innsight (version 0.1.0)

Gradient: Vanilla Gradient Method

Description

This method computes the gradients (also known as 'Vanilla Gradients') of the outputs with respect to the input variables, i.e. for all input variable \(i\) and output class \(j\) $$d f(x)_j / d x_i.$$ If the argument times_input is TRUE, the gradients are multiplied by the respective input value ('Gradient x Input'), i.e. $$x_i * d f(x)_j / d x_i.$$

Super classes

innsight::InterpretingMethod -> innsight::GradientBased -> Gradient

Methods

Public methods

• Gradient$new()

• Gradient$clone()

• innsight::InterpretingMethod$get_result()

• innsight::GradientBased$boxplot()

• innsight::GradientBased$plot()

Method new()

Create a new instance of the Vanilla Gradient method.

Usage

Gradient$new(
  converter,
  data,
  channels_first = TRUE,
  output_idx = NULL,
  ignore_last_act = TRUE,
  times_input = FALSE,
  dtype = "float"
)

Arguments

converter

An instance of the R6 class Converter.

data

The data for which the gradients are to be calculated. It has to be an array or array-like format of size (batch_size, dim_in).

channels_first

The format of the given data, i.e. channels on last dimension (FALSE) or after the batch dimension (TRUE). If the data has no channels, use the default value TRUE.

output_idx

This vector determines for which outputs the method will be applied. By default (NULL), all outputs (but limited to the first 10) are considered.

ignore_last_act

A boolean value to include the last activation into all the calculations, or not (default: TRUE). In some cases, the last activation leads to a saturation problem.

times_input

Multiplies the gradients with the input features. This method is called 'Gradient x Input'. Default: FALSE.

dtype

The data type for the calculations. Use either 'float' for torch::torch_float or 'double' for torch::torch_double.

Method clone()

The objects of this class are cloneable with this method.

Usage

Gradient$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

# NOT RUN {
#----------------------- Example 1: Torch ----------------------------------
library(torch)

# Create nn_sequential model and data
model <- nn_sequential(
  nn_linear(5, 12),
  nn_relu(),
  nn_linear(12, 2),
  nn_softmax(dim = 2)
)
data <- torch_randn(25, 5)

# Create Converter with input and output names
converter <- Converter$new(model,
  input_dim = c(5),
  input_names = list(c("Car", "Cat", "Dog", "Plane", "Horse")),
  output_names = list(c("Buy it!", "Don't buy it!"))
)

# Calculate the Gradients
grad <- Gradient$new(converter, data)

# Print the result as a data.frame for first 5 rows
grad$get_result("data.frame")[1:5,]

# Plot the result for both classes
plot(grad, output_idx = 1:2)

# Plot the boxplot of all datapoints
boxplot(grad, output_idx = 1:2)

# ------------------------- Example 2: Neuralnet ---------------------------
library(neuralnet)
data(iris)

# Train a neural network
nn <- neuralnet(Species ~ ., iris,
  linear.output = FALSE,
  hidden = c(10, 5),
  act.fct = "logistic",
  rep = 1
)

# Convert the trained model
converter <- Converter$new(nn)

# Calculate the gradients
gradient <- Gradient$new(converter, iris[, -5], times_input = TRUE)

# Plot the result for the first and 60th data point and all classes
plot(gradient, data_idx = c(1, 60), output_idx = 1:3)

# Calculate Gradients x Input and do not ignore the last activation
gradient <- Gradient$new(converter, iris[, -5], ignore_last_act = FALSE)

# Plot the result again
plot(gradient, data_idx = c(1, 60), output_idx = 1:3)

# ------------------------- Example 3: Keras -------------------------------
library(keras)

if (is_keras_available()) {
  data <- array(rnorm(64 * 60 * 3), dim = c(64, 60, 3))

  model <- keras_model_sequential()
  model %>%
    layer_conv_1d(
      input_shape = c(60, 3), kernel_size = 8, filters = 8,
      activation = "softplus", padding = "valid"
    ) %>%
    layer_conv_1d(
      kernel_size = 8, filters = 4, activation = "tanh",
      padding = "same"
    ) %>%
    layer_conv_1d(
      kernel_size = 4, filters = 2, activation = "relu",
      padding = "valid"
    ) %>%
    layer_flatten() %>%
    layer_dense(units = 64, activation = "relu") %>%
    layer_dense(units = 16, activation = "relu") %>%
    layer_dense(units = 3, activation = "softmax")

  # Convert the model
  converter <- Converter$new(model)

  # Apply the Gradient method
  gradient <- Gradient$new(converter, data, channels_first = FALSE)

  # Plot the result for the first datapoint and all classes
  plot(gradient, output_idx = 1:3)

  # Plot the result as boxplots for first two classes
  boxplot(gradient, output_idx = 1:2)

  # You can also create an interactive plot with plotly.
  # This is a suggested package, so make sure that it is installed
  library(plotly)

  # Result as boxplots
  boxplot(gradient, as_plotly = TRUE)

  # Result of the second data point
  plot(gradient, data_idx = 2, as_plotly = TRUE)
}

# ------------------------- Advanced: Plotly -------------------------------
# If you want to create an interactive plot of your results with custom
# changes, you can take use of the method plotly::ggplotly
library(ggplot2)
library(plotly)
library(neuralnet)
data(iris)

nn <- neuralnet(Species ~ .,
  iris,
  linear.output = FALSE,
  hidden = c(10, 8), act.fct = "tanh", rep = 1, threshold = 0.5
)
# create an converter for this model
converter <- Converter$new(nn)

# create new instance of 'Gradient'
gradient <- Gradient$new(converter, iris[, -5])

library(plotly)

# Get the ggplot and add your changes
p <- plot(gradient, output_idx = 1, data_idx = 1:2) +
  theme_bw() +
  scale_fill_gradient2(low = "green", mid = "black", high = "blue")

# Now apply the method plotly::ggplotly with argument tooltip = "text"
plotly::ggplotly(p, tooltip = "text")
# }