├── 0 - Introduction.ipynb
├── 1 - Simple linear regression in Keras.ipynb
├── 2 - Simple linear regression in Keras with uncertainty.ipynb
├── 3 - Probabilistic regression TensorFlow Probability Probabilistic Layers.ipynb
├── 4 - Basic neural network in TensorFlow 2.0.ipynb
├── 5 - Basic neural network in TensorFlow 2.0 -- DenseVariational layer.ipynb
├── 6 - TensorFlow CNN.ipynb
├── 7 - TensorFlow CNN with Aleatoric uncertainty binary classification.ipynb
├── 8 - TensorFlow CNN Bayesian.ipynb
├── 9 - TensorFlow CNN Bayesian-Copy1.ipynb
├── 91 - Final.ipynb
├── README.md
├── Resources -- 3a - Basic neural network in TensorFlow 2.0 -- DenseVariational layer.ipynb
├── Resources -- 3c - Basic neural network in TensorFlow 2.0 -- Aleatoric uncertainty.ipynb
├── Typical_cnn.png
└── saved_models
    ├── aleatoric_cnn.ckpt.data-00000-of-00001
    ├── aleatoric_cnn.ckpt.index
    ├── aleatoric_cnn_history.pkl
    ├── base_cnn_fresh.ckpt.data-00000-of-00001
    ├── base_cnn_fresh.ckpt.index
    ├── bayes_cnn_history.pkl
    ├── bayesian_cnn_conv2dflipout.ckpt.data-00000-of-00001
    ├── bayesian_cnn_conv2dflipout.ckpt.index
    ├── binary_cnn_history.pkl
    ├── checkpoint
    ├── cnn_history.pkl
    ├── mnist_fashion.ckpt.data-00000-of-00001
    ├── mnist_fashion.ckpt.index
    ├── regular_binary_cnn.ckpt.data-00000-of-00001
    └── regular_binary_cnn.ckpt.index


/0 - Introduction.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "markdown",
  5 |    "metadata": {},
  6 |    "source": [
  7 |     "# Bayesian Deep Learning in TensorFlow Probability and TensorFlow 2.0 -- The How and the Why"
  8 |    ]
  9 |   },
 10 |   {
 11 |    "cell_type": "markdown",
 12 |    "metadata": {},
 13 |    "source": [
 14 |     " "
 15 |    ]
 16 |   },
 17 |   {
 18 |    "cell_type": "markdown",
 19 |    "metadata": {},
 20 |    "source": [
 21 |     " "
 22 |    ]
 23 |   },
 24 |   {
 25 |    "cell_type": "markdown",
 26 |    "metadata": {},
 27 |    "source": [
 28 |     "\n",
 29 |     "    \n",
 30 |     "![Hello](https://media1.giphy.com/media/ASd0Ukj0y3qMM/giphy.gif \"Wave\")\n"
 31 |    ]
 32 |   },
 33 |   {
 34 |    "cell_type": "markdown",
 35 |    "metadata": {},
 36 |    "source": [
 37 |     "## What this talk is\n",
 38 |     "\n",
 39 |     "- An overivew of the `layers` module of the recently-released TensorFlow Probability package and its integration into TensorFlow 2.0 `keras`\n",
 40 |     "- An explanation of how to use `tfp.layers` in order to fit distributions over weights of neural networks and why you would want to do that\n"
 41 |    ]
 42 |   },
 43 |   {
 44 |    "cell_type": "markdown",
 45 |    "metadata": {},
 46 |    "source": [
 47 |     " \n",
 48 |     " "
 49 |    ]
 50 |   },
 51 |   {
 52 |    "cell_type": "markdown",
 53 |    "metadata": {},
 54 |    "source": [
 55 |     "## What this talk isn't\n",
 56 |     "- An introduction to probabilistic programming, Bayesian reasoning, and their subtopics in a general fashion\n",
 57 |     "- A deep dive into the mechanics of variational inference and other techniques implemented in TensorFlow Probability\n",
 58 |     "- An introduction to vanilla neural networks and their applications"
 59 |    ]
 60 |   },
 61 |   {
 62 |    "cell_type": "markdown",
 63 |    "metadata": {},
 64 |    "source": [
 65 |     "## Who this talk is for\n",
 66 |     "\n",
 67 |     "- Pracitioners! Anyone interested in applying the latest in TensorFlow and TensorFlow Probability to solve interesting problems.\n",
 68 |     "- People who are interested in marrying probabilistic techniques with deep learning frameworks, but haven't yet delved in themselves."
 69 |    ]
 70 |   },
 71 |   {
 72 |    "cell_type": "markdown",
 73 |    "metadata": {},
 74 |    "source": [
 75 |     "## Why does this matter?"
 76 |    ]
 77 |   },
 78 |   {
 79 |    "cell_type": "code",
 80 |    "execution_count": 2,
 81 |    "metadata": {},
 82 |    "outputs": [
 83 |     {
 84 |      "data": {
 85 |       "text/html": [
 86 |        "<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/HumFmLu3CJ8\" frameborder=\"0\" allow=\"accelerometer; autoplay; encrypted-media; gyroscope; picture-in-picture\" allowfullscreen></iframe>"
 87 |       ],
 88 |       "text/plain": [
 89 |        "<IPython.core.display.HTML object>"
 90 |       ]
 91 |      },
 92 |      "execution_count": 2,
 93 |      "metadata": {},
 94 |      "output_type": "execute_result"
 95 |     }
 96 |    ],
 97 |    "source": [
 98 |     "from IPython.display import HTML\n",
 99 |     "\n",
100 |     "HTML('<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/HumFmLu3CJ8\" frameborder=\"0\" allow=\"accelerometer; autoplay; encrypted-media; gyroscope; picture-in-picture\" allowfullscreen></iframe>')"
101 |    ]
102 |   },
103 |   {
104 |    "cell_type": "markdown",
105 |    "metadata": {},
106 |    "source": [
107 |     "Probabilistic deep learning is a field that's experienced hype in the recent past, but its depth of required knowledge and lack of high-level tools has discouraged many from being able to participate. Frameworks like Pyro on PyTorch have sought to address this problem, but so far uptake remains low.\n",
108 |     "\n",
109 |     "With the recent release of TensorFlow 2.0, the probabilistic learning framework TensorFlow Probability is included as a first class member of the TensorFlow ecosystem, and interoperates cleanly with TensorFlow 2.0 `keras`. This makes previous problems like fitting priors on the weights in a convolutional neural network layer much more approachable from a computational standpoint, allowing practitioners to focus on the theory and problems they are trying to solve."
110 |    ]
111 |   }
112 |  ],
113 |  "metadata": {
114 |   "kernelspec": {
115 |    "display_name": "Python 3",
116 |    "language": "python",
117 |    "name": "python3"
118 |   },
119 |   "language_info": {
120 |    "codemirror_mode": {
121 |     "name": "ipython",
122 |     "version": 3
123 |    },
124 |    "file_extension": ".py",
125 |    "mimetype": "text/x-python",
126 |    "name": "python",
127 |    "nbconvert_exporter": "python",
128 |    "pygments_lexer": "ipython3",
129 |    "version": "3.7.3"
130 |   }
131 |  },
132 |  "nbformat": 4,
133 |  "nbformat_minor": 4
134 | }
135 | 


--------------------------------------------------------------------------------
/91 - Final.ipynb:
--------------------------------------------------------------------------------
 1 | {
 2 |  "cells": [
 3 |   {
 4 |    "cell_type": "markdown",
 5 |    "metadata": {},
 6 |    "source": [
 7 |     "# Final Takeaways"
 8 |    ]
 9 |   },
10 |   {
11 |    "cell_type": "markdown",
12 |    "metadata": {},
13 |    "source": [
14 |     "Let's go over some of the things we've learned here. You were introduced to TensorFlow Probability and the `layers` and `distributions` modules and shown how these topics can interoperate cleanly with TensorFlow 2.0 `keras` in order to train neural networks of near-arbitrary depth and complexity with full support for aleatoric uncertainty, epistemic uncertainty, or both.\n",
15 |     "\n",
16 |     "You also received a broad overview of the strengths of probabilistic programming and reasoning with uncertainty and gained a greated appreciation for the hidden uncertainties that you're potentially already exposed to in your current work."
17 |    ]
18 |   },
19 |   {
20 |    "cell_type": "markdown",
21 |    "metadata": {},
22 |    "source": [
23 |     " "
24 |    ]
25 |   },
26 |   {
27 |    "cell_type": "markdown",
28 |    "metadata": {},
29 |    "source": [
30 |     "# Further research"
31 |    ]
32 |   },
33 |   {
34 |    "cell_type": "markdown",
35 |    "metadata": {},
36 |    "source": [
37 |     "I hope you're encouraged to learn more! Here is a compendium of resources I've found interesting and recommend to others:\n",
38 |     "\n",
39 |     "## Books:\n",
40 |     "- [Probabilistic Programming and Bayesian Methods for Hackers](https://github.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers)\n",
41 |     "  - This is one of the best possible entrypoints to probabilistic programming in a general sense, and it was recently ported from PyMC3 (an excellent language) to TensorFlow Probability.\n",
42 |     "- [Probabilistic Models of Cognition](https://probmods.org)\n",
43 |     "  - Probmods is an excellent theoretical treatment of probabilistic programming and its application to artificial intelligence\n",
44 |     "\n",
45 |     "## Blog posts:\n",
46 |     "- [Thomas Wiecki, Bayesian Deep Learning](https://twiecki.io/blog/2016/06/01/bayesian-deep-learning/)\n",
47 |     "  - Wiecki is one of the authors of PyMC3, originally written in Theano but now in the process of being ported to TensorFlow Probability for PyMC4\n",
48 |     "- [Variational Autoencoders with TensorFlow Probability Layers](https://medium.com/tensorflow/variational-autoencoders-with-tensorflow-probability-layers-d06c658931b7)\n",
49 |     "  - By the TensorFlow Probability Team, this is a reasonable next step from the material that we've covered here.\n",
50 |     "\n",
51 |     "## Talks\n",
52 |     "- [Eric Ma, An Attempt at Demystifying Bayesian Deep Learning](https://www.youtube.com/watch?v=s0S6HFdPtlA)\n",
53 |     "- [Josh Dillon, TensorFlow Probability: Learning with Confidence](https://www.youtube.com/watch?v=BrwKURU-wpk)"
54 |    ]
55 |   }
56 |  ],
57 |  "metadata": {
58 |   "kernelspec": {
59 |    "display_name": "Python 3",
60 |    "language": "python",
61 |    "name": "python3"
62 |   },
63 |   "language_info": {
64 |    "codemirror_mode": {
65 |     "name": "ipython",
66 |     "version": 3
67 |    },
68 |    "file_extension": ".py",
69 |    "mimetype": "text/x-python",
70 |    "name": "python",
71 |    "nbconvert_exporter": "python",
72 |    "pygments_lexer": "ipython3",
73 |    "version": "3.7.3"
74 |   }
75 |  },
76 |  "nbformat": 4,
77 |  "nbformat_minor": 4
78 | }
79 | 


--------------------------------------------------------------------------------
/README.md:
--------------------------------------------------------------------------------
1 | # ProbabilisticDeepLearningTensorFlow
2 | Material for ODSC Europe presentation -- Probabilistic Deep Learning in TensorFlow, the why and the how
3 | 


--------------------------------------------------------------------------------
/Resources -- 3c - Basic neural network in TensorFlow 2.0 -- Aleatoric uncertainty.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "markdown",
  5 |    "metadata": {},
  6 |    "source": [
  7 |     "## 3 - Deep neural network in TensorFlow 2.0\n",
  8 |     "\n",
  9 |     "\n",
 10 |     "\n"
 11 |    ]
 12 |   },
 13 |   {
 14 |    "cell_type": "code",
 15 |    "execution_count": 1,
 16 |    "metadata": {},
 17 |    "outputs": [
 18 |     {
 19 |      "name": "stdout",
 20 |      "output_type": "stream",
 21 |      "text": [
 22 |       "2.0.0\n"
 23 |      ]
 24 |     }
 25 |    ],
 26 |    "source": [
 27 |     "from __future__ import absolute_import, division, print_function, unicode_literals\n",
 28 |     "\n",
 29 |     "import pandas as pd\n",
 30 |     "\n",
 31 |     "# TensorFlow and tf.keras\n",
 32 |     "import tensorflow as tf\n",
 33 |     "from tensorflow import keras\n",
 34 |     "from tensorflow_probability import distributions as tfd\n",
 35 |     "import tensorflow_probability as tfp\n",
 36 |     "\n",
 37 |     "# Helper libraries\n",
 38 |     "import numpy as np\n",
 39 |     "import matplotlib.pyplot as plt\n",
 40 |     "\n",
 41 |     "print(tf.__version__)"
 42 |    ]
 43 |   },
 44 |   {
 45 |    "cell_type": "code",
 46 |    "execution_count": 2,
 47 |    "metadata": {},
 48 |    "outputs": [],
 49 |    "source": [
 50 |     "negloglik = lambda y, p_y: -p_y.log_prob(y)\n"
 51 |    ]
 52 |   },
 53 |   {
 54 |    "cell_type": "markdown",
 55 |    "metadata": {},
 56 |    "source": [
 57 |     "We've seen how to solve simple regression problems in TensorFlow 2.0 and the Keras layers library, but how about doing something deeper?"
 58 |    ]
 59 |   },
 60 |   {
 61 |    "cell_type": "code",
 62 |    "execution_count": 3,
 63 |    "metadata": {},
 64 |    "outputs": [],
 65 |    "source": [
 66 |     "fashion_mnist = keras.datasets.fashion_mnist\n",
 67 |     "\n",
 68 |     "(train_images, train_labels), (test_images, test_labels) = fashion_mnist.load_data()"
 69 |    ]
 70 |   },
 71 |   {
 72 |    "cell_type": "code",
 73 |    "execution_count": 4,
 74 |    "metadata": {},
 75 |    "outputs": [],
 76 |    "source": [
 77 |     "class_names = ['T-shirt/top', \n",
 78 |     "               'Trouser', \n",
 79 |     "               'Pullover', \n",
 80 |     "               'Dress', \n",
 81 |     "               'Coat',\n",
 82 |     "               'Sandal', \n",
 83 |     "               'Shirt', \n",
 84 |     "               'Sneaker', \n",
 85 |     "               'Bag', \n",
 86 |     "               'Ankle boot']"
 87 |    ]
 88 |   },
 89 |   {
 90 |    "cell_type": "markdown",
 91 |    "metadata": {},
 92 |    "source": [
 93 |     "Let's explore these data"
 94 |    ]
 95 |   },
 96 |   {
 97 |    "cell_type": "code",
 98 |    "execution_count": 5,
 99 |    "metadata": {},
100 |    "outputs": [
101 |     {
102 |      "data": {
103 |       "text/plain": [
104 |        "(60000, 28, 28)"
105 |       ]
106 |      },
107 |      "execution_count": 5,
108 |      "metadata": {},
109 |      "output_type": "execute_result"
110 |     }
111 |    ],
112 |    "source": [
113 |     "train_images.shape"
114 |    ]
115 |   },
116 |   {
117 |    "cell_type": "code",
118 |    "execution_count": 6,
119 |    "metadata": {},
120 |    "outputs": [
121 |     {
122 |      "data": {
123 |       "text/plain": [
124 |        "60000"
125 |       ]
126 |      },
127 |      "execution_count": 6,
128 |      "metadata": {},
129 |      "output_type": "execute_result"
130 |     }
131 |    ],
132 |    "source": [
133 |     "len(train_labels)"
134 |    ]
135 |   },
136 |   {
137 |    "cell_type": "markdown",
138 |    "metadata": {},
139 |    "source": [
140 |     "All of the image data we have is represented as a series of pixels, each with an integer r/g/b value of between 0 and 255 -- let's transform those such that they're usable by a neural network."
141 |    ]
142 |   },
143 |   {
144 |    "cell_type": "code",
145 |    "execution_count": 7,
146 |    "metadata": {},
147 |    "outputs": [
148 |     {
149 |      "data": {
150 |       "text/plain": [
151 |        "array([[  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
152 |        "          0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
153 |        "          0,   0],\n",
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155 |        "          0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
156 |        "          0,   0],\n",
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192 |        "         92,   0],\n",
193 |        "       [  0,   0,   1,   4,   6,   7,   2,   0,   0,   0,   0,   0, 237,\n",
194 |        "        226, 217, 223, 222, 219, 222, 221, 216, 223, 229, 215, 218, 255,\n",
195 |        "         77,   0],\n",
196 |        "       [  0,   3,   0,   0,   0,   0,   0,   0,   0,  62, 145, 204, 228,\n",
197 |        "        207, 213, 221, 218, 208, 211, 218, 224, 223, 219, 215, 224, 244,\n",
198 |        "        159,   0],\n",
199 |        "       [  0,   0,   0,   0,  18,  44,  82, 107, 189, 228, 220, 222, 217,\n",
200 |        "        226, 200, 205, 211, 230, 224, 234, 176, 188, 250, 248, 233, 238,\n",
201 |        "        215,   0],\n",
202 |        "       [  0,  57, 187, 208, 224, 221, 224, 208, 204, 214, 208, 209, 200,\n",
203 |        "        159, 245, 193, 206, 223, 255, 255, 221, 234, 221, 211, 220, 232,\n",
204 |        "        246,   0],\n",
205 |        "       [  3, 202, 228, 224, 221, 211, 211, 214, 205, 205, 205, 220, 240,\n",
206 |        "         80, 150, 255, 229, 221, 188, 154, 191, 210, 204, 209, 222, 228,\n",
207 |        "        225,   0],\n",
208 |        "       [ 98, 233, 198, 210, 222, 229, 229, 234, 249, 220, 194, 215, 217,\n",
209 |        "        241,  65,  73, 106, 117, 168, 219, 221, 215, 217, 223, 223, 224,\n",
210 |        "        229,  29],\n",
211 |        "       [ 75, 204, 212, 204, 193, 205, 211, 225, 216, 185, 197, 206, 198,\n",
212 |        "        213, 240, 195, 227, 245, 239, 223, 218, 212, 209, 222, 220, 221,\n",
213 |        "        230,  67],\n",
214 |        "       [ 48, 203, 183, 194, 213, 197, 185, 190, 194, 192, 202, 214, 219,\n",
215 |        "        221, 220, 236, 225, 216, 199, 206, 186, 181, 177, 172, 181, 205,\n",
216 |        "        206, 115],\n",
217 |        "       [  0, 122, 219, 193, 179, 171, 183, 196, 204, 210, 213, 207, 211,\n",
218 |        "        210, 200, 196, 194, 191, 195, 191, 198, 192, 176, 156, 167, 177,\n",
219 |        "        210,  92],\n",
220 |        "       [  0,   0,  74, 189, 212, 191, 175, 172, 175, 181, 185, 188, 189,\n",
221 |        "        188, 193, 198, 204, 209, 210, 210, 211, 188, 188, 194, 192, 216,\n",
222 |        "        170,   0],\n",
223 |        "       [  2,   0,   0,   0,  66, 200, 222, 237, 239, 242, 246, 243, 244,\n",
224 |        "        221, 220, 193, 191, 179, 182, 182, 181, 176, 166, 168,  99,  58,\n",
225 |        "          0,   0],\n",
226 |        "       [  0,   0,   0,   0,   0,   0,   0,  40,  61,  44,  72,  41,  35,\n",
227 |        "          0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
228 |        "          0,   0],\n",
229 |        "       [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
230 |        "          0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
231 |        "          0,   0],\n",
232 |        "       [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
233 |        "          0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
234 |        "          0,   0]], dtype=uint8)"
235 |       ]
236 |      },
237 |      "execution_count": 7,
238 |      "metadata": {},
239 |      "output_type": "execute_result"
240 |     }
241 |    ],
242 |    "source": [
243 |     "train_images[0]"
244 |    ]
245 |   },
246 |   {
247 |    "cell_type": "code",
248 |    "execution_count": 8,
249 |    "metadata": {},
250 |    "outputs": [
251 |     {
252 |      "data": {
253 |       "text/plain": [
254 |        "array([9, 0, 0, ..., 3, 0, 5], dtype=uint8)"
255 |       ]
256 |      },
257 |      "execution_count": 8,
258 |      "metadata": {},
259 |      "output_type": "execute_result"
260 |     }
261 |    ],
262 |    "source": [
263 |     "train_labels"
264 |    ]
265 |   },
266 |   {
267 |    "cell_type": "code",
268 |    "execution_count": 9,
269 |    "metadata": {},
270 |    "outputs": [
271 |     {
272 |      "data": {
273 |       "image/png": "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\n",
274 |       "text/plain": [
275 |        "<Figure size 432x288 with 2 Axes>"
276 |       ]
277 |      },
278 |      "metadata": {
279 |       "needs_background": "light"
280 |      },
281 |      "output_type": "display_data"
282 |     }
283 |    ],
284 |    "source": [
285 |     "plt.figure()\n",
286 |     "plt.imshow(train_images[0])\n",
287 |     "plt.colorbar()\n",
288 |     "plt.grid(False)\n",
289 |     "plt.show()"
290 |    ]
291 |   },
292 |   {
293 |    "cell_type": "markdown",
294 |    "metadata": {},
295 |    "source": [
296 |     "Standardize the r/g/b pixel values to between zero and one."
297 |    ]
298 |   },
299 |   {
300 |    "cell_type": "code",
301 |    "execution_count": 10,
302 |    "metadata": {},
303 |    "outputs": [],
304 |    "source": [
305 |     "train_images = train_images / 255.0\n",
306 |     "\n",
307 |     "test_images = test_images / 255.0"
308 |    ]
309 |   },
310 |   {
311 |    "cell_type": "markdown",
312 |    "metadata": {},
313 |    "source": [
314 |     "Let's see what these look like!"
315 |    ]
316 |   },
317 |   {
318 |    "cell_type": "code",
319 |    "execution_count": 11,
320 |    "metadata": {},
321 |    "outputs": [
322 |     {
323 |      "data": {
324 |       "image/png": 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\n",
325 |       "text/plain": [
326 |        "<Figure size 720x720 with 25 Axes>"
327 |       ]
328 |      },
329 |      "metadata": {},
330 |      "output_type": "display_data"
331 |     }
332 |    ],
333 |    "source": [
334 |     "plt.figure(figsize=(10,10))\n",
335 |     "for i in range(25):\n",
336 |     "    plt.subplot(5,5,i+1)\n",
337 |     "    plt.xticks([])\n",
338 |     "    plt.yticks([])\n",
339 |     "    plt.grid(False)\n",
340 |     "    plt.imshow(train_images[i], cmap=plt.cm.binary)\n",
341 |     "    plt.xlabel(class_names[train_labels[i]])\n",
342 |     "plt.show()"
343 |    ]
344 |   },
345 |   {
346 |    "cell_type": "markdown",
347 |    "metadata": {},
348 |    "source": [
349 |     "Now that we've seen what these images look like and made them consumable by a TensorFlow 2.0 model, let's construct such a model and plug them in."
350 |    ]
351 |   },
352 |   {
353 |    "cell_type": "code",
354 |    "execution_count": 12,
355 |    "metadata": {},
356 |    "outputs": [],
357 |    "source": [
358 |     "model = keras.Sequential([\n",
359 |     "    keras.layers.Flatten(input_shape=(28, 28)),\n",
360 |     "    keras.layers.Dense(128, activation='relu'),\n",
361 |     "    keras.layers.Dense(10, activation='softmax'),\n",
362 |     "    tfp.layers.DistributionLambda(\n",
363 |     "      lambda t: tfd.Categorical(probs=t)),\n",
364 |     "])"
365 |    ]
366 |   },
367 |   {
368 |    "cell_type": "markdown",
369 |    "metadata": {},
370 |    "source": [
371 |     "For a loss function, we'll choose sparse softmax cross entropy loss between logit outputs and labels -- this is a great loss function for mutually exclusive discrete classification tasks."
372 |    ]
373 |   },
374 |   {
375 |    "cell_type": "code",
376 |    "execution_count": 13,
377 |    "metadata": {},
378 |    "outputs": [],
379 |    "source": [
380 |     "model.compile(optimizer='adam',\n",
381 |     "              loss=negloglik,  # tf.nn.sparse_softmax_cross_entropy_with_logits\n",
382 |     "              metrics=['accuracy'])"
383 |    ]
384 |   },
385 |   {
386 |    "cell_type": "code",
387 |    "execution_count": 14,
388 |    "metadata": {},
389 |    "outputs": [
390 |     {
391 |      "name": "stdout",
392 |      "output_type": "stream",
393 |      "text": [
394 |       "Train on 60000 samples\n",
395 |       "Epoch 1/10\n",
396 |       "60000/60000 [==============================] - 6s 98us/sample - loss: 2.3038 - accuracy: 0.0539\n",
397 |       "Epoch 2/10\n",
398 |       "60000/60000 [==============================] - 6s 93us/sample - loss: 2.3024 - accuracy: 0.0517\n",
399 |       "Epoch 3/10\n",
400 |       "60000/60000 [==============================] - 5s 91us/sample - loss: 2.3022 - accuracy: 0.0592\n",
401 |       "Epoch 4/10\n",
402 |       "60000/60000 [==============================] - 5s 91us/sample - loss: 2.3021 - accuracy: 0.0613\n",
403 |       "Epoch 5/10\n",
404 |       "60000/60000 [==============================] - 5s 91us/sample - loss: 2.3020 - accuracy: 0.0507\n",
405 |       "Epoch 6/10\n",
406 |       "60000/60000 [==============================] - 6s 99us/sample - loss: 2.3019 - accuracy: 0.0539\n",
407 |       "Epoch 7/10\n",
408 |       "60000/60000 [==============================] - 6s 94us/sample - loss: 2.3018 - accuracy: 0.0613s - los\n",
409 |       "Epoch 8/10\n",
410 |       "60000/60000 [==============================] - 6s 93us/sample - loss: 2.3017 - accuracy: 0.0480\n",
411 |       "Epoch 9/10\n",
412 |       "60000/60000 [==============================] - 6s 93us/sample - loss: 2.3016 - accuracy: 0.0645\n",
413 |       "Epoch 10/10\n",
414 |       "60000/60000 [==============================] - 6s 97us/sample - loss: 2.3018 - accuracy: 0.0549\n"
415 |      ]
416 |     },
417 |     {
418 |      "data": {
419 |       "text/plain": [
420 |        "<tensorflow.python.keras.callbacks.History at 0x15374c908>"
421 |       ]
422 |      },
423 |      "execution_count": 14,
424 |      "metadata": {},
425 |      "output_type": "execute_result"
426 |     }
427 |    ],
428 |    "source": [
429 |     "model.fit(train_images, train_labels.astype('int32'), epochs=10)"
430 |    ]
431 |   },
432 |   {
433 |    "cell_type": "code",
434 |    "execution_count": 15,
435 |    "metadata": {},
436 |    "outputs": [
437 |     {
438 |      "name": "stdout",
439 |      "output_type": "stream",
440 |      "text": [
441 |       "10000/1 - 1s - loss: 2.2962 - accuracy: 0.0319\n",
442 |       "\n",
443 |       "Test accuracy: 0.031948883\n"
444 |      ]
445 |     }
446 |    ],
447 |    "source": [
448 |     "test_loss, test_acc = model.evaluate(test_images,  test_labels/1.0, verbose=2)\n",
449 |     "\n",
450 |     "print('\\nTest accuracy:', test_acc)"
451 |    ]
452 |   },
453 |   {
454 |    "cell_type": "markdown",
455 |    "metadata": {},
456 |    "source": [
457 |     "Our model trains fairly quickly, even without a GPU. Fortunately, it's small and fairly shallow as deep neural networks go. Let's take a look at the predictions."
458 |    ]
459 |   },
460 |   {
461 |    "cell_type": "code",
462 |    "execution_count": 16,
463 |    "metadata": {},
464 |    "outputs": [],
465 |    "source": [
466 |     "predictions = [model.predict(test_images) for _ in range(10)]"
467 |    ]
468 |   },
469 |   {
470 |    "cell_type": "code",
471 |    "execution_count": 17,
472 |    "metadata": {},
473 |    "outputs": [
474 |     {
475 |      "data": {
476 |       "text/plain": [
477 |        "2"
478 |       ]
479 |      },
480 |      "execution_count": 17,
481 |      "metadata": {},
482 |      "output_type": "execute_result"
483 |     }
484 |    ],
485 |    "source": [
486 |     "predictions[0][0]"
487 |    ]
488 |   },
489 |   {
490 |    "cell_type": "markdown",
491 |    "metadata": {},
492 |    "source": [
493 |     "Our softmax function effectively squished the model outputs into a distribution and seems to be most heavily activated on class 9. Let's verify that."
494 |    ]
495 |   },
496 |   {
497 |    "cell_type": "code",
498 |    "execution_count": 18,
499 |    "metadata": {},
500 |    "outputs": [
501 |     {
502 |      "data": {
503 |       "text/plain": [
504 |        "0"
505 |       ]
506 |      },
507 |      "execution_count": 18,
508 |      "metadata": {},
509 |      "output_type": "execute_result"
510 |     }
511 |    ],
512 |    "source": [
513 |     "np.argmax(predictions[0][0])"
514 |    ]
515 |   },
516 |   {
517 |    "cell_type": "code",
518 |    "execution_count": 19,
519 |    "metadata": {},
520 |    "outputs": [
521 |     {
522 |      "data": {
523 |       "text/plain": [
524 |        "9"
525 |       ]
526 |      },
527 |      "execution_count": 19,
528 |      "metadata": {},
529 |      "output_type": "execute_result"
530 |     }
531 |    ],
532 |    "source": [
533 |     "test_labels[0]"
534 |    ]
535 |   },
536 |   {
537 |    "cell_type": "code",
538 |    "execution_count": 20,
539 |    "metadata": {},
540 |    "outputs": [],
541 |    "source": [
542 |     "def plot_image(i, predictions_array, true_label, img):\n",
543 |     "    predictions_array, true_label, img = predictions_array, true_label[i], img[i]\n",
544 |     "    plt.grid(False)\n",
545 |     "    plt.xticks([])\n",
546 |     "    plt.yticks([])\n",
547 |     "\n",
548 |     "    plt.imshow(img, cmap=plt.cm.binary)\n",
549 |     "\n",
550 |     "    predicted_label = np.argmax(predictions_array)\n",
551 |     "    if predicted_label == true_label:\n",
552 |     "        color = 'blue'\n",
553 |     "    else:\n",
554 |     "        color = 'red'\n",
555 |     "\n",
556 |     "    plt.xlabel(\"{} {:2.0f}% ({})\".format(class_names[predicted_label],\n",
557 |     "                                100*np.max(predictions_array),\n",
558 |     "                                class_names[true_label]),\n",
559 |     "                                color=color)\n",
560 |     "\n",
561 |     "def plot_value_array(i, predictions_array, true_label):\n",
562 |     "    predictions_array, true_label = predictions_array, true_label[i]\n",
563 |     "    plt.grid(False)\n",
564 |     "    plt.xticks(range(10))\n",
565 |     "    plt.yticks([])\n",
566 |     "    thisplot = plt.bar(range(10), predictions_array, color=\"#777777\")\n",
567 |     "    plt.ylim([0, 1])\n",
568 |     "    predicted_label = np.argmax(predictions_array)\n",
569 |     "\n",
570 |     "    thisplot[predicted_label].set_color('red')\n",
571 |     "    thisplot[true_label].set_color('blue')"
572 |    ]
573 |   },
574 |   {
575 |    "cell_type": "code",
576 |    "execution_count": 21,
577 |    "metadata": {},
578 |    "outputs": [
579 |     {
580 |      "ename": "IndexError",
581 |      "evalue": "list index out of range",
582 |      "output_type": "error",
583 |      "traceback": [
584 |       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
585 |       "\u001b[0;31mIndexError\u001b[0m                                Traceback (most recent call last)",
586 |       "\u001b[0;32m<ipython-input-21-69392c8b20c5>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfigsize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m6\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msubplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0mplot_image\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpredictions\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtest_labels\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtest_images\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      5\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msubplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0mplot_value_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpredictions\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m  \u001b[0mtest_labels\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
587 |       "\u001b[0;32m<ipython-input-20-7485cf980009>\u001b[0m in \u001b[0;36mplot_image\u001b[0;34m(i, predictions_array, true_label, img)\u001b[0m\n\u001b[1;32m     13\u001b[0m         \u001b[0mcolor\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'red'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     14\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 15\u001b[0;31m     plt.xlabel(\"{} {:2.0f}% ({})\".format(class_names[predicted_label],\n\u001b[0m\u001b[1;32m     16\u001b[0m                                 \u001b[0;36m100\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpredictions_array\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     17\u001b[0m                                 class_names[true_label]),\n",
588 |       "\u001b[0;31mIndexError\u001b[0m: list index out of range"
589 |      ]
590 |     },
591 |     {
592 |      "data": {
593 |       "image/png": "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\n",
594 |       "text/plain": [
595 |        "<Figure size 432x216 with 1 Axes>"
596 |       ]
597 |      },
598 |      "metadata": {},
599 |      "output_type": "display_data"
600 |     }
601 |    ],
602 |    "source": [
603 |     "i = 0\n",
604 |     "plt.figure(figsize=(6,3))\n",
605 |     "plt.subplot(1,2,1)\n",
606 |     "plot_image(i, predictions[i], test_labels, test_images)\n",
607 |     "plt.subplot(1,2,2)\n",
608 |     "plot_value_array(i, predictions[i],  test_labels)\n",
609 |     "plt.show()"
610 |    ]
611 |   },
612 |   {
613 |    "cell_type": "markdown",
614 |    "metadata": {},
615 |    "source": [
616 |     "Question: are the other outputs of the softmax function probabilities?"
617 |    ]
618 |   },
619 |   {
620 |    "cell_type": "markdown",
621 |    "metadata": {},
622 |    "source": [
623 |     "Let's now try to plot predictions from the test set generally and see how well we're doing."
624 |    ]
625 |   },
626 |   {
627 |    "cell_type": "code",
628 |    "execution_count": 29,
629 |    "metadata": {},
630 |    "outputs": [
631 |     {
632 |      "data": {
633 |       "image/png": 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634 |       "text/plain": [
635 |        "<Figure size 864x720 with 30 Axes>"
636 |       ]
637 |      },
638 |      "metadata": {
639 |       "needs_background": "light"
640 |      },
641 |      "output_type": "display_data"
642 |     }
643 |    ],
644 |    "source": [
645 |     "# Plot the first X test images, their predicted labels, and the true labels.\n",
646 |     "# Color correct predictions in blue and incorrect predictions in red.\n",
647 |     "num_rows = 5\n",
648 |     "num_cols = 3\n",
649 |     "num_images = num_rows*num_cols\n",
650 |     "plt.figure(figsize=(2*2*num_cols, 2*num_rows))\n",
651 |     "for i in range(num_images):\n",
652 |     "    plt.subplot(num_rows, 2*num_cols, 2*i+1)\n",
653 |     "    plot_image(i, predictions[i], test_labels, test_images)\n",
654 |     "    plt.subplot(num_rows, 2*num_cols, 2*i+2)\n",
655 |     "    plot_value_array(i, predictions[i], test_labels)\n",
656 |     "plt.tight_layout()\n",
657 |     "plt.show()"
658 |    ]
659 |   },
660 |   {
661 |    "cell_type": "markdown",
662 |    "metadata": {},
663 |    "source": [
664 |     "Looks good! Just one mistake out of the first fifteen tries."
665 |    ]
666 |   }
667 |  ],
668 |  "metadata": {
669 |   "kernelspec": {
670 |    "display_name": "Python 3",
671 |    "language": "python",
672 |    "name": "python3"
673 |   },
674 |   "language_info": {
675 |    "codemirror_mode": {
676 |     "name": "ipython",
677 |     "version": 3
678 |    },
679 |    "file_extension": ".py",
680 |    "mimetype": "text/x-python",
681 |    "name": "python",
682 |    "nbconvert_exporter": "python",
683 |    "pygments_lexer": "ipython3",
684 |    "version": "3.7.2"
685 |   }
686 |  },
687 |  "nbformat": 4,
688 |  "nbformat_minor": 4
689 | }
690 | 


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2 | all_model_checkpoint_paths: "mnist_fashion.ckpt"
3 | 


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