# 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)
ref <- torch_randn(1, 5)
# Create Converter
converter <- Converter$new(model, input_dim = c(5))
# Apply method DeepLift
deeplift <- DeepLift$new(converter, data, x_ref = ref)
# Print the result as a torch tensor for first two data points
deeplift$get_result("torch.tensor")[1:2]
# Plot the result for both classes
plot(deeplift, output_idx = 1:2)
# Plot the boxplot of all datapoints
boxplot(deeplift, output_idx = 1:2)
# ------------------------- Example 2: Neuralnet ---------------------------
library(neuralnet)
data(iris)
# Train a neural network
nn <- neuralnet((Species == "setosa") ~ Petal.Length + Petal.Width,
iris,
linear.output = FALSE,
hidden = c(3, 2), act.fct = "tanh", rep = 1
)
# Convert the model
converter <- Converter$new(nn)
# Apply DeepLift with rescale-rule and a reference input of the feature
# means
x_ref <- matrix(colMeans(iris[, c(3, 4)]), nrow = 1)
deeplift_rescale <- DeepLift$new(converter, iris[, c(3, 4)], x_ref = x_ref)
# Get the result as a dataframe and show first 5 rows
deeplift_rescale$get_result(type = "data.frame")[1:5, ]
# Plot the result for the first datapoint in the data
plot(deeplift_rescale, data_idx = 1)
# Plot the result as boxplots
boxplot(deeplift_rescale)
# ------------------------- Example 3: Keras -------------------------------
library(keras)
if (is_keras_available()) {
data <- array(rnorm(10 * 32 * 32 * 3), dim = c(10, 32, 32, 3))
model <- keras_model_sequential()
model %>%
layer_conv_2d(
input_shape = c(32, 32, 3), kernel_size = 8, filters = 8,
activation = "softplus", padding = "valid"
) %>%
layer_conv_2d(
kernel_size = 8, filters = 4, activation = "tanh",
padding = "same"
) %>%
layer_conv_2d(
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 = 2, activation = "softmax")
# Convert the model
converter <- Converter$new(model)
# Apply the DeepLift method with reveal-cancel rule
deeplift_revcancel <- DeepLift$new(converter, data,
channels_first = FALSE,
rule_name = "reveal_cancel"
)
# Plot the result for the first image and both classes
plot(deeplift_revcancel, output_idx = 1:2)
# Plot the result as boxplots for first class
boxplot(deeplift_revcancel, output_idx = 1)
# You can also create an interactive plot with plotly.
# This is a suggested package, so make sure that it is installed
library(plotly)
boxplot(deeplift_revcancel, 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(neuralnet)
library(plotly)
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$new(nn)
# create new instance of 'DeepLift'
deeplift <- DeepLift$new(converter, iris[, -5])
# Get the ggplot and add your changes
p <- plot(deeplift, 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")
# }
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