@Tilo Wรผnscheโ
To continue training an existing Keras/TensorFlow model that is stored in MLFlow, you need to follow the steps below:
- Load the model from MLFlow using mlflow.keras.load_model method.
import mlflow.keras
model = mlflow.keras.load_model("model_uri")
- Freeze the layers of the loaded model that you don't want to retrain.
for layer in model.layers[:-5]:
layer.trainable = False
In this example, the last five layers will be trainable and the rest of the layers will be frozen.
- Compile the model with the desired learning rate and optimizer.
from tensorflow.keras.optimizers import Adam
optimizer = Adam(lr=0.0001)
model.compile(loss="categorical_crossentropy", optimizer=optimizer, metrics=["accuracy"])
In this example, the learning rate is set to 0.0001 and the Adam optimizer is used.
- Continue training the model with the new data. Use the fit method to continue training the model.
model.fit(new_X_train, new_y_train, epochs=10, batch_size=32, validation_data=(X_val, y_val))
In this example, the model is trained for 10 epochs with a batch size of 32.
With these steps, you should be able to load an existing Keras/TensorFlow model stored in MLFlow and continue training it with a different learning rate.