理工酷

我有一个简单的NN模型，用于使用Keras（Theano后端）从用python编写的28x28px图像中检测手写数字：

model0 = Sequential()
#number of epochs to train for
nb_epoch = 12#amount of data each iteration in an epoch sees
batch_size = 128

model0.add(Flatten(input_shape=(1, img_rows, img_cols)))
model0.add(Dense(nb_classes))
model0.add(Activation('softmax'))
model0.compile(loss='categorical_crossentropy', 
         optimizer='sgd',
         metrics=['accuracy'])

model0.fit(X_train, Y_train, batch_size=batch_size, nb_epoch=nb_epoch,
      verbose=1, validation_data=(X_test, Y_test))

score = model0.evaluate(X_test, Y_test, verbose=0)

print('Test score:', score[0])
print('Test accuracy:', score[1])

这样运行良好，我得到了约90％的准确度。然后，我执行以下命令，通过执行print(model0.summary（)）来获得网络结构的信息。

Layer (type)         Output Shape   Param #     Connected to                     
=====================================================================
flatten_1 (Flatten)   (None, 784)     0           flatten_input_1[0][0]            
dense_1 (Dense)     (None, 10)       7850        flatten_1[0][0]                  
activation_1        (None, 10)          0           dense_1[0][0]                    
======================================================================
Total params: 7850

我不明白如何达到7850个总参数，这实际上意味着什么？