How can i get precision & recall instead of accuracy in Tensorflow - machine-learning

Question

I'm seeing Spam Prediction classifying messages as Spam and Ham made by other person.
[source code]
https://github.com/nfmcclure/tensorflow_cookbook/blob/master/09_Recurrent_Neural_Networks/02_Implementing_RNN_for_Spam_Prediction/02_implementing_rnn.py
The program produces the following values. (loss, accuracy)
Veiw Result Screenshot
In this code, the result is only loss, accuracy,
I think Accuracy has no meaning.
I need Precision, Recall value (for F1 measure)
However, since the my code analysis is not working properly,
I know Precision and Recall.
But I do not know how to calculate(code embedding) Precision and Recall in this code.

Answer 1

I succeeded it myself, hurray !!
here is the code:
actuals = tf.cast(y_output, tf.int64)
predictions = tf.argmax(logits_out, 1)
ones_like_actuals = tf.ones_like(actuals)
zeros_like_actuals = tf.zeros_like(actuals)
ones_like_predictions = tf.ones_like(predictions)
zeros_like_predictions = tf.zeros_like(predictions)
tp_op = tf.reduce_sum(
tf.cast(
tf.logical_and(
tf.equal(actuals, ones_like_actuals),
tf.equal(predictions, ones_like_predictions)
),
"float"
)
)
tn_op = tf.reduce_sum(
tf.cast(
tf.logical_and(
tf.equal(actuals, zeros_like_actuals),
tf.equal(predictions, zeros_like_predictions)
),
"float"
)
)
fp_op = tf.reduce_sum(
tf.cast(
tf.logical_and(
tf.equal(actuals, zeros_like_actuals),
tf.equal(predictions, ones_like_predictions)
),
"float"
)
)
fn_op = tf.reduce_sum(
tf.cast(
tf.logical_and(
tf.equal(actuals, ones_like_actuals),
tf.equal(predictions, zeros_like_predictions)
),
"float"
)
)
I saw confusion matrix open source in github thank you #Mistobaan !!
https://gist.github.com/Mistobaan/337222ac3acbfc00bdac