Instantiates the EfficientNetB6 architecture.
application_efficientnet_b6(
include_top = TRUE,
weights = "imagenet",
input_tensor = NULL,
input_shape = NULL,
pooling = NULL,
classes = 1000L,
classifier_activation = "softmax",
name = "efficientnetb6",
...
)A model instance.
Whether to include the fully-connected
layer at the top of the network. Defaults to TRUE.
One of NULL (random initialization),
"imagenet" (pre-training on ImageNet),
or the path to the weights file to be loaded.
Defaults to "imagenet".
Optional Keras tensor
(i.e. output of keras_input())
to use as image input for the model.
Optional shape tuple, only to be specified
if include_top is FALSE.
It should have exactly 3 inputs channels.
Optional pooling mode for feature extraction
when include_top is FALSE. Defaults to NULL.
NULL means that the output of the model will be
the 4D tensor output of the
last convolutional layer.
avg means that global average pooling
will be applied to the output of the
last convolutional layer, and thus
the output of the model will be a 2D tensor.
max means that global max pooling will
be applied.
Optional number of classes to classify images
into, only to be specified if include_top is TRUE, and
if no weights argument is specified. 1000 is how many
ImageNet classes there are. Defaults to 1000.
A str or callable. The activation function to use
on the "top" layer. Ignored unless include_top=TRUE. Set
classifier_activation=NULL to return the logits of the "top" layer.
Defaults to 'softmax'.
When loading pretrained weights, classifier_activation can only
be NULL or "softmax".
The name of the model (string).
For forward/backward compatability.
This function returns a Keras image classification model, optionally loaded with weights pre-trained on ImageNet.
For image classification use cases, see this page for detailed examples.
For transfer learning use cases, make sure to read the guide to transfer learning & fine-tuning.