Loading a saved model to resume training

Question

I'm training a ResNet model to classify car brands.

I saved the weights during training for every epoch.

For a test, I stopped the training at epoch 3.

# checkpoint = ModelCheckpoint("best_model.hdf5", monitor='loss', verbose=1)
checkpoint_path = "weights/cp-{epoch:04d}.ckpt"
checkpoint_dir = os.path.dirname(checkpoint_path)

cp_callback = tf.keras.callbacks.ModelCheckpoint(
   checkpoint_path, verbose=1, 
   # Save weights, every epoch.
   save_freq='epoch')

model.save_weights(checkpoint_path.format(epoch=0))

history = model.fit_generator(
    training_set,
    validation_data = test_set,
    epochs = 50,
    steps_per_epoch = len(training_set),
    validation_steps = len(test_set),
    callbacks = [cp_callback]
)

However, when loading them, I am unsure if it is resuming from the last epoch saved one since it says epoch 1/50 again. Below is the code I use to load the last saved model.

from keras.models import Sequential, load_model
# load the model
new_model = load_model('./weights/cp-0003.ckpt')

# fit the model
history = new_model.fit_generator(
    training_set,
    validation_data = test_set,
    epochs = 50,
    steps_per_epoch = len(training_set),
    validation_steps = len(test_set),
    callbacks = [cp_callback]
)

This is what it looks like: Image showing that running the saved weight starts from epoch 1/50 again

Can someone please help?

Answer 1

You can use the initial_epoch argument of the fit_generator. By default, it is set to 0 but you can set it to any positive number:

from keras.models import Sequential, load_model
import tensorflow as tf

checkpoint_path = "weights/cp-{epoch:04d}.ckpt"
checkpoint_dir = os.path.dirname(checkpoint_path)

cp_callback = tf.keras.callbacks.ModelCheckpoint(
   checkpoint_path, verbose=1, 
   # Save weights, every epoch.
   save_freq='epoch')

model.save_weights(checkpoint_path.format(epoch=0))

history = model.fit_generator(
    training_set,
    validation_data=test_set,
    epochs=3,
    steps_per_epoch=len(training_set),
    validation_steps=len(test_set),
    callbacks = [cp_callback]
)


new_model = load_model('./weights/cp-0003.ckpt')

# fit the model
history = new_model.fit_generator(
    training_set,
    validation_data=test_set,
    epochs=50,
    steps_per_epoch=len(training_set),
    validation_steps=len(test_set),
    callbacks=[cp_callback],
    initial_epoch=3
)

This will train your model for 50 - 3 = 47 additional epochs.

---

Some remarks regarding your code if you use Tensorflow 2.X:

• fit_generator is deprecated since fit supports generator now
• you should replace your import from keras.... to from tensorflow.keras...