怎么看keras.engine.sequential.Sequential

Question

我是 Keras 和深度学习的新手，正在与 Keras 上的 MNIST 合作。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'))

然后我打印出来

print(model)

输出是

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

我的问题是有什么方法可以看到 Keras 的更好结果，这意味着如果我打印 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

你也可以试试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)

怎么看keras.engine.sequential.Sequential

How to see keras.engine.sequential.Sequential

deep-learning

keras

keras-layer