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Instantiates the ResNet101 architecture.
tf.keras.applications.resnet.ResNet101(
include_top=True,
weights='imagenet',
input_tensor=None,
input_shape=None,
pooling=None,
classes=1000,
**kwargs
)
Reference:
- 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.
Args | |
---|---|
include_top
|
whether to include the fully-connected layer at the top of the network. |
weights
|
one of None (random initialization),
'imagenet' (pre-training on ImageNet),
or the path to the weights file to be loaded.
|
input_tensor
|
optional Keras tensor (i.e. output of layers.Input() )
to use as image input for the model.
|
input_shape
|
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.
|
pooling
|
Optional pooling mode for feature extraction
when include_top is False .
|
classes
|
optional number of classes to classify images
into, only to be specified if include_top is True, and
if no weights argument is specified.
|
classifier_activation
|
A str or callable. The activation function to use
on the "top" layer. Ignored unless include_top=True . Set
classifier_activation=None to return the logits of the "top" layer.
When loading pretrained weights, classifier_activation can only
be None or "softmax" .
|
Returns | |
---|---|
A Keras model instance. |