[英]Is it possible to set a middle layer as an output layer in keras
I would like to try out an idea about autoencoder.我想尝试一下关于自动编码器的想法。 The model is like this:
model是这样的:
input (pictures) - conv2d - pooling - dense - dense(supervised output) - dense - conv - upsampling - output (pictures)
If it is possible to train the NN having desired outputs for dense(supervised output)
and output (pictures)
?是否可以训练具有
dense(supervised output)
和output (pictures)
所需输出的 NN? In other words, I want to make a classifier-and-back.换句话说,我想制作一个分类器并返回。
This can be done with the Keras functional API ( https://keras.io/getting-started/functional-api-guide/ ).这可以通过 Keras 功能 API ( https://keras.io/getting-started/functional-api-guide/ ) 来完成。
A minimal example, where the model has 2 outputs, one from an intermediate layer, and one from the final layer:一个最小的示例,其中 model 有 2 个输出,一个来自中间层,一个来自最后一层:
import keras
input = keras.layers.Input(shape=(3,))
intermediate = keras.layers.Dense(10)(input)
final_output = keras.layers.Dense(3)(intermediate)
model = keras.Model(inputs=input, outputs=[intermediate, final_output])
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