189 lines
10 KiB
Plaintext
189 lines
10 KiB
Plaintext
|
{
|
||
|
|
"cells": [
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 5,
|
||
|
|
"metadata": {
|
||
|
|
"collapsed": false,
|
||
|
|
"deletable": true,
|
||
|
|
"editable": true
|
||
|
|
},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"import matplotlib\n",
|
||
|
|
"%matplotlib inline\n",
|
||
|
|
"matplotlib.rcParams['figure.figsize'] = (20, 10)\n",
|
||
|
|
"matplotlib.rcParams['font.size'] = 24\n",
|
||
|
|
"matplotlib.rcParams['lines.linewidth'] = 5\n",
|
||
|
|
"matplotlib.rcParams['lines.markersize'] = 20\n",
|
||
|
|
"\n",
|
||
|
|
"import tensorflow as tf\n",
|
||
|
|
"from tensorflow.python.client import device_lib\n",
|
||
|
|
"\n",
|
||
|
|
"import time\n",
|
||
|
|
"import numpy as np\n",
|
||
|
|
"from matplotlib import pyplot\n",
|
||
|
|
"from collections import defaultdict\n",
|
||
|
|
"from ipy_table import make_table, set_row_style, set_column_style\n",
|
||
|
|
"from jupyter_progressbar import ProgressBar\n",
|
||
|
|
"\n",
|
||
|
|
"from sklearn.preprocessing import LabelEncoder\n",
|
||
|
|
"from sklearn.model_selection import train_test_split\n",
|
||
|
|
"from keras.models import Sequential\n",
|
||
|
|
"from keras.layers import Activation\n",
|
||
|
|
"from keras.optimizers import SGD, Adam\n",
|
||
|
|
"from keras.layers import Dense, Dropout, Flatten\n",
|
||
|
|
"from keras.regularizers import l1, l2\n",
|
||
|
|
"from keras import regularizers\n",
|
||
|
|
"import numpy\n",
|
||
|
|
"import pickle\n",
|
||
|
|
"import tensorflow as tf"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 6,
|
||
|
|
"metadata": {
|
||
|
|
"collapsed": false,
|
||
|
|
"deletable": true,
|
||
|
|
"editable": true
|
||
|
|
},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/html": [
|
||
|
|
"<table border=\"1\" cellpadding=\"3\" cellspacing=\"0\" style=\"border:black; border-collapse:collapse;\"><tr><td style=\"border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;\"><b>name</b></td><td style=\"border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;\"><b>type</b></td></tr><tr><td style=\"border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;\">/device:CPU:0</td><td style=\"border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;\">CPU</td></tr><tr><td style=\"border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;\">/device:GPU:0</td><td style=\"border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;\">GPU</td></tr></table>"
|
||
|
|
],
|
||
|
|
"text/plain": [
|
||
|
|
"<ipy_table.ipy_table.IpyTable at 0x7f8d84227cc0>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 6,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"devices = device_lib.list_local_devices()\n",
|
||
|
|
"\n",
|
||
|
|
"make_table([[\"name\", \"type\"]] + [\n",
|
||
|
|
" [device.name, device.device_type]\n",
|
||
|
|
" for device in devices\n",
|
||
|
|
"])\n",
|
||
|
|
"set_row_style(0, bold=True)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 11,
|
||
|
|
"metadata": {
|
||
|
|
"collapsed": false,
|
||
|
|
"deletable": true,
|
||
|
|
"editable": true
|
||
|
|
},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"6250/6250 [==============================] - 1s 167us/step\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
|
||
|
|
"6250/6250 [==============================] - 0s 60us/step\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"results = defaultdict(dict)\n",
|
||
|
|
"\n",
|
||
|
|
"for device in devices:\n",
|
||
|
|
" with tf.device(device.name):\n",
|
||
|
|
" with open(\"CatsAndDogs.pickle\", 'rb') as f:\n",
|
||
|
|
" data = pickle.load(f)\n",
|
||
|
|
"\n",
|
||
|
|
" X = data[\"dataset\"]\n",
|
||
|
|
" y = data[\"labels\"]\n",
|
||
|
|
" del data\n",
|
||
|
|
"\n",
|
||
|
|
" (X_train, X_test, y_train, y_test) = train_test_split(\n",
|
||
|
|
" X, y, test_size = 0.25, random_state = 42)\n",
|
||
|
|
"\n",
|
||
|
|
" model = Sequential()\n",
|
||
|
|
" model.add(Flatten(input_shape=X_train.shape[1:]))\n",
|
||
|
|
"\n",
|
||
|
|
" model.add(Dense(768, kernel_initializer=\"uniform\", activation=\"relu\"))\n",
|
||
|
|
" model.add(Dropout(0.2))\n",
|
||
|
|
"\n",
|
||
|
|
" model.add(Dense(384, kernel_initializer=\"uniform\", activation=\"relu\"))\n",
|
||
|
|
" model.add(Dropout(0.2))\n",
|
||
|
|
"\n",
|
||
|
|
" model.add(Dense(128, kernel_initializer=\"uniform\", activation=\"relu\"))\n",
|
||
|
|
" model.add(Dropout(0.2))\n",
|
||
|
|
"\n",
|
||
|
|
" model.add(Dense(2, activation=\"softmax\"))\n",
|
||
|
|
"\n",
|
||
|
|
" model.compile(loss=\"binary_crossentropy\", optimizer=Adam(lr=0.001), metrics=[\"accuracy\"])\n",
|
||
|
|
" \n",
|
||
|
|
" t0 = time.time()\n",
|
||
|
|
" model.fit(X_train, y_train, validation_split=0.25, epochs=5, batch_size=128, verbose=0)\n",
|
||
|
|
" t1 = time.time()\n",
|
||
|
|
" (loss, accuracy) = model.evaluate(X_test, y_test, verbose=0)\n",
|
||
|
|
" t2 = time.time()\n",
|
||
|
|
" \n",
|
||
|
|
" results[device.name]['training'] = t1 - t0\n",
|
||
|
|
" results[device.name]['evaluating'] = t2 - t1"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 12,
|
||
|
|
"metadata": {
|
||
|
|
"collapsed": false
|
||
|
|
},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/html": [
|
||
|
|
"<table border=\"1\" cellpadding=\"3\" cellspacing=\"0\" style=\"border:black; border-collapse:collapse;\"><tr><td style=\"border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;text-align:right;\"><b>device</b></td><td style=\"border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;text-align:right;\"><b>execution time (s) training</b></td><td style=\"border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;text-align:right;\"><b>execution time (s) evaluating</b></td></tr><tr><td style=\"border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;text-align:right;\"><b>/device:GPU:0</b></td><td style=\"border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;\">8.3381</td><td style=\"border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;\">0.3781</td></tr><tr><td style=\"border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;text-align:right;\"><b>/device:CPU:0</b></td><td style=\"border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;\">38.5678</td><td style=\"border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;\">1.0451</td></tr></table>"
|
||
|
|
],
|
||
|
|
"text/plain": [
|
||
|
|
"<ipy_table.ipy_table.IpyTable at 0x7f8d7faa7b00>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 12,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"make_table([[\"device\", \"execution time (s) training\", \"execution time (s) evaluating\"]] + [\n",
|
||
|
|
" [device, result['training'], result['evaluating']]\n",
|
||
|
|
" for device, result in results.items()\n",
|
||
|
|
"])\n",
|
||
|
|
"set_column_style(0, bold=True, align='right')\n",
|
||
|
|
"set_row_style(0, bold=True, align='right')"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"metadata": {
|
||
|
|
"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": "3.5.2"
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"nbformat": 4,
|
||
|
|
"nbformat_minor": 2
|
||
|
|
}
|