tf.keras.callbacks.modelcheckpoint忽略蒙台参数，并始终使用损失

Question

我正在使用准确度度量来运行tf.keras.callbacks.modelcheckpoint，但损失用于保存最佳检查点。我已经在不同的地方（我的计算机和协作）和两个不同的代码进行了测试，并面临同一问题。这是一个示例代码和结果：

import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers
import os
import shutil

def get_uncompiled_model():
    inputs = keras.Input(shape=(784,), name="digits")
    x = layers.Dense(64, activation="relu", name="dense_1")(inputs)
    x = layers.Dense(64, activation="relu", name="dense_2")(x)
    outputs = layers.Dense(10, activation="softmax", name="predictions")(x)
    model = keras.Model(inputs=inputs, outputs=outputs)
    return model

def get_compiled_model():
    model = get_uncompiled_model()
    model.compile(
        optimizer="rmsprop",
        loss="sparse_categorical_crossentropy",
        metrics=["accuracy"],
    )
    return model

(x_train, y_train), (x_test, y_test) = keras.datasets.mnist.load_data()

# Preprocess the data (these are NumPy arrays)
x_train = x_train.reshape(60000, 784).astype("float32") / 255
x_test = x_test.reshape(10000, 784).astype("float32") / 255

y_train = y_train.astype("float32")
y_test = y_test.astype("float32")

# Reserve 10,000 samples for validation
x_val = x_train[-10000:]
y_val = y_train[-10000:]
x_train = x_train[:-10000]
y_train = y_train[:-10000]


ckpt_folder = os.path.join(os.getcwd(), 'ckpt')
if os.path.exists(ckpt_folder):
    shutil.rmtree(ckpt_folder)

ckpt_path = os.path.join(r'D:\deep_learning\tf_keras\semantic_segmentation\logs', 'mymodel_{epoch}')


callbacks = [
    tf.keras.callbacks.ModelCheckpoint(
        # Path where to save the model
        # The two parameters below mean that we will overwrite
        # the current checkpoint if and only if
        # the `val_loss` score has improved.
        # The saved model name will include the current epoch.
        filepath=ckpt_path,
        montior="val_accuracy",
        # save the model weights with best validation accuracy
        mode='max',
        save_best_only=True,  # only save the best weights
        save_weights_only=False,
        # only save model weights (not whole model)
        verbose=1
    )
]

model = get_compiled_model()


model.fit(
    x_train, y_train, epochs=3, batch_size=1, callbacks=callbacks, validation_split=0.2, steps_per_epoch=1
)

1/1 [========================================= ：2.6475-准确性：0.0000E+00 epoch 1：val_loss从-inf提高到2.32311，将模型保存到d：\ deep_learning \ tf_keras \ semantic_sementation \ logs \ mymodel_1 1/1 [============================================================== 00 -Val_loss：2.3231 -Val_accuracy：0.1142

时期2/3 1/1 [=========================================== EPOCH 2：Val_loss从2.32311提高到2.34286，将模型保存到D：\ deep_learning \ tf_keras \ semantic_sementation \ logs \ logs \ mymodel_2 1/1 [==============================] - 5s 5s/step - loss: 1.9612 - accuracy: 1.0000 - val_loss ：2.3429 -Val_accuracy：0.1187

时期3/3 1/1 [========================================== Epoch 3：Val_loss并未从2.34286提高 1/1 [=============================================================================================================================================================================================== 00 -Val_loss：2.2943 -Val_accuracy：0.1346

Answer 1

在你的代码中，你写的是 montior 而不是 monitor，并且该函数没有这个词作为参数，然后使用默认值，如果你像下面这样写，你会得到你想要什么：

callbacks = [
    tf.keras.callbacks.ModelCheckpoint(
        filepath=ckpt_path,
        monitor="val_accuracy",
        mode='max',
        save_best_only=True,
        save_weights_only=False,
        verbose=1
    )
]