TensorBoard, Checkpoints, and Custom Hooks in Keras

Content Overview Introduction Setup Keras callbacks overview An overview of callback methods Global methods Batch-level methods for training/testing/predicting Epoch-level methods (training only) A basic example Usage of logs dict Usage of self.model attribute Examples of Keras callback applications Early stopping at minimum loss Learning rate scheduling Built-in Keras callbacks Introduction Introduction Setup Setup Keras callbacks overview Keras callbacks overview An overview of callback methods An overview of callback methods Global methods Global methods Batch-level methods for training/testing/predicting Batch-level methods for training/testing/predicting Epoch-level methods (training only) Epoch-level methods (training only) A basic example A basic example Usage of logs dict Usage of logs dict Usage of self.model attribute Usage of self.model attribute Examples of Keras callback applications Examples of Keras callback applications Early stopping at minimum loss Early stopping at minimum loss Learning rate scheduling Learning rate scheduling Built-in Keras callbacks Built-in Keras callbacks Introduction A callback is a powerful tool to customize the behavior of a Keras model during training, evaluation, or inference. Examples include tf.keras.callbacks.TensorBoard to visualize training progress and results with TensorBoard, or tf.keras.callbacks.ModelCheckpoint to periodically save your model during training. tf.keras.callbacks.TensorBoard tf.keras.callbacks.ModelCheckpoint In this guide, you will learn what a Keras callback is, what it can do, and how you can build your own. We provide a few demos of simple callback applications to get you started. Setup import tensorflow as tf from tensorflow import keras import tensorflow as tf from tensorflow import keras Keras callbacks overview All callbacks subclass the keras.callbacks.Callback class, and override a set of methods called at various stages of training, testing, and predicting. Callbacks are useful to get a view on internal states and statistics of the model during training. keras.callbacks.Callback You can pass a list of callbacks (as the keyword argument callbacks) to the following model methods: callbacks keras.Model.fit() keras.Model.evaluate() keras.Model.predict() keras.Model.fit() keras.Model.fit() keras.Model.evaluate() keras.Model.evaluate() keras.Model.predict() keras.Model.predict() An overview of callback methods Global methods on_(train|test|predict)_begin(self, logs=None) on_(train|test|predict)_begin(self, logs=None) Called at the beginning of fit/evaluate/predict. fit evaluate predict on_(train|test|predict)_end(self, logs=None) on_(train|test|predict)_end(self, logs=None) Called at the end of fit/evaluate/predict. fit evaluate predict Batch-level methods for training/testing/predicting on_(train|test|predict)_batch_begin(self, batch, logs=None) on_(train|test|predict)_batch_begin(self, batch, logs=None) Called right before processing a batch during training/testing/predicting. on_(train|test|predict)_batch_end(self, batch, logs=None) on_(train|test|predict)_batch_end(self, batch, logs=None) Called at the end of training/testing/predicting a batch. Within this method, logs is a dict containing the metrics results. logs Epoch-level methods (training only) on_epoch_begin(self, epoch, logs=None) on_epoch_begin(self, epoch, logs=None) Called at the beginning of an epoch during training. on_epoch_end(self, epoch, logs=None) on_epoch_end(self, epoch, logs=None) Called at the end of an epoch during training. A basic example Let's take a look at a concrete example. To get started, let's import tensorflow and define a simple Sequential Keras model: # Define the Keras model to add callbacks to def get_model(): model = keras.Sequential() model.add(keras.layers.Dense(1, input_dim=784)) model.compile( optimizer=keras.optimizers.RMSprop(learning_rate=0.1), loss="mean_squared_error", metrics=["mean_absolute_error"], ) return model # Define the Keras model to add callbacks to def get_model(): model = keras.Sequential() model.add(keras.layers.Dense(1, input_dim=784)) model.compile( optimizer=keras.optimizers.RMSprop(learning_rate=0.1), loss="mean_squared_error", metrics=["mean_absolute_error"], ) return model Then, load the MNIST data for training and testing from Keras datasets API: # Load example MNIST data and pre-process it (x_train, y_train), (x_test, y_test) = tf.keras.datasets.mnist.load_data() x_train = x_train.reshape(-1, 784).astype("float32") / 255.0 x_test = x_test.reshape(-1, 784).astype("float32") / 255.0 # Limit the data to 1000 samples x_train = x_train[:1000] y_train = y_train[:1000] x_test = x_test[:1000] y_test = y_test[:1000] # Load example MNIST data and pre-process it (x_train, y_train), (x_test, y_test) = tf.keras.datasets.mnist.load_data() x_train = x_train.reshape(-1, 784).astype("float32") / 255.0 x_test = x_test.reshape(-1, 784).astype("float32") / 255.0 # Limit the data to 1000 samples x_train = x_train[:1000] y_train = y_train[:1000] x_test = x_test[:1000] y_test = y_test[:1000] Now, define a simple custom callback that logs: When fit/evaluate/predict starts & ends When each epoch starts & ends When each training batch starts & ends When each evaluation (test) batch starts & ends When each inference (prediction) batch starts & ends When fit/evaluate/predict starts & ends fit evaluate predict When each epoch starts & ends When each training batch starts & ends When each evaluation (test) batch starts & ends When each inference (prediction) batch starts & ends class CustomCallback(keras.callbacks.Callback): def on_train_begin(self, logs=None): keys = list(logs.keys()) print("Starting training; got log keys: {}".format(keys)) def on_train_end(self, logs=None): keys = list(logs.keys()) print("Stop training; got log keys: {}".format(keys)) def on_epoch_begin(self, epoch, logs=None): keys = list(logs.keys()) print("Start epoch {} of training; got log keys: {}".format(epoch, keys)) def on_epoch_end(self, epoch, logs=None): keys = list(logs.keys()) print("End epoch {} of training; got log keys: {}".format(epoch, keys)) def on_test_begin(self, logs=None): keys = list(logs.keys()) print("Start testing; got log keys: {}".format(keys)) def on_test_end(self, logs=None): keys = list(logs.keys()) print("Stop testing; got log keys: {}".format(keys)) def on_predict_begin(self, logs=None): keys = list(logs.keys()) print("Start predicting; got log keys: {}".format(keys)) def on_predict_end(self, logs=None): keys = list(logs.keys()) print("Stop predicting; got log keys: {}".format(keys)) def on_train_batch_begin(self, batch, logs=None): keys = list(logs.keys()) print("...Training: start of batch {}; got log keys: {}".format(batch, keys)) def on_train_batch_end(self, batch, logs=None): keys = list(logs.keys()) print("...Training: end of batch {}; got log keys: {}".format(batch, keys)) def on_test_batch_begin(self, batch, logs=None): keys = list(logs.keys()) print("...Evaluating: start of batch {}; got log keys: {}".format(batch, keys)) def on_test_batch_end(self, batch, logs=None): keys = list(logs.keys()) print("...Evaluating: end of batch {}; got log keys: {}".format(batch, keys)) def on_predict_batch_begin(self, batch, logs=None): keys = list(logs.keys()) print("...Predicting: start of batch {}; got log keys: {}".format(batch, keys)) def on_predict_batch_end(self, batch, logs=None): keys = list(logs.keys()) print("...Predicting: end of batch {}; got log keys: {}".format(batch, keys)) class CustomCallback(keras.callbacks.Callback): def on_train_begin(self, logs=None): keys = list(logs.keys()) print("Starting training; got log keys: {}".format(keys)) def on_train_end(self, logs=None): keys = list(logs.keys()) print("Stop training; got log keys: {}".format(keys)) def on_epoch_begin(self, epoch, logs=None): keys = list(logs.keys()) print("Start epoch {} of training; got log keys: {}".format(epoch, keys)) def on_epoch_end(self, epoch, logs=None): keys = list(logs.keys()) print("End epoch {} of training; got log keys: {}".format(epoch, keys)) def on_test_begin(self, logs=None): keys = list(logs.keys()) print("Start testing; got log keys: {}".format(keys)) def on_test_end(self, logs=None): keys = list(logs.keys()) print("Stop testing; got log keys: {}".format(keys)) def on_predict_begin(self, logs=None): keys = list(logs.keys()) print("Start predicting; got log keys: {}".format(keys)) def on_predict_end(self, logs=None): keys = list(logs.keys()) print("Stop predicting; got log keys: {}".format(keys)) def on_train_batch_begin(self, batch, logs=None): keys = list(logs.keys()) print("...Training: start of batch {}; got log keys: {}".format(batch, keys)) def on_train_batch_end(self, batch, logs=None): keys = list(logs.keys()) print("...Training: end of batch {}; got log keys: {}".format(batch, keys)) def on_test_batch_begin(self, batch, logs=None): keys = list(logs.keys()) print("...Evaluating: start of batch {}; got log keys: {}".format(batch, keys)) def on_test_batch_end(self, batch, logs=None): keys = list(logs.keys()) print("...Evaluating: end of batch {}; got log keys: {}".format(batch, keys)) def on_predict_batch_begin(self, batch, logs=None): keys = list(logs.keys()) print("...Predicting: start of batch {}; got log keys: {}".format(batch, keys)) def on_predict_batch_end(self, batch, logs=None): keys = list(logs.keys()) print("...Predicting: end of batch {}; got log keys: {}".format(batch, keys)) Let's try it out: model = get_model() model.fit( x_train, y_train, batch_size=128, epochs=1, verbose=0, validation_split=0.5, callbacks=[CustomCallback()], ) res = model.evaluate( x_test, y_test, batch_size=128, verbose=0, callbacks=[CustomCallback()] ) res = model.predict(x_test, batch_size=128, callbacks=[CustomCallback()]) model = get_model() model.fit( x_train, y_train, batch_size=128, epochs=1, verbose=0, validation_split=0.5, callbacks=[CustomCallback()], ) res = model.evaluate( x_test, y_test, batch_size=128, verbose=0, callbacks=[CustomCallback()] ) res = model.predict(x_test, batch_size=128, callbacks=[CustomCallback()]) Starting training; got log keys: [] Start epoch 0 of training; got log keys: [] ...Training: start of batch 0; got log keys: [] ...Training: end of batch 0; got log keys: ['loss', 'mean_absolute_error'] ...Training: start of batch 1; got log keys: [] ...Training: end of batch 1; got log keys: ['loss', 'mean_absolute_error'] ...Training: start of batch 2; got log keys: [] ...Training: end of batch 2; got log keys: ['loss', 'mean_absolute_error'] ...Training: start of batch 3; got log keys: [] ...Training: end of batch 3; got log keys: ['loss', 'mean_absolute_error'] Start testing; got log keys: [] ...Evaluating: start of batch 0; got log keys: [] ...Evaluating: end of batch 0; got log keys: ['loss', 'mean_absolute_error'] ...Evaluating: start of batch 1; got log keys: [] ...Evaluating: end of batch 1; got log keys: ['loss', 'mean_absolute_error'] ...Evaluating: start of batch 2; got log keys: [] ...Evaluating: end of batch 2; got log keys: ['loss', 'mean_absolute_error'] ...Evaluating: start of batch 3; got log keys: [] ...Evaluating: end of batch 3; got log keys: ['loss', 'mean_absolute_error'] Stop testing; got log keys: ['loss', 'mean_absolute_error'] End epoch 0 of training; got log keys: ['loss', 'mean_absolute_error', 'val_loss', 'val_mean_absolute_error'] Stop training; got log keys: ['loss', 'mean_absolute_error', 'val_loss', 'val_mean_absolute_error'] Start testing; got log keys: [] ...Evaluating: start of batch 0; got log keys: [] ...Evaluating: end of batch 0; got log keys: ['loss', 'mean_absolute_error'] ...Evaluating: start of batch 1; got log keys: [] ...Evaluating: end of batch 1; got log keys: ['loss', 'mean_absolute_error'] ...Evaluating: start of batch 2; got log keys: [] ...Evaluating: end of batch 2; got log keys: ['loss', 'mean_absolute_error'] ...Evaluating: start of batch 3; got log keys: [] ...Evaluating: end of batch 3; got log keys: ['loss', 'mean_absolute_error'] ...Evaluating: start of batch 4; got log keys: [] ...Evaluating: end of batch 4; got log keys: ['loss', 'mean_absolute_error'] ...Evaluating: start of batch 5; got log keys: [] ...Evaluating: end of batch 5; got log keys: ['loss', 'mean_absolute_error'] ...Evaluating: start of batch 6; got log keys: [] ...Evaluating: end of batch 6; got log keys: ['loss', 'mean_absolute_error'] ...Evaluating: start of batch 7; got log keys: [] ...Evaluating: end of batch 7; got log keys: ['loss', 'mean_absolute_error'] Stop testing; got log keys: ['loss', 'mean_absolute_error'] Start predicting; got log keys: [] ...Predicting: start of batch 0; got log keys: [] ...Predicting: end of batch 0; got log keys: ['outputs'] 1/8 [==>...........................] - ETA: 0s...Predicting: start of batch 1; got log keys: [] ...Predicting: end of batch 1; got log keys: ['outputs'] ...Predicting: start of batch 2; got log keys: [] ...Predicting: end of batch 2; got log keys: ['outputs'] ...Predicting: start of batch 3; got log keys: [] ...Predicting: end of batch 3; got log keys: ['outputs'] ...Predicting: start of batch 4; got log keys: [] ...Predicting: end of batch 4; got log keys: ['outputs'] ...Predicting: start of batch 5; got log keys: [] ...Predicting: end of batch 5; got log keys: ['outputs'] ...Predicting: start of batch 6; got log keys: [] ...Predicting: end of batch 6; got log keys: ['outputs'] ...Predicting: start of batch 7; got log keys: [] ...Predicting: end of batch 7; got log keys: ['outputs'] Stop predicting; got log keys: [] 8/8 [==============================] - 0s 2ms/step Starting training; got log keys: [] Start epoch 0 of training; got log keys: [] ...Training: start of batch 0; got log keys: [] ...Training: end of batch 0; got log keys: ['loss', 'mean_absolute_error'] ...Training: start of batch 1; got log keys: [] ...Training: end of batch 1; got log keys: ['loss', 'mean_absolute_error'] ...Training: start of batch 2; got log keys: [] ...Training: end of batch 2; got log keys: ['loss', 'mean_absolute_error'] ...Training: start of batch 3; got log keys: [] ...Training: end of batch 3; got log keys: ['loss', 'mean_absolute_error'] Start testing; got log keys: [] ...Evaluating: start of batch 0; got log keys: [] ...Evaluating: end of batch 0; got log keys: ['loss', 'mean_absolute_error'] ...Evaluating: start of batch 1; got log keys: [] ...Evaluating: end of batch 1; got log keys: ['loss', 'mean_absolute_error'] ...Evaluating: start of batch 2; got log keys: [] ...Evaluating: end of batch 2; got log keys: ['loss', 'mean_absolute_error'] ...Evaluating: start of batch 3; got log keys: [] ...Evaluating: end of batch 3; got log keys: ['loss', 'mean_absolute_error'] Stop testing; got log keys: ['loss', 'mean_absolute_error'] End epoch 0 of training; got log keys: ['loss', 'mean_absolute_error', 'val_loss', 'val_mean_absolute_error'] Stop training; got log keys: ['loss', 'mean_absolute_error', 'val_loss', 'val_mean_absolute_error'] Start testing; got log keys: [] ...Evaluating: start of batch 0; got log keys: [] ...Evaluating: end of batch 0; got log keys: ['loss', 'mean_absolute_error'] ...Evaluating: start of batch 1; got log keys: [] ...Evaluating: end of batch 1; got log keys: ['loss', 'mean_absolute_error'] ...Evaluating: start of batch 2; got log keys: [] ...Evaluating: end of batch 2; got log keys: ['loss', 'mean_absolute_error'] ...Evaluating: start of batch 3; got log keys: [] ...Evaluating: end of batch 3; got log keys: ['loss', 'mean_absolute_error'] ...Evaluating: start of batch 4; got log keys: [] ...Evaluating: end of batch 4; got log keys: ['loss', 'mean_absolute_error'] ...Evaluating: start of batch 5; got log keys: [] ...Evaluating: end of batch 5; got log keys: ['loss', 'mean_absolute_error'] ...Evaluating: start of batch 6; got log keys: [] ...Evaluating: end of batch 6; got log keys: ['loss', 'mean_absolute_error'] ...Evaluating: start of batch 7; got log keys: [] ...Evaluating: end of batch 7; got log keys: ['loss', 'mean_absolute_error'] Stop testing; got log keys: ['loss', 'mean_absolute_error'] Start predicting; got log keys: [] ...Predicting: start of batch 0; got log keys: [] ...Predicting: end of batch 0; got log keys: ['outputs'] 1/8 [==>...........................] - ETA: 0s...Predicting: start of batch 1; got log keys: [] ...Predicting: end of batch 1; got log keys: ['outputs'] ...Predicting: start of batch 2; got log keys: [] ...Predicting: end of batch 2; got log keys: ['outputs'] ...Predicting: start of batch 3; got log keys: [] ...Predicting: end of batch 3; got log keys: ['outputs'] ...Predicting: start of batch 4; got log keys: [] ...Predicting: end of batch 4; got log keys: ['outputs'] ...Predicting: start of batch 5; got log keys: [] ...Predicting: end of batch 5; got log keys: ['outputs'] ...Predicting: start of batch 6; got log keys: [] ...Predicting: end of batch 6; got log keys: ['outputs'] ...Predicting: start of batch 7; got log keys: [] ...Predicting: end of batch 7; got log keys: ['outputs'] Stop predicting; got log keys: [] 8/8 [==============================] - 0s 2ms/step Usage of logs dict logs The logs dict contains the loss value, and all the metrics at the end of a batch or epoch. Example includes the loss and mean absolute error. logs class LossAndErrorPrintingCallback(keras.callbacks.Callback): def on_train_batch_end(self, batch, logs=None): print( "Up to batch {}, the average loss is {:7.2f}.".format(batch, logs["loss"]) ) def on_test_batch_end(self, batch, logs=None): print( "Up to batch {}, the average loss is {:7.2f}.".format(batch, logs["loss"]) ) def on_epoch_end(self, epoch, logs=None): print( "The average loss for epoch {} is {:7.2f} " "and mean absolute error is {:7.2f}.".format( epoch, logs["loss"], logs["mean_absolute_error"] ) ) model = get_model() model.fit( x_train, y_train, batch_size=128, epochs=2, verbose=0, callbacks=[LossAndErrorPrintingCallback()], ) res = model.evaluate( x_test, y_test, batch_size=128, verbose=0, callbacks=[LossAndErrorPrintingCallback()], ) class LossAndErrorPrintingCallback(keras.callbacks.Callback): def on_train_batch_end(self, batch, logs=None): print( "Up to batch {}, the average loss is {:7.2f}.".format(batch, logs["loss"]) ) def on_test_batch_end(self, batch, logs=None): print( "Up to batch {}, the average loss is {:7.2f}.".format(batch, logs["loss"]) ) def on_epoch_end(self, epoch, logs=None): print( "The average loss for epoch {} is {:7.2f} " "and mean absolute error is {:7.2f}.".format( epoch, logs["loss"], logs["mean_absolute_error"] ) ) model = get_model() model.fit( x_train, y_train, batch_size=128, epochs=2, verbose=0, callbacks=[LossAndErrorPrintingCallback()], ) res = model.evaluate( x_test, y_test, batch_size=128, verbose=0, callbacks=[LossAndErrorPrintingCallback()], ) Up to batch 0, the average loss is 33.08. Up to batch 1, the average loss is 429.70. Up to batch 2, the average loss is 293.82. Up to batch 3, the average loss is 222.52. Up to batch 4, the average loss is 179.47. Up to batch 5, the average loss is 150.49. Up to batch 6, the average loss is 129.87. Up to batch 7, the average loss is 116.92. The average loss for epoch 0 is 116.92 and mean absolute error is 5.88. Up to batch 0, the average loss is 5.29. Up to batch 1, the average loss is 4.86. Up to batch 2, the average loss is 4.66. Up to batch 3, the average loss is 4.54. Up to batch 4, the average loss is 4.50. Up to batch 5, the average loss is 4.38. Up to batch 6, the average loss is 4.39. Up to batch 7, the average loss is 4.33. The average loss for epoch 1 is 4.33 and mean absolute error is 1.68. Up to batch 0, the average loss is 5.21. Up to batch 1, the average loss is 4.73. Up to batch 2, the average loss is 4.68. Up to batch 3, the average loss is 4.57. Up to batch 4, the average loss is 4.70. Up to batch 5, the average loss is 4.71. Up to batch 6, the average loss is 4.63. Up to batch 7, the average loss is 4.56. Up to batch 0, the average loss is 33.08. Up to batch 1, the average loss is 429.70. Up to batch 2, the average loss is 293.82. Up to batch 3, the average loss is 222.52. Up to batch 4, the average loss is 179.47. Up to batch 5, the average loss is 150.49. Up to batch 6, the average loss is 129.87. Up to batch 7, the average loss is 116.92. The average loss for epoch 0 is 116.92 and mean absolute error is 5.88. Up to batch 0, the average loss is 5.29. Up to batch 1, the average loss is 4.86. Up to batch 2, the average loss is 4.66. Up to batch 3, the average loss is 4.54. Up to batch 4, the average loss is 4.50. Up to batch 5, the average loss is 4.38. Up to batch 6, the average loss is 4.39. Up to batch 7, the average loss is 4.33. The average loss for epoch 1 is 4.33 and mean absolute error is 1.68. Up to batch 0, the average loss is 5.21. Up to batch 1, the average loss is 4.73. Up to batch 2, the average loss is 4.68. Up to batch 3, the average loss is 4.57. Up to batch 4, the average loss is 4.70. Up to batch 5, the average loss is 4.71. Up to batch 6, the average loss is 4.63. Up to batch 7, the average loss is 4.56. Usage of self.model attribute self.model In addition to receiving log information when one of their methods is called, callbacks have access to the model associated with the current round of training/evaluation/inference: self.model. self.model Here are a few of the things you can do with self.model in a callback: self.model Set self.model.stop_training = True to immediately interrupt training. Mutate hyperparameters of the optimizer (available as self.model.optimizer), such as self.model.optimizer.learning_rate. Save the model at period intervals. Record the output of model.predict() on a few test samples at the end of each epoch, to use as a sanity check during training. Extract visualizations of intermediate features at the end of each epoch, to monitor what the model is learning over time. etc. Set self.model.stop_training = True to immediately interrupt training. self.model.stop_training = True Mutate hyperparameters of the optimizer (available as self.model.optimizer), such as self.model.optimizer.learning_rate. self.model.optimizer self.model.optimizer.learning_rate Save the model at period intervals. Record the output of model.predict() on a few test samples at the end of each epoch, to use as a sanity check during training. model.predict() Extract visualizations of intermediate features at the end of each epoch, to monitor what the model is learning over time. etc. Let's see this in action in a couple of examples. Examples of Keras callback applications Early stopping at minimum loss This first example shows the creation of a Callback that stops training when the minimum of loss has been reached, by setting the attribute self.model.stop_training (boolean). Optionally, you can provide an argument patience to specify how many epochs we should wait before stopping after having reached a local minimum. Callback self.model.stop_training patience tf.keras.callbacks.EarlyStopping provides a more complete and general implementation. tf.keras.callbacks.EarlyStopping import numpy as np class EarlyStoppingAtMinLoss(keras.callbacks.Callback): """Stop training when the loss is at its min, i.e. the loss stops decreasing. Arguments: patience: Number of epochs to wait after min has been hit. After this number of no improvement, training stops. """ def __init__(self, patience=0): super().__init__() self.patience = patience # best_weights to store the weights at which the minimum loss occurs. self.best_weights = None def on_train_begin(self, logs=None): # The number of epoch it has waited when loss is no longer minimum. self.wait = 0 # The epoch the training stops at. self.stopped_epoch = 0 # Initialize the best as infinity. self.best = np.inf def on_epoch_end(self, epoch, logs=None): current = logs.get("loss") if np.less(current, self.best): self.best = current self.wait = 0 # Record the best weights if current results is better (less). self.best_weights = self.model.get_weights() else: self.wait += 1 if self.wait >= self.patience: self.stopped_epoch = epoch self.model.stop_training = True print("Restoring model weights from the end of the best epoch.") self.model.set_weights(self.best_weights) def on_train_end(self, logs=None): if self.stopped_epoch > 0: print("Epoch %05d: early stopping" % (self.stopped_epoch + 1)) model = get_model() model.fit( x_train, y_train, batch_size=64, steps_per_epoch=5, epochs=30, verbose=0, callbacks=[LossAndErrorPrintingCallback(), EarlyStoppingAtMinLoss()], ) import numpy as np class EarlyStoppingAtMinLoss(keras.callbacks.Callback): """Stop training when the loss is at its min, i.e. the loss stops decreasing. Arguments: patience: Number of epochs to wait after min has been hit. After this number of no improvement, training stops. """ def __init__(self, patience=0): super().__init__() self.patience = patience # best_weights to store the weights at which the minimum loss occurs. self.best_weights = None def on_train_begin(self, logs=None): # The number of epoch it has waited when loss is no longer minimum. self.wait = 0 # The epoch the training stops at. self.stopped_epoch = 0 # Initialize the best as infinity. self.best = np.inf def on_epoch_end(self, epoch, logs=None): current = logs.get("loss") if np.less(current, self.best): self.best = current self.wait = 0 # Record the best weights if current results is better (less). self.best_weights = self.model.get_weights() else: self.wait += 1 if self.wait >= self.patience: self.stopped_epoch = epoch self.model.stop_training = True print("Restoring model weights from the end of the best epoch.") self.model.set_weights(self.best_weights) def on_train_end(self, logs=None): if self.stopped_epoch > 0: print("Epoch %05d: early stopping" % (self.stopped_epoch + 1)) model = get_model() model.fit( x_train, y_train, batch_size=64, steps_per_epoch=5, epochs=30, verbose=0, callbacks=[LossAndErrorPrintingCallback(), EarlyStoppingAtMinLoss()], ) Up to batch 0, the average loss is 23.53. Up to batch 1, the average loss is 480.92. Up to batch 2, the average loss is 328.49. Up to batch 3, the average loss is 248.52. Up to batch 4, the average loss is 200.53. The average loss for epoch 0 is 200.53 and mean absolute error is 8.30. Up to batch 0, the average loss is 5.02. Up to batch 1, the average loss is 5.80. Up to batch 2, the average loss is 5.51. Up to batch 3, the average loss is 5.38. Up to batch 4, the average loss is 5.42. The average loss for epoch 1 is 5.42 and mean absolute error is 1.90. Up to batch 0, the average loss is 5.80. Up to batch 1, the average loss is 6.89. Up to batch 2, the average loss is 6.68. Up to batch 3, the average loss is 6.35. Up to batch 4, the average loss is 6.57. The average loss for epoch 2 is 6.57 and mean absolute error is 2.07. Restoring model weights from the end of the best epoch. Epoch 00003: early stopping Up to batch 0, the average loss is 23.53. Up to batch 1, the average loss is 480.92. Up to batch 2, the average loss is 328.49. Up to batch 3, the average loss is 248.52. Up to batch 4, the average loss is 200.53. The average loss for epoch 0 is 200.53 and mean absolute error is 8.30. Up to batch 0, the average loss is 5.02. Up to batch 1, the average loss is 5.80. Up to batch 2, the average loss is 5.51. Up to batch 3, the average loss is 5.38. Up to batch 4, the average loss is 5.42. The average loss for epoch 1 is 5.42 and mean absolute error is 1.90. Up to batch 0, the average loss is 5.80. Up to batch 1, the average loss is 6.89. Up to batch 2, the average loss is 6.68. Up to batch 3, the average loss is 6.35. Up to batch 4, the average loss is 6.57. The average loss for epoch 2 is 6.57 and mean absolute error is 2.07. Restoring model weights from the end of the best epoch. Epoch 00003: early stopping Learning rate scheduling In this example, we show how a custom Callback can be used to dynamically change the learning rate of the optimizer during the course of training. See callbacks.LearningRateScheduler for a more general implementations. callbacks.LearningRateScheduler class CustomLearningRateScheduler(keras.callbacks.Callback): """Learning rate scheduler which sets the learning rate according to schedule. Arguments: schedule: a function that takes an epoch index (integer, indexed from 0) and current learning rate as inputs and returns a new learning rate as output (float). """ def __init__(self, schedule): super().__init__() self.schedule = schedule def on_epoch_begin(self, epoch, logs=None): if not hasattr(self.model.optimizer, "lr"): raise ValueError('Optimizer must have a "lr" attribute.') # Get the current learning rate from model's optimizer. lr = float(tf.keras.backend.get_value(self.model.optimizer.learning_rate)) # Call schedule function to get the scheduled learning rate. scheduled_lr = self.schedule(epoch, lr) # Set the value back to the optimizer before this epoch starts tf.keras.backend.set_value(self.model.optimizer.lr, scheduled_lr) print("\nEpoch %05d: Learning rate is %6.4f." % (epoch, scheduled_lr)) LR_SCHEDULE = [ # (epoch to start, learning rate) tuples (3, 0.05), (6, 0.01), (9, 0.005), (12, 0.001), ] def lr_schedule(epoch, lr): """Helper function to retrieve the scheduled learning rate based on epoch.""" if epoch LR_SCHEDULE[-1][0]: return lr for i in range(len(LR_SCHEDULE)): if epoch == LR_SCHEDULE[i][0]: return LR_SCHEDULE[i][1] return lr model = get_model() model.fit( x_train, y_train, batch_size=64, steps_per_epoch=5, epochs=15, verbose=0, callbacks=[ LossAndErrorPrintingCallback(), CustomLearningRateScheduler(lr_schedule), ], ) class CustomLearningRateScheduler(keras.callbacks.Callback): """Learning rate scheduler which sets the learning rate according to schedule. Arguments: schedule: a function that takes an epoch index (integer, indexed from 0) and current learning rate as inputs and returns a new learning rate as output (float). """ def __init__(self, schedule): super().__init__() self.schedule = schedule def on_epoch_begin(self, epoch, logs=None): if not hasattr(self.model.optimizer, "lr"): raise ValueError('Optimizer must have a "lr" attribute.') # Get the current learning rate from model's optimizer. lr = float(tf.keras.backend.get_value(self.model.optimizer.learning_rate)) # Call schedule function to get the scheduled learning rate. scheduled_lr = self.schedule(epoch, lr) # Set the value back to the optimizer before this epoch starts tf.keras.backend.set_value(self.model.optimizer.lr, scheduled_lr) print("\nEpoch %05d: Learning rate is %6.4f." % (epoch, scheduled_lr)) LR_SCHEDULE = [ # (epoch to start, learning rate) tuples (3, 0.05), (6, 0.01), (9, 0.005), (12, 0.001), ] def lr_schedule(epoch, lr): """Helper function to retrieve the scheduled learning rate based on epoch.""" if epoch LR_SCHEDULE[-1][0]: return lr for i in range(len(LR_SCHEDULE)): if epoch == LR_SCHEDULE[i][0]: return LR_SCHEDULE[i][1] return lr model = get_model() model.fit( x_train, y_train, batch_size=64, steps_per_epoch=5, epochs=15, verbose=0, callbacks=[ LossAndErrorPrintingCallback(), CustomLearningRateScheduler(lr_schedule), ], ) Epoch 00000: Learning rate is 0.1000. Up to batch 0, the average loss is 25.33. Up to batch 1, the average loss is 434.31. Up to batch 2, the average loss is 298.47. Up to batch 3, the average loss is 226.43. Up to batch 4, the average loss is 182.22. The average loss for epoch 0 is 182.22 and mean absolute error is 8.09. Epoch 00001: Learning rate is 0.1000. Up to batch 0, the average loss is 5.55. Up to batch 1, the average loss is 5.56. Up to batch 2, the average loss is 6.20. Up to batch 3, the average loss is 6.24. Up to batch 4, the average loss is 6.34. The average loss for epoch 1 is 6.34 and mean absolute error is 2.09. Epoch 00002: Learning rate is 0.1000. Up to batch 0, the average loss is 7.28. Up to batch 1, the average loss is 7.82. Up to batch 2, the average loss is 7.52. Up to batch 3, the average loss is 7.33. Up to batch 4, the average loss is 7.52. The average loss for epoch 2 is 7.52 and mean absolute error is 2.27. Epoch 00003: Learning rate is 0.0500. Up to batch 0, the average loss is 10.56. Up to batch 1, the average loss is 7.01. Up to batch 2, the average loss is 6.36. Up to batch 3, the average loss is 6.18. Up to batch 4, the average loss is 5.55. The average loss for epoch 3 is 5.55 and mean absolute error is 1.90. Epoch 00004: Learning rate is 0.0500. Up to batch 0, the average loss is 3.26. Up to batch 1, the average loss is 3.70. Up to batch 2, the average loss is 3.75. Up to batch 3, the average loss is 3.73. Up to batch 4, the average loss is 3.79. The average loss for epoch 4 is 3.79 and mean absolute error is 1.56. Epoch 00005: Learning rate is 0.0500. Up to batch 0, the average loss is 5.90. Up to batch 1, the average loss is 5.09. Up to batch 2, the average loss is 4.59. Up to batch 3, the average loss is 4.39. Up to batch 4, the average loss is 4.50. The average loss for epoch 5 is 4.50 and mean absolute error is 1.66. Epoch 00006: Learning rate is 0.0100. Up to batch 0, the average loss is 6.34. Up to batch 1, the average loss is 6.46. Up to batch 2, the average loss is 5.29. Up to batch 3, the average loss is 4.89. Up to batch 4, the average loss is 4.68. The average loss for epoch 6 is 4.68 and mean absolute error is 1.74. Epoch 00007: Learning rate is 0.0100. Up to batch 0, the average loss is 3.67. Up to batch 1, the average loss is 3.06. Up to batch 2, the average loss is 3.25. Up to batch 3, the average loss is 3.45. Up to batch 4, the average loss is 3.34. The average loss for epoch 7 is 3.34 and mean absolute error is 1.43. Epoch 00008: Learning rate is 0.0100. Up to batch 0, the average loss is 3.35. Up to batch 1, the average loss is 3.74. Up to batch 2, the average loss is 3.50. Up to batch 3, the average loss is 3.38. Up to batch 4, the average loss is 3.58. The average loss for epoch 8 is 3.58 and mean absolute error is 1.52. Epoch 00009: Learning rate is 0.0050. Up to batch 0, the average loss is 2.08. Up to batch 1, the average loss is 2.52. Up to batch 2, the average loss is 2.76. Up to batch 3, the average loss is 2.72. Up to batch 4, the average loss is 2.85. The average loss for epoch 9 is 2.85 and mean absolute error is 1.31. Epoch 00010: Learning rate is 0.0050. Up to batch 0, the average loss is 3.64. Up to batch 1, the average loss is 3.39. Up to batch 2, the average loss is 3.42. Up to batch 3, the average loss is 3.83. Up to batch 4, the average loss is 3.85. The average loss for epoch 10 is 3.85 and mean absolute error is 1.56. Epoch 00011: Learning rate is 0.0050. Up to batch 0, the average loss is 3.33. Up to batch 1, the average loss is 3.18. Up to batch 2, the average loss is 2.98. Up to batch 3, the average loss is 3.02. Up to batch 4, the average loss is 2.85. The average loss for epoch 11 is 2.85 and mean absolute error is 1.31. Epoch 00012: Learning rate is 0.0010. Up to batch 0, the average loss is 3.58. Up to batch 1, the average loss is 3.22. Up to batch 2, the average loss is 3.27. Up to batch 3, the average loss is 3.24. Up to batch 4, the average loss is 3.02. The average loss for epoch 12 is 3.02 and mean absolute error is 1.32. Epoch 00013: Learning rate is 0.0010. Up to batch 0, the average loss is 3.37. Up to batch 1, the average loss is 3.55. Up to batch 2, the average loss is 3.31. Up to batch 3, the average loss is 3.28. Up to batch 4, the average loss is 3.27. The average loss for epoch 13 is 3.27 and mean absolute error is 1.43. Epoch 00014: Learning rate is 0.0010. Up to batch 0, the average loss is 2.02. Up to batch 1, the average loss is 2.66. Up to batch 2, the average loss is 2.61. Up to batch 3, the average loss is 2.56. Up to batch 4, the average loss is 2.82. The average loss for epoch 14 is 2.82 and mean absolute error is 1.27. Epoch 00000: Learning rate is 0.1000. Up to batch 0, the average loss is 25.33. Up to batch 1, the average loss is 434.31. Up to batch 2, the average loss is 298.47. Up to batch 3, the average loss is 226.43. Up to batch 4, the average loss is 182.22. The average loss for epoch 0 is 182.22 and mean absolute error is 8.09. Epoch 00001: Learning rate is 0.1000. Up to batch 0, the average loss is 5.55. Up to batch 1, the average loss is 5.56. Up to batch 2, the average loss is 6.20. Up to batch 3, the average loss is 6.24. Up to batch 4, the average loss is 6.34. The average loss for epoch 1 is 6.34 and mean absolute error is 2.09. Epoch 00002: Learning rate is 0.1000. Up to batch 0, the average loss is 7.28. Up to batch 1, the average loss is 7.82. Up to batch 2, the average loss is 7.52. Up to batch 3, the average loss is 7.33. Up to batch 4, the average loss is 7.52. The average loss for epoch 2 is 7.52 and mean absolute error is 2.27. Epoch 00003: Learning rate is 0.0500. Up to batch 0, the average loss is 10.56. Up to batch 1, the average loss is 7.01. Up to batch 2, the average loss is 6.36. Up to batch 3, the average loss is 6.18. Up to batch 4, the average loss is 5.55. The average loss for epoch 3 is 5.55 and mean absolute error is 1.90. Epoch 00004: Learning rate is 0.0500. Up to batch 0, the average loss is 3.26. Up to batch 1, the average loss is 3.70. Up to batch 2, the average loss is 3.75. Up to batch 3, the average loss is 3.73. Up to batch 4, the average loss is 3.79. The average loss for epoch 4 is 3.79 and mean absolute error is 1.56. Epoch 00005: Learning rate is 0.0500. Up to batch 0, the average loss is 5.90. Up to batch 1, the average loss is 5.09. Up to batch 2, the average loss is 4.59. Up to batch 3, the average loss is 4.39. Up to batch 4, the average loss is 4.50. The average loss for epoch 5 is 4.50 and mean absolute error is 1.66. Epoch 00006: Learning rate is 0.0100. Up to batch 0, the average loss is 6.34. Up to batch 1, the average loss is 6.46. Up to batch 2, the average loss is 5.29. Up to batch 3, the average loss is 4.89. Up to batch 4, the average loss is 4.68. The average loss for epoch 6 is 4.68 and mean absolute error is 1.74. Epoch 00007: Learning rate is 0.0100. Up to batch 0, the average loss is 3.67. Up to batch 1, the average loss is 3.06. Up to batch 2, the average loss is 3.25. Up to batch 3, the average loss is 3.45. Up to batch 4, the average loss is 3.34. The average loss for epoch 7 is 3.34 and mean absolute error is 1.43. Epoch 00008: Learning rate is 0.0100. Up to batch 0, the average loss is 3.35. Up to batch 1, the average loss is 3.74. Up to batch 2, the average loss is 3.50. Up to batch 3, the average loss is 3.38. Up to batch 4, the average loss is 3.58. The average loss for epoch 8 is 3.58 and mean absolute error is 1.52. Epoch 00009: Learning rate is 0.0050. Up to batch 0, the average loss is 2.08. Up to batch 1, the average loss is 2.52. Up to batch 2, the average loss is 2.76. Up to batch 3, the average loss is 2.72. Up to batch 4, the average loss is 2.85. The average loss for epoch 9 is 2.85 and mean absolute error is 1.31. Epoch 00010: Learning rate is 0.0050. Up to batch 0, the average loss is 3.64. Up to batch 1, the average loss is 3.39. Up to batch 2, the average loss is 3.42. Up to batch 3, the average loss is 3.83. Up to batch 4, the average loss is 3.85. The average loss for epoch 10 is 3.85 and mean absolute error is 1.56. Epoch 00011: Learning rate is 0.0050. Up to batch 0, the average loss is 3.33. Up to batch 1, the average loss is 3.18. Up to batch 2, the average loss is 2.98. Up to batch 3, the average loss is 3.02. Up to batch 4, the average loss is 2.85. The average loss for epoch 11 is 2.85 and mean absolute error is 1.31. Epoch 00012: Learning rate is 0.0010. Up to batch 0, the average loss is 3.58. Up to batch 1, the average loss is 3.22. Up to batch 2, the average loss is 3.27. Up to batch 3, the average loss is 3.24. Up to batch 4, the average loss is 3.02. The average loss for epoch 12 is 3.02 and mean absolute error is 1.32. Epoch 00013: Learning rate is 0.0010. Up to batch 0, the average loss is 3.37. Up to batch 1, the average loss is 3.55. Up to batch 2, the average loss is 3.31. Up to batch 3, the average loss is 3.28. Up to batch 4, the average loss is 3.27. The average loss for epoch 13 is 3.27 and mean absolute error is 1.43. Epoch 00014: Learning rate is 0.0010. Up to batch 0, the average loss is 2.02. Up to batch 1, the average loss is 2.66. Up to batch 2, the average loss is 2.61. Up to batch 3, the average loss is 2.56. Up to batch 4, the average loss is 2.82. The average loss for epoch 14 is 2.82 and mean absolute error is 1.27. Built-in Keras callbacks Be sure to check out the existing Keras callbacks by reading the API docs. Applications include logging to CSV, saving the model, visualizing metrics in TensorBoard, and a lot more! API docs Originally published on the TensorFlow website, this article appears here under a new headline and is licensed under CC BY 4.0. Code samples shared under the Apache 2.0 License. Originally published on the TensorFlow website, this article appears here under a new headline and is licensed under CC BY 4.0. Code samples shared under the Apache 2.0 License. Originally published on the TensorFlow website, this article appears here under a new headline and is licensed under CC BY 4.0. Code samples shared under the Apache 2.0 License. TensorFlow TensorFlow