MLP（多层感知器神经网络）即多层全连接神经网络模型。
from keras.datasets import mnist from keras.utils import np_utils from keras.models import Sequential from keras.layers import Dense,Dropout,Activation batch_size=128 nb_classes=10 nb_epoch=10 img_size=28*28 (x_train,y_train),(x_test,y_test)=mnist.load_data("E:\Code\PycharmProjects\KerasStudying\data\mnist.npz") x_train=x_train.reshape(-1,img_size).astype('float32')/255 x_test=x_test.reshape(-1,img_size).astype('float32')/255 y_train=np_utils.to_categorical(y_train,nb_classes) y_test=np_utils.to_categorical(y_test,nb_classes) model=Sequential([ Dense(512,input_shape=(img_size,),activation='relu',), Dropout(0.2), Dense(512,input_shape=(512,),activation='relu'), Dropout(0.2), Dense(10,input_shape=(512,),activation='softmax'), ]) model.summary() model.compile(optimizer='rmsprop',loss='categorical_crossentropy',metrics=['accuracy']) model.fit(x_train,y_train,batch_size=batch_size,epochs=10,verbose=0,validation_data=(x_test,y_test)) score=model.evaluate(x_test,y_test,verbose=0) print('accuracy:'+str(score[1]))