fit
Trains the model for a fixed number of epochs (iterations over a dataset).
Return
A TrainingHistory object. Its TrainingHistory.batchHistory attribute is a record of training loss values and metrics values per each batch and epoch.
Parameters
The train dataset that combines input data (X) and target data (Y).
Number of epochs to train the model. An epoch is an iteration over the entire x and y data provided.
Number of samples per gradient update. True (default) = Weights are initialized at the beginning of the training phase. False = Weights are not initialized during training phase. It should be initialized before (via transfer learning or init() method call).
Callback to be used during training phase.
Trains the model for a fixed number of epochs (iterations over a dataset).
Return
A TrainingHistory object. Its TrainingHistory.batchHistory attribute is a record of training loss values and metrics values per each batch and epoch.
Parameters
The train dataset that combines input data (X) and target data (Y).
Number of epochs to train the model. An epoch is an iteration over the entire x and y data provided.
Number of samples per gradient update. True (default) = Weights are initialized at the beginning of the training phase. False = Weights are not initialized during training phase. It should be initialized before (via transfer learning or init() method call).
Callbacks to be used during training phase.
Trains the model for a fixed number of epochs (iterations over a dataset).
Return
A TrainingHistory object. It contains records with training/validation loss values and metrics per each batch and epoch.
Parameters
The train dataset that combines input data (X) and target data (Y).
The validation dataset that combines input data (X) and target data (Y).
Number of epochs to train the model. An epoch is an iteration over the entire x and y data provided.
Number of samples per gradient update.
Number of samples per validation batch. True (default) = optimizer variables are initialized at the beginning of the training phase. False = optimizer variables are not initialized during training phase. It should be initialized before (via transfer learning).
Callback to be used during training phase.
Trains the model for a fixed number of epochs (iterations over a dataset).
Return
A TrainingHistory object. It contains records with training/validation loss values and metrics per each batch and epoch.
Parameters
The train dataset that combines input data (X) and target data (Y).
The validation dataset that combines input data (X) and target data (Y).
Number of epochs to train the model. An epoch is an iteration over the entire x and y data provided.
Number of samples per gradient update.
Number of samples per validation batch. True (default) = optimizer variables are initialized at the beginning of the training phase. False = optimizer variables are not initialized during training phase. It should be initialized before (via transfer learning).
Callbacks to be used during training phase.
Trains the model for a fixed number of epochs (iterations on a dataset).
Return
A TrainingHistory object. It contains records with training/validation loss values and metrics per each batch and epoch.
Parameters
The dataset that combines input data (X) and target data (Y). It will be split on train and validation sub-datasets.
Number between 0.0 and 1.0. The proportion of validation data from initially passed dataset.
Number of epochs to train the model. An epoch is an iteration over the entire x and y data provided.
Number of samples per gradient update.
Number of samples per validation batch.
Callback to be used during training phase.
Trains the model for a fixed number of epochs (iterations on a dataset).
Return
A TrainingHistory object. It contains records with training/validation loss values and metrics per each batch and epoch.
Parameters
The dataset that combines input data (X) and target data (Y). It will be split on train and validation sub-datasets.
Number between 0.0 and 1.0. The proportion of validation data from initially passed dataset.
Number of epochs to train the model. An epoch is an iteration over the entire x and y data provided.
Number of samples per gradient update.
Number of samples per validation batch.
Callbacks to be used during training phase.