Keras 3 API documentation / KerasNLP / Models / FNet / FNetClassifier model

FNetClassifier model

[source]

FNetClassifier class

keras_nlp.models.FNetClassifier(
    backbone, num_classes, preprocessor=None, activation=None, dropout=0.1, **kwargs
)

An end-to-end f_net model for classification tasks.

This model attaches a classification head to a keras_nlp.model.FNetBackbone instance, mapping from the backbone outputs to logits suitable for a classification task. For usage of this model with pre-trained weights, use the from_preset() constructor.

This model can optionally be configured with a preprocessor layer, in which case it will automatically apply preprocessing to raw inputs during fit(), predict(), and evaluate(). This is done by default when creating the model with from_preset().

Disclaimer: Pre-trained models are provided on an "as is" basis, without warranties or conditions of any kind.

Arguments

  • backbone: A keras_nlp.models.FNetBackbone instance.
  • num_classes: int. Number of classes to predict.
  • preprocessor: A keras_nlp.models.FNetPreprocessor or None. If None, this model will not apply preprocessing, and inputs should be preprocessed before calling the model.
  • activation: Optional str or callable. The activation function to use on the model outputs. Set activation="softmax" to return output probabilities. Defaults to None.
  • hidden_dim: int. The size of the pooler layer.
  • dropout: float. The dropout probability value, applied after the dense layer.

Examples

Raw string data.

features = ["The quick brown fox jumped.", "I forgot my homework."]
labels = [0, 3]

# Pretrained classifier.
classifier = keras_nlp.models.FNetClassifier.from_preset(
    "f_net_base_en",
    num_classes=4,
)
classifier.fit(x=features, y=labels, batch_size=2)
classifier.predict(x=features, batch_size=2)

# Re-compile (e.g., with a new learning rate).
classifier.compile(
    loss=keras.losses.SparseCategoricalCrossentropy(from_logits=True),
    optimizer=keras.optimizers.Adam(5e-5),
    jit_compile=True,
)
# Access backbone programmatically (e.g., to change `trainable`).
classifier.backbone.trainable = False
# Fit again.
classifier.fit(x=features, y=labels, batch_size=2)

Preprocessed integer data.

features = {
    "token_ids": np.ones(shape=(2, 12), dtype="int32"),
    "segment_ids": np.array([[0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0]] * 2),
}
labels = [0, 3]

# Pretrained classifier without preprocessing.
classifier = keras_nlp.models.FNetClassifier.from_preset(
    "f_net_base_en",
    num_classes=4,
    preprocessor=None,
)
classifier.fit(x=features, y=labels, batch_size=2)

[source]

from_preset method

FNetClassifier.from_preset()

Instantiate FNetClassifier model from preset architecture and weights.

Arguments

  • preset: string. Must be one of "f_net_base_en", "f_net_large_en".
  • load_weights: Whether to load pre-trained weights into model. Defaults to True.

Examples

# Load architecture and weights from preset
model = FNetClassifier.from_preset("f_net_base_en")

# Load randomly initialized model from preset architecture
model = FNetClassifier.from_preset(
    "f_net_base_en",
    load_weights=False
)
Preset name Parameters Description
f_net_base_en 82.86M 12-layer FNet model where case is maintained. Trained on the C4 dataset.
f_net_large_en 236.95M 24-layer FNet model where case is maintained. Trained on the C4 dataset.

backbone property

keras_nlp.models.FNetClassifier.backbone

A keras.Model instance providing the backbone sub-model.


preprocessor property

keras_nlp.models.FNetClassifier.preprocessor

A keras.layers.Layer instance used to preprocess inputs.