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I am really new to Keras so forgive me if my query is a bit silly. I installed Keras in my system using the default methods and it works fine. I want to add a new optimizer to Keras so that I can easily mention "optimizer = mynewone " under the model.compile function. How do I go about changing the " optimizer.py " code in Keras and ensuring that the change is reflected on my Keras environment. Here is what I tried:
Suppose I change the optimizer name from rmsprop to rmsprops in the code I get the following error:
model.compile(loss='binary_crossentropy', optimizer='rmsprops', metrics= ['accuracy'])
Traceback (most recent call last):
File "<ipython-input-33-40773d534448>", line 1, in <module>
model.compile(loss='binary_crossentropy', optimizer='rmsprops', metrics=['accuracy'])
File "/home/kiran/anaconda/lib/python3.5/site-packages/keras/models.py", line 589, in compile
**kwargs)
File "/home/kiran/anaconda/lib/python3.5/site-packages/keras/engine/training.py", line 469, in compile
self.optimizer = optimizers.get(optimizer)
File "/home/kiran/anaconda/lib/python3.5/site-packages/keras/optimizers.py", line 614, in get
# Instantiate a Keras optimizer
File "/home/kiran/anaconda/lib/python3.5/site-packages/keras/utils/generic_utils.py", line 16, in get_from_module
str(identifier))
ValueError: Invalid optimizer: rmsprops
Then when I click on optimizers.py I get the code developed by Keras in my environment. After that in the code I replaced all "rmsprop" keywords with "rmsprops" and saved the file. So I thought I must have the updated optimizers.py in my system. But when I go back to my original file and run model.compile it throws the same error.
Any help would be really appreciated. Thanks in advance.
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The constant learning rate is the default schedule in all Keras Optimizers. For example, in the SGD optimizer, the learning rate defaults to 0.01 . To use a custom learning rate, simply instantiate an SGD optimizer and pass the argument learning_rate=0.01 .
Optimizer that implements the RMSprop algorithm. The gist of RMSprop is to: Maintain a moving (discounted) average of the square of gradients. Divide the gradient by the root of this average.
I think your approach is complicated and it doesn't have to be. Let's say you implement your own optimizer by subclassing keras.optimizers.Optimizer:
class MyOptimizer(Optimizer):
optimizer functions here.
Then to instantiate it in your model you can do this:
myOpt = MyOptimizer()
model.compile(loss='binary_crossentropy', optimizer=myOpt, metrics= ['accuracy'])
Just pass an instance of your optimizer as the optimizer parameter of model.compile and that's it, Keras will now use your optimizer.
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