["python","keras"]
Keras AttributeError: 'Sequential' object has no attribute 'predict_classes'

Im attempting to find model performance metrics (F1 score, accuracy, recall) following this guide https://machinelearningmastery.com/how-to-calculate-precision-recall-f1-and-more-for-deep-learning-models/ This exact code was working a few months ago but now returning all sorts of errors, very confusing since i havent changed one character of this code. Maybe a package update has changed things? I fit the sequential model with model.fit, then used model.evaluate to find test accuracy. Now i am attempting to use model.predict_classes to make class predictions (model is a multi-class classifier). Code shown below: model = Sequential() model.add(Dense(24, input_dim=13, activation='relu')) model.add(Dense(18, activation='relu')) model.add(Dense(6, activation='softmax')) model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy']) - history = model.fit(X_train, y_train, batch_size = 256, epochs = 10, verbose = 2, validation_split = 0.2) - score, acc = model.evaluate(X_test, y_test,verbose=2, batch_size= 256) print('test accuracy:', acc) - yhat_classes = model.predict_classes(X_test) last line returns error "AttributeError: 'Sequential' object has no attribute 'predict_classes'" This exact code was working not long ago so struggling a bit, thanks for any help
This function was removed in TensorFlow version 2.6. According to the keras in rstudio reference update to predict_x=model.predict(X_test) classes_x=np.argmax(predict_x,axis=1) Or use TensorFlow 2.5.x . If you are using TensorFlow version 2.5, you will receive the following warning: tensorflow\python\keras\engine\sequential.py:455: UserWarning: model.predict_classes() is deprecated and will be removed after 2021-01-01. Please use instead:* np.argmax(model.predict(x), axis=-1), if your model does multi-class classification (e.g. if it uses a softmax last-layer activation).* (model.predict(x) > 0.5).astype("int32"), if your model does binary classification (e.g. if it uses a sigmoid last-layer activation).