![]() I solved the problem using opencv to read and resize image, but I'd. ![]() The problem is that the accuracy on validation set is very high, around the 90, but on test set the accuracy is very bad, less that 1. Hi, I see you’re using testgenerator to evaluate the model, What evaluate generator does is it takes its inputs exactly like fitgenerator, meaning it expects both the input data and actual target data, and calls predict generator on the input data, finally compares the predictions with the actual target data to return the loss. compile ( optimizer = 'rmsprop', loss = 'categorical_crossentropy' ) model. I'm new with keras with tensorflow backend and I'm trying to do transfer learning with pretrained net. Otherwise, the directory structure is ignored. Xception ( weights = None, input_shape = ( 256, 256, 3 ), classes = 10 ) model. If labels is inferred, it should contain subdirectories, each containing images for a class. image_dataset_from_directory ( directory = 'validation_data/', labels = 'inferred', label_mode = 'categorical', batch_size = 32, image_size = ( 256, 256 )) model = keras. Keras model provides a function, evaluate which does the evaluation of the model. image_dataset_from_directory ( directory = 'training_data/', labels = 'inferred', label_mode = 'categorical', batch_size = 32, image_size = ( 256, 256 )) validation_ds = keras. Evaluation is a process during development of the model to check whether the model is best fit for the given problem and corresponding data. We start with the imports that would be required for this tutorial. From tensorflow import keras train_ds = keras. Creating a data generator Few useful data generator properties Visualizing data generator tensors for a quick correctness test Training using the data generator Predicting using the data generator Training, validation and test set creation 1.
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