如何看待keras.engine.sequential.Sequential

Question

我是Keras和深入学习的新手，并在Keras上与MNIST合作。当我创建一个模型时

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'))

然后我印了出来

print(model)

输出是

<keras.engine.sequential.Sequential at 0x7f3d554f6710>

我的问题是，有没有办法看到Keras的效果更好，也就是说，如果我打印model，我可以看到我有3个隐藏层，第一个隐藏层有512个隐藏单元和784个输入单元，第二个隐藏层有512个输入单元和32个隐藏单元等等。

Answer 1

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
____________________________

Answer 2

您也可以尝试plot_model()

model = tf.keras.Sequential()
model.add(tf.keras.layers.Dense(512,activation = 'relu',input_shape=(28*28,)))
model.add(tf.keras.layers.Dense(32,activation ='relu'))
model.add(tf.keras.layers.Dense(10,activation='softmax'))
model.summary()

from keras.utils.vis_utils import plot_model  
plot_model(model, show_shapes=True, show_layer_names=True)

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