Tensorflow confusion matrix using one-hot code

I have multi-class classification using RNN and here is my main code for RNN:

def RNN(x, weights, biases):
    x = tf.unstack(x, input_size, 1)
    lstm_cell = rnn.BasicLSTMCell(num_unit, forget_bias=1.0, state_is_tuple=True) 
    stacked_lstm = rnn.MultiRNNCell([lstm_cell]*lstm_size, state_is_tuple=True) 
    outputs, states = tf.nn.static_rnn(stacked_lstm, x, dtype=tf.float32)

    return tf.matmul(outputs[-1], weights) + biases

logits = RNN(X, weights, biases)
prediction = tf.nn.softmax(logits)

cost =tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=logits, labels=Y))
optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate)
train_op = optimizer.minimize(cost)

correct_pred = tf.equal(tf.argmax(prediction, 1), tf.argmax(Y, 1)) 
accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))

I have to classify all inputs to 6 classes and each of classes is composed of one-hot code label as the follow:

happy = [1, 0, 0, 0, 0, 0]
angry = [0, 1, 0, 0, 0, 0]
neutral = [0, 0, 1, 0, 0, 0]
excited = [0, 0, 0, 1, 0, 0]
embarrassed = [0, 0, 0, 0, 1, 0]
sad = [0, 0, 0, 0, 0, 1]

The problem is I cannot print confusion matrix using tf.confusion_matrix() function.

Is there any way to print confusion matrix using those labels?

If not, how can I convert one-hot code to integer indices only when I need to print confusion matrix?

You cannot generate confusion matrix using one-hot vectors as input parameters of labels and predictions. You will have to supply it a 1D tensor containing your labels directly.

To convert your one hot vector to normal label, make use of argmax function:

label = tf.argmax(one_hot_tensor, axis = 1)

After that you can print your confusion_matrix like this:

import tensorflow as tf

num_classes = 2
prediction_arr = tf.constant([1,  1, 1, 1,  0, 0, 0, 0,  1, 1])
labels_arr     = tf.constant([0,  1, 1, 1,  1, 1, 1, 1,  0, 0])

confusion_matrix = tf.confusion_matrix(labels_arr, prediction_arr, num_classes)
with tf.Session() as sess:
    print(confusion_matrix.eval())

Output: