kerastuneR (version 0.1.0.2)

HyperResNet: HyperResNet

Description

A ResNet HyperModel.

Usage

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`.

Value

a pre-trained ResNet model

Examples

# NOT RUN {
# }
# NOT RUN {
library(keras)
library(dplyr)
library(kerastuneR)

kerastuneR::install_kerastuner()

cifar <- dataset_cifar10()

hypermodel = kerastuneR::HyperResNet(input_shape = list(32L, 32L, 3L), classes = 10L)
hypermodel2 = kerastuneR::HyperXception(input_shape = list(32L, 32L, 3L), classes = 10L)


tuner = kerastuneR::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)
# }