Predict visual signal strength

This function predicts the visual signal strength using the provided keras model, input array and optional auxiliary input array.

Usage

KERAS_WRAPPER$predict(
  input_array,
  auxiliary = NULL,
  keras_model = self$keras_model,
  node_index = self$node_index,
  extract_featrue_from_layer = NULL
)

Arguments

input_array

Array/Numpy array. An input array, usually of the shape (batch_size, height, width, channels).

auxiliary

Array/Data frame. An auxiliary input array of the shape (batch_size, number_of_auxiliary_inputs). This is only needed if the keras model takes multiple inputs.

keras_model

Keras model. A trained computer vision model.

node_index

Integer. An index indicating which node of the output layer contains the visual signal strength. This is particularly useful when the keras model has more than one output nodes.

extract_feature_from_layer

Character/Integer. A layer name or an integer layer index for extracting features from a layer.

Value

A tibble. The first column is vss which is the prediction, the rest of the columns are features extracted from a layer.

Examples

keras_model <- try(get_keras_model("vss_phn_32"))
if (!inherits(keras_model, "try-error")) {
  wrapper <- keras_wrapper(keras_model)

  # Provide one 32 * 32 RGB image and one vector of length 5 as input
  wrapper$predict(input_array = array(255, dim = c(1, 32, 32, 3)),
                  auxiliary = matrix(1, ncol = 5))
}
#> ✔ Predict visual signal strength for 1 image.
#> # A tibble: 1 × 1
#>     vss
#>   <dbl>
#> 1  3.21