How to plot confusion matrix using Tensor Flow model maker

plot_confusion_matrix_tfmodel_maker.py

%matplotlib inline
from sklearn.metrics import confusion_matrix
import itertools
import matplotlib.pyplot as plt
import tensorflow as tf
assert tf.__version__.startswith('2')
from tflite_model_maker import image_classifier

predicts = model.predict_top_k(test_data)
y_pred = []
y_true = []
for i, (image, label) in enumerate(test_data.gen_dataset().unbatch().take(100)):
  pred_label = predicts[i][0][0]
  true_label = test_data.index_to_label[label.numpy()]
  y_pred.append(pred_label)
  y_true.append(true_label)

def plot_confusion_matrix(cm, classes,
                        normalize=False,
                        title='Confusion matrix',
                        cmap=plt.cm.Blues):
    """
    This function prints and plots the confusion matrix.
    Normalization can be applied by setting `normalize=True`.
    """
    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)

    if normalize:
        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]
        print("Normalized confusion matrix")
    else:
        print('Confusion matrix, without normalization')

    print(cm)

    thresh = cm.max() / 2.
    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):
        plt.text(j, i, cm[i, j],
            horizontalalignment="center",
            color="white" if cm[i, j] > thresh else "black")

    plt.tight_layout()
    plt.ylabel('True label')
    plt.xlabel('Predicted label')

classes = test_data.index_to_label

cm = confusion_matrix(y_true=y_true, y_pred=y_pred)

plot_confusion_matrix(cm=cm, classes=classes, title='Confusion Matrix')