application_resnet152_v2 {keras3}

Instantiates the ResNet152V2 architecture.

Description

Instantiates the ResNet152V2 architecture.

Usage

application_resnet152_v2(
  include_top = TRUE,
  weights = "imagenet",
  input_tensor = NULL,
  input_shape = NULL,
  pooling = NULL,
  classes = 1000L,
  classifier_activation = "softmax",
  name = "resnet152v2"
)

Arguments

include_top

whether to include the fully-connected layer at the top of the network.

weights

one of NULL (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 keras_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.

• NULL 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.

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=NULL to return the logits of the "top" layer. When loading pretrained weights, classifier_activation can only be NULL or "softmax".

name

The name of the model (string).

Value

A Model instance.

Reference

• Identity Mappings in Deep Residual Networks (CVPR 2016)

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.

Note

Each Keras Application expects a specific kind of input preprocessing. For ResNet, call application_preprocess_inputs() on your inputs before passing them to the model. application_preprocess_inputs() will scale input pixels between -1 and 1.

See Also

• https://keras.io/api/applications/resnet#resnet152v2-function