Tensorflow2-CallBack

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Keras callbacks overview

所有的callbacks子类都继承自 keras.callbacks.Callback 类；

可以把callbacks的lists传递给如下的接口： （参数名为 callbacks）

  • keras.Model.fit()
  • keras.Model.evaluate()
  • keras.Model.predict()

An overview of callback methods

Global methods

on_(train|test|predict)_begin(self, logs=None)

Called at the beginning of fit/evaluate/predict.

on_(train|test|predict)_end(self, logs=None)

Called at the end of fit/evaluate/predict.

Batch-level methods for training/testing/predicting

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)

Called at the end of training/testing/predicting a batch. Within this method, logs is a dict containing the metrics results.

Epoch-level methods (training only)

on_epoch_begin(self, epoch, logs=None)

Called at the beginning of an epoch during training.

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:

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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"],
  )
  
# 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]

定义一个做如下工作的callback :

  • 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
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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))
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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()])

Usage of logs dict

logs 字典包含了损失值，全部的metrics，在每一个batch或者epoch之后。例子如下：

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if default_keys:
       assert min(default_keys) <= len(
           args), "Not enough arguments (%s, %s, %s)" % (args, default_keys,
                                                         self.arg_names)
     
     AssertionError: Not enough arguments ((), [2], ['true', 'pred', 'num_classes'])