Cannot print confusion matrix for model

Question

I implemented an MLP and it works perfectly. However, I'm having an issue trying to print the confusion matrix.

My model is defined as...

logits = layers(X, weights, biases)

Where...

def layers(x, weights, biases):
    layer_1 = tf.add(tf.matmul(x, weights['h1']), biases['b1'])
    layer_2 = tf.add(tf.matmul(layer_1, weights['h2']), biases['b2'])
    out_layer = tf.matmul(layer_2, weights['out']) + biases['out']

    return out_layer

I train the model on the mnist dataset. After training I am am able to print out the accuracy of the model successfully...

pred = tf.nn.softmax(logits)

correct_prediction = tf.equal(tf.argmax(pred, 1), tf.argmax(y, 1))

accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float"))
print("Accuracy: ", accuracy.eval({X:mnist.test.images, y:mnist.test.labels}))

The accuracy gives me 90%. Now I want to print out the confusion matrix of the results. I tried the following...

confusion = tf.confusion_matrix(
         labels=mnist.test.labels, predictions=correct_prediction)

But this gives me errors...

ValueError: Can not squeeze dim[1], expected a dimension of 1, got 10 for 'confusion_matrix/remove_squeezable_dimensions/Squeeze' (op: 'Squeeze') with input shapes: [10000,10].

What is the proper way to print out the confusion matrix? I've been struggling for some time.

Answer 1

Looks like one of the arguments for tf.confusion_matrix has a 10 as second dim. The question is if mnist.test.labels or correct_prediction are one-hot encoded? That would explain it. You need the labels there as one dim tensors. Can you print the shapes for those two tensors?

Also it looks like correct_prediction is a boolean tensor to flag whether your predictions are accurate. For the confusion matrix you want the predicted label, that would be tf.argmax( pred, 1 ) instead. Similarly, if your labels are one-hot encoded, you want to decode them for the confusion matrix. So try this line for confusion:

confusion = tf.confusion_matrix(
     labels = tf.argmax( mnist.test.labels, 1 ),
     predictions = tf.argmax( pred, 1 ) )

In order to print the confusion matrix itself, it is necessary to use eval with the final result:

print(confusion.eval({x:mnist.test.images, y:mnist.test.labels}))