Confusion Matrix with Tensorflow - tensorflow

Question

I am using finetune AlexNet architecture written by #kratzert on my own dataset which, works properly (I got the code from here: https://github.com/kratzert/finetune_alexnet_with_tensorflow) and I want to figure out how to build confusion matrix from his code. I have tried to use tf.confusion_matrix(labels, predictions, num_classes) to build confusion matrix but I can't. I am confused what should be the values for labels and predictions, I mean, I know what should be but each time I feed these value got an error. Can anyone help me on this or have a look at the code (above link) and guide me?
I added these two lines in finetune.py exactly after calculating accuracy to make the labels and the predictions as the number of the class.
with tf.name_scope("accuracy"):
correct_pred = tf.equal(tf.argmax(score, 1), tf.argmax(y, 1))
accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))
**true_class = tf.argmax(y, 1)
predicted_class = tf.argmax(score, 1)**
and I have added tf.confusion_matrix() inside my session at the very bottom before saving checkpoint of the model
for _ in range(val_batches_per_epoch):
img_batch, label_batch = sess.run(next_batch)
acc, cost = sess.run([accuracy, loss], feed_dict={x: img_batch,
y: label_batch,
keep_prob: 1.})
test_acc += acc
test_count += 1
test_acc /= test_count
print("{} Validation Accuracy = {:.4f} -- Validation Loss = {:.4f}".format(datetime.now(),test_acc, cost))
print("{} Saving checkpoint of model...".format(datetime.now()))
**print(sess.run(tf.confusion_matrix(true_class, predicted_class, num_classes)))**
# save checkpoint of the model
checkpoint_name = os.path.join(checkpoint_path,
'model_epoch'+str(epoch+1)+'.ckpt')
save_path = saver.save(sess, checkpoint_name)
print("{} Model checkpoint saved at {}".format(datetime.now(),
checkpoint_name))
I have tried other places as well but each time I will get an error:
Caused by op 'Placeholder_1', defined at:
File "/home/armin/Desktop/Alexnet_DataPipeline/finetune.py", line 85, in <module>
y = tf.placeholder(tf.float32, [batch_size, num_classes])
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/ops/array_ops.py", line 1777, in placeholder
return gen_array_ops.placeholder(dtype=dtype, shape=shape, name=name)
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/ops/gen_array_ops.py", line 4521, in placeholder
"Placeholder", dtype=dtype, shape=shape, name=name)
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/framework/op_def_library.py", line 787, in _apply_op_helper
op_def=op_def)
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/framework/ops.py", line 3290, in create_op
op_def=op_def)
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/framework/ops.py", line 1654, in __init__
self._traceback = self._graph._extract_stack() # pylint: disable=protected-access
InvalidArgumentError (see above for traceback): You must feed a value for placeholder tensor 'Placeholder_1' with dtype float and shape [128,3]
any help will be appreciated, Thanks.

Answer 1

It's a fairly long piece of code you're referring to, and you did not specify where you put your confusion matrix line.
Just by experience, the most frequent problem with confusion matrices is that tf.confusion_matrix() requires both the labels and the predictions as the number of the class, not as one-hot vectors. In other words, the label and the prediction should be in the form of the number 5 instead of [ 0, 0, 0, 0, 0, 1, 0, 0, 0, 0 ].
In the code you refer to, y is in the one-hot format. The output of the network, score is a vector, giving the probability of each class. That is also not the required format. You need to do something like
true_class = tf.argmax( y, 1 )
predicted_class = tf.argmax( score, 1 )
and use those with the confusion matrix like
tf.confusion_matrix( true_class, predicted_class, num_classes )
(Basically, if you take a look at line 123 of finetune.py, that has both of those elements for determining accuracy, but they are not saved in separate tensors.)
If you want to keep a running total of confusion matrices of all batches, you just have to add them up - since each cell of the matrix counts the number of examples falling into that category, an element-wise addition creates the confusion matrix for the whole set:
cm_running_total = None
cm_nupmy_array = sess.run(tf.confusion_matrix(true_class, predicted_class, num_classes), feed_dict={x: img_batch, y: label_batch, keep_prob: 1.} )
if cm_running_total is None:
cm_running_total = cm_numpy_array
else:
cm_running_total += cm_numpy_array