Instantiates the ResNet101 architecture.
application_resnet101(
include_top = TRUE,
weights = "imagenet",
input_tensor = NULL,
input_shape = NULL,
pooling = NULL,
classes = 1000L,
classifier_activation = "softmax",
name = "resnet101"
)A Model instance.
whether to include the fully-connected layer at the top of the network.
one of NULL (random initialization),
"imagenet" (pre-training on ImageNet), or the path to the weights
file to be loaded.
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 (otherwise the input shape has to be (224, 224, 3)
(with "channels_last" data format) or (3, 224, 224)
(with "channels_first" data format). It should have exactly 3
inputs channels, and width and height should be no smaller than 32.
E.g. (200, 200, 3) would be one valid value.
Optional pooling mode for feature extraction when include_top
is FALSE.
NULL means that the output of the model will be the 4D tensor
output of the last convolutional block.
avg means that global average pooling will be applied to the output
of the last convolutional block, 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.
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.
When loading pretrained weights, classifier_activation can only
be NULL or "softmax".
The name of the model (string).
Deep Residual Learning for Image Recognition (CVPR 2015)
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.