“Could not interpret optimizer identifier” error in Keras

Question

I got this error when I tried to modify the learning rate parameter of SGD optimizer in Keras. Did I miss something in my codes or my Keras was not installed properly?

Answer 1

I tried the following and it worked for me:

from keras import optimizers

sgd = optimizers.SGD(lr=0.01)

model.compile(loss='mean_squared_error', optimizer=sgd)

Answer 2

Running the Keras documentaion example https://keras.io/examples/cifar10_cnn/ and installing the latest keras and tensor flow versions

(at the time of this writing tensorflow 2.0.0a0 and Keras version 2.2.4 )

I had to import explicitly the optimizer the keras the example is using,specifically the line on top of the example :

opt = tensorflow.keras.optimizers.rmsprop(lr=0.0001, decay=1e-6)

was replaced by

from tensorflow.keras.optimizers import RMSprop

opt = RMSprop(lr=0.0001, decay=1e-6)

In the recent version the api "broke" and keras.stuff in a lot of cases became tensorflow.keras.stuff.

Answer 3

I am bit late here, Your issue is you have mixed Tensorflow keras and keras API in your code. The optimizer and the model should come from same layer definition. Use Keras API for everything as below:

from keras.models import Sequential
from keras.layers import Dense, Dropout, LSTM, BatchNormalization
from keras.callbacks import TensorBoard
from keras.callbacks import ModelCheckpoint
from keras.optimizers import adam

# Set Model
model = Sequential()
model.add(LSTM(128, input_shape=(train_x.shape[1:]), return_sequences=True))
model.add(Dropout(0.2))
model.add(BatchNormalization())

# Set Optimizer
opt = adam(lr=0.001, decay=1e-6)

# Compile model
model.compile(
    loss='sparse_categorical_crossentropy',
    optimizer=opt,
    metrics=['accuracy']
)

I have used adam in this example. Please use your relevant optimizer as per above code.

Hope this helps.

Answer 4

This problem is mainly caused due to different versions. The tensorflow.keras version may not be same as the keras. Thus causing the error as mentioned by @Priyanka.

For me, whenever this error arises, I pass in the name of the optimizer as a string, and the backend figures it out. For example instead of

tf.keras.optimizers.Adam

or

keras.optimizers.Adam

I do

model.compile(optimizer= 'adam' , loss= keras.losses.binary_crossentropy, metrics=['accuracy'])

Answer 5

Just give

optimizer = 'sgd' / 'RMSprop'

Answer 6

In my case it was because I missed the parentheses. I am using tensorflow_addons so my code was like

model.compile(optimizer=tfa.optimizers.LAMB, loss='binary_crossentropy',
              metrics=['binary_accuracy'])

And it gives

ValueError: ('Could not interpret optimizer identifier:', <class tensorflow_addons.optimizers.lamb.LAMB'>)

Then I changed my code into:

model.compile(optimizer=tfa.optimizers.LAMB(), loss='binary_crossentropy',
              metrics=['binary_accuracy'])

and it works.