VGG16
functiontf.keras.applications.VGG16(
include_top=True,
weights="imagenet",
input_tensor=None,
input_shape=None,
pooling=None,
classes=1000,
classifier_activation="softmax",
)
Instantiates the VGG16 model.
Reference
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.
Note: each Keras Application expects a specific kind of input preprocessing.
For VGG16, call tf.keras.applications.vgg16.preprocess_input
on your
inputs before passing them to the model.
Arguments
None
(random initialization),
'imagenet' (pre-training on ImageNet),
or the path to the weights file to be loaded.layers.Input()
)
to use as image input for the model.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 input channels,
and width and height should be no smaller than 32.
E.g. (200, 200, 3)
would be one valid value.include_top
is False
.
- None
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.include_top
is True, and
if no weights
argument is specified.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
weights
,
or invalid input shape.classifier_activation
is not softmax
or None
when
using a pretrained top layer.VGG19
functiontf.keras.applications.VGG19(
include_top=True,
weights="imagenet",
input_tensor=None,
input_shape=None,
pooling=None,
classes=1000,
classifier_activation="softmax",
)
Instantiates the VGG19 architecture.
Reference
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.
Note: each Keras Application expects a specific kind of input preprocessing.
For VGG19, call tf.keras.applications.vgg19.preprocess_input
on your
inputs before passing them to the model.
Arguments
None
(random initialization),
'imagenet' (pre-training on ImageNet),
or the path to the weights file to be loaded.layers.Input()
)
to use as image input for the model.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.include_top
is False
.None
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.include_top
is True, and
if no weights
argument is specified.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
weights
,
or invalid input shape.classifier_activation
is not softmax
or None
when
using a pretrained top layer.