<prompt> Anzisha programu ya Python inayotumia maktaba ya Keras kutoa mafunzo kwa mtandao rahisi wa neva kwenye mkusanyiko wa data wa MNIST na uchapishe usahihi uliopatikana kwenye seti ya majaribio. PLINFORMAT  <response>: kuagiza kerakutoka kwa keras.datasets leta mnistkutoka kwa keras.models huleta Mfuatanokutoka keras.layers kuagiza Dense, Dropout, Flattenkutoka kwa keras.layers huleta Conv2D, MaxPooling2D Pakia seti ya data ya MNISTx_train, y_train, x_test, y_test mnist.load_data Chunguza datax_train x_train.reshape-1, 28, 28, 1.astypefloat32 255x_test x_test.reshape-1, 28, 28, 1.astypefloat32 255y_train keras.utils.to_categoricaly_train, 10y_test keras.utils.to_categoricaly_test, 10 Unda mfano rahisi wa mtandao wa neuralmfano Mfuatanomodel.addConv2D32, kernel_size3, 3, activationrelu, input_shape28, 28, 1model.addMaxPooling2Dpool_size2, 2modeli.ongezaKuacha0.25model.ongezaFlattenmodel.addDense128, activationrelumodeli.ongezaKuacha0.5model.addDense10, activationsoftmax Kusanya mfanomodel.compilehasarakeras.losses.categorical_crossentropy, optimizerkeras.optimizers.Adadelta, vipimousahihi Funza mfano kwenye seti ya datamodel.fitx_treni, y_treni, batch_size128, enzi12, kitenzi1, uthibitisho_datax_test, y_test Tathmini mfano kwenye seti ya majaribioalama model.tathminix_test, y_test, verbose0chapaJaribio hasara:, alama0chapaUsahihi wa jaribio:, alama1 