XceptionBackbone model

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XceptionBackbone class

keras_hub.models.XceptionBackbone(
    stackwise_conv_filters,
    stackwise_pooling,
    image_shape=(None, None, 3),
    data_format=None,
    dtype=None,
    **kwargs
)

Xception core network with hyperparameters.

This class implements a Xception backbone as described in Xception: Deep Learning with Depthwise Separable Convolutions.

Most users will want the pretrained presets available with this model. If you are creating a custom backbone, this model provides customizability through the stackwise_conv_filters and stackwise_pooling arguments. This backbone assumes the same basic structure as the original Xception mode: * Residuals and pre-activation everywhere but the first and last block. * Conv layers for the first block only, separable conv layers elsewhere.

Arguments

• stackwise_conv_filters: list of list of ints. Each outermost list entry represents a block, and each innermost list entry a conv layer. The integer value specifies the number of filters for the conv layer.
• stackwise_pooling: list of bools. A list of booleans per block, where each entry is true if the block should includes a max pooling layer and false if it should not.
• image_shape: tuple. The input shape without the batch size. Defaults to (None, None, 3).
• data_format: None or str. If specified, either "channels_last" or "channels_first". If unspecified, the Keras default will be used.
• dtype: None or str or keras.mixed_precision.DTypePolicy. The dtype to use for the model's computations and weights.

Examples

input_data = np.random.uniform(0, 1, size=(2, 224, 224, 3))

# Pretrained Xception backbone.
model = keras_hub.models.Backbone.from_preset("xception_41_imagenet")
model(input_data)

# Randomly initialized Xception backbone with a custom config.
model = keras_hub.models.XceptionBackbone(
    stackwise_conv_filters=[[32, 64], [64, 128], [256, 256]],
    stackwise_pooling=[True, True, False],
)
model(input_data)

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from_preset method

XceptionBackbone.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,
)