Loading a trained Keras model and continue training
Question:
I was wondering if it was possible to save a partly trained Keras model and continue the training after loading the model again.
The reason for this is that I will have more training data in the future and I do not want to retrain the whole model again.
The functions which I am using are:
#Partly train model
model.fit(first_training, first_classes, batch_size=32, nb_epoch=20)
#Save partly trained model
model.save('partly_trained.h5')
#Load partly trained model
from keras.models import load_model
model = load_model('partly_trained.h5')
#Continue training
model.fit(second_training, second_classes, batch_size=32, nb_epoch=20)
Edit 1: added fully working example
With the first dataset after 10 epochs the loss of the last epoch will be 0.0748 and the accuracy 0.9863.
After saving, deleting and reloading the model the loss and accuracy of the model trained on the second dataset will be 0.1711 and 0.9504 respectively.
Is this caused by the new training data or by a completely re-trained model?
"""
Model by: http://machinelearningmastery.com/
"""
# load (downloaded if needed) the MNIST dataset
import numpy
from keras.datasets import mnist
from keras.models import Sequential
from keras.layers import Dense
from keras.utils import np_utils
from keras.models import load_model
numpy.random.seed(7)
def baseline_model():
model = Sequential()
model.add(Dense(num_pixels, input_dim=num_pixels, init='normal', activation='relu'))
model.add(Dense(num_classes, init='normal', activation='softmax'))
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
return model
if __name__ == '__main__':
# load data
(X_train, y_train), (X_test, y_test) = mnist.load_data()
# flatten 28*28 images to a 784 vector for each image
num_pixels = X_train.shape[1] * X_train.shape[2]
X_train = X_train.reshape(X_train.shape[0], num_pixels).astype('float32')
X_test = X_test.reshape(X_test.shape[0], num_pixels).astype('float32')
# normalize inputs from 0-255 to 0-1
X_train = X_train / 255
X_test = X_test / 255
# one hot encode outputs
y_train = np_utils.to_categorical(y_train)
y_test = np_utils.to_categorical(y_test)
num_classes = y_test.shape[1]
# build the model
model = baseline_model()
#Partly train model
dataset1_x = X_train[:3000]
dataset1_y = y_train[:3000]
model.fit(dataset1_x, dataset1_y, nb_epoch=10, batch_size=200, verbose=2)
# Final evaluation of the model
scores = model.evaluate(X_test, y_test, verbose=0)
print("Baseline Error: %.2f%%" % (100-scores[1]*100))
#Save partly trained model
model.save('partly_trained.h5')
del model
#Reload model
model = load_model('partly_trained.h5')
#Continue training
dataset2_x = X_train[3000:]
dataset2_y = y_train[3000:]
model.fit(dataset2_x, dataset2_y, nb_epoch=10, batch_size=200, verbose=2)
scores = model.evaluate(X_test, y_test, verbose=0)
print("Baseline Error: %.2f%%" % (100-scores[1]*100))
Edit 2: tensorflow.keras remarks
For tensorflow.keras change the parameter nb_epochs to epochs in the model fit. The imports and basemodel function are:
import numpy
from tensorflow.keras.datasets import mnist
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense
from tensorflow.keras.utils import to_categorical
from tensorflow.keras.models import load_model
numpy.random.seed(7)
def baseline_model():
model = Sequential()
model.add(Dense(num_pixels, input_dim=num_pixels, activation='relu'))
model.add(Dense(num_classes, activation='softmax'))
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
return model
Answers:
Actually – model.save saves all information need for restarting training in your case. The only thing which could be spoiled by reloading model is your optimizer state. To check that – try to save and reload model and train it on training data.
Notice that Keras sometimes has issues with loaded models, as in here.
This might explain cases in which you don’t start from the same trained accuracy.
The problem might be that you use a different optimizer – or different arguments to your optimizer. I just had the same issue with a custom pretrained model, using
reduce_lr = ReduceLROnPlateau(monitor='loss', factor=lr_reduction_factor,
patience=patience, min_lr=min_lr, verbose=1)
for the pretrained model, whereby the original learning rate starts at 0.0003 and during pre-training it is reduced to the min_learning rate, which is 0.000003
I just copied that line over to the script which uses the pre-trained model and got really bad accuracies. Until I noticed that the last learning rate of the pretrained model was the min learning rate, i.e. 0.000003. And if I start with that learning rate, I get exactly the same accuracies to start with as the output of the pretrained model – which makes sense, as starting with a learning rate that is 100 times bigger than the last learning rate used in the pretrained model will result in a huge overshoot of GD and hence in heavily decreased accuracies.
All above helps, you must resume from same learning rate() as the LR when the model and weights were saved. Set it directly on the optimizer.
Note that improvement from there is not guaranteed, because the model may have reached the local minimum, which may be global. There is no point to resume a model in order to search for another local minimum, unless you intent to increase the learning rate in a controlled fashion and nudge the model into a possibly better minimum not far away.
You might also be hitting Concept Drift, see Should you retrain a model when new observations are available. There’s also the concept of catastrophic forgetting which a bunch of academic papers discuss. Here’s one with MNIST Empirical investigation of catastrophic forgetting
Most of the above answers covered important points. If you are using recent Tensorflow (TF2.1 or above), Then the following example will help you. The model part of the code is from Tensorflow website.
import tensorflow as tf
from tensorflow import keras
mnist = tf.keras.datasets.mnist
(x_train, y_train),(x_test, y_test) = mnist.load_data()
x_train, x_test = x_train / 255.0, x_test / 255.0
def create_model():
model = tf.keras.models.Sequential([
tf.keras.layers.Flatten(input_shape=(28, 28)),
tf.keras.layers.Dense(512, activation=tf.nn.relu),
tf.keras.layers.Dropout(0.2),
tf.keras.layers.Dense(10, activation=tf.nn.softmax)
])
model.compile(optimizer='adam', loss='sparse_categorical_crossentropy',metrics=['accuracy'])
return model
# Create a basic model instance
model=create_model()
model.fit(x_train, y_train, epochs = 10, validation_data = (x_test,y_test),verbose=1)
Please save the model in *.tf format. From my experience, if you have any custom_loss defined, *.h5 format will not save optimizer status and hence will not serve your purpose if you want to retrain the model from where we left.
# saving the model in tensorflow format
model.save('./MyModel_tf',save_format='tf')
# loading the saved model
loaded_model = tf.keras.models.load_model('./MyModel_tf')
# retraining the model
loaded_model.fit(x_train, y_train, epochs = 10, validation_data = (x_test,y_test),verbose=1)
This approach will restart the training where we left before saving the model. As mentioned by others, if you want to save weights of best model or you want to save weights of model every epoch you need to use keras callbacks function (ModelCheckpoint) with options such as save_weights_only=True, save_freq='epoch', and save_best_only.
For more details, please check here and another example here.
If you are using TF2, use the new saved_model method(format pb). More information available here and here.
model.fit(x=X_train, y=y_train, epochs=10,callbacks=[model_callback])#your first training
tf.saved_model.save(model, save_to_dir_path) #save the model
del model #to delete the model
model = tf.keras.models.load_model(save_to_dir_path)
model.fit(x=X_train, y=y_train, epochs=10,callbacks=[model_callback])#your second training
It is completely okay to train a model with a saved model. I trained the saved model with the same data and found it was giving good accuracy. Moreover, the time taken was quite less in each epoch.
Here is the code have a look:
from keras.models import load_model
model = load_model('/content/drive/MyDrive/CustomResNet/saved_models/model_1.h5')
history=model.fit(train_gen,validation_data=valid_gen,epochs=5)
I was wondering if it was possible to save a partly trained Keras model and continue the training after loading the model again.
The reason for this is that I will have more training data in the future and I do not want to retrain the whole model again.
The functions which I am using are:
#Partly train model
model.fit(first_training, first_classes, batch_size=32, nb_epoch=20)
#Save partly trained model
model.save('partly_trained.h5')
#Load partly trained model
from keras.models import load_model
model = load_model('partly_trained.h5')
#Continue training
model.fit(second_training, second_classes, batch_size=32, nb_epoch=20)
Edit 1: added fully working example
With the first dataset after 10 epochs the loss of the last epoch will be 0.0748 and the accuracy 0.9863.
After saving, deleting and reloading the model the loss and accuracy of the model trained on the second dataset will be 0.1711 and 0.9504 respectively.
Is this caused by the new training data or by a completely re-trained model?
"""
Model by: http://machinelearningmastery.com/
"""
# load (downloaded if needed) the MNIST dataset
import numpy
from keras.datasets import mnist
from keras.models import Sequential
from keras.layers import Dense
from keras.utils import np_utils
from keras.models import load_model
numpy.random.seed(7)
def baseline_model():
model = Sequential()
model.add(Dense(num_pixels, input_dim=num_pixels, init='normal', activation='relu'))
model.add(Dense(num_classes, init='normal', activation='softmax'))
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
return model
if __name__ == '__main__':
# load data
(X_train, y_train), (X_test, y_test) = mnist.load_data()
# flatten 28*28 images to a 784 vector for each image
num_pixels = X_train.shape[1] * X_train.shape[2]
X_train = X_train.reshape(X_train.shape[0], num_pixels).astype('float32')
X_test = X_test.reshape(X_test.shape[0], num_pixels).astype('float32')
# normalize inputs from 0-255 to 0-1
X_train = X_train / 255
X_test = X_test / 255
# one hot encode outputs
y_train = np_utils.to_categorical(y_train)
y_test = np_utils.to_categorical(y_test)
num_classes = y_test.shape[1]
# build the model
model = baseline_model()
#Partly train model
dataset1_x = X_train[:3000]
dataset1_y = y_train[:3000]
model.fit(dataset1_x, dataset1_y, nb_epoch=10, batch_size=200, verbose=2)
# Final evaluation of the model
scores = model.evaluate(X_test, y_test, verbose=0)
print("Baseline Error: %.2f%%" % (100-scores[1]*100))
#Save partly trained model
model.save('partly_trained.h5')
del model
#Reload model
model = load_model('partly_trained.h5')
#Continue training
dataset2_x = X_train[3000:]
dataset2_y = y_train[3000:]
model.fit(dataset2_x, dataset2_y, nb_epoch=10, batch_size=200, verbose=2)
scores = model.evaluate(X_test, y_test, verbose=0)
print("Baseline Error: %.2f%%" % (100-scores[1]*100))
Edit 2: tensorflow.keras remarks
For tensorflow.keras change the parameter nb_epochs to epochs in the model fit. The imports and basemodel function are:
import numpy
from tensorflow.keras.datasets import mnist
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense
from tensorflow.keras.utils import to_categorical
from tensorflow.keras.models import load_model
numpy.random.seed(7)
def baseline_model():
model = Sequential()
model.add(Dense(num_pixels, input_dim=num_pixels, activation='relu'))
model.add(Dense(num_classes, activation='softmax'))
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
return model
Actually – model.save saves all information need for restarting training in your case. The only thing which could be spoiled by reloading model is your optimizer state. To check that – try to save and reload model and train it on training data.
Notice that Keras sometimes has issues with loaded models, as in here.
This might explain cases in which you don’t start from the same trained accuracy.
The problem might be that you use a different optimizer – or different arguments to your optimizer. I just had the same issue with a custom pretrained model, using
reduce_lr = ReduceLROnPlateau(monitor='loss', factor=lr_reduction_factor,
patience=patience, min_lr=min_lr, verbose=1)
for the pretrained model, whereby the original learning rate starts at 0.0003 and during pre-training it is reduced to the min_learning rate, which is 0.000003
I just copied that line over to the script which uses the pre-trained model and got really bad accuracies. Until I noticed that the last learning rate of the pretrained model was the min learning rate, i.e. 0.000003. And if I start with that learning rate, I get exactly the same accuracies to start with as the output of the pretrained model – which makes sense, as starting with a learning rate that is 100 times bigger than the last learning rate used in the pretrained model will result in a huge overshoot of GD and hence in heavily decreased accuracies.
All above helps, you must resume from same learning rate() as the LR when the model and weights were saved. Set it directly on the optimizer.
Note that improvement from there is not guaranteed, because the model may have reached the local minimum, which may be global. There is no point to resume a model in order to search for another local minimum, unless you intent to increase the learning rate in a controlled fashion and nudge the model into a possibly better minimum not far away.
You might also be hitting Concept Drift, see Should you retrain a model when new observations are available. There’s also the concept of catastrophic forgetting which a bunch of academic papers discuss. Here’s one with MNIST Empirical investigation of catastrophic forgetting
Most of the above answers covered important points. If you are using recent Tensorflow (TF2.1 or above), Then the following example will help you. The model part of the code is from Tensorflow website.
import tensorflow as tf
from tensorflow import keras
mnist = tf.keras.datasets.mnist
(x_train, y_train),(x_test, y_test) = mnist.load_data()
x_train, x_test = x_train / 255.0, x_test / 255.0
def create_model():
model = tf.keras.models.Sequential([
tf.keras.layers.Flatten(input_shape=(28, 28)),
tf.keras.layers.Dense(512, activation=tf.nn.relu),
tf.keras.layers.Dropout(0.2),
tf.keras.layers.Dense(10, activation=tf.nn.softmax)
])
model.compile(optimizer='adam', loss='sparse_categorical_crossentropy',metrics=['accuracy'])
return model
# Create a basic model instance
model=create_model()
model.fit(x_train, y_train, epochs = 10, validation_data = (x_test,y_test),verbose=1)
Please save the model in *.tf format. From my experience, if you have any custom_loss defined, *.h5 format will not save optimizer status and hence will not serve your purpose if you want to retrain the model from where we left.
# saving the model in tensorflow format
model.save('./MyModel_tf',save_format='tf')
# loading the saved model
loaded_model = tf.keras.models.load_model('./MyModel_tf')
# retraining the model
loaded_model.fit(x_train, y_train, epochs = 10, validation_data = (x_test,y_test),verbose=1)
This approach will restart the training where we left before saving the model. As mentioned by others, if you want to save weights of best model or you want to save weights of model every epoch you need to use keras callbacks function (ModelCheckpoint) with options such as save_weights_only=True, save_freq='epoch', and save_best_only.
For more details, please check here and another example here.
If you are using TF2, use the new saved_model method(format pb). More information available here and here.
model.fit(x=X_train, y=y_train, epochs=10,callbacks=[model_callback])#your first training
tf.saved_model.save(model, save_to_dir_path) #save the model
del model #to delete the model
model = tf.keras.models.load_model(save_to_dir_path)
model.fit(x=X_train, y=y_train, epochs=10,callbacks=[model_callback])#your second training
It is completely okay to train a model with a saved model. I trained the saved model with the same data and found it was giving good accuracy. Moreover, the time taken was quite less in each epoch.
Here is the code have a look:
from keras.models import load_model
model = load_model('/content/drive/MyDrive/CustomResNet/saved_models/model_1.h5')
history=model.fit(train_gen,validation_data=valid_gen,epochs=5)