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class Model(object): |
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from hpolib.benchmarks.ml import svm_benchmark | |
from ConfigSpace.hyperparameters import FloatHyperparameter, IntegerHyperparameter | |
from skopt import gp_minimize | |
svm_bench = svm_benchmark.SvmOnMnist() | |
bounds = svm_bench.get_meta_information()["bounds"] | |
hyperparams = svm_bench.configuration_space.get_hyperparameters() | |
new_bounds = [] | |
for i, (l_b, u_b) in enumerate(bounds): |
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from sklearn.datasets import load_boston | |
from sklearn.model_selection import train_test_split | |
from sklearn.tree import DecisionTreeRegressor | |
from sklearn.ensemble import RandomForestRegressor | |
boston = load_boston() | |
X, y = boston.data, boston.target | |
X_train, X_test, y_train, y_test = train_test_split( | |
X, y, train_size=0.6, test_size=0.4, random_state=0) | |
X_train = np.array(X_train, dtype=np.float32) |
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[array([ 1.93743793, 2.30290486, 14.52240377, 2.30262299, | |
1.95685067, 2.30586127, 1.14091716, 1.91978845, | |
2.30250415, 1.53107564, 2.30258038, 2.30215575, | |
2.3025886 , 2.26822514, 2.1281669 , 2.32953785, | |
2.18480186, 2.31662579, 2.30259784, 2.11116244, | |
2.30262142, 2.30337757, 2.06764852, 2.31965076, | |
2.3030167 , 2.0474523 , 2.12057801, 2.06735832, | |
2.30270004, 2.31166795, 2.30260678, 2.16387814, | |
2.10967536, 2.30661086, 2.11461189, 2.3029234 , | |
2.07641528, 2.08500446, 2.0747728 , 2.30185462, |
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#!/bin/bash | |
#PBS -l nodes=1:ppn=1:gpus=1 | |
#PBS -l walltime=8:00:00 | |
#PBS -l mem=50GB | |
#PBS -N name_of_script | |
#PBS -M emailid | |
#PBS -m b -m e -m a -m abe | |
#PBS -j oe | |
module purge |
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1. ssh mks542@hpc.nyu.edu | |
2. ssh -X mercer | |
To create an interactive job on the GPU: | |
# You should increase the mem and walltime argument depending on the requirement. | |
3. qsub -I -l nodes=1:ppn=1:gpus=1,mem=32GB,walltime=03:00:00 | |
4. module load librosa/0.4.1 | |
5. module avail tensorflow | |
# Choose the version you would like. | |
6. module load tensorflow/python2.7/20161207 |
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[array([-0.2792, -0.2379, -0.2677, -0.1 , -0.1 , -0.1 , -0.0927, | |
-0.2448, -0.3269, -0.1142, -0.1002, -0.6134, -0.1902, -0.1 , | |
-0.0988, -0.1 , -0.1 , -0.1 , -0.1058, -0.1 , -0.1073, | |
-0.4381, -0.1824, -0.1832, -0.1785, -0.106 , -0.2397, -0.1648, | |
-0.1088, -0.1 , -0.163 , -0.1 , -0.1531, -0.1 , -0.1 , | |
-0.1 , -0.1 , -0.1049, -0.1 , -0.1 , -0.1509, -0.144 , | |
-0.1056, -0.1207, -0.1779, -0.1 , -0.1213, -0.1 , -0.496 , | |
-0.1816])] | |
Mean optimum: -0.6134 |
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import tensorflow as tf | |
class test(object): | |
def test_func(self, pass=1): | |
to_hidden_W = tf.get_variable( | |
"to_hidden_W", | |
shape=[2, 2]) | |
with tf.variable_scope("Model", reuse=True): | |
a = test() |
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import os | |
import numpy as np | |
import scipy.io.wavfile as wav | |
def convert_mp3_to_wav(folder, sampling_freq=44.1): | |
""" | |
Converts a directory with mp3 files to wav files. |
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Image features: | |
array([1971, 1979, 1980, 1981, 1982, 1983, 1984, 1985, 1986, 1987, 1988, | |
1989, 1990, 1991, 1992, 1993, 1994, 1995, 1996, 1997, 1998, 1999, | |
2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, | |
2011, 2012, 2013, 2014]) | |
Voice traits (Continuous) | |
array([1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, | |
2009, 2010, 2011, 2012]) | |
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