backend argmax has none for gradients. Can you even define one?

I am using Keras.Backend.armax() in a gamma layer. The model compiles fine but throws an error during fit().

ValueError: An operation has `None` for gradient. Please make sure that all of your ops have a gradient defined (i.e. are differentiable). Common ops without gradient: K.argmax, K.round, K.eval.

My model:

latent_dim = 512
encoder_inputs = Input(shape=(train_data.shape[1],))
encoder_dense = Dense(vocabulary, activation='softmax')
encoder_outputs = Embedding(vocabulary, latent_dim)(encoder_inputs)
encoder_outputs = LSTM(latent_dim, return_sequences=True)(encoder_outputs)
encoder_outputs = Dropout(0.5)(encoder_outputs)
encoder_outputs = encoder_dense(encoder_outputs)
encoder_outputs = Lambda(K.argmax, arguments={'axis':-1})(encoder_outputs)
encoder_outputs = Lambda(K.cast, arguments={'dtype':'float32'})(encoder_outputs)

encoder_dense1 = Dense(train_label.shape[1], activation='softmax')
decoder_embedding = Embedding(vocabulary, latent_dim)
decoder_lstm1 = LSTM(latent_dim, return_sequences=True)
decoder_lstm2 = LSTM(latent_dim, return_sequences=True)
decoder_dense2 = Dense(vocabulary, activation='softmax')

decoder_outputs = encoder_dense1(encoder_outputs)
decoder_outputs = decoder_embedding(decoder_outputs)
decoder_outputs = decoder_lstm1(decoder_outputs)
decoder_outputs = decoder_lstm2(decoder_outputs)
decoder_outputs = Dropout(0.5)(decoder_outputs)
decoder_outputs = decoder_dense2(decoder_outputs)
model = Model(encoder_inputs, decoder_outputs)
model.summary()

Model summary for easy visualizing:

_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
input_7 (InputLayer)         (None, 32)                0         
_________________________________________________________________
embedding_13 (Embedding)     (None, 32, 512)           2018816   
_________________________________________________________________
lstm_19 (LSTM)               (None, 32, 512)           2099200   
_________________________________________________________________
dropout_10 (Dropout)         (None, 32, 512)           0         
_________________________________________________________________
dense_19 (Dense)             (None, 32, 3943)          2022759   
_________________________________________________________________
lambda_5 (Lambda)            (None, 32)                0         
_________________________________________________________________
lambda_6 (Lambda)            (None, 32)                0         
_________________________________________________________________
dense_20 (Dense)             (None, 501)               16533     
_________________________________________________________________
embedding_14 (Embedding)     (None, 501, 512)          2018816   
_________________________________________________________________
lstm_20 (LSTM)               (None, 501, 512)          2099200   
_________________________________________________________________
lstm_21 (LSTM)               (None, 501, 512)          2099200   
_________________________________________________________________
dropout_11 (Dropout)         (None, 501, 512)          0         
_________________________________________________________________
dense_21 (Dense)             (None, 501, 3943)         2022759   
=================================================================
Total params: 14,397,283
Trainable params: 14,397,283
Non-trainable params: 0
_________________________________________________________________

I googled for the solution but almost all were about a faulty model. Some recommended to not use functions causing that are causing issues. However, as you can see, I cannot create this model without K.argmax (If you know any other way then do tell me).

Also, how can you even define gradient of argmax! I am guessing its an issue in Keras, if not, pls tell me how to define its gradient.

[sunwei317]

I have the same problem. There is no any problem for train and evaluation, and Ok for saving the model in H5. However, when loading the saved model, the error message pops up: ValueError: An operation has None for gradient. Please make sure that all of your ops have a gradient defined (i.e. are differentiable). Common ops without gradient: K.argmax, K.round, K.eval. Do you have idea to fix this issue. Otherwise, the model cannot be used for prediction. Thank you.

[yli192]

I monitored the precision, recall and accuracy while training, the model was getting better. If the model was saved with Keras.save, then the error above appears with Keras.load_model. However, if the model was saved with Keras.mode_to_json and Keras.save_weights, then everything is fine when loading the saved model.

I implemented this solution and it worked for me. This is all you will need:

Save model to JSON

model_json = model.to_json() with open(“model.json”, “w”) as json_file: json_file.write(model_json)

serialize weights to HDF5

model.save_weights(“model.h5”) print(“Saved model to disk”)

Load JSON model

json_file = open(‘model.json’, ‘r’) loaded_model_json = json_file.read() json_file.close() loaded_model = model_from_json(loaded_model_json)

load weights into new model

loaded_model.load_weights(“model.h5”) print(“Loaded model from disk”)