"Could not interpret optimizer identifier" error in Keras

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:\TensorFlow\Keras\ResNet-50\test_sgd.py", line 10, in model.compile(loss='mean_squared_error', optimizer=SGD(lr=0.01), metrics=['accuracy']) File "C:\Users\nsugiant\AppData\Local\Programs\Python\Python35\lib\site-packages\tensorflow\python\keras_impl\keras\models.py", line 787, in compile **kwargs) File "C:\Users\nsugiant\AppData\Local\Programs\Python\Python35\lib\site-packages\tensorflow\python\keras_impl\keras\engine\training.py", line 632, in compile self.optimizer = optimizers.get(optimizer) File "C:\Users\nsugiant\AppData\Local\Programs\Python\Python35\lib\site-packages\tensorflow\python\keras_impl\keras\optimizers.py", line 788, in get raise ValueError('Could not interpret optimizer identifier:', identifier) ValueError: ('Could not interpret optimizer identifier:', )

Solution 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.

Solution 2:

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.

Solution 3:

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'])

Solution 4:

from tensorflow.keras.optimizers import SGD

This works well.

Since Tensorflow 2.0, there is a new API available directly via tensorflow:

• https://www.pyimagesearch.com/2019/10/21/keras-vs-tf-keras-whats-the-difference-in-tensorflow-2-0/

Solution works for tensorflow==2.2.0rc2, Keras==2.2.4 (on Win10)

Please also note that the version above uses learning_rate as parameter and no longer lr.