|TensorFlow 1 version||View source on GitHub|
Instantiates the ResNet50V2 architecture.
See Migration guide for more details.
tf.keras.applications.ResNet50V2( include_top=True, weights='imagenet', input_tensor=None, input_shape=None, pooling=None, classes=1000, classifier_activation='softmax' )
Optionally loads weights pre-trained on ImageNet.
Note that the data format convention used by the model is
the one specified in your Keras config at
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_topis 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
Nonemeans that the output of the model will be the 4D tensor output of the last convolutional block.
avgmeans 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.
maxmeans that global max pooling will be applied.
classes: optional number of classes to classify images into, only to be specified if
include_topis True, and if no
weightsargument is specified.
stror callable. The activation function to use on the "top" layer. Ignored unless
classifier_activation=Noneto return the logits of the "top" layer.