tensorflow.분류(중간층).relu,sigmoid 비교

#속성 4개 3중 분류
# RMSPropOptimizer
# 중간층 사용
# 뉴런 10개, 활성화 함수 relu
iris=load_iris()
X=tf.placeholder(tf.float32,shape=(None,4))
y=tf.placeholder(tf.float32,shape=(None,3))

w=tf.Variable(tf.random.normal([4,10],0,0.1))
b=tf.Variable(tf.random.normal([10],0,0.1))
u=tf.nn.relu(X@w+b)

ww=tf.Variable(tf.random.normal([10,3],0,0.1))
bb=tf.Variable(tf.random.normal([3],0,0.1))
pred_y=u@ww+bb

##
entropy = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits_v2(labels=y,logits=pred_y))
##

optimizer=tf.train.RMSPropOptimizer(learning_rate=0.01)
train_op=optimizer.minimize(entropy)

costs1=[]
sess=tf.InteractiveSession()
tf.global_variables_initializer().run()

from sklearn.model_selection import train_test_split
X_trian,X_test,y_train,y_test=train_test_split(iris.data,np.eye(3)[iris.target])

for i in range(2000):
    entropy_val,_=sess.run([entropy,train_op],feed_dict={X:X_trian,y:y_train})
    costs1.append(entropy_val)
import matplotlib.pyplot as plt    
plt.plot(costs)

#속성 4개 3중 분류
# RMSPropOptimizer
# 중간층 사용
# 뉴런 10개, 활성화 함수 sigmoid
iris=load_iris()
X=tf.placeholder(tf.float32,shape=(None,4))
y=tf.placeholder(tf.float32,shape=(None,3))

w=tf.Variable(tf.random.normal([4,10],0,0.1))
b=tf.Variable(tf.random.normal([10],0,0.1))
u=tf.nn.sigmoid(X@w+b)

ww=tf.Variable(tf.random.normal([10,3],0,0.1))
bb=tf.Variable(tf.random.normal([3],0,0.1))
pred_y=u@ww+bb

##
entropy = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits_v2(labels=y,logits=pred_y))
##

optimizer=tf.train.RMSPropOptimizer(learning_rate=0.01)
train_op=optimizer.minimize(entropy)

costs2=[]
sess=tf.InteractiveSession()
tf.global_variables_initializer().run()

from sklearn.model_selection import train_test_split
X_trian,X_test,y_train,y_test=train_test_split(iris.data,np.eye(3)[iris.target])

for i in range(2000):
    entropy_val,_=sess.run([entropy,train_op],feed_dict={X:X_trian,y:y_train})
    costs2.append(entropy_val)
import matplotlib.pyplot as plt    
plt.plot(costs)