# Get precision and recall value with Tensorflow CNN implementation [closed]

I was wondering if there was a simple solution to get recall and precision value for the classes of my classifier?

To put some context, I implemented a 20 classes CNN classifier using Tensorflow with the help of Denny Britz code : https://github.com/dennybritz/cnn-text-classification-tf .

As you can see at the end of text_cnn.py he implements a simple function to compute the global accuracy :

# Accuracy
with tf.name_scope("accuracy"):
correct_predictions = tf.equal(self.predictions, tf.argmax(self.input_y, 1))
self.accuracy = tf.reduce_mean(tf.cast(correct_predictions, "float"), name="accuracy")


Any ideas on how i could do something similar to get the recall and precision value for the differents categories?

Maybe my question will sound dumb but I'm a bit lost with this to be honest.

## closed as off-topic by gung♦, kjetil b halvorsen, Peter Flom♦Jun 23 '17 at 11:48

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## 2 Answers

Ok so i solved my problem, just posting my solution here in case somebody has the same issue.

Basically what I did is construct "by hand" a confusion matrix which is a 2D list of 20 rows/20 columns (20 because I had 20 categories). I filled this matrix at every step of the training by comparing the predicted category and the labeled category.

Example when predicted category is number 16 and the labeled category is 7:

confusion_matrix+=1


This confusion matrix allowed me to compute recall and precision values in the end by using the classic formula you can see here : https://en.wikipedia.org/wiki/Precision_and_recall

I'm not an expert in tensorflow, but you can obtain the confusion matrix as

y_ = tf.placeholder(tf.float32, shape=[None, 2])
y = last_layer(d/2,2,h_fc1_drop)
confusion_matrix_tf = tf.confusion_matrix(tf.argmax(y, 1), tf.argmax(y_, 1))


Then after training you can obtain the CM as

cm = confusion_matrix_tf.eval(feed_dict={x: X_train, y_: y_train, keep_prob: 1.0})


Prediction and recall can be derived from cm using the typical formulas. For those interested I have created these 3 helpers:

def beautyCM(cm, ind=['True pos', 'True neg'], cols=['Pred pos', 'Pred neg']):
return pd.DataFrame(cm, index=ind, columns=cols)

def precision(cm):
# prec = TP / (TP + FP)
try:
return round(float(cm.loc['True pos', 'Pred pos']) / \
(cm.loc['True pos', 'Pred pos'] + cm.loc['True neg', 'Pred pos']), 4)
except ZeroDivisionError:
return 1.0

def recall(cm):
# prec = TP / (TP + FN)
try:
return round(float(cm.loc['True pos', 'Pred pos']) / \
(cm.loc['True pos', 'Pred pos'] + cm.loc['True pos', 'Pred neg']), 4)
except ZeroDivisionError:
return 1.0


Just copy-pasted, so many variables defined from previous code. Let me know if there is any problem