#Test data generator keras generator#

#Test data generator keras generator#

After optimizing a model with optimal meta-parameters the test data is used to get a fair estimate of the model performance. 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. The validation data is used to make choices about the meta-parameters, e.g. To do so we will create a DataGenerator class which.

Training data is used to optimize the model parameters. Now that we have a bit idea about how python generators work let us create a custom data generator. The Keras documentation uses three different sets of data: training data, validation data and test data. Since Linux uses 'fork(2)' to spawn child processes and the initialization of the data generator was happening outside of the MP part, all workers were using the same seed and were generating equal batches. Y_true = np.array( * 1000 + * 1000)Īdditional note on test and validation data Note that in the test generator we only want to rescale the data and set the validation split, as augmenting the images is only beneficial for training the model and may decrease test performance. Turns out the problem was a combination of OS behavior and my data generator, which was internally doing some shuffling using np.random. Compute the confusion matrix based on the label predictionsįor example, compare the probabilities with the case that there are 1000 cats and 1000 dogs respectively.Probabilities = model.predict_generator(generator, 2000) Then I train and test my CNN with the following code. Shuffle=False) # keep data in same order as labels A Python generator or returning (inputs, targets) or (inputs, targets, sampleweights). In (588, 120, 120, 1) shape input data, there are total 588 samples each with a shape of (120, 120, 1).

To get a confusion matrix from the test data you should go througt two steps:įor example, use model.predict_generator to predict the first 2000 probabilities from the test generator.