added benchmark notebooks
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01. Matrix multiplication.ipynb
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01. Matrix multiplication.ipynb
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02. Simple Neural Network training + evaluation.ipynb
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02. Simple Neural Network training + evaluation.ipynb
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"cells": [
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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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"deletable": true,
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"editable": true
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},
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"outputs": [],
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"source": [
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"import matplotlib\n",
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"%matplotlib inline\n",
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"matplotlib.rcParams['figure.figsize'] = (20, 10)\n",
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"matplotlib.rcParams['font.size'] = 24\n",
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"matplotlib.rcParams['lines.linewidth'] = 5\n",
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"matplotlib.rcParams['lines.markersize'] = 20\n",
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"\n",
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"import tensorflow as tf\n",
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"from tensorflow.python.client import device_lib\n",
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"\n",
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"import time\n",
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"import numpy as np\n",
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"from matplotlib import pyplot\n",
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"from collections import defaultdict\n",
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"from ipy_table import make_table, set_row_style, set_column_style\n",
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"from jupyter_progressbar import ProgressBar\n",
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"\n",
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"from sklearn.preprocessing import LabelEncoder\n",
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"from sklearn.model_selection import train_test_split\n",
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"from keras.models import Sequential\n",
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"from keras.layers import Activation\n",
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"from keras.optimizers import SGD, Adam\n",
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"from keras.layers import Dense, Dropout, Flatten\n",
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"from keras.regularizers import l1, l2\n",
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"from keras import regularizers\n",
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"import numpy\n",
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"import pickle\n",
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"import tensorflow as tf"
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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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"deletable": true,
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"editable": true
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},
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"outputs": [
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{
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"data": {
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"text/html": [
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"<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>"
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],
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"text/plain": [
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"<ipy_table.ipy_table.IpyTable at 0x7f8d84227cc0>"
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]
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},
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"execution_count": 6,
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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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"devices = device_lib.list_local_devices()\n",
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"\n",
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"make_table([[\"name\", \"type\"]] + [\n",
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" [device.name, device.device_type]\n",
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" for device in devices\n",
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"])\n",
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"set_row_style(0, bold=True)"
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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": 11,
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"metadata": {
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"collapsed": false,
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"deletable": true,
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"editable": true
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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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"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",
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"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"
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]
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}
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],
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"source": [
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"results = defaultdict(dict)\n",
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"\n",
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"for device in devices:\n",
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" with tf.device(device.name):\n",
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" with open(\"CatsAndDogs.pickle\", 'rb') as f:\n",
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" data = pickle.load(f)\n",
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"\n",
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" X = data[\"dataset\"]\n",
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" y = data[\"labels\"]\n",
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" del data\n",
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"\n",
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" (X_train, X_test, y_train, y_test) = train_test_split(\n",
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" X, y, test_size = 0.25, random_state = 42)\n",
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"\n",
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" model = Sequential()\n",
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" model.add(Flatten(input_shape=X_train.shape[1:]))\n",
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"\n",
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" model.add(Dense(768, kernel_initializer=\"uniform\", activation=\"relu\"))\n",
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" model.add(Dropout(0.2))\n",
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"\n",
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" model.add(Dense(384, kernel_initializer=\"uniform\", activation=\"relu\"))\n",
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" model.add(Dropout(0.2))\n",
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"\n",
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" model.add(Dense(128, kernel_initializer=\"uniform\", activation=\"relu\"))\n",
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" model.add(Dropout(0.2))\n",
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"\n",
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" model.add(Dense(2, activation=\"softmax\"))\n",
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"\n",
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" model.compile(loss=\"binary_crossentropy\", optimizer=Adam(lr=0.001), metrics=[\"accuracy\"])\n",
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" \n",
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" t0 = time.time()\n",
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" model.fit(X_train, y_train, validation_split=0.25, epochs=5, batch_size=128, verbose=0)\n",
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" t1 = time.time()\n",
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" (loss, accuracy) = model.evaluate(X_test, y_test, verbose=0)\n",
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" t2 = time.time()\n",
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" \n",
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" results[device.name]['training'] = t1 - t0\n",
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" results[device.name]['evaluating'] = t2 - t1"
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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": 12,
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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/html": [
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"<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>"
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],
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"text/plain": [
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"<ipy_table.ipy_table.IpyTable at 0x7f8d7faa7b00>"
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]
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},
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"execution_count": 12,
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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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"make_table([[\"device\", \"execution time (s) training\", \"execution time (s) evaluating\"]] + [\n",
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" [device, result['training'], result['evaluating']]\n",
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" for device, result in results.items()\n",
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"])\n",
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"set_column_style(0, bold=True, align='right')\n",
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"set_row_style(0, bold=True, align='right')"
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]
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}
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],
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"metadata": {
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"hide_input": false,
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.5.2"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}
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03. Popular image classification models.ipynb
Normal file
256
03. Popular image classification models.ipynb
Normal file
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {
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"collapsed": false,
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"deletable": true,
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"editable": true
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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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"Using TensorFlow backend.\n"
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]
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|
}
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|
],
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|
"source": [
|
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|
"import os.path\n",
|
||||||
|
"import pickle\n",
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|
"\n",
|
||||||
|
"from sklearn.model_selection import train_test_split\n",
|
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|
"from sklearn.linear_model import LogisticRegression\n",
|
||||||
|
"from sklearn.metrics import classification_report\n",
|
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|
"\n",
|
||||||
|
"from jupyter_progressbar import ProgressBar\n",
|
||||||
|
"import numpy as np\n",
|
||||||
|
"\n",
|
||||||
|
"from keras.models import Sequential\n",
|
||||||
|
"from keras.layers import Dense, Flatten, GlobalAveragePooling2D\n",
|
||||||
|
"from keras.optimizers import SGD\n",
|
||||||
|
"from keras import backend as K\n",
|
||||||
|
"from keras.applications.inception_v3 import preprocess_input as preprocess, InceptionV3\n",
|
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|
"\n",
|
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|
"import matplotlib\n",
|
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|
"import matplotlib.pyplot as plt\n",
|
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|
"%matplotlib inline\n",
|
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|
"matplotlib.rcParams['figure.figsize'] = (20, 10)\n",
|
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|
"matplotlib.rcParams['font.size'] = 24\n",
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"matplotlib.rcParams['lines.linewidth'] = 5\n",
|
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|
"matplotlib.rcParams['lines.markersize'] = 20\n",
|
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|
"\n",
|
||||||
|
"import tensorflow as tf\n",
|
||||||
|
"from tensorflow.python.client import device_lib\n",
|
||||||
|
"\n",
|
||||||
|
"import time\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",
|
||||||
|
"# Disable progressbar VGG-batches\n",
|
||||||
|
"ProgressBar = lambda x: x"
|
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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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"deletable": true,
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"editable": true
|
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|
},
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"outputs": [
|
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|
{
|
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|
"data": {
|
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"text/html": [
|
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|
"<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 0x7fc8a18d95f8>"
|
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|
]
|
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|
},
|
||||||
|
"execution_count": 2,
|
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|
"metadata": {},
|
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|
"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)"
|
||||||
|
]
|
||||||
|
},
|
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|
{
|
||||||
|
"cell_type": "code",
|
||||||
|
"execution_count": 3,
|
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|
"metadata": {
|
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|
"collapsed": false,
|
||||||
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"deletable": true,
|
||||||
|
"editable": true
|
||||||
|
},
|
||||||
|
"outputs": [],
|
||||||
|
"source": [
|
||||||
|
"with open('creepycrawly_and_cats.p3', 'rb') as f:\n",
|
||||||
|
" X, y = pickle.load(f)\n",
|
||||||
|
"classes = ['creepy crawly', 'cat']"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "code",
|
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|
"execution_count": 4,
|
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|
"metadata": {
|
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|
"collapsed": true,
|
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|
"deletable": true,
|
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|
"editable": true
|
||||||
|
},
|
||||||
|
"outputs": [],
|
||||||
|
"source": [
|
||||||
|
"import numpy\n",
|
||||||
|
"from skimage.transform import resize\n",
|
||||||
|
"\n",
|
||||||
|
"def resize_for_model(X, model):\n",
|
||||||
|
" target = tuple([x.value for x in model.input.get_shape()][1:3])\n",
|
||||||
|
" if target[0] is None or target[1] is None:\n",
|
||||||
|
" return X\n",
|
||||||
|
" result = numpy.zeros((X.shape[0], target[0], target[1], X.shape[3]), dtype=X.dtype)\n",
|
||||||
|
" \n",
|
||||||
|
" for i in range(X.shape[0]):\n",
|
||||||
|
" result[i] = resize(X[i], target + (X.shape[3], ))\n",
|
||||||
|
" return result"
|
||||||
|
]
|
||||||
|
},
|
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|
{
|
||||||
|
"cell_type": "code",
|
||||||
|
"execution_count": 5,
|
||||||
|
"metadata": {
|
||||||
|
"collapsed": false,
|
||||||
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"deletable": true,
|
||||||
|
"editable": true
|
||||||
|
},
|
||||||
|
"outputs": [],
|
||||||
|
"source": [
|
||||||
|
"results = defaultdict(dict)\n",
|
||||||
|
"for device in devices:\n",
|
||||||
|
" from keras.applications.inception_v3 import InceptionV3, preprocess_input, decode_predictions\n",
|
||||||
|
" \n",
|
||||||
|
" with tf.device(device.name):\n",
|
||||||
|
" model = InceptionV3(weights='imagenet')\n",
|
||||||
|
" X_ = preprocess_input(resize_for_model(X, model))\n",
|
||||||
|
" t0 = time.time()\n",
|
||||||
|
" preds = model.predict(X_, verbose=0)\n",
|
||||||
|
" t1 = time.time()\n",
|
||||||
|
" \n",
|
||||||
|
" results[device.name]['inceptionv3'] = t1 - t0\n",
|
||||||
|
" K.clear_session()\n",
|
||||||
|
" \n",
|
||||||
|
" from keras.applications.resnet50 import ResNet50, preprocess_input, decode_predictions\n",
|
||||||
|
" with tf.device(device.name):\n",
|
||||||
|
" model = ResNet50(weights='imagenet')\n",
|
||||||
|
" X_ = preprocess_input(resize_for_model(X, model))\n",
|
||||||
|
" t0 = time.time()\n",
|
||||||
|
" preds = model.predict(X_, verbose=0)\n",
|
||||||
|
" t1 = time.time()\n",
|
||||||
|
" \n",
|
||||||
|
" results[device.name]['resnet50'] = t1 - t0\n",
|
||||||
|
" K.clear_session()\n",
|
||||||
|
"\n",
|
||||||
|
" vgg_batch_size = 2\n",
|
||||||
|
" \n",
|
||||||
|
" from keras.applications.vgg16 import VGG16, preprocess_input, decode_predictions\n",
|
||||||
|
" with tf.device(device.name):\n",
|
||||||
|
" model = VGG16(weights='imagenet', include_top=True)\n",
|
||||||
|
" X_ = preprocess_input(resize_for_model(X, model))\n",
|
||||||
|
" t0 = time.time()\n",
|
||||||
|
" for start, end in zip(\n",
|
||||||
|
" range(0, len(X), vgg_batch_size),\n",
|
||||||
|
" ProgressBar(range(vgg_batch_size, len(X), vgg_batch_size))\n",
|
||||||
|
" ):\n",
|
||||||
|
" model.predict(X_[start:end], verbose=0)\n",
|
||||||
|
" t1 = time.time()\n",
|
||||||
|
" \n",
|
||||||
|
" results[device.name]['vgg16'] = t1 - t0\n",
|
||||||
|
" K.clear_session()\n",
|
||||||
|
" \n",
|
||||||
|
" from keras.applications.vgg19 import VGG19, preprocess_input, decode_predictions\n",
|
||||||
|
" \n",
|
||||||
|
" with tf.device(device.name):\n",
|
||||||
|
" model = VGG19(weights='imagenet', include_top=True)\n",
|
||||||
|
" X_ = preprocess_input(resize_for_model(X, model))\n",
|
||||||
|
" t0 = time.time()\n",
|
||||||
|
" for start, end in zip(\n",
|
||||||
|
" range(0, len(X), vgg_batch_size),\n",
|
||||||
|
" ProgressBar(range(vgg_batch_size, len(X), vgg_batch_size))\n",
|
||||||
|
" ):\n",
|
||||||
|
" model.predict(X_[start:end], verbose=0)\n",
|
||||||
|
" t1 = time.time()\n",
|
||||||
|
" \n",
|
||||||
|
" results[device.name]['vgg19'] = t1 - t0\n",
|
||||||
|
" K.clear_session()"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"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;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>resnet50</b></td><td style=\"border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;text-align:right;\"><b>vgg19</b></td><td style=\"border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;text-align:right;\"><b>inceptionv3</b></td><td style=\"border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;text-align:right;\"><b>vgg16</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;\">59.2201</td><td style=\"border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;\">156.9875</td><td style=\"border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;\">15.0459</td><td style=\"border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;\">129.3934</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;\">605.1631</td><td style=\"border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;\">1857.0126</td><td style=\"border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;\">89.1597</td><td style=\"border-left: 1px solid;border-right: 1px solid;border-top: 1px solid;border-bottom: 1px solid;\">1445.7997</td></tr></table>"
|
||||||
|
],
|
||||||
|
"text/plain": [
|
||||||
|
"<ipy_table.ipy_table.IpyTable at 0x7fc88813c860>"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
"execution_count": 6,
|
||||||
|
"metadata": {},
|
||||||
|
"output_type": "execute_result"
|
||||||
|
}
|
||||||
|
],
|
||||||
|
"source": [
|
||||||
|
"model_names = list(next(iter(results.values())).keys())\n",
|
||||||
|
"\n",
|
||||||
|
"make_table([[\"device\"] + model_names] + [\n",
|
||||||
|
" [device] + [result[model_name] for model_name in model_names]\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
|
||||||
|
}
|
||||||
Loading…
Reference in New Issue
Block a user