# Default value of learning rate in adam optimizer - Keras

I am working on a image classification problem using Transfer Learning.

Parameters used given below:
Adam optimizer with learning rate - 0.0001

adamOpti = Adam(lr = 0.0001)
model.compile(optimizer = adamOpti, loss = "categorical_crossentropy, metrics = ["accuracy"])


For testing I used adam optimizer without explicitly specifying any parameter (default value lr = 0.001).

With the default value of learning rate the accuracy of training and validation got stuck at around 50%.
And when I use learning rate = 0.0001 in the first epoch itself I could see that the accuracy is going to 90%.

1. why with lower value of learning rate the accuracy is increasing rapidly?
2. Also which of the above used learning rate is the better?
3. How could I make use of decaying learning rate in Keras?

Thank you

1. With too large a learning rate, you might bounce around an optimum, or you might start off by sling-shooting out to a part of the parameter space where the gradients vanish. (With too small, you might take too long to converge to an optimum, or you might find a poor local optimum. These effects are lessened by adams momentum effect.)
3. There is a parameter decay for simple decay, or you can set up a callback for step decays.