Created
January 4, 2019 08:28
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| # show the confusion matrix of our predictions | |
| # compute predictions | |
| predictions = model.predict_generator(generator=validation_generator) | |
| y_pred = [np.argmax(probas) for probas in predictions] | |
| y_test = validation_generator.classes | |
| class_names = validation_generator.class_indices.keys() | |
| from sklearn.metrics import confusion_matrix | |
| import itertools | |
| def plot_confusion_matrix(cm, classes, title='Confusion matrix', cmap=plt.cm.Blues): | |
| cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis] | |
| plt.figure(figsize=(10,10)) | |
| plt.imshow(cm, interpolation='nearest', cmap=cmap) | |
| plt.title(title) | |
| plt.colorbar() | |
| tick_marks = np.arange(len(classes)) | |
| plt.xticks(tick_marks, classes, rotation=45) | |
| plt.yticks(tick_marks, classes) | |
| fmt = '.2f' | |
| thresh = cm.max() / 2. | |
| for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])): | |
| plt.text(j, i, format(cm[i, j], fmt), | |
| horizontalalignment="center", | |
| color="white" if cm[i, j] > thresh else "black") | |
| plt.ylabel('True label') | |
| plt.xlabel('Predicted label') | |
| plt.tight_layout() | |
| # compute confusion matrix | |
| cnf_matrix = confusion_matrix(y_test, y_pred) | |
| np.set_printoptions(precision=2) | |
| # plot normalized confusion matrix | |
| plt.figure() | |
| plot_confusion_matrix(cnf_matrix, classes=class_names, title='Normalized confusion matrix') | |
| plt.show() |
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