[Data Science] Deep Learning - Dropout(TF_2.0)

Dropout - TF 2.0

Dropout 이란 overfitting을 막기위한 방법입니다. 데이터에서 어느정도의 비율을 제외하고 학습을 시키는 방법입니다. 하지만 Dropout의 경우 정해진 비율안에 랜덤으로 제외시키고 제외된 항목이 계속 변경되어 좀더 효율적이게 학습시키는 방법이라고 보시면 되겠습니다.

핵심코드(2.0 버전)는 하기와 같습니다.

keras_model.add(Dense(420, activation='relu', kernel_initializer='he_uniform'))
keras_model.add(Dropout(0.3))

MNIST_Dropout 예제

import numpy as np
import pandas as pd
import tensorflow as tf
from sklearn.preprocessing import MinMaxScaler, OneHotEncoder # Normalization
from sklearn.model_selection import train_test_split # train, test 데이터분리
from sklearn.model_selection import KFold # cross validation
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Flatten, Dense, Dropout
from tensorflow.keras.optimizers import SGD, Adam
from scipy import stats
from tensorflow.keras.utils import to_categorical
from sklearn.metrics import classification_report


df = pd.read_csv('/Users/admin/Downloads/Digit_Recognizer_train.csv')
display(df)
# 결측치확인
print(df.isnull().sum())


# Data split
x_data_train, x_data_test, t_data_train, t_data_test =\
train_test_split(df.iloc[:,1:], df.iloc[:,0], test_size = 0.3, random_state=0)

# Normalization
x_data_scaler = MinMaxScaler()
x_data_scaler.fit(x_data_train)
x_data_train_norm = x_data_scaler.transform(x_data_train)
x_data_test_norm = x_data_scaler.transform(x_data_test)
t_data_train_onehot = to_categorical(t_data_train)
t_data_test_onehot = to_categorical(t_data_test)


# TF 2.0 구현 
keras_model = Sequential()
keras_model.add(Flatten(input_shape=(784,))) # 아래행처럼 더하기가 가능
keras_model.add(Dense(420, activation='relu', kernel_initializer='he_uniform'))
keras_model.add(Dropout(0.3))
keras_model.add(Dense(258, 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(learning_rate=1e-3), loss='categorical_crossentropy',
                    metrics=['categorical_accuracy'])

''' t_data를 onehot encoding 하지 않을경우 하기 코드를 사용하셔도 무방합니다. 
keras_model.compile(optimizer=Adam(learning_rate=1e-3),
                    loss='sparse_categorical_crossentropy',
                    metrics=['sparse_categorical_accuracy'])
'''

history = keras_model.fit(x_data_train_norm, t_data_train_onehot, 
                         epochs = 100, 
                          verbose = 1, 
                          batch_size=128, 
                          validation_split=0.3)

predict_val = np.argmax(keras_model.predict(x_data_test_norm), axis=1)

import matplotlib.pyplot as plt
print(classification_report(t_data_test, predict_val.ravel()))

print(type(history))
print(history.history.keys())
plt.plot(history.history['val_categorical_accuracy'], color='b')
plt.plot(history.history['categorical_accuracy'], color='r')
plt.show()