Keras P/R metrics at different thresholds during training

I’m training a binary classifier and I’d like to see Precision/Recall metrics at different thresholds.

Tensorflow 2.3 introduced tf.keras.metrics.Precision and tf.keras.metrics.Recall which take a thresholds parameter, where you can specify one or multiple thresholds for which you want the metrics computed. This all works as advertised i.e.

m = tf.keras.metrics.Precision(thresholds=[0.1, 0.2, 0.3, 0.4, 0.5, 0.6])
m.update_state([0, 1, 0, 1], [0.4, 0.5, 0.3, 0.8])
m.result().numpy()

Returns the precision value at each threshold [0.5, 0.5, 0.6666667, 1., 1., 1.], as per the documentation.

However when passed as metrics to Model.compile I get a single metric regardless of how many thresholds I have.

pr_thresholds = list(np.arange(0.05, 0.95, 0.05))
model.compile(
    'adam',
    'binary_crossentropy',
    metrics=[ 
        keras.metrics.Precision(thresholds=pr_thresholds),
        keras.metrics.Recall(thresholds=pr_thresholds),
    ]
)

I get

Epoch 34/50
395/395 [==============================] - 22s 54ms/step - loss: 0.4314 - precision: 0.7886 - recall: 0.9008 - val_loss: 0.5113 - val_precision: 0.7434 - val_recall: 0.8769

What’s happening here ? Does it always use the default threshold value of 0.5 in this case ?

Is there a way I can get it to display the values for multiple thresholds during training ?

model.compile(
optimizer=keras.optimizers.Adam(learning_rate=1e-2),
loss='categorical_crossentropy',
metrics=[metrics.Recall(thresholds=0.6), 
         metrics.Recall(thresholds=0.9)])

model.fit(X_train, y_train, epochs=10, validation_data=(X_test, y_test))

thresholds = [0.6, 0.7, 0.9]
metrics_objs_list=[metrics.Recall(thresholds=thr) for thr in thresholds]