Supervised Learning - Softmax Classfication (multi-variable 2)

Softmax Classifiation : 주어진 입력에 따라 3개 이상의 class에서의 예측

(=Multi Classification)

Linear : H(x) ∈ (-inf, inf)

Softmax : H(x) ∈ [0,1]

 

 

 

Step1) Hypothesis

Step2) Cost function 

Step3) Training - gradient descent method 

 

 

 

 

Tensorflow

w/ min-max scaling

---

#load module
import numpy as np 
import tensorflow as tf
import matplotlib.pyplot as plt

 

#input & label
x_input = tf.constant([[25,22],[25,26],[25,30],[35,22],[35,26],[35,30],[45,22],
                       [45,26],[45,30],[55,22],[55,26],[55,30],[65,22],[65,26],[65,30],[73,22],[73,26],[73,30]], dtype= tf.float32)
labels = tf.constant([[1,0,0],[0,1,0],[0,0,1],[1,0,0],[0,1,0],[1,0,0],[1,0,0],
                      [1,0,0],[0,1,0],[1,0,0],[0,1,0],[0,0,1],[0,1,0],[0,0,1],[0,0,1],[0,1,0],[0,0,1],[0,0,1]], dtype= tf.float32)
# 나이와 BMI 가 입력되면, 혈압등급을 0 : 정상,  1: 주의,  2: 경고  등급으로 구분하는 코드를 구현


#weight and bias
n_var, n_class = 2, 3
W = tf.Variable(tf.random.normal((n_var, n_class),dtype=tf.float32))
B = tf.Variable(tf.random.normal((n_class,),dtype=tf.float32))

 

#min-max scaling 
x_input_org = x_input
xmin, xmax = np.min(x_input, axis = 0), np.max(x_input, axis = 0)
x_input = (x_input - xmin)/(xmax-xmin)

 

#Function Generation 
def logits(x): 
    return tf.matmul(x,W) + B

def Hypothesis(x): 
    return tf.nn.softmax(logits(x))

def Cost(): 
    logit_value = logits(x_input)
    return tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=logit_value, labels=labels))

def Predict(x):
    return Hypothesis((x-xmin)/(xmax-xmin))

 

#Parameter 
epochs = 10000
lr = 0.5
opt = tf.keras.optimizers.SGD(learning_rate = lr) 

training_idx = np.arange(0, epochs+1, 1)
cost_graph = np.zeros(epochs+1)

#Training 
for cnt in range(0, epochs+1):
    cost_graph[cnt] = Cost()
    if cnt % (epochs//5) == 0:
        print("[Epochs : {:>6}] Cost = {:>10.4}".format(cnt, cost_graph[cnt]))
        print("W = {:}".format(W.numpy()))
        print("B = {:}".format(B.numpy()))
        
    opt.minimize(Cost, [W,B])

#Prediction (using input value)
test_set = tf.constant([[20, 25], [50, 30],[70, 20]], dtype = tf.float32)
Hx = Predict(test_set)
H = np.argmax(Hx, axis = 1)
print(Hx, '\n')


for i in range(test_set.shape[0]):
    print("Input : {} => Group : {} , Pred : {}".format(test_set[i], H[i], Hx[i]))

#Cost function graph
plt.title("'Cost / Epochs' Graph")
plt.xlabel("Epochs")
plt.ylabel("Cost")
plt.plot(training_idx, cost_graph)
plt.xlim(0, epochs)
plt.grid(True)
plt.semilogy()
plt.show()