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form.onsubmit = function(e) { e.preventDefault(); var query = document.getElementById('search-input').value; window.location.href = '/search.html?query=' + query; return False } </script> </div> <div class='k-main-inner' id='k-main-id'> <div class='k-location-slug'> ► <a href='/guides/'>Developer guides</a> / Writing your own callbacks </div> <div class='k-content'> <h1 id="writing-your-own-callbacks">Writing your own callbacks</h1> Authors: Rick Chao, Francois Chollet Date created: 2019/03/20 Last modified: 2023/06/25 Description: Complete guide to writing new Keras callbacks. <img class="k-inline-icon" src="https://colab.research.google.com/img/colab_favicon.ico"/> <a href="https://colab.research.google.com/github/keras-team/keras-io/blob/master/guides/ipynb/writing_your_own_callbacks.ipynb">View in Colab</a> •<img class="k-inline-icon" src="https://github.com/favicon.ico"/> <a href="https://github.com/keras-team/keras-io/blob/master/guides/writing_your_own_callbacks.py">GitHub source</a> <hr /> <h2 id="introduction">Introduction</h2> A callback is a powerful tool to customize the behavior of a Keras model during training, evaluation, or inference. Examples include <a href="/api/callbacks/tensorboard#tensorboard-class"><code>keras.callbacks.TensorBoard</code></a> to visualize training progress and results with TensorBoard, or <a href="/api/callbacks/model_checkpoint#modelcheckpoint-class"><code>keras.callbacks.ModelCheckpoint</code></a> to periodically save your model during training. 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. <hr /> <h2 id="setup">Setup</h2> <div class="codehilite"><pre><code>import numpy as np import keras </code></pre></div> <hr /> <h2 id="keras-callbacks-overview">Keras callbacks overview</h2> All callbacks subclass the <a href="/api/callbacks/base_callback#callback-class"><code>keras.callbacks.Callback</code></a> 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. You can pass a list of callbacks (as the keyword argument <code>callbacks</code>) to the following model methods: <ul> <li><code>keras.Model.fit()</code></li> <li><code>keras.Model.evaluate()</code></li> <li><code>keras.Model.predict()</code></li> </ul> <hr /> <h2 id="an-overview-of-callback-methods">An overview of callback methods</h2> <h3 id="global-methods">Global methods</h3> <h4 id="ontraintestpredictbeginself-logsnone"><code>on_(train|test|predict)_begin(self, logs=None)</code></h4> Called at the beginning of <code>fit</code>/<code>evaluate</code>/<code>predict</code>. <h4 id="ontraintestpredictendself-logsnone"><code>on_(train|test|predict)_end(self, logs=None)</code></h4> Called at the end of <code>fit</code>/<code>evaluate</code>/<code>predict</code>. <h3 id="batchlevel-methods-for-trainingtestingpredicting">Batch-level methods for training/testing/predicting</h3> <h4 id="ontraintestpredictbatchbeginself-batch-logsnone"><code>on_(train|test|predict)_batch_begin(self, batch, logs=None)</code></h4> Called right before processing a batch during training/testing/predicting. <h4 id="ontraintestpredictbatchendself-batch-logsnone"><code>on_(train|test|predict)_batch_end(self, batch, logs=None)</code></h4> Called at the end of training/testing/predicting a batch. Within this method, <code>logs</code> is a dict containing the metrics results. <h3 id="epochlevel-methods-training-only">Epoch-level methods (training only)</h3> <h4 id="onepochbeginself-epoch-logsnone"><code>on_epoch_begin(self, epoch, logs=None)</code></h4> Called at the beginning of an epoch during training. <h4 id="onepochendself-epoch-logsnone"><code>on_epoch_end(self, epoch, logs=None)</code></h4> Called at the end of an epoch during training. <hr /> <h2 id="a-basic-example">A basic example</h2> Let's take a look at a concrete example. To get started, let's import tensorflow and define a simple Sequential Keras model: <div class="codehilite"><pre><code># Define the Keras model to add callbacks to def get_model(): model = keras.Sequential() model.add(keras.layers.Dense(1)) model.compile( optimizer=keras.optimizers.RMSprop(learning_rate=0.1), loss="mean_squared_error", metrics=["mean_absolute_error"], ) return model </code></pre></div> Then, load the MNIST data for training and testing from Keras datasets API: <div class="codehilite"><pre><code># Load example MNIST data and pre-process it (x_train, y_train), (x_test, y_test) = 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] </code></pre></div> Now, define a simple custom callback that logs: <ul> <li>When <code>fit</code>/<code>evaluate</code>/<code>predict</code> starts & ends</li> <li>When each epoch starts & ends</li> <li>When each training batch starts & ends</li> <li>When each evaluation (test) batch starts & ends</li> <li>When each inference (prediction) batch starts & ends</li> </ul> <div class="codehilite"><pre><code>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)) </code></pre></div> Let's try it out: <div class="codehilite"><pre><code>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()]) </code></pre></div> <div class="k-default-codeblock"> <div class="codehilite"><pre><code>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 ━━[37m━━━━━━━━━━━━━━━━━━ 0s 13ms/step...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 5ms/step </code></pre></div> </div> <h3 id="usage-of-logs-dict">Usage of <code>logs</code> dict</h3> The <code>logs</code> 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. <div class="codehilite"><pre><code>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()], ) </code></pre></div> <div class="k-default-codeblock"> <div class="codehilite"><pre><code>Up to batch 0, the average loss is 29.25. Up to batch 1, the average loss is 485.36. Up to batch 2, the average loss is 330.94. Up to batch 3, the average loss is 250.62. Up to batch 4, the average loss is 202.20. Up to batch 5, the average loss is 169.51. Up to batch 6, the average loss is 145.98. Up to batch 7, the average loss is 128.48. The average loss for epoch 0 is 128.48 and mean absolute error is 6.01. Up to batch 0, the average loss is 5.10. Up to batch 1, the average loss is 4.80. Up to batch 2, the average loss is 4.96. Up to batch 3, the average loss is 4.96. Up to batch 4, the average loss is 4.82. Up to batch 5, the average loss is 4.69. Up to batch 6, the average loss is 4.51. Up to batch 7, the average loss is 4.53. The average loss for epoch 1 is 4.53 and mean absolute error is 1.72. Up to batch 0, the average loss is 5.08. Up to batch 1, the average loss is 4.66. Up to batch 2, the average loss is 4.64. Up to batch 3, the average loss is 4.72. Up to batch 4, the average loss is 4.82. Up to batch 5, the average loss is 4.83. Up to batch 6, the average loss is 4.77. Up to batch 7, the average loss is 4.72. </code></pre></div> </div> <hr /> <h2 id="usage-of-selfmodel-attribute">Usage of <code>self.model</code> attribute</h2> 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: <code>self.model</code>. Here are a few of the things you can do with <code>self.model</code> in a callback: <ul> <li>Set <code>self.model.stop_training = True</code> to immediately interrupt training.</li> <li>Mutate hyperparameters of the optimizer (available as <code>self.model.optimizer</code>), such as <code>self.model.optimizer.learning_rate</code>.</li> <li>Save the model at period intervals.</li> <li>Record the output of <code>model.predict()</code> on a few test samples at the end of each epoch, to use as a sanity check during training.</li> <li>Extract visualizations of intermediate features at the end of each epoch, to monitor what the model is learning over time.</li> <li>etc.</li> </ul> Let's see this in action in a couple of examples. <hr /> <h2 id="examples-of-keras-callback-applications">Examples of Keras callback applications</h2> <h3 id="early-stopping-at-minimum-loss">Early stopping at minimum loss</h3> This first example shows the creation of a <code>Callback</code> that stops training when the minimum of loss has been reached, by setting the attribute <code>self.model.stop_training</code> (boolean). Optionally, you can provide an argument <code>patience</code> to specify how many epochs we should wait before stopping after having reached a local minimum. <a href="/api/callbacks/early_stopping#earlystopping-class"><code>keras.callbacks.EarlyStopping</code></a> provides a more complete and general implementation. <div class="codehilite"><pre><code>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(f"Epoch {self.stopped_epoch + 1}: early stopping") model = get_model() model.fit( x_train, y_train, batch_size=64, epochs=30, verbose=0, callbacks=[LossAndErrorPrintingCallback(), EarlyStoppingAtMinLoss()], ) </code></pre></div> <div class="k-default-codeblock"> <div class="codehilite"><pre><code>Up to batch 0, the average loss is 25.57. Up to batch 1, the average loss is 471.66. Up to batch 2, the average loss is 322.55. Up to batch 3, the average loss is 243.88. Up to batch 4, the average loss is 196.53. Up to batch 5, the average loss is 165.02. Up to batch 6, the average loss is 142.34. Up to batch 7, the average loss is 125.17. Up to batch 8, the average loss is 111.83. Up to batch 9, the average loss is 101.35. Up to batch 10, the average loss is 92.60. Up to batch 11, the average loss is 85.16. Up to batch 12, the average loss is 79.02. Up to batch 13, the average loss is 73.71. Up to batch 14, the average loss is 69.23. Up to batch 15, the average loss is 65.26. The average loss for epoch 0 is 65.26 and mean absolute error is 3.89. Up to batch 0, the average loss is 3.92. Up to batch 1, the average loss is 4.34. Up to batch 2, the average loss is 5.39. Up to batch 3, the average loss is 6.58. Up to batch 4, the average loss is 10.55. Up to batch 5, the average loss is 19.29. Up to batch 6, the average loss is 31.58. Up to batch 7, the average loss is 38.20. Up to batch 8, the average loss is 41.96. Up to batch 9, the average loss is 41.30. Up to batch 10, the average loss is 39.31. Up to batch 11, the average loss is 37.09. Up to batch 12, the average loss is 35.08. Up to batch 13, the average loss is 33.27. Up to batch 14, the average loss is 31.54. Up to batch 15, the average loss is 30.00. The average loss for epoch 1 is 30.00 and mean absolute error is 4.23. Up to batch 0, the average loss is 5.70. Up to batch 1, the average loss is 6.90. Up to batch 2, the average loss is 7.74. Up to batch 3, the average loss is 8.85. Up to batch 4, the average loss is 12.53. Up to batch 5, the average loss is 21.55. Up to batch 6, the average loss is 35.70. Up to batch 7, the average loss is 44.16. Up to batch 8, the average loss is 44.82. Up to batch 9, the average loss is 43.07. Up to batch 10, the average loss is 40.51. Up to batch 11, the average loss is 38.44. Up to batch 12, the average loss is 36.69. Up to batch 13, the average loss is 34.77. Up to batch 14, the average loss is 32.97. Up to batch 15, the average loss is 31.32. The average loss for epoch 2 is 31.32 and mean absolute error is 4.39. Restoring model weights from the end of the best epoch. Epoch 3: early stopping <keras.src.callbacks.history.History at 0x1187b7430> </code></pre></div> </div> <h3 id="learning-rate-scheduling">Learning rate scheduling</h3> 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 <code>callbacks.LearningRateScheduler</code> for a more general implementations. <div class="codehilite"><pre><code>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, "learning_rate"): raise ValueError('Optimizer must have a "learning_rate" attribute.') # Get the current learning rate from model's optimizer. lr = 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 self.model.optimizer.learning_rate = scheduled_lr print(f"\nEpoch {epoch}: Learning rate is {float(np.array(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[0][0] or 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, epochs=15, verbose=0, callbacks=[ LossAndErrorPrintingCallback(), CustomLearningRateScheduler(lr_schedule), ], ) </code></pre></div> <div class="k-default-codeblock"> <div class="codehilite"><pre><code>Epoch 0: Learning rate is 0.10000000149011612. Up to batch 0, the average loss is 27.90. Up to batch 1, the average loss is 439.49. Up to batch 2, the average loss is 302.08. Up to batch 3, the average loss is 228.83. Up to batch 4, the average loss is 184.97. Up to batch 5, the average loss is 155.25. Up to batch 6, the average loss is 134.03. Up to batch 7, the average loss is 118.29. Up to batch 8, the average loss is 105.65. Up to batch 9, the average loss is 95.53. Up to batch 10, the average loss is 87.25. Up to batch 11, the average loss is 80.33. Up to batch 12, the average loss is 74.48. Up to batch 13, the average loss is 69.46. Up to batch 14, the average loss is 65.05. Up to batch 15, the average loss is 61.31. The average loss for epoch 0 is 61.31 and mean absolute error is 3.85. </code></pre></div> </div> <div class="k-default-codeblock"> <div class="codehilite"><pre><code>Epoch 1: Learning rate is 0.10000000149011612. Up to batch 0, the average loss is 57.96. Up to batch 1, the average loss is 55.11. Up to batch 2, the average loss is 52.81. Up to batch 3, the average loss is 51.06. Up to batch 4, the average loss is 50.58. Up to batch 5, the average loss is 51.49. Up to batch 6, the average loss is 53.24. Up to batch 7, the average loss is 54.20. Up to batch 8, the average loss is 54.39. Up to batch 9, the average loss is 54.31. Up to batch 10, the average loss is 53.83. Up to batch 11, the average loss is 52.93. Up to batch 12, the average loss is 51.73. Up to batch 13, the average loss is 50.34. Up to batch 14, the average loss is 48.94. Up to batch 15, the average loss is 47.65. The average loss for epoch 1 is 47.65 and mean absolute error is 4.30. </code></pre></div> </div> <div class="k-default-codeblock"> <div class="codehilite"><pre><code>Epoch 2: Learning rate is 0.10000000149011612. Up to batch 0, the average loss is 46.38. Up to batch 1, the average loss is 45.16. Up to batch 2, the average loss is 44.03. Up to batch 3, the average loss is 43.11. Up to batch 4, the average loss is 42.52. Up to batch 5, the average loss is 42.32. Up to batch 6, the average loss is 43.06. Up to batch 7, the average loss is 44.58. Up to batch 8, the average loss is 45.33. Up to batch 9, the average loss is 45.15. Up to batch 10, the average loss is 44.59. Up to batch 11, the average loss is 43.88. Up to batch 12, the average loss is 43.17. Up to batch 13, the average loss is 42.40. Up to batch 14, the average loss is 41.74. Up to batch 15, the average loss is 41.19. The average loss for epoch 2 is 41.19 and mean absolute error is 4.27. </code></pre></div> </div> <div class="k-default-codeblock"> <div class="codehilite"><pre><code>Epoch 3: Learning rate is 0.05. Up to batch 0, the average loss is 40.85. Up to batch 1, the average loss is 40.11. Up to batch 2, the average loss is 39.38. Up to batch 3, the average loss is 38.69. Up to batch 4, the average loss is 38.01. Up to batch 5, the average loss is 37.38. Up to batch 6, the average loss is 36.77. Up to batch 7, the average loss is 36.18. Up to batch 8, the average loss is 35.61. Up to batch 9, the average loss is 35.08. Up to batch 10, the average loss is 34.54. Up to batch 11, the average loss is 34.04. Up to batch 12, the average loss is 33.56. Up to batch 13, the average loss is 33.08. Up to batch 14, the average loss is 32.64. Up to batch 15, the average loss is 32.25. The average loss for epoch 3 is 32.25 and mean absolute error is 3.64. </code></pre></div> </div> <div class="k-default-codeblock"> <div class="codehilite"><pre><code>Epoch 4: Learning rate is 0.05000000074505806. Up to batch 0, the average loss is 31.83. Up to batch 1, the average loss is 31.42. Up to batch 2, the average loss is 31.05. Up to batch 3, the average loss is 30.72. Up to batch 4, the average loss is 30.49. Up to batch 5, the average loss is 30.37. Up to batch 6, the average loss is 30.15. Up to batch 7, the average loss is 29.94. Up to batch 8, the average loss is 29.75. Up to batch 9, the average loss is 29.56. Up to batch 10, the average loss is 29.27. Up to batch 11, the average loss is 28.96. Up to batch 12, the average loss is 28.67. Up to batch 13, the average loss is 28.39. Up to batch 14, the average loss is 28.11. Up to batch 15, the average loss is 27.80. The average loss for epoch 4 is 27.80 and mean absolute error is 3.43. </code></pre></div> </div> <div class="k-default-codeblock"> <div class="codehilite"><pre><code>Epoch 5: Learning rate is 0.05000000074505806. Up to batch 0, the average loss is 27.51. Up to batch 1, the average loss is 27.25. Up to batch 2, the average loss is 27.05. Up to batch 3, the average loss is 26.88. Up to batch 4, the average loss is 26.76. Up to batch 5, the average loss is 26.60. Up to batch 6, the average loss is 26.44. Up to batch 7, the average loss is 26.25. Up to batch 8, the average loss is 26.08. Up to batch 9, the average loss is 25.89. Up to batch 10, the average loss is 25.71. Up to batch 11, the average loss is 25.48. Up to batch 12, the average loss is 25.26. Up to batch 13, the average loss is 25.03. Up to batch 14, the average loss is 24.81. Up to batch 15, the average loss is 24.58. The average loss for epoch 5 is 24.58 and mean absolute error is 3.25. </code></pre></div> </div> <div class="k-default-codeblock"> <div class="codehilite"><pre><code>Epoch 6: Learning rate is 0.01. Up to batch 0, the average loss is 24.36. Up to batch 1, the average loss is 24.14. Up to batch 2, the average loss is 23.93. Up to batch 3, the average loss is 23.71. Up to batch 4, the average loss is 23.52. Up to batch 5, the average loss is 23.32. Up to batch 6, the average loss is 23.12. Up to batch 7, the average loss is 22.93. Up to batch 8, the average loss is 22.74. Up to batch 9, the average loss is 22.55. Up to batch 10, the average loss is 22.37. Up to batch 11, the average loss is 22.19. Up to batch 12, the average loss is 22.01. Up to batch 13, the average loss is 21.83. Up to batch 14, the average loss is 21.67. Up to batch 15, the average loss is 21.50. The average loss for epoch 6 is 21.50 and mean absolute error is 2.98. </code></pre></div> </div> <div class="k-default-codeblock"> <div class="codehilite"><pre><code>Epoch 7: Learning rate is 0.009999999776482582. Up to batch 0, the average loss is 21.33. Up to batch 1, the average loss is 21.17. Up to batch 2, the average loss is 21.01. Up to batch 3, the average loss is 20.85. Up to batch 4, the average loss is 20.71. Up to batch 5, the average loss is 20.57. Up to batch 6, the average loss is 20.41. Up to batch 7, the average loss is 20.27. Up to batch 8, the average loss is 20.13. Up to batch 9, the average loss is 19.98. Up to batch 10, the average loss is 19.83. Up to batch 11, the average loss is 19.69. Up to batch 12, the average loss is 19.57. Up to batch 13, the average loss is 19.44. Up to batch 14, the average loss is 19.32. Up to batch 15, the average loss is 19.19. The average loss for epoch 7 is 19.19 and mean absolute error is 2.77. </code></pre></div> </div> <div class="k-default-codeblock"> <div class="codehilite"><pre><code>Epoch 8: Learning rate is 0.009999999776482582. Up to batch 0, the average loss is 19.07. Up to batch 1, the average loss is 18.95. Up to batch 2, the average loss is 18.83. Up to batch 3, the average loss is 18.70. Up to batch 4, the average loss is 18.58. Up to batch 5, the average loss is 18.46. Up to batch 6, the average loss is 18.35. Up to batch 7, the average loss is 18.24. Up to batch 8, the average loss is 18.12. Up to batch 9, the average loss is 18.01. Up to batch 10, the average loss is 17.90. Up to batch 11, the average loss is 17.79. Up to batch 12, the average loss is 17.68. Up to batch 13, the average loss is 17.58. Up to batch 14, the average loss is 17.48. Up to batch 15, the average loss is 17.38. The average loss for epoch 8 is 17.38 and mean absolute error is 2.61. </code></pre></div> </div> <div class="k-default-codeblock"> <div class="codehilite"><pre><code>Epoch 9: Learning rate is 0.005. Up to batch 0, the average loss is 17.28. Up to batch 1, the average loss is 17.18. Up to batch 2, the average loss is 17.08. Up to batch 3, the average loss is 16.99. Up to batch 4, the average loss is 16.90. Up to batch 5, the average loss is 16.80. Up to batch 6, the average loss is 16.71. Up to batch 7, the average loss is 16.62. Up to batch 8, the average loss is 16.53. Up to batch 9, the average loss is 16.44. Up to batch 10, the average loss is 16.35. Up to batch 11, the average loss is 16.26. Up to batch 12, the average loss is 16.17. Up to batch 13, the average loss is 16.09. Up to batch 14, the average loss is 16.00. Up to batch 15, the average loss is 15.92. The average loss for epoch 9 is 15.92 and mean absolute error is 2.48. </code></pre></div> </div> <div class="k-default-codeblock"> <div class="codehilite"><pre><code>Epoch 10: Learning rate is 0.004999999888241291. Up to batch 0, the average loss is 15.84. Up to batch 1, the average loss is 15.76. Up to batch 2, the average loss is 15.68. Up to batch 3, the average loss is 15.61. Up to batch 4, the average loss is 15.53. Up to batch 5, the average loss is 15.45. Up to batch 6, the average loss is 15.37. Up to batch 7, the average loss is 15.29. Up to batch 8, the average loss is 15.23. Up to batch 9, the average loss is 15.15. Up to batch 10, the average loss is 15.08. Up to batch 11, the average loss is 15.00. Up to batch 12, the average loss is 14.93. Up to batch 13, the average loss is 14.86. Up to batch 14, the average loss is 14.79. Up to batch 15, the average loss is 14.72. The average loss for epoch 10 is 14.72 and mean absolute error is 2.37. </code></pre></div> </div> <div class="k-default-codeblock"> <div class="codehilite"><pre><code>Epoch 11: Learning rate is 0.004999999888241291. Up to batch 0, the average loss is 14.65. Up to batch 1, the average loss is 14.58. Up to batch 2, the average loss is 14.52. Up to batch 3, the average loss is 14.45. Up to batch 4, the average loss is 14.39. Up to batch 5, the average loss is 14.33. Up to batch 6, the average loss is 14.26. Up to batch 7, the average loss is 14.20. Up to batch 8, the average loss is 14.14. Up to batch 9, the average loss is 14.08. Up to batch 10, the average loss is 14.02. Up to batch 11, the average loss is 13.96. Up to batch 12, the average loss is 13.90. Up to batch 13, the average loss is 13.84. Up to batch 14, the average loss is 13.78. Up to batch 15, the average loss is 13.72. The average loss for epoch 11 is 13.72 and mean absolute error is 2.27. </code></pre></div> </div> <div class="k-default-codeblock"> <div class="codehilite"><pre><code>Epoch 12: Learning rate is 0.001. Up to batch 0, the average loss is 13.67. Up to batch 1, the average loss is 13.60. Up to batch 2, the average loss is 13.55. Up to batch 3, the average loss is 13.49. Up to batch 4, the average loss is 13.44. Up to batch 5, the average loss is 13.38. Up to batch 6, the average loss is 13.33. Up to batch 7, the average loss is 13.28. Up to batch 8, the average loss is 13.22. Up to batch 9, the average loss is 13.17. Up to batch 10, the average loss is 13.12. Up to batch 11, the average loss is 13.07. Up to batch 12, the average loss is 13.02. Up to batch 13, the average loss is 12.97. Up to batch 14, the average loss is 12.92. Up to batch 15, the average loss is 12.87. The average loss for epoch 12 is 12.87 and mean absolute error is 2.19. </code></pre></div> </div> <div class="k-default-codeblock"> <div class="codehilite"><pre><code>Epoch 13: Learning rate is 0.0010000000474974513. Up to batch 0, the average loss is 12.82. Up to batch 1, the average loss is 12.77. Up to batch 2, the average loss is 12.72. Up to batch 3, the average loss is 12.68. Up to batch 4, the average loss is 12.63. Up to batch 5, the average loss is 12.58. Up to batch 6, the average loss is 12.53. Up to batch 7, the average loss is 12.49. Up to batch 8, the average loss is 12.45. Up to batch 9, the average loss is 12.40. Up to batch 10, the average loss is 12.35. Up to batch 11, the average loss is 12.30. Up to batch 12, the average loss is 12.26. Up to batch 13, the average loss is 12.22. Up to batch 14, the average loss is 12.17. Up to batch 15, the average loss is 12.13. The average loss for epoch 13 is 12.13 and mean absolute error is 2.12. </code></pre></div> </div> <div class="k-default-codeblock"> <div class="codehilite"><pre><code>Epoch 14: Learning rate is 0.0010000000474974513. Up to batch 0, the average loss is 12.09. Up to batch 1, the average loss is 12.05. Up to batch 2, the average loss is 12.01. Up to batch 3, the average loss is 11.97. Up to batch 4, the average loss is 11.92. Up to batch 5, the average loss is 11.88. Up to batch 6, the average loss is 11.84. Up to batch 7, the average loss is 11.80. Up to batch 8, the average loss is 11.76. Up to batch 9, the average loss is 11.72. Up to batch 10, the average loss is 11.68. Up to batch 11, the average loss is 11.64. Up to batch 12, the average loss is 11.60. Up to batch 13, the average loss is 11.57. Up to batch 14, the average loss is 11.54. Up to batch 15, the average loss is 11.50. The average loss for epoch 14 is 11.50 and mean absolute error is 2.06. <keras.src.callbacks.history.History at 0x168619c60> </code></pre></div> </div> <h3 id="builtin-keras-callbacks">Built-in Keras callbacks</h3> Be sure to check out the existing Keras callbacks by reading the <a href="https://keras.io/api/callbacks/">API docs</a>. Applications include logging to CSV, saving the model, visualizing metrics in TensorBoard, and a lot more! </div> <div class='k-outline'> <div class='k-outline-depth-1'> <a href='#writing-your-own-callbacks'>Writing your own callbacks</a> </div> <div class='k-outline-depth-2'> ◆ <a href='#introduction'>Introduction</a> </div> <div class='k-outline-depth-2'> ◆ <a href='#setup'>Setup</a> </div> <div class='k-outline-depth-2'> ◆ <a href='#keras-callbacks-overview'>Keras callbacks overview</a> </div> <div class='k-outline-depth-2'> ◆ <a href='#an-overview-of-callback-methods'>An overview of callback methods</a> </div> <div class='k-outline-depth-3'> <a href='#global-methods'>Global methods</a> </div> <div class='k-outline-depth-3'> <a href='#batchlevel-methods-for-trainingtestingpredicting'>Batch-level methods for training/testing/predicting</a> </div> <div class='k-outline-depth-3'> <a href='#epochlevel-methods-training-only'>Epoch-level methods (training only)</a> </div> <div class='k-outline-depth-2'> ◆ <a href='#a-basic-example'>A basic example</a> </div> <div class='k-outline-depth-3'> <a href='#usage-of-logs-dict'>Usage of <code>logs</code> dict</a> </div> <div class='k-outline-depth-2'> ◆ <a href='#usage-of-selfmodel-attribute'>Usage of <code>self.model</code> attribute</a> </div> <div class='k-outline-depth-2'> ◆ <a href='#examples-of-keras-callback-applications'>Examples of Keras callback applications</a> </div> <div class='k-outline-depth-3'> <a href='#early-stopping-at-minimum-loss'>Early stopping at minimum loss</a> </div> <div class='k-outline-depth-3'> <a href='#learning-rate-scheduling'>Learning rate scheduling</a> </div> <div class='k-outline-depth-3'> <a href='#builtin-keras-callbacks'>Built-in Keras callbacks</a> </div> </div> </div> </div> </div> </body> <footer style="float: left; width: 100%; padding: 1em; border-top: solid 1px #bbb;"> <a href="https://policies.google.com/terms">Terms</a> | <a href="https://policies.google.com/privacy">Privacy</a> </footer> </html>