GoogLeNet#
eqxvision.models.GoogLeNet
#
A simple port of torchvision.models.GoogLeNet
__init__(self, num_classes: int = 1000, aux_logits: bool = False, blocks: Optional[List[eqx.Module]] = None, dropout: float = 0.2, dropout_aux: float = 0.7, *, key: Optional[jax.random.PRNGKey] = None)
#
Arguments:
num_classes
: Number of classes in the classification task. Also controls the final output shape(num_classes,)
. Defaults to1000
aux_logits
: IfTrue
, two auxiliary branches are added to the network. Defaults toFalse
blocks
: Blocks for constructing the networkdropout
: Dropout applied on themain
branch. Defaults to0.2
dropout_aux
: Dropout applied on theaux
branches. Defaults to0.7
key
: Ajax.random.PRNGKey
used to provide randomness for parameter initialisation. (Keyword only argument.)
__call__(self, x: Array, *, key: jax.random.PRNGKey) -> Optional[Array]
#
Arguments:
x
: The input. Should be a JAX array with3
channelskey
: Required parameter. Utilised by few layers such asDropout
orDropPath
eqxvision.models.googlenet(torch_weights: str = None, **kwargs: Any) -> GoogLeNet
#
GoogLeNet (Inception v1) model architecture from Going Deeper with Convolutions. The required minimum input size of the model is 15x15.
Arguments:
torch_weights
: APath
orURL
for thePyTorch
weights. Defaults toNone