Keras model.summary（）结果-了解参数数- AskGo

Keras model.summary（）结果-了解参数数

keras

machine-learning

neural-network

python

109

我有一个简单的NN模型，用于使用Keras（Theano后端）从以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])

这样可以很好地运行，我的准确率约为90％。然后，我通过执行print(model0.summary())来执行以下命令，以获取网络结构的摘要。输出以下内容：

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

我不明白它们如何达到7850个总参数，这实际上意味着什么？

参考资料：

Stack Overflow

2020-05-29 21:13:59 更新

Carl Agnes• 709

共 4 个回答

高赞时间活跃

形状中的“无”表示它没有预定义的数字。例如，它可以是您在训练期间使用的批次大小，并且您希望通过不为其分配任何值来使其灵活，以便可以更改批次大小。该模型将从图层的上下文中推断形状。

要使节点连接到每一层，可以执行以下操作：

for layer in model.layers:
    print(layer.name, layer.inbound_nodes, layer.outbound_nodes)

2020-05-29 21:14:20 更新

东方鹏天• 689

对于密集层：

output_size * (input_size + 1) == number_parameters

对于转换层：

output_channels * (input_channels * window_size + 1) == number_parameters

考虑以下示例，

model = Sequential([
Conv2D(32, (3, 3), activation='relu', input_shape=input_shape),
Conv2D(64, (3, 3), activation='relu'),
Conv2D(128, (3, 3), activation='relu'),
Dense(num_classes, activation='softmax')
])

model.summary()
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
conv2d_1 (Conv2D)            (None, 222, 222, 32)      896       
_________________________________________________________________
conv2d_2 (Conv2D)            (None, 220, 220, 64)      18496     
_________________________________________________________________
conv2d_3 (Conv2D)            (None, 218, 218, 128)     73856     
_________________________________________________________________
dense_9 (Dense)              (None, 218, 218, 10)      1290      
=================================================================

计算参数

assert 32 * (3 * (3*3) + 1) == 896
assert 64 * (32 * (3*3) + 1) == 18496
assert 128 * (64 * (3*3) + 1) == 73856
assert num_classes * (128 + 1) == 1290

2020-05-29 21:14:10 更新

谈胤文• 678

我将514维实值输入提供给Keras中的Sequential模型。我的模型按以下方式构造：

    predictivemodel = Sequential()
    predictivemodel.add(Dense(514, input_dim=514, W_regularizer=WeightRegularizer(l1=0.000001,l2=0.000001), init='normal'))
    predictivemodel.add(Dense(257, W_regularizer=WeightRegularizer(l1=0.000001,l2=0.000001), init='normal'))
    predictivemodel.compile(loss='mean_squared_error', optimizer='adam', metrics=['accuracy'])

当我打印model.summary() ，得到以下结果：

Layer (type)    Output Shape  Param #     Connected to                   
================================================================
dense_1 (Dense) (None, 514)   264710      dense_input_1[0][0]              
________________________________________________________________
activation_1    (None, 514)   0           dense_1[0][0]                    
________________________________________________________________
dense_2 (Dense) (None, 257)   132355      activation_1[0][0]               
================================================================
Total params: 397065
________________________________________________________________

对于density_1层，参数数量为264710。其获得方式为：514（输入值）* 514（第一层中的神经元）+ 514（偏置值）

对于密集_2层，参数数目为132355。其获得为：514（输入值）* 257（第二层中的神经元）+ 257（第二层中神经元的偏置值）

2020-05-29 21:14:04 更新