Skip to contents

Instantiates the ResNet152 architecture.

Source: R/applications.R
application_resnet152.Rd

Instantiates the ResNet152 architecture.

Usage

application_resnet152(
  include_top = TRUE,
  weights = "imagenet",
  input_tensor = NULL,
  input_shape = NULL,
  pooling = NULL,
  classes = 1000L,
  classifier_activation = "softmax",
  name = "resnet152"
)

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

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 convert the input images from RGB to BGR, then will zero-center each color channel with respect to the ImageNet dataset, without scaling.

See also