysis (PCA) from scikit-learn to reduce the array-lengths of the transfer-values from 2048 to 2 so they can be plotted."
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from sklearn.decomposition import PCA"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Create a new PCA-object and set the target array-length to 2."
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"pca = PCA(n_components=2)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"It takes a while to compute the PCA. In this case the data-set is not so large, but otherwise you could select a smaller part of the training-set to speed up the computation."
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# transfer_values = transfer_values_train[0:3000]\n",
"transfer_values = transfer_values_train"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Get the class-numbers for the samples you selected."
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# cls = cls_train[0:3000]\n",
"cls = cls_train"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Check that the array has 4170 samples and 2048 transfer-values for each sample."
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"(4170, 2048)"
]
},
"execution_count": 43,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"transfer_values.shape"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Use PCA to reduce the transfer-value arrays from 2048 to 2 elements."
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"transfer_values_reduced = pca.fit_transform(transfer_values)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Check that it is now an array with 4170 samples and 2 values per sample."
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"(4170, 2)"
]
},
"execution_count": 45,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"transfer_values_reduced.shape"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Helper-function for plotting the reduced transfer-values."
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def plot_scatter(values, cls):\n",
" # Create a color-map with a different color for each class.\n",
" import matplotlib.cm as cm\n",
" cmap = cm.rainbow(np.linspace(0.0, 1.0, num_classes))\n",
"\n",
" # Create an index with a random permutation to make a better plot.\n",
" idx = np.random.permutation(len(values))\n",
" \n",
" # Get the color for each sample.\n",
" colors = cmap[cls[idx]]\n",
"\n",
" # Extract the x- and y-values.\n",
" x = values[idx, 0]\n",
" y = values[idx, 1]\n",
"\n",
" # Plot it.\n",
" plt.scatter(x, y, color=colors, alpha=0.5)\n",
" plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Plot the transfer-values that have been reduced using PCA. There are 3 different colors for the different classes in the Knifey-Spoony data-set. The colors have very large overlap. This may be because PCA cannot properly separate the transfer-values."
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAYAAAAD8CAYAAAB+UHOxAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAAAAAElFTkSuQmCC\n",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plot_scatter(transfer_values_reduced, cls=cls)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"
Analysis of Transfer Values using t-SNE
"
]
},
{
"cell_type": "code",
"execution_count": 48,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from sklearn.manifold import TSNE"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Another method for doing dimensionality reduction is t-SNE. Unfortunately, t-SNE is very slow so we first use PCA to reduce the transfer-values from 2048 to 50 elements."
]
},
{
"cell_type": "code",
"execution_count": 49,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"pca = PCA(n_components=50)\n",
"transfer_values_50d = pca.fit_transform(transfer_values)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Create a new t-SNE object for the final dimensionality reduction and set the target to 2-dim."
]
},
{
"cell_type": "code",
"execution_count": 50,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"tsne = TSNE(n_components=2)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Perform the final reduction using t-SNE. The current implemenation of t-SNE in scikit-learn cannot handle data with many samples so this might crash if you use the full training-set."
]
},
{
"cell_type": "code",
"execution_count": 51,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"transfer_values_reduced = tsne.fit_transform(transfer_values_50d) "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Check that it is now an array with 4170 samples and 2 transfer-values per sample."
]
},
{
"cell_type": "code",
"execution_count": 52,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"(4170, 2)"
]
},
"execution_count": 52,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"transfer_values_reduced.shape"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Plot the transfer-values that have been reduced to 2-dim using t-SNE, which shows better separation than the PCA-plot above.\n",
"\n",
"This means the transfer-values from the Inception model appear to contain enough information to separate the Knifey-Spoony images into classes, although there is still some overlap so the separation is not perfect."
]
},
{
"cell_type": "code",
"execution_count": 53,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAXwAAAD8CAYAAAB0IB+mAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nMEswgm8wGAyzBCP4BoPBMEv4/wgcdnLzYpllAAAAAElFTkSuQmCC\n",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plot_scatter(transfer_values_reduced, cls=cls)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"
New Classifier in TensorFlow
"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now we will create another neural network in TensorFlow. This network will take as input the transfer-values from the Inception model and output the predicted classes for Knifey-Spoony images.\n",
"\n",
"It is assumed that you are already familiar with how to build neural networks in TensorFlow, otherwise see e.g. Tutorial #03."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"
Placeholder Variables
\n",
"\n",
"First we need the array-length for transfer-values which is stored as a variable in the object for the Inception model."
]
},
{
"cell_type": "code",
"execution_count": 54,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"transfer_len = model.transfer_len"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now create a placeholder variable for inputting the transfer-values from the Inception model into the new network that we are building. The shape of this variable is `[None, transfer_len]` which means it takes an input array with an arbitrary number of samples as indicated by the keyword `None` and each sample has 2048 elements, equal to `transfer_len`."
]
},
{
"cell_type": "code",
"execution_count": 55,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"x = tf.placeholder(tf.float32, shape=[None, transfer_len], name='x')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Create another placeholder variable for inputting the true class-label of each image. These are so-called One-Hot encoded arrays with 3 elements, one for each possible class in the data-set."
]
},
{
"cell_type": "code",
"execution_count": 56,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"y_true = tf.placeholder(tf.float32, shape=[None, num_classes], name='y_true')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Calculate the true class as an integer. This could also be a placeholder variable."
]
},
{
"cell_type": "code",
"execution_count": 57,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"WARNING:tensorflow:From :1: calling argmax (from tensorflow.python.ops.math_ops) with dimension is deprecated and will be removed in a future version.\n",
"Instructions for updating:\n",
"Use the `axis` argument instead\n"
]
}
],
"source": [
"y_true_cls = tf.argmax(y_true, dimension=1)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"
Neural Network
"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Create the neural network for doing the classification on the Knifey-Spoony data-set. This takes as input the transfer-values from the Inception model which will be fed into the placeholder variable `x`. The network outputs the predicted class in `y_pred`.\n",
"\n",
"See Tutorial #03 for more details on how to use Pretty Tensor to construct neural networks."
]
},
{
"cell_type": "code",
"execution_count": 58,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"WARNING:tensorflow:From C:\\Users\\Reasonable\\Anaconda3\\lib\\site-packages\\tensorflow\\contrib\\nn\\python\\ops\\cross_entropy.py:68: softmax_cross_entropy_with_logits (from tensorflow.python.ops.nn_ops) is deprecated and will be removed in a future version.\n",
"Instructions for updating:\n",
"\n",
"Future major versions of TensorFlow will allow gradients to flow\n",
"into the labels input on backprop by default.\n",
"\n",
"See tf.nn.softmax_cross_entropy_with_logits_v2.\n",
"\n"
]
}
],
"source": [
"# Wrap the transfer-values as a Pretty Tensor object.\n",
"x_pretty = pt.wrap(x)\n",
"\n",
"with pt.defaults_scope(activation_fn=tf.nn.relu):\n",
" y_pred, loss = x_pretty.\\\n",
" fully_connected(size=1024, name='layer_fc1').\\\n",
" softmax_classifier(num_classes=num_classes, labels=y_true)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"
Optimization Method
\n",
"\n",
"Create a variable for keeping track of the number of optimization iterations performed."
]
},
{
"cell_type": "code",
"execution_count": 59,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"global_step = tf.Variable(initial_value=0,\n",
" name='global_step', trainable=False)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Method for optimizing the new neural network."
]
},
{
"cell_type": "code",
"execution_count": 60,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"optimizer = tf.train.AdamOptimizer(learning_rate=1e-4).minimize(loss, global_step)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"
Classification Accuracy
\n",
"\n",
"The output of the network `y_pred` is an array with 3 elements. The class number is the index of the largest element in the array."
]
},
{
"cell_type": "code",
"execution_count": 61,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"y_pred_cls = tf.argmax(y_pred, dimension=1)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Create an array of booleans whether the predicted class equals the true class of each image."
]
},
{
"cell_type": "code",
"execution_count": 62,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"correct_prediction = tf.equal(y_pred_cls, y_true_cls)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The classification accuracy is calculated by first type-casting the array of booleans to floats, so that False becomes 0 and True becomes 1, and then taking the average of these numbers."
]
},
{
"cell_type": "code",
"execution_count": 63,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"
Create TensorFlow Session
\n",
"\n",
"Once the TensorFlow graph has been created, we have to create a TensorFlow session which is used to execute the graph."
]
},
{
"cell_type": "code",
"execution_count": 64,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"session = tf.Session()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"
Initialize Variables
"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The variables for the new network must be initialized before we start optimizing them."
]
},
{
"cell_type": "code",
"execution_count": 65,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"session.run(tf.global_variables_initializer())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"
Helper-Function for Random Training Batch
"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"There are 4170 images (and arrays with transfer-values for the images) in the training-set. It takes a long time to calculate the gradient of the model using all these images (transfer-values). We therefore only use a small batch of images (transfer-values) in each iteration of the optimizer.\n",
"\n",
"If your computer crashes or becomes very slow because you run out of RAM, then you may try and lower this number, but you may then need to perform more optimization iterations."
]
},
{
"cell_type": "code",
"execution_count": 66,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"train_batch_size = 64"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Function for selecting a random batch of transfer-values from the training-set."
]
},
{
"cell_type": "code",
"execution_count": 67,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def random_batch():\n",
" # Number of images (transfer-values) in the training-set.\n",
" num_images = len(transfer_values_train)\n",
"\n",
" # Create a random index.\n",
" idx = np.random.choice(num_images,\n",
" size=train_batch_size,\n",
" replace=False)\n",
"\n",
" # Use the random index to select random x and y-values.\n",
" # We use the transfer-values instead of images as x-values.\n",
" x_batch = transfer_values_train[idx]\n",
" y_batch = labels_train[idx]\n",
"\n",
" return x_batch, y_batch"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"
Helper-Function to Perform Optimization
\n",
"\n",
"This function performs a number of optimization iterations so as to gradually improve the variables of the neural network. In each iteration, a new batch of data is selected from the training-set and then TensorFlow executes the optimizer using those training samples. The progress is printed every 100 iterations."
]
},
{
"cell_type": "code",
"execution_count": 68,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def optimize(num_iterations):\n",
" # Start-time used for printing time-usage below.\n",
" start_time = time.time()\n",
"\n",
" for i in range(num_iterations):\n",
" # Get a batch of training examples.\n",
" # x_batch now holds a batch of images (transfer-values) and\n",
" # y_true_batch are the true labels for those images.\n",
" x_batch, y_true_batch = random_batch()\n",
"\n",
" # Put the batch into a dict with the proper names\n",
" # for placeholder variables in the TensorFlow graph.\n",
" feed_dict_train = {x: x_batch,\n",
" y_true: y_true_batch}\n",
"\n",
" # Run the optimizer using this batch of training data.\n",
" # TensorFlow assigns the variables in feed_dict_train\n",
" # to the placeholder variables and then runs the optimizer.\n",
" # We also want to retrieve the global_step counter.\n",
" i_global, _ = session.run([global_step, optimizer],\n",
" feed_dict=feed_dict_train)\n",
"\n",
" # Print status to screen every 100 iterations (and last).\n",
" if (i_global % 100 == 0) or (i == num_iterations - 1):\n",
" # Calculate the accuracy on the training-batch.\n",
" batch_acc = session.run(accuracy,\n",
" feed_dict=feed_dict_train)\n",
"\n",
" # Print status.\n",
" msg = \"Global Step: {0:>6}, Training Batch Accuracy: {1:>6.1%}\"\n",
" print(msg.format(i_global, batch_acc))\n",
"\n",
" # Ending time.\n",
" end_time = time.time()\n",
"\n",
" # Difference between start and end-times.\n",
" time_dif = end_time - start_time\n",
"\n",
" # Print the time-usage.\n",
" print(\"Time usage: \" + str(timedelta(seconds=int(round(time_dif)))))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"
Helper-Function to Plot Example Errors
\n",
"\n",
"Function for plotting examples of images from the test-set that have been mis-classified."
]
},
{
"cell_type": "code",
"execution_count": 69,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def plot_example_errors(cls_pred, correct):\n",
" # This function is called from print_test_accuracy() below.\n",
"\n",
" # cls_pred is an array of the predicted class-number for\n",
" # all images in the test-set.\n",
"\n",
" # correct is a boolean array whether the predicted class\n",
" # is equal to the true class for each image in the test-set.\n",
"\n",
" # Negate the boolean array.\n",
" incorrect = (correct == False)\n",
"\n",
" # Get the indices for the incorrectly classified images.\n",
" idx = np.flatnonzero(incorrect)\n",
"\n",
" # Number of images to select, max 9.\n",
" n = min(len(idx), 9)\n",
" \n",
" # Randomize and select n indices.\n",
" idx = np.random.choice(idx,\n",
" size=n,\n",
" replace=False)\n",
"\n",
" # Get the predicted classes for those images.\n",
" cls_pred = cls_pred[idx]\n",
"\n",
" # Get the true classes for those images.\n",
" cls_true = cls_test[idx]\n",
"\n",
" # Load the corresponding images from the test-set.\n",
" # Note: We cannot do image_paths_test[idx] on lists of strings.\n",
" image_paths = [image_paths_test[i] for i in idx]\n",
" images = load_images(image_paths)\n",
"\n",
" # Plot the images.\n",
" plot_images(images=images,\n",
" cls_true=cls_true,\n",
" cls_pred=cls_pred)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"
Helper-Function to Plot Confusion Matrix
"
]
},
{
"cell_type": "code",
"execution_count": 70,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Import a function from sklearn to calculate the confusion-matrix.\n",
"from sklearn.metrics import confusion_matrix\n",
"\n",
"def plot_confusion_matrix(cls_pred):\n",
" # This is called from print_test_accuracy() below.\n",
"\n",
" # cls_pred is an array of the predicted class-number for\n",
" # all images in the test-set.\n",
"\n",
" # Get the confusion matrix using sklearn.\n",
" cm = confusion_matrix(y_true=cls_test, # True class for test-set.\n",
" y_pred=cls_pred) # Predicted class.\n",
"\n",
" # Print the confusion matrix as text.\n",
" for i in range(num_classes):\n",
" # Append the class-name to each line.\n",
" class_name = \"({}) {}\".format(i, class_names[i])\n",
" print(cm[i, :], class_name)\n",
"\n",
" # Print the class-numbers for easy reference.\n",
" class_numbers = [\" ({0})\".format(i) for i in range(num_classes)]\n",
" print(\"\".join(class_numbers))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"
Helper-Function for Calculating Classifications
\n",
"\n",
"This function calculates the predicted classes of images and also returns a boolean array whether the classification of each image is correct.\n",
"\n",
"The calculation is done in batches because it might use too much RAM otherwise. If your computer crashes then you can try and lower the batch-size."
]
},
{
"cell_type": "code",
"execution_count": 71,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Split the data-set in batches of this size to limit RAM usage.\n",
"batch_size = 256\n",
"\n",
"def predict_cls(transfer_values, labels, cls_true):\n",
" # Number of images.\n",
" num_images = len(transfer_values)\n",
"\n",
" # Allocate an array for the predicted classes which\n",
" # will be calculated in batches and filled into this array.\n",
" cls_pred = np.zeros(shape=num_images, dtype=np.int)\n",
"\n",
" # Now calculate the predicted classes for the batches.\n",
" # We will just iterate through all the batches.\n",
" # There might be a more clever and Pythonic way of doing this.\n",
"\n",
" # The starting index for the next batch is denoted i.\n",
" i = 0\n",
"\n",
" while i < num_images:\n",
" # The ending index for the next batch is denoted j.\n",
" j = min(i + batch_size, num_images)\n",
"\n",
" # Create a feed-dict with the images and labels\n",
" # between index i and j.\n",
" feed_dict = {x: transfer_values[i:j],\n",
" y_true: labels[i:j]}\n",
"\n",
" # Calculate the predicted class using TensorFlow.\n",
" cls_pred[i:j] = session.run(y_pred_cls, feed_dict=feed_dict)\n",
"\n",
" # Set the start-index for the next batch to the\n",
" # end-index of the current batch.\n",
" i = j\n",
" \n",
" # Create a boolean array whether each image is correctly classified.\n",
" correct = (cls_true == cls_pred)\n",
"\n",
" return correct, cls_pred"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Calculate the predicted class for the test-set."
]
},
{
"cell_type": "code",
"execution_count": 72,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def predict_cls_test():\n",
" return predict_cls(transfer_values = transfer_values_test,\n",
" labels = labels_test,\n",
" cls_true = cls_test)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"
Helper-Function for Calculating Classification Accuracy
\n",
"\n",
"This function calculates the classification accuracy given a boolean array whether each image was correctly classified. E.g. `classification_accuracy([True, True, False, False, False]) = 2/5 = 0.4`. The function also returns the number of correct classifications."
]
},
{
"cell_type": "code",
"execution_count": 73,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def classification_accuracy(correct):\n",
" # When averaging a boolean array, False means 0 and True means 1.\n",
" # So we are calculating: number of True / len(correct) which is\n",
" # the same as the classification accuracy.\n",
"\n",
" # Return the classification accuracy\n",
" # and the number of correct classifications.\n",
" return correct.mean(), correct.sum()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"
Helper-Function for Showing the Classification Accuracy
"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Function for printing the classification accuracy on the test-set.\n",
"\n",
"It takes a while to compute the classification for all the images in the test-set, that's why the results are re-used by calling the above functions directly from this function, so the classifications don't have to be recalculated by each function."
]
},
{
"cell_type": "code",
"execution_count": 74,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def print_test_accuracy(show_example_errors=False,\n",
" show_confusion_matrix=False):\n",
"\n",
" # For all the images in the test-set,\n",
" # calculate the predicted classes and whether they are correct.\n",
" correct, cls_pred = predict_cls_test()\n",
" \n",
" # Classification accuracy and the number of correct classifications.\n",
" acc, num_correct = classification_accuracy(correct)\n",
" \n",
" # Number of images being classified.\n",
" num_images = len(correct)\n",
"\n",
" # Print the accuracy.\n",
" msg = \"Accuracy on Test-Set: {0:.1%} ({1} / {2})\"\n",
" print(msg.format(acc, num_correct, num_images))\n",
"\n",
" # Plot some examples of mis-classifications, if desired.\n",
" if show_example_errors:\n",
" print(\"Example errors:\")\n",
" plot_example_errors(cls_pred=cls_pred, correct=correct)\n",
"\n",
" # Plot the confusion matrix, if desired.\n",
" if show_confusion_matrix:\n",
" print(\"Confusion Matrix:\")\n",
" plot_confusion_matrix(cls_pred=cls_pred)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"
\n",
"PERFORMANCE BEFORE OPTIMIZATION\n",
"
"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The classification accuracy on the test-set is very low because the model variables have only been initialized and not optimized at all, so it just classifies the images randomly."
]
},
{
"cell_type": "code",
"execution_count": 76,
"metadata": {
"scrolled": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Accuracy on Test-Set: 52.6% (279 / 530)\n",
"Confusion Matrix:\n",
"[90 0 61] (0) forky\n",
"[91 0 46] (1) knifey\n",
"[ 53 0 189] (2) spoony\n",
" (0) (1) (2)\n"
]
}
],
"source": [
"print_test_accuracy(show_example_errors=False,\n",
" show_confusion_matrix=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"
\n",
"PERFORMANCE AFTER 1,000 OPTIMIZATION ITERATIONS\n",
"
"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We are now done using TensorFlow, so we close the session to release its resources. Note that we have two TensorFlow-sessions so we close both."
]
},
{
"cell_type": "code",
"execution_count": 79,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# This can be commented out in case you want to modify and experiment\n",
"# with the Notebook without having to restart it.\n",
"model.close()\n",
"session.close()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"
\n",
"SUMMARY\n",
"
\n",
"\n",
"This tutorial showed how to use your own images in Transfer Learning with the Inception model. Thousands of images were used in this tutorial which were generated from just a few minutes of video recordings using a Python [script](https://github.com/Hvass-Labs/TensorFlow-Tutorials/blob/master/convert.py).\n",
"\n",
"However, the classification accuracy was not very good on the Knifey-Spoony data-set, especially for images of forks. The reason for this may be that the Inception model was originally trained on the ImageNet data-set which only contains 16 images of forks, while it contains more than 1200 images of spoons and more than 1300 images of knives. So it is possible that the Inception model cannot properly recognize forks.\n",
"\n",
"We therefore need another technique for fine-tuning the Inception model so it becomes better at recognizing images of forks."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"
\n",
"CHALLENGE\n",
"
\n",
"\n",
"These are a few suggestions for exercises that may help improve your skills with TensorFlow. It is important to get hands-on experience with TensorFlow in order to learn how to use it properly.\n",
"\n",
"You may want to backup this Notebook and the other files before making any changes.\n",
"\n",
"* Try changing the neural network for doing the new classification. What happens if you remove the fully-connected layer, or add more fully-connected layers?\n",
"* What happens if you perform fewer or more optimization iterations?\n",
"* Try and delete some of the spoony-images in the training-set so there is a similar amount of images in each of the 3 classes (take a backup first). You also need to delete all the cache-files with `*.pkl` filenames and re-run this Notebook. Does this improve the classification accuracy? Compare the confusion matrix before and after this change.\n",
"* Create your own data-set using the `convert.py` script. For example, make video recordings of cars and motorcycles and build a classification system that can tell those two classes apart.\n",
"* Is it necessary to delete unclear images from the training-set you have created? What happens to the classification accuracy if you delete the unclear images?\n",
"* Make changes to this Notebook so you can input a single image instead of a whole data-set. You don't have to cache the transfer-values from the Inception model.\n",
"* Can you build a better or faster Neural Network for your data-set instead of using the Inception model with Transfer Learning?\n",
"* Explain to a friend how the program works."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"
\n",
"LICENSE (MIT)\n",
"
\n",
"\n",
"Copyright (c) 2016 by [Magnus Erik Hvass Pedersen](http://www.hvass-labs.org/)\n",
"\n",
"Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the \"Software\"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:\n",
"\n",
"The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.\n",
"\n",
"THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"anaconda-cloud": {},
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.4"
}
},
"nbformat": 4,
"nbformat_minor": 1
}