TensorFlow 1 version | View source on GitHub |
Instantiates the VGG19 architecture.
tf.keras.applications.VGG19(
include_top=True, weights='imagenet', input_tensor=None, input_shape=None,
pooling=None, classes=1000, classifier_activation='softmax'
)
By default, it loads weights pre-trained on ImageNet. Check 'weights' for other options.
This model can be built both with 'channels_first' data format (channels, height, width) or 'channels_last' data format (height, width, channels).
The default input size for this model is 224x224.
Arguments | |
---|---|
include_top
|
whether to include the 3 fully-connected layers 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.
|
Returns | |
---|---|
A keras.Model instance.
|
Raises | |
---|---|
ValueError
|
in case of invalid argument for weights ,
or invalid input shape.
|
ValueError
|
if classifier_activation is not softmax or None when
using a pretrained top layer.
|