구글 코랩 시작하기
본 포스팅은 제가 대학원 수업 조교를 하며 colab사용법과 딥러닝과 Keras 툴킷의 기본을 설명한 포스팅입니다. 추가적으로 음성파일에 대한 mfcc추출 등도 내용에 있습니다.
사양확인
!cat /proc/cpuinfo
processor : 0
vendor_id : GenuineIntel
cpu family : 6
model : 63
model name : Intel(R) Xeon(R) CPU @ 2.30GHz
stepping : 0
microcode : 0x1
cpu MHz : 2300.000
cache size : 46080 KB
physical id : 0
siblings : 2
core id : 0
cpu cores : 1
apicid : 0
initial apicid : 0
fpu : yes
fpu_exception : yes
cpuid level : 13
wp : yes
flags : fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ss ht syscall nx pdpe1gb rdtscp lm constant_tsc rep_good nopl xtopology nonstop_tsc cpuid pni pclmulqdq ssse3 fma cx16 sse4_1 sse4_2 x2apic movbe popcnt aes xsave avx f16c rdrand hypervisor lahf_lm abm pti ssbd ibrs ibpb stibp fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms xsaveopt arat arch_capabilities
bugs : cpu_meltdown spectre_v1 spectre_v2 spec_store_bypass l1tf
bogomips : 4600.00
clflush size : 64
cache_alignment : 64
address sizes : 46 bits physical, 48 bits virtual
power management:
processor : 1
vendor_id : GenuineIntel
cpu family : 6
model : 63
model name : Intel(R) Xeon(R) CPU @ 2.30GHz
stepping : 0
microcode : 0x1
cpu MHz : 2300.000
cache size : 46080 KB
physical id : 0
siblings : 2
core id : 0
cpu cores : 1
apicid : 1
initial apicid : 1
fpu : yes
fpu_exception : yes
cpuid level : 13
wp : yes
flags : fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ss ht syscall nx pdpe1gb rdtscp lm constant_tsc rep_good nopl xtopology nonstop_tsc cpuid pni pclmulqdq ssse3 fma cx16 sse4_1 sse4_2 x2apic movbe popcnt aes xsave avx f16c rdrand hypervisor lahf_lm abm pti ssbd ibrs ibpb stibp fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms xsaveopt arat arch_capabilities
bugs : cpu_meltdown spectre_v1 spectre_v2 spec_store_bypass l1tf
bogomips : 4600.00
clflush size : 64
cache_alignment : 64
address sizes : 46 bits physical, 48 bits virtual
power management:
!cat /proc/meminfo
MemTotal: 13335212 kB
MemFree: 8513476 kB
MemAvailable: 11822980 kB
Buffers: 127904 kB
Cached: 3096336 kB
SwapCached: 0 kB
Active: 1325920 kB
Inactive: 3094080 kB
Active(anon): 945144 kB
Inactive(anon): 5448 kB
Active(file): 380776 kB
Inactive(file): 3088632 kB
Unevictable: 0 kB
Mlocked: 0 kB
SwapTotal: 0 kB
SwapFree: 0 kB
Dirty: 1028 kB
Writeback: 0 kB
AnonPages: 1195632 kB
Mapped: 514604 kB
Shmem: 5968 kB
Slab: 203064 kB
SReclaimable: 163508 kB
SUnreclaim: 39556 kB
KernelStack: 3872 kB
PageTables: 8772 kB
NFS_Unstable: 0 kB
Bounce: 0 kB
WritebackTmp: 0 kB
CommitLimit: 6667604 kB
Committed_AS: 2970144 kB
VmallocTotal: 34359738367 kB
VmallocUsed: 0 kB
VmallocChunk: 0 kB
AnonHugePages: 0 kB
ShmemHugePages: 0 kB
ShmemPmdMapped: 0 kB
HugePages_Total: 0
HugePages_Free: 0
HugePages_Rsvd: 0
HugePages_Surp: 0
Hugepagesize: 2048 kB
DirectMap4k: 167924 kB
DirectMap2M: 6123520 kB
DirectMap1G: 9437184 kB
!nvidia-smi
Wed Nov 28 06:03:48 2018
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 396.44 Driver Version: 396.44 |
|-------------------------------+----------------------+----------------------+
| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |
| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |
|===============================+======================+======================|
| 0 Tesla K80 Off | 00000000:00:04.0 Off | 0 |
| N/A 36C P0 69W / 149W | 202MiB / 11441MiB | 0% Default |
+-------------------------------+----------------------+----------------------+
+-----------------------------------------------------------------------------+
| Processes: GPU Memory |
| GPU PID Type Process name Usage |
|=============================================================================|
+-----------------------------------------------------------------------------+
Library 설치
- numpy : Numpy는 파이썬이 계산과학분야에 이용될때 핵심 역할을 하는 라이브러리입니다. Numpy는 고성능의 다차원 배열 객체와 이를 다룰 도구를 제공합니다.
- Tensorflow : Google이 개발/배포하는 딥러닝용 라이브러리
- Keras : Tensorflow를 추상화하여 더욱 쉽게 사용할 수 있도록 하는 라이브러리
- librosa : Audio와 관련된 다양한 feature extraction, Visualization 등을 제공
!pip install numpy keras librosa matplotlib
Requirement already satisfied: numpy in /usr/local/lib/python2.7/dist-packages (1.14.6)
Requirement already satisfied: keras in /usr/local/lib/python2.7/dist-packages (2.2.4)
Requirement already satisfied: librosa in /usr/local/lib/python2.7/dist-packages (0.6.2)
Requirement already satisfied: matplotlib in /usr/local/lib/python2.7/dist-packages (2.1.2)
Requirement already satisfied: keras-applications>=1.0.6 in /usr/local/lib/python2.7/dist-packages (from keras) (1.0.6)
Requirement already satisfied: scipy>=0.14 in /usr/local/lib/python2.7/dist-packages (from keras) (1.1.0)
Requirement already satisfied: keras-preprocessing>=1.0.5 in /usr/local/lib/python2.7/dist-packages (from keras) (1.0.5)
Requirement already satisfied: h5py in /usr/local/lib/python2.7/dist-packages (from keras) (2.8.0)
Requirement already satisfied: pyyaml in /usr/local/lib/python2.7/dist-packages (from keras) (3.13)
Requirement already satisfied: six>=1.9.0 in /usr/local/lib/python2.7/dist-packages (from keras) (1.11.0)
Requirement already satisfied: decorator>=3.0.0 in /usr/local/lib/python2.7/dist-packages (from librosa) (4.3.0)
Requirement already satisfied: joblib>=0.12 in /usr/local/lib/python2.7/dist-packages (from librosa) (0.13.0)
Requirement already satisfied: audioread>=2.0.0 in /usr/local/lib/python2.7/dist-packages (from librosa) (2.1.6)
Requirement already satisfied: numba>=0.38.0 in /usr/local/lib/python2.7/dist-packages (from librosa) (0.40.1)
Requirement already satisfied: scikit-learn!=0.19.0,>=0.14.0 in /usr/local/lib/python2.7/dist-packages (from librosa) (0.19.2)
Requirement already satisfied: resampy>=0.2.0 in /usr/local/lib/python2.7/dist-packages (from librosa) (0.2.1)
Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python2.7/dist-packages (from matplotlib) (0.10.0)
Requirement already satisfied: backports.functools-lru-cache in /usr/local/lib/python2.7/dist-packages (from matplotlib) (1.5)
Requirement already satisfied: subprocess32 in /usr/local/lib/python2.7/dist-packages (from matplotlib) (3.5.3)
Requirement already satisfied: pytz in /usr/local/lib/python2.7/dist-packages (from matplotlib) (2018.7)
Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python2.7/dist-packages (from matplotlib) (2.5.3)
Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python2.7/dist-packages (from matplotlib) (2.3.0)
Requirement already satisfied: funcsigs in /usr/local/lib/python2.7/dist-packages (from numba>=0.38.0->librosa) (1.0.2)
Requirement already satisfied: enum34 in /usr/local/lib/python2.7/dist-packages (from numba>=0.38.0->librosa) (1.1.6)
Requirement already satisfied: llvmlite>=0.25.0dev0 in /usr/local/lib/python2.7/dist-packages (from numba>=0.38.0->librosa) (0.25.0)
Requirement already satisfied: singledispatch in /usr/local/lib/python2.7/dist-packages (from numba>=0.38.0->librosa) (3.4.0.3)
import os
디렉토리 내용 확인
os.listdir('./')
['.config', 'sample_data', 'drive']
Google drive 마운트
인증을 통해서 google drive마운트하기
from google.colab import drive
drive.mount('/content/drive')
Mounted at /content/drive
os.listdir('/content/drive/My Drive/Colab Notebooks')
['Untitled1.ipynb',
'Untitled0.ipynb',
'Untitled2.ipynb',
'Untitled3.ipynb',
'category',
'Untitled',
'Untitled4.ipynb',
'\xe1\x84\x89\xe1\x85\xb5\xe1\x86\xaf\xe1\x84\x89\xe1\x85\xb3\xe1\x86\xb8\xe1\x84\x89\xe1\x85\xb5\xe1\x86\xaf\xe1\x84\x89\xe1\x85\xb3\xe1\x86\xb8.ipynb']
import librosa
import numpy as np
import librosa.display
from matplotlib import pyplot as plt
%matplotlib inline
librosa example audio를 이용한 data visualization
!apt-get install -y -qq ffmpeg
y, sr = librosa.load(librosa.util.example_audio_file(), duration=10)
y:np.ndarray [shape=(n,) or (2, n)]
audio time series
sr:number > 0 [scalar]
sampling rate of y
Sampling rate 확인
sr
22050
Audio time series와 dimension 확인
print(y,y.shape)
(array([ 0. , 0. , 0. , ..., -0.07654281, -0.0804726 , -0.09472633], dtype=float32), (220500,))
plt.figure(figsize=(15,7))
librosa.display.waveplot(y,sr=sr)
<matplotlib.collections.PolyCollection at 0x7f342b54c490>
plt.figure(figsize=(15,7))
librosa.display.waveplot(y,sr=sr)
plt.title('Raw waveform')
plt.show()
import IPython.display as ipd
ipd.Audio(y,rate=sr)
Sample 파일 MFCC 추출
reference : https://librosa.github.io/librosa/generated/librosa.feature.mfcc.html#librosa-feature-mfcc
mfcc = librosa.feature.mfcc(y=y, sr=sr)
mfcc
array([[-585.74258221, -536.98026502, -420.33729707, ..., -312.36725669, -294.1104376 , -296.64193221],
[ 0. , 60.42567623, 147.08731492, ..., 100.49875748, 98.6326375 , 129.67881767],
[ 0. , 41.15048816, 35.22410725, ..., 67.2011451 , 74.83373746, 78.34393586],
...,
[ 0. , -9.40651646, -5.60343754, ..., 6.0100009 , 5.52348393, 2.21866423],
[ 0. , -7.86510647, -5.9375782 , ..., 3.39014456, 4.68482683, 8.28399921],
[ 0. , -4.03834951, -4.45187158, ..., -5.21105313, -6.87013638, -5.5506121 ]])
mfcc의 array의 dimension 확인
-기본적으로 20차 MFCC를 추출하도록 되어있음.
mfcc.shape
(20, 431)
13차 mfcc 추출
- 20ms window, 10ms stride
mfcc = librosa.feature.mfcc(y=y, sr=sr,n_mfcc=13,
n_fft=int(0.02*sr),hop_length=int(0.01*sr))
mfcc.shape
(13, 1003)
np.transpose(mfcc).shape
(1003, 13)
np.transpose(mfcc)[5]
array([-616.80164198, 112.90440116, 36.59436053, 5.24560461, 8.94744557, 20.13686159, 32.53575185,
29.90577324, 11.01739721, 2.37518004, 7.64154294, 6.40403061, -1.77340686])
plt.figure(figsize=(10, 4))
librosa.display.specshow(mfcc, x_axis='frames')
plt.colorbar()
plt.title('13dim-MFCC')
plt.tight_layout()
plt.figure(figsize=(10, 4))
delta=librosa.feature.delta(mfcc)
librosa.display.specshow(delta, x_axis='frames')
plt.colorbar()
plt.title('13dim-$\Delta$MFCC')
plt.tight_layout()
plt.figure(figsize=(10, 4))
delta2=librosa.feature.delta(mfcc,order=2)
librosa.display.specshow(delta2, x_axis='frames')
plt.colorbar()
plt.title('13dim-$\Delta^2$MFCC')
plt.tight_layout()
con_mfcc=np.concatenate((mfcc,delta,delta2),axis=0)
con_mfcc.shape
(39, 1003)
plt.figure(figsize=(10, 4))
librosa.display.specshow(con_mfcc, x_axis='frames')
plt.colorbar()
plt.title('mfcc+delta+deltadelta')
plt.tight_layout()
MNIST Classification
from keras.layers import Input, Dense
from keras.models import Model
from keras.datasets import mnist #MNIST dataset load
from keras.utils import np_utils
from keras.datasets import mnist
from keras.models import Sequential
from keras.layers import Dense, Activation
(x_train,y_train),(x_test,y_test) = mnist.load_data()
print x_train.shape, x_test.shape , y_train.shape, y_test.shape
(60000, 28, 28) (10000, 28, 28) (60000,) (10000,)
print x_train[0].shape
(28, 28)
np.set_printoptions(linewidth=4*29)
print x_train[0]
[[ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
[ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
[ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
[ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
[ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
[ 0 0 0 0 0 0 0 0 0 0 0 0 3 18 18 18 126 136 175 26 166 255 247 127 0 0 0 0]
[ 0 0 0 0 0 0 0 0 30 36 94 154 170 253 253 253 253 253 225 172 253 242 195 64 0 0 0 0]
[ 0 0 0 0 0 0 0 49 238 253 253 253 253 253 253 253 253 251 93 82 82 56 39 0 0 0 0 0]
[ 0 0 0 0 0 0 0 18 219 253 253 253 253 253 198 182 247 241 0 0 0 0 0 0 0 0 0 0]
[ 0 0 0 0 0 0 0 0 80 156 107 253 253 205 11 0 43 154 0 0 0 0 0 0 0 0 0 0]
[ 0 0 0 0 0 0 0 0 0 14 1 154 253 90 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
[ 0 0 0 0 0 0 0 0 0 0 0 139 253 190 2 0 0 0 0 0 0 0 0 0 0 0 0 0]
[ 0 0 0 0 0 0 0 0 0 0 0 11 190 253 70 0 0 0 0 0 0 0 0 0 0 0 0 0]
[ 0 0 0 0 0 0 0 0 0 0 0 0 35 241 225 160 108 1 0 0 0 0 0 0 0 0 0 0]
[ 0 0 0 0 0 0 0 0 0 0 0 0 0 81 240 253 253 119 25 0 0 0 0 0 0 0 0 0]
[ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 45 186 253 253 150 27 0 0 0 0 0 0 0 0]
[ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 16 93 252 253 187 0 0 0 0 0 0 0 0]
[ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 249 253 249 64 0 0 0 0 0 0 0]
[ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 46 130 183 253 253 207 2 0 0 0 0 0 0 0]
[ 0 0 0 0 0 0 0 0 0 0 0 0 39 148 229 253 253 253 250 182 0 0 0 0 0 0 0 0]
[ 0 0 0 0 0 0 0 0 0 0 24 114 221 253 253 253 253 201 78 0 0 0 0 0 0 0 0 0]
[ 0 0 0 0 0 0 0 0 23 66 213 253 253 253 253 198 81 2 0 0 0 0 0 0 0 0 0 0]
[ 0 0 0 0 0 0 18 171 219 253 253 253 253 195 80 9 0 0 0 0 0 0 0 0 0 0 0 0]
[ 0 0 0 0 55 172 226 253 253 253 253 244 133 11 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
[ 0 0 0 0 136 253 253 253 212 135 132 16 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
[ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
[ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
[ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]]
plt.imshow(x_train[0]/255.0)
plt.gray()
plt.axis('off')
plt.show()
y_train[0]
5
#DATA normalize and reshape
x_train = x_train.reshape(60000,28*28).astype('float32') / 255.0
x_test = x_test.reshape(10000,28*28).astype('float32') / 255.0
x_train[0]
array([0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0.01176471,
0.07058824, 0.07058824, 0.07058824, 0.49411765, 0.53333336, 0.6862745 , 0.10196079, 0.6509804 , 1. ,
0.96862745, 0.49803922, 0. , 0. , 0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. , 0.11764706, 0.14117648, 0.36862746, 0.6039216 ,
0.6666667 , 0.99215686, 0.99215686, 0.99215686, 0.99215686, 0.99215686, 0.88235295, 0.6745098 , 0.99215686,
0.9490196 , 0.7647059 , 0.2509804 , 0. , 0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. , 0.19215687, 0.93333334, 0.99215686, 0.99215686,
0.99215686, 0.99215686, 0.99215686, 0.99215686, 0.99215686, 0.99215686, 0.9843137 , 0.3647059 , 0.32156864,
0.32156864, 0.21960784, 0.15294118, 0. , 0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. , 0. , 0.07058824, 0.85882354, 0.99215686,
0.99215686, 0.99215686, 0.99215686, 0.99215686, 0.7764706 , 0.7137255 , 0.96862745, 0.94509804, 0. ,
0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0.3137255 ,
0.6117647 , 0.41960785, 0.99215686, 0.99215686, 0.8039216 , 0.04313726, 0. , 0.16862746, 0.6039216 ,
0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. ,
0. , 0.05490196, 0.00392157, 0.6039216 , 0.99215686, 0.3529412 , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0.54509807, 0.99215686, 0.74509805, 0.00784314, 0. ,
0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. , 0.04313726, 0.74509805, 0.99215686, 0.27450982,
0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. , 0. , 0. , 0.13725491, 0.94509804,
0.88235295, 0.627451 , 0.42352942, 0.00392157, 0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. ,
0.31764707, 0.9411765 , 0.99215686, 0.99215686, 0.46666667, 0.09803922, 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0.1764706 , 0.7294118 , 0.99215686, 0.99215686, 0.5882353 , 0.10588235, 0. ,
0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0.0627451 , 0.3647059 , 0.9882353 , 0.99215686, 0.73333335,
0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. , 0. , 0. , 0.9764706 , 0.99215686,
0.9764706 , 0.2509804 , 0. , 0. , 0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. , 0.18039216, 0.50980395, 0.7176471 , 0.99215686,
0.99215686, 0.8117647 , 0.00784314, 0. , 0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0.15294118, 0.5803922 , 0.8980392 , 0.99215686, 0.99215686,
0.99215686, 0.98039216, 0.7137255 , 0. , 0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0.09411765, 0.44705883, 0.8666667 , 0.99215686, 0.99215686, 0.99215686,
0.99215686, 0.7882353 , 0.30588236, 0. , 0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0.09019608, 0.25882354, 0.8352941 , 0.99215686, 0.99215686, 0.99215686, 0.99215686,
0.7764706 , 0.31764707, 0.00784314, 0. , 0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. ,
0. , 0.07058824, 0.67058825, 0.85882354, 0.99215686, 0.99215686, 0.99215686, 0.99215686, 0.7647059 ,
0.3137255 , 0.03529412, 0. , 0. , 0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. ,
0.21568628, 0.6745098 , 0.8862745 , 0.99215686, 0.99215686, 0.99215686, 0.99215686, 0.95686275, 0.52156866,
0.04313726, 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. ,
0. , 0.53333336, 0.99215686, 0.99215686, 0.99215686, 0.83137256, 0.5294118 , 0.5176471 , 0.0627451 ,
0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. ,
0. ], dtype=float32)
y_train
array([5, 0, 4, ..., 5, 6, 8], dtype=uint8)
One hot encoding for training
y_train= np_utils.to_categorical(y_train)
## result
y_train
array([[0., 0., 0., ..., 0., 0., 0.],
[1., 0., 0., ..., 0., 0., 0.],
[0., 0., 0., ..., 0., 0., 0.],
...,
[0., 0., 0., ..., 0., 0., 0.],
[0., 0., 0., ..., 0., 0., 0.],
[0., 0., 0., ..., 0., 1., 0.]], dtype=float32)
y_train[0]
array([0., 0., 0., 0., 0., 1., 0., 0., 0., 0.], dtype=float32)
y_test = np_utils.to_categorical(y_test)
#Design Model for classification MNIST
input = 784
H1 = 512
H2= 512
O = 10
[출처 : https://github.com/wxs/keras-mnist-tutorial/raw/8824b7b56963a92ef879f09acd99cf3a210db2b8/figure.png ]
#input dimension은 이전 Layer의 output dimesion과 같이 해주면 됩니다.
model = Sequential()
model.add(Dense(units=512,input_dim=28*28,activation='relu'))
model.add(Dense(units=512,input_dim=512,activation='relu'))
model.add(Dense(units=10,activation='softmax'))
model.compile(loss='mean_squared_error',optimizer='sgd',metrics=['accuracy'])
hist = model.fit(x_train,y_train,epochs=10,batch_size=32)
Epoch 1/10
60000/60000 [==============================] - 10s 165us/step - loss: 0.0886 - acc: 0.2661
Epoch 2/10
60000/60000 [==============================] - 9s 156us/step - loss: 0.0828 - acc: 0.4897
Epoch 3/10
60000/60000 [==============================] - 10s 159us/step - loss: 0.0702 - acc: 0.5782
Epoch 4/10
60000/60000 [==============================] - 10s 160us/step - loss: 0.0516 - acc: 0.7253
Epoch 5/10
60000/60000 [==============================] - 10s 161us/step - loss: 0.0368 - acc: 0.8049
Epoch 6/10
60000/60000 [==============================] - 10s 160us/step - loss: 0.0280 - acc: 0.8525
Epoch 7/10
60000/60000 [==============================] - 10s 160us/step - loss: 0.0232 - acc: 0.8719
Epoch 8/10
60000/60000 [==============================] - 10s 159us/step - loss: 0.0204 - acc: 0.8830
Epoch 9/10
60000/60000 [==============================] - 10s 160us/step - loss: 0.0186 - acc: 0.8903
Epoch 10/10
60000/60000 [==============================] - 10s 160us/step - loss: 0.0173 - acc: 0.8967
model.evaluate(x_train,y_train)
60000/60000 [==============================] - 4s 61us/step
[0.016724505480378866, 0.8993333333333333]
model.evaluate(x_test,y_test)
10000/10000 [==============================] - 1s 61us/step
[0.015871783668361603, 0.9038]
from sklearn.metrics import confusion_matrix
model.predict(x_train)[0]
array([1.1833468e-02, 5.9616595e-04, 2.1906586e-02, 4.8277041e-01, 1.2570148e-04, 4.4862482e-01, 3.2751362e-03,
1.0664076e-02, 1.4754170e-02, 5.4494580e-03], dtype=float32)
model.predict_classes(x_train)
array([3, 0, 4, ..., 5, 6, 8])
model.predict_classes(x_test)
array([7, 2, 1, ..., 4, 8, 6])
y_test[0]
array([0., 0., 0., 0., 0., 0., 0., 1., 0., 0.], dtype=float32)
confusion_matrix(model.predict_classes(x_test),y_test.argmax(axis=1))
array([[ 952, 0, 13, 4, 1, 13, 15, 5, 11, 7],
[ 0, 1109, 4, 1, 5, 6, 3, 19, 4, 6],
[ 4, 5, 910, 28, 5, 9, 5, 35, 12, 9],
[ 1, 4, 12, 885, 1, 39, 0, 1, 25, 12],
[ 0, 0, 16, 0, 903, 21, 12, 10, 13, 47],
[ 8, 1, 3, 34, 0, 732, 20, 0, 22, 15],
[ 10, 4, 21, 3, 21, 22, 898, 0, 14, 1],
[ 1, 1, 16, 23, 1, 10, 1, 920, 9, 24],
[ 4, 11, 33, 23, 6, 35, 4, 3, 849, 8],
[ 0, 0, 4, 9, 39, 5, 0, 35, 15, 880]])
print plt.style.available
[u'seaborn-darkgrid', u'Solarize_Light2', u'seaborn-notebook', u'classic', u'seaborn-ticks', u'grayscale', u'bmh', u'seaborn-talk', u'dark_background', u'ggplot', u'fivethirtyeight', u'_classic_test', u'seaborn-colorblind', u'seaborn-deep', u'seaborn-whitegrid', u'seaborn-bright', u'seaborn-poster', u'seaborn-muted', u'seaborn-paper', u'seaborn-white', u'fast', u'seaborn-pastel', u'seaborn-dark', u'seaborn', u'seaborn-dark-palette']
import itertools
plt.style.use('classic')
def plot_confusion_matrix(cm, classes,
normalize=False,
title='Confusion matrix',
cmap=plt.cm.Blues):
"""
This function prints and plots the confusion matrix.
Normalization can be applied by setting `normalize=True`.
"""
if normalize:
cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]
print("Normalized confusion matrix")
else:
print('Confusion matrix, without normalization')
#print(cm)
fig = plt.figure(figsize=(15,10))
plt.imshow(cm, interpolation='nearest', cmap=cmap)
plt.title(title)
plt.colorbar()
tick_marks = np.arange(len(classes))
plt.xticks(tick_marks, classes, rotation=45)
plt.yticks(tick_marks, classes)
fmt = '.2f' if normalize else 'd'
thresh = cm.max() / 2.
for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):
plt.text(j, i, format(cm[i, j], fmt),
horizontalalignment="center",
color="white" if cm[i, j] > thresh else "black")
plt.ylabel('True label')
plt.xlabel('Predicted label')
plt.tight_layout()
a = plot_confusion_matrix(confusion_matrix(model.predict_classes(x_test),y_test.argmax(axis=1)),range(0,10),normalize=False)
Confusion matrix, without normalization
