\n",
"We saw in the previous Tutorial #07 how to use the pre-trained Inception model for classifying images. Unfortunately the Inception model seemed unable to classify images of people. The reason was the data-set used for training the Inception model, which had some confusing text-labels for classes.\n",
"
\n",
"The Inception model is actually quite capable of extracting useful information from an image. So we can instead train the Inception model using another data-set. But it takes several weeks using a very powerful and expensive computer to fully train the Inception model on a new data-set.\n",
"
\n",
"We can instead re-use the pre-trained Inception model and merely replace the layer that does the final classification. This is called Transfer Learning.\n",
"
\n",
"This tutorial builds on the previous tutorials so you should be familiar with Tutorial #07 on the Inception model, as well as earlier tutorials on how to build and train Neural Networks in TensorFlow. A part of the source-code for this tutorial is located in the `inception.py` file.\n",
"
\n",
"\n",
" \n",
"\n",
"
[Click here to follow along with the video on YouTube](https://www.youtube.com/watch?v=upfgTWrhkpg&list=PL9Hr9sNUjfsmEu1ZniY0XpHSzl5uihcXZ&t=0s&index=12)
\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",
"The reason for using a cache-file is that it takes a long time to process an image with the Inception model. My laptop computer with a Quad-Core 2 GHz CPU can process about 3 images per second using the Inception model. If each image is processed more than once then we can save a lot of time by caching the transfer-values.\n",
"\n",
"The transfer-values are also sometimes called bottleneck-values, but that is a confusing term so it is not used here.\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",
"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": false
},
"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": [
"
\n",
"LOAD DATA FOR CIFAR-10\n",
"
\n",
"\n",
"
Imports
"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"from data import cifar10"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The data dimensions have already been defined in the cifar10 module, so we just need to import the ones we need."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"from data.cifar10 import num_classes"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Set the path for storing the data-set on your computer."
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"cifar10.data_path = \"data/CIFAR-10/\""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The CIFAR-10 data-set is about 163 MB and will be downloaded automatically if it is not located in the given path."
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Data has apparently already been downloaded and unpacked.\n"
]
}
],
"source": [
"cifar10.maybe_download_and_extract()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Load the class-names."
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Loading data: data/CIFAR-10/cifar-10-batches-py/batches.meta\n"
]
},
{
"data": {
"text/plain": [
"['airplane',\n",
" 'automobile',\n",
" 'bird',\n",
" 'cat',\n",
" 'deer',\n",
" 'dog',\n",
" 'frog',\n",
" 'horse',\n",
" 'ship',\n",
" 'truck']"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"class_names = cifar10.load_class_names()\n",
"class_names"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Load the training-set. This returns the images, the class-numbers as integers, and the class-numbers as One-Hot encoded arrays called labels."
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Loading data: data/CIFAR-10/cifar-10-batches-py/data_batch_1\n",
"Loading data: data/CIFAR-10/cifar-10-batches-py/data_batch_2\n",
"Loading data: data/CIFAR-10/cifar-10-batches-py/data_batch_3\n",
"Loading data: data/CIFAR-10/cifar-10-batches-py/data_batch_4\n",
"Loading data: data/CIFAR-10/cifar-10-batches-py/data_batch_5\n"
]
}
],
"source": [
"images_train, cls_train, labels_train = cifar10.load_training_data()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Load the test-set."
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Loading data: data/CIFAR-10/cifar-10-batches-py/test_batch\n"
]
}
],
"source": [
"images_test, cls_test, labels_test = cifar10.load_test_data()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The CIFAR-10 data-set has now been loaded and consists of 60,000 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": 12,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Size of:\n",
"- Training-set:\t\t50000\n",
"- Test-set:\t\t10000\n"
]
}
],
"source": [
"print(\"Size of:\")\n",
"print(\"- Training-set:\\t\\t{}\".format(len(images_train)))\n",
"print(\"- Test-set:\\t\\t{}\".format(len(images_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": 13,
"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": [
"
Plot a few Images to See if Data is Correct
"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
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vT7AVrk9FhOpRXDanU85yyHJzy9RlbzI9djQaoadNEffEAw0Y8dff8KUvf/lY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\nw8PDY0j8f/sJk3mN/fWjAAAAAElFTkSuQmCC\n",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Get the first images from the test-set.\n",
"images = images_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=False)"
]
},
{
"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": 15,
"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": 16,
"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": 17,
"metadata": {
"collapsed": true,
"scrolled": false
},
"outputs": [],
"source": [
"model = inception.Inception()"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"
\n",
"CALCULATE TRANSFER VALUES\n",
"
"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Import a helper-function for caching the transfer-values of the Inception model."
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"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": 19,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"file_path_cache_train = os.path.join(cifar10.data_path, 'inception_cifar10_train.pkl')\n",
"file_path_cache_test = os.path.join(cifar10.data_path, 'inception_cifar10_test.pkl')"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Processing Inception transfer-values for training-images ...\n",
"- Processing image: 50000 / 50000\n",
"- Data saved to cache-file: data/CIFAR-10/inception_cifar10_train.pkl\n"
]
}
],
"source": [
"print(\"Processing Inception transfer-values for training-images ...\")\n",
"\n",
"# Scale images because Inception needs pixels to be between 0 and 255,\n",
"# while the CIFAR-10 functions return pixels between 0.0 and 1.0\n",
"images_scaled = images_train * 255.0\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",
" images=images_scaled,\n",
" model=model)"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Processing Inception transfer-values for test-images ...\n",
"- Processing image: 10000 / 10000\n",
"- Data saved to cache-file: data/CIFAR-10/inception_cifar10_test.pkl\n"
]
}
],
"source": [
"print(\"Processing Inception transfer-values for test-images ...\")\n",
"\n",
"# Scale images because Inception needs pixels to be between 0 and 255,\n",
"# while the CIFAR-10 functions return pixels between 0.0 and 1.0\n",
"images_scaled = images_test * 255.0\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",
" images=images_scaled,\n",
" model=model)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Check the shape of the array with the transfer-values. There are 50,000 images in the training-set and for each image there are 2048 transfer-values."
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(50000, 2048)"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"transfer_values_train.shape"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Similarly, there are 10,000 images in the test-set with 2048 transfer-values for each image."
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(10000, 2048)"
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"transfer_values_test.shape"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"
\n",
"HELPER-FUNCTION FOR PLOTTING TRANSFER VALUES\n",
"
"
]
},
{
"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": 27,
"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": 28,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"pca = PCA(n_components=2)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"It takes a while to compute the PCA so the number of samples has been limited to 3000. You can try and use the full training-set if you like."
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"transfer_values = transfer_values_train[0:3000]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Get the class-numbers for the samples you selected."
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"cls = cls_train[0:3000]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Check that the array has 3000 samples and 2048 transfer-values for each sample."
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"(3000, 2048)"
]
},
"execution_count": 31,
"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": 32,
"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 3000 samples and 2 values per sample."
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"(3000, 2)"
]
},
"execution_count": 33,
"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": 34,
"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",
" # Get the color for each sample.\n",
" colors = cmap[cls]\n",
"\n",
" # Extract the x- and y-values.\n",
" x = values[:, 0]\n",
" y = values[:, 1]\n",
"\n",
" # Plot it.\n",
" plt.scatter(x, y, color=colors)\n",
" plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Plot the transfer-values that have been reduced using PCA. There are 10 different colors for the different classes in the CIFAR-10 data-set. The colors are grouped together but with very large overlap. This may be because PCA cannot properly separate the transfer-values."
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {
"scrolled": false
},
"outputs": [
{
"data": {
"image/png": 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+m9HLWkoNXQ/T3D6TypIcfglDJzXzX0ju4Nbh7+BT02wLaspzaPT3ttLSPovy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\ngg==\n",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plot_scatter(transfer_values_reduced, cls)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"
\n",
"ANALYSIS OF TRANSFER VALUES USING T-SNE\n",
"
"
]
},
{
"cell_type": "code",
"execution_count": 36,
"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": 37,
"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": 38,
"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": 39,
"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 3000 samples and 2 transfer-values per sample."
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"(3000, 2)"
]
},
"execution_count": 40,
"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 CIFAR-10 images into classes, although there is still some overlap so the separation is not perfect."
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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imW1K9FrbVlCrVJxVBEwYtynIR1uCnNdQ46MBUXb2sCo4/Ub427aFiv0hCgvh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\nSUVORK5CYII=\n",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plot_scatter(transfer_values_reduced, cls)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"
\n",
"NEW CLASSIFIER IN TENSORFLOW\n",
"
"
]
},
{
"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 CIFAR-10 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
"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"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": 42,
"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": 43,
"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 10 elements, one for each possible class in the data-set."
]
},
{
"cell_type": "code",
"execution_count": 44,
"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": 45,
"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 CIFAR-10 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": 46,
"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": [
"
\n",
"\n",
"The output of the network `y_pred` is an array with 10 elements. The class number is the index of the largest element in the array."
]
},
{
"cell_type": "code",
"execution_count": 49,
"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": 50,
"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": 51,
"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": 52,
"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": 53,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"session.run(tf.global_variables_initializer())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"
Helper-Function for Random Batch
"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"There are 50,000 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": 54,
"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": 55,
"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
"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"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": 56,
"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": 57,
"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 images from the test-set that have been\n",
" # incorrectly classified.\n",
" images = images_test[incorrect]\n",
" \n",
" # Get the predicted classes for those images.\n",
" cls_pred = cls_pred[incorrect]\n",
"\n",
" # Get the true classes for those images.\n",
" cls_true = cls_test[incorrect]\n",
"\n",
" n = min(9, len(images))\n",
" \n",
" # Plot the first n images.\n",
" plot_images(images=images[0:n],\n",
" cls_true=cls_true[0:n],\n",
" cls_pred=cls_pred[0:n])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"
Helper-Function to Plot Confusion Matrix
"
]
},
{
"cell_type": "code",
"execution_count": 58,
"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": 59,
"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": 60,
"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": [
"
Create TensorFlow Session
\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": 61,
"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": 62,
"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",
"
\n",
"\n",
"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": 63,
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Accuracy on Test-Set: 12.0% (1201 / 10000)\n"
]
}
],
"source": [
"print_test_accuracy(show_example_errors=False,\n",
" show_confusion_matrix=False)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"
\n",
"PERFORMANCE AFTER 10,000 OPTIMIZATIONS\n",
"
"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"After 10,000 optimization iterations, the classification accuracy is about 90% on the test-set. Compare this to the basic Convolutional Neural Network from Tutorial #06 which had less than 80% accuracy on the test-set."
]
},
{
"cell_type": "code",
"execution_count": 64,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Global Step: 100, Training Batch Accuracy: 84.4%\n",
"Global Step: 200, Training Batch Accuracy: 90.6%\n",
"Global Step: 300, Training Batch Accuracy: 92.2%\n",
"Global Step: 400, Training Batch Accuracy: 87.5%\n",
"Global Step: 500, Training Batch Accuracy: 85.9%\n",
"Global Step: 600, Training Batch Accuracy: 89.1%\n",
"Global Step: 700, Training Batch Accuracy: 89.1%\n",
"Global Step: 800, Training Batch Accuracy: 95.3%\n",
"Global Step: 900, Training Batch Accuracy: 89.1%\n",
"Global Step: 1000, Training Batch Accuracy: 87.5%\n",
"Global Step: 1100, Training Batch Accuracy: 90.6%\n",
"Global Step: 1200, Training Batch Accuracy: 87.5%\n",
"Global Step: 1300, Training Batch Accuracy: 96.9%\n",
"Global Step: 1400, Training Batch Accuracy: 92.2%\n",
"Global Step: 1500, Training Batch Accuracy: 85.9%\n",
"Global Step: 1600, Training Batch Accuracy: 93.8%\n",
"Global Step: 1700, Training Batch Accuracy: 92.2%\n",
"Global Step: 1800, Training Batch Accuracy: 87.5%\n",
"Global Step: 1900, Training Batch Accuracy: 92.2%\n",
"Global Step: 2000, Training Batch Accuracy: 93.8%\n",
"Global Step: 2100, Training Batch Accuracy: 90.6%\n",
"Global Step: 2200, Training Batch Accuracy: 90.6%\n",
"Global Step: 2300, Training Batch Accuracy: 89.1%\n",
"Global Step: 2400, Training Batch Accuracy: 93.8%\n",
"Global Step: 2500, Training Batch Accuracy: 92.2%\n",
"Global Step: 2600, Training Batch Accuracy: 89.1%\n",
"Global Step: 2700, Training Batch Accuracy: 89.1%\n",
"Global Step: 2800, Training Batch Accuracy: 93.8%\n",
"Global Step: 2900, Training Batch Accuracy: 92.2%\n",
"Global Step: 3000, Training Batch Accuracy: 90.6%\n",
"Global Step: 3100, Training Batch Accuracy: 95.3%\n",
"Global Step: 3200, Training Batch Accuracy: 93.8%\n",
"Global Step: 3300, Training Batch Accuracy: 93.8%\n",
"Global Step: 3400, Training Batch Accuracy: 95.3%\n",
"Global Step: 3500, Training Batch Accuracy: 92.2%\n",
"Global Step: 3600, Training Batch Accuracy: 96.9%\n",
"Global Step: 3700, Training Batch Accuracy: 96.9%\n",
"Global Step: 3800, Training Batch Accuracy: 92.2%\n",
"Global Step: 3900, Training Batch Accuracy: 93.8%\n",
"Global Step: 4000, Training Batch Accuracy: 89.1%\n",
"Global Step: 4100, Training Batch Accuracy: 93.8%\n",
"Global Step: 4200, Training Batch Accuracy: 95.3%\n",
"Global Step: 4300, Training Batch Accuracy: 92.2%\n",
"Global Step: 4400, Training Batch Accuracy: 92.2%\n",
"Global Step: 4500, Training Batch Accuracy: 95.3%\n",
"Global Step: 4600, Training Batch Accuracy: 100.0%\n",
"Global Step: 4700, Training Batch Accuracy: 93.8%\n",
"Global Step: 4800, Training Batch Accuracy: 93.8%\n",
"Global Step: 4900, Training Batch Accuracy: 95.3%\n",
"Global Step: 5000, Training Batch Accuracy: 93.8%\n",
"Global Step: 5100, Training Batch Accuracy: 95.3%\n",
"Global Step: 5200, Training Batch Accuracy: 98.4%\n",
"Global Step: 5300, Training Batch Accuracy: 98.4%\n",
"Global Step: 5400, Training Batch Accuracy: 92.2%\n",
"Global Step: 5500, Training Batch Accuracy: 90.6%\n",
"Global Step: 5600, Training Batch Accuracy: 98.4%\n",
"Global Step: 5700, Training Batch Accuracy: 95.3%\n",
"Global Step: 5800, Training Batch Accuracy: 98.4%\n",
"Global Step: 5900, Training Batch Accuracy: 89.1%\n",
"Global Step: 6000, Training Batch Accuracy: 92.2%\n",
"Global Step: 6100, Training Batch Accuracy: 92.2%\n",
"Global Step: 6200, Training Batch Accuracy: 96.9%\n",
"Global Step: 6300, Training Batch Accuracy: 92.2%\n",
"Global Step: 6400, Training Batch Accuracy: 92.2%\n",
"Global Step: 6500, Training Batch Accuracy: 93.8%\n",
"Global Step: 6600, Training Batch Accuracy: 98.4%\n",
"Global Step: 6700, Training Batch Accuracy: 95.3%\n",
"Global Step: 6800, Training Batch Accuracy: 95.3%\n",
"Global Step: 6900, Training Batch Accuracy: 96.9%\n",
"Global Step: 7000, Training Batch Accuracy: 96.9%\n",
"Global Step: 7100, Training Batch Accuracy: 95.3%\n",
"Global Step: 7200, Training Batch Accuracy: 95.3%\n",
"Global Step: 7300, Training Batch Accuracy: 96.9%\n",
"Global Step: 7400, Training Batch Accuracy: 93.8%\n",
"Global Step: 7500, Training Batch Accuracy: 96.9%\n",
"Global Step: 7600, Training Batch Accuracy: 95.3%\n",
"Global Step: 7700, Training Batch Accuracy: 95.3%\n",
"Global Step: 7800, Training Batch Accuracy: 98.4%\n",
"Global Step: 7900, Training Batch Accuracy: 98.4%\n",
"Global Step: 8000, Training Batch Accuracy: 93.8%\n",
"Global Step: 8100, Training Batch Accuracy: 96.9%\n",
"Global Step: 8200, Training Batch Accuracy: 95.3%\n",
"Global Step: 8300, Training Batch Accuracy: 98.4%\n",
"Global Step: 8400, Training Batch Accuracy: 93.8%\n",
"Global Step: 8500, Training Batch Accuracy: 93.8%\n",
"Global Step: 8600, Training Batch Accuracy: 95.3%\n",
"Global Step: 8700, Training Batch Accuracy: 98.4%\n",
"Global Step: 8800, Training Batch Accuracy: 96.9%\n",
"Global Step: 8900, Training Batch Accuracy: 100.0%\n",
"Global Step: 9000, Training Batch Accuracy: 92.2%\n",
"Global Step: 9100, Training Batch Accuracy: 98.4%\n",
"Global Step: 9200, Training Batch Accuracy: 96.9%\n",
"Global Step: 9300, Training Batch Accuracy: 93.8%\n",
"Global Step: 9400, Training Batch Accuracy: 95.3%\n",
"Global Step: 9500, Training Batch Accuracy: 100.0%\n",
"Global Step: 9600, Training Batch Accuracy: 98.4%\n",
"Global Step: 9700, Training Batch Accuracy: 95.3%\n",
"Global Step: 9800, Training Batch Accuracy: 98.4%\n",
"Global Step: 9900, Training Batch Accuracy: 96.9%\n",
"Global Step: 10000, Training Batch Accuracy: 100.0%\n",
"Time usage: 0:00:51\n"
]
}
],
"source": [
"optimize(num_iterations=10000)"
]
},
{
"cell_type": "code",
"execution_count": 65,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Accuracy on Test-Set: 90.7% (9069 / 10000)\n",
"Example errors:\n"
]
},
{
"data": {
"image/png": 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d3BB8hsQcY0tMNsPaKTbPcWHJ5dUldf0dxijmRyc8e/4pH/36U07Pfk2eHyG6xAVNHPKv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\nV4DOaHbo0KHDFaAzmh06dOhwBfj/NfX1FX0aAFMAAAAASUVORK5CYII=\n",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Confusion Matrix:\n",
"[914 8 17 4 3 1 1 2 38 12] (0) airplane\n",
"[ 5 951 2 4 0 3 1 1 4 29] (1) automobile\n",
"[ 14 0 903 21 28 10 16 5 1 2] (2) bird\n",
"[ 6 3 26 801 28 106 17 10 2 1] (3) cat\n",
"[ 6 0 32 18 888 14 14 25 2 1] (4) deer\n",
"[ 4 0 11 52 19 897 3 13 1 0] (5) dog\n",
"[ 3 1 25 19 23 13 912 1 3 0] (6) frog\n",
"[ 4 0 11 12 25 35 2 908 1 2] (7) horse\n",
"[ 35 4 1 2 0 1 1 0 952 4] (8) ship\n",
"[ 7 31 4 0 0 1 1 1 12 943] (9) truck\n",
" (0) (1) (2) (3) (4) (5) (6) (7) (8) (9)\n"
]
}
],
"source": [
"print_test_accuracy(show_example_errors=True,\n",
" show_confusion_matrix=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"
Close TensorFlow Session
"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We are now done using TensorFlow, so we close the session to release its resources. Note that there are two TensorFlow-sessions so we close both, one session is inside the model-object."
]
},
{
"cell_type": "code",
"execution_count": 66,
"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",
"In the previous Tutorial #06 it took 15 hours on a laptop PC to train a neural network for classifying the CIFAR-10 data-set with an accuracy of about 80% on the test-set.\n",
"\n",
"In this tutorial we used the Inception model from Tutorial #07 to achieve a classification accuracy of about 90% on the CIFAR-10 data-set. This was done by feeding all the images from the CIFAR-10 data-set through the Inception model and caching the so-called transfer-values prior to the final classification layer. We then built another neural network that took these transfer-values as input and produced a CIFAR-10 class as output.\n",
"\n",
"The CIFAR-10 data-set contains a total of 60,000 images. It took about 6 hours to calculate the transfer-values of the Inception model for all these images, using a laptop PC without a GPU. And training the new classifier on top of these transfer-values only took a few minutes. So the combined time-usage of this tranfer-learning was less than half the time it took to train a neural network directly for the CIFAR-10 data-set, and it achieved significantly higher classification accuracy.\n",
"\n",
"So transfer-learning with the Inception model is useful for building an image classifier for your own data-set."
]
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"
\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 using the full training-set in the PCA and t-SNE plots. What happens?\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",
"* What happens if you change the `learning_rate` for the optimizer?\n",
"* How would you implement random distortions to the CIFAR-10 images as was done in Tutorial #06? You can no longer use the cache because each input image is different.\n",
"* Try using the MNIST data-set instead of the CIFAR-10 data-set.\n",
"* Explain to a friend how the program works."
]
},
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"
\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."
]
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