也许有人可以帮助我 - 我正在顺序模型中研究学习率。我希望每次更改学习率时都从头开始优化，以便公平比较每个学习率在结果中的表现。那么如何在Python中创建一个函数来生成新模型来优化循环中的学习率呢？
""" optimizing learning rate"""
# Create list of learning rates: lr_to_test
lr_to_test = [0.000001, 0.01, 1]
# Loop over learning rates
for lr in lr_to_test:
print('\n\nTesting model with learning rate: %f\n'%lr )
# Build new model to test, unaffected by previous models
model = Sequential()
# Add the layers
model.add(Dense(50, activation='relu', input_shape=(n_cols,)))
model.add(Dense(32, activation='relu'))
model.add(Dense(1))
# Create SGD optimizer with specified learning rate: my_optimizer
my_optimizer = SGD(lr=lr)
# Compile the model
model.compile(optimizer=my_optimizer, loss='mean_squared_error')
# Fit the model
model.fit(predictors, target, epochs=10)
作为我得到的结果:
Testing model with learning rate: 0.000001
Epoch 1/10
534/534 [==============================] - 0s 661us/step - loss: 120.5427
Epoch 2/10
534/534 [==============================] - 0s 29us/step - loss: 111.6158
.....
Epoch 10/10
534/534 [==============================] - 0s 59us/step - loss: 65.8593
Testing model with learning rate: 0.010000
Epoch 1/10
534/534 [==============================] - 0s 693us/step - loss: nan
Epoch 2/10
534/534 [==============================] - 0s 59us/step - loss: nan
Epoch 3/10
534/534 [==============================] - 0s 29us/step - loss: nan
....<>
最佳答案
您可以循环学习速率列表并在最后评估结果，以了解哪种速率最适合您。
learning_rates = [0.00001, 0.0001, 0.001, 0.01, 0.1]
best_lr = 0
best_rmse = 999999
for lr in learning_rates:
"""Build sequential model"""
my_optimizer = SGD(lr=lr)
"""Compile, fit and evaluate"""
rmse = "Calculate your evaluation metric"
if rmse < best_rmse:
best_rmse = rmse
best_lr = lr
关于python - 如何创建一个模型，每次更改学习率时都从头开始优化？，我们在Stack Overflow上找到一个类似的问题： https://stackoverflow.com/questions/49746668/