python - "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?

Here is my code:

from tensorflow.python.keras.models import Sequential
from tensorflow.python.keras.layers import Dense, Flatten, GlobalAveragePooling2D, Activation
import keras
from keras.optimizers import SGD

model = Sequential()
model.add(Dense(64, kernel_initializer='uniform', input_shape=(10,)))
model.add(Activation('softmax'))
model.compile(loss='mean_squared_error', optimizer=SGD(lr=0.01), metrics= ['accuracy'])*

and here is the error message:

Traceback (most recent call last): File "C:TensorFlowKerasResNet-50est_sgd.py", line 10, in model.compile(loss='mean_squared_error', optimizer=SGD(lr=0.01), metrics=['accuracy']) File "C:Users sugiantAppDataLocalProgramsPythonPython35libsite-packagesensorflowpythonkeras_implkerasmodels.py", line 787, in compile **kwargs) File "C:Users sugiantAppDataLocalProgramsPythonPython35libsite-packagesensorflowpythonkeras_implkerasengineraining.py", line 632, in compile self.optimizer = optimizers.get(optimizer) File "C:Users sugiantAppDataLocalProgramsPythonPython35libsite-packagesensorflowpythonkeras_implkerasoptimizers.py", line 788, in get raise ValueError('Could not interpret optimizer identifier:', identifier) ValueError: ('Could not interpret optimizer identifier:', )

Answer 1

The reason is you are using tensorflow.python.keras API for model and layers and keras.optimizers for SGD. They are two different Keras versions of TensorFlow and pure Keras. They could not work together. You have to change everything to one version. Then it should work.