问丢失keras中的Nan Resnet-50

EN

from tensorflow import keras

base_model = keras.applications.resnet50.ResNet50(include_top = False, weights='imagenet')

for layer in base_model.layers:
    layer.trainable = False

avg = keras.layers.GlobalAveragePooling2D(name="global_avg")(base_model.output)
output = keras.layers.Dense(1, activation = 'sigmoid', name = "predictions")(avg)
model = keras.Model(inputs = base_model.input, outputs = output, name = "ResNet-50")

optimizer = keras.optimizers.SGD(lr=0.01, momentum=0.9, decay=0.0001, clipnorm = 0.1)
reduce_LROP = keras.callbacks.ReduceLROnPlateau(monitor='val_loss', factor=0.1, patience=10, verbose=0, mode='auto',
    min_delta=0.0001, cooldown=0, min_lr=0)
model.compile(loss=tf.keras.losses.BinaryCrossentropy(), optimizer = optimizer, metrics = ['accuracy'])

history = model.fit(tri, y_train, epochs = 10, batch_size = 32, validation_data = (vai, y_val),
                    callbacks = [reduce_LROP])