Model training APIs

[source]

compile method

Model.compile(
    optimizer="rmsprop",
    loss=None,
    loss_weights=None,
    metrics=None,
    weighted_metrics=None,
    run_eagerly=False,
    steps_per_execution=1,
    jit_compile="auto",
    auto_scale_loss=True,
)

Configures the model for training.

Example

model.compile(
    optimizer=keras.optimizers.Adam(learning_rate=1e-3),
    loss=keras.losses.BinaryCrossentropy(),
    metrics=[
        keras.metrics.BinaryAccuracy(),
        keras.metrics.FalseNegatives(),
    ],
)

Arguments

• optimizer: String (name of optimizer) or optimizer instance. See keras.optimizers.
• loss: Loss function. May be a string (name of loss function), or a keras.losses.Loss instance. See keras.losses. A loss function is any callable with the signature loss = fn(y_true, y_pred), where y_true are the ground truth values, and y_pred are the model's predictions. y_true should have shape (batch_size, d0, .. dN) (except in the case of sparse loss functions such as sparse categorical crossentropy which expects integer arrays of shape (batch_size, d0, .. dN-1)). y_pred should have shape (batch_size, d0, .. dN). The loss function should return a float tensor.
• loss_weights: Optional list or dictionary specifying scalar coefficients (Python floats) to weight the loss contributions of different model outputs. The loss value that will be minimized by the model will then be the weighted sum of all individual losses, weighted by the loss_weights coefficients. If a list, it is expected to have a 1:1 mapping to the model's outputs. If a dict, it is expected to map output names (strings) to scalar coefficients.
• metrics: List of metrics to be evaluated by the model during training and testing. Each of this can be a string (name of a built-in function), function or a keras.metrics.Metric instance. See keras.metrics. Typically you will use metrics=['accuracy']. A function is any callable with the signature result = fn(y_true, _pred). To specify different metrics for different outputs of a multi-output model, you could also pass a dictionary, such as metrics={'a':'accuracy', 'b':['accuracy', 'mse']}. You can also pass a list to specify a metric or a list of metrics for each output, such as metrics=[['accuracy'], ['accuracy', 'mse']] or metrics=['accuracy', ['accuracy', 'mse']]. When you pass the strings 'accuracy' or 'acc', we convert this to one of keras.metrics.BinaryAccuracy, keras.metrics.CategoricalAccuracy, keras.metrics.SparseCategoricalAccuracy based on the shapes of the targets and of the model output. A similar conversion is done for the strings "crossentropy" and "ce" as well. The metrics passed here are evaluated without sample weighting; if you would like sample weighting to apply, you can specify your metrics via the weighted_metrics argument instead.
• weighted_metrics: List of metrics to be evaluated and weighted by sample_weight or class_weight during training and testing.
• run_eagerly: Bool. If True, this model's forward pass will never be compiled. It is recommended to leave this as False when training (for best performance), and to set it to True when debugging.
• steps_per_execution: Int. The number of batches to run during each a single compiled function call. Running multiple batches inside a single compiled function call can greatly improve performance on TPUs or small models with a large Python overhead. At most, one full epoch will be run each execution. If a number larger than the size of the epoch is passed, the execution will be truncated to the size of the epoch. Note that if steps_per_execution is set to N, Callback.on_batch_begin and Callback.on_batch_end methods will only be called every N batches (i.e. before/after each compiled function execution). Not supported with the PyTorch backend.
• jit_compile: Bool or "auto". Whether to use XLA compilation when compiling a model. For jax and tensorflow backends, jit_compile="auto" enables XLA compilation if the model supports it, and disabled otherwise. For torch backend, "auto" will default to eager execution and jit_compile=True will run with torch.compile with the "inductor" backend.
• auto_scale_loss: Bool. If True and the model dtype policy is "mixed_float16", the passed optimizer will be automatically wrapped in a LossScaleOptimizer, which will dynamically scale the loss to prevent underflow.

[source]

fit method

Model.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,
)

Trains the model for a fixed number of epochs (dataset iterations).

Arguments

• x: Input data. It can be:
  • A NumPy array (or array-like), or a list of arrays (in case the model has multiple inputs).
  • A backend-native tensor, or a list of tensors (in case the model has multiple inputs).
  • A dict mapping input names to the corresponding array/tensors, if the model has named inputs.
  • A keras.utils.PyDataset returning (inputs, targets) or (inputs, targets, sample_weights).
  • A tf.data.Dataset yielding (inputs, targets) or (inputs, targets, sample_weights).
  • A torch.utils.data.DataLoader yielding (inputs, targets) or (inputs, targets, sample_weights).
  • A Python generator function yielding (inputs, targets) or (inputs, targets, sample_weights).
• y: Target data. Like the input data x, it can be either NumPy array(s) or backend-native tensor(s). If x is a keras.utils.PyDataset, tf.data.Dataset, torch.utils.data.DataLoader or a Python generator function, y should not be specified since targets will be obtained from x.
• batch_size: Integer or None. Number of samples per gradient update. If unspecified, batch_size will default to 32. Do not specify the batch_size if your input data x is a keras.utils.PyDataset, tf.data.Dataset, torch.utils.data.DataLoader or Python generator function since they generate batches.
• epochs: Integer. Number of epochs to train the model. An epoch is an iteration over the entire x and y data provided (unless the steps_per_epoch flag is set to something other than None). Note that in conjunction with initial_epoch, epochs is to be understood as "final epoch". The model is not trained for a number of iterations given by epochs, but merely until the epoch of index epochs is reached.
• verbose: "auto", 0, 1, or 2. Verbosity mode. 0 = silent, 1 = progress bar, 2 = one line per epoch. "auto" becomes 1 for most cases. Note that the progress bar is not particularly useful when logged to a file, so verbose=2 is recommended when not running interactively (e.g., in a production environment). Defaults to "auto".
• callbacks: List of keras.callbacks.Callback instances. List of callbacks to apply during training. See keras.callbacks. Note keras.callbacks.ProgbarLogger and keras.callbacks.History callbacks are created automatically and need not be passed to model.fit(). keras.callbacks.ProgbarLogger is created or not based on the verbose argument in model.fit().
• validation_split: Float between 0 and 1. Fraction of the training data to be used as validation data. The model will set apart this fraction of the training data, will not train on it, and will evaluate the loss and any model metrics on this data at the end of each epoch. The validation data is selected from the last samples in the x and y data provided, before shuffling. This argument is only supported when x and y are made of NumPy arrays or tensors. If both validation_data and validation_split are provided, validation_data will override validation_split.
• validation_data: Data on which to evaluate the loss and any model metrics at the end of each epoch. The model will not be trained on this data. Thus, note the fact that the validation loss of data provided using validation_split or validation_data is not affected by regularization layers like noise and dropout. validation_data will override validation_split. It can be:
  • A tuple (x_val, y_val) of NumPy arrays or tensors.
  • A tuple (x_val, y_val, val_sample_weights) of NumPy arrays.
  • A keras.utils.PyDataset, a tf.data.Dataset, a torch.utils.data.DataLoader yielding (inputs, targets) or a Python generator function yielding (x_val, y_val) or (inputs, targets, sample_weights).
• shuffle: Boolean, whether to shuffle the training data before each epoch. This argument is ignored when x is a keras.utils.PyDataset, tf.data.Dataset, torch.utils.data.DataLoader or Python generator function.
• class_weight: Optional dictionary mapping class indices (integers) to a weight (float) value, used for weighting the loss function (during training only). This can be useful to tell the model to "pay more attention" to samples from an under-represented class. When class_weight is specified and targets have a rank of 2 or greater, either y must be one-hot encoded, or an explicit final dimension of 1 must be included for sparse class labels.
• sample_weight: Optional NumPy array or tensor of weights for the training samples, used for weighting the loss function (during training only). You can either pass a flat (1D) NumPy array or tensor with the same length as the input samples (1:1 mapping between weights and samples), or in the case of temporal data, you can pass a 2D NumPy array or tensor with shape (samples, sequence_length) to apply a different weight to every timestep of every sample. This argument is not supported when x is a keras.utils.PyDataset, tf.data.Dataset, torch.utils.data.DataLoader or Python generator function. Instead, provide sample_weights as the third element of x. Note that sample weighting does not apply to metrics specified via the metrics argument in compile(). To apply sample weighting to your metrics, you can specify them via the weighted_metrics in compile() instead.
• initial_epoch: Integer. Epoch at which to start training (useful for resuming a previous training run).
• steps_per_epoch: Integer or None. Total number of steps (batches of samples) before declaring one epoch finished and starting the next epoch. When training with input tensors or NumPy arrays, the default None means that the value used is the number of samples in your dataset divided by the batch size, or 1 if that cannot be determined. If x is a keras.utils.PyDataset, tf.data.Dataset, torch.utils.data.DataLoader or Python generator function, the epoch will run until the input dataset is exhausted. When passing an infinitely repeating dataset, you must specify the steps_per_epoch argument, otherwise the training will run indefinitely.
• validation_steps: Integer or None. Only relevant if validation_data is provided. Total number of steps (batches of samples) to draw before stopping when performing validation at the end of every epoch. If validation_steps is None, validation will run until the validation_data dataset is exhausted. In the case of an infinitely repeating dataset, it will run indefinitely. If validation_steps is specified and only part of the dataset is consumed, the evaluation will start from the beginning of the dataset at each epoch. This ensures that the same validation samples are used every time.
• validation_batch_size: Integer or None. Number of samples per validation batch. If unspecified, will default to batch_size. Do not specify the validation_batch_size if your data is a keras.utils.PyDataset, tf.data.Dataset, torch.utils.data.DataLoader or Python generator function since they generate batches.
• validation_freq: Only relevant if validation data is provided. Specifies how many training epochs to run before a new validation run is performed, e.g. validation_freq=2 runs validation every 2 epochs.

Unpacking behavior for iterator-like inputs: A common pattern is to pass an iterator like object such as a tf.data.Dataset or a keras.utils.PyDataset to fit(), which will in fact yield not only features (x) but optionally targets (y) and sample weights (sample_weight). Keras requires that the output of such iterator-likes be unambiguous. The iterator should return a tuple of length 1, 2, or 3, where the optional second and third elements will be used for y and sample_weight respectively. Any other type provided will be wrapped in a length-one tuple, effectively treating everything as x. When yielding dicts, they should still adhere to the top-level tuple structure, e.g. ({"x0": x0, "x1": x1}, y). Keras will not attempt to separate features, targets, and weights from the keys of a single dict. A notable unsupported data type is the namedtuple. The reason is that it behaves like both an ordered datatype (tuple) and a mapping datatype (dict). So given a namedtuple of the form: namedtuple("example_tuple", ["y", "x"]) it is ambiguous whether to reverse the order of the elements when interpreting the value. Even worse is a tuple of the form: namedtuple("other_tuple", ["x", "y", "z"]) where it is unclear if the tuple was intended to be unpacked into x, y, and sample_weight or passed through as a single element to x.

Returns

A History object. Its History.history attribute is a record of training loss values and metrics values at successive epochs, as well as validation loss values and validation metrics values (if applicable).

[source]

evaluate method

Model.evaluate(
    x=None,
    y=None,
    batch_size=None,
    verbose="auto",
    sample_weight=None,
    steps=None,
    callbacks=None,
    return_dict=False,
    **kwargs
)

Returns the loss value & metrics values for the model in test mode.

Computation is done in batches (see the batch_size arg.)

Arguments

• x: Input data. It can be:
  • A NumPy array (or array-like), or a list of arrays (in case the model has multiple inputs).
  • A backend-native tensor, or a list of tensors (in case the model has multiple inputs).
  • A dict mapping input names to the corresponding array/tensors, if the model has named inputs.
  • A keras.utils.PyDataset returning (inputs, targets) or (inputs, targets, sample_weights).
  • A tf.data.Dataset yielding (inputs, targets) or (inputs, targets, sample_weights).
  • A torch.utils.data.DataLoader yielding (inputs, targets) or (inputs, targets, sample_weights).
  • A Python generator function yielding (inputs, targets) or (inputs, targets, sample_weights).
• y: Target data. Like the input data x, it can be either NumPy array(s) or backend-native tensor(s). If x is a keras.utils.PyDataset, tf.data.Dataset, torch.utils.data.DataLoader or a Python generator function, y should not be specified since targets will be obtained from x.
• batch_size: Integer or None. Number of samples per batch of computation. If unspecified, batch_size will default to 32. Do not specify the batch_size if your input data x is a keras.utils.PyDataset, tf.data.Dataset, torch.utils.data.DataLoader or Python generator function since they generate batches.
• verbose: "auto", 0, 1, or 2. Verbosity mode. 0 = silent, 1 = progress bar, 2 = single line. "auto" becomes 1 for most cases. Note that the progress bar is not particularly useful when logged to a file, so verbose=2 is recommended when not running interactively (e.g. in a production environment). Defaults to "auto".
• sample_weight: Optional NumPy array or tensor of weights for the training samples, used for weighting the loss function (during training only). You can either pass a flat (1D) NumPy array or tensor with the same length as the input samples (1:1 mapping between weights and samples), or in the case of temporal data, you can pass a 2D NumPy array or tensor with shape (samples, sequence_length) to apply a different weight to every timestep of every sample. This argument is not supported when x is a keras.utils.PyDataset, tf.data.Dataset, torch.utils.data.DataLoader or Python generator function. Instead, provide sample_weights as the third element of x. Note that sample weighting does not apply to metrics specified via the metrics argument in compile(). To apply sample weighting to your metrics, you can specify them via the weighted_metrics in compile() instead.
• steps: Integer or None. Total number of steps (batches of samples) to draw before declaring the evaluation round finished. If steps is None, it will run until x is exhausted. In the case of an infinitely repeating dataset, it will run indefinitely.
• callbacks: List of keras.callbacks.Callback instances. List of callbacks to apply during evaluation.
• return_dict: If True, loss and metric results are returned as a dict, with each key being the name of the metric. If False, they are returned as a list.

Returns

Scalar test loss (if the model has a single output and no metrics) or list of scalars (if the model has multiple outputs and/or metrics). The attribute model.metrics_names will give you the display labels for the scalar outputs.

[source]

predict method

Model.predict(x, batch_size=None, verbose="auto", steps=None, callbacks=None)

Generates output predictions for the input samples.

Computation is done in batches. This method is designed for batch processing of large numbers of inputs. It is not intended for use inside of loops that iterate over your data and process small numbers of inputs at a time.

For small numbers of inputs that fit in one batch, directly use __call__() for faster execution, e.g., model(x), or model(x, training=False) if you have layers such as BatchNormalization that behave differently during inference.

Note: See this FAQ entry for more details about the difference between Model methods predict() and __call__().

Arguments

• x: Input data. It can be:
  • A NumPy array (or array-like), or a list of arrays (in case the model has multiple inputs).
  • A backend-native tensor, or a list of tensors (in case the model has multiple inputs).
  • A dict mapping input names to the corresponding array/tensors, if the model has named inputs.
  • A keras.utils.PyDataset.
  • A tf.data.Dataset.
  • A torch.utils.data.DataLoader.
  • A Python generator function.
• batch_size: Integer or None. Number of samples per batch of computation. If unspecified, batch_size will default to 32. Do not specify the batch_size if your input data x is a keras.utils.PyDataset, tf.data.Dataset, torch.utils.data.DataLoader or Python generator function since they generate batches.
• verbose: "auto", 0, 1, or 2. Verbosity mode. 0 = silent, 1 = progress bar, 2 = single line. "auto" becomes 1 for most cases. Note that the progress bar is not particularly useful when logged to a file, so verbose=2 is recommended when not running interactively (e.g. in a production environment). Defaults to "auto".
• steps: Total number of steps (batches of samples) to draw before declaring the prediction round finished. If steps is None, it will run until x is exhausted. In the case of an infinitely repeating dataset, it will run indefinitely.
• callbacks: List of keras.callbacks.Callback instances. List of callbacks to apply during prediction.

Returns

NumPy array(s) of predictions.

[source]

train_on_batch method

Model.train_on_batch(
    x, y=None, sample_weight=None, class_weight=None, return_dict=False
)

Runs a single gradient update on a single batch of data.

Arguments

• x: Input data. Must be array-like.
• y: Target data. Must be array-like.
• sample_weight: Optional array of the same length as x, containing weights to apply to the model's loss for each sample. In the case of temporal data, you can pass a 2D array with shape (samples, sequence_length), to apply a different weight to every timestep of every sample.
• class_weight: Optional dictionary mapping class indices (integers) to a weight (float) to apply to the model's loss for the samples from this class during training. This can be useful to tell the model to "pay more attention" to samples from an under-represented class. When class_weight is specified and targets have a rank of 2 or greater, either y must be one-hot encoded, or an explicit final dimension of 1 must be included for sparse class labels.
• return_dict: If True, loss and metric results are returned as a dict, with each key being the name of the metric. If False, they are returned as a list.

Returns

A scalar loss value (when no metrics and return_dict=False), a list of loss and metric values (if there are metrics and return_dict=False), or a dict of metric and loss values (if return_dict=True).

[source]

test_on_batch method

Model.test_on_batch(x, y=None, sample_weight=None, return_dict=False)

Test the model on a single batch of samples.

Arguments

• x: Input data. Must be array-like.
• y: Target data. Must be array-like.
• sample_weight: Optional array of the same length as x, containing weights to apply to the model's loss for each sample. In the case of temporal data, you can pass a 2D array with shape (samples, sequence_length), to apply a different weight to every timestep of every sample.
• return_dict: If True, loss and metric results are returned as a dict, with each key being the name of the metric. If False, they are returned as a list.

Returns

A scalar loss value (when no metrics and return_dict=False), a list of loss and metric values (if there are metrics and return_dict=False), or a dict of metric and loss values (if return_dict=True).

[source]

predict_on_batch method

Model.predict_on_batch(x)

Returns predictions for a single batch of samples.

Arguments

• x: Input data. It must be array-like.

Returns

NumPy array(s) of predictions.