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Class CSVLogger
Callback that streams epoch results to a csv file.
Inherits From: Callback
Aliases:
Supports all values that can be represented as a string, including 1D iterables such as np.ndarray.
Example:
csv_logger = CSVLogger('training.log')
model.fit(X_train, Y_train, callbacks=[csv_logger])
Arguments:
filename: filename of the csv file, e.g. 'run/log.csv'.separator: string used to separate elements in the csv file.append: True: append if file exists (useful for continuing training). False: overwrite existing file,
__init__
__init__(
filename,
separator=',',
append=False
)
Initialize self. See help(type(self)) for accurate signature.
Methods
tf.keras.callbacks.CSVLogger.on_batch_begin
on_batch_begin(
batch,
logs=None
)
A backwards compatibility alias for on_train_batch_begin.
tf.keras.callbacks.CSVLogger.on_batch_end
on_batch_end(
batch,
logs=None
)
A backwards compatibility alias for on_train_batch_end.
tf.keras.callbacks.CSVLogger.on_epoch_begin
on_epoch_begin(
epoch,
logs=None
)
Called at the start of an epoch.
Subclasses should override for any actions to run. This function should only be called during TRAIN mode.
Arguments:
epoch: integer, index of epoch.logs: dict. Currently no data is passed to this argument for this method but that may change in the future.
tf.keras.callbacks.CSVLogger.on_epoch_end
on_epoch_end(
epoch,
logs=None
)
Called at the end of an epoch.
Subclasses should override for any actions to run. This function should only be called during TRAIN mode.
Arguments:
epoch: integer, index of epoch.logs: dict, metric results for this training epoch, and for the validation epoch if validation is performed. Validation result keys are prefixed withval_.
tf.keras.callbacks.CSVLogger.on_predict_batch_begin
on_predict_batch_begin(
batch,
logs=None
)
Called at the beginning of a batch in predict methods.
Subclasses should override for any actions to run.
Arguments:
batch: integer, index of batch within the current epoch.logs: dict. Has keysbatchandsizerepresenting the current batch number and the size of the batch.
tf.keras.callbacks.CSVLogger.on_predict_batch_end
on_predict_batch_end(
batch,
logs=None
)
Called at the end of a batch in predict methods.
Subclasses should override for any actions to run.
Arguments:
batch: integer, index of batch within the current epoch.logs: dict. Metric results for this batch.
tf.keras.callbacks.CSVLogger.on_predict_begin
on_predict_begin(logs=None)
Called at the beginning of prediction.
Subclasses should override for any actions to run.
Arguments:
logs: dict. Currently no data is passed to this argument for this method but that may change in the future.
tf.keras.callbacks.CSVLogger.on_predict_end
on_predict_end(logs=None)
Called at the end of prediction.
Subclasses should override for any actions to run.
Arguments:
logs: dict. Currently no data is passed to this argument for this method but that may change in the future.
tf.keras.callbacks.CSVLogger.on_test_batch_begin
on_test_batch_begin(
batch,
logs=None
)
Called at the beginning of a batch in evaluate methods.
Also called at the beginning of a validation batch in the fit
methods, if validation data is provided.
Subclasses should override for any actions to run.
Arguments:
batch: integer, index of batch within the current epoch.logs: dict. Has keysbatchandsizerepresenting the current batch number and the size of the batch.
tf.keras.callbacks.CSVLogger.on_test_batch_end
on_test_batch_end(
batch,
logs=None
)
Called at the end of a batch in evaluate methods.
Also called at the end of a validation batch in the fit
methods, if validation data is provided.
Subclasses should override for any actions to run.
Arguments:
batch: integer, index of batch within the current epoch.logs: dict. Metric results for this batch.
tf.keras.callbacks.CSVLogger.on_test_begin
on_test_begin(logs=None)
Called at the beginning of evaluation or validation.
Subclasses should override for any actions to run.
Arguments:
logs: dict. Currently no data is passed to this argument for this method but that may change in the future.
tf.keras.callbacks.CSVLogger.on_test_end
on_test_end(logs=None)
Called at the end of evaluation or validation.
Subclasses should override for any actions to run.
Arguments:
logs: dict. Currently no data is passed to this argument for this method but that may change in the future.
tf.keras.callbacks.CSVLogger.on_train_batch_begin
on_train_batch_begin(
batch,
logs=None
)
Called at the beginning of a training batch in fit methods.
Subclasses should override for any actions to run.
Arguments:
batch: integer, index of batch within the current epoch.logs: dict. Has keysbatchandsizerepresenting the current batch number and the size of the batch.
tf.keras.callbacks.CSVLogger.on_train_batch_end
on_train_batch_end(
batch,
logs=None
)
Called at the end of a training batch in fit methods.
Subclasses should override for any actions to run.
Arguments:
batch: integer, index of batch within the current epoch.logs: dict. Metric results for this batch.
tf.keras.callbacks.CSVLogger.on_train_begin
on_train_begin(logs=None)
Called at the beginning of training.
Subclasses should override for any actions to run.
Arguments:
logs: dict. Currently no data is passed to this argument for this method but that may change in the future.
tf.keras.callbacks.CSVLogger.on_train_end
on_train_end(logs=None)
Called at the end of training.
Subclasses should override for any actions to run.
Arguments:
logs: dict. Currently no data is passed to this argument for this method but that may change in the future.
tf.keras.callbacks.CSVLogger.set_model
set_model(model)
tf.keras.callbacks.CSVLogger.set_params
set_params(params)