如何在 Keras 中返回多输出模型的回波损耗历史？

Question

I use Python 3.7 and Keras 2.2.4. I created a Keras model with two output layers:

self.df_model = Model(inputs=input, outputs=[out1,out2])

As the loss history only returns one loss value per epoch, I want to get the loss of each output layer. How is it possible to get two loss values per epoch, one for each output layer?

Answer 1

Each model in Keras has a default History callback which stores all the loss and metric values of all the epochs, both the aggregate values as well as per output layer. This callback creates a History object which is returned when fit model is called and you can access all of these values by using the history property of that object (it is actually a dictionary):

history = model.fit(...)
print(history.history)  # <-- a dict which contains all the loss and metric values per epoch

A minimal reproducible example:

from keras import layers
from keras import Model
import numpy as np

inp = layers.Input((1,))
out1 = layers.Dense(2, name="output1")(inp)
out2 = layers.Dense(3, name="output2")(inp)

model = Model(inp, [out1, out2])
model.compile(loss='mse', optimizer='adam')

x = np.random.rand(2, 1)
y1 = np.random.rand(2, 2)
y2 = np.random.rand(2, 3)
history = model.fit(x, [y1,y2], epochs=5)

print(history.history)

#{'loss': [1.0881365537643433, 1.084699034690857, 1.081269383430481, 1.0781562328338623, 1.0747418403625488],
# 'output1_loss': [0.87154925, 0.8690172, 0.86648905, 0.8641926, 0.8616721],
# 'output2_loss': [0.21658726, 0.21568182, 0.2147803, 0.21396361, 0.2130697]}