10 janv. 2022 · Evaluation on the test data. We'll use MNIST data for this example. (x_train, y_train), (x_test, y_test) = keras.datasets.mnist.load_data() # Preprocess the data (these are NumPy arrays) x_train = x_train.reshape(60000, 784).astype("float32") / 255. x_test = x_test.reshape(10000, 784).astype("float32") / 255. Python Model.evaluate_generator - 34 examples found. These are the top rated real world Python examples of keras.models.Model.evaluate_generator extracted from open source projects. You can rate examples to help us improve the quality of examples. 26 avr. 2019 · def evaluate_predcitions (model, generator): predictions = model.predict_generator (generator=generator, steps=generator.samples/nBatches, max_queue_size=10, workers=1, use_multiprocessing=False, verbose=0) # Evaluate predictions predictedClass = np.argmax (predictions, axis=1) trueClass = generator.classes classLabels = list. Python Model.evaluate_generator - 30 examples found. These are the top rated real world Python examples of tensorflow.keras.models.Model.evaluate_generator extracted from open source projects. You can rate examples to help us improve the quality of examples. Python Model.evaluate_generator - 6 examples found. These are the top rated real world Python examples of pytoune.framework.Model.evaluate_generator extracted from open source projects. You can rate examples to help us improve the quality of examples. 1 mars 2019 · In the first end-to-end example you saw, we used the validation_data argument to pass a tuple of NumPy arrays (x_val, y_val) to the model for evaluating a validation loss and validation metrics at the end of each epoch. 24 sept. 2020 · 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. Share. 6 nov. 2018 · n_features = 1. series = series.reshape((len(series), n_features)) The TimeseriesGenerator will then split the series into samples with the shape [ batch, n_input, 1] or [8, 2, 1] for all eight samples in the generator and the two lag observations used as time steps. The complete example is listed below.