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TensorFlow 1 version
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View source on GitHub
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Instantiates the VGG16 model.
tf.keras.applications.VGG16(
include_top=True, weights='imagenet', input_tensor=None,
input_shape=None, pooling=None, classes=1000,
classifier_activation='softmax'
)
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.
Arguments | |
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include_top
|
whether to include the 3 fully-connected layers at the top of the network. |
weights
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one of None (random initialization),
'imagenet' (pre-training on ImageNet),
or the path to the weights file to be loaded.
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input_tensor
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optional Keras tensor
(i.e. output of layers.Input())
to use as image input for the model.
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input_shape
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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 input channels,
and width and height should be no smaller than 32.
E.g. (200, 200, 3) would be one valid value.
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pooling
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Optional pooling mode for feature extraction
when include_top is False.
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classes
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optional number of classes to classify images
into, only to be specified if include_top is True, and
if no weights argument is specified.
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classifier_activation
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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.
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Returns | |
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A keras.Model instance.
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Raises | |
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ValueError
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in case of invalid argument for weights,
or invalid input shape.
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ValueError
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if classifier_activation is not softmax or None when
using a pretrained top layer.
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