nn_arch.py 文件源码

def small_nn(self):
        model = Sequential()
        model.add(Conv2D(64, (self.stride, self.stride,), name='conv1',
                         padding='same',
                         activation='relu',
                         input_shape=self.ip_shape[1:]))
        model.add(MaxPooling2D(pool_size=(2, 2), name='pool1'))
        model.add(BatchNormalization())

        model.add(Flatten())
        model.add(Dense(32, activation='relu', name='dense1'))
        model.add(BatchNormalization())
        model.add(Dropout(0.5))
        model.add(Dense(10, activation='softmax', name='dense2'))
        adam = keras.optimizers.Adam(lr=self.learning_rate, beta_1=0.9, beta_2=0.999, epsilon=1e-08, decay=0.0)

        model.compile(loss='categorical_crossentropy', optimizer=adam, metrics=["accuracy"])
        return model