I've got multiple outputs from my model from multiple Dense layers. My model has 'accuracy' as the only metric in compilation. I'd like to know the loss and accuracy for each output. This is some part of my code. scores = model.evaluate(X_test, [y_test_one, y_test_two], verbose=1) When I printed out the scores, this is the result.

2020年9月24日 · model.evaluate() just takes your neural network as it is (at epoch 100), computes predictions, and then calculates the loss. Thus, the minimum loss is likely to be less (although only slightly for good hyperparameters), than the model.evaluate(), but model.evaluate() tells you where your NN is currently.

2019年11月4日 · No module named 'model_evaluation_utils' Is there any pip installation or conda that could solve this problem? from keras.preprocessing.image import load_img, img_to_array, array_to_img from keras.models import load_model import …

2017年6月30日 · Since originally asked, a lot has happened, including the docs significantly improving; so I'll include a link here to the Keras API for Tensorflow 2.x Python API for "Model": compile (Configures the model for training); fit (Trains the model for a fixed number of epochs); evaluate (Returns the loss value & metrics values for the model in test mode); predict …

2020年2月1日 · model.eval() is a kind of switch for some specific layers/parts of the model that behave differently during training and inference (evaluating) time. For example, Dropouts Layers, BatchNorm Layers etc. You need to turn them off during model …

2020年3月20日 · I'm wondering what the best way is to evaluate a fitted binary classification model using Apache Spark 2.4.5 and PySpark (Python). I want to consider different metrics such as accuracy, precision, ...

2018年2月23日 · If I call model.evaluate() on the training set I get the loss value for the whole set. So, intuitively if I call model.evaluate() on a single instance of the training set I should get a value that is a fraction of the value I would get calling model.evaluate() on the whole training set. But instead I get a value that is close or even bigger by ...

2020年9月1日 · So you will have a bit of variation in the accuracy. If you have used non-augmented validation or test data in the validation_data of fit_generator and also for model.evaluate() or model.evaluate_generator, then there wouldn't be any change in the accuracy. Below is the simple Cat and Dog Classification program that I have ran for one epoch-

2019年3月15日 · In the end of epoch 5 (which is the last epoch and final model) this model should correspond to the evaluation. Same happens for a validation set (not shown). The validation accuracy during training is not the same as after evalutation.

2018年8月24日 · I am new to machine learning and created a neural network for regression output. I have ~95000 training examples and ~24000 test examples. I want to know how can I evaluate my model and get train and test errors? How to know the accuracy of this regression model? My Y variable values ranges between 100-200 and X have 9 input features in the ...