[DL] accuracy

04 Dec 2020

머신러닝

TF2(relu,he’s initialization , dropout)(mnist 예제)

TF2를 활용해 DNN을 구현한다. 여기서 주목해야 할 점은 activation function으로 relu를, 초기값 설정을 위해 he’s initialization 방법을, overfitting을 막기 위해 dropout을 사용하는것에 초점을 둔다.

Library 및 Training_data

library

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import tensorflow as tf

from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Dropout
from tensorflow.keras.optimizers import Adam

from sklearn.preprocessing import MinMaxScaler     
from sklearn.model_selection import train_test_split
from sklearn.metrics import classification_report

DataSet

df = pd.read_csv('./data/mnist/train.csv')

## Data Split
x_data_train, x_data_test, t_data_train, t_data_test = \
train_test_split(df.drop('label', axis=1, inplace=False), df['label'], test_size=0.3, random_state=0)
## Min-Max Normalization
scaler = MinMaxScaler()  
scaler.fit(x_data_train)
x_data_train_norm = scaler.transform(x_data_train)
x_data_test_norm = scaler.transform(x_data_test)

del x_data_train
del x_data_test

모델 생성 및 학습

model 생성 및 compile

keras_model = Sequential()
keras_model.add(Dense(256, activation='relu', kernel_initializer='he_uniform' input_shape=(x_data_train_norm ,)))
keras_model.add(dropout(0.3))
keras_model.add(Dense(128, activation='relu', kernel_initializer='he_uniform'))
keras_model.add(dropout(0.3))
keras_model.add(Dense(10, activation='softmax', kernel_initializer='he_uniform'))

keras_model.compile(optimizer = Adam(learninig_rate=1e-2),
                    loss = 'sparse_categorical_crossentropy',
                    accuracy = ['sparse_categorical_accuracy'])

학습

history =keras_model.fit(x_data_train_norm, t_data_train, epochs=100, batch_size=128, validataion_split=0.3 ,verbose=0 )

결과

result = tf.argmax(keras_model.predict(x_data_test_norm), axis=1)
print(classification_report(t_data_test, result))
##               precision    recall  f1-score   support
## 
##            0       0.98      0.97      0.97      1242
##            1       0.98      0.98      0.98      1429
##            2       0.97      0.97      0.97      1276
##            3       0.98      0.96      0.97      1298
##            4       0.97      0.97      0.97      1236
##            5       0.97      0.96      0.97      1119
##            6       0.97      0.98      0.98      1243
##            7       0.97      0.97      0.97      1334
##            8       0.92      0.95      0.93      1204
##            9       0.96      0.95      0.95      1219
## 
##     accuracy                           0.97     12600
##    macro avg       0.97      0.97      0.97     12600
## weighted avg       0.97      0.97      0.97     12600

그래프로 accuracy 확인

plt.plot(history.history['sparse_categorical_accuracy'])
plt.plot(history.history['val_sparse_categorical_accuracy'])
plt.show()