我对Keras和深度学习并不熟悉,并且正在MNIST上研究Keras。当我使用
创建模型时model = models.Sequential()
model.add(layers.Dense(512,activation = 'relu',input_shape=(28*28,)))
model.add(layers.Dense(32,activation ='relu'))
model.add(layers.Dense(10,activation='softmax'))
然后我打印了它
print(model)
输出是
<keras.engine.sequential.Sequential at 0x7f3d554f6710>
[我的问题是,有什么办法可以看到更好的Keras结果,这意味着如果我打印model
,我可以看到我有3个隐藏层,其中第一个隐藏层具有512个隐藏单元和784个输入单元,第二个隐藏层具有512个输入单元和32个隐藏单元等。
model.summary()将为您打印整个模型。
model = Sequential()
model.add(Dense(512,activation = 'relu',input_shape=(28*28,)))
model.add(Dense(32,activation ='relu'))
model.add(Dense(10,activation='softmax'))
model.summary()
Model: "sequential_1"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
dense (Dense) (None, 512) 401920
_________________________________________________________________
dense_1 (Dense) (None, 32) 16416
_________________________________________________________________
dense_2 (Dense) (None, 10) 330
=================================================================
Total params: 418,666
Trainable params: 418,666
Non-trainable params: 0
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