fit ( x = None, y = None, batch_size = None, epochs = 1, verbose = "auto", callbacks = None, validation_split = 0.0, validation_data = None, shuffle = True, class_weight = None, sample_weight = None, initial_epoch = 0, steps_per_epoch = None, validation_steps = None, validation_batch_size = None, validation_freq = 1, ) Wrapped in a LossScaleOptimizer, which will dynamically "mixed_float16", the passed optimizer will be automatically Jit_compile="auto" enables XLA compilation if the modelįor torch backend, "auto" will default to eagerĮxecution and jit_compile=True will run with pile Whether to use XLA compilation whenĬompiling a model. Will only be called every N batches (i.e. Note that if steps_per_execution is set to N,Ĭallback.on_batch_begin and Callback.on_batch_end methods Passed, the execution will be truncated to the size of theĮpoch. If a number larger than the size of the epoch is At most, one full epoch will be run eachĮxecution. Greatly improve performance on TPUs or small models with a large Running multipleīatches inside a single compiled function call can The number of batches to runĭuring each a single compiled function call. It is recommended to leave thisĪs False when training (for best performance), Sample_weight or class_weight during training and testing. weighted_metrics: List of metrics to be evaluated and weighted by.Your metrics via the weighted_metrics argument instead. If you would like sample weighting to apply, you can specify The metrics passed here are evaluated without sample weighting A similarĬonversion is done for the strings "crossentropy" Shapes of the targets and of the model output. The strings 'accuracy' or 'acc', we convert this to one of You can also pass a list to specify a metric or a list of Metrics for different outputs of a multi-output model, you could Each of this can be a string (name of aīuilt-in function), function or a metrics: List of metrics to be evaluated by the model during.IfĪ dict, it is expected to map output names (strings) to scalar It is expected to have a 1:1 mapping to the model's outputs. Losses, weighted by the loss_weights coefficients. The loss value that will be minimizedīy the model will then be the weighted sum of all individual loss_weights: Optional list or dictionary specifying scalarĬoefficients (Python floats) to weight the loss contributions ofĭifferent model outputs.The loss function should return a float tensor. Y_pred should have shape (batch_size, d0. Sparse categorical crossentropy which expects integer arrays of (except in the case of sparse loss functions such as Y_true should have shape (batch_size, d0. Values, and y_pred are the model's predictions. Loss = fn(y_true, y_pred), where y_true are the ground truth Loss function is any callable with the signature May be a string (name of loss function), orĪ instance. optimizer: String (name of optimizer) or optimizer instance.Adam ( learning_rate = 1e-3 ), loss = keras.
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