DensNetBackbone model

[source]

DenseNetBackbone class

keras_hub.models.DenseNetBackbone(
    stackwise_num_repeats,
    image_shape=(None, None, 3),
    compression_ratio=0.5,
    growth_rate=32,
    **kwargs
)

Instantiates the DenseNet architecture.

This class implements a DenseNet backbone as described in Densely Connected Convolutional Networks (CVPR 2017).

Arguments

• stackwise_num_repeats: list of ints, number of repeated convolutional blocks per dense block.
• image_shape: optional shape tuple, defaults to (None, None, 3).
• compression_ratio: float, compression rate at transition layers, defaults to 0.5.
• growth_rate: int, number of filters added by each dense block, defaults to 32

Examples

input_data = np.ones(shape=(8, 224, 224, 3))

# Pretrained backbone
model = keras_hub.models.DenseNetBackbone.from_preset("densenet121_imagenet")
model(input_data)

# Randomly initialized backbone with a custom config
model = keras_hub.models.DenseNetBackbone(
    stackwise_num_repeats=[6, 12, 24, 16],
)
model(input_data)

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from_preset method

DenseNetBackbone.from_preset(preset, load_weights=True, **kwargs)

Instantiate a keras_hub.models.Backbone from a model preset.

A preset is a directory of configs, weights and other file assets used to save and load a pre-trained model. The preset can be passed as a one of:

1. a built-in preset identifier like 'bert_base_en'
2. a Kaggle Models handle like 'kaggle://user/bert/keras/bert_base_en'
3. a Hugging Face handle like 'hf://user/bert_base_en'
4. a path to a local preset directory like './bert_base_en'

This constructor can be called in one of two ways. Either from the base class like keras_hub.models.Backbone.from_preset(), or from a model class like keras_hub.models.GemmaBackbone.from_preset(). If calling from the base class, the subclass of the returning object will be inferred from the config in the preset directory.

For any Backbone subclass, you can run cls.presets.keys() to list all built-in presets available on the class.

Arguments

• preset: string. A built-in preset identifier, a Kaggle Models handle, a Hugging Face handle, or a path to a local directory.
• load_weights: bool. If True, the weights will be loaded into the model architecture. If False, the weights will be randomly initialized.

Examples

# Load a Gemma backbone with pre-trained weights.
model = keras_hub.models.Backbone.from_preset(
    "gemma_2b_en",
)

# Load a Bert backbone with a pre-trained config and random weights.
model = keras_hub.models.Backbone.from_preset(
    "bert_base_en",
    load_weights=False,
)