plot_vae {VAExprs} | R Documentation

## Visualization for the variational autoencoder

### Description

You can create a plot of the VAE model. This plot can help you check that the model is connected the way you intended. The node colors indicate the components of the VAE.

### Usage

    plot_vae(x, node_color = list(encoder_col = "tomato",
                                mean_vector_col = "orange",
                                stddev_vector_col = "lavender",
                                latent_vector_col = "lightblue",
                                decoder_col = "palegreen",
                                condition_col = "gray"))

### Arguments

`x` | VAE model
`node_color` | node colors for encoder(default: tomato), mean vector(default: orange), standard deviation vector(default: lavender), latent_vector(default: lightblue), decoder(default: palegreen), and condition(default: gray)

### Value

plot for the model architecture

### Author(s)

Dongmin Jung

### See Also

purrr::map, purrr::map_chr, purrr::pluck, purrr::imap_dfr, DiagrammeR::grViz

### Examples

    if (keras::is_keras_available() & reticulate::py_available()) {
        ### simulate differentially expressed genes
        set.seed(1)
        g <- 3
        n <- 100
        m <- 1000
        mu <- 5
        sigma <- 5
        mat <- matrix(rnorm(n*m*g, mu, sigma), m, n*g)
        rownames(mat) <- paste0("gene", seq_len(m))
        colnames(mat) <- paste0("cell", seq_len(n*g))
        group <- factor(sapply(seq_len(g), function(x) {
            rep(paste0("group", x), n)
        }))
        names(group) <- colnames(mat)
        mu_upreg <- 6
        sigma_upreg <- 10
        deg <- 100
        for (i in seq_len(g)) {
            mat[(deg*(i-1) + 1):(deg*i), group == paste0("group", i)] <-
                mat[1:deg, group==paste0("group", i)] + rnorm(deg, mu_upreg, sigma_upreg)
        }
        # positive expression only
        mat[mat < 0] <- 0
        x_train <- as.matrix(t(mat))

        ### model
        batch_size <- 32
        original_dim <- 1000
        intermediate_dim <- 512
        epochs <- 2
        # VAE
        vae_result <- fit_vae(x_train = x_train,
                            encoder_layers = list(layer_input(shape = c(original_dim)),
                                                layer_dense(units = intermediate_dim,
                                                            activation = "relu")),
                            decoder_layers = list(layer_dense(units = intermediate_dim,
                                                            activation = "relu"),
                                                layer_dense(units = original_dim,
                                                            activation = "sigmoid")),
                            epochs = epochs, batch_size = batch_size,
                            validation_split = 0.5,
                            use_generator = FALSE,
                            callbacks = keras::callback_early_stopping(
                                monitor = "val_loss",
                                patience = 10,
                                restore_best_weights = TRUE))
        # plot
        plot_vae(vae_result$model)
    }

[Package VAExprs version 1.0.1 Index]