When using mectrics in model.compile in keras, report ValueError: ('Unknown metric function', ':f1score')
I suspect you are using Keras 2.X. As explained in https://keras.io/metrics/, you can create custom metrics. These metrics appear to take only (y_true, y_pred)
as function arguments, so a generalized implementation of fbeta is not possible.
Here is an implementation of f1_score
based on the keras 1.2.2 source code.
import keras.backend as K
def f1_score(y_true, y_pred):
# Count positive samples.
c1 = K.sum(K.round(K.clip(y_true * y_pred, 0, 1)))
c2 = K.sum(K.round(K.clip(y_pred, 0, 1)))
c3 = K.sum(K.round(K.clip(y_true, 0, 1)))
# If there are no true samples, fix the F1 score at 0.
if c3 == 0:
return 0
# How many selected items are relevant?
precision = c1 / c2
# How many relevant items are selected?
recall = c1 / c3
# Calculate f1_score
f1_score = 2 * (precision * recall) / (precision + recall)
return f1_score
To use, simply add f1_score
to your list of metrics when you compile your model, after defining the custom metric. For example:
model.compile(loss='categorical_crossentropy',
optimizer='adam',
metrics=['accuracy',f1_score])