https://www.kaggle.com/code/emilyjiminroh/cnn-fashion-mnist
Import 라이브러리
In [1]:
import sys
import os
In [2]:
from keras.datasets import mnist
from keras.utils import np_utils # 원-핫 인코딩. np_utils.to_categorical(클래스, 클래스의 개수)
In [3]:
from keras.models import Sequential
from keras.layers import Dense, Dropout, Flatten, Conv2D, MaxPooling2D
from keras.callbacks import ModelCheckpoint, EarlyStopping
In [4]:
linkcode
import pandas as pd
import numpy as np
import tensorflow as tf
import matplotlib.pyplot as plt
import seaborn as sns
데이터 살피기
In [5]:
train = pd.read_csv('../input/fashionmnist/fashion-mnist_train.csv')
test = pd.read_csv('../input/fashionmnist/fashion-mnist_test.csv')
In [6]:
train.head()
Out[6]:
labelpixel1pixel2pixel3pixel4pixel5pixel6pixel7pixel8pixel9...pixel775pixel776pixel777pixel778pixel779pixel780pixel781pixel782pixel783pixel78401234
| 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 9 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 6 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 5 | 0 | ... | 0 | 0 | 0 | 30 | 43 | 0 | 0 | 0 | 0 | 0 |
| 0 | 0 | 0 | 0 | 1 | 2 | 0 | 0 | 0 | 0 | ... | 3 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 |
| 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
5 rows × 785 columns
In [7]:
train.info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 60000 entries, 0 to 59999
Columns: 785 entries, label to pixel784
dtypes: int64(785)
memory usage: 359.3 MB
In [8]:
test.head()
Out[8]:
labelpixel1pixel2pixel3pixel4pixel5pixel6pixel7pixel8pixel9...pixel775pixel776pixel777pixel778pixel779pixel780pixel781pixel782pixel783pixel78401234
| 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 9 | 8 | ... | 103 | 87 | 56 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 34 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 2 | 0 | 0 | 0 | 0 | 0 | 0 | 14 | 53 | 99 | ... | 0 | 0 | 0 | 0 | 63 | 53 | 31 | 0 | 0 | 0 |
| 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 137 | 126 | 140 | 0 | 133 | 224 | 222 | 56 | 0 | 0 |
| 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
5 rows × 785 columns
In [9]:
test.info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 10000 entries, 0 to 9999
Columns: 785 entries, label to pixel784
dtypes: int64(785)
memory usage: 59.9 MB
- label과 pixel이 함께 있다. => 분리해야 함!!
Label 분리
In [10]:
X_train = train.drop(["label"],axis=1)
Y_train = train["label"]
In [11]:
X_test = test.drop(["label"],axis=1)
Y_test = test["label"]
In [12]:
X_train.values[0]
Out[12]:
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, 4, 0, 0,
0, 0, 0, 62, 61, 21, 29, 23, 51, 136, 61, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 88, 201, 228, 225, 255, 115, 62, 137, 255, 235, 222,
255, 135, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 47, 252, 234, 238, 224, 215, 215, 229, 108, 180, 207,
214, 224, 231, 249, 254, 45, 0, 0, 0, 0, 0, 0, 0,
0, 1, 0, 0, 214, 222, 210, 213, 224, 225, 217, 220, 254,
233, 219, 221, 217, 223, 221, 240, 254, 0, 0, 1, 0, 0,
0, 1, 0, 0, 0, 128, 237, 207, 224, 224, 207, 216, 214,
210, 208, 211, 221, 208, 219, 213, 226, 211, 237, 150, 0, 0,
0, 0, 0, 0, 2, 0, 0, 237, 222, 215, 207, 210, 212,
213, 206, 214, 213, 214, 213, 210, 215, 214, 206, 199, 218, 255,
13, 0, 2, 0, 0, 0, 4, 0, 85, 228, 210, 218, 200,
211, 208, 203, 215, 210, 209, 209, 210, 213, 211, 210, 217, 206,
213, 231, 175, 0, 0, 0, 0, 0, 0, 0, 217, 224, 215,
206, 205, 204, 217, 230, 222, 215, 224, 233, 228, 232, 228, 224,
207, 212, 215, 213, 229, 31, 0, 4, 0, 1, 0, 21, 225,
212, 212, 203, 211, 225, 193, 139, 136, 195, 147, 156, 139, 128,
162, 197, 223, 207, 220, 213, 232, 177, 0, 0, 0, 0, 0,
123, 226, 207, 211, 209, 205, 228, 158, 90, 103, 186, 138, 100,
121, 147, 158, 183, 226, 208, 214, 209, 216, 255, 13, 0, 1,
0, 0, 226, 219, 202, 208, 206, 205, 216, 184, 156, 150, 193,
170, 164, 168, 188, 186, 200, 219, 216, 213, 213, 211, 233, 148,
0, 0, 0, 45, 227, 204, 214, 211, 218, 222, 221, 230, 229,
221, 213, 224, 233, 226, 220, 219, 221, 224, 223, 217, 210, 218,
213, 254, 0, 0, 0, 157, 226, 203, 207, 211, 209, 215, 205,
198, 207, 208, 201, 201, 197, 203, 205, 210, 207, 213, 214, 214,
214, 213, 208, 234, 107, 0, 0, 235, 213, 204, 211, 210, 209,
213, 202, 197, 204, 215, 217, 213, 212, 210, 206, 212, 203, 211,
218, 215, 214, 208, 209, 222, 230, 0, 52, 255, 207, 200, 208,
213, 210, 210, 208, 207, 202, 201, 209, 216, 216, 216, 216, 214,
212, 205, 215, 201, 228, 208, 214, 212, 218, 25, 118, 217, 201,
206, 208, 213, 208, 205, 206, 210, 211, 202, 199, 207, 208, 209,
210, 207, 210, 210, 245, 139, 119, 255, 202, 203, 236, 114, 171,
238, 212, 203, 220, 216, 217, 209, 207, 205, 210, 211, 206, 204,
206, 209, 211, 215, 210, 206, 221, 242, 0, 224, 234, 230, 181,
26, 39, 145, 201, 255, 157, 115, 250, 200, 207, 206, 207, 213,
216, 206, 205, 206, 207, 206, 215, 207, 221, 238, 0, 0, 188,
85, 0, 0, 0, 0, 0, 31, 0, 129, 253, 190, 207, 208,
208, 208, 209, 211, 211, 209, 209, 209, 212, 201, 226, 165, 0,
0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 89, 254, 199,
199, 192, 196, 198, 199, 201, 202, 203, 204, 203, 203, 200, 222,
155, 0, 3, 3, 3, 2, 0, 0, 0, 1, 5, 0, 0,
255, 218, 226, 232, 228, 224, 222, 220, 219, 219, 217, 221, 220,
212, 236, 95, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 155, 194, 168, 170, 171, 173, 173, 179, 177, 175, 172,
171, 167, 161, 180, 0, 0, 1, 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, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0])In [13]:
Y_train
Out[13]:
0 2
1 9
2 6
3 0
4 3
..
59995 9
59996 1
59997 8
59998 8
59999 7
Name: label, Length: 60000, dtype: int64- train/test 모두 X와 Y로 분리 완료!!
X 데이터 전처리
- type 확인 -> to_numpy()
In [14]:
type(X_train)
Out[14]:
pandas.core.frame.DataFrameIn [15]:
type(X_test)
Out[15]:
pandas.core.frame.DataFrameIn [16]:
X_test = X_test.to_numpy()
In [17]:
X_train = X_train.to_numpy()
In [18]:
type(X_train)
Out[18]:
numpy.ndarrayIn [19]:
type(X_test)
Out[19]:
numpy.ndarray- reshape() 1차원으로 -> float64 -> 255로 나눠 normalize
In [20]:
X_train = X_train.reshape(X_train.shape[0], 28,28,1).astype('float64') / 255
In [21]:
X_test = X_test.reshape(X_test.shape[0], 28,28,1).astype('float64') / 255
- 정규화 확인
In [22]:
print(X_train[0])
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[[0. ]
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[0.86666667]
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[[0.00392157]
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[0.88627451]
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[0. ]
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[[0. ]
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[0.78039216]
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[1. ]
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[0.00784314]
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[[0. ]
[0.01568627]
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[0.33333333]
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[0.82352941]
[0.85098039]
[0.80784314]
[0.83529412]
[0.90588235]
[0.68627451]
[0. ]
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[[0. ]
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[0.85098039]
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[0.84313725]
[0.80784314]
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[0.8 ]
[0.85098039]
[0.90196078]
[0.87058824]
[0.84313725]
[0.87843137]
[0.91372549]
[0.89411765]
[0.90980392]
[0.89411765]
[0.87843137]
[0.81176471]
[0.83137255]
[0.84313725]
[0.83529412]
[0.89803922]
[0.12156863]
[0. ]
[0.01568627]
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[[0.00392157]
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[0.8745098 ]
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[0.8627451 ]
[0.83529412]
[0.90980392]
[0.69411765]
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[0.4745098 ]
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[1. ]
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[0.75686275]
[0.66666667]
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[0. ]
[0. ]]
[[0. ]
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[0.8 ]
[0.83921569]
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[0. ]
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[[0. ]
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[0.82745098]
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[0.81960784]
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[0.79215686]
[0.77254902]
[0.8 ]
[0.84313725]
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[0.82352941]
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[0. ]]
[[0.20392157]
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[[0.4627451 ]
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[1. ]
[0.79215686]
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[0.44705882]]
[[0.67058824]
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[0.8 ]
[0.80784314]
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[0.84313725]
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[0.87843137]
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[[0.15294118]
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[0.84313725]
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[0.93333333]
[0. ]
[0. ]
[0.7372549 ]
[0.33333333]
[0. ]
[0. ]]
[[0. ]
[0. ]
[0. ]
[0.12156863]
[0. ]
[0.50588235]
[0.99215686]
[0.74509804]
[0.81176471]
[0.81568627]
[0.81568627]
[0.81568627]
[0.81960784]
[0.82745098]
[0.82745098]
[0.81960784]
[0.81960784]
[0.81960784]
[0.83137255]
[0.78823529]
[0.88627451]
[0.64705882]
[0. ]
[0. ]
[0. ]
[0. ]
[0. ]
[0. ]]
[[0.00784314]
[0. ]
[0. ]
[0. ]
[0. ]
[0.34901961]
[0.99607843]
[0.78039216]
[0.78039216]
[0.75294118]
[0.76862745]
[0.77647059]
[0.78039216]
[0.78823529]
[0.79215686]
[0.79607843]
[0.8 ]
[0.79607843]
[0.79607843]
[0.78431373]
[0.87058824]
[0.60784314]
[0. ]
[0.01176471]
[0.01176471]
[0.01176471]
[0.00784314]
[0. ]]
[[0. ]
[0. ]
[0.00392157]
[0.01960784]
[0. ]
[0. ]
[1. ]
[0.85490196]
[0.88627451]
[0.90980392]
[0.89411765]
[0.87843137]
[0.87058824]
[0.8627451 ]
[0.85882353]
[0.85882353]
[0.85098039]
[0.86666667]
[0.8627451 ]
[0.83137255]
[0.9254902 ]
[0.37254902]
[0. ]
[0.00784314]
[0. ]
[0. ]
[0. ]
[0. ]]
[[0. ]
[0. ]
[0. ]
[0. ]
[0. ]
[0. ]
[0.60784314]
[0.76078431]
[0.65882353]
[0.66666667]
[0.67058824]
[0.67843137]
[0.67843137]
[0.70196078]
[0.69411765]
[0.68627451]
[0.6745098 ]
[0.67058824]
[0.65490196]
[0.63137255]
[0.70588235]
[0. ]
[0. ]
[0.00392157]
[0. ]
[0.00392157]
[0. ]
[0. ]]
[[0. ]
[0. ]
[0. ]
[0. ]
[0. ]
[0. ]
[0. ]
[0. ]
[0. ]
[0. ]
[0. ]
[0. ]
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[0. ]
[0. ]
[0. ]
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[[0. ]
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[0. ]]
[[0. ]
[0. ]
[0. ]
[0. ]
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[0. ]
[0. ]]]
In [23]:
print(X_test)
[[[[0. ]
[0. ]
[0. ]
...
[0. ]
[0. ]
[0. ]]
[[0. ]
[0. ]
[0.01568627]
...
[0. ]
[0. ]
[0. ]]
[[0. ]
[0. ]
[0.00392157]
...
[0. ]
[0. ]
[0. ]]
...
[[0. ]
[0. ]
[0. ]
...
[0. ]
[0. ]
[0. ]]
[[0. ]
[0. ]
[0. ]
...
[0. ]
[0. ]
[0. ]]
[[0. ]
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[0. ]
...
[0. ]
[0. ]
[0. ]]]
[[[0. ]
[0. ]
[0. ]
...
[0. ]
[0. ]
[0. ]]
[[0. ]
[0. ]
[0. ]
...
[0. ]
[0. ]
[0. ]]
[[0. ]
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[0. ]
...
[0. ]
[0. ]
[0. ]]
...
[[0. ]
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...
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[[0. ]
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...
[0. ]
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[0. ]]
[[0. ]
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...
[0. ]
[0. ]
[0. ]]]
[[[0. ]
[0. ]
[0. ]
...
[0. ]
[0. ]
[0. ]]
[[0. ]
[0. ]
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...
[0. ]
[0. ]
[0. ]]
[[0. ]
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...
[0. ]
[0. ]
[0. ]]
...
[[0. ]
[0. ]
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...
[0. ]
[0. ]
[0. ]]
[[0. ]
[0. ]
[0. ]
...
[0. ]
[0. ]
[0. ]]
[[0. ]
[0. ]
[0. ]
...
[0. ]
[0. ]
[0. ]]]
...
[[[0. ]
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...
[0. ]
[0. ]
[0. ]]
[[0. ]
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...
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[0. ]]
[[0. ]
[0. ]
[0. ]
...
[0. ]
[0. ]
[0. ]]
...
[[0. ]
[0. ]
[0.01176471]
...
[0. ]
[0. ]
[0. ]]
[[0. ]
[0. ]
[0.00784314]
...
[0. ]
[0. ]
[0. ]]
[[0. ]
[0. ]
[0. ]
...
[0. ]
[0.00392157]
[0. ]]]
[[[0. ]
[0.00392157]
[0.01176471]
...
[0. ]
[0.00784314]
[0. ]]
[[0. ]
[0. ]
[0. ]
...
[0. ]
[0. ]
[0. ]]
[[0. ]
[0. ]
[0.45490196]
...
[0.78431373]
[0.11372549]
[0. ]]
...
[[0. ]
[0. ]
[0.01568627]
...
[0. ]
[0.01568627]
[0. ]]
[[0. ]
[0. ]
[0. ]
...
[0.00784314]
[0. ]
[0. ]]
[[0. ]
[0. ]
[0. ]
...
[0. ]
[0. ]
[0. ]]]
[[[0. ]
[0. ]
[0. ]
...
[0. ]
[0. ]
[0. ]]
[[0. ]
[0. ]
[0. ]
...
[0. ]
[0. ]
[0. ]]
[[0. ]
[0. ]
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...
[0. ]
[0. ]
[0. ]]
...
[[0. ]
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...
[0. ]
[0. ]
[0. ]]
[[0. ]
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...
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[0. ]]
[[0. ]
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[0. ]
...
[0. ]
[0. ]
[0. ]]]]
Y 데이터 바이너리화 (원-핫 인코딩)
- class 확인하기
<총 10개의 class> 0: T-shirt/top\ 1: Trouser\ 2: Pullover\ 3: Dress\ 4: Coat\ 5: Sandal\ 6: Shirt\ 7: Sneaker\ 8: Bag\ 9: Ankle boot
In [24]:
class_names = ['T_shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat',
'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle boot']
plt.figure(figsize=(20, 20))
for k in range(10):
XX = X_train[Y_train == k]
YY = Y_train[Y_train == k].reset_index()['label']
for i in range(10):
plt.subplot(10, 10, k*10 + i + 1)
plt.xticks([])
plt.yticks([])
plt.grid(False)
plt.imshow(XX[i][:,:,0], cmap='gray')
label_index = int(YY[i])
plt.title('{}. {}'.format(k, class_names[label_index]))
plt.show()
In [25]:
Y_train = np_utils.to_categorical(Y_train, 10)
Y_test = np_utils.to_categorical(Y_test, 10)
In [26]:
print(Y_train[0])
[0. 0. 1. 0. 0. 0. 0. 0. 0. 0.]
In [27]:
print(Y_test)
[[1. 0. 0. ... 0. 0. 0.]
[0. 1. 0. ... 0. 0. 0.]
[0. 0. 1. ... 0. 0. 0.]
...
[0. 0. 0. ... 0. 1. 0.]
[0. 0. 0. ... 0. 1. 0.]
[0. 1. 0. ... 0. 0. 0.]]
CNN 모델
In [28]:
# 컨볼루션 신경망 설정
model = Sequential()
model.add(Conv2D(32, kernel_size=(3, 3), input_shape = (28, 28, 1), activation='relu')) # 다름
model.add(Conv2D(64, (3, 3), activation='relu'))
model.add(MaxPooling2D(pool_size=2))
model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(128, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(10, activation='softmax'))
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
2022-02-15 08:09:48.668516: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2022-02-15 08:09:48.744371: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2022-02-15 08:09:48.745080: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2022-02-15 08:09:48.746180: I tensorflow/core/platform/cpu_feature_guard.cc:142] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX2 AVX512F FMA
To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
2022-02-15 08:09:48.747363: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2022-02-15 08:09:48.748012: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2022-02-15 08:09:48.748691: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2022-02-15 08:09:50.315065: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2022-02-15 08:09:50.315921: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2022-02-15 08:09:50.316562: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2022-02-15 08:09:50.317133: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1510] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 15403 MB memory: -> device: 0, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:04.0, compute capability: 6.0
In [29]:
model.summary()
Model: "sequential"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
conv2d (Conv2D) (None, 26, 26, 32) 320
_________________________________________________________________
conv2d_1 (Conv2D) (None, 24, 24, 64) 18496
_________________________________________________________________
max_pooling2d (MaxPooling2D) (None, 12, 12, 64) 0
_________________________________________________________________
dropout (Dropout) (None, 12, 12, 64) 0
_________________________________________________________________
flatten (Flatten) (None, 9216) 0
_________________________________________________________________
dense (Dense) (None, 128) 1179776
_________________________________________________________________
dropout_1 (Dropout) (None, 128) 0
_________________________________________________________________
dense_1 (Dense) (None, 10) 1290
=================================================================
Total params: 1,199,882
Trainable params: 1,199,882
Non-trainable params: 0
_________________________________________________________________
- 모델 최적화
In [30]:
# 모델 최적화 설정
MODEL_DIR = './model/'
if not os.path.exists(MODEL_DIR):
os.mkdir(MODEL_DIR)
modelpath='./model/{epoch:02d}-{val_loss:.4f}.hdf5'
checkpointer = ModelCheckpoint(filepath=modelpath, monitor='val_loss', verbose=1, save_best_only=True)
early_stopping_callback = EarlyStopping(monitor='val_loss', patience=10)
In [31]:
# 모델의 실행
history = model.fit(X_train, Y_train, validation_data=(X_test, Y_test), epochs=30, batch_size=200, verbose=0, callbacks=[early_stopping_callback, checkpointer])
2022-02-15 08:09:51.615996: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:185] None of the MLIR Optimization Passes are enabled (registered 2)
2022-02-15 08:09:52.761190: I tensorflow/stream_executor/cuda/cuda_dnn.cc:369] Loaded cuDNN version 8005
Epoch 00001: val_loss improved from inf to 0.32456, saving model to ./model/01-0.3246.hdf5
Epoch 00002: val_loss improved from 0.32456 to 0.26909, saving model to ./model/02-0.2691.hdf5
Epoch 00003: val_loss improved from 0.26909 to 0.24979, saving model to ./model/03-0.2498.hdf5
Epoch 00004: val_loss improved from 0.24979 to 0.23318, saving model to ./model/04-0.2332.hdf5
Epoch 00005: val_loss improved from 0.23318 to 0.22127, saving model to ./model/05-0.2213.hdf5
Epoch 00006: val_loss improved from 0.22127 to 0.20935, saving model to ./model/06-0.2093.hdf5
Epoch 00007: val_loss improved from 0.20935 to 0.20668, saving model to ./model/07-0.2067.hdf5
Epoch 00008: val_loss improved from 0.20668 to 0.20279, saving model to ./model/08-0.2028.hdf5
Epoch 00009: val_loss did not improve from 0.20279
Epoch 00010: val_loss improved from 0.20279 to 0.19836, saving model to ./model/10-0.1984.hdf5
Epoch 00011: val_loss did not improve from 0.19836
Epoch 00012: val_loss improved from 0.19836 to 0.19533, saving model to ./model/12-0.1953.hdf5
Epoch 00013: val_loss did not improve from 0.19533
Epoch 00014: val_loss did not improve from 0.19533
Epoch 00015: val_loss did not improve from 0.19533
Epoch 00016: val_loss did not improve from 0.19533
Epoch 00017: val_loss did not improve from 0.19533
Epoch 00018: val_loss did not improve from 0.19533
Epoch 00019: val_loss did not improve from 0.19533
Epoch 00020: val_loss did not improve from 0.19533
Epoch 00021: val_loss did not improve from 0.19533
Epoch 00022: val_loss did not improve from 0.19533
In [32]:
# 테스트 정확도 출력
print("\n Test Accuracy: %.4f" % (model.evaluate(X_test, Y_test)[1]))
313/313 [==============================] - 1s 3ms/step - loss: 0.2232 - accuracy: 0.9375
Test Accuracy: 0.9375
- 14번째 에포크에서 최적화 모델 생성 (정확도: 93%)
오차 그래프 출력
In [33]:
# 오차 = 1 - 학습셋 정확도
# 테스트 셋의 오차
y_vloss = history.history['val_loss']
# 학습셋의 오차
y_loss = history.history['loss']
# 그래프로 표현
# numpy.arange(n) => 0~n-1를 담은 리스트 생성 (예제 아래)
x_len = np.arange(len(y_loss))
# point marker 형태로 label 명을 붙인 y_vloss, y_loss 그래프를 각각 색상 다르게 생성
plt.plot(x_len, y_vloss, marker='.', c="red", label='Testset_loss')
plt.plot(x_len, y_loss, marker='.', c="blue", label='Trainset_loss')
# 그래프에 그리드를 주고 레이블을 표시
plt.legend(loc='upper right')
# plt.axis([0, 20, 0, 0.35])
plt.grid()
plt.xlabel('epoch')
plt.ylabel('loss')
plt.show()
In [ ]:
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