tf.keras.callbacks.ModelCheckpoint Type Error : Unable to serialize 1.0000000656873453e-05 to JSON

I am creating my custom layers tf.keras model using the mobile net pre-trained layer. Model training is running fine but when saving the best-picked model it is giving an error. Below is the snippet of the code that I used

pretrained_model = tf.keras.applications.MobileNetV2(
                                                    weights='imagenet',
                                                    include_top=False,
                                                    input_shape=[*IMAGE_SIZE, IMG_CHANNELS])
pretrained_model.trainable = True #fine tuning
model = tf.keras.Sequential([
                            tf.keras.layers.Lambda(# Convert image from int[0, 255] to the format expect by this model
                            lambda data:tf.keras.applications.mobilenet.preprocess_input(
                                tf.cast(data, tf.float32)), input_shape=[*IMAGE_SIZE, 3]),
                            pretrained_model,
                            tf.keras.layers.GlobalAveragePooling2D()])

model.add(tf.keras.layers.Dense(64, name='object_dense',kernel_regularizer=tf.keras.regularizers.l2(l2=0.001)))
model.add(tf.keras.layers.BatchNormalization(scale=False, center = False))
model.add(tf.keras.layers.Activation('relu', name='relu_dense_64'))
model.add(tf.keras.layers.Dropout(rate=0.2, name='dropout_dense_64'))
model.add(tf.keras.layers.Dense(32, name='object_dense_2',kernel_regularizer=tf.keras.regularizers.l2(l2=0.01)))
model.add(tf.keras.layers.BatchNormalization(scale=False, center = False))
model.add(tf.keras.layers.Activation('relu', name='relu_dense_32'))
model.add(tf.keras.layers.Dropout(rate=0.2, name='dropout_dense_32'))
model.add(tf.keras.layers.Dense(16, name='object_dense_16', kernel_regularizer=tf.keras.regularizers.l2(l2=0.01)))
model.add(tf.keras.layers.Dense(len(CLASS_NAMES), activation='softmax', name='object_prob'))
m1 = tf.keras.metrics.CategoricalAccuracy()
m2 = tf.keras.metrics.Recall()
m3 = tf.keras.metrics.Precision()



optimizers = [
    tfa.optimizers.AdamW(learning_rate=lr * .001 , weight_decay=wd),
    tfa.optimizers.AdamW(learning_rate=lr, weight_decay=wd)
           ]

optimizers_and_layers = [(optimizers[0], model.layers[0]), (optimizers[1], model.layers[1:])]

optimizer = tfa.optimizers.MultiOptimizer(optimizers_and_layers)

model.compile(
    optimizer= optimizer,
    loss = 'categorical_crossentropy',
    metrics=[m1, m2, m3],
    )

checkpoint_path = os.getcwd() + os.sep + 'keras_model'
checkpoint_cb = tf.keras.callbacks.ModelCheckpoint(filepath=os.path.join(checkpoint_path), 
                                                    monitor = 'categorical_accuracy',
                                                    save_best_only=True,
                                                    save_weights_only=False)

history = model.fit(train_data, validation_data=test_data, epochs=N_EPOCHS, callbacks=[checkpoint_cb])

At tf.keras.callbacks.ModelCheckpoint is giving me an error

TypeError: Unable to serialize 1.0000000656873453e-05 to JSON. Unrecognized type <class ‘tensorflow.python.framework.ops.EagerTensor’>.

I am using tensorflow 2.7

[rchao]

Correct.

[chilin0525]

Sure, but I have no time to reproduce the error recently. If I want to provide full stack trace just put following code at the beginning right?

tf.debugging.disable_traceback_filtering()