Get learning rate of keras model

Get learning rate of keras model

To get the learning rate of a Keras model during training, you can follow these steps:

  1. Retrieve Optimizer from Model: First, you need to retrieve the optimizer used in your Keras model. The optimizer is an instance of a Keras optimizer class (e.g., Adam, SGD, RMSprop, etc.).

  2. Access Learning Rate: Once you have the optimizer instance, you can access its learning rate by using the lr attribute. Different optimizers may have different attribute names for the learning rate, so make sure to check the documentation for the specific optimizer you're using.

Here's an example of how you can get the learning rate for a model using the Adam optimizer:

from keras.models import Sequential from keras.layers import Dense from keras.optimizers import Adam # Create a simple sequential model model = Sequential() model.add(Dense(10, input_dim=5, activation='relu')) model.add(Dense(1, activation='sigmoid')) # Compile the model with an optimizer (e.g., Adam) optimizer = Adam(learning_rate=0.001) # Set your desired learning rate model.compile(optimizer=optimizer, loss='binary_crossentropy', metrics=['accuracy']) # Get the learning rate from the optimizer learning_rate = model.optimizer.lr.numpy() # Access the 'lr' attribute print("Learning rate:", learning_rate)

In this example, we create a simple model with the Adam optimizer and then access the learning rate using model.optimizer.lr. Remember that the attribute names and methods for retrieving the learning rate may vary depending on the optimizer used.

For optimizers like SGD and RMSprop, you can use model.optimizer.lr in a similar way to retrieve the learning rate.

Keep in mind that the lr attribute provides access to the learning rate value that was set during the optimizer's initialization or later updates. If you modify the learning rate during training, the lr attribute will reflect the updated value.

Examples

  1. How to retrieve the learning rate of a Keras model during training?

    • Description: This query aims to find a method to access the learning rate of a Keras model while it is being trained.
    from keras.optimizers import SGD # Example model compilation with SGD optimizer model.compile(optimizer=SGD(lr=0.01), loss='mse') # Accessing the learning rate of the optimizer learning_rate = model.optimizer.lr print("Learning rate:", learning_rate)

  2. Python code to get the learning rate of a Keras model using optimizer's attribute?

    • Description: This query explores accessing the learning rate of a Keras model by directly querying the optimizer's attribute.
    # Example model compilation with Adam optimizer model.compile(optimizer='adam', loss='mse') # Accessing the learning rate of the optimizer learning_rate = model.optimizer.lr print("Learning rate:", learning_rate)

  3. How to extract the learning rate from a Keras model's optimizer configuration?

    • Description: This query focuses on extracting the learning rate from the configuration of a Keras model's optimizer.
    # Example model compilation with RMSprop optimizer model.compile(optimizer='rmsprop', loss='mse') # Extracting the learning rate from the optimizer's configuration learning_rate = model.optimizer.get_config()['learning_rate'] print("Learning rate:", learning_rate)

  4. Python code to access the learning rate of a Keras model during training iteration?

    • Description: This query seeks to access the learning rate of a Keras model at each training iteration.
    from keras.callbacks import LambdaCallback # Callback function to print learning rate during training print_lr_callback = LambdaCallback(on_epoch_begin=lambda epoch, logs: print("Learning rate:", model.optimizer.lr)) # Example model training with callback model.fit(X_train, y_train, epochs=10, callbacks=[print_lr_callback])

  5. How to print the learning rate of a Keras model during training using callback?

    • Description: This query explores using a callback function to print the learning rate of a Keras model during training.
    from keras.callbacks import LambdaCallback # Callback function to print learning rate during training print_lr_callback = LambdaCallback(on_epoch_begin=lambda epoch, logs: print("Learning rate:", model.optimizer.lr)) # Example model training with callback model.fit(X_train, y_train, epochs=10, callbacks=[print_lr_callback])

  6. Python code to access the learning rate of an SGD optimizer in Keras model?

    • Description: This query aims to access the learning rate of an SGD optimizer used in a Keras model.
    from keras.optimizers import SGD # Example model compilation with SGD optimizer model.compile(optimizer=SGD(lr=0.01), loss='mse') # Accessing the learning rate of the SGD optimizer learning_rate = model.optimizer.lr print("Learning rate:", learning_rate)

  7. How to retrieve the learning rate from a Keras model's SGD optimizer configuration?

    • Description: This query focuses on retrieving the learning rate from the configuration of an SGD optimizer used in a Keras model.
    from keras.optimizers import SGD # Example model compilation with SGD optimizer model.compile(optimizer=SGD(lr=0.01), loss='mse') # Extracting the learning rate from the SGD optimizer's configuration learning_rate = model.optimizer.get_config()['lr'] print("Learning rate:", learning_rate)

  8. Python code to get the learning rate of an Adam optimizer in Keras model?

    • Description: This query aims to get the learning rate of an Adam optimizer used in a Keras model.
    # Example model compilation with Adam optimizer model.compile(optimizer='adam', loss='mse') # Accessing the learning rate of the Adam optimizer learning_rate = model.optimizer.lr print("Learning rate:", learning_rate)

  9. How to access the learning rate from a Keras model's Adam optimizer configuration?

    • Description: This query seeks to access the learning rate from the configuration of an Adam optimizer used in a Keras model.
    # Example model compilation with Adam optimizer model.compile(optimizer='adam', loss='mse') # Extracting the learning rate from the Adam optimizer's configuration learning_rate = model.optimizer.get_config()['learning_rate'] print("Learning rate:", learning_rate)

  10. Python code to obtain the learning rate of a Keras model's optimizer during training?

    • Description: This query aims to obtain the learning rate of a Keras model's optimizer during the training process.
    from keras.callbacks import LambdaCallback # Callback function to print learning rate during training print_lr_callback = LambdaCallback(on_epoch_begin=lambda epoch, logs: print("Learning rate:", model.optimizer.lr)) # Example model training with callback model.fit(X_train, y_train, epochs=10, callbacks=[print_lr_callback])

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