是否有任何方法可以显示TensorFlow线性估计量的训练进度：tf.estimator.LinearClassifier（）.train（）类似于model.fit每个历元的www.example.com（）进度输出方式？tensorflow==2.9.2

Epoch 1/10
1875/1875 [==============================] - 5s 3ms/step - loss: 0.4964 - accuracy: 0.8270
Epoch 2/10
1875/1875 [==============================] - 4s 2ms/step - loss: 0.3751 - accuracy: 0.8652
Epoch 3/10
1875/1875 [==============================] - 5s 3ms/step - loss: 0.3382 - accuracy: 0.8762

下面是我的代码示例

#input function 
def make_input_fn(data_df, label_df, num_epochs=1000, shuffle=True, batch_size=32):
  def input_function():  # inner function, this will be returned
    ds = tf.data.Dataset.from_tensor_slices((dict(data_df), label_df))  # create tf.data.Dataset object with data and its label
    if shuffle:
      ds = ds.shuffle(1000)  # randomize order of data
    ds = ds.batch(batch_size).repeat(num_epochs)  # split dataset into batches of 32 and repeat process for number of epochs
    return ds  # return a batch of the dataset
  return input_function  # return a function object for use

train_input_fn = make_input_fn(dftrain, y_train)  # here we will call the input_function that was returned to us to get a dataset object we can feed to the model
eval_input_fn = make_input_fn(dfeval, y_eval, num_epochs=1, shuffle=False)
pre_input_fn = make_input_fn(dfpre, y_pre, num_epochs=1, shuffle=False)

linear_est = tf.estimator.LinearClassifier(feature_columns=feature_columns)
 
linear_est.train(train_input_fn)  # train
result = linear_est.evaluate(eval_input_fn)