python - How to print the Learning Rate at each epoch with Adam optimizer in Keras?

Python - How to print the Learning Rate at each epoch with Adam optimizer in Keras?

You can print the learning rate at each epoch when using the Adam optimizer in Keras by defining a custom callback. Here's how you can do it:

from keras.callbacks import Callback import keras.backend as K class LearningRateTracker(Callback): def on_epoch_end(self, epoch, logs=None): lr = float(K.get_value(self.model.optimizer.learning_rate)) print(f'Learning rate at epoch {epoch + 1}: {lr}') # Example of usage from keras.models import Sequential from keras.layers import Dense from keras.optimizers import Adam # Define your model model = Sequential() model.add(Dense(64, input_shape=(10,), activation='relu')) model.add(Dense(1, activation='sigmoid')) # Compile your model with the Adam optimizer adam_optimizer = Adam(lr=0.001) # specify the learning rate if not using default model.compile(optimizer=adam_optimizer, loss='binary_crossentropy', metrics=['accuracy']) # Create an instance of the custom callback lr_tracker = LearningRateTracker() # Train your model with fit() method, passing the callback model.fit(X_train, y_train, epochs=10, batch_size=32, callbacks=[lr_tracker])

In this example:

  • We define a custom callback LearningRateTracker that inherits from Callback.
  • Inside the on_epoch_end method of this callback, we retrieve the current learning rate value from the optimizer using K.get_value(self.model.optimizer.learning_rate) and print it.
  • During model training, we create an instance of LearningRateTracker and pass it as a callback to the fit() method.

This way, the learning rate will be printed at the end of each epoch during training. Adjustments can be made to the printing format or the frequency of printing based on your requirements.

Examples

  1. "Print Learning Rate each epoch Keras Adam optimizer"

    • Description: This query seeks a method to display the learning rate during training using the Adam optimizer in Keras.
    from keras.optimizers import Adam # Define the optimizer with the desired learning rate optimizer = Adam(lr=0.001)

  2. "Keras Adam optimizer learning rate per epoch"

    • Description: Users want to monitor how the learning rate changes across epochs when using the Adam optimizer in Keras.
    from keras.callbacks import LearningRateScheduler # Define a learning rate scheduler callback def print_lr(epoch): lr = float(model.optimizer.lr) print(f'Learning rate for epoch {epoch + 1}: {lr}') lr_scheduler = LearningRateScheduler(print_lr)

  3. "Python Keras Adam optimizer LR print per epoch"

    • Description: This query is similar to the previous ones but specifically mentions Python.
    # Inside the model training loop model.fit(x_train, y_train, epochs=epochs, callbacks=[lr_scheduler])

  4. "Display learning rate Adam optimizer Keras"

    • Description: This query is more focused on displaying the learning rate rather than configuring it.
    print("Learning Rate: ", model.optimizer.lr)

  5. "Keras Adam optimizer learning rate tracking"

    • Description: Users want to track how the learning rate changes throughout training while using the Adam optimizer in Keras.
    # Inside the model training loop history = model.fit(x_train, y_train, epochs=epochs, callbacks=[lr_scheduler])

  6. "How to get learning rate per epoch in Keras Adam"

    • Description: This query is about obtaining the learning rate for each epoch specifically with the Adam optimizer in Keras.
    # Define a custom callback to print learning rate per epoch class PrintLR(keras.callbacks.Callback): def on_epoch_end(self, epoch, logs=None): print(f'Learning rate for epoch {epoch + 1}: {float(model.optimizer.lr)}') print_lr_callback = PrintLR()

  7. "Print learning rate Adam optimizer per epoch Keras"

    • Description: This query wants a method to print the learning rate during each epoch when using the Adam optimizer in Keras.
    # Inside the model training loop model.fit(x_train, y_train, epochs=epochs, callbacks=[print_lr_callback])

  8. "Keras Adam optimizer learning rate visualization"

    • Description: Users are looking for a way to visualize the changes in learning rate over epochs with the Adam optimizer in Keras.
    import matplotlib.pyplot as plt # Plotting learning rate over epochs plt.plot(history.history['lr']) plt.title('Learning Rate Over Epochs') plt.xlabel('Epoch') plt.ylabel('Learning Rate') plt.show()

  9. "Access learning rate Adam optimizer Keras"

    • Description: This query is about accessing the learning rate value specifically when using the Adam optimizer in Keras.
    # Accessing the learning rate lr_value = float(model.optimizer.lr)

  10. "Keras Adam optimizer learning rate change visualization"

    • Description: Similar to previous queries, this one emphasizes visualizing the changes in learning rate over epochs.
    # Plotting learning rate change plt.plot(history.history['lr'], marker='o') plt.title('Learning Rate Change Over Epochs') plt.xlabel('Epoch') plt.ylabel('Learning Rate') plt.grid(True) plt.show()

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