def ae(a):
	from sklearn.datasets import load_iris
	from sklearn.neural_network import MLPClassifier
	iris=load_iris()
	type(iris)
	if a=='tanh':
		print('tanh')
	elif a=='relu':
		print('relu')
	#print(iris.feature_names)
	#print(iris.data)
	X=iris.data
	y=iris.target

	knn=MLPClassifier(activation=a,hidden_layer_sizes=(2),solver='sgd',learning_rate_init=0.01,max_iter=500,verbose=True)
	print(knn)

	knn.fit(X,y)
	print(knn.predict([[3,5,4,2]]))
	p=knn.predict(X)
	from sklearn.metrics import confusion_matrix
	print(confusion_matrix(y,p))
	print(knn.coefs_)
a=ae('tanh')
#print(---------------------------------------------------)
a=ae('relu')
