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818 lines
66 KiB
Plaintext
818 lines
66 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"<h1 align=\"center\">TensorFlow Neural Network Lab</h1>"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"<img src=\"image/notmnist.png\">\n",
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"In this lab, you'll use all the tools you learned from *Introduction to TensorFlow* to label images of English letters! The data you are using, <a href=\"http://yaroslavvb.blogspot.com/2011/09/notmnist-dataset.html\">notMNIST</a>, consists of images of a letter from A to J in differents font.\n",
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"\n",
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"The above images are a few examples of the data you'll be training on. After training the network, you will compare your prediction model against test data. Your goal, by the end of this lab, is to make predictions against that test set with at least an 80% accuracy. Let's jump in!"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"To start this lab, you first need to import all the necessary modules. Run the code below. If it runs successfully, it will print \"`All modules imported`\"."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {
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"collapsed": false
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"All modules imported.\n"
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]
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}
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],
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"source": [
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"import hashlib\n",
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"import os\n",
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"import pickle\n",
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"from urllib.request import urlretrieve\n",
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"\n",
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"import numpy as np\n",
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"from PIL import Image\n",
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"from sklearn.model_selection import train_test_split\n",
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"from sklearn.preprocessing import LabelBinarizer\n",
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"from sklearn.utils import resample\n",
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"from tqdm import tqdm\n",
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"from zipfile import ZipFile\n",
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"\n",
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"print('All modules imported.')"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"The notMNIST dataset is too large for many computers to handle. It contains 500,000 images for just training. You'll be using a subset of this data, 15,000 images for each label (A-J)."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {
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"collapsed": false
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Downloading notMNIST_train.zip...\n",
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"Download Finished\n",
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"Downloading notMNIST_test.zip...\n",
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"Download Finished\n",
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"All files downloaded.\n"
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]
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}
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],
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"source": [
|
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"def download(url, file):\n",
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" \"\"\"\n",
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" Download file from <url>\n",
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" :param url: URL to file\n",
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" :param file: Local file path\n",
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" \"\"\"\n",
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" if not os.path.isfile(file):\n",
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" print('Downloading ' + file + '...')\n",
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" urlretrieve(url, file)\n",
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" print('Download Finished')\n",
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"\n",
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"# Download the training and test dataset.\n",
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"download('https://s3.amazonaws.com/udacity-sdc/notMNIST_train.zip', 'notMNIST_train.zip')\n",
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"download('https://s3.amazonaws.com/udacity-sdc/notMNIST_test.zip', 'notMNIST_test.zip')\n",
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"\n",
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"# Make sure the files aren't corrupted\n",
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"assert hashlib.md5(open('notMNIST_train.zip', 'rb').read()).hexdigest() == 'c8673b3f28f489e9cdf3a3d74e2ac8fa',\\\n",
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" 'notMNIST_train.zip file is corrupted. Remove the file and try again.'\n",
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"assert hashlib.md5(open('notMNIST_test.zip', 'rb').read()).hexdigest() == '5d3c7e653e63471c88df796156a9dfa9',\\\n",
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" 'notMNIST_test.zip file is corrupted. Remove the file and try again.'\n",
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"\n",
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"# Wait until you see that all files have been downloaded.\n",
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"print('All files downloaded.')"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"metadata": {
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"collapsed": false
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},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"100%|██████████| 210001/210001 [00:26<00:00, 7964.58files/s]\n",
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"100%|██████████| 10001/10001 [00:01<00:00, 8404.50files/s]\n"
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]
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"All features and labels uncompressed.\n"
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]
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}
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],
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"source": [
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"def uncompress_features_labels(file):\n",
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" \"\"\"\n",
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" Uncompress features and labels from a zip file\n",
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" :param file: The zip file to extract the data from\n",
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" \"\"\"\n",
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" features = []\n",
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" labels = []\n",
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"\n",
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" with ZipFile(file) as zipf:\n",
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" # Progress Bar\n",
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" filenames_pbar = tqdm(zipf.namelist(), unit='files')\n",
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" \n",
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" # Get features and labels from all files\n",
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" for filename in filenames_pbar:\n",
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" # Check if the file is a directory\n",
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" if not filename.endswith('/'):\n",
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" with zipf.open(filename) as image_file:\n",
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" image = Image.open(image_file)\n",
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" image.load()\n",
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" # Load image data as 1 dimensional array\n",
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" # We're using float32 to save on memory space\n",
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" feature = np.array(image, dtype=np.float32).flatten()\n",
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"\n",
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" # Get the the letter from the filename. This is the letter of the image.\n",
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" label = os.path.split(filename)[1][0]\n",
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"\n",
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" features.append(feature)\n",
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" labels.append(label)\n",
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" return np.array(features), np.array(labels)\n",
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"\n",
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"# Get the features and labels from the zip files\n",
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"train_features, train_labels = uncompress_features_labels('notMNIST_train.zip')\n",
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"test_features, test_labels = uncompress_features_labels('notMNIST_test.zip')\n",
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"\n",
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"# Limit the amount of data to work with a docker container\n",
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"docker_size_limit = 150000\n",
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"train_features, train_labels = resample(train_features, train_labels, n_samples=docker_size_limit)\n",
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"\n",
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"# Set flags for feature engineering. This will prevent you from skipping an important step.\n",
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"is_features_normal = False\n",
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"is_labels_encod = False\n",
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"\n",
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"# Wait until you see that all features and labels have been uncompressed.\n",
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"print('All features and labels uncompressed.')"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"<img src=\"image/Mean Variance - Image.png\" style=\"height: 75%;width: 75%; position: relative; right: 5%\">\n",
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"## Problem 1\n",
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"The first problem involves normalizing the features for your training and test data.\n",
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"\n",
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"Implement Min-Max scaling in the `normalize()` function to a range of `a=0.1` and `b=0.9`. After scaling, the values of the pixels in the input data should range from 0.1 to 0.9.\n",
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"\n",
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"Since the raw notMNIST image data is in [grayscale](https://en.wikipedia.org/wiki/Grayscale), the current values range from a min of 0 to a max of 255.\n",
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"\n",
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"Min-Max Scaling:\n",
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"$\n",
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"X'=a+{\\frac {\\left(X-X_{\\min }\\right)\\left(b-a\\right)}{X_{\\max }-X_{\\min }}}\n",
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"$\n",
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"\n",
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"*If you're having trouble solving problem 1, you can view the solution [here](https://github.com/udacity/deep-learning/blob/master/intro-to-tensorFlow/intro_to_tensorflow_solution.ipynb).*"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 37,
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"metadata": {
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"collapsed": false
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},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"0.9"
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]
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},
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"execution_count": 37,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"t = train_features[0]\n",
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"t_ = 0.1 + ((t-t.min())*(0.9 - 0.1))/(t.max()-t.min())\n",
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"0.1 + (255*0.8/255)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 40,
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"metadata": {
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"collapsed": false
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Tests Passed!\n"
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]
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}
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],
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"source": [
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"# Problem 1 - Implement Min-Max scaling for grayscale image data\n",
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"def normalize_grayscale(image_data):\n",
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" \"\"\"\n",
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" Normalize the image data with Min-Max scaling to a range of [0.1, 0.9]\n",
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" :param image_data: The image data to be normalized\n",
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" :return: Normalized image data\n",
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" \"\"\"\n",
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" t = image_data\n",
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" # TODO: Implement Min-Max scaling for grayscale image data\n",
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" return 0.1 + ((t-t.min())*(0.9 - 0.1))/(t.max()-t.min())\n",
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"\n",
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"\n",
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"### DON'T MODIFY ANYTHING BELOW ###\n",
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"# Test Cases\n",
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"np.testing.assert_array_almost_equal(\n",
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" normalize_grayscale(np.array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 255])),\n",
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" [0.1, 0.103137254902, 0.106274509804, 0.109411764706, 0.112549019608, 0.11568627451, 0.118823529412, 0.121960784314,\n",
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" 0.125098039216, 0.128235294118, 0.13137254902, 0.9],\n",
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" decimal=3)\n",
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"np.testing.assert_array_almost_equal(\n",
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" normalize_grayscale(np.array([0, 1, 10, 20, 30, 40, 233, 244, 254,255])),\n",
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" [0.1, 0.103137254902, 0.13137254902, 0.162745098039, 0.194117647059, 0.225490196078, 0.830980392157, 0.865490196078,\n",
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" 0.896862745098, 0.9])\n",
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"\n",
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"if not is_features_normal:\n",
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" train_features = normalize_grayscale(train_features)\n",
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" test_features = normalize_grayscale(test_features)\n",
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" is_features_normal = True\n",
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"\n",
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"print('Tests Passed!')"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 45,
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"metadata": {
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"collapsed": false
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Labels One-Hot Encoded\n"
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]
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}
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],
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"source": [
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"if not is_labels_encod:\n",
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" # Turn labels into numbers and apply One-Hot Encoding\n",
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" encoder = LabelBinarizer()\n",
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" encoder.fit(train_labels)\n",
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" train_labels = encoder.transform(train_labels)\n",
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" test_labels = encoder.transform(test_labels)\n",
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"\n",
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" # Change to float32, so it can be multiplied against the features in TensorFlow, which are float32\n",
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" train_labels = train_labels.astype(np.float32)\n",
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" test_labels = test_labels.astype(np.float32)\n",
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" is_labels_encod = True\n",
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"\n",
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"print('Labels One-Hot Encoded')"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 46,
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"metadata": {
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"collapsed": false
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Training features and labels randomized and split.\n"
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]
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}
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],
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"source": [
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"assert is_features_normal, 'You skipped the step to normalize the features'\n",
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"assert is_labels_encod, 'You skipped the step to One-Hot Encode the labels'\n",
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"\n",
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"# Get randomized datasets for training and validation\n",
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"train_features, valid_features, train_labels, valid_labels = train_test_split(\n",
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" train_features,\n",
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" train_labels,\n",
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" test_size=0.05,\n",
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" random_state=832289)\n",
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"\n",
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"print('Training features and labels randomized and split.')"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 47,
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"metadata": {
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"collapsed": false
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Saving data to pickle file...\n",
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"Data cached in pickle file.\n"
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]
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}
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],
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"source": [
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"# Save the data for easy access\n",
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"pickle_file = 'notMNIST.pickle'\n",
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"if not os.path.isfile(pickle_file):\n",
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" print('Saving data to pickle file...')\n",
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" try:\n",
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" with open('notMNIST.pickle', 'wb') as pfile:\n",
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" pickle.dump(\n",
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" {\n",
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" 'train_dataset': train_features,\n",
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" 'train_labels': train_labels,\n",
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" 'valid_dataset': valid_features,\n",
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" 'valid_labels': valid_labels,\n",
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" 'test_dataset': test_features,\n",
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" 'test_labels': test_labels,\n",
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" },\n",
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" pfile, pickle.HIGHEST_PROTOCOL)\n",
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" except Exception as e:\n",
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" print('Unable to save data to', pickle_file, ':', e)\n",
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" raise\n",
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"\n",
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"print('Data cached in pickle file.')"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Checkpoint\n",
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"All your progress is now saved to the pickle file. If you need to leave and comeback to this lab, you no longer have to start from the beginning. Just run the code block below and it will load all the data and modules required to proceed."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 48,
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"metadata": {
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"collapsed": false
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Data and modules loaded.\n"
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]
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}
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],
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"source": [
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"%matplotlib inline\n",
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"\n",
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"# Load the modules\n",
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"import pickle\n",
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"import math\n",
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"\n",
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"import numpy as np\n",
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"import tensorflow as tf\n",
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"from tqdm import tqdm\n",
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"import matplotlib.pyplot as plt\n",
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"\n",
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"# Reload the data\n",
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"pickle_file = 'notMNIST.pickle'\n",
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"with open(pickle_file, 'rb') as f:\n",
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" pickle_data = pickle.load(f)\n",
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" train_features = pickle_data['train_dataset']\n",
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" train_labels = pickle_data['train_labels']\n",
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" valid_features = pickle_data['valid_dataset']\n",
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" valid_labels = pickle_data['valid_labels']\n",
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" test_features = pickle_data['test_dataset']\n",
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" test_labels = pickle_data['test_labels']\n",
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" del pickle_data # Free up memory\n",
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"\n",
|
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"print('Data and modules loaded.')"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"\n",
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"## Problem 2\n",
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"\n",
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"Now it's time to build a simple neural network using TensorFlow. Here, your network will be just an input layer and an output layer.\n",
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"\n",
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"<img src=\"image/network_diagram.png\" style=\"height: 40%;width: 40%; position: relative; right: 10%\">\n",
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"\n",
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"For the input here the images have been flattened into a vector of $28 \\times 28 = 784$ features. Then, we're trying to predict the image digit so there are 10 output units, one for each label. Of course, feel free to add hidden layers if you want, but this notebook is built to guide you through a single layer network. \n",
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"\n",
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"For the neural network to train on your data, you need the following <a href=\"https://www.tensorflow.org/resources/dims_types.html#data-types\">float32</a> tensors:\n",
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" - `features`\n",
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" - Placeholder tensor for feature data (`train_features`/`valid_features`/`test_features`)\n",
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" - `labels`\n",
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" - Placeholder tensor for label data (`train_labels`/`valid_labels`/`test_labels`)\n",
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" - `weights`\n",
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" - Variable Tensor with random numbers from a truncated normal distribution.\n",
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" - See <a href=\"https://www.tensorflow.org/api_docs/python/constant_op.html#truncated_normal\">`tf.truncated_normal()` documentation</a> for help.\n",
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" - `biases`\n",
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" - Variable Tensor with all zeros.\n",
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" - See <a href=\"https://www.tensorflow.org/api_docs/python/constant_op.html#zeros\"> `tf.zeros()` documentation</a> for help.\n",
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"\n",
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"*If you're having trouble solving problem 2, review \"TensorFlow Linear Function\" section of the class. If that doesn't help, the solution for this problem is available [here](intro_to_tensorflow_solution.ipynb).*"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 159,
|
|
"metadata": {
|
|
"collapsed": false
|
|
},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Tests Passed!\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"# All the pixels in the image (28 * 28 = 784)\n",
|
|
"features_count = 784\n",
|
|
"# All the labels\n",
|
|
"labels_count = 10\n",
|
|
"\n",
|
|
"# TODO: Set the features and labels tensors\n",
|
|
"features = tf.placeholder(tf.float32, [None, features_count])\n",
|
|
"labels = tf.placeholder(tf.float32, [None, labels_count])\n",
|
|
"\n",
|
|
"# TODO: Set the weights and biases tensors\n",
|
|
"weights = tf.Variable(tf.truncated_normal([features_count, labels_count]))\n",
|
|
"biases = tf.Variable(tf.zeros([labels_count]))\n",
|
|
"\n",
|
|
"\n",
|
|
"\n",
|
|
"### DON'T MODIFY ANYTHING BELOW ###\n",
|
|
"\n",
|
|
"#Test Cases\n",
|
|
"from tensorflow.python.ops.variables import Variable\n",
|
|
"\n",
|
|
"assert features._op.name.startswith('Placeholder'), 'features must be a placeholder'\n",
|
|
"assert labels._op.name.startswith('Placeholder'), 'labels must be a placeholder'\n",
|
|
"assert isinstance(weights, Variable), 'weights must be a TensorFlow variable'\n",
|
|
"assert isinstance(biases, Variable), 'biases must be a TensorFlow variable'\n",
|
|
"\n",
|
|
"assert features._shape == None or (\\\n",
|
|
" features._shape.dims[0].value is None and\\\n",
|
|
" features._shape.dims[1].value in [None, 784]), 'The shape of features is incorrect'\n",
|
|
"assert labels._shape == None or (\\\n",
|
|
" labels._shape.dims[0].value is None and\\\n",
|
|
" labels._shape.dims[1].value in [None, 10]), 'The shape of labels is incorrect'\n",
|
|
"assert weights._variable._shape == (784, 10), 'The shape of weights is incorrect'\n",
|
|
"assert biases._variable._shape == (10), 'The shape of biases is incorrect'\n",
|
|
"\n",
|
|
"assert features._dtype == tf.float32, 'features must be type float32'\n",
|
|
"assert labels._dtype == tf.float32, 'labels must be type float32'\n",
|
|
"\n",
|
|
"# Feed dicts for training, validation, and test session\n",
|
|
"train_feed_dict = {features: train_features, labels: train_labels}\n",
|
|
"valid_feed_dict = {features: valid_features, labels: valid_labels}\n",
|
|
"test_feed_dict = {features: test_features, labels: test_labels}\n",
|
|
"\n",
|
|
"# Linear Function WX + b\n",
|
|
"logits = tf.matmul(features, weights) + biases\n",
|
|
"\n",
|
|
"prediction = tf.nn.softmax(logits)\n",
|
|
"\n",
|
|
"# Cross entropy\n",
|
|
"cross_entropy = -tf.reduce_sum(labels * tf.log(prediction), reduction_indices=1)\n",
|
|
"\n",
|
|
"# Training loss\n",
|
|
"loss = tf.reduce_mean(cross_entropy)\n",
|
|
"\n",
|
|
"\n",
|
|
"optimizer = tf.train.AdamOptimizer(0.003).minimize(loss) \n",
|
|
"\n",
|
|
"# Create an operation that initializes all variables\n",
|
|
"init = tf.global_variables_initializer()\n",
|
|
"\n",
|
|
"# Test Cases\n",
|
|
"with tf.Session() as session:\n",
|
|
" session.run(init)\n",
|
|
" session.run(loss, feed_dict=train_feed_dict)\n",
|
|
" session.run(loss, feed_dict=valid_feed_dict)\n",
|
|
" session.run(loss, feed_dict=test_feed_dict)\n",
|
|
" biases_data = session.run(biases)\n",
|
|
"\n",
|
|
"assert not np.count_nonzero(biases_data), 'biases must be zeros'\n",
|
|
"\n",
|
|
"print('Tests Passed!')"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 160,
|
|
"metadata": {
|
|
"collapsed": false
|
|
},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Accuracy function created.\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"# Determine if the predictions are correct\n",
|
|
"is_correct_prediction = tf.equal(tf.argmax(prediction, 1), tf.argmax(labels, 1))\n",
|
|
"# Calculate the accuracy of the predictions\n",
|
|
"accuracy = tf.reduce_mean(tf.cast(is_correct_prediction, tf.float32))\n",
|
|
"\n",
|
|
"print('Accuracy function created.')"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"<img src=\"image/Learn Rate Tune - Image.png\" style=\"height: 70%;width: 70%\">\n",
|
|
"## Problem 3\n",
|
|
"Below are 2 parameter configurations for training the neural network. In each configuration, one of the parameters has multiple options. For each configuration, choose the option that gives the best acccuracy.\n",
|
|
"\n",
|
|
"Parameter configurations:\n",
|
|
"\n",
|
|
"Configuration 1\n",
|
|
"* **Epochs:** 1\n",
|
|
"* **Learning Rate:**\n",
|
|
" * 0.8\n",
|
|
" * 0.5\n",
|
|
" * 0.1\n",
|
|
" * 0.05\n",
|
|
" * 0.01\n",
|
|
"\n",
|
|
"Configuration 2\n",
|
|
"* **Epochs:**\n",
|
|
" * 1\n",
|
|
" * 2\n",
|
|
" * 3\n",
|
|
" * 4\n",
|
|
" * 5\n",
|
|
"* **Learning Rate:** 0.2\n",
|
|
"\n",
|
|
"The code will print out a Loss and Accuracy graph, so you can see how well the neural network performed.\n",
|
|
"\n",
|
|
"*If you're having trouble solving problem 3, you can view the solution [here](intro_to_tensorflow_solution.ipynb).*"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 161,
|
|
"metadata": {
|
|
"collapsed": false
|
|
},
|
|
"outputs": [
|
|
{
|
|
"name": "stderr",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Epoch 1/5: 100%|██████████| 557/557 [00:02<00:00, 246.49batches/s]\n",
|
|
"Epoch 2/5: 100%|██████████| 557/557 [00:02<00:00, 256.03batches/s]\n",
|
|
"Epoch 3/5: 100%|██████████| 557/557 [00:02<00:00, 252.19batches/s]\n",
|
|
"Epoch 4/5: 100%|██████████| 557/557 [00:02<00:00, 241.44batches/s]\n",
|
|
"Epoch 5/5: 100%|██████████| 557/557 [00:02<00:00, 238.71batches/s]\n"
|
|
]
|
|
},
|
|
{
|
|
"data": {
|
|
"image/png": 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dO3Slb6e+9O3Ul36d+lX9XXk26MCMgQkFzoLiAuavm8+CggVVYWrl5pUUbS+q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\nLR/zc9RFAAAAAElFTkSuQmCC\n",
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"text/plain": [
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"<matplotlib.figure.Figure at 0x7fe0bd505e10>"
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]
|
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},
|
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"metadata": {},
|
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"output_type": "display_data"
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},
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{
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"name": "stdout",
|
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"output_type": "stream",
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"text": [
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"Validation accuracy at 0.7875999808311462\n"
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|
]
|
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}
|
|
],
|
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"source": [
|
|
"# Change if you have memory restrictions\n",
|
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"batch_size = 256\n",
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"\n",
|
|
"# TODO: Find the best parameters for each configuration\n",
|
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"epochs = 5\n",
|
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"#learning_rate = 0.01\n",
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" \n",
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"\n",
|
|
"### DON'T MODIFY ANYTHING BELOW ###\n",
|
|
"# Gradient Descent\n",
|
|
"#optimizer = tf.train.AdamOptimizer().minimize(loss) \n",
|
|
"#with tf.Session() as sess:\n",
|
|
"# sess.run(init)\n",
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"\n",
|
|
"# The accuracy measured against the validation set\n",
|
|
"validation_accuracy = 0.0\n",
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"\n",
|
|
"# Measurements use for graphing loss and accuracy\n",
|
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"log_batch_step = 50\n",
|
|
"batches = []\n",
|
|
"loss_batch = []\n",
|
|
"train_acc_batch = []\n",
|
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"valid_acc_batch = []\n",
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"\n",
|
|
"with tf.Session() as session:\n",
|
|
" session.run(init)\n",
|
|
" batch_count = int(math.ceil(len(train_features)/batch_size))\n",
|
|
"\n",
|
|
" for epoch_i in range(epochs):\n",
|
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" \n",
|
|
" # Progress bar\n",
|
|
" batches_pbar = tqdm(range(batch_count), desc='Epoch {:>2}/{}'.format(epoch_i+1, epochs), unit='batches')\n",
|
|
" \n",
|
|
" # The training cycle\n",
|
|
" for batch_i in batches_pbar:\n",
|
|
" # Get a batch of training features and labels\n",
|
|
" batch_start = batch_i*batch_size\n",
|
|
" batch_features = train_features[batch_start:batch_start + batch_size]\n",
|
|
" batch_labels = train_labels[batch_start:batch_start + batch_size]\n",
|
|
"\n",
|
|
" # Run optimizer and get loss\n",
|
|
" _, l = session.run(\n",
|
|
" [optimizer, loss],\n",
|
|
" feed_dict={features: batch_features, labels: batch_labels})\n",
|
|
"\n",
|
|
" # Log every 50 batches\n",
|
|
" if not batch_i % log_batch_step:\n",
|
|
" # Calculate Training and Validation accuracy\n",
|
|
" training_accuracy = session.run(accuracy, feed_dict=train_feed_dict)\n",
|
|
" validation_accuracy = session.run(accuracy, feed_dict=valid_feed_dict)\n",
|
|
"\n",
|
|
" # Log batches\n",
|
|
" previous_batch = batches[-1] if batches else 0\n",
|
|
" batches.append(log_batch_step + previous_batch)\n",
|
|
" loss_batch.append(l)\n",
|
|
" train_acc_batch.append(training_accuracy)\n",
|
|
" valid_acc_batch.append(validation_accuracy)\n",
|
|
"\n",
|
|
" # Check accuracy against Validation data\n",
|
|
" validation_accuracy = session.run(accuracy, feed_dict=valid_feed_dict)\n",
|
|
"\n",
|
|
"loss_plot = plt.subplot(211)\n",
|
|
"loss_plot.set_title('Loss')\n",
|
|
"loss_plot.plot(batches, loss_batch, 'g')\n",
|
|
"loss_plot.set_xlim([batches[0], batches[-1]])\n",
|
|
"acc_plot = plt.subplot(212)\n",
|
|
"acc_plot.set_title('Accuracy')\n",
|
|
"acc_plot.plot(batches, train_acc_batch, 'r', label='Training Accuracy')\n",
|
|
"acc_plot.plot(batches, valid_acc_batch, 'x', label='Validation Accuracy')\n",
|
|
"acc_plot.set_ylim([0, 1.0])\n",
|
|
"acc_plot.set_xlim([batches[0], batches[-1]])\n",
|
|
"acc_plot.legend(loc=4)\n",
|
|
"plt.tight_layout()\n",
|
|
"plt.show()\n",
|
|
"\n",
|
|
"print('Validation accuracy at {}'.format(validation_accuracy))"
|
|
]
|
|
},
|
|
{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Test\n",
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"You're going to test your model against your hold out dataset/testing data. This will give you a good indicator of how well the model will do in the real world. You should have a test accuracy of at least 80%."
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]
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},
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{
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"cell_type": "code",
|
|
"execution_count": 162,
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"metadata": {
|
|
"collapsed": false
|
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},
|
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"outputs": [
|
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"Epoch 1/5: 100%|██████████| 557/557 [00:00<00:00, 1103.36batches/s]\n",
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"Epoch 2/5: 100%|██████████| 557/557 [00:00<00:00, 1257.45batches/s]\n",
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"Epoch 3/5: 100%|██████████| 557/557 [00:00<00:00, 1494.07batches/s]\n",
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"Epoch 4/5: 100%|██████████| 557/557 [00:00<00:00, 1577.33batches/s]\n",
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"Epoch 5/5: 100%|██████████| 557/557 [00:00<00:00, 1609.31batches/s]"
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]
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},
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{
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"name": "stdout",
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|
"output_type": "stream",
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|
"text": [
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"Nice Job! Test Accuracy is 0.855400025844574\n"
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|
]
|
|
},
|
|
{
|
|
"name": "stderr",
|
|
"output_type": "stream",
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"text": [
|
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"\n"
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]
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}
|
|
],
|
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"source": [
|
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"### DON'T MODIFY ANYTHING BELOW ###\n",
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"# The accuracy measured against the test set\n",
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"test_accuracy = 0.0\n",
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"\n",
|
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"with tf.Session() as session:\n",
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" \n",
|
|
" session.run(init)\n",
|
|
" batch_count = int(math.ceil(len(train_features)/batch_size))\n",
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"\n",
|
|
" for epoch_i in range(epochs):\n",
|
|
" e\n",
|
|
" # Progress bar\n",
|
|
" batches_pbar = tqdm(range(batch_count), desc='Epoch {:>2}/{}'.format(epoch_i+1, epochs), unit='batches')\n",
|
|
" \n",
|
|
" # The training cycle\n",
|
|
" for batch_i in batches_pbar:\n",
|
|
" # Get a batch of training features and labels\n",
|
|
" batch_start = batch_i*batch_size\n",
|
|
" batch_features = train_features[batch_start:batch_start + batch_size]\n",
|
|
" batch_labels = train_labels[batch_start:batch_start + batch_size]\n",
|
|
"\n",
|
|
" # Run optimizer\n",
|
|
" _ = session.run(optimizer, feed_dict={features: batch_features, labels: batch_labels})\n",
|
|
"\n",
|
|
" # Check accuracy against Test data\n",
|
|
" test_accuracy = session.run(accuracy, feed_dict=test_feed_dict)\n",
|
|
"\n",
|
|
"\n",
|
|
"assert test_accuracy >= 0.80, 'Test accuracy at {}, should be equal to or greater than 0.80'.format(test_accuracy)\n",
|
|
"print('Nice Job! Test Accuracy is {}'.format(test_accuracy))"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"# Multiple layers\n",
|
|
"Good job! You built a one layer TensorFlow network! However, you might want to build more than one layer. This is deep learning after all! In the next section, you will start to satisfy your need for more layers."
|
|
]
|
|
}
|
|
],
|
|
"metadata": {
|
|
"anaconda-cloud": {},
|
|
"hide_input": false,
|
|
"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": "3.5.1"
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 0
|
|
}
|