XceptionBackbone
classkeras_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
(None, None, 3)
.None
or str. If specified, either "channels_last"
or
"channels_first"
. If unspecified, the Keras default will be used.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)
from_preset
methodXceptionBackbone.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:
'bert_base_en'
'kaggle://user/bert/keras/bert_base_en'
'hf://user/bert_base_en'
'./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
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,
)
Preset | Parameters | Description |
---|---|---|
xception_41_imagenet | 20.86M | 41-layer Xception model pre-trained on ImageNet 1k. |