ModelCheckPoint problem

[redaelhail]

Hello,

Thank you for your work.

I am doing domain adaptation with DANN. I would like to save the best model using model checkpoint based on the loss value of the task network:

    chk = ModelCheckpoint(os.path.join(model_directory_name,'Model'),
                          monitor="loss",
                          verbose=1,
                          save_best_only=True,
                          save_weights_only=False,
                          mode='min',
                          save_freq=1)

During the trainning, i keep receiving this warning:

WARNING:tensorflow:Model's `__init__()` arguments contain non-serializable objects. Please implement a `get_config()` method in the subclassed Model for proper saving and loading. Defaulting to empty config.

This is the training code:

# define callbacks
    callbacks_list = [chk]

    # Build model
    model = DANN(encoder=encoder(), task=task(), discriminator=discriminitor(),
                 Xt=Xt,lambda_= 0.1, metrics=["acc"],random_state=0)
    # start training
    model_log = model.fit(Xs, ys,epochs = 2, callbacks=callbacks_list, verbose=1, class_weight=class_weights)

[antoinedemathelin]

Hi @redaelhail, Thank you for reporting the issue.

Yes, saving tensorflow objects is not easy... A suggestion is to only save the weights of the netwoks.

Here is a little example that should work:

import numpy as np
import tensorflow as tf
from tensorflow.keras.callbacks import ModelCheckpoint
from adapt.feature_based import DANN

np.random.seed(0)
Xt = np.random.randn(100, 2)
Xs = np.random.randn(100, 2)
ys = np.random.choice(2, 100)

def encoder():
  mod = tf.keras.Sequential()
  mod.add(tf.keras.layers.Dense(10, activation="relu", input_shape=(2,)))
  return mod

def task():
  mod = tf.keras.Sequential()
  mod.add(tf.keras.layers.Dense(1, activation="sigmoid"))
  return mod

def discriminitor():
  mod = tf.keras.Sequential()
  mod.add(tf.keras.layers.Dense(1, activation="sigmoid"))
  return mod


chk = ModelCheckpoint('Model.hdf5',
                        monitor="loss",
                        verbose=1,
                        save_best_only=True,
                        save_weights_only=True,
                        mode='min',
                        save_freq=1)

# define callbacks
callbacks_list = [chk]

# Build model
model = DANN(encoder=encoder(), task=task(), discriminator=discriminitor(),
             Xt=Xt, lambda_= 0.1, loss="bce", metrics=["acc"], random_state=0)
# start training
model_log = model.fit(Xs, ys,epochs = 2, callbacks=callbacks_list, verbose=1)

# Load saved weights
model.load_weights('Model.hdf5')

If you want to load the weights in a new model, you still have to call the fit function to instantiate the variables:

new_model = DANN(encoder=encoder(), task=task(), discriminator=discriminitor(),
                 Xt=Xt, lambda_= 0.1, metrics=["acc"], random_state=0)
new_model.fit(Xs, ys,epochs = 0)
new_model.load_weights('Model.hdf5')

You can set epochs=0 to avoid the training process.

PS: I see that, in your code, you use the accuracy metric. I guess you are solving a classification problem? Be careful with the default parameters of DANN. The loss by default is "mse", but I think you need to use the binary cross-entropy ("bce") or the cross-entropy for multiclass? You can also change the default optimizer in the DANN arguments.

[redaelhail]

yes, that worked, i can now now save the best run. Thank you @antoinedemathelin.

For your remark, I am using the cross-entropy since i am dealing with multiclass classification; it was clear in the documentation that is should be written within the DANN class. Thank you