Loading a trained Keras model and continue training

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.

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 tffrom tensorflow import kerasmnist = 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 / 255.0def 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 instancemodel=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 formatmodel.save('./MyModel_tf',save_format='tf')# loading the saved modelloaded_model = tf.keras.models.load_model('./MyModel_tf')# retraining the modelloaded_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.

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.