Trjtdtk

I have code that computes the accuracy, but now I would like to compute the f1 score.

accuracy_1 = tf.reduce_mean(tf.cast(tf.equal(
 tf.argmax(output_1, axis=-1),
 tf.argmax(y_1, axis=-1)), tf.float32), name="accuracy_1")
accuracy_2 = tf.reduce_mean(tf.cast(tf.equal(
 tf.argmax(output_2, axis=-1),
 tf.argmax(y_2, axis=-1)), tf.float32), name="accuracy_2")

How can I compute f1 equivalent for the above code? I'm finding it difficult as I am very new to tensorflow.

To compute f1_score, first, use this function of python sklearn library to produce confusion matrix, after that, from the confusion matrix generate TP,TN,FP,FN then use them to calculate:

Recall = TP/TP+FN and Precision = TP/TP+FP

and then from the above 2 metrics you can easly calculate:

f1_score = 2 * (precision * recall) / (precision + recall)

OR

you can use another function of the same library here to compute f1_score directly from the generated y_true and y_pred like below:

F1 = f1_score(y_true, y_pred, average='binary')

finally, the library links consists of a helpful explanation, you should read them carefully.

f1 can be Defined as a custom metric. Keras will evaluate this metric on each batch/epoch as applicable.

import keras.backend as K

def f1_metric(y_true, y_pred):
 true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1)))
 possible_positives = K.sum(K.round(K.clip(y_true, 0, 1)))
 predicted_positives = K.sum(K.round(K.clip(y_pred, 0, 1)))
 precision = true_positives / (predicted_positives + K.epsilon())
 recall = true_positives / (possible_positives + K.epsilon())
 f1_val = 2*(precision*recall)/(precision+recall+K.epsilon())
 return f1_val


model.compile(...,metrics=['accuracy', f1_metric])