When you encounter the error message “module ‘keras.api._v2.keras.metrics’ has no attribute ‘f1score'”, it means that the keras.metrics module does not have a method named f1score().
The f1score() is a common evaluation metric used in classification tasks. It calculates the F1 score, which is the harmonic mean of precision and recall. This metric is commonly used when the data is imbalanced, and both high precision and recall are desired.
However, as mentioned earlier, the keras.metrics module in the current version you are using does not have a direct method named f1score(). Instead, you can make use of f1_score from the sklearn.metrics module to calculate the F1 score.
Here’s an example of how you can calculate the F1 score using f1_score from sklearn.metrics in Keras:
from sklearn.metrics import f1_score
# Assuming you have true labels and predicted labels
y_true = [0, 1, 0, 1, 1]
y_pred = [1, 1, 0, 1, 0]
f1 = f1_score(y_true, y_pred)
print("F1 Score:", f1)
In the above example, we import f1_score from sklearn.metrics. We then create two lists, y_true and y_pred, representing the true labels and predicted labels, respectively. Finally, we calculate the F1 score using f1_score(y_true, y_pred).
Remember, to use sklearn.metrics.f1_score, you need to have scikit-learn installed. If it’s not already installed, you can add it to your project by running pip install scikit-learn in your terminal.