Keras learning rate not changing despite decay in SGD

Question

For some reason my learning rate does not appear to change eventhough I set a decay factor. I added a callback to view the learning rate and it appears to be the same after each epoch. Why is it not changing

class LearningRatePrinter(Callback):
    def init(self):
        super(LearningRatePrinter, self).init()

    def on_epoch_begin(self, epoch, logs={}):
        print('lr:', self.model.optimizer.lr.get_value())

lr_printer = LearningRatePrinter()

model = Sequential()
model.add(Flatten(input_shape = (28, 28)))
model.add(Dense(200, activation = 'tanh'))
model.add(Dropout(0.5))
model.add(Dense(20, activation = 'tanh'))
model.add(Dense(10, activation = 'softmax'))

print('Compiling Model')
sgd = SGD(lr = 0.01, decay = 0.1, momentum = 0.9, nesterov = True)
model.compile(loss = 'categorical_crossentropy', optimizer = sgd)
print('Fitting Data')
model.fit(x_train, y_train, batch_size = 128, nb_epoch = 400, validation_data = (x_test, y_test), callbacks = [lr_printer])


lr: 0.009999999776482582
Epoch 24/400
60000/60000 [==============================] - 0s - loss: 0.7580 - val_loss: 0.6539
lr: 0.009999999776482582
Epoch 25/400
60000/60000 [==============================] - 0s - loss: 0.7573 - val_loss: 0.6521
lr: 0.009999999776482582
Epoch 26/400
60000/60000 [==============================] - 0s - loss: 0.7556 - val_loss: 0.6503
lr: 0.009999999776482582
Epoch 27/400
60000/60000 [==============================] - 0s - loss: 0.7525 - val_loss: 0.6485
lr: 0.009999999776482582
Epoch 28/400
60000/60000 [==============================] - 0s - loss: 0.7502 - val_loss: 0.6469
lr: 0.009999999776482582
Epoch 29/400
60000/60000 [==============================] - 0s - loss: 0.7494 - val_loss: 0.6453
lr: 0.009999999776482582
Epoch 30/400
60000/60000 [==============================] - 0s - loss: 0.7483 - val_loss: 0.6438
lr: 0.009999999776482582
Epoch 31/400

Answer 1

This is changing just fine, the problem is the field you are trying to access stores initial learning rate , not current one. Current one is calculated from scratch during each iteration through equation

lr = self.lr * (1. / (1. + self.decay * self.iterations))

and it is never stored , thus you cannot monitor it this way, you simply have to calculate it on your own, using this equation.

see line :126 of https://github.com/fchollet/keras/blob/master/keras/optimizers.py