A ResNet HyperModel.

HyperResNet(
  include_top = TRUE,
  input_shape = NULL,
  input_tensor = NULL,
  classes = NULL,
  ...
)

Arguments

include_top

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

input_shape

Optional shape list, e.g. `(256, 256, 3)`. One of `input_shape` or `input_tensor` must be specified.

input_tensor

Optional Keras tensor (i.e. output of `layers.Input()`) to use as image input for the model. One of `input_shape` or `input_tensor` must be specified.

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. **kwargs: Additional keyword arguments that apply to all HyperModels. See `kerastuner.HyperModel`.

...

Additional keyword arguments that apply to all HyperModels.

Value

a pre-trained ResNet model

Examples

if (FALSE) { cifar <- dataset_cifar10() hypermodel = HyperResNet(input_shape = list(32L, 32L, 3L), classes = 10L) hypermodel2 = HyperXception(input_shape = list(32L, 32L, 3L), classes = 10L) tuner = Hyperband( hypermodel = hypermodel, objective = 'accuracy', loss = 'sparse_categorical_crossentropy', max_epochs = 1, directory = 'my_dir', project_name='helloworld') train_data = cifar$train$x[1:30,1:32,1:32,1:3] test_data = cifar$train$y[1:30,1] %>% as.matrix() tuner %>% fit_tuner(train_data,test_data, epochs = 1) }