<prompt> Andika hati ya Python inayotumia maktaba ya Keras kuunda mtandao rahisi wa neva wa kuainisha tarakimu zilizoandikwa kwa mkono kutoka kwa seti ya data ya MNIST.PLINFORMAT  <response>: ingiza numpy kama npkutoka kwa keras.datasets leta mnistkutoka kwa keras.models huleta Mfuatanokutoka keras.layers kuagiza Dense, Dropout, Flattenkutoka kwa keras.layers.convolutional kuagiza Conv2D, MaxPooling2Dkutoka kwa keras.utils huagiza np_utilsnp.mbegu.nasibu42X_train, y_train, X_test, y_test mnist.load_dataX_train X_train.reshapeX_train.shape0, 28, 28, 1.astypefloat32X_test X_test.reshapeX_test.shape0, 28, 28, 1.astypefloat32X_treni 255Mtihani wa X 255y_train np_utils.to_categoricaly_trainy_test np_utils.to_categoricaly_testnum_classes y_test.shape1def create_model: mfano Mfuatano model.addConv2D32, 3, 3, input_shape28, 28, 1, kuwezesharelu model.addMaxPooling2Dpool_size2, 2 modeli.ongezaKuacha0.2 model.ongezaFlatten model.addDense128, activationrelu model.addDensenum_classes, activationsoftmax model.compilehasaracategorical_crossentropy, optimizeradam, metricsusahihi mfano wa kurudimodel create_modelmodel.fitX_train, y_train, validation_dataX_test, y_test, epochs10, batch_size200alama model.evaluateX_test, y_test, verbose0printHitilafu ya CNN: .2f 100 - alama1 100 