1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
|
{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "aQkTGc-d8I1k"
},
"source": [
"This notebook runs a basic speed test for a simple algorithm that implements the process described in Collatz Conjecture.\n",
"\n",
"https://en.wikipedia.org/wiki/Collatz_conjecture"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "x5ChBlH09jk_"
},
"source": [
"### Imports"
]
},
{
"cell_type": "code",
"execution_count": 0,
"metadata": {
"colab": {
"autoexec": {
"startup": false,
"wait_interval": 0
}
},
"colab_type": "code",
"id": "X-QAUpWdPxUh"
},
"outputs": [],
"source": [
"!pip install -U -q tf-nightly"
]
},
{
"cell_type": "code",
"execution_count": 0,
"metadata": {
"colab": {
"autoexec": {
"startup": false,
"wait_interval": 0
}
},
"colab_type": "code",
"id": "wiKQu3w05eCa"
},
"outputs": [],
"source": [
"import numpy as np\n",
"from matplotlib import pyplot as plt\n",
"import tensorflow as tf\n",
"from tensorflow.contrib import autograph as ag\n",
"from tensorflow.python.eager import context"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "_cRFTcwT9mnn"
},
"source": [
"### Plotting helpers"
]
},
{
"cell_type": "code",
"execution_count": 0,
"metadata": {
"colab": {
"autoexec": {
"startup": false,
"wait_interval": 0
}
},
"colab_type": "code",
"id": "ww7rc0GQ9pMu"
},
"outputs": [],
"source": [
"def plot_results(counts, times, title):\n",
" plt.plot(counts, np.array(times) * 1000., 'o')\n",
" plt.ylabel('Time (milliseconds)')\n",
" plt.xlabel('Collatz counter')\n",
" plt.title(title)\n",
" plt.ylim(0, 30)\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "ESZGw9s9-Y5_"
},
"source": [
"### Collatz function definition"
]
},
{
"cell_type": "code",
"execution_count": 0,
"metadata": {
"colab": {
"autoexec": {
"startup": false,
"wait_interval": 0
}
},
"colab_type": "code",
"id": "qeunWm9m-dT7"
},
"outputs": [],
"source": [
"def collatz(a):\n",
" count = 0\n",
" while a \u003e 1.1:\n",
" if a % 2 \u003c 0.1:\n",
" a //= 2\n",
" else:\n",
" a = 3 * a + 1\n",
" count += 1\n",
" return count\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "nnFmPDvScsDo"
},
"source": [
"# AutoGraph"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"colab": {
"autoexec": {
"startup": false,
"wait_interval": 0
},
"height": 301
},
"colab_type": "code",
"executionInfo": {
"elapsed": 9153,
"status": "ok",
"timestamp": 1531757473651,
"user": {
"displayName": "",
"photoUrl": "",
"userId": ""
},
"user_tz": 240
},
"id": "6fU4vlxYcsDe",
"outputId": "11b50f28-aced-4506-a743-4b749e9645c3"
},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAYkAAAEcCAYAAAAydkhNAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
"text/plain": [
"\u003cmatplotlib.figure.Figure at 0x7fc3b259add0\u003e"
]
},
"metadata": {
"tags": []
},
"output_type": "display_data"
}
],
"source": [
"counts = []\n",
"times = []\n",
"for n in np.logspace(0, 7, 50):\n",
"\n",
" with tf.Graph().as_default():\n",
" tf_collatz = ag.to_graph(collatz)\n",
" count = tf_collatz(tf.constant(n, dtype=tf.float32))\n",
" with tf.Session() as sess:\n",
" count_value = sess.run(count)\n",
"\n",
" res = %timeit -n10 -r1 -o -q sess.run(count)\n",
" counts.append(count_value)\n",
" times.append(res.best)\n",
" \n",
"plot_results(counts, times, 'AutoGraph')"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "RRENYzLRF_f3"
},
"source": [
"# Eager"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"colab": {
"autoexec": {
"startup": false,
"wait_interval": 0
},
"height": 301
},
"colab_type": "code",
"executionInfo": {
"elapsed": 5003,
"status": "ok",
"timestamp": 1531757478713,
"user": {
"displayName": "",
"photoUrl": "",
"userId": ""
},
"user_tz": 240
},
"id": "dhDf8LLdF_f-",
"outputId": "3de0a5a5-7a11-4b41-8ab0-e4e21ce8d59b"
},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAYkAAAEcCAYAAAAydkhNAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
"text/plain": [
"\u003cmatplotlib.figure.Figure at 0x7fc3af690a50\u003e"
]
},
"metadata": {
"tags": []
},
"output_type": "display_data"
}
],
"source": [
"with context.eager_mode():\n",
"\n",
" counts = []\n",
" times = [] \n",
" for n in np.logspace(0, 7, 50):\n",
"\n",
" n_tensor = tf.constant(n, dtype=tf.float32)\n",
" count = collatz(n_tensor)\n",
"\n",
" res = %timeit -n10 -r1 -o -q collatz(n_tensor)\n",
" times.append(res.best)\n",
" counts.append(count)\n",
" \n",
"plot_results(counts, times, 'Eager')\n"
]
}
],
"metadata": {
"colab": {
"collapsed_sections": [
"x5ChBlH09jk_",
"_cRFTcwT9mnn"
],
"default_view": {},
"last_runtime": {
"build_target": "",
"kind": "local"
},
"name": "Autograph vs. Eager Collatz speed test",
"provenance": [
{
"file_id": "0B8bm7KvwJklpMUQtbnVpYkdJUjRtOTRyWVVfSEhpRl9HYm5n",
"timestamp": 1531512047714
}
],
"version": "0.3.2",
"views": {}
},
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
|
