Python 인공신경망 예제

활성함수(Activation)

계단함수(Step Function) : 0과 1로만 내보난다

예제

import numpy as np

def step_function(x):

    return np.array(x > 0, dtype=int)

x = np.arange(-5,6,0.1)

res = step_function(x)

import matplotlib.pyplot as plt

plt.plot(x,res)

plt.show()

Sigmoid Function

예제

# h(x) = 1 / (1 + exp(-x)) : exp = 2.718281828...

def sigmoid(x):

    return 1 / (1 + np.exp(-x))

x = np.arange(-5.0,5.0,0.1)

y = sigmoid(x)

plt.figure(figsize=(3,2))

plt.plot(x,y)

plt.ylim(-0.1,1.1)

plt.show()

예제

# 계단함수와 시그모이드 함수의 출력값을 한개의 차트에 그려서 비교해보세요

plt.figure(figsize=(5,2))

x = np.arange(-10,10,0.1)

y = step_function(x)

y2 = sigmoid(x)

plt.plot(x,y,'r--',label='step function')

plt.plot(x,y2,label='sigmoid')

plt.legend()

plt.show()

ReLU(Rectified Linear Unit) : x <= 0, -> 0, x > 0 -> x

예제

def relu(x):

    return np.maximum(0,x) # arg1, arg2를 비교하여 큰 값 리턴

arr = np.arange(-10,11,1)

y = relu(arr)

plt.figure(figsize=(5,2))

plt.plot(arr,y)

plt.show()

Softmax : 다중분류

예제

# softmax : 다중분류

# 분류 클래스 수만큼의 값이 산출됨

# 산출된 값을 합하면 1이 됨

#!pip install tensorflow

import tensorflow as tf

from tensorflow.keras.models import Sequential

from tensorflow.keras.layers import Dense

np.random.seed(0)

tf.random.set_seed(3)

from sklearn.model_selection import train_test_split

import pandas as pd

df = pd.read_csv('housing.csv',delim_whitespace=True,header=None)

0	1	2	3	4	5	6	7	8	9	10	11	12	13
0	0.00632	18.0	2.31	0	0.538	6.575	65.2	4.0900	1	296.0	15.3	396.90	4.98	24.0
1	0.02731	0.0	7.07	0	0.469	6.421	78.9	4.9671	2	242.0	17.8	396.90	9.14	21.6
2	0.02729	0.0	7.07	0	0.469	7.185	61.1	4.9671	2	242.0	17.8	392.83	4.03	34.7
3	0.03237	0.0	2.18	0	0.458	6.998	45.8	6.0622	3	222.0	18.7	394.63	2.94	33.4
4	0.06905	0.0	2.18	0	0.458	7.147	54.2	6.0622	3	222.0	18.7	396.90	5.33	36.2
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
501	0.06263	0.0	11.93	0	0.573	6.593	69.1	2.4786	1	273.0	21.0	391.99	9.67	22.4
502	0.04527	0.0	11.93	0	0.573	6.120	76.7	2.2875	1	273.0	21.0	396.90	9.08	20.6
503	0.06076	0.0	11.93	0	0.573	6.976	91.0	2.1675	1	273.0	21.0	396.90	5.64	23.9
504	0.10959	0.0	11.93	0	0.573	6.794	89.3	2.3889	1	273.0	21.0	393.45	6.48	22.0
505	0.04741	0.0	11.93	0	0.573	6.030	80.8	2.5050	1	273.0	21.0	396.90	7.88	11.9

506 rows × 14 columns

dataset = df.values

X = dataset[:,:13]

y = dataset[:,13]

X_train, X_test, y_train, y_test = train_test_split(X,y,test_size=0.3,random_state=0)

X_train.shape # (354, 13)

X_test.shape # (152, 13)

y_train.shape # (354,)

y_test.shape # (152,)

model = Sequential()

model.add(Dense(30,input_dim=13, activation='relu'))

model.add(Dense(20,activation='relu')) # 히든레이어

model.add(Dense(10,activation='relu')) # 히든레이어

model.add(Dense(15,activation='relu')) # 히든레이어

model.add(Dense(1)) # 결과

model.summary()

Model: "sequential_1"
_________________________________________________________________
 Layer (type)                Output Shape              Param #   
=================================================================
 dense_2 (Dense)             (None, 30)                420       
                                                                 
 dense_3 (Dense)             (None, 20)                620       
                                                                 
 dense_4 (Dense)             (None, 10)                210       
                                                                 
 dense_5 (Dense)             (None, 15)                165       
                                                                 
 dense_6 (Dense)             (None, 1)                 16        
                                                                 
=================================================================
Total params: 1,431
Trainable params: 1,431
Non-trainable params: 0
_________________________________________________________________

# Loss함수 'adam' 사용

model.compile(loss='mse', optimizer='adam')

# 학습내역

history = model.fit(X_train,y_train,epochs=200,batch_size=10) # epoch : 반복횟수, batch_size : 10개의 입력을 처리하고 손실함수를 기반으로 가중치 조정, 현재 1개 입력은 13의 input을 말한다

# 인공신경망이 계산한 집값

model.predict(X_test).flatten()

# 학습내역

history.history['loss']

# 학습내역 그래프

plt.plot(history.history['loss'])

plt.show()