WebOct 19, 2024 · It may be a late answer, but I got the same problem and below is the solution # Don't use categorical_features= [10] in encoder init from sklearn.preprocessing import OneHotEncoder onehotencoder=OneHotEncoder () Y= onehotencoder.fit_transform (X [:, [10]]).toarray () Share Improve this answer Follow answered Feb 13, 2024 at 7:40 vikas … WebJul 3, 2024 · I'm using linear model on MNIST dataset searching for optimal regularization parameters. >>> Imports: #coding=utf-8 try: import keras except: pass try: from keras …
I kept getting error
WebAttributeError: 'Dense' object has no attribute 'kernel' Any idea what's wrong? Related Topics . Machine learning Computer science Information & communications technology … WebJul 2, 2024 · 1 Answer Sorted by: 2 I had this same problem this week. It seems that the tf.keras Additive attention does not return the attention weights, only the context vector. Therefore you just need to eliminate "attention_weights" when calling AdditiveAttention () and you should be good. Share Improve this answer Follow answered Jul 12, 2024 at 7:20 fastest pitches mlb the show 22
WebMay 30, 2024 · You're adding a type to your list, not an instance of the type. What you're doing is essentially the same as this: class Experience: pass buffer = [] buffer.append(Experience) WebAug 27, 2024 · You are mixing the usage/imports of the keras and tf.keras packages, these packages are not compatible with each other, you must make all relevant imports from one package only. Share Improve this answer Follow answered Aug 27, 2024 at 21:30 Dr. Snoopy 54.7k 7 120 140 Add a comment Your Answer Post Your Answer WebOct 31, 2024 · If your input data is univariate (e.g. 1D sequence), then num_channels=1 - thus: model.add (LSTM (128, activation='relu', input_shape= (1000, 1), return_sequences=True)) Lastly, for 'binary_crossentropy', a better output layer would be Dense (1, activation='sigmoid'). For more info, see this answer. fastest pitchers in baseball