├── .gitignore
├── LICENSE
├── README.md
└── first_deep_learning_model.ipynb
/.gitignore:
--------------------------------------------------------------------------------
1 | *.screenflow/
2 | *.mp4
3 | .DS_Store
4 |
5 | # Byte-compiled / optimized / DLL files
6 | __pycache__/
7 | *.py[cod]
8 | *$py.class
9 |
10 | # C extensions
11 | *.so
12 |
13 | # Distribution / packaging
14 | .Python
15 | build/
16 | develop-eggs/
17 | dist/
18 | downloads/
19 | eggs/
20 | .eggs/
21 | lib/
22 | lib64/
23 | parts/
24 | sdist/
25 | var/
26 | wheels/
27 | *.egg-info/
28 | .installed.cfg
29 | *.egg
30 | MANIFEST
31 |
32 | # PyInstaller
33 | # Usually these files are written by a python script from a template
34 | # before PyInstaller builds the exe, so as to inject date/other infos into it.
35 | *.manifest
36 | *.spec
37 |
38 | # Installer logs
39 | pip-log.txt
40 | pip-delete-this-directory.txt
41 |
42 | # Unit test / coverage reports
43 | htmlcov/
44 | .tox/
45 | .coverage
46 | .coverage.*
47 | .cache
48 | nosetests.xml
49 | coverage.xml
50 | *.cover
51 | .hypothesis/
52 | .pytest_cache/
53 |
54 | # Translations
55 | *.mo
56 | *.pot
57 |
58 | # Django stuff:
59 | *.log
60 | local_settings.py
61 | db.sqlite3
62 |
63 | # Flask stuff:
64 | instance/
65 | .webassets-cache
66 |
67 | # Scrapy stuff:
68 | .scrapy
69 |
70 | # Sphinx documentation
71 | docs/_build/
72 |
73 | # PyBuilder
74 | target/
75 |
76 | # Jupyter Notebook
77 | .ipynb_checkpoints
78 |
79 | # pyenv
80 | .python-version
81 |
82 | # celery beat schedule file
83 | celerybeat-schedule
84 |
85 | # SageMath parsed files
86 | *.sage.py
87 |
88 | # Environments
89 | .env
90 | .venv
91 | env/
92 | venv/
93 | ENV/
94 | env.bak/
95 | venv.bak/
96 |
97 | # Spyder project settings
98 | .spyderproject
99 | .spyproject
100 |
101 | # Rope project settings
102 | .ropeproject
103 |
104 | # mkdocs documentation
105 | /site
106 |
107 | # mypy
108 | .mypy_cache/
109 |
--------------------------------------------------------------------------------
/LICENSE:
--------------------------------------------------------------------------------
1 | MIT License
2 |
3 | Copyright (c) 2018 Zero to Deep Learning
4 |
5 | Permission is hereby granted, free of charge, to any person obtaining a copy
6 | of this software and associated documentation files (the "Software"), to deal
7 | in the Software without restriction, including without limitation the rights
8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
9 | copies of the Software, and to permit persons to whom the Software is
10 | furnished to do so, subject to the following conditions:
11 |
12 | The above copyright notice and this permission notice shall be included in all
13 | copies or substantial portions of the Software.
14 |
15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
21 | SOFTWARE.
22 |
--------------------------------------------------------------------------------
/README.md:
--------------------------------------------------------------------------------
1 | ## [Check our Zero To Deep Learning Online Bootcamp](https://www.zerotodeeplearning.com/?utm_source=github.com&utm_medium=affiliate&utm_campaign=https%3A%2F%2Fgithub.com%2Fzerotodeeplearning%2Ffirst_deep_learning_model&utm_content=README.md)
2 |
3 | --
4 |
5 |
6 | # First Deep Learning Model
7 |
8 |
9 | Build your first deep learning model in 10 minutes.
10 |
11 | [Guided video walkthrough](https://www.youtube.com/watch?v=m8_B_eL7gNY)
12 |
13 | Follow the get started guide below to set up your computer. The video will walk you through how to build your first model.
14 |
15 | You can run this notebook on Google Colab:
16 |
17 |
--------------------------------------------------------------------------------
/first_deep_learning_model.ipynb:
--------------------------------------------------------------------------------
1 | {
2 | "cells": [
3 | {
4 | "cell_type": "markdown",
5 | "metadata": {},
6 | "source": [
7 | "
"
8 | ]
9 | },
10 | {
11 | "cell_type": "markdown",
12 | "metadata": {},
13 | "source": [
14 | "## Learn with us: www.zerotodeeplearning.com\n",
15 | "\n",
16 | "Copyright © 2020: Zero to Deep Learning ® Catalit LLC."
17 | ]
18 | },
19 | {
20 | "cell_type": "code",
21 | "execution_count": null,
22 | "metadata": {},
23 | "outputs": [],
24 | "source": [
25 | "# Licensed under the Apache License, Version 2.0 (the \"License\");\n",
26 | "# you may not use this file except in compliance with the License.\n",
27 | "# You may obtain a copy of the License at\n",
28 | "#\n",
29 | "# https://www.apache.org/licenses/LICENSE-2.0\n",
30 | "#\n",
31 | "# Unless required by applicable law or agreed to in writing, software\n",
32 | "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
33 | "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
34 | "# See the License for the specific language governing permissions and\n",
35 | "# limitations under the License."
36 | ]
37 | },
38 | {
39 | "cell_type": "markdown",
40 | "metadata": {},
41 | "source": [
42 | "# First Deep Learning Model\n",
43 | "\n",
44 | "Hello, this is our first model."
45 | ]
46 | },
47 | {
48 | "cell_type": "code",
49 | "execution_count": 1,
50 | "metadata": {},
51 | "outputs": [],
52 | "source": [
53 | "import numpy as np"
54 | ]
55 | },
56 | {
57 | "cell_type": "code",
58 | "execution_count": 2,
59 | "metadata": {},
60 | "outputs": [],
61 | "source": [
62 | "import matplotlib.pyplot as plt"
63 | ]
64 | },
65 | {
66 | "cell_type": "code",
67 | "execution_count": 3,
68 | "metadata": {},
69 | "outputs": [],
70 | "source": [
71 | "from sklearn.datasets import make_circles"
72 | ]
73 | },
74 | {
75 | "cell_type": "code",
76 | "execution_count": 4,
77 | "metadata": {},
78 | "outputs": [],
79 | "source": [
80 | "X, y = make_circles(n_samples=1000,\n",
81 | " noise=0.1,\n",
82 | " factor=0.2,\n",
83 | " random_state=0)"
84 | ]
85 | },
86 | {
87 | "cell_type": "code",
88 | "execution_count": 5,
89 | "metadata": {},
90 | "outputs": [
91 | {
92 | "data": {
93 | "text/plain": [
94 | "array([[ 0.24265541, 0.0383196 ],\n",
95 | " [ 0.04433036, -0.05667334],\n",
96 | " [-0.78677748, -0.75718576],\n",
97 | " ...,\n",
98 | " [ 0.0161236 , -0.00548034],\n",
99 | " [ 0.20624715, 0.09769677],\n",
100 | " [-0.19186631, 0.08916672]])"
101 | ]
102 | },
103 | "execution_count": 5,
104 | "metadata": {},
105 | "output_type": "execute_result"
106 | }
107 | ],
108 | "source": [
109 | "X"
110 | ]
111 | },
112 | {
113 | "cell_type": "code",
114 | "execution_count": 6,
115 | "metadata": {},
116 | "outputs": [
117 | {
118 | "data": {
119 | "text/plain": [
120 | "(1000, 2)"
121 | ]
122 | },
123 | "execution_count": 6,
124 | "metadata": {},
125 | "output_type": "execute_result"
126 | }
127 | ],
128 | "source": [
129 | "X.shape"
130 | ]
131 | },
132 | {
133 | "cell_type": "code",
134 | "execution_count": 6,
135 | "metadata": {},
136 | "outputs": [
137 | {
138 | "data": {
139 | "image/png": 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",
140 | "text/plain": [
141 | ""
142 | ]
143 | },
144 | "metadata": {
145 | "needs_background": "light"
146 | },
147 | "output_type": "display_data"
148 | }
149 | ],
150 | "source": [
151 | "plt.figure(figsize=(5, 5))\n",
152 | "plt.plot(X[y==0, 0], X[y==0, 1], 'ob', alpha=0.5)\n",
153 | "plt.plot(X[y==1, 0], X[y==1, 1], 'xr', alpha=0.5)\n",
154 | "plt.xlim(-1.5, 1.5)\n",
155 | "plt.ylim(-1.5, 1.5)\n",
156 | "plt.legend(['0', '1'])\n",
157 | "plt.title(\"Blue circles and red crosses\");"
158 | ]
159 | },
160 | {
161 | "cell_type": "code",
162 | "execution_count": 7,
163 | "metadata": {},
164 | "outputs": [],
165 | "source": [
166 | "from tensorflow.keras.models import Sequential\n",
167 | "from tensorflow.keras.layers import Dense\n",
168 | "from tensorflow.keras.optimizers import SGD"
169 | ]
170 | },
171 | {
172 | "cell_type": "code",
173 | "execution_count": 8,
174 | "metadata": {},
175 | "outputs": [],
176 | "source": [
177 | "model = Sequential([\n",
178 | " Dense(4, input_shape=(2,), activation='tanh'),\n",
179 | " Dense(1, activation='sigmoid'),\n",
180 | "])\n",
181 | "\n",
182 | "model.compile(SGD(lr=0.5),\n",
183 | " 'binary_crossentropy',\n",
184 | " metrics=['accuracy'])"
185 | ]
186 | },
187 | {
188 | "cell_type": "code",
189 | "execution_count": 9,
190 | "metadata": {},
191 | "outputs": [
192 | {
193 | "name": "stdout",
194 | "output_type": "stream",
195 | "text": [
196 | "Train on 1000 samples\n",
197 | "Epoch 1/15\n",
198 | "1000/1000 [==============================] - 1s 509us/sample - loss: 0.6976 - accuracy: 0.5090\n",
199 | "Epoch 2/15\n",
200 | "1000/1000 [==============================] - 0s 46us/sample - loss: 0.6930 - accuracy: 0.5840\n",
201 | "Epoch 3/15\n",
202 | "1000/1000 [==============================] - 0s 51us/sample - loss: 0.6801 - accuracy: 0.5560\n",
203 | "Epoch 4/15\n",
204 | "1000/1000 [==============================] - 0s 44us/sample - loss: 0.6514 - accuracy: 0.6430\n",
205 | "Epoch 5/15\n",
206 | "1000/1000 [==============================] - 0s 53us/sample - loss: 0.5879 - accuracy: 0.7770\n",
207 | "Epoch 6/15\n",
208 | "1000/1000 [==============================] - 0s 45us/sample - loss: 0.5060 - accuracy: 0.8420\n",
209 | "Epoch 7/15\n",
210 | "1000/1000 [==============================] - 0s 47us/sample - loss: 0.4281 - accuracy: 0.8580\n",
211 | "Epoch 8/15\n",
212 | "1000/1000 [==============================] - 0s 50us/sample - loss: 0.3569 - accuracy: 0.8730\n",
213 | "Epoch 9/15\n",
214 | "1000/1000 [==============================] - 0s 50us/sample - loss: 0.2809 - accuracy: 0.9160\n",
215 | "Epoch 10/15\n",
216 | "1000/1000 [==============================] - 0s 76us/sample - loss: 0.2122 - accuracy: 0.9830\n",
217 | "Epoch 11/15\n",
218 | "1000/1000 [==============================] - 0s 68us/sample - loss: 0.1623 - accuracy: 0.9980\n",
219 | "Epoch 12/15\n",
220 | "1000/1000 [==============================] - 0s 54us/sample - loss: 0.1290 - accuracy: 0.9990\n",
221 | "Epoch 13/15\n",
222 | "1000/1000 [==============================] - 0s 67us/sample - loss: 0.1062 - accuracy: 0.9990\n",
223 | "Epoch 14/15\n",
224 | "1000/1000 [==============================] - 0s 55us/sample - loss: 0.0902 - accuracy: 1.0000\n",
225 | "Epoch 15/15\n",
226 | "1000/1000 [==============================] - 0s 57us/sample - loss: 0.0783 - accuracy: 1.0000\n"
227 | ]
228 | },
229 | {
230 | "data": {
231 | "text/plain": [
232 | ""
233 | ]
234 | },
235 | "execution_count": 9,
236 | "metadata": {},
237 | "output_type": "execute_result"
238 | }
239 | ],
240 | "source": [
241 | "model.fit(X, y, epochs=15)"
242 | ]
243 | },
244 | {
245 | "cell_type": "code",
246 | "execution_count": 10,
247 | "metadata": {},
248 | "outputs": [],
249 | "source": [
250 | "hticks = np.linspace(-1.5, 1.5, 101)\n",
251 | "vticks = np.linspace(-1.5, 1.5, 101)\n",
252 | "aa, bb = np.meshgrid(hticks, vticks)\n",
253 | "ab = np.c_[aa.ravel(), bb.ravel()]\n",
254 | "c = model.predict(ab)\n",
255 | "cc = c.reshape(aa.shape)"
256 | ]
257 | },
258 | {
259 | "cell_type": "code",
260 | "execution_count": 11,
261 | "metadata": {},
262 | "outputs": [
263 | {
264 | "data": {
265 | "image/png": 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",
266 | "text/plain": [
267 | ""
268 | ]
269 | },
270 | "metadata": {
271 | "needs_background": "light"
272 | },
273 | "output_type": "display_data"
274 | }
275 | ],
276 | "source": [
277 | "plt.figure(figsize=(5, 5))\n",
278 | "plt.contourf(aa, bb, cc, cmap='bwr', alpha=0.2)\n",
279 | "plt.plot(X[y==0, 0], X[y==0, 1], 'ob', alpha=0.5)\n",
280 | "plt.plot(X[y==1, 0], X[y==1, 1], 'xr', alpha=0.5)\n",
281 | "plt.xlim(-1.5, 1.5)\n",
282 | "plt.ylim(-1.5, 1.5)\n",
283 | "plt.legend(['0', '1'])\n",
284 | "plt.title(\"Blue circles and red crosses\");"
285 | ]
286 | },
287 | {
288 | "cell_type": "code",
289 | "execution_count": null,
290 | "metadata": {},
291 | "outputs": [],
292 | "source": []
293 | }
294 | ],
295 | "metadata": {
296 | "kernelspec": {
297 | "display_name": "Python 3",
298 | "language": "python",
299 | "name": "python3"
300 | },
301 | "language_info": {
302 | "codemirror_mode": {
303 | "name": "ipython",
304 | "version": 3
305 | },
306 | "file_extension": ".py",
307 | "mimetype": "text/x-python",
308 | "name": "python",
309 | "nbconvert_exporter": "python",
310 | "pygments_lexer": "ipython3",
311 | "version": "3.7.6"
312 | }
313 | },
314 | "nbformat": 4,
315 | "nbformat_minor": 2
316 | }
317 |
--------------------------------------------------------------------------------