How do i create Confusion matrix of predicted and ground truth labels with Tensorflow?

Question

I have implemented a Nueral Network model for a classification with the help of using TensorFlow. But, i don\'t know how can i able to draw confusion matrix by using predic

Answer 1

For the moment, I use this solution to obtain the confusion matrix :

# load the data
(train_x, train_y), (dev_x, dev_y), (test_x, test_y) = dataLoader.load()

# build the classifier
classifier = tf.estimator.DNNClassifier(...)

# train the classifier
classifier.train(input_fn=lambda:train_input_fn(), steps=1000)

# evaluate and prediction on the test set
test_evaluate = classifier.evaluate(input_fn=lambda:eval_input_fn())
test_predict = classifier.predict(input_fn = lambda:eval_input_fn())

# parse the prediction to retrieve the predicted labels
predictions = []

for i in list(test_predict):
    predictions.append(i['class_ids'][0])

# build the prediction matrix
matrix = tf.confusion_matrix(test_y, predictions)

#display the prediction matrix
with tf.Session():
    print(str(tf.Tensor.eval(matrix)))

But I am not convince by my loop to retrieve the predicted labels...there should be a better Python way to do this...(or a TensorFlow way...)